0:\\n data.label_samples(selected_samples, selected_labels)\\n return selected_samples\",\n \"def MNIST_experiment():\\n tsetlin_machine = TsetlinMachine(number_clauses=1000,\\n number_action_states=1000,\\n precision=3.0,\\n threshold=10)\\n\\n X, y, val_X, val_y = MNIST()\\n\\n tsetlin_machine.fit(X, y, val_X, val_y, 300)\\n print('Final training accuracy:', tsetlin_machine.accuracy(X, y))\\n print('Final validation accuracy:', tsetlin_machine.accuracy(val_X, val_y))\",\n \"def X_train(self):\\n return self.train.drop(self.target_param, axis=1)\",\n \"def load_own_target():\\n data = Data()\\n target = data.get_label_col()\\n return target.values\",\n \"def test_train_with_early_stopping(task_dataset, mocker):\\n wandb_log = mocker.patch.object(wandb, 'log')\\n\\n early_stopping = learning.EarlyStopping(patience=PATIENCE)\\n # Cannot possible go lower! So we should stop after PATIENCE steps.\\n early_stopping(float('-inf'))\\n\\n probe = nn.Linear(N_DIMS_PER_REP, N_UNIQUE_FEATS)\\n before = probe.weight.data.clone()\\n\\n learning.train(probe,\\n task_dataset,\\n epochs=EPOCHS,\\n stopper=early_stopping,\\n also_log_to_wandb=True)\\n after = probe.weight.data\\n\\n assert not before.equal(after)\\n assert wandb_log.call_args_list == [\\n mocker.call({'train loss': mocker.ANY}),\\n ] * (PATIENCE + 1)\",\n \"def build_newstest_finetune(self):\\n # Note that this function is purposefully similar to build_newscomment_only\\n # The two datasets have very similar structure and it would just be more\\n # confusing to refactor code, creating multiple overlapping paths.\\n logging.info('Building newstest finetune dataset')\\n logging.info(self.configs[NEWSTEST])\\n builder = tfds.builder(WMT_BASE_DATASET_NAME,\\n config=self.configs[NEWSTEST],\\n data_dir=self.data_dir)\\n self.default_builder_obj = builder\\n shard_spec = self.build_shard_spec()\\n logging.info('Training on TFDS dataset %s with split %s',\\n WMT_BASE_DATASET_NAME, 'train' + shard_spec)\\n train_data = builder.as_dataset(split='train' + shard_spec,\\n shuffle_files=self.shuffle_train_files)\\n eval_data = self.default_eval_builder(builder, shard_spec)\\n return train_data, eval_data\",\n \"def train_one_epoch(self):\\n raise NotImplementedError\",\n \"def train_step(self):\\n pass\",\n \"def get_data():\\n iris = datasets.load_iris()\\n xall = np.asarray(iris[\\\"data\\\"], dtype=np.float64)\\n yall = np.asarray(iris[\\\"target\\\"], dtype=np.float64)\\n xall = np.vstack([xall, (7, 2.0, 4.5, 1)])\\n yall = np.append(yall, n_classes)\\n X, Xval, y, yval = train_test_split(\\n xall, yall, test_size=0.2, shuffle=True, random_state=12345\\n )\\n y = tf.one_hot(y, n_classes)\\n yval = tf.one_hot(yval, n_classes)\\n return X, y, Xval, yval\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def getDataset(self, train=True):\\n \\n device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\") \\n \\n if self.dataset == \\\"ELLIPSE\\\":\\n a = np.array([[0,1.0],[1.0,2.0]]) \\n b = a*0.5 \\n myE = el.ellipse(device, 500, 100, a, b) \\n if train == True:\\n return myE.create_dataset(myE.examples)\\n return myE.create_dataset(myE.valid) \\n \\n if self.dataset == \\\"SWISS\\\": \\n myS = sw.SwissRoll(device, 500, 0.2) \\n if train == True:\\n return myS.create_dataset(myS.examples)\\n return myS.create_dataset(myS.valid)\\n \\n \\n #open file\\n myFile = h5py.File(self.dataString, 'r', self.driver)\\n \\n if train == True: \\n inputString = \\\"train_inputs\\\"\\n labelsString = \\\"train_labels\\\"\\n \\n else:\\n inputString = \\\"test_inputs\\\"\\n labelsString = \\\"test_labels\\\"\\n \\n #get hdf5 datsets\\n features = myFile.get(inputString)\\n labels = myFile.get(labelsString)\\n \\n #convert to tensors\\n features = torch.from_numpy(np.array(features))\\n labels = torch.from_numpy(np.array(labels))\\n \\n #close file to ensure dataset is in memory\\n myFile.close()\\n \\n #conver to correct datatypes\\n features = features.float()\\n \\n if self.conv_sg == False:\\n labels = labels.long() \\n \\n dataset = torch.utils.data.TensorDataset(features, labels)\\n \\n return dataset\",\n \"def get_init_fn():\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n variables_to_restore.append(var)\\n\\n checkpoint_path = tf.train.latest_checkpoint(\\\"./base_checkpoint\\\")\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=False)\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\n raise NotImplementedError()\",\n \"def get_mnist_data(batch=128):\\n \\n def transformer(data, label):\\n data = data.flatten().expand_dims(0).astype(np.float32)/255\\n data = data-0.13/0.31\\n label = label.astype(np.float32)\\n return data, label\\n\\n train_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=True, transform=transformer)\\n validation_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=False, transform=transformer)\\n train_dataloader = gluon.data.DataLoader(train_dataset, batch_size=batch, last_batch='keep',shuffle=True)\\n validation_dataloader = gluon.data.DataLoader(validation_dataset, batch_size=batch, last_batch='keep')\\n \\n return train_dataloader, validation_dataloader\",\n \"def test_train_spl(data, testsize):\\n test = data.tail(testsize)\\n train = data.head(data.shape[0] - testsize)\\n return test, train\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def get_train_data():\\n # train set\\n train = pd.read_csv(\\\"train.csv\\\")\\n\\n return train\",\n \"def getTrainInstance(self): #NOTE: Probably faster way of doing this than additional 'if' statement every learning iteration\\r\\n return [self.currentTrainState, self.currentTrainPhenotype] #Return unadulterated training data\\r\",\n \"def get_train_iterator(self) -> tf.contrib.data.Iterator:\\n return self.train.make_initializable_iterator()\",\n \"def target_dummy(config: Configuration, seed: int) -> int:\\n return seed\",\n \"def build_pseudo_ref(self):\\n logging.info('Building pseudo finetune dataset')\\n logging.info(self.configs[NEWSTEST])\\n builder = tfds.builder(WMT_BASE_DATASET_NAME,\\n config=self.configs[NEWSTEST],\\n data_dir=self.data_dir)\\n self.default_builder_obj = builder\\n shard_spec = self.build_shard_spec()\\n eval_data = self.default_eval_builder(builder, shard_spec)\\n\\n new_data = pickle.load(tf.io.gfile.GFile(self.pseudo_path, 'rb'))\\n # Create tensorflow dataset\\n tf_pre_dataset = {'inputs': [], 'targets': []}\\n for data in new_data:\\n inp = data[-2]\\n targ = data[-1] # [1:] # Targets have dummy first variable\\n tf_pre_dataset['inputs'].append(inp)\\n tf_pre_dataset['targets'].append(targ)\\n\\n tf_dataset = tf.data.Dataset.from_tensor_slices(tf_pre_dataset)\\n return tf_dataset, eval_data\",\n \"def get_eval_input(self, prev, i):\\n del prev, i\\n # The attention_decoder will fetch image features from the net, no need for\\n # extra inputs.\\n return self._zero_label\",\n \"def import_training_data(target_col = 'label'):\\n dir = os.path.dirname(os.path.dirname(__file__)) # go up one level to get root of this experiment\\n path = os.path.join(dir, 'data','train.csv')\\n utils_logr.info('Loading data from {} as pandas df'.format(path))\\n df = pd.read_csv(path)\\n y = df[target_col]\\n df = df.drop(target_col, axis=1)\\n return df, y\",\n \"def switch_to_train_data(self) -> None:\\n if self._train_name not in self._datasets:\\n raise ValueError(\\\"Training data not provided.\\\")\\n self.switch_to_dataset(self._train_name)\",\n \"def readMNISTData():\\n mnist = input_data.read_data_sets(\\\"MNIST_data\\\",one_hot=True) \\n return mnist\",\n \"def infer(\\n self, data: xr.Dataset, num_samples: int, seed: int, **infer_args\\n ) -> xr.Dataset:\\n raise NotImplementedError\",\n \"def _train_stub(self, idx):\\n x, y, ID = self.get_samples(idx=[idx], split='train', as_list=False, simple_IDs=True)\\n return np.squeeze(x), np.squeeze(y), ID[0]\",\n \"def test(self) -> tf.contrib.data.Dataset:\\n return self.__test_dataset\",\n \"def get_data_train(self):\\n return self.get_data(self.file_train, self.batch)\",\n \"def get_init_fn():\\n checkpoint_exclude_scopes = [\\\"InceptionV1/Logits\\\", \\\"InceptionV1/AuxLogits\\\"]\\n\\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n return slim.assign_from_checkpoint_fn(\\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\\n variables_to_restore)\",\n \"def eval_step(data_laoder, model, device, fold):\\n\\tmodel.eval()\\n\\ttrue_labels, pred_labels = [], []\\n\\tfor inputs, input_lens, labels in tqdm.tqdm(data_laoder, ncols=100, desc=f\\\"eval--- F: {fold} -- E: n\\\"):\\n\\t\\tinputs = inputs.to(device)\\n\\t\\tlabels = labels.to(device)\\n\\t\\t#input_lens = input_lens.to(device)\\n\\n\\t\\tpreds = model(inputs, input_lens)\\n\\n\\t\\ttrue_labels.extend(labels.detach().squeeze().cpu().numpy().tolist())\\n\\t\\tpred_labels.extend(preds.detach().squeeze().cpu().numpy().tolist())\\n\\n\\treturn true_labels, pred_labels\",\n \"def train_data():\\n raw = datasets.load_iris()\\n iris = pd.DataFrame(raw.data, columns=raw.feature_names)\\n iris = iris.join(pd.DataFrame(raw.target))\\n iris.columns = [\\\"SepalLength\\\", \\\"SepalWidth\\\", \\\"PetalLength\\\", \\\"PetalWidth\\\", \\\"Species\\\"]\\n iris[\\\"Species\\\"] = iris[\\\"Species\\\"].astype(\\\"category\\\")\\n iris.Species.cat.categories = raw.target_names\\n return iris.iloc[:, 0:4], iris[\\\"Species\\\"]\",\n \"def get_train_examples(self, data_dir):\\n\\t\\traise NotImplementedError()\",\n \"def _get_init_fn():\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\\n\\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)\",\n \"def train(\\n self, training_data: Dataset, validation_data: Optional[Dataset] = None\\n ) -> Predictor:\\n raise NotImplementedError\",\n \"def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):\\n\\n epoch = K.eval(self._ckpt_saved_epoch)\\n if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0:\\n # The most recently saved epoch is one epoch prior to the epoch it\\n # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.\\n return epoch + 1\\n return initial_epoch\",\n \"def get_train_input(self, prev, i):\\n if i == 0:\\n prev = self._zero_label\\n else:\\n prev = self._labels_one_hot[:, i - 1, :]\\n image_feature = self.get_image_feature(i)\\n return tf.concat([image_feature, prev], 1)\",\n \"def test_early_stopping_no_val_step(tmpdir):\\n\\n class CurrentModel(EvalModelTemplate):\\n def training_step(self, *args, **kwargs):\\n output = super().training_step(*args, **kwargs)\\n output.update({'my_train_metric': output['loss']}) # could be anything else\\n return output\\n\\n model = CurrentModel()\\n model.validation_step = None\\n model.val_dataloader = None\\n\\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\\n trainer = Trainer(\\n default_root_dir=tmpdir,\\n early_stop_callback=stopping,\\n overfit_pct=0.20,\\n max_epochs=5,\\n )\\n result = trainer.fit(model)\\n\\n assert result == 1, 'training failed to complete'\\n assert trainer.current_epoch < trainer.max_epochs\",\n \"def target(self, example):\\n return example[self.target_index]\",\n \"def get_train_data(batch_size=8):\\n transform_train = transforms.Compose([\\n transforms.RandomResizedCrop(224),\\n transforms.RandomFlipLeftRight(),\\n transforms.RandomColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),\\n transforms.RandomLighting(0.1),\\n transforms.ToTensor(),\\n transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\\n ])\\n\\n img_folder, img_file = get_data_path()\\n td = MultilabelDataset(data_folder=img_folder, data_file=img_file)\\n train_data = DataLoader(td.transform_first(transform_train), batch_size=batch_size, shuffle=True)\\n return train_data\",\n \"def trainingset(self, batchsize=None, flatten=True):\\n if batchsize is None:\\n batchsize = self.batchsize\\n\\n if self.x_train is None:\\n raise AttributeError('No fold initialized... Try calling next_leaveout')\\n\\n return self.GENERATOR(self.x_train, self.y_train, batchsize, flatten=flatten, evaluate=False)\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_train_examples(self, data_dir):\\r\\n raise NotImplementedError()\",\n \"def get_dataset(self):\\n\\n trainset = datasets.ImageNet('datasets/ImageNet/train/', split='train', transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.ImageNet('datasets/ImageNet/val/', split='val', transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def train(esncell, states, labels):\\n Who = lstsq_stable(states, labels)\\n return esncell + (Who,)\",\n \"def _get_train_ensemble(self):\\n self._validate_train_ensemble()\\n return deepcopy(self.train_ensemble)\",\n \"def get_dataset(self):\\n\\n trainset = datasets.SVHN('datasets/SVHN/train/', split='train', transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.SVHN('datasets/SVHN/test/', split='test', transform=self.val_transforms,\\n target_transform=None, download=True)\\n extraset = datasets.SVHN('datasets/SVHN/extra', split='extra', transform=self.train_transforms,\\n target_transform=None, download=True)\\n\\n trainset = torch.utils.data.ConcatDataset([trainset, extraset])\\n\\n return trainset, valset\",\n \"def get_dataset_sampler(self):\\n return None\",\n \"def test_train(self):\\n tf.reset_default_graph()\\n if os.path.isdir(self.training_dir):\\n shutil.rmtree(self.training_dir)\\n\\n nas_trainer = EarlyStopNASTrainer(\\n encoded_network=self.net_nsc,\\n input_shape=infer_data_shape(self.train_data),\\n n_classes=infer_n_classes(self.train_labels),\\n batch_size=self.batch_size,\\n log_path=self.training_dir,\\n mu=0.5,\\n rho=0.5,\\n variable_scope=\\\"cnn\\\"\\n )\\n\\n nas_trainer.train(\\n train_data=self.train_data,\\n train_labels=self.train_labels,\\n train_input_fn=\\\"default\\\"\\n )\\n\\n self.assertTrue(os.path.isdir(self.training_dir))\",\n \"def get_mnist():\\n if GLOBALS['project_root']=='':\\n print('please initialize project_root in GLOBALS first')\\n return None\\n data_path = os.path.join(GLOBALS['project_root'], 'data/MNIST/')\\n pickle_path = os.path.join(data_path, 'mnist.pickle')\\n if os.path.exists(pickle_path):\\n with open(pickle_path, 'rb') as f:\\n mnist = pickle.load(f)\\n else:\\n from tensorflow.examples.tutorials.mnist import input_data\\n mnist = input_data.read_data_sets(data_path, one_hot=True)\\n with open(pickle_path, 'wb') as f:\\n pickle.dump(mnist, f, pickle.HIGHEST_PROTOCOL)\\n\\n return mnist\",\n \"def get_init_fn():\\n\\tif train_config['checkpoint_path'] is None:\\n\\t\\treturn None\\n\\t# Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n\\t# ignoring the checkpoint anyway.\\n\\tif tf.train.latest_checkpoint(train_config['checkpoint_path']):\\n\\t\\ttf.logging.info(\\n\\t\\t\\t'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n\\t\\t\\t% train_config['checkpoint_path'])\\n\\t\\treturn None\\n\\n\\texclusions = []\\n\\tif train_config['checkpoint_exclude_scopes']:\\n\\t\\texclusions = [scope.strip()\\n\\t\\t for scope in train_config['checkpoint_exclude_scopes'].split(',')]\\n\\n\\t\\tvariable_to_restore = []\\n\\t\\tfor var in slim.get_model_variables():\\n\\t\\t\\texcluded = False\\n\\t\\t\\tfor exclusion in exclusions:\\n\\t\\t\\t\\tif var.op.name.startswith(exclusion):\\n\\t\\t\\t\\t\\texcluded = True\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tif not excluded:\\n\\t\\t\\t\\tvariable_to_restore.append(var)\\n\\n\\t\\tif tf.gfile.IsDirectory(train_config['checkpoint_path']):\\n\\t\\t\\tcheckpoint_path = tf.train.latest_checkpoint(train_config['checkpoint_path'])\\n\\t\\telse:\\n\\t\\t\\tcheckpoint_path = train_config['checkpoint_path']\\n\\n\\t\\ttf.logging.info('Fune-tuning from %s' % checkpoint_path)\\n\\n\\t\\treturn slim.assign_from_checkpoint_fn(\\n\\t\\t\\tcheckpoint_path,\\n\\t\\t\\tvariable_to_restore,\\n\\t\\t\\tignore_missing_vars=train_config['ignore_missing_vars'])\",\n \"def test_intent_classifier_get_training_samples(self):\\n pass\",\n \"def train_imed(esncell, states, inputs, labels, sigma=1.):\\n Who = imed_lstsq_stable(states, inputs, labels, sigma)\\n return esncell + (Who,)\",\n \"def partition_dataset_train():\\n dataset = datasets.MNIST(\\n './data',\\n train=True,\\n download=True,\\n transform=transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307, ), (0.3081, ))\\n ]))\\n size = dist.get_world_size()\\n bsz = int(128 / float(size))\\n partition_sizes = [1.0 / size for _ in range(size)]\\n partition = DataPartitioner(dataset, partition_sizes)\\n partition = partition.use(dist.get_rank())\\n train_set = torch.utils.data.DataLoader(\\n partition, batch_size=bsz, shuffle=True)\\n return train_set, bsz\",\n \"def preprocess_train_dataset(dataset):\\n return (dataset\\n # Shuffle according to the largest client dataset\\n .shuffle(buffer_size=MAX_CLIENT_DATASET_SIZE)\\n # Repeat to do multiple local epochs\\n .repeat(CLIENT_EPOCHS_PER_ROUND)\\n # Batch to a fixed client batch size\\n .batch(CLIENT_BATCH_SIZE, drop_remainder=False)\\n # Preprocessing step\\n .map(reshape_emnist_element))\",\n \"def getTrainingData(self):\\n raise NotImplementedError\",\n \"def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\\n i = offset_steps\\n lval = splitsize\\n if not do_offset:\\n i_val = allind[:lval]\\n i_train = allind[lval:]\\n else:\\n i_val = allind[i * lval:(i + 1) * lval]\\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\\n if len(i_val) <= 0:\\n print(\\\"Warning: #Validation data is 0, take 1 training sample instead\\\")\\n i_val = i_train[:1]\\n\\n return i_train, i_val\",\n \"def train_model(self, save_folder: str = \\\"latest_model\\\"):\\n dataset_folder = self.dataset_config[\\\"dataset_folder\\\"]\\n if not path.exists(dataset_folder):\\n raise ValueError(f\\\"Folder {dataset_folder} is not exists\\\")\\n\\n files_list = [path.join(dataset_folder, f) for f in listdir(dataset_folder) if path.isfile(path.join(dataset_folder, f)) and f.endswith(\\\".yml\\\")]\\n\\n df, _, _, synonym_dict = make_dataframe(files=files_list)\\n\\n self.synonym_dict.update(synonym_dict)\\n self.config[\\\"model\\\"][\\\"synonym\\\"] = self.synonym_dict\\n\\n dataset = DIETClassifierDataset(dataframe=df, tokenizer=self.tokenizer, entities=self.entities[1:], intents=self.intents)\\n\\n trainer = DIETTrainer(model=self.model, dataset=dataset,\\n train_range=self.training_config[\\\"train_range\\\"],\\n num_train_epochs=self.training_config[\\\"num_train_epochs\\\"],\\n per_device_train_batch_size=self.training_config[\\\"per_device_train_batch_size\\\"],\\n per_device_eval_batch_size=self.training_config[\\\"per_device_eval_batch_size\\\"],\\n warmup_steps=self.training_config[\\\"warmup_steps\\\"],\\n weight_decay=self.training_config[\\\"weight_decay\\\"],\\n logging_dir=self.training_config[\\\"logging_dir\\\"],\\n early_stopping_patience=self.training_config[\\\"early_stopping_patience\\\"],\\n early_stopping_threshold=self.training_config[\\\"early_stopping_threshold\\\"],\\n output_dir=self.training_config[\\\"output_dir\\\"])\\n\\n trainer.train()\\n\\n self.save_pretrained(directory=save_folder)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72998613","0.713837","0.66336054","0.6162712","0.5902305","0.57828236","0.5423811","0.5414221","0.53984654","0.5350862","0.52556276","0.52556276","0.5214808","0.5211418","0.51941895","0.51866794","0.5174885","0.5166052","0.51568496","0.5156731","0.5136225","0.5072823","0.5066894","0.50632143","0.50560087","0.50398403","0.5035402","0.5030994","0.5027986","0.5027658","0.50203013","0.5001609","0.49958393","0.49866885","0.4985053","0.49766284","0.49549928","0.49450916","0.4940079","0.49132943","0.4911617","0.4909662","0.48625436","0.48536265","0.48507872","0.48444986","0.48358288","0.48315182","0.4829959","0.4829959","0.48297745","0.48259473","0.48207542","0.48173624","0.48171353","0.48060986","0.4802608","0.479634","0.47952297","0.478678","0.47834346","0.4766419","0.47598895","0.4755531","0.47552508","0.47454974","0.4745225","0.47434002","0.47368288","0.47355026","0.47318977","0.4729839","0.47208393","0.47136712","0.47110504","0.47089392","0.47075713","0.47070742","0.4700354","0.4700354","0.4700354","0.4700354","0.4700354","0.4700354","0.4700354","0.46957892","0.4690633","0.46864882","0.4682267","0.46773544","0.4676698","0.46766788","0.46734145","0.46728534","0.46727505","0.46725297","0.46522093","0.46513703","0.46509153","0.4644901"],"string":"[\n \"0.72998613\",\n \"0.713837\",\n \"0.66336054\",\n \"0.6162712\",\n \"0.5902305\",\n \"0.57828236\",\n \"0.5423811\",\n \"0.5414221\",\n \"0.53984654\",\n \"0.5350862\",\n \"0.52556276\",\n \"0.52556276\",\n \"0.5214808\",\n \"0.5211418\",\n \"0.51941895\",\n \"0.51866794\",\n \"0.5174885\",\n \"0.5166052\",\n \"0.51568496\",\n \"0.5156731\",\n \"0.5136225\",\n \"0.5072823\",\n \"0.5066894\",\n \"0.50632143\",\n \"0.50560087\",\n \"0.50398403\",\n \"0.5035402\",\n \"0.5030994\",\n \"0.5027986\",\n \"0.5027658\",\n \"0.50203013\",\n \"0.5001609\",\n \"0.49958393\",\n \"0.49866885\",\n \"0.4985053\",\n \"0.49766284\",\n \"0.49549928\",\n \"0.49450916\",\n \"0.4940079\",\n \"0.49132943\",\n \"0.4911617\",\n \"0.4909662\",\n \"0.48625436\",\n \"0.48536265\",\n \"0.48507872\",\n \"0.48444986\",\n \"0.48358288\",\n \"0.48315182\",\n \"0.4829959\",\n \"0.4829959\",\n \"0.48297745\",\n \"0.48259473\",\n \"0.48207542\",\n \"0.48173624\",\n \"0.48171353\",\n \"0.48060986\",\n \"0.4802608\",\n \"0.479634\",\n \"0.47952297\",\n \"0.478678\",\n \"0.47834346\",\n \"0.4766419\",\n \"0.47598895\",\n \"0.4755531\",\n \"0.47552508\",\n \"0.47454974\",\n \"0.4745225\",\n \"0.47434002\",\n \"0.47368288\",\n \"0.47355026\",\n \"0.47318977\",\n \"0.4729839\",\n \"0.47208393\",\n \"0.47136712\",\n \"0.47110504\",\n \"0.47089392\",\n \"0.47075713\",\n \"0.47070742\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.4700354\",\n \"0.46957892\",\n \"0.4690633\",\n \"0.46864882\",\n \"0.4682267\",\n \"0.46773544\",\n \"0.4676698\",\n \"0.46766788\",\n \"0.46734145\",\n \"0.46728534\",\n \"0.46727505\",\n \"0.46725297\",\n \"0.46522093\",\n \"0.46513703\",\n \"0.46509153\",\n \"0.4644901\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81183374"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18478,"cells":{"query":{"kind":"string","value":"Get a `DatasetSentinel` representing an Experiment's `fold_validation_input` Returns"},"document":{"kind":"string","value":"def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())"},"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 train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())","def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())","def holdout_input(self):\n return DatasetSentinel(\n \"holdout_input\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())","def get_train_input(self, prev, i):\n return self.get_eval_input(prev, i)","def get_train_input(self, prev, i):\n return self.get_eval_input(prev, i)","def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\n dataset = input_data.Data(Xtrain_ul,\n Xtrain_l,\n Xtest,\n ytrain_ul,\n ytrain_l,\n ytest,\n num_labeled, \n batch_size, \n shuffle=True)\n return dataset.next_batch()","def load_input(io_config):\n\n fold = io_config['fold']\n\n train_data_fnames = io_config['file']['train_data']\n\n train_fname = [t[1] for t in train_data_fnames\n if t[0] == fold][0]\n\n return train_fname","def get_eval_input(self, prev, i):\n del prev, i\n # The attention_decoder will fetch image features from the net, no need for\n # extra inputs.\n return self._zero_label","def get_input(self, prev, i):\n if self.is_training():\n return self.get_train_input(prev, i)\n else:\n return self.get_eval_input(prev, i)","def get_train_inputs(self, example):\n return example","def get_init_fn():\n checkpoint_exclude_scopes = [\"InceptionV1/Logits\", \"InceptionV1/AuxLogits\"]\n\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n return slim.assign_from_checkpoint_fn(\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n variables_to_restore)","def holdout_target(self):\n return DatasetSentinel(\n \"holdout_target\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def _dataset_sentinel_helper(self):\n params = self.cross_experiment_key.parameters\n return dict(\n dataset_hash=params[\"train_dataset\"],\n cv_type=params[\"cross_experiment_params\"].get(\n \"cv_type\", params[\"cross_experiment_params\"].get(\"cross_validation_type\", None)\n ),\n global_random_seed=params[\"cross_experiment_params\"][\"global_random_seed\"],\n random_seeds=params[\"cross_experiment_params\"][\"random_seeds\"],\n )","def get_train_input(self, prev, i):\n pass","def validation(self) -> tf.contrib.data.Dataset:\n return self.__validation_dataset","def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\n i = offset_steps\n lval = splitsize\n if not do_offset:\n i_val = allind[:lval]\n i_train = allind[lval:]\n else:\n i_val = allind[i * lval:(i + 1) * lval]\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\n if len(i_val) <= 0:\n print(\"Warning: #Validation data is 0, take 1 training sample instead\")\n i_val = i_train[:1]\n\n return i_train, i_val","def get_train(self, preprocess=False):\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')","def _input_fn(input_pipeline_context=None):\n return _create_dataset(options, is_training, input_pipeline_context)","def get_eval_input(self, prev, i):\n del prev\n return self.get_image_feature(i)","def _partition_elms(\n self, max_elms: int = None, fold: int = None\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n # get ELM indices from datafile\n elm_index, _ = self._read_file()\n\n # limit the data according to the max number of events passed\n if max_elms is not None and max_elms != -1:\n LOGGER.info(f\"Limiting data read to {max_elms} events.\")\n n_elms = max_elms\n else:\n n_elms = len(elm_index)\n\n # split the data into train, validation and test sets\n training_elms, test_elms = model_selection.train_test_split(\n elm_index[:n_elms],\n test_size=self.fraction_test,\n shuffle=True,\n random_state=config.seed,\n )\n\n # kfold cross validation\n if self.kfold and fold is None:\n raise Exception(\n f\"K-fold cross validation is passed but fold index in range [0, {config.folds}) is not specified.\"\n )\n\n if self.kfold:\n LOGGER.info(\"Using K-fold cross validation\")\n self._kfold_cross_val(training_elms)\n training_elms = self.df[self.df[\"fold\"] != fold][\"elm_events\"]\n validation_elms = self.df[self.df[\"fold\"] == fold][\"elm_events\"]\n else:\n LOGGER.info(\n \"Creating training and validation datasets by simple splitting\"\n )\n training_elms, validation_elms = model_selection.train_test_split(\n training_elms, test_size=self.fraction_validate\n )\n LOGGER.info(f\"Number of training ELM events: {training_elms.size}\")\n LOGGER.info(f\"Number of validation ELM events: {validation_elms.size}\")\n LOGGER.info(f\"Number of test ELM events: {test_elms.size}\")\n\n return training_elms, validation_elms, test_elms","def _kfold_cross_val(self, training_elms: np.ndarray) -> None:\n kf = model_selection.KFold(\n n_splits=config.folds, shuffle=True, random_state=config.seed\n )\n self.df[\"elm_events\"] = training_elms\n self.df[\"fold\"] = -1\n for f_, (_, valid_idx) in enumerate(kf.split(X=training_elms)):\n self.df.loc[valid_idx, \"fold\"] = f_","def get_train_input(self, prev, i):\n if i == 0:\n return self._zero_label\n else:\n # TODO(gorban): update to gradually introduce gt labels.\n return self._labels_one_hot[:, i - 1, :]","def get_init_fn():\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n variables_to_restore.append(var)\n\n checkpoint_path = tf.train.latest_checkpoint(\"./base_checkpoint\")\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=False)","def _get_init_fn():\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\n\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)","def get_validation_batch(self, batch_size):\n if batch_size > len(self.val_indices):\n return self.data_handler.slice_data(self.val_indices)\n else:\n return self.data_handler.slice_data(list(np.random.choice(self.val_indices, size=batch_size)))","def get_eval_input(self, prev, i):\n raise AssertionError('Not implemented')","def test_early_stopping_no_val_step(tmpdir):\n\n class CurrentModel(EvalModelTemplate):\n def training_step(self, *args, **kwargs):\n output = super().training_step(*args, **kwargs)\n output.update({'my_train_metric': output['loss']}) # could be anything else\n return output\n\n model = CurrentModel()\n model.validation_step = None\n model.val_dataloader = None\n\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\n trainer = Trainer(\n default_root_dir=tmpdir,\n early_stop_callback=stopping,\n overfit_pct=0.20,\n max_epochs=5,\n )\n result = trainer.fit(model)\n\n assert result == 1, 'training failed to complete'\n assert trainer.current_epoch < trainer.max_epochs","def get_input_fn(is_train):\n d = DataInfo(ddir,evalddir)\n hparams = d.generate()\n params = utils.Params(**hparams)\n\n if is_train:\n input_fn = data.get_input_fn(dataset_fn=data.get_train_dataset, mode=TRAIN, params=params, shuffle_queue=10000, repeat=False)\n \n else:\n input_fn = data.get_input_fn(dataset_fn=data.get_eval_dataset, mode=EVAL, params=params, shuffle_queue=10000, repeat=False)\n \n return input_fn, params","def get_eval_input(self, prev, i):\n if i == 0:\n prev = self._zero_label\n else:\n logit = self.char_logit(prev, char_index=i - 1)\n prev = self.char_one_hot(logit)\n image_feature = self.get_image_feature(char_index=i)\n return tf.concat([image_feature, prev], 1)","def from_checkpoint(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n ds_iter: Optional[tf.data.Iterator] = None,\n init_rng: Optional[jnp.ndarray] = None\n ) -> Optional[train_state_lib.TrainState]:\n train_states = list(\n self.from_checkpoints(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng))\n if len(train_states) > 1:\n raise ValueError(\n f'Expected at most 1 checkpoint but got {len(train_states)} for '\n f'config(s): {ckpt_cfgs}')\n return (train_states[0]) if train_states else None","def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):\n\n epoch = K.eval(self._ckpt_saved_epoch)\n if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0:\n # The most recently saved epoch is one epoch prior to the epoch it\n # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.\n return epoch + 1\n return initial_epoch","def _get_init_fn():\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n if tf.train.latest_checkpoint(FLAGS.train_dir):\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % FLAGS.train_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n # TODO(sguada) variables.filter_variables()\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def _get_init_fn():\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n if tf.train.latest_checkpoint(FLAGS.train_dir):\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % FLAGS.train_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n # TODO(sguada) variables.filter_variables()\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def get_infer_fn(infer_step: InferStepCallable, batch_size: int,\n train_state_axes: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner):\n infer_step = partitioner.partition(\n infer_step,\n in_axis_resources=(train_state_axes.params, PartitionSpec('data',)),\n out_axis_resources=PartitionSpec('data',))\n\n data_layout = partitioner.get_data_layout(batch_size)\n shard_id = data_layout.shard_id\n num_shards = data_layout.num_shards\n\n per_shard_batch_size = batch_size // num_shards\n\n def infer_fn(ds: tf.data.Dataset, train_state: train_state_lib.TrainState):\n ds_shapes = jax.tree_map(lambda x: jnp.array(x.shape), ds.element_spec)\n multihost_utils.assert_same(\n ds_shapes, 'Dataset element shapes do not agree across hosts. '\n 'This could be an indication that the dataset is nondeterministic.')\n try:\n dataset_remainder = len(ds) % batch_size # pytype:disable=wrong-arg-types\n logging.info('length of dataset = %s', len(ds))\n except TypeError as e:\n if str(e) == 'dataset length is unknown.':\n logging.warning(\n 'The following error is likely due to the use of TensorFlow v1 in '\n 'your dataset pipeline. Verify you are not importing from '\n '`tf.compat.v1` as part of your pipeline.')\n raise e\n\n if dataset_remainder:\n dataset_pad_amt = batch_size - dataset_remainder\n logging.info(\n 'Padding infer dataset with %d examples for even per-replica shards.',\n dataset_pad_amt)\n # Pad with the first example using an index of -1 so seqio will ignore.\n pad_ds = ds.take(1).map(lambda i, x: (np.int64(-1), x)).repeat(\n dataset_pad_amt)\n ds = ds.concatenate(pad_ds)\n\n # Shard the infer dataset across replica sets.\n sharded_ds = ds.shard(num_shards, shard_id).batch(\n per_shard_batch_size, drop_remainder=True)\n multihost_utils.assert_same(\n jnp.array(len(sharded_ds)),\n 'Dataset lengths do not agree across hosts.')\n\n logging.info(\n 'The infer dataset is sharded into %d shards with per-shard '\n 'batch size of %d', num_shards, per_shard_batch_size)\n\n # Run inference for each replica set.\n batched_results, all_indices = [], []\n for index, infer_batch in sharded_ds.as_numpy_iterator():\n # Run fast inference on batch.\n # [B, ...] -> [B, ...]\n batch_result = infer_step(train_state.params, infer_batch)\n logging.info('Inference of batch %s done.', index)\n # Issue asynchronous copy request which serves as prefetching to the host.\n # The result value is synchronized with host_allgather in the loop below.\n try:\n jax.tree_map(lambda x: x.copy_to_host_async(), batch_result)\n except AttributeError:\n # Similar to jax.device_get, we skip transfers for non DeviceArrays.\n pass\n batched_results.append(batch_result)\n all_indices.append(index)\n logging.info('Inference of all batches done.')\n all_inferences = []\n for batch_result in batched_results:\n # [B, ...] -> [H, B, ...]\n batch_result = multihost_utils.host_allgather(\n batch_result, num_shards, shard_id,\n data_layout.is_first_host_in_replica_set)\n all_inferences.append(batch_result)\n\n # List[H, B, ...] -> List[B, H, ...]\n all_inferences = jax.tree_map(lambda x: np.moveaxis(x, 0, 1),\n all_inferences)\n\n # List[B, H, ...] -> [B * batch_count, H, ...]\n all_inferences = jax.tree_multimap(lambda *args: np.concatenate(args),\n *all_inferences)\n # List[B] -> [B * batch_count]\n all_indices = np.concatenate(all_indices)\n # Collect all batches from across hosts.\n # [B * batch_count] -> [H, B * batch_count]\n all_indices = multihost_utils.host_allgather(\n all_indices, num_shards, shard_id,\n data_layout.is_first_host_in_replica_set)\n # [H, B * batch_count] -> [B * batch_count, H]\n all_indices = np.transpose(all_indices)\n all_inferences, all_indices = _deshard_and_remove_padding(\n all_inferences, all_indices)\n\n # Translate [B, ...] -> List[...] by flattening inferences making sure to\n # preserve structure of individual elements (inferences are not assumed to\n # be simple np.array). Finally, zip inferences with corresponding indices\n # and convert leaf np.arrays into lists.\n all_inferences, struct = jax.tree_flatten(all_inferences)\n all_inferences = map(\n functools.partial(jax.tree_unflatten, struct), zip(*all_inferences))\n indices_and_outputs = list(zip(all_indices, all_inferences))\n indices_and_outputs = jax.tree_map(lambda x: np.array(x).tolist(),\n indices_and_outputs)\n return indices_and_outputs\n\n return infer_fn","def test_no_fit(self, example_dataset):\n\n transformer = PreprocessFeatures()\n\n with pytest.raises(NotFittedError):\n transformer.transform(example_dataset)","def get_fit_intercept(self):\n return self.fit_intercept","def get_inputs_train():\n x = tf.constant(extract_pandas_data(x_train))\n y = tf.constant(y_train.values)\n return x, y","def find_input_dataset(stage, rconfig, data_type=None):\n\n # Use the stage-to-data mapping to find the data_type if none was handed in\n if data_type is None:\n data_type = DOCUMENT_PROCESSING_IO[stage]['in']\n # Get all data sets D for input name\n dirname = os.path.join(rconfig.target_path, 'data', data_type)\n datasets1 = [ds for ds in os.listdir(dirname) if ds.isdigit()]\n datasets2 = [DataSet(stage, data_type, rconfig, ds) for ds in datasets1]\n # Filer the datasets making sure that d.trace + d.head matches\n # rconfig.pipeline(txt).trace\n datasets3 = [ds for ds in datasets2 if ds.input_matches_global_config()]\n # If there is one result, return it, otherwise write a warning and exit\n if len(datasets3) == 1:\n return datasets3[0]\n elif len(datasets3) > 1:\n print \"WARNING, more than one approriate training set:\"\n for ds in datasets3:\n print ' ', ds\n sys.exit(\"Exiting...\")\n elif len(datasets3) == 0:\n print \"WARNING: no datasets available to meet input requirements\"\n sys.exit(\"Exiting...\")","def _get_training_dataset(self):\n\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))","def _generate_validation_fold(self):\n\n for offset in range(self.nb_folds):\n # Load all the data from cache (do this to save memory)\n with open(self.data_cache_path_str + \"data_cache.pkl\", \"rb\") as f:\n data_df, target_df = pickle.load(f)\n\n # Generate train and test sets\n data_dates_lst = data_df[\"date\"].drop_duplicates().sort_values().tolist()\n train_start_day = len(data_dates_lst) - ((self.nb_folds - offset) * self.test_nb_days + self.train_nb_days)\n train_end_day = train_start_day + self.train_nb_days\n test_start_day = train_end_day\n test_end_day = test_start_day + self.test_nb_days\n\n train_dates_lst = data_dates_lst[train_start_day:train_end_day]\n test_dates_lst = data_dates_lst[test_start_day:test_end_day]\n\n # Generate train and test labels\n training_set_df = data_df.loc[data_df[\"date\"].isin(train_dates_lst)].reset_index(drop = True)\n testing_set_df = data_df.loc[data_df[\"date\"].isin(test_dates_lst)].reset_index(drop = True)\n new_target_df = target_df.loc[data_df[\"date\"].isin(train_dates_lst)].reset_index(drop = True)\n truth_df = target_df.loc[data_df[\"date\"].isin(test_dates_lst)].reset_index(drop = True)\n\n # Reduce memory usage\n del data_df, target_df\n gc.collect()\n\n # Return result\n yield (training_set_df, testing_set_df, new_target_df, truth_df)","def get_validation_iterator(self) -> tf.contrib.data.Iterator:\n return self.validation.make_initializable_iterator()","def get_inputs_test_S():\n x = tf.constant(extract_pandas_data(x_test_S))\n y = tf.constant(y_test_S.values)\n return x, y","def train_input_fn():\n # When choosing shuffle buffer sizes, larger sizes result in better\n # randomness, while smaller sizes use less memory. MNIST is a small\n # enough dataset that we can easily shuffle the full epoch.\n ds = dtrain(flags_obj.data_dir)\n ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)\n\n # Iterate through the dataset a set number (`epochs_between_evals`) of times\n # during each training session.\n ds = ds.repeat()\n return ds","def _evaluate_during_fit(self, test_loader, epoch):","def get_eval_input(self, prev, i):\n if i == 0:\n return self._zero_label\n else:\n logit = self.char_logit(prev, char_index=i - 1)\n return self.char_one_hot(logit)","def train(\n self, training_data: Dataset, validation_data: Optional[Dataset] = None\n ) -> Predictor:\n raise NotImplementedError","def test_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,\n with_preprocessor):\n input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)\n estimator = clone(estimator)\n estimator.set_params(preprocessor=preprocessor)\n set_random_state(estimator)\n with pytest.raises(NotFittedError):\n estimator.predict(input_data)","def locate_input(self,il):\n if il.tp == opmod.no_input or il.val is None:\n return None\n elif il.tp == opmod.basic_type:\n return il.val\n elif il.tp == opmod.entire_workflow:\n wf = self.workflows[il.val]\n stk,diag = wf.execution_stack()\n self.prepare_wf(wf,stk)\n return wf\n #return self.workflows[il.val]\n elif il.tp == opmod.plugin_item:\n if isinstance(il.val,list):\n return [self.plugin_manager.get_data_from_uri(v) for v in il.val]\n else:\n return self.plugin_manager.get_data_from_uri(il.val)\n # changed: let basic_type inputs be loaded directly,\n # without using InputLocators.\n #elif il.tp == opmod.basic_type:\n # return il.val","def get_init_fn():\n\tif train_config['checkpoint_path'] is None:\n\t\treturn None\n\t# Warn the user if a checkpoint exists in the train_dir. Then we'll be\n\t# ignoring the checkpoint anyway.\n\tif tf.train.latest_checkpoint(train_config['checkpoint_path']):\n\t\ttf.logging.info(\n\t\t\t'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n\t\t\t% train_config['checkpoint_path'])\n\t\treturn None\n\n\texclusions = []\n\tif train_config['checkpoint_exclude_scopes']:\n\t\texclusions = [scope.strip()\n\t\t for scope in train_config['checkpoint_exclude_scopes'].split(',')]\n\n\t\tvariable_to_restore = []\n\t\tfor var in slim.get_model_variables():\n\t\t\texcluded = False\n\t\t\tfor exclusion in exclusions:\n\t\t\t\tif var.op.name.startswith(exclusion):\n\t\t\t\t\texcluded = True\n\t\t\t\t\tbreak\n\t\t\tif not excluded:\n\t\t\t\tvariable_to_restore.append(var)\n\n\t\tif tf.gfile.IsDirectory(train_config['checkpoint_path']):\n\t\t\tcheckpoint_path = tf.train.latest_checkpoint(train_config['checkpoint_path'])\n\t\telse:\n\t\t\tcheckpoint_path = train_config['checkpoint_path']\n\n\t\ttf.logging.info('Fune-tuning from %s' % checkpoint_path)\n\n\t\treturn slim.assign_from_checkpoint_fn(\n\t\t\tcheckpoint_path,\n\t\t\tvariable_to_restore,\n\t\t\tignore_missing_vars=train_config['ignore_missing_vars'])","def get_inputs_test():\n x = tf.constant(extract_pandas_data(x_test))\n y = tf.constant(y_test.values)\n return x, y","def _make_train_input_fn(is_classification):\n\n def _input_fn():\n features = dict(FEATURES_DICT)\n if is_classification:\n labels = CLASSIFICATION_LABELS\n else:\n labels = REGRESSION_LABELS\n return features, labels\n\n return _input_fn","def input_fn(evaluate=False) -> tf.data.Dataset:\n\n # The dataset\n ds = tf.data.Dataset.from_generator( generator=train_generator, \n output_types=( { 'character' : tf.string } , tf.string ),\n output_shapes=( { 'character' : (Model.SEQUENCE_LENGHT,) } , () )\n )\n\n ds = ds.batch(64)\n ds = ds.prefetch(1)\n\n return ds","def _restore_data_inputs(self):\n super()._restore_data_inputs()\n self.training_data = (\n self._data.training_data.data if self._data.training_data and self._data.training_data.data else None\n )\n self.validation_data = (\n self._data.validation_data.data if self._data.validation_data and self._data.validation_data.data else None\n )","def prepare_train_validation(self) -> Tuple:\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\n\n Xt = self.dataset.prepare_input_samples(Xt)\n Yt = self.dataset.prepare_output_samples(Yt)\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\n traindataset = traindataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n Xv = self.dataset.prepare_input_samples(Xv)\n Yv = self.dataset.prepare_output_samples(Yv)\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\n validdataset = validdataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n return traindataset, validdataset","def input_fn(self, ctx=None):\n dataset = self.make_parsed_dataset(ctx)\n\n # Prefetch overlaps in-feed with training\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n\n if self.is_training:\n options = tf.data.Options()\n options.experimental_deterministic = False\n dataset = dataset.with_options(options)\n\n return dataset","def validation_step(self, *args: Any, **kwargs: Any) -> None:\n batch = args[0]\n batch_idx = args[1]\n x = batch[\"image\"]\n y = batch[\"mask\"]\n y_hat = self(x)\n y_hat_hard = y_hat.argmax(dim=1)\n\n loss = self.loss(y_hat, y)\n\n self.log(\"val_loss\", loss, on_step=False, on_epoch=True)\n self.val_metrics(y_hat_hard, y)\n\n if (\n batch_idx < 10\n and hasattr(self.trainer, \"datamodule\")\n and self.logger\n and hasattr(self.logger, \"experiment\")\n and hasattr(self.logger.experiment, \"add_figure\")\n ):\n try:\n datamodule = self.trainer.datamodule\n batch[\"prediction\"] = y_hat_hard\n for key in [\"image\", \"mask\", \"prediction\"]:\n batch[key] = batch[key].cpu()\n sample = unbind_samples(batch)[0]\n fig = datamodule.plot(sample)\n summary_writer = self.logger.experiment\n summary_writer.add_figure(\n f\"image/{batch_idx}\", fig, global_step=self.global_step\n )\n plt.close()\n except ValueError:\n pass","def get_data(self):\n if self.config['model'] == 'vggnet':\n if self.is_training:\n return self.data.shuffle(self.shuffle).batch(self.batch_size)\n elif self.is_testing:\n return self.data.batch(self.batch_size)\n elif not self.is_testing and not self.is_training:\n return self.data.batch(self.batch_size)\n else:\n raise NotImplementedError('In dataset.py: default input not specified for this model!')","def make_input_fn(step_output):\n return tf.nn.embedding_lookup(embeddings, step_output.predictions)","def _get_init_fn(checkpoints_dir_y):\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n # checkpoints_dir_y = namings.generate_checkpoints_dir(FLAGS, image_size)\n if tf.train.latest_checkpoint(checkpoints_dir_y): #FLAGS.checkpoints_dir): # YY\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % checkpoints_dir_y) # YY: FLAGS.checkpoints_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def get_input_fn(options, is_training):\n if not isinstance(options, reader_pb2.VCRReader):\n raise ValueError('options has to be an instance of Reader.')\n\n def _input_fn(input_pipeline_context=None):\n \"\"\"Returns a python dictionary.\n\n Returns:\n A dataset that can be fed to estimator.\n \"\"\"\n return _create_dataset(options, is_training, input_pipeline_context)\n\n return _input_fn","def get_test(self, preprocess=False):\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')","def predict_single_fold(self, model: TorchBasedLinearEstimator, dataset: TabularDataset) -> np.ndarray:\n pred = model.predict(dataset.data)\n\n return pred","def from_checkpoint_or_scratch(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n init_rng: Array,\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\n return (self.from_checkpoint(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng)\n or self.from_scratch(init_rng))","def get_train_input(self, prev, i):\n if i == 0:\n prev = self._zero_label\n else:\n prev = self._labels_one_hot[:, i - 1, :]\n image_feature = self.get_image_feature(i)\n return tf.concat([image_feature, prev], 1)","def input_fn_by_dataset_with_fake_data(input_size, padding_length):\n def _input_fn(params):\n \"\"\"A `input_fn` returning features and labels.\"\"\"\n batch_size = params['batch_size']\n inputs, labels, lengths = sequence_example_lib.get_fake_data_batch(\n batch_size, input_size, padding_length)\n\n features = {\n 'inputs': inputs,\n 'lengths': lengths\n }\n return features, labels\n return _input_fn","def get_input_fn(batch_size, dataset_path, train):\n\n with open(dataset_path, 'rb') as f:\n cifar = pickle.load(f, encoding='bytes')\n images_array = cifar[b'data']\n labels_array = cifar[b'coarse_labels']\n\n images_array = (images_array.reshape((-1, 3, 32, 32))\n .transpose((0, 2, 3, 1)))\n labels_array = LabelBinarizer().fit_transform(labels_array)\n init_hook = InitHook()\n\n def input_fn():\n images_input = tf.placeholder(dtype=tf.float32,\n shape=images_array.shape,\n name='images')\n labels_input = tf.placeholder(dtype=tf.float32,\n shape=labels_array.shape,\n name='labels')\n\n inputs = (images_input, labels_input)\n dset = tf.data.Dataset.from_tensor_slices(inputs)\n dset = dset.shuffle(len(images_array)).batch(batch_size)\n if train:\n dset = dset.repeat()\n\n iterator = dset.make_initializable_iterator()\n init_hook.init = iterator.initializer\n init_hook.feed_dict = {images_input: images_array,\n labels_input: labels_array}\n next_images, next_labels = iterator.get_next()\n return {'images': next_images}, next_labels\n\n return input_fn, init_hook","def get_validation_data(self):\n return self.unzip_batch(self.valid)","def test_fit_returns_self(self):\n\n df = d.create_df_1()\n\n x = BaseTransformer(columns=\"a\")\n\n x_fitted = x.fit(df)\n\n assert x_fitted is x, \"Returned value from BaseTransformer.fit not as expected.\"","def input_fn(self, ctx=None):\n sup_dataset = self.supervised_input.make_parsed_dataset(ctx)\n unsup_dataset = self.unsupervised_input.make_parsed_dataset(ctx)\n\n dataset = tf.data.Dataset.zip((sup_dataset, unsup_dataset))\n dataset = dataset.map(_combine_sup_unsup_datasets)\n\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n\n options = tf.data.Options()\n options.experimental_deterministic = False\n dataset = dataset.with_options(options)\n\n return dataset","def _get_input(cfg,\n mode='train',\n rank=None,\n dir_name_init: Optional[list] = None,\n img_name_init: Optional[list] = None,\n min_size: Optional[tuple] = None,\n image_only_test: bool = True):\n def _validate_shape(cfg, image, mask, i):\n if image is None:\n return\n\n if cfg.DATASET.LOAD_2D:\n assert image[i].shape[1:] == mask[i].shape[1:]\n else:\n assert image[i].shape == mask[i].shape[-3:]\n\n assert mode in ['train', 'val', 'test']\n dir_path = cfg.DATASET.INPUT_PATH\n if dir_name_init is not None:\n dir_name = dir_name_init\n else:\n dir_name = _get_file_list(dir_path)\n\n if mode == 'val':\n img_name = cfg.DATASET.VAL_IMAGE_NAME\n label_name = cfg.DATASET.VAL_LABEL_NAME\n valid_mask_name = cfg.DATASET.VAL_VALID_MASK_NAME\n pad_size = cfg.DATASET.VAL_PAD_SIZE\n else:\n img_name = cfg.DATASET.IMAGE_NAME\n label_name = cfg.DATASET.LABEL_NAME\n valid_mask_name = cfg.DATASET.VALID_MASK_NAME\n pad_size = cfg.DATASET.PAD_SIZE\n assert not all([elem == None for elem in [img_name, label_name]]), \\\n \"At least one of img_name and label_name should not be None!\"\n\n volume, label, valid_mask = None, None, None\n if img_name_init is not None:\n img_name = img_name_init\n\n if img_name is not None:\n img_name = _get_file_list(img_name, prefix=dir_path)\n img_name = _make_path_list(cfg, dir_name, img_name, rank)\n volume = [None] * len(img_name)\n print(rank, len(img_name), list(map(os.path.basename, img_name)))\n\n if _load_label_condition(label_name, mode, image_only_test):\n label_name = _get_file_list(label_name, prefix=dir_path)\n label_name = _make_path_list(cfg, dir_name, label_name, rank)\n label = [None]*len(label_name)\n print(rank, len(label_name), list(map(os.path.basename, label_name)))\n\n if _load_label_condition(valid_mask_name, mode, image_only_test):\n valid_mask_name = _get_file_list(valid_mask_name, prefix=dir_path)\n valid_mask_name = _make_path_list(cfg, dir_name, valid_mask_name, rank)\n valid_mask = [None]*len(valid_mask_name)\n\n pad_mode = cfg.DATASET.PAD_MODE\n read_fn = readvol if not cfg.DATASET.LOAD_2D else readimg_as_vol\n num_vols = len(img_name) if img_name is not None else len(label_name)\n\n for i in range(num_vols):\n if volume is not None:\n volume[i] = read_fn(img_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\n print(f\"volume shape (original): {volume[i].shape}\")\n if cfg.DATASET.NORMALIZE_RANGE:\n volume[i] = normalize_range(volume[i])\n volume[i] = _rescale(volume[i], cfg.DATASET.IMAGE_SCALE, order=3)\n volume[i] = _pad(volume[i], pad_size, pad_mode)\n volume[i] = _resize2target(volume[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\n order=3, target_size=min_size)\n print(f\"volume shape (after scaling and padding): {volume[i].shape}\")\n\n if label is not None:\n label[i] = read_fn(label_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\n if cfg.DATASET.LABEL_VAST:\n label[i] = vast2Seg(label[i])\n if label[i].ndim == 2: # make it into 3D volume\n label[i] = label[i][None, :]\n if cfg.DATASET.LABEL_BINARY and label[i].max() > 1:\n label[i] = label[i] // 255\n if cfg.DATASET.LABEL_MAG != 0:\n label[i] = (label[i]/cfg.DATASET.LABEL_MAG).astype(np.float32)\n\n label[i] = _rescale(label[i], cfg.DATASET.LABEL_SCALE, order=0) # nearest\n label[i] = _pad(label[i], pad_size, pad_mode)\n label[i] = _resize2target(label[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\n order=0, target_size=min_size)\n print(f\"label shape (after scaling and padding): {label[i].shape}\")\n _validate_shape(cfg, volume, label, i)\n\n if valid_mask is not None:\n valid_mask[i] = read_fn(valid_mask_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\n valid_mask[i] = _rescale(valid_mask[i], cfg.DATASET.VALID_MASK_SCALE, order=0)\n valid_mask[i] = _pad(valid_mask[i], pad_size, pad_mode)\n valid_mask[i] = _resize2target(valid_mask[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\n order=0, target_size=min_size)\n print(f\"valid_mask shape (after scaling and padding): {valid_mask[i].shape}\")\n _validate_shape(cfg, volume, valid_mask, i)\n\n return volume, label, valid_mask","def experiment(\n self,\n dataset: Union[str, dict, pd.DataFrame] = None,\n training_set: Union[str, dict, pd.DataFrame] = None,\n validation_set: Union[str, dict, pd.DataFrame] = None,\n test_set: Union[str, dict, pd.DataFrame] = None,\n training_set_metadata: Union[str, dict] = None,\n data_format: str = None,\n experiment_name: str = \"experiment\",\n model_name: str = \"run\",\n model_load_path: str = None,\n model_resume_path: str = None,\n eval_split: str = TEST,\n skip_save_training_description: bool = False,\n skip_save_training_statistics: bool = False,\n skip_save_model: bool = False,\n skip_save_progress: bool = False,\n skip_save_log: bool = False,\n skip_save_processed_input: bool = False,\n skip_save_unprocessed_output: bool = False,\n skip_save_predictions: bool = False,\n skip_save_eval_stats: bool = False,\n skip_collect_predictions: bool = False,\n skip_collect_overall_stats: bool = False,\n output_directory: str = \"results\",\n random_seed: int = default_random_seed,\n **kwargs,\n ) -> Tuple[Optional[dict], TrainingStats, PreprocessedDataset, str]:\n if self._user_config.get(HYPEROPT):\n print_boxed(\"WARNING\")\n logger.warning(HYPEROPT_WARNING)\n\n (train_stats, preprocessed_data, output_directory) = self.train(\n dataset=dataset,\n training_set=training_set,\n validation_set=validation_set,\n test_set=test_set,\n training_set_metadata=training_set_metadata,\n data_format=data_format,\n experiment_name=experiment_name,\n model_name=model_name,\n model_load_path=model_load_path,\n model_resume_path=model_resume_path,\n skip_save_training_description=skip_save_training_description,\n skip_save_training_statistics=skip_save_training_statistics,\n skip_save_model=skip_save_model,\n skip_save_progress=skip_save_progress,\n skip_save_log=skip_save_log,\n skip_save_processed_input=skip_save_processed_input,\n skip_save_unprocessed_output=skip_save_unprocessed_output,\n output_directory=output_directory,\n random_seed=random_seed,\n )\n\n (training_set, validation_set, test_set, training_set_metadata) = preprocessed_data\n\n eval_set = validation_set\n if eval_split == TRAINING:\n eval_set = training_set\n elif eval_split == VALIDATION:\n eval_set = validation_set\n elif eval_split == TEST:\n eval_set = test_set\n else:\n logger.warning(f\"Eval split {eval_split} not supported. \" f\"Using validation set instead\")\n\n if eval_set is not None:\n trainer_dict = self.config_obj.trainer.to_dict()\n batch_size = trainer_dict.get(EVAL_BATCH_SIZE, trainer_dict.get(BATCH_SIZE, None))\n\n # predict\n try:\n eval_stats, _, _ = self.evaluate(\n eval_set,\n data_format=data_format,\n batch_size=batch_size,\n output_directory=output_directory,\n skip_save_unprocessed_output=skip_save_unprocessed_output,\n skip_save_predictions=skip_save_predictions,\n skip_save_eval_stats=skip_save_eval_stats,\n collect_predictions=not skip_collect_predictions,\n collect_overall_stats=not skip_collect_overall_stats,\n return_type=\"dict\",\n )\n except NotImplementedError:\n logger.warning(\n \"Skipping evaluation as the necessary methods are not \"\n \"supported. Full exception below:\\n\"\n f\"{traceback.format_exc()}\"\n )\n eval_stats = None\n else:\n logger.warning(f\"The evaluation set {eval_set} was not provided. \" f\"Skipping evaluation\")\n eval_stats = None\n\n return eval_stats, train_stats, preprocessed_data, output_directory","def cross_validation_datasets(self, fold):\n if fold > len(self): fold = len(self) / 2\n stratified = self.stratified_bunches(fold)\n datasets = []\n for index in range(len(stratified)):\n gold = GoldInstances(training_as_gold(stratified[index]))\n rest = flatten(stratified[:index]) + flatten(stratified[index + 1:])\n training = TrainingInstances(rest)\n datasets.append((training, gold))\n return datasets","def keep_cross_validation_fold_assignment(self):\n return self._parms.get(\"keep_cross_validation_fold_assignment\")","def fold_data(fold, datas):\n training, validation = fold\n folded = {}\n\n # fold Xtrain\n if datas.get('Xtrain') is not None:\n folded['Xtrain'] = datas['Xtrain'][training, :]\n folded['Xtest'] = datas['Xtrain'][validation, :]\n\n # fold Ktrain\n if datas.get('Ktrain') is not None:\n folded['Ktrain'] = datas['Ktrain'][training, :][:, training]\n folded['Ktest'] = datas['Ktrain'][validation, ][:, training]\n # if datas.get('Ktest') is not None:\n # folded['Ktest'] = datas['Ktest'][:, validation]\n # else:\n # folded['Ktest'] = folded['Xtest'].dot(folded['Xtrain'].T)\n\n # fold Ytrain\n if datas.get('Ytrain') is not None:\n folded['Ytrain'] = datas['Ytrain'][training, :]\n folded['Ytest'] = datas['Ytrain'][validation, :]\n\n return folded","def input_example(self, max_batch=1, max_dim=8192):\n device = next(self.parameters()).device\n input_example = torch.randn(max_batch, self._feat_in, max_dim, device=device)\n lens = torch.full(size=(input_example.shape[0],), fill_value=max_dim, device=device)\n return tuple([input_example, lens])","def _verify_training_data(self):\n if self.training_fluxes.shape != self.training_flux_uncertainties.shape:\n raise ValueError(\n \"the training flux and uncertainty arrays should \"\n \"have the same shape\")\n\n if len(self.training_labels) == 0 \\\n or self.training_labels.dtype.names is None:\n raise ValueError(\"no named labels provided for the training set\")\n\n if len(self.training_labels) != self.training_fluxes.shape[0]:\n raise ValueError(\n \"the first axes of the training flux array should \"\n \"have the same shape as the nuber of rows in the label table \"\n \"(N_stars, N_pixels)\")\n\n if self.dispersion is not None:\n dispersion = np.atleast_1d(self.dispersion).flatten()\n if dispersion.size != self.training_fluxes.shape[1]:\n raise ValueError(\n \"mis-match between the number of wavelength \"\n \"points ({0}) and flux values ({1})\".format(\n self.training_fluxes.shape[1], dispersion.size))\n return None","def train_input_fn():\n # Initialize `iterator` with training data.\n train_filenames = [os.path.join(FLAGS.out_dir, FLAGS.train_records)]\n return _input_fn(train_filenames, FLAGS.epochs, FLAGS.batch_size, shuffle=True)","def unpack_validation_data(validation_data, raise_if_ambiguous=True):\n if (isinstance(validation_data, (iterator_ops.Iterator,\n iterator_ops.IteratorBase,\n data_types.DatasetV2,\n data_utils.Sequence))\n or not hasattr(validation_data, '__len__')):\n val_x = validation_data\n val_y = None\n val_sample_weight = None\n elif len(validation_data) == 2:\n try:\n val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence\n val_sample_weight = None\n except ValueError:\n val_x, val_y, val_sample_weight = validation_data, None, None\n elif len(validation_data) == 3:\n try:\n val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence\n except ValueError:\n val_x, val_y, val_sample_weight = validation_data, None, None\n else:\n if raise_if_ambiguous:\n raise ValueError(\n 'When passing a `validation_data` argument, '\n 'it must contain either 2 items (x_val, y_val), '\n 'or 3 items (x_val, y_val, val_sample_weights), '\n 'or alternatively it could be a dataset or a '\n 'dataset or a dataset iterator. '\n 'However we received `validation_data=%s`' % validation_data)\n val_x, val_y, val_sample_weight = validation_data, None, None\n return val_x, val_y, val_sample_weight","def example(self):\n result = getattr(self, '_example', None)\n if result is None:\n # No example batch was found, so get one from the `.train` dataset\n result = next(iter(self.train))\n # And cache it for next time\n self._example = result\n return result","def build_training_input_fn():\n transformed_metadata = metadata_io.read_metadata(\n os.path.join(\n MODEL_DIR, transform_fn_io.TRANSFORMED_METADATA_DIR))\n transformed_feature_spec = transformed_metadata.schema.as_feature_spec()\n\n def input_fn():\n \"\"\"Input function for training and eval.\"\"\"\n dataset = tf.contrib.data.make_batched_features_dataset(\n file_pattern=os.path.join(TFRECORD_DIR, '*'),\n batch_size=BATCH_SIZE,\n features=transformed_feature_spec,\n reader=tf.data.TFRecordDataset,\n shuffle=True)\n transformed_features = dataset.make_one_shot_iterator().get_next()\n # Extract features and labels from the transformed tensors.\n label_cols = set(['TotalVolume', 'Density', 'Temperature', 'Humidity', 'Energy', 'Problems'])\n transformed_labels = {key: value for (key, value) in transformed_features.items() if key in label_cols}\n transformed_features = {key: value for (key, value) in transformed_features.items() if key not in label_cols}\n return transformed_features, transformed_labels\n\n return input_fn","def input(self):\n try:\n return self.inputs[-1]\n except IndexError:\n pass\n raise ValueError(\"The sample method has not been called\")","def train_input_fn(self) -> types.FeatureAndLabelTensors:\n return self._input_fn_from_file(self._train_path)","def load_folds(options=None, df=None):\n if df is not None and 'fold' in df.columns:\n i_train = df.query(\"fold != 'test'\").index.to_numpy()\n i_test = df.query(\"fold == 'test'\").index.to_numpy()\n return [(i_train, i_test)]\n print('No folds specified in CSV file')\n\n if options.folds == 'weak':\n return save_weak_folds(df)\n return save_folds(df)","def k_fold_inf(\n config,\n df,\n log_folder=None,\n use_full_size=True,\n global_threshold=None,\n use_tta=False,\n save=False,\n save_all_tta=False,\n):\n folds = df[config.cv_column].unique()\n scores = []\n\n for i, fold in enumerate(folds):\n if i in config.selected_folds:\n print(f\"\\n------------- Fold {i + 1} / {len(folds)} -------------\\n\")\n df_val = df[df[config.cv_column] == fold].reset_index()\n\n val_images = df_val[\"tile_name\"].apply(lambda x: x.split(\"_\")[0]).unique()\n\n model = define_model(\n config.decoder,\n config.encoder,\n num_classes=config.num_classes,\n encoder_weights=config.encoder_weights,\n ).to(config.device)\n model.zero_grad()\n model.eval()\n\n load_model_weights(\n model, log_folder + f\"{config.decoder}_{config.encoder}_{i}.pt\"\n )\n\n scores += validate_inf(\n model,\n config,\n val_images,\n log_folder=log_folder,\n use_full_size=use_full_size,\n global_threshold=global_threshold,\n use_tta=use_tta,\n save=save,\n save_all_tta=save_all_tta,\n )\n\n return scores","def create_master(exp, var, data=None, new_fields=[]):\n\n if isinstance(var, basestring):\n assert data is not None\n data_dict = data\n new_fields.append(var)\n else:\n # see if it's a Var, and access metadata from the associated\n # Experiment\n if data is None:\n data_dict = var.data\n else:\n data_dict = data\n new_fields.append(var.varname)\n new_fields.extend(var.oldvar)\n\n all_case_vals = exp.all_case_vals()\n\n # Post-process the case inspection a bit:\n # 1) Promote any single-value case to a list with one entry\n for i, case_vals in enumerate(all_case_vals):\n if isinstance(case_vals, str):\n all_case_vals[i] = list(case_vals)\n\n # 2) Make sure they're all still in the data dictionary. This is\n # circular but a necessary sanity check\n for case_bits in product(*all_case_vals):\n assert case_bits in data_dict\n\n # Discover the type of the data passed into this method. If\n # it's an xarray type, we'll preserve that. If it's an iris type,\n # then we need to crash for now.\n first_case = next(exp.all_cases())\n\n proto = data_dict[first_case]\n if isinstance(proto, Dataset):\n return _master_dataset(exp, data_dict, new_fields)\n elif isinstance(proto, DataArray):\n return _master_dataarray(exp, data_dict)\n # elif isinstance(proto, Cube):\n # raise NotImplementedError(\"Cube handling not yet implemented\")\n else:\n raise ValueError(\"Data must be an xarray type\")","def get_feed(data_feeds, data_loader, model_config, is_train):\n input_feed_dict = {}\n excluded_example_count = 0\n\n for data_feed in data_feeds:\n tmp_contexts, tmp_targets, tmp_lines = [], [], []\n tmp_masked_contexts, tmp_masked_words = [], []\n example_in_batch_count = 0\n while example_in_batch_count < model_config.batch_size:\n if model_config.it_train:\n example = next(data_loader.data_it, None)\n else:\n # TODO: not tested\n example = data_loader.get_sample()\n\n # mainly used in evaluation when testset file reaches EOF, create a dummy input to feed model\n if example is None:\n example = {}\n example['contexts'] = [0] * model_config.max_context_len\n example['target'] = {'pos_id': 0,\n 'abbr_id': 0,\n 'abbr': None,\n 'sense_id': 0,\n 'sense': None,\n 'line_id': data_loader.size,\n 'inst_id': 0\n }\n example['line'] = ''\n # sample['def'] = [0] * model_config.max_def_len\n # sample['stype'] = 0\n excluded_example_count += 1 # Assume eval use single GPU\n\n # print(example_in_batch_count)\n # print(excluded_example_count)\n # print(example)\n\n tmp_contexts.append(example['contexts'])\n tmp_targets.append(example['target'])\n tmp_lines.append(example['line'])\n # print('input:\\t%s\\t%s.' % (sample['line'], sample['target']))\n if model_config.lm_mask_rate and 'cur_masked_contexts' in example:\n tmp_masked_contexts.append(example['cur_masked_contexts'])\n tmp_masked_words.append(example['masked_words'])\n\n # print('done one example, current len(batch)=%d' % len(tmp_contexts))\n example_in_batch_count += 1\n\n for step in range(model_config.max_context_len):\n input_feed_dict[data_feed['contexts'][step].name] = [\n tmp_contexts[batch_idx][step]\n for batch_idx in range(model_config.batch_size)]\n\n if model_config.hub_module_embedding:\n input_feed_dict[data_feed['text_input'].name] = [\n tmp_lines[batch_idx]\n for batch_idx in range(model_config.batch_size)]\n\n input_feed_dict[data_feed['abbr_inp'].name] = [\n tmp_targets[batch_idx]['abbr_id']\n for batch_idx in range(model_config.batch_size)\n ]\n\n input_feed_dict[data_feed['sense_inp'].name] = [\n tmp_targets[batch_idx]['sense_id']\n for batch_idx in range(model_config.batch_size)\n ]\n\n if model_config.lm_mask_rate and tmp_masked_contexts:\n i = 0\n while len(tmp_masked_contexts) < model_config.batch_size:\n tmp_masked_contexts.append(tmp_masked_contexts[i % len(tmp_masked_contexts)])\n tmp_masked_words.append(tmp_masked_words[i % len(tmp_masked_contexts)])\n i += 1\n\n for step in range(model_config.max_context_len):\n input_feed_dict[data_feed['masked_contexts'][step].name] = [\n tmp_masked_contexts[batch_idx][step]\n for batch_idx in range(model_config.batch_size)]\n\n for step in range(model_config.max_subword_len):\n input_feed_dict[data_feed['masked_words'][step].name] = [\n tmp_masked_words[batch_idx][1][step]\n for batch_idx in range(model_config.batch_size)]\n\n return input_feed_dict, excluded_example_count, tmp_targets","def get_train_index():\n data_size = (NUM_CLASS - 1) * NUM_DATA_PER_CLASS\n return np.array([i for i in range(0, data_size)])","def get_input_datasets(cfg):\n input_data = mlr.get_input_data(cfg,\n pattern=cfg.get('pattern'),\n ignore=cfg.get('ignore'))\n tags = list(group_metadata(input_data, 'tag').keys())\n if len(tags) != 1:\n raise ValueError(\n f\"Expected unique 'tag' for all input datasets, got {len(tags):d} \"\n f\"different ones ({tags})\")\n return input_data","def input_example(self, max_batch=1, max_dim=256):\n input_example = torch.randn(max_batch, self.input_feat_in, max_dim).to(next(self.parameters()).device)\n return tuple([input_example])","def fit_predict_single_fold(\n self, train: TabularDataset, valid: TabularDataset\n ) -> Tuple[TorchBasedLinearEstimator, np.ndarray]:\n if type(train) is PandasDataset:\n train = train.to_numpy()\n valid = valid.to_numpy()\n\n model = self._infer_params()\n\n model.fit(\n train.data,\n train.target,\n train.weights,\n valid.data,\n valid.target,\n valid.weights,\n )\n\n val_pred = model.predict(valid.data)\n\n return model, val_pred","def cross_validate(self, pre_train=None, **kwargs):\n \n inferred = np.nan * np.ones_like(self.labels_array)\n N_training_set, N_labels = inferred.shape\n N_stop_at = kwargs.pop(\"N\", N_training_set)\n\n debug = kwargs.pop(\"debug\", False)\n \n kwds = { \"threads\": self.threads }\n kwds.update(kwargs)\n\n for i in range(N_training_set):\n \n training_set = np.ones(N_training_set, dtype=bool)\n training_set[i] = False\n\n # Create a clean model to use so we don't overwrite self.\n model = self.__class__(\n self.training_labels[training_set],\n self.training_fluxes[training_set],\n self.training_flux_uncertainties[training_set],\n **kwds)\n\n # Initialise and run any pre-training function.\n for _attribute in self._descriptive_attributes:\n setattr(model, _attribute[1:], getattr(self, _attribute[1:]))\n\n if pre_train is not None:\n pre_train(self, model)\n\n # Train and solve.\n model.train()\n\n try:\n inferred[i, :] = model.fit(self.training_fluxes[i],\n self.training_flux_uncertainties[i], full_output=False)\n\n except:\n logger.exception(\"Exception during cross-validation on object \"\n \"with index {0}:\".format(i))\n if debug: raise\n\n if i == N_stop_at + 1:\n break\n\n return inferred[:N_stop_at, :]","def make_predict_step(self):\n return self.make_eval_step()","def getTrainingData(self):\n raise NotImplementedError","def _save_input(self, mod, i):\n if mod.training:\n self.state[mod][\"x\"] = i[0]","def test_tracin_require_inputs_dataset(\n self,\n reduction,\n tracin_constructor: Callable,\n ) -> None:\n with tempfile.TemporaryDirectory() as tmpdir:\n (\n net,\n train_dataset,\n test_samples,\n test_labels,\n ) = get_random_model_and_data(tmpdir, unpack_inputs=False)\n\n criterion = nn.MSELoss(reduction=reduction)\n\n tracin = tracin_constructor(\n net,\n train_dataset,\n tmpdir,\n loss_fn=criterion,\n batch_size=1,\n )\n with self.assertRaisesRegex(AssertionError, \"required.\"):\n tracin.influence(None, k=None)","def __get_x_y_from_training_validation(\n logger, training, validation, predictors, target):\n if training is not None:\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\n if validation is not None:\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\n return training_X, training_Y, validation_X, validation_Y","def convert_data_to_examples(train, test, data_column, label_column):\r\n train_InputExamples = train.apply(lambda x: InputExample(guid=None,\r\n text_a = x[data_column],\r\n text_b = None,\r\n label = x[label_column]), axis = 1)\r\n\r\n\r\n validation_InputExamples = test.apply(lambda x: InputExample(guid=None,\r\n text_a = x[data_column],\r\n text_b = None,\r\n label = x[label_column]), axis = 1)\r\n\r\n return train_InputExamples, validation_InputExamples","def test_scl_only(self):\n task = SentinelHubInputTask(\n bands_feature=None,\n additional_data=[(FeatureType.DATA, 'SCL')],\n size=self.size,\n maxcc=self.maxcc,\n time_difference=self.time_difference,\n data_collection=DataCollection.SENTINEL2_L2A,\n max_threads=self.max_threads\n )\n\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\n scl = eopatch[(FeatureType.DATA, 'SCL')]\n\n width, height = self.size\n self.assertTrue(scl.shape == (4, height, width, 1))","def test_train_test_split_uni_exo(load_uni_exo_data_target):\n data, target = load_uni_exo_data_target\n\n ####################################\n #### Continuous fh without Gaps ####\n ####################################\n\n #### Integer fh ----\n exp = TSForecastingExperiment()\n fh = 12\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test.index == data.iloc[-fh:].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(1, 10) # 9 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [1, 2, 3, 4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #################################\n #### Continuous fh with Gaps ####\n #################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(7, 13) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n ####################################\n #### Discontinuous fh with Gaps ####\n ####################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6, 10, 11, 12]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)"],"string":"[\n \"def train_input(self):\\n return DatasetSentinel(\\\"train_input\\\", **self._dataset_sentinel_helper())\",\n \"def validation_target(self):\\n return DatasetSentinel(\\\"validation_target\\\", **self._dataset_sentinel_helper())\",\n \"def holdout_input(self):\\n return DatasetSentinel(\\n \\\"holdout_input\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def train_target(self):\\n return DatasetSentinel(\\\"train_target\\\", **self._dataset_sentinel_helper())\",\n \"def get_train_input(self, prev, i):\\n return self.get_eval_input(prev, i)\",\n \"def get_train_input(self, prev, i):\\n return self.get_eval_input(prev, i)\",\n \"def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\\n dataset = input_data.Data(Xtrain_ul,\\n Xtrain_l,\\n Xtest,\\n ytrain_ul,\\n ytrain_l,\\n ytest,\\n num_labeled, \\n batch_size, \\n shuffle=True)\\n return dataset.next_batch()\",\n \"def load_input(io_config):\\n\\n fold = io_config['fold']\\n\\n train_data_fnames = io_config['file']['train_data']\\n\\n train_fname = [t[1] for t in train_data_fnames\\n if t[0] == fold][0]\\n\\n return train_fname\",\n \"def get_eval_input(self, prev, i):\\n del prev, i\\n # The attention_decoder will fetch image features from the net, no need for\\n # extra inputs.\\n return self._zero_label\",\n \"def get_input(self, prev, i):\\n if self.is_training():\\n return self.get_train_input(prev, i)\\n else:\\n return self.get_eval_input(prev, i)\",\n \"def get_train_inputs(self, example):\\n return example\",\n \"def get_init_fn():\\n checkpoint_exclude_scopes = [\\\"InceptionV1/Logits\\\", \\\"InceptionV1/AuxLogits\\\"]\\n\\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n return slim.assign_from_checkpoint_fn(\\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\\n variables_to_restore)\",\n \"def holdout_target(self):\\n return DatasetSentinel(\\n \\\"holdout_target\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def _dataset_sentinel_helper(self):\\n params = self.cross_experiment_key.parameters\\n return dict(\\n dataset_hash=params[\\\"train_dataset\\\"],\\n cv_type=params[\\\"cross_experiment_params\\\"].get(\\n \\\"cv_type\\\", params[\\\"cross_experiment_params\\\"].get(\\\"cross_validation_type\\\", None)\\n ),\\n global_random_seed=params[\\\"cross_experiment_params\\\"][\\\"global_random_seed\\\"],\\n random_seeds=params[\\\"cross_experiment_params\\\"][\\\"random_seeds\\\"],\\n )\",\n \"def get_train_input(self, prev, i):\\n pass\",\n \"def validation(self) -> tf.contrib.data.Dataset:\\n return self.__validation_dataset\",\n \"def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\\n i = offset_steps\\n lval = splitsize\\n if not do_offset:\\n i_val = allind[:lval]\\n i_train = allind[lval:]\\n else:\\n i_val = allind[i * lval:(i + 1) * lval]\\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\\n if len(i_val) <= 0:\\n print(\\\"Warning: #Validation data is 0, take 1 training sample instead\\\")\\n i_val = i_train[:1]\\n\\n return i_train, i_val\",\n \"def get_train(self, preprocess=False):\\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')\",\n \"def _input_fn(input_pipeline_context=None):\\n return _create_dataset(options, is_training, input_pipeline_context)\",\n \"def get_eval_input(self, prev, i):\\n del prev\\n return self.get_image_feature(i)\",\n \"def _partition_elms(\\n self, max_elms: int = None, fold: int = None\\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n # get ELM indices from datafile\\n elm_index, _ = self._read_file()\\n\\n # limit the data according to the max number of events passed\\n if max_elms is not None and max_elms != -1:\\n LOGGER.info(f\\\"Limiting data read to {max_elms} events.\\\")\\n n_elms = max_elms\\n else:\\n n_elms = len(elm_index)\\n\\n # split the data into train, validation and test sets\\n training_elms, test_elms = model_selection.train_test_split(\\n elm_index[:n_elms],\\n test_size=self.fraction_test,\\n shuffle=True,\\n random_state=config.seed,\\n )\\n\\n # kfold cross validation\\n if self.kfold and fold is None:\\n raise Exception(\\n f\\\"K-fold cross validation is passed but fold index in range [0, {config.folds}) is not specified.\\\"\\n )\\n\\n if self.kfold:\\n LOGGER.info(\\\"Using K-fold cross validation\\\")\\n self._kfold_cross_val(training_elms)\\n training_elms = self.df[self.df[\\\"fold\\\"] != fold][\\\"elm_events\\\"]\\n validation_elms = self.df[self.df[\\\"fold\\\"] == fold][\\\"elm_events\\\"]\\n else:\\n LOGGER.info(\\n \\\"Creating training and validation datasets by simple splitting\\\"\\n )\\n training_elms, validation_elms = model_selection.train_test_split(\\n training_elms, test_size=self.fraction_validate\\n )\\n LOGGER.info(f\\\"Number of training ELM events: {training_elms.size}\\\")\\n LOGGER.info(f\\\"Number of validation ELM events: {validation_elms.size}\\\")\\n LOGGER.info(f\\\"Number of test ELM events: {test_elms.size}\\\")\\n\\n return training_elms, validation_elms, test_elms\",\n \"def _kfold_cross_val(self, training_elms: np.ndarray) -> None:\\n kf = model_selection.KFold(\\n n_splits=config.folds, shuffle=True, random_state=config.seed\\n )\\n self.df[\\\"elm_events\\\"] = training_elms\\n self.df[\\\"fold\\\"] = -1\\n for f_, (_, valid_idx) in enumerate(kf.split(X=training_elms)):\\n self.df.loc[valid_idx, \\\"fold\\\"] = f_\",\n \"def get_train_input(self, prev, i):\\n if i == 0:\\n return self._zero_label\\n else:\\n # TODO(gorban): update to gradually introduce gt labels.\\n return self._labels_one_hot[:, i - 1, :]\",\n \"def get_init_fn():\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n variables_to_restore.append(var)\\n\\n checkpoint_path = tf.train.latest_checkpoint(\\\"./base_checkpoint\\\")\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=False)\",\n \"def _get_init_fn():\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\\n\\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)\",\n \"def get_validation_batch(self, batch_size):\\n if batch_size > len(self.val_indices):\\n return self.data_handler.slice_data(self.val_indices)\\n else:\\n return self.data_handler.slice_data(list(np.random.choice(self.val_indices, size=batch_size)))\",\n \"def get_eval_input(self, prev, i):\\n raise AssertionError('Not implemented')\",\n \"def test_early_stopping_no_val_step(tmpdir):\\n\\n class CurrentModel(EvalModelTemplate):\\n def training_step(self, *args, **kwargs):\\n output = super().training_step(*args, **kwargs)\\n output.update({'my_train_metric': output['loss']}) # could be anything else\\n return output\\n\\n model = CurrentModel()\\n model.validation_step = None\\n model.val_dataloader = None\\n\\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\\n trainer = Trainer(\\n default_root_dir=tmpdir,\\n early_stop_callback=stopping,\\n overfit_pct=0.20,\\n max_epochs=5,\\n )\\n result = trainer.fit(model)\\n\\n assert result == 1, 'training failed to complete'\\n assert trainer.current_epoch < trainer.max_epochs\",\n \"def get_input_fn(is_train):\\n d = DataInfo(ddir,evalddir)\\n hparams = d.generate()\\n params = utils.Params(**hparams)\\n\\n if is_train:\\n input_fn = data.get_input_fn(dataset_fn=data.get_train_dataset, mode=TRAIN, params=params, shuffle_queue=10000, repeat=False)\\n \\n else:\\n input_fn = data.get_input_fn(dataset_fn=data.get_eval_dataset, mode=EVAL, params=params, shuffle_queue=10000, repeat=False)\\n \\n return input_fn, params\",\n \"def get_eval_input(self, prev, i):\\n if i == 0:\\n prev = self._zero_label\\n else:\\n logit = self.char_logit(prev, char_index=i - 1)\\n prev = self.char_one_hot(logit)\\n image_feature = self.get_image_feature(char_index=i)\\n return tf.concat([image_feature, prev], 1)\",\n \"def from_checkpoint(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n ds_iter: Optional[tf.data.Iterator] = None,\\n init_rng: Optional[jnp.ndarray] = None\\n ) -> Optional[train_state_lib.TrainState]:\\n train_states = list(\\n self.from_checkpoints(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng))\\n if len(train_states) > 1:\\n raise ValueError(\\n f'Expected at most 1 checkpoint but got {len(train_states)} for '\\n f'config(s): {ckpt_cfgs}')\\n return (train_states[0]) if train_states else None\",\n \"def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):\\n\\n epoch = K.eval(self._ckpt_saved_epoch)\\n if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0:\\n # The most recently saved epoch is one epoch prior to the epoch it\\n # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.\\n return epoch + 1\\n return initial_epoch\",\n \"def _get_init_fn():\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n if tf.train.latest_checkpoint(FLAGS.train_dir):\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % FLAGS.train_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n # TODO(sguada) variables.filter_variables()\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def _get_init_fn():\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n if tf.train.latest_checkpoint(FLAGS.train_dir):\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % FLAGS.train_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n # TODO(sguada) variables.filter_variables()\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def get_infer_fn(infer_step: InferStepCallable, batch_size: int,\\n train_state_axes: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner):\\n infer_step = partitioner.partition(\\n infer_step,\\n in_axis_resources=(train_state_axes.params, PartitionSpec('data',)),\\n out_axis_resources=PartitionSpec('data',))\\n\\n data_layout = partitioner.get_data_layout(batch_size)\\n shard_id = data_layout.shard_id\\n num_shards = data_layout.num_shards\\n\\n per_shard_batch_size = batch_size // num_shards\\n\\n def infer_fn(ds: tf.data.Dataset, train_state: train_state_lib.TrainState):\\n ds_shapes = jax.tree_map(lambda x: jnp.array(x.shape), ds.element_spec)\\n multihost_utils.assert_same(\\n ds_shapes, 'Dataset element shapes do not agree across hosts. '\\n 'This could be an indication that the dataset is nondeterministic.')\\n try:\\n dataset_remainder = len(ds) % batch_size # pytype:disable=wrong-arg-types\\n logging.info('length of dataset = %s', len(ds))\\n except TypeError as e:\\n if str(e) == 'dataset length is unknown.':\\n logging.warning(\\n 'The following error is likely due to the use of TensorFlow v1 in '\\n 'your dataset pipeline. Verify you are not importing from '\\n '`tf.compat.v1` as part of your pipeline.')\\n raise e\\n\\n if dataset_remainder:\\n dataset_pad_amt = batch_size - dataset_remainder\\n logging.info(\\n 'Padding infer dataset with %d examples for even per-replica shards.',\\n dataset_pad_amt)\\n # Pad with the first example using an index of -1 so seqio will ignore.\\n pad_ds = ds.take(1).map(lambda i, x: (np.int64(-1), x)).repeat(\\n dataset_pad_amt)\\n ds = ds.concatenate(pad_ds)\\n\\n # Shard the infer dataset across replica sets.\\n sharded_ds = ds.shard(num_shards, shard_id).batch(\\n per_shard_batch_size, drop_remainder=True)\\n multihost_utils.assert_same(\\n jnp.array(len(sharded_ds)),\\n 'Dataset lengths do not agree across hosts.')\\n\\n logging.info(\\n 'The infer dataset is sharded into %d shards with per-shard '\\n 'batch size of %d', num_shards, per_shard_batch_size)\\n\\n # Run inference for each replica set.\\n batched_results, all_indices = [], []\\n for index, infer_batch in sharded_ds.as_numpy_iterator():\\n # Run fast inference on batch.\\n # [B, ...] -> [B, ...]\\n batch_result = infer_step(train_state.params, infer_batch)\\n logging.info('Inference of batch %s done.', index)\\n # Issue asynchronous copy request which serves as prefetching to the host.\\n # The result value is synchronized with host_allgather in the loop below.\\n try:\\n jax.tree_map(lambda x: x.copy_to_host_async(), batch_result)\\n except AttributeError:\\n # Similar to jax.device_get, we skip transfers for non DeviceArrays.\\n pass\\n batched_results.append(batch_result)\\n all_indices.append(index)\\n logging.info('Inference of all batches done.')\\n all_inferences = []\\n for batch_result in batched_results:\\n # [B, ...] -> [H, B, ...]\\n batch_result = multihost_utils.host_allgather(\\n batch_result, num_shards, shard_id,\\n data_layout.is_first_host_in_replica_set)\\n all_inferences.append(batch_result)\\n\\n # List[H, B, ...] -> List[B, H, ...]\\n all_inferences = jax.tree_map(lambda x: np.moveaxis(x, 0, 1),\\n all_inferences)\\n\\n # List[B, H, ...] -> [B * batch_count, H, ...]\\n all_inferences = jax.tree_multimap(lambda *args: np.concatenate(args),\\n *all_inferences)\\n # List[B] -> [B * batch_count]\\n all_indices = np.concatenate(all_indices)\\n # Collect all batches from across hosts.\\n # [B * batch_count] -> [H, B * batch_count]\\n all_indices = multihost_utils.host_allgather(\\n all_indices, num_shards, shard_id,\\n data_layout.is_first_host_in_replica_set)\\n # [H, B * batch_count] -> [B * batch_count, H]\\n all_indices = np.transpose(all_indices)\\n all_inferences, all_indices = _deshard_and_remove_padding(\\n all_inferences, all_indices)\\n\\n # Translate [B, ...] -> List[...] by flattening inferences making sure to\\n # preserve structure of individual elements (inferences are not assumed to\\n # be simple np.array). Finally, zip inferences with corresponding indices\\n # and convert leaf np.arrays into lists.\\n all_inferences, struct = jax.tree_flatten(all_inferences)\\n all_inferences = map(\\n functools.partial(jax.tree_unflatten, struct), zip(*all_inferences))\\n indices_and_outputs = list(zip(all_indices, all_inferences))\\n indices_and_outputs = jax.tree_map(lambda x: np.array(x).tolist(),\\n indices_and_outputs)\\n return indices_and_outputs\\n\\n return infer_fn\",\n \"def test_no_fit(self, example_dataset):\\n\\n transformer = PreprocessFeatures()\\n\\n with pytest.raises(NotFittedError):\\n transformer.transform(example_dataset)\",\n \"def get_fit_intercept(self):\\n return self.fit_intercept\",\n \"def get_inputs_train():\\n x = tf.constant(extract_pandas_data(x_train))\\n y = tf.constant(y_train.values)\\n return x, y\",\n \"def find_input_dataset(stage, rconfig, data_type=None):\\n\\n # Use the stage-to-data mapping to find the data_type if none was handed in\\n if data_type is None:\\n data_type = DOCUMENT_PROCESSING_IO[stage]['in']\\n # Get all data sets D for input name\\n dirname = os.path.join(rconfig.target_path, 'data', data_type)\\n datasets1 = [ds for ds in os.listdir(dirname) if ds.isdigit()]\\n datasets2 = [DataSet(stage, data_type, rconfig, ds) for ds in datasets1]\\n # Filer the datasets making sure that d.trace + d.head matches\\n # rconfig.pipeline(txt).trace\\n datasets3 = [ds for ds in datasets2 if ds.input_matches_global_config()]\\n # If there is one result, return it, otherwise write a warning and exit\\n if len(datasets3) == 1:\\n return datasets3[0]\\n elif len(datasets3) > 1:\\n print \\\"WARNING, more than one approriate training set:\\\"\\n for ds in datasets3:\\n print ' ', ds\\n sys.exit(\\\"Exiting...\\\")\\n elif len(datasets3) == 0:\\n print \\\"WARNING: no datasets available to meet input requirements\\\"\\n sys.exit(\\\"Exiting...\\\")\",\n \"def _get_training_dataset(self):\\n\\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))\",\n \"def _generate_validation_fold(self):\\n\\n for offset in range(self.nb_folds):\\n # Load all the data from cache (do this to save memory)\\n with open(self.data_cache_path_str + \\\"data_cache.pkl\\\", \\\"rb\\\") as f:\\n data_df, target_df = pickle.load(f)\\n\\n # Generate train and test sets\\n data_dates_lst = data_df[\\\"date\\\"].drop_duplicates().sort_values().tolist()\\n train_start_day = len(data_dates_lst) - ((self.nb_folds - offset) * self.test_nb_days + self.train_nb_days)\\n train_end_day = train_start_day + self.train_nb_days\\n test_start_day = train_end_day\\n test_end_day = test_start_day + self.test_nb_days\\n\\n train_dates_lst = data_dates_lst[train_start_day:train_end_day]\\n test_dates_lst = data_dates_lst[test_start_day:test_end_day]\\n\\n # Generate train and test labels\\n training_set_df = data_df.loc[data_df[\\\"date\\\"].isin(train_dates_lst)].reset_index(drop = True)\\n testing_set_df = data_df.loc[data_df[\\\"date\\\"].isin(test_dates_lst)].reset_index(drop = True)\\n new_target_df = target_df.loc[data_df[\\\"date\\\"].isin(train_dates_lst)].reset_index(drop = True)\\n truth_df = target_df.loc[data_df[\\\"date\\\"].isin(test_dates_lst)].reset_index(drop = True)\\n\\n # Reduce memory usage\\n del data_df, target_df\\n gc.collect()\\n\\n # Return result\\n yield (training_set_df, testing_set_df, new_target_df, truth_df)\",\n \"def get_validation_iterator(self) -> tf.contrib.data.Iterator:\\n return self.validation.make_initializable_iterator()\",\n \"def get_inputs_test_S():\\n x = tf.constant(extract_pandas_data(x_test_S))\\n y = tf.constant(y_test_S.values)\\n return x, y\",\n \"def train_input_fn():\\n # When choosing shuffle buffer sizes, larger sizes result in better\\n # randomness, while smaller sizes use less memory. MNIST is a small\\n # enough dataset that we can easily shuffle the full epoch.\\n ds = dtrain(flags_obj.data_dir)\\n ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)\\n\\n # Iterate through the dataset a set number (`epochs_between_evals`) of times\\n # during each training session.\\n ds = ds.repeat()\\n return ds\",\n \"def _evaluate_during_fit(self, test_loader, epoch):\",\n \"def get_eval_input(self, prev, i):\\n if i == 0:\\n return self._zero_label\\n else:\\n logit = self.char_logit(prev, char_index=i - 1)\\n return self.char_one_hot(logit)\",\n \"def train(\\n self, training_data: Dataset, validation_data: Optional[Dataset] = None\\n ) -> Predictor:\\n raise NotImplementedError\",\n \"def test_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,\\n with_preprocessor):\\n input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)\\n estimator = clone(estimator)\\n estimator.set_params(preprocessor=preprocessor)\\n set_random_state(estimator)\\n with pytest.raises(NotFittedError):\\n estimator.predict(input_data)\",\n \"def locate_input(self,il):\\n if il.tp == opmod.no_input or il.val is None:\\n return None\\n elif il.tp == opmod.basic_type:\\n return il.val\\n elif il.tp == opmod.entire_workflow:\\n wf = self.workflows[il.val]\\n stk,diag = wf.execution_stack()\\n self.prepare_wf(wf,stk)\\n return wf\\n #return self.workflows[il.val]\\n elif il.tp == opmod.plugin_item:\\n if isinstance(il.val,list):\\n return [self.plugin_manager.get_data_from_uri(v) for v in il.val]\\n else:\\n return self.plugin_manager.get_data_from_uri(il.val)\\n # changed: let basic_type inputs be loaded directly,\\n # without using InputLocators.\\n #elif il.tp == opmod.basic_type:\\n # return il.val\",\n \"def get_init_fn():\\n\\tif train_config['checkpoint_path'] is None:\\n\\t\\treturn None\\n\\t# Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n\\t# ignoring the checkpoint anyway.\\n\\tif tf.train.latest_checkpoint(train_config['checkpoint_path']):\\n\\t\\ttf.logging.info(\\n\\t\\t\\t'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n\\t\\t\\t% train_config['checkpoint_path'])\\n\\t\\treturn None\\n\\n\\texclusions = []\\n\\tif train_config['checkpoint_exclude_scopes']:\\n\\t\\texclusions = [scope.strip()\\n\\t\\t for scope in train_config['checkpoint_exclude_scopes'].split(',')]\\n\\n\\t\\tvariable_to_restore = []\\n\\t\\tfor var in slim.get_model_variables():\\n\\t\\t\\texcluded = False\\n\\t\\t\\tfor exclusion in exclusions:\\n\\t\\t\\t\\tif var.op.name.startswith(exclusion):\\n\\t\\t\\t\\t\\texcluded = True\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tif not excluded:\\n\\t\\t\\t\\tvariable_to_restore.append(var)\\n\\n\\t\\tif tf.gfile.IsDirectory(train_config['checkpoint_path']):\\n\\t\\t\\tcheckpoint_path = tf.train.latest_checkpoint(train_config['checkpoint_path'])\\n\\t\\telse:\\n\\t\\t\\tcheckpoint_path = train_config['checkpoint_path']\\n\\n\\t\\ttf.logging.info('Fune-tuning from %s' % checkpoint_path)\\n\\n\\t\\treturn slim.assign_from_checkpoint_fn(\\n\\t\\t\\tcheckpoint_path,\\n\\t\\t\\tvariable_to_restore,\\n\\t\\t\\tignore_missing_vars=train_config['ignore_missing_vars'])\",\n \"def get_inputs_test():\\n x = tf.constant(extract_pandas_data(x_test))\\n y = tf.constant(y_test.values)\\n return x, y\",\n \"def _make_train_input_fn(is_classification):\\n\\n def _input_fn():\\n features = dict(FEATURES_DICT)\\n if is_classification:\\n labels = CLASSIFICATION_LABELS\\n else:\\n labels = REGRESSION_LABELS\\n return features, labels\\n\\n return _input_fn\",\n \"def input_fn(evaluate=False) -> tf.data.Dataset:\\n\\n # The dataset\\n ds = tf.data.Dataset.from_generator( generator=train_generator, \\n output_types=( { 'character' : tf.string } , tf.string ),\\n output_shapes=( { 'character' : (Model.SEQUENCE_LENGHT,) } , () )\\n )\\n\\n ds = ds.batch(64)\\n ds = ds.prefetch(1)\\n\\n return ds\",\n \"def _restore_data_inputs(self):\\n super()._restore_data_inputs()\\n self.training_data = (\\n self._data.training_data.data if self._data.training_data and self._data.training_data.data else None\\n )\\n self.validation_data = (\\n self._data.validation_data.data if self._data.validation_data and self._data.validation_data.data else None\\n )\",\n \"def prepare_train_validation(self) -> Tuple:\\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\\n\\n Xt = self.dataset.prepare_input_samples(Xt)\\n Yt = self.dataset.prepare_output_samples(Yt)\\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\\n traindataset = traindataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n Xv = self.dataset.prepare_input_samples(Xv)\\n Yv = self.dataset.prepare_output_samples(Yv)\\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\\n validdataset = validdataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n return traindataset, validdataset\",\n \"def input_fn(self, ctx=None):\\n dataset = self.make_parsed_dataset(ctx)\\n\\n # Prefetch overlaps in-feed with training\\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\\n\\n if self.is_training:\\n options = tf.data.Options()\\n options.experimental_deterministic = False\\n dataset = dataset.with_options(options)\\n\\n return dataset\",\n \"def validation_step(self, *args: Any, **kwargs: Any) -> None:\\n batch = args[0]\\n batch_idx = args[1]\\n x = batch[\\\"image\\\"]\\n y = batch[\\\"mask\\\"]\\n y_hat = self(x)\\n y_hat_hard = y_hat.argmax(dim=1)\\n\\n loss = self.loss(y_hat, y)\\n\\n self.log(\\\"val_loss\\\", loss, on_step=False, on_epoch=True)\\n self.val_metrics(y_hat_hard, y)\\n\\n if (\\n batch_idx < 10\\n and hasattr(self.trainer, \\\"datamodule\\\")\\n and self.logger\\n and hasattr(self.logger, \\\"experiment\\\")\\n and hasattr(self.logger.experiment, \\\"add_figure\\\")\\n ):\\n try:\\n datamodule = self.trainer.datamodule\\n batch[\\\"prediction\\\"] = y_hat_hard\\n for key in [\\\"image\\\", \\\"mask\\\", \\\"prediction\\\"]:\\n batch[key] = batch[key].cpu()\\n sample = unbind_samples(batch)[0]\\n fig = datamodule.plot(sample)\\n summary_writer = self.logger.experiment\\n summary_writer.add_figure(\\n f\\\"image/{batch_idx}\\\", fig, global_step=self.global_step\\n )\\n plt.close()\\n except ValueError:\\n pass\",\n \"def get_data(self):\\n if self.config['model'] == 'vggnet':\\n if self.is_training:\\n return self.data.shuffle(self.shuffle).batch(self.batch_size)\\n elif self.is_testing:\\n return self.data.batch(self.batch_size)\\n elif not self.is_testing and not self.is_training:\\n return self.data.batch(self.batch_size)\\n else:\\n raise NotImplementedError('In dataset.py: default input not specified for this model!')\",\n \"def make_input_fn(step_output):\\n return tf.nn.embedding_lookup(embeddings, step_output.predictions)\",\n \"def _get_init_fn(checkpoints_dir_y):\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n # checkpoints_dir_y = namings.generate_checkpoints_dir(FLAGS, image_size)\\n if tf.train.latest_checkpoint(checkpoints_dir_y): #FLAGS.checkpoints_dir): # YY\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % checkpoints_dir_y) # YY: FLAGS.checkpoints_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def get_input_fn(options, is_training):\\n if not isinstance(options, reader_pb2.VCRReader):\\n raise ValueError('options has to be an instance of Reader.')\\n\\n def _input_fn(input_pipeline_context=None):\\n \\\"\\\"\\\"Returns a python dictionary.\\n\\n Returns:\\n A dataset that can be fed to estimator.\\n \\\"\\\"\\\"\\n return _create_dataset(options, is_training, input_pipeline_context)\\n\\n return _input_fn\",\n \"def get_test(self, preprocess=False):\\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')\",\n \"def predict_single_fold(self, model: TorchBasedLinearEstimator, dataset: TabularDataset) -> np.ndarray:\\n pred = model.predict(dataset.data)\\n\\n return pred\",\n \"def from_checkpoint_or_scratch(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n init_rng: Array,\\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\\n return (self.from_checkpoint(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng)\\n or self.from_scratch(init_rng))\",\n \"def get_train_input(self, prev, i):\\n if i == 0:\\n prev = self._zero_label\\n else:\\n prev = self._labels_one_hot[:, i - 1, :]\\n image_feature = self.get_image_feature(i)\\n return tf.concat([image_feature, prev], 1)\",\n \"def input_fn_by_dataset_with_fake_data(input_size, padding_length):\\n def _input_fn(params):\\n \\\"\\\"\\\"A `input_fn` returning features and labels.\\\"\\\"\\\"\\n batch_size = params['batch_size']\\n inputs, labels, lengths = sequence_example_lib.get_fake_data_batch(\\n batch_size, input_size, padding_length)\\n\\n features = {\\n 'inputs': inputs,\\n 'lengths': lengths\\n }\\n return features, labels\\n return _input_fn\",\n \"def get_input_fn(batch_size, dataset_path, train):\\n\\n with open(dataset_path, 'rb') as f:\\n cifar = pickle.load(f, encoding='bytes')\\n images_array = cifar[b'data']\\n labels_array = cifar[b'coarse_labels']\\n\\n images_array = (images_array.reshape((-1, 3, 32, 32))\\n .transpose((0, 2, 3, 1)))\\n labels_array = LabelBinarizer().fit_transform(labels_array)\\n init_hook = InitHook()\\n\\n def input_fn():\\n images_input = tf.placeholder(dtype=tf.float32,\\n shape=images_array.shape,\\n name='images')\\n labels_input = tf.placeholder(dtype=tf.float32,\\n shape=labels_array.shape,\\n name='labels')\\n\\n inputs = (images_input, labels_input)\\n dset = tf.data.Dataset.from_tensor_slices(inputs)\\n dset = dset.shuffle(len(images_array)).batch(batch_size)\\n if train:\\n dset = dset.repeat()\\n\\n iterator = dset.make_initializable_iterator()\\n init_hook.init = iterator.initializer\\n init_hook.feed_dict = {images_input: images_array,\\n labels_input: labels_array}\\n next_images, next_labels = iterator.get_next()\\n return {'images': next_images}, next_labels\\n\\n return input_fn, init_hook\",\n \"def get_validation_data(self):\\n return self.unzip_batch(self.valid)\",\n \"def test_fit_returns_self(self):\\n\\n df = d.create_df_1()\\n\\n x = BaseTransformer(columns=\\\"a\\\")\\n\\n x_fitted = x.fit(df)\\n\\n assert x_fitted is x, \\\"Returned value from BaseTransformer.fit not as expected.\\\"\",\n \"def input_fn(self, ctx=None):\\n sup_dataset = self.supervised_input.make_parsed_dataset(ctx)\\n unsup_dataset = self.unsupervised_input.make_parsed_dataset(ctx)\\n\\n dataset = tf.data.Dataset.zip((sup_dataset, unsup_dataset))\\n dataset = dataset.map(_combine_sup_unsup_datasets)\\n\\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\\n\\n options = tf.data.Options()\\n options.experimental_deterministic = False\\n dataset = dataset.with_options(options)\\n\\n return dataset\",\n \"def _get_input(cfg,\\n mode='train',\\n rank=None,\\n dir_name_init: Optional[list] = None,\\n img_name_init: Optional[list] = None,\\n min_size: Optional[tuple] = None,\\n image_only_test: bool = True):\\n def _validate_shape(cfg, image, mask, i):\\n if image is None:\\n return\\n\\n if cfg.DATASET.LOAD_2D:\\n assert image[i].shape[1:] == mask[i].shape[1:]\\n else:\\n assert image[i].shape == mask[i].shape[-3:]\\n\\n assert mode in ['train', 'val', 'test']\\n dir_path = cfg.DATASET.INPUT_PATH\\n if dir_name_init is not None:\\n dir_name = dir_name_init\\n else:\\n dir_name = _get_file_list(dir_path)\\n\\n if mode == 'val':\\n img_name = cfg.DATASET.VAL_IMAGE_NAME\\n label_name = cfg.DATASET.VAL_LABEL_NAME\\n valid_mask_name = cfg.DATASET.VAL_VALID_MASK_NAME\\n pad_size = cfg.DATASET.VAL_PAD_SIZE\\n else:\\n img_name = cfg.DATASET.IMAGE_NAME\\n label_name = cfg.DATASET.LABEL_NAME\\n valid_mask_name = cfg.DATASET.VALID_MASK_NAME\\n pad_size = cfg.DATASET.PAD_SIZE\\n assert not all([elem == None for elem in [img_name, label_name]]), \\\\\\n \\\"At least one of img_name and label_name should not be None!\\\"\\n\\n volume, label, valid_mask = None, None, None\\n if img_name_init is not None:\\n img_name = img_name_init\\n\\n if img_name is not None:\\n img_name = _get_file_list(img_name, prefix=dir_path)\\n img_name = _make_path_list(cfg, dir_name, img_name, rank)\\n volume = [None] * len(img_name)\\n print(rank, len(img_name), list(map(os.path.basename, img_name)))\\n\\n if _load_label_condition(label_name, mode, image_only_test):\\n label_name = _get_file_list(label_name, prefix=dir_path)\\n label_name = _make_path_list(cfg, dir_name, label_name, rank)\\n label = [None]*len(label_name)\\n print(rank, len(label_name), list(map(os.path.basename, label_name)))\\n\\n if _load_label_condition(valid_mask_name, mode, image_only_test):\\n valid_mask_name = _get_file_list(valid_mask_name, prefix=dir_path)\\n valid_mask_name = _make_path_list(cfg, dir_name, valid_mask_name, rank)\\n valid_mask = [None]*len(valid_mask_name)\\n\\n pad_mode = cfg.DATASET.PAD_MODE\\n read_fn = readvol if not cfg.DATASET.LOAD_2D else readimg_as_vol\\n num_vols = len(img_name) if img_name is not None else len(label_name)\\n\\n for i in range(num_vols):\\n if volume is not None:\\n volume[i] = read_fn(img_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\\n print(f\\\"volume shape (original): {volume[i].shape}\\\")\\n if cfg.DATASET.NORMALIZE_RANGE:\\n volume[i] = normalize_range(volume[i])\\n volume[i] = _rescale(volume[i], cfg.DATASET.IMAGE_SCALE, order=3)\\n volume[i] = _pad(volume[i], pad_size, pad_mode)\\n volume[i] = _resize2target(volume[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\\n order=3, target_size=min_size)\\n print(f\\\"volume shape (after scaling and padding): {volume[i].shape}\\\")\\n\\n if label is not None:\\n label[i] = read_fn(label_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\\n if cfg.DATASET.LABEL_VAST:\\n label[i] = vast2Seg(label[i])\\n if label[i].ndim == 2: # make it into 3D volume\\n label[i] = label[i][None, :]\\n if cfg.DATASET.LABEL_BINARY and label[i].max() > 1:\\n label[i] = label[i] // 255\\n if cfg.DATASET.LABEL_MAG != 0:\\n label[i] = (label[i]/cfg.DATASET.LABEL_MAG).astype(np.float32)\\n\\n label[i] = _rescale(label[i], cfg.DATASET.LABEL_SCALE, order=0) # nearest\\n label[i] = _pad(label[i], pad_size, pad_mode)\\n label[i] = _resize2target(label[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\\n order=0, target_size=min_size)\\n print(f\\\"label shape (after scaling and padding): {label[i].shape}\\\")\\n _validate_shape(cfg, volume, label, i)\\n\\n if valid_mask is not None:\\n valid_mask[i] = read_fn(valid_mask_name[i], drop_channel=cfg.DATASET.DROP_CHANNEL)\\n valid_mask[i] = _rescale(valid_mask[i], cfg.DATASET.VALID_MASK_SCALE, order=0)\\n valid_mask[i] = _pad(valid_mask[i], pad_size, pad_mode)\\n valid_mask[i] = _resize2target(valid_mask[i], enabled=cfg.DATASET.ENSURE_MIN_SIZE,\\n order=0, target_size=min_size)\\n print(f\\\"valid_mask shape (after scaling and padding): {valid_mask[i].shape}\\\")\\n _validate_shape(cfg, volume, valid_mask, i)\\n\\n return volume, label, valid_mask\",\n \"def experiment(\\n self,\\n dataset: Union[str, dict, pd.DataFrame] = None,\\n training_set: Union[str, dict, pd.DataFrame] = None,\\n validation_set: Union[str, dict, pd.DataFrame] = None,\\n test_set: Union[str, dict, pd.DataFrame] = None,\\n training_set_metadata: Union[str, dict] = None,\\n data_format: str = None,\\n experiment_name: str = \\\"experiment\\\",\\n model_name: str = \\\"run\\\",\\n model_load_path: str = None,\\n model_resume_path: str = None,\\n eval_split: str = TEST,\\n skip_save_training_description: bool = False,\\n skip_save_training_statistics: bool = False,\\n skip_save_model: bool = False,\\n skip_save_progress: bool = False,\\n skip_save_log: bool = False,\\n skip_save_processed_input: bool = False,\\n skip_save_unprocessed_output: bool = False,\\n skip_save_predictions: bool = False,\\n skip_save_eval_stats: bool = False,\\n skip_collect_predictions: bool = False,\\n skip_collect_overall_stats: bool = False,\\n output_directory: str = \\\"results\\\",\\n random_seed: int = default_random_seed,\\n **kwargs,\\n ) -> Tuple[Optional[dict], TrainingStats, PreprocessedDataset, str]:\\n if self._user_config.get(HYPEROPT):\\n print_boxed(\\\"WARNING\\\")\\n logger.warning(HYPEROPT_WARNING)\\n\\n (train_stats, preprocessed_data, output_directory) = self.train(\\n dataset=dataset,\\n training_set=training_set,\\n validation_set=validation_set,\\n test_set=test_set,\\n training_set_metadata=training_set_metadata,\\n data_format=data_format,\\n experiment_name=experiment_name,\\n model_name=model_name,\\n model_load_path=model_load_path,\\n model_resume_path=model_resume_path,\\n skip_save_training_description=skip_save_training_description,\\n skip_save_training_statistics=skip_save_training_statistics,\\n skip_save_model=skip_save_model,\\n skip_save_progress=skip_save_progress,\\n skip_save_log=skip_save_log,\\n skip_save_processed_input=skip_save_processed_input,\\n skip_save_unprocessed_output=skip_save_unprocessed_output,\\n output_directory=output_directory,\\n random_seed=random_seed,\\n )\\n\\n (training_set, validation_set, test_set, training_set_metadata) = preprocessed_data\\n\\n eval_set = validation_set\\n if eval_split == TRAINING:\\n eval_set = training_set\\n elif eval_split == VALIDATION:\\n eval_set = validation_set\\n elif eval_split == TEST:\\n eval_set = test_set\\n else:\\n logger.warning(f\\\"Eval split {eval_split} not supported. \\\" f\\\"Using validation set instead\\\")\\n\\n if eval_set is not None:\\n trainer_dict = self.config_obj.trainer.to_dict()\\n batch_size = trainer_dict.get(EVAL_BATCH_SIZE, trainer_dict.get(BATCH_SIZE, None))\\n\\n # predict\\n try:\\n eval_stats, _, _ = self.evaluate(\\n eval_set,\\n data_format=data_format,\\n batch_size=batch_size,\\n output_directory=output_directory,\\n skip_save_unprocessed_output=skip_save_unprocessed_output,\\n skip_save_predictions=skip_save_predictions,\\n skip_save_eval_stats=skip_save_eval_stats,\\n collect_predictions=not skip_collect_predictions,\\n collect_overall_stats=not skip_collect_overall_stats,\\n return_type=\\\"dict\\\",\\n )\\n except NotImplementedError:\\n logger.warning(\\n \\\"Skipping evaluation as the necessary methods are not \\\"\\n \\\"supported. Full exception below:\\\\n\\\"\\n f\\\"{traceback.format_exc()}\\\"\\n )\\n eval_stats = None\\n else:\\n logger.warning(f\\\"The evaluation set {eval_set} was not provided. \\\" f\\\"Skipping evaluation\\\")\\n eval_stats = None\\n\\n return eval_stats, train_stats, preprocessed_data, output_directory\",\n \"def cross_validation_datasets(self, fold):\\n if fold > len(self): fold = len(self) / 2\\n stratified = self.stratified_bunches(fold)\\n datasets = []\\n for index in range(len(stratified)):\\n gold = GoldInstances(training_as_gold(stratified[index]))\\n rest = flatten(stratified[:index]) + flatten(stratified[index + 1:])\\n training = TrainingInstances(rest)\\n datasets.append((training, gold))\\n return datasets\",\n \"def keep_cross_validation_fold_assignment(self):\\n return self._parms.get(\\\"keep_cross_validation_fold_assignment\\\")\",\n \"def fold_data(fold, datas):\\n training, validation = fold\\n folded = {}\\n\\n # fold Xtrain\\n if datas.get('Xtrain') is not None:\\n folded['Xtrain'] = datas['Xtrain'][training, :]\\n folded['Xtest'] = datas['Xtrain'][validation, :]\\n\\n # fold Ktrain\\n if datas.get('Ktrain') is not None:\\n folded['Ktrain'] = datas['Ktrain'][training, :][:, training]\\n folded['Ktest'] = datas['Ktrain'][validation, ][:, training]\\n # if datas.get('Ktest') is not None:\\n # folded['Ktest'] = datas['Ktest'][:, validation]\\n # else:\\n # folded['Ktest'] = folded['Xtest'].dot(folded['Xtrain'].T)\\n\\n # fold Ytrain\\n if datas.get('Ytrain') is not None:\\n folded['Ytrain'] = datas['Ytrain'][training, :]\\n folded['Ytest'] = datas['Ytrain'][validation, :]\\n\\n return folded\",\n \"def input_example(self, max_batch=1, max_dim=8192):\\n device = next(self.parameters()).device\\n input_example = torch.randn(max_batch, self._feat_in, max_dim, device=device)\\n lens = torch.full(size=(input_example.shape[0],), fill_value=max_dim, device=device)\\n return tuple([input_example, lens])\",\n \"def _verify_training_data(self):\\n if self.training_fluxes.shape != self.training_flux_uncertainties.shape:\\n raise ValueError(\\n \\\"the training flux and uncertainty arrays should \\\"\\n \\\"have the same shape\\\")\\n\\n if len(self.training_labels) == 0 \\\\\\n or self.training_labels.dtype.names is None:\\n raise ValueError(\\\"no named labels provided for the training set\\\")\\n\\n if len(self.training_labels) != self.training_fluxes.shape[0]:\\n raise ValueError(\\n \\\"the first axes of the training flux array should \\\"\\n \\\"have the same shape as the nuber of rows in the label table \\\"\\n \\\"(N_stars, N_pixels)\\\")\\n\\n if self.dispersion is not None:\\n dispersion = np.atleast_1d(self.dispersion).flatten()\\n if dispersion.size != self.training_fluxes.shape[1]:\\n raise ValueError(\\n \\\"mis-match between the number of wavelength \\\"\\n \\\"points ({0}) and flux values ({1})\\\".format(\\n self.training_fluxes.shape[1], dispersion.size))\\n return None\",\n \"def train_input_fn():\\n # Initialize `iterator` with training data.\\n train_filenames = [os.path.join(FLAGS.out_dir, FLAGS.train_records)]\\n return _input_fn(train_filenames, FLAGS.epochs, FLAGS.batch_size, shuffle=True)\",\n \"def unpack_validation_data(validation_data, raise_if_ambiguous=True):\\n if (isinstance(validation_data, (iterator_ops.Iterator,\\n iterator_ops.IteratorBase,\\n data_types.DatasetV2,\\n data_utils.Sequence))\\n or not hasattr(validation_data, '__len__')):\\n val_x = validation_data\\n val_y = None\\n val_sample_weight = None\\n elif len(validation_data) == 2:\\n try:\\n val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence\\n val_sample_weight = None\\n except ValueError:\\n val_x, val_y, val_sample_weight = validation_data, None, None\\n elif len(validation_data) == 3:\\n try:\\n val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence\\n except ValueError:\\n val_x, val_y, val_sample_weight = validation_data, None, None\\n else:\\n if raise_if_ambiguous:\\n raise ValueError(\\n 'When passing a `validation_data` argument, '\\n 'it must contain either 2 items (x_val, y_val), '\\n 'or 3 items (x_val, y_val, val_sample_weights), '\\n 'or alternatively it could be a dataset or a '\\n 'dataset or a dataset iterator. '\\n 'However we received `validation_data=%s`' % validation_data)\\n val_x, val_y, val_sample_weight = validation_data, None, None\\n return val_x, val_y, val_sample_weight\",\n \"def example(self):\\n result = getattr(self, '_example', None)\\n if result is None:\\n # No example batch was found, so get one from the `.train` dataset\\n result = next(iter(self.train))\\n # And cache it for next time\\n self._example = result\\n return result\",\n \"def build_training_input_fn():\\n transformed_metadata = metadata_io.read_metadata(\\n os.path.join(\\n MODEL_DIR, transform_fn_io.TRANSFORMED_METADATA_DIR))\\n transformed_feature_spec = transformed_metadata.schema.as_feature_spec()\\n\\n def input_fn():\\n \\\"\\\"\\\"Input function for training and eval.\\\"\\\"\\\"\\n dataset = tf.contrib.data.make_batched_features_dataset(\\n file_pattern=os.path.join(TFRECORD_DIR, '*'),\\n batch_size=BATCH_SIZE,\\n features=transformed_feature_spec,\\n reader=tf.data.TFRecordDataset,\\n shuffle=True)\\n transformed_features = dataset.make_one_shot_iterator().get_next()\\n # Extract features and labels from the transformed tensors.\\n label_cols = set(['TotalVolume', 'Density', 'Temperature', 'Humidity', 'Energy', 'Problems'])\\n transformed_labels = {key: value for (key, value) in transformed_features.items() if key in label_cols}\\n transformed_features = {key: value for (key, value) in transformed_features.items() if key not in label_cols}\\n return transformed_features, transformed_labels\\n\\n return input_fn\",\n \"def input(self):\\n try:\\n return self.inputs[-1]\\n except IndexError:\\n pass\\n raise ValueError(\\\"The sample method has not been called\\\")\",\n \"def train_input_fn(self) -> types.FeatureAndLabelTensors:\\n return self._input_fn_from_file(self._train_path)\",\n \"def load_folds(options=None, df=None):\\n if df is not None and 'fold' in df.columns:\\n i_train = df.query(\\\"fold != 'test'\\\").index.to_numpy()\\n i_test = df.query(\\\"fold == 'test'\\\").index.to_numpy()\\n return [(i_train, i_test)]\\n print('No folds specified in CSV file')\\n\\n if options.folds == 'weak':\\n return save_weak_folds(df)\\n return save_folds(df)\",\n \"def k_fold_inf(\\n config,\\n df,\\n log_folder=None,\\n use_full_size=True,\\n global_threshold=None,\\n use_tta=False,\\n save=False,\\n save_all_tta=False,\\n):\\n folds = df[config.cv_column].unique()\\n scores = []\\n\\n for i, fold in enumerate(folds):\\n if i in config.selected_folds:\\n print(f\\\"\\\\n------------- Fold {i + 1} / {len(folds)} -------------\\\\n\\\")\\n df_val = df[df[config.cv_column] == fold].reset_index()\\n\\n val_images = df_val[\\\"tile_name\\\"].apply(lambda x: x.split(\\\"_\\\")[0]).unique()\\n\\n model = define_model(\\n config.decoder,\\n config.encoder,\\n num_classes=config.num_classes,\\n encoder_weights=config.encoder_weights,\\n ).to(config.device)\\n model.zero_grad()\\n model.eval()\\n\\n load_model_weights(\\n model, log_folder + f\\\"{config.decoder}_{config.encoder}_{i}.pt\\\"\\n )\\n\\n scores += validate_inf(\\n model,\\n config,\\n val_images,\\n log_folder=log_folder,\\n use_full_size=use_full_size,\\n global_threshold=global_threshold,\\n use_tta=use_tta,\\n save=save,\\n save_all_tta=save_all_tta,\\n )\\n\\n return scores\",\n \"def create_master(exp, var, data=None, new_fields=[]):\\n\\n if isinstance(var, basestring):\\n assert data is not None\\n data_dict = data\\n new_fields.append(var)\\n else:\\n # see if it's a Var, and access metadata from the associated\\n # Experiment\\n if data is None:\\n data_dict = var.data\\n else:\\n data_dict = data\\n new_fields.append(var.varname)\\n new_fields.extend(var.oldvar)\\n\\n all_case_vals = exp.all_case_vals()\\n\\n # Post-process the case inspection a bit:\\n # 1) Promote any single-value case to a list with one entry\\n for i, case_vals in enumerate(all_case_vals):\\n if isinstance(case_vals, str):\\n all_case_vals[i] = list(case_vals)\\n\\n # 2) Make sure they're all still in the data dictionary. This is\\n # circular but a necessary sanity check\\n for case_bits in product(*all_case_vals):\\n assert case_bits in data_dict\\n\\n # Discover the type of the data passed into this method. If\\n # it's an xarray type, we'll preserve that. If it's an iris type,\\n # then we need to crash for now.\\n first_case = next(exp.all_cases())\\n\\n proto = data_dict[first_case]\\n if isinstance(proto, Dataset):\\n return _master_dataset(exp, data_dict, new_fields)\\n elif isinstance(proto, DataArray):\\n return _master_dataarray(exp, data_dict)\\n # elif isinstance(proto, Cube):\\n # raise NotImplementedError(\\\"Cube handling not yet implemented\\\")\\n else:\\n raise ValueError(\\\"Data must be an xarray type\\\")\",\n \"def get_feed(data_feeds, data_loader, model_config, is_train):\\n input_feed_dict = {}\\n excluded_example_count = 0\\n\\n for data_feed in data_feeds:\\n tmp_contexts, tmp_targets, tmp_lines = [], [], []\\n tmp_masked_contexts, tmp_masked_words = [], []\\n example_in_batch_count = 0\\n while example_in_batch_count < model_config.batch_size:\\n if model_config.it_train:\\n example = next(data_loader.data_it, None)\\n else:\\n # TODO: not tested\\n example = data_loader.get_sample()\\n\\n # mainly used in evaluation when testset file reaches EOF, create a dummy input to feed model\\n if example is None:\\n example = {}\\n example['contexts'] = [0] * model_config.max_context_len\\n example['target'] = {'pos_id': 0,\\n 'abbr_id': 0,\\n 'abbr': None,\\n 'sense_id': 0,\\n 'sense': None,\\n 'line_id': data_loader.size,\\n 'inst_id': 0\\n }\\n example['line'] = ''\\n # sample['def'] = [0] * model_config.max_def_len\\n # sample['stype'] = 0\\n excluded_example_count += 1 # Assume eval use single GPU\\n\\n # print(example_in_batch_count)\\n # print(excluded_example_count)\\n # print(example)\\n\\n tmp_contexts.append(example['contexts'])\\n tmp_targets.append(example['target'])\\n tmp_lines.append(example['line'])\\n # print('input:\\\\t%s\\\\t%s.' % (sample['line'], sample['target']))\\n if model_config.lm_mask_rate and 'cur_masked_contexts' in example:\\n tmp_masked_contexts.append(example['cur_masked_contexts'])\\n tmp_masked_words.append(example['masked_words'])\\n\\n # print('done one example, current len(batch)=%d' % len(tmp_contexts))\\n example_in_batch_count += 1\\n\\n for step in range(model_config.max_context_len):\\n input_feed_dict[data_feed['contexts'][step].name] = [\\n tmp_contexts[batch_idx][step]\\n for batch_idx in range(model_config.batch_size)]\\n\\n if model_config.hub_module_embedding:\\n input_feed_dict[data_feed['text_input'].name] = [\\n tmp_lines[batch_idx]\\n for batch_idx in range(model_config.batch_size)]\\n\\n input_feed_dict[data_feed['abbr_inp'].name] = [\\n tmp_targets[batch_idx]['abbr_id']\\n for batch_idx in range(model_config.batch_size)\\n ]\\n\\n input_feed_dict[data_feed['sense_inp'].name] = [\\n tmp_targets[batch_idx]['sense_id']\\n for batch_idx in range(model_config.batch_size)\\n ]\\n\\n if model_config.lm_mask_rate and tmp_masked_contexts:\\n i = 0\\n while len(tmp_masked_contexts) < model_config.batch_size:\\n tmp_masked_contexts.append(tmp_masked_contexts[i % len(tmp_masked_contexts)])\\n tmp_masked_words.append(tmp_masked_words[i % len(tmp_masked_contexts)])\\n i += 1\\n\\n for step in range(model_config.max_context_len):\\n input_feed_dict[data_feed['masked_contexts'][step].name] = [\\n tmp_masked_contexts[batch_idx][step]\\n for batch_idx in range(model_config.batch_size)]\\n\\n for step in range(model_config.max_subword_len):\\n input_feed_dict[data_feed['masked_words'][step].name] = [\\n tmp_masked_words[batch_idx][1][step]\\n for batch_idx in range(model_config.batch_size)]\\n\\n return input_feed_dict, excluded_example_count, tmp_targets\",\n \"def get_train_index():\\n data_size = (NUM_CLASS - 1) * NUM_DATA_PER_CLASS\\n return np.array([i for i in range(0, data_size)])\",\n \"def get_input_datasets(cfg):\\n input_data = mlr.get_input_data(cfg,\\n pattern=cfg.get('pattern'),\\n ignore=cfg.get('ignore'))\\n tags = list(group_metadata(input_data, 'tag').keys())\\n if len(tags) != 1:\\n raise ValueError(\\n f\\\"Expected unique 'tag' for all input datasets, got {len(tags):d} \\\"\\n f\\\"different ones ({tags})\\\")\\n return input_data\",\n \"def input_example(self, max_batch=1, max_dim=256):\\n input_example = torch.randn(max_batch, self.input_feat_in, max_dim).to(next(self.parameters()).device)\\n return tuple([input_example])\",\n \"def fit_predict_single_fold(\\n self, train: TabularDataset, valid: TabularDataset\\n ) -> Tuple[TorchBasedLinearEstimator, np.ndarray]:\\n if type(train) is PandasDataset:\\n train = train.to_numpy()\\n valid = valid.to_numpy()\\n\\n model = self._infer_params()\\n\\n model.fit(\\n train.data,\\n train.target,\\n train.weights,\\n valid.data,\\n valid.target,\\n valid.weights,\\n )\\n\\n val_pred = model.predict(valid.data)\\n\\n return model, val_pred\",\n \"def cross_validate(self, pre_train=None, **kwargs):\\n \\n inferred = np.nan * np.ones_like(self.labels_array)\\n N_training_set, N_labels = inferred.shape\\n N_stop_at = kwargs.pop(\\\"N\\\", N_training_set)\\n\\n debug = kwargs.pop(\\\"debug\\\", False)\\n \\n kwds = { \\\"threads\\\": self.threads }\\n kwds.update(kwargs)\\n\\n for i in range(N_training_set):\\n \\n training_set = np.ones(N_training_set, dtype=bool)\\n training_set[i] = False\\n\\n # Create a clean model to use so we don't overwrite self.\\n model = self.__class__(\\n self.training_labels[training_set],\\n self.training_fluxes[training_set],\\n self.training_flux_uncertainties[training_set],\\n **kwds)\\n\\n # Initialise and run any pre-training function.\\n for _attribute in self._descriptive_attributes:\\n setattr(model, _attribute[1:], getattr(self, _attribute[1:]))\\n\\n if pre_train is not None:\\n pre_train(self, model)\\n\\n # Train and solve.\\n model.train()\\n\\n try:\\n inferred[i, :] = model.fit(self.training_fluxes[i],\\n self.training_flux_uncertainties[i], full_output=False)\\n\\n except:\\n logger.exception(\\\"Exception during cross-validation on object \\\"\\n \\\"with index {0}:\\\".format(i))\\n if debug: raise\\n\\n if i == N_stop_at + 1:\\n break\\n\\n return inferred[:N_stop_at, :]\",\n \"def make_predict_step(self):\\n return self.make_eval_step()\",\n \"def getTrainingData(self):\\n raise NotImplementedError\",\n \"def _save_input(self, mod, i):\\n if mod.training:\\n self.state[mod][\\\"x\\\"] = i[0]\",\n \"def test_tracin_require_inputs_dataset(\\n self,\\n reduction,\\n tracin_constructor: Callable,\\n ) -> None:\\n with tempfile.TemporaryDirectory() as tmpdir:\\n (\\n net,\\n train_dataset,\\n test_samples,\\n test_labels,\\n ) = get_random_model_and_data(tmpdir, unpack_inputs=False)\\n\\n criterion = nn.MSELoss(reduction=reduction)\\n\\n tracin = tracin_constructor(\\n net,\\n train_dataset,\\n tmpdir,\\n loss_fn=criterion,\\n batch_size=1,\\n )\\n with self.assertRaisesRegex(AssertionError, \\\"required.\\\"):\\n tracin.influence(None, k=None)\",\n \"def __get_x_y_from_training_validation(\\n logger, training, validation, predictors, target):\\n if training is not None:\\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\\n if validation is not None:\\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\\n return training_X, training_Y, validation_X, validation_Y\",\n \"def convert_data_to_examples(train, test, data_column, label_column):\\r\\n train_InputExamples = train.apply(lambda x: InputExample(guid=None,\\r\\n text_a = x[data_column],\\r\\n text_b = None,\\r\\n label = x[label_column]), axis = 1)\\r\\n\\r\\n\\r\\n validation_InputExamples = test.apply(lambda x: InputExample(guid=None,\\r\\n text_a = x[data_column],\\r\\n text_b = None,\\r\\n label = x[label_column]), axis = 1)\\r\\n\\r\\n return train_InputExamples, validation_InputExamples\",\n \"def test_scl_only(self):\\n task = SentinelHubInputTask(\\n bands_feature=None,\\n additional_data=[(FeatureType.DATA, 'SCL')],\\n size=self.size,\\n maxcc=self.maxcc,\\n time_difference=self.time_difference,\\n data_collection=DataCollection.SENTINEL2_L2A,\\n max_threads=self.max_threads\\n )\\n\\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\\n scl = eopatch[(FeatureType.DATA, 'SCL')]\\n\\n width, height = self.size\\n self.assertTrue(scl.shape == (4, height, width, 1))\",\n \"def test_train_test_split_uni_exo(load_uni_exo_data_target):\\n data, target = load_uni_exo_data_target\\n\\n ####################################\\n #### Continuous fh without Gaps ####\\n ####################################\\n\\n #### Integer fh ----\\n exp = TSForecastingExperiment()\\n fh = 12\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test.index == data.iloc[-fh:].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(1, 10) # 9 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [1, 2, 3, 4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #################################\\n #### Continuous fh with Gaps ####\\n #################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(7, 13) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n ####################################\\n #### Discontinuous fh with Gaps ####\\n ####################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6, 10, 11, 12]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7417075","0.7251253","0.6297536","0.6207659","0.5739387","0.5739387","0.57109046","0.5591265","0.55240226","0.54016316","0.53963184","0.5390611","0.5354384","0.5352668","0.52997166","0.50982046","0.509454","0.5057065","0.50273144","0.49092582","0.48859358","0.48842114","0.48813173","0.48661986","0.4842402","0.48323932","0.4805341","0.47921005","0.47847784","0.47741088","0.4765554","0.4752834","0.47509432","0.47507173","0.4750272","0.47403243","0.47303084","0.47179595","0.47167894","0.47069234","0.46997404","0.4699154","0.46973708","0.46970397","0.46844682","0.46781877","0.4667766","0.46650583","0.46624961","0.46546105","0.4647282","0.46388832","0.4635214","0.46351016","0.463103","0.46304336","0.46292576","0.46251783","0.4605373","0.46045727","0.4600562","0.4577458","0.4570513","0.45656776","0.45654193","0.45643297","0.45574716","0.45570028","0.45550674","0.45536086","0.45375502","0.45374992","0.4536036","0.45355222","0.45323437","0.45315072","0.45269388","0.4526249","0.4515045","0.45026895","0.44959185","0.44932422","0.4491634","0.44903627","0.44869554","0.44849557","0.44800675","0.44795159","0.44785935","0.4477125","0.4476732","0.44761682","0.4471871","0.44703805","0.44660136","0.44552505","0.44383684","0.44357818","0.44339827","0.44336215"],"string":"[\n \"0.7417075\",\n \"0.7251253\",\n \"0.6297536\",\n \"0.6207659\",\n \"0.5739387\",\n \"0.5739387\",\n \"0.57109046\",\n \"0.5591265\",\n \"0.55240226\",\n \"0.54016316\",\n \"0.53963184\",\n \"0.5390611\",\n \"0.5354384\",\n \"0.5352668\",\n \"0.52997166\",\n \"0.50982046\",\n \"0.509454\",\n \"0.5057065\",\n \"0.50273144\",\n \"0.49092582\",\n \"0.48859358\",\n \"0.48842114\",\n \"0.48813173\",\n \"0.48661986\",\n \"0.4842402\",\n \"0.48323932\",\n \"0.4805341\",\n \"0.47921005\",\n \"0.47847784\",\n \"0.47741088\",\n \"0.4765554\",\n \"0.4752834\",\n \"0.47509432\",\n \"0.47507173\",\n \"0.4750272\",\n \"0.47403243\",\n \"0.47303084\",\n \"0.47179595\",\n \"0.47167894\",\n \"0.47069234\",\n \"0.46997404\",\n \"0.4699154\",\n \"0.46973708\",\n \"0.46970397\",\n \"0.46844682\",\n \"0.46781877\",\n \"0.4667766\",\n \"0.46650583\",\n \"0.46624961\",\n \"0.46546105\",\n \"0.4647282\",\n \"0.46388832\",\n \"0.4635214\",\n \"0.46351016\",\n \"0.463103\",\n \"0.46304336\",\n \"0.46292576\",\n \"0.46251783\",\n \"0.4605373\",\n \"0.46045727\",\n \"0.4600562\",\n \"0.4577458\",\n \"0.4570513\",\n \"0.45656776\",\n \"0.45654193\",\n \"0.45643297\",\n \"0.45574716\",\n \"0.45570028\",\n \"0.45550674\",\n \"0.45536086\",\n \"0.45375502\",\n \"0.45374992\",\n \"0.4536036\",\n \"0.45355222\",\n \"0.45323437\",\n \"0.45315072\",\n \"0.45269388\",\n \"0.4526249\",\n \"0.4515045\",\n \"0.45026895\",\n \"0.44959185\",\n \"0.44932422\",\n \"0.4491634\",\n \"0.44903627\",\n \"0.44869554\",\n \"0.44849557\",\n \"0.44800675\",\n \"0.44795159\",\n \"0.44785935\",\n \"0.4477125\",\n \"0.4476732\",\n \"0.44761682\",\n \"0.4471871\",\n \"0.44703805\",\n \"0.44660136\",\n \"0.44552505\",\n \"0.44383684\",\n \"0.44357818\",\n \"0.44339827\",\n \"0.44336215\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79688627"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18479,"cells":{"query":{"kind":"string","value":"Get a `DatasetSentinel` representing an Experiment's `fold_validation_target` Returns"},"document":{"kind":"string","value":"def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())"},"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 train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())","def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())","def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())","def holdout_target(self):\n return DatasetSentinel(\n \"holdout_target\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def holdout_input(self):\n return DatasetSentinel(\n \"holdout_input\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def validation(self) -> tf.contrib.data.Dataset:\n return self.__validation_dataset","def training_target(training_data):\n return training_data.status == \"DEFAULT\"","def __get_x_y_from_training_validation(\n logger, training, validation, predictors, target):\n if training is not None:\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\n if validation is not None:\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\n return training_X, training_Y, validation_X, validation_Y","def _dataset_sentinel_helper(self):\n params = self.cross_experiment_key.parameters\n return dict(\n dataset_hash=params[\"train_dataset\"],\n cv_type=params[\"cross_experiment_params\"].get(\n \"cv_type\", params[\"cross_experiment_params\"].get(\"cross_validation_type\", None)\n ),\n global_random_seed=params[\"cross_experiment_params\"][\"global_random_seed\"],\n random_seeds=params[\"cross_experiment_params\"][\"random_seeds\"],\n )","def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\n dataset = input_data.Data(Xtrain_ul,\n Xtrain_l,\n Xtest,\n ytrain_ul,\n ytrain_l,\n ytest,\n num_labeled, \n batch_size, \n shuffle=True)\n return dataset.next_batch()","def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\n i = offset_steps\n lval = splitsize\n if not do_offset:\n i_val = allind[:lval]\n i_train = allind[lval:]\n else:\n i_val = allind[i * lval:(i + 1) * lval]\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\n if len(i_val) <= 0:\n print(\"Warning: #Validation data is 0, take 1 training sample instead\")\n i_val = i_train[:1]\n\n return i_train, i_val","def test_early_stopping_no_val_step(tmpdir):\n\n class CurrentModel(EvalModelTemplate):\n def training_step(self, *args, **kwargs):\n output = super().training_step(*args, **kwargs)\n output.update({'my_train_metric': output['loss']}) # could be anything else\n return output\n\n model = CurrentModel()\n model.validation_step = None\n model.val_dataloader = None\n\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\n trainer = Trainer(\n default_root_dir=tmpdir,\n early_stop_callback=stopping,\n overfit_pct=0.20,\n max_epochs=5,\n )\n result = trainer.fit(model)\n\n assert result == 1, 'training failed to complete'\n assert trainer.current_epoch < trainer.max_epochs","def get_train_input(self, prev, i):\n return self.get_eval_input(prev, i)","def get_train_input(self, prev, i):\n return self.get_eval_input(prev, i)","def get_init_fn():\n checkpoint_exclude_scopes = [\"InceptionV1/Logits\", \"InceptionV1/AuxLogits\"]\n\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n return slim.assign_from_checkpoint_fn(\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n variables_to_restore)","def _generate_validation_fold(self):\n\n for offset in range(self.nb_folds):\n # Load all the data from cache (do this to save memory)\n with open(self.data_cache_path_str + \"data_cache.pkl\", \"rb\") as f:\n data_df, target_df = pickle.load(f)\n\n # Generate train and test sets\n data_dates_lst = data_df[\"date\"].drop_duplicates().sort_values().tolist()\n train_start_day = len(data_dates_lst) - ((self.nb_folds - offset) * self.test_nb_days + self.train_nb_days)\n train_end_day = train_start_day + self.train_nb_days\n test_start_day = train_end_day\n test_end_day = test_start_day + self.test_nb_days\n\n train_dates_lst = data_dates_lst[train_start_day:train_end_day]\n test_dates_lst = data_dates_lst[test_start_day:test_end_day]\n\n # Generate train and test labels\n training_set_df = data_df.loc[data_df[\"date\"].isin(train_dates_lst)].reset_index(drop = True)\n testing_set_df = data_df.loc[data_df[\"date\"].isin(test_dates_lst)].reset_index(drop = True)\n new_target_df = target_df.loc[data_df[\"date\"].isin(train_dates_lst)].reset_index(drop = True)\n truth_df = target_df.loc[data_df[\"date\"].isin(test_dates_lst)].reset_index(drop = True)\n\n # Reduce memory usage\n del data_df, target_df\n gc.collect()\n\n # Return result\n yield (training_set_df, testing_set_df, new_target_df, truth_df)","def test_train_test_split_uni_exo(load_uni_exo_data_target):\n data, target = load_uni_exo_data_target\n\n ####################################\n #### Continuous fh without Gaps ####\n ####################################\n\n #### Integer fh ----\n exp = TSForecastingExperiment()\n fh = 12\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test.index == data.iloc[-fh:].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(1, 10) # 9 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [1, 2, 3, 4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #################################\n #### Continuous fh with Gaps ####\n #################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(7, 13) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n ####################################\n #### Discontinuous fh with Gaps ####\n ####################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6, 10, 11, 12]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)","def _kfold_cross_val(self, training_elms: np.ndarray) -> None:\n kf = model_selection.KFold(\n n_splits=config.folds, shuffle=True, random_state=config.seed\n )\n self.df[\"elm_events\"] = training_elms\n self.df[\"fold\"] = -1\n for f_, (_, valid_idx) in enumerate(kf.split(X=training_elms)):\n self.df.loc[valid_idx, \"fold\"] = f_","def get_eval_input(self, prev, i):\n del prev, i\n # The attention_decoder will fetch image features from the net, no need for\n # extra inputs.\n return self._zero_label","def get_validation_batch(self, batch_size):\n if batch_size > len(self.val_indices):\n return self.data_handler.slice_data(self.val_indices)\n else:\n return self.data_handler.slice_data(list(np.random.choice(self.val_indices, size=batch_size)))","def sample_train_data(dataset ,target,data_len, resp = True ):\r\n np.random.seed(222)\r\n ixes = np.random.choice(dataset.index, data_len, replace = False)\r\n print(ixes)\r\n under_df = dataset.iloc[ixes]\r\n if resp==True:\r\n under_target = target.iloc[ixes]\r\n return under_df, under_target\r\n else:\r\n return under_df","def get_train(self, preprocess=False):\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')","def load_input(io_config):\n\n fold = io_config['fold']\n\n train_data_fnames = io_config['file']['train_data']\n\n train_fname = [t[1] for t in train_data_fnames\n if t[0] == fold][0]\n\n return train_fname","def cross_validation_datasets(self, fold):\n if fold > len(self): fold = len(self) / 2\n stratified = self.stratified_bunches(fold)\n datasets = []\n for index in range(len(stratified)):\n gold = GoldInstances(training_as_gold(stratified[index]))\n rest = flatten(stratified[:index]) + flatten(stratified[index + 1:])\n training = TrainingInstances(rest)\n datasets.append((training, gold))\n return datasets","def _partition_elms(\n self, max_elms: int = None, fold: int = None\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n # get ELM indices from datafile\n elm_index, _ = self._read_file()\n\n # limit the data according to the max number of events passed\n if max_elms is not None and max_elms != -1:\n LOGGER.info(f\"Limiting data read to {max_elms} events.\")\n n_elms = max_elms\n else:\n n_elms = len(elm_index)\n\n # split the data into train, validation and test sets\n training_elms, test_elms = model_selection.train_test_split(\n elm_index[:n_elms],\n test_size=self.fraction_test,\n shuffle=True,\n random_state=config.seed,\n )\n\n # kfold cross validation\n if self.kfold and fold is None:\n raise Exception(\n f\"K-fold cross validation is passed but fold index in range [0, {config.folds}) is not specified.\"\n )\n\n if self.kfold:\n LOGGER.info(\"Using K-fold cross validation\")\n self._kfold_cross_val(training_elms)\n training_elms = self.df[self.df[\"fold\"] != fold][\"elm_events\"]\n validation_elms = self.df[self.df[\"fold\"] == fold][\"elm_events\"]\n else:\n LOGGER.info(\n \"Creating training and validation datasets by simple splitting\"\n )\n training_elms, validation_elms = model_selection.train_test_split(\n training_elms, test_size=self.fraction_validate\n )\n LOGGER.info(f\"Number of training ELM events: {training_elms.size}\")\n LOGGER.info(f\"Number of validation ELM events: {validation_elms.size}\")\n LOGGER.info(f\"Number of test ELM events: {test_elms.size}\")\n\n return training_elms, validation_elms, test_elms","def _get_training_dataset(self):\n\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))","def example(self):\n result = getattr(self, '_example', None)\n if result is None:\n # No example batch was found, so get one from the `.train` dataset\n result = next(iter(self.train))\n # And cache it for next time\n self._example = result\n return result","def _get_init_fn():\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\n\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)","def keep_cross_validation_fold_assignment(self):\n return self._parms.get(\"keep_cross_validation_fold_assignment\")","def get_target_encoder(self, train: NumpyOrPandas) -> Optional[type]:\n target_encoder = None\n if train.folds is not None:\n if train.task.name in [\"binary\", \"reg\"]:\n target_encoder = TargetEncoder\n else:\n n_classes = train.target.max() + 1\n if n_classes <= self.multiclass_te_co:\n target_encoder = MultiClassTargetEncoder\n\n return target_encoder","def get_init_fn():\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n variables_to_restore.append(var)\n\n checkpoint_path = tf.train.latest_checkpoint(\"./base_checkpoint\")\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=False)","def get_test(self, preprocess=False):\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')","def get_train_input(self, prev, i):\n pass","def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):\n\n epoch = K.eval(self._ckpt_saved_epoch)\n if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0:\n # The most recently saved epoch is one epoch prior to the epoch it\n # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.\n return epoch + 1\n return initial_epoch","def prepare_train_validation(self) -> Tuple:\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\n\n Xt = self.dataset.prepare_input_samples(Xt)\n Yt = self.dataset.prepare_output_samples(Yt)\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\n traindataset = traindataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n Xv = self.dataset.prepare_input_samples(Xv)\n Yv = self.dataset.prepare_output_samples(Yv)\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\n validdataset = validdataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n return traindataset, validdataset","def _evaluate_during_fit(self, test_loader, epoch):","def validation_step(self, *args: Any, **kwargs: Any) -> None:\n batch = args[0]\n batch_idx = args[1]\n x = batch[\"image\"]\n y = batch[\"mask\"]\n y_hat = self(x)\n y_hat_hard = y_hat.argmax(dim=1)\n\n loss = self.loss(y_hat, y)\n\n self.log(\"val_loss\", loss, on_step=False, on_epoch=True)\n self.val_metrics(y_hat_hard, y)\n\n if (\n batch_idx < 10\n and hasattr(self.trainer, \"datamodule\")\n and self.logger\n and hasattr(self.logger, \"experiment\")\n and hasattr(self.logger.experiment, \"add_figure\")\n ):\n try:\n datamodule = self.trainer.datamodule\n batch[\"prediction\"] = y_hat_hard\n for key in [\"image\", \"mask\", \"prediction\"]:\n batch[key] = batch[key].cpu()\n sample = unbind_samples(batch)[0]\n fig = datamodule.plot(sample)\n summary_writer = self.logger.experiment\n summary_writer.add_figure(\n f\"image/{batch_idx}\", fig, global_step=self.global_step\n )\n plt.close()\n except ValueError:\n pass","def get_train_inputs(self, example):\n return example","def from_checkpoint(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n ds_iter: Optional[tf.data.Iterator] = None,\n init_rng: Optional[jnp.ndarray] = None\n ) -> Optional[train_state_lib.TrainState]:\n train_states = list(\n self.from_checkpoints(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng))\n if len(train_states) > 1:\n raise ValueError(\n f'Expected at most 1 checkpoint but got {len(train_states)} for '\n f'config(s): {ckpt_cfgs}')\n return (train_states[0]) if train_states else None","def predict_single_fold(self, model: TorchBasedLinearEstimator, dataset: TabularDataset) -> np.ndarray:\n pred = model.predict(dataset.data)\n\n return pred","def get_infer_fn(infer_step: InferStepCallable, batch_size: int,\n train_state_axes: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner):\n infer_step = partitioner.partition(\n infer_step,\n in_axis_resources=(train_state_axes.params, PartitionSpec('data',)),\n out_axis_resources=PartitionSpec('data',))\n\n data_layout = partitioner.get_data_layout(batch_size)\n shard_id = data_layout.shard_id\n num_shards = data_layout.num_shards\n\n per_shard_batch_size = batch_size // num_shards\n\n def infer_fn(ds: tf.data.Dataset, train_state: train_state_lib.TrainState):\n ds_shapes = jax.tree_map(lambda x: jnp.array(x.shape), ds.element_spec)\n multihost_utils.assert_same(\n ds_shapes, 'Dataset element shapes do not agree across hosts. '\n 'This could be an indication that the dataset is nondeterministic.')\n try:\n dataset_remainder = len(ds) % batch_size # pytype:disable=wrong-arg-types\n logging.info('length of dataset = %s', len(ds))\n except TypeError as e:\n if str(e) == 'dataset length is unknown.':\n logging.warning(\n 'The following error is likely due to the use of TensorFlow v1 in '\n 'your dataset pipeline. Verify you are not importing from '\n '`tf.compat.v1` as part of your pipeline.')\n raise e\n\n if dataset_remainder:\n dataset_pad_amt = batch_size - dataset_remainder\n logging.info(\n 'Padding infer dataset with %d examples for even per-replica shards.',\n dataset_pad_amt)\n # Pad with the first example using an index of -1 so seqio will ignore.\n pad_ds = ds.take(1).map(lambda i, x: (np.int64(-1), x)).repeat(\n dataset_pad_amt)\n ds = ds.concatenate(pad_ds)\n\n # Shard the infer dataset across replica sets.\n sharded_ds = ds.shard(num_shards, shard_id).batch(\n per_shard_batch_size, drop_remainder=True)\n multihost_utils.assert_same(\n jnp.array(len(sharded_ds)),\n 'Dataset lengths do not agree across hosts.')\n\n logging.info(\n 'The infer dataset is sharded into %d shards with per-shard '\n 'batch size of %d', num_shards, per_shard_batch_size)\n\n # Run inference for each replica set.\n batched_results, all_indices = [], []\n for index, infer_batch in sharded_ds.as_numpy_iterator():\n # Run fast inference on batch.\n # [B, ...] -> [B, ...]\n batch_result = infer_step(train_state.params, infer_batch)\n logging.info('Inference of batch %s done.', index)\n # Issue asynchronous copy request which serves as prefetching to the host.\n # The result value is synchronized with host_allgather in the loop below.\n try:\n jax.tree_map(lambda x: x.copy_to_host_async(), batch_result)\n except AttributeError:\n # Similar to jax.device_get, we skip transfers for non DeviceArrays.\n pass\n batched_results.append(batch_result)\n all_indices.append(index)\n logging.info('Inference of all batches done.')\n all_inferences = []\n for batch_result in batched_results:\n # [B, ...] -> [H, B, ...]\n batch_result = multihost_utils.host_allgather(\n batch_result, num_shards, shard_id,\n data_layout.is_first_host_in_replica_set)\n all_inferences.append(batch_result)\n\n # List[H, B, ...] -> List[B, H, ...]\n all_inferences = jax.tree_map(lambda x: np.moveaxis(x, 0, 1),\n all_inferences)\n\n # List[B, H, ...] -> [B * batch_count, H, ...]\n all_inferences = jax.tree_multimap(lambda *args: np.concatenate(args),\n *all_inferences)\n # List[B] -> [B * batch_count]\n all_indices = np.concatenate(all_indices)\n # Collect all batches from across hosts.\n # [B * batch_count] -> [H, B * batch_count]\n all_indices = multihost_utils.host_allgather(\n all_indices, num_shards, shard_id,\n data_layout.is_first_host_in_replica_set)\n # [H, B * batch_count] -> [B * batch_count, H]\n all_indices = np.transpose(all_indices)\n all_inferences, all_indices = _deshard_and_remove_padding(\n all_inferences, all_indices)\n\n # Translate [B, ...] -> List[...] by flattening inferences making sure to\n # preserve structure of individual elements (inferences are not assumed to\n # be simple np.array). Finally, zip inferences with corresponding indices\n # and convert leaf np.arrays into lists.\n all_inferences, struct = jax.tree_flatten(all_inferences)\n all_inferences = map(\n functools.partial(jax.tree_unflatten, struct), zip(*all_inferences))\n indices_and_outputs = list(zip(all_indices, all_inferences))\n indices_and_outputs = jax.tree_map(lambda x: np.array(x).tolist(),\n indices_and_outputs)\n return indices_and_outputs\n\n return infer_fn","def _get__target(self):\n if self._saved_target is None:\n self._saved_target = self._metadata.get_callable()()\n \n return self._saved_target","def get_train_input(self, prev, i):\n if i == 0:\n return self._zero_label\n else:\n # TODO(gorban): update to gradually introduce gt labels.\n return self._labels_one_hot[:, i - 1, :]","def get_init_fn():\n\tif train_config['checkpoint_path'] is None:\n\t\treturn None\n\t# Warn the user if a checkpoint exists in the train_dir. Then we'll be\n\t# ignoring the checkpoint anyway.\n\tif tf.train.latest_checkpoint(train_config['checkpoint_path']):\n\t\ttf.logging.info(\n\t\t\t'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n\t\t\t% train_config['checkpoint_path'])\n\t\treturn None\n\n\texclusions = []\n\tif train_config['checkpoint_exclude_scopes']:\n\t\texclusions = [scope.strip()\n\t\t for scope in train_config['checkpoint_exclude_scopes'].split(',')]\n\n\t\tvariable_to_restore = []\n\t\tfor var in slim.get_model_variables():\n\t\t\texcluded = False\n\t\t\tfor exclusion in exclusions:\n\t\t\t\tif var.op.name.startswith(exclusion):\n\t\t\t\t\texcluded = True\n\t\t\t\t\tbreak\n\t\t\tif not excluded:\n\t\t\t\tvariable_to_restore.append(var)\n\n\t\tif tf.gfile.IsDirectory(train_config['checkpoint_path']):\n\t\t\tcheckpoint_path = tf.train.latest_checkpoint(train_config['checkpoint_path'])\n\t\telse:\n\t\t\tcheckpoint_path = train_config['checkpoint_path']\n\n\t\ttf.logging.info('Fune-tuning from %s' % checkpoint_path)\n\n\t\treturn slim.assign_from_checkpoint_fn(\n\t\t\tcheckpoint_path,\n\t\t\tvariable_to_restore,\n\t\t\tignore_missing_vars=train_config['ignore_missing_vars'])","def eval_step(data_laoder, model, device, fold):\n\tmodel.eval()\n\ttrue_labels, pred_labels = [], []\n\tfor inputs, input_lens, labels in tqdm.tqdm(data_laoder, ncols=100, desc=f\"eval--- F: {fold} -- E: n\"):\n\t\tinputs = inputs.to(device)\n\t\tlabels = labels.to(device)\n\t\t#input_lens = input_lens.to(device)\n\n\t\tpreds = model(inputs, input_lens)\n\n\t\ttrue_labels.extend(labels.detach().squeeze().cpu().numpy().tolist())\n\t\tpred_labels.extend(preds.detach().squeeze().cpu().numpy().tolist())\n\n\treturn true_labels, pred_labels","def get_dataset(self):\n\n trainset = datasets.MNIST('datasets/MNIST/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.MNIST('datasets/MNIST/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def create_master(exp, var, data=None, new_fields=[]):\n\n if isinstance(var, basestring):\n assert data is not None\n data_dict = data\n new_fields.append(var)\n else:\n # see if it's a Var, and access metadata from the associated\n # Experiment\n if data is None:\n data_dict = var.data\n else:\n data_dict = data\n new_fields.append(var.varname)\n new_fields.extend(var.oldvar)\n\n all_case_vals = exp.all_case_vals()\n\n # Post-process the case inspection a bit:\n # 1) Promote any single-value case to a list with one entry\n for i, case_vals in enumerate(all_case_vals):\n if isinstance(case_vals, str):\n all_case_vals[i] = list(case_vals)\n\n # 2) Make sure they're all still in the data dictionary. This is\n # circular but a necessary sanity check\n for case_bits in product(*all_case_vals):\n assert case_bits in data_dict\n\n # Discover the type of the data passed into this method. If\n # it's an xarray type, we'll preserve that. If it's an iris type,\n # then we need to crash for now.\n first_case = next(exp.all_cases())\n\n proto = data_dict[first_case]\n if isinstance(proto, Dataset):\n return _master_dataset(exp, data_dict, new_fields)\n elif isinstance(proto, DataArray):\n return _master_dataarray(exp, data_dict)\n # elif isinstance(proto, Cube):\n # raise NotImplementedError(\"Cube handling not yet implemented\")\n else:\n raise ValueError(\"Data must be an xarray type\")","def MNIST_experiment():\n tsetlin_machine = TsetlinMachine(number_clauses=1000,\n number_action_states=1000,\n precision=3.0,\n threshold=10)\n\n X, y, val_X, val_y = MNIST()\n\n tsetlin_machine.fit(X, y, val_X, val_y, 300)\n print('Final training accuracy:', tsetlin_machine.accuracy(X, y))\n print('Final validation accuracy:', tsetlin_machine.accuracy(val_X, val_y))","def _next_train_batch(self, batch_size=128, replace=False):\n mask = np.random.choice(self.train_data.shape[0], batch_size, replace=replace)\n return self.train_data[mask], self.train_label[mask]","def train(\n self, training_data: Dataset, validation_data: Optional[Dataset] = None\n ) -> Predictor:\n raise NotImplementedError","def _get_init_fn():\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n if tf.train.latest_checkpoint(FLAGS.train_dir):\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % FLAGS.train_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n # TODO(sguada) variables.filter_variables()\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def _get_init_fn():\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n if tf.train.latest_checkpoint(FLAGS.train_dir):\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % FLAGS.train_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n # TODO(sguada) variables.filter_variables()\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def test_train_with_early_stopping(task_dataset, mocker):\n wandb_log = mocker.patch.object(wandb, 'log')\n\n early_stopping = learning.EarlyStopping(patience=PATIENCE)\n # Cannot possible go lower! So we should stop after PATIENCE steps.\n early_stopping(float('-inf'))\n\n probe = nn.Linear(N_DIMS_PER_REP, N_UNIQUE_FEATS)\n before = probe.weight.data.clone()\n\n learning.train(probe,\n task_dataset,\n epochs=EPOCHS,\n stopper=early_stopping,\n also_log_to_wandb=True)\n after = probe.weight.data\n\n assert not before.equal(after)\n assert wandb_log.call_args_list == [\n mocker.call({'train loss': mocker.ANY}),\n ] * (PATIENCE + 1)","def test_no_fit(self, example_dataset):\n\n transformer = PreprocessFeatures()\n\n with pytest.raises(NotFittedError):\n transformer.transform(example_dataset)","def get_input(self, prev, i):\n if self.is_training():\n return self.get_train_input(prev, i)\n else:\n return self.get_eval_input(prev, i)","def all2one(df, val_size=2, seed=0xBadCafe):\n folds = sorted(df.fold.unique())\n split = []\n for f in folds:\n idx_b = df[df.fold != f].index.tolist()\n test_ids = df[df.fold == f].index.tolist()\n train_ids, val_ids = train_test_split(idx_b, test_size=val_size, random_state=seed)\n split.append([train_ids, val_ids, test_ids])\n return split","def ae_finetuning(decoder, train_loader, valid_loader, criterion, optimizer, resume, options):\n from tensorboardX import SummaryWriter\n\n log_dir = os.path.join(options.output_dir, 'log_dir', 'fold_' + str(options.split), 'ConvAutoencoder')\n visualization_path = os.path.join(options.output_dir, 'visualize', 'fold_' + str(options.split))\n best_model_dir = os.path.join(options.output_dir, 'best_model_dir', 'fold_' + str(options.split), 'ConvAutoencoder')\n filename = os.path.join(log_dir, 'training.tsv')\n\n if not resume:\n check_and_clean(best_model_dir)\n check_and_clean(visualization_path)\n check_and_clean(log_dir)\n columns = ['epoch', 'iteration', 'loss_train', 'mean_loss_train', 'loss_valid', 'mean_loss_valid']\n results_df = pd.DataFrame(columns=columns)\n with open(filename, 'w') as f:\n results_df.to_csv(f, index=False, sep='\\t')\n options.beginning_epoch = 0\n\n else:\n if not os.path.exists(filename):\n raise ValueError('The training.tsv file of the resumed experiment does not exist.')\n truncated_tsv = pd.read_csv(filename, sep='\\t')\n truncated_tsv.set_index(['epoch', 'iteration'], inplace=True)\n truncated_tsv.drop(options.beginning_epoch, level=0, inplace=True)\n truncated_tsv.to_csv(filename, index=True, sep='\\t')\n\n # Create writers\n writer_train = SummaryWriter(os.path.join(log_dir, 'train'))\n writer_valid = SummaryWriter(os.path.join(log_dir, 'valid'))\n\n decoder.train()\n first_visu = True\n print(decoder)\n\n if options.gpu:\n decoder.cuda()\n\n # Initialize variables\n best_loss_valid = np.inf\n\n early_stopping = EarlyStopping('min', min_delta=options.tolerance, patience=options.patience)\n loss_valid = None\n epoch = options.beginning_epoch\n\n print(\"Beginning training\")\n while epoch < options.epochs and not early_stopping.step(loss_valid):\n print(\"At %d-th epoch.\" % epoch)\n\n decoder.zero_grad()\n evaluation_flag = True\n step_flag = True\n for i, data in enumerate(train_loader):\n if options.gpu:\n imgs = data['image'].cuda()\n else:\n imgs = data['image']\n\n train_output = decoder(imgs)\n loss = criterion(train_output, imgs)\n loss.backward()\n\n del imgs, train_output\n\n if (i+1) % options.accumulation_steps == 0:\n step_flag = False\n optimizer.step()\n optimizer.zero_grad()\n\n # Evaluate the decoder only when no gradients are accumulated\n if (i+1) % options.evaluation_steps == 0:\n evaluation_flag = False\n print('Iteration %d' % i)\n loss_train = test_ae(decoder, train_loader, options.gpu, criterion)\n mean_loss_train = loss_train / (len(train_loader) * train_loader.batch_size)\n\n loss_valid = test_ae(decoder, valid_loader, options.gpu, criterion)\n mean_loss_valid = loss_valid / (len(valid_loader) * valid_loader.batch_size)\n decoder.train()\n\n writer_train.add_scalar('loss', mean_loss_train, i + epoch * len(train_loader))\n writer_valid.add_scalar('loss', mean_loss_valid, i + epoch * len(train_loader))\n print(\"Scan level validation loss is %f at the end of iteration %d\" % (loss_valid, i))\n row = np.array([epoch, i, loss_train, mean_loss_train, loss_valid, mean_loss_valid]).reshape(1, -1)\n row_df = pd.DataFrame(row, columns=columns)\n with open(filename, 'a') as f:\n row_df.to_csv(f, header=False, index=False, sep='\\t')\n\n # If no step has been performed, raise Exception\n if step_flag:\n raise Exception('The model has not been updated once in the epoch. The accumulation step may be too large.')\n\n # If no evaluation has been performed, warn the user\n if evaluation_flag:\n warnings.warn('Your evaluation steps are too big compared to the size of the dataset.'\n 'The model is evaluated only once at the end of the epoch')\n\n # Always test the results and save them once at the end of the epoch\n print('Last checkpoint at the end of the epoch %d' % epoch)\n\n loss_train = test_ae(decoder, train_loader, options.gpu, criterion)\n mean_loss_train = loss_train / (len(train_loader) * train_loader.batch_size)\n\n loss_valid = test_ae(decoder, valid_loader, options.gpu, criterion)\n mean_loss_valid = loss_valid / (len(valid_loader) * valid_loader.batch_size)\n decoder.train()\n\n writer_train.add_scalar('loss', mean_loss_train, i + epoch * len(train_loader))\n writer_valid.add_scalar('loss', mean_loss_valid, i + epoch * len(train_loader))\n print(\"Scan level validation loss is %f at the end of iteration %d\" % (loss_valid, i))\n\n row = np.array([epoch, i, loss_train, mean_loss_train, loss_valid, mean_loss_valid]).reshape(1, -1)\n row_df = pd.DataFrame(row, columns=columns)\n with open(filename, 'a') as f:\n row_df.to_csv(f, header=False, index=False, sep='\\t')\n\n is_best = loss_valid < best_loss_valid\n best_loss_valid = min(best_loss_valid, loss_valid)\n # Always save the model at the end of the epoch and update best model\n save_checkpoint({'model': decoder.state_dict(),\n 'iteration': i,\n 'epoch': epoch,\n 'loss_valid': loss_valid},\n False, is_best,\n best_model_dir)\n # Save optimizer state_dict to be able to reload\n save_checkpoint({'optimizer': optimizer.state_dict(),\n 'epoch': epoch,\n 'name': options.optimizer,\n },\n False, False,\n best_model_dir,\n filename='optimizer.pth.tar')\n\n if epoch % 10 == 0:\n visualize_subject(decoder, train_loader, visualization_path, options, epoch=epoch, save_input=first_visu)\n first_visu = False\n\n epoch += 1\n\n visualize_subject(decoder, train_loader, visualization_path, options, epoch=epoch, save_input=first_visu)","def test_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,\n with_preprocessor):\n input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)\n estimator = clone(estimator)\n estimator.set_params(preprocessor=preprocessor)\n set_random_state(estimator)\n with pytest.raises(NotFittedError):\n estimator.predict(input_data)","def _get_init_fn(checkpoints_dir_y):\n if FLAGS.checkpoint_path is None:\n return None\n\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\n # ignoring the checkpoint anyway.\n # checkpoints_dir_y = namings.generate_checkpoints_dir(FLAGS, image_size)\n if tf.train.latest_checkpoint(checkpoints_dir_y): #FLAGS.checkpoints_dir): # YY\n tf.logging.info(\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\n % checkpoints_dir_y) # YY: FLAGS.checkpoints_dir)\n return None\n\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\n\n return slim.assign_from_checkpoint_fn(\n checkpoint_path,\n variables_to_restore,\n ignore_missing_vars=FLAGS.ignore_missing_vars)","def getLabeledXYonly(self, trainingData):\r\n labeledData, unlabeledData = trainingData\r\n return labeledData","def get_fit_intercept(self):\n return self.fit_intercept","def target(self, example):\n return example[self.target_index]","def from_checkpoint_or_scratch(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n init_rng: Array,\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\n return (self.from_checkpoint(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng)\n or self.from_scratch(init_rng))","def get_dataset(self):\n trainset = datasets.KMNIST('datasets/KMNIST/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.KMNIST('datasets/KMNIST/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def imit_test(self, epoch, best): \n a_loss, t_loss = 0., 0.\n data_valid_iter = batch_iter(self.data_valid[0], self.data_valid[1], self.data_valid[2], self.data_valid[3])\n for i, data in enumerate(data_valid_iter):\n loss_a, loss_t = self.user_loop(data)\n a_loss += loss_a.item()\n t_loss += loss_t.item()\n \n a_loss /= i\n t_loss /= i\n logging.debug('<<user simulator>> validation, epoch {}, loss_a:{}, loss_t:{}'.format(epoch, a_loss, t_loss))\n loss = a_loss + t_loss\n if loss < best:\n logging.info('<<user simulator>> best model saved')\n best = loss\n self.save(self.save_dir, 'best')\n \n a_loss, t_loss = 0., 0.\n data_test_iter = batch_iter(self.data_test[0], self.data_test[1], self.data_test[2], self.data_test[3])\n for i, data in enumerate(data_test_iter):\n loss_a, loss_t = self.user_loop(data)\n a_loss += loss_a.item()\n t_loss += loss_t.item()\n \n a_loss /= i\n t_loss /= i\n logging.debug('<<user simulator>> test, epoch {}, loss_a:{}, loss_t:{}'.format(epoch, a_loss, t_loss))\n return best","def clfs_predict(self, nidxs_target, data=None, to_eval=False, fill=True, nidxs_train=None, eval_idx=None):\n\n if data is None:\n data = self.datas[self.train_idx]\n train_idx0 = self.train_idx\n\n if eval_idx is None:\n for i, d in enumerate(self.datas):\n if i != self.train_idx and data.p_data == d.p_data:\n eval_idx = i\n break\n\n # Still doesn't find the right data\n if eval_idx is None:\n print('\\n { Cannot find eval data }\\n')\n return None\n\n # Combine training and testing data into layers of new Data object used to retrain clf-net during expansion\n # The training and test data will be in different layers\n # This is so training data is not lost during expansive training iterations\n self.datas.append(self.blend_data(idxs=list({train_idx0, eval_idx})))\n blend_idx = len(self.datas) - 1\n if nidxs_train is not None:\n nidxs_train_blend = [self.datas[blend_idx].nidxconvert[train_idx0][n] for n in nidxs_train]\n else:\n nidxs_train_blend = [self.datas[blend_idx].nidxconvert[train_idx0][n] for n in self.datas[train_idx0].nidx_train]\n\n # Compute expansion path\n path = data.recruit(nidxs_remain=set(nidxs_target))\n n_steps = len(path)\n print('\\n { Network expansion path contains %d steps }' % n_steps)\n\n # Retain original base-classifiers\n clf_pkg = self.archive_clfs()\n\n # Retain original multilayer state and change to train multilayers\n train_multilayers0 = self.train_multilayers\n self.train_multilayers = True\n\n nidxs = []\n predictions = None\n for istep in range(n_steps):\n\n nidxs += [self.datas[blend_idx].nidxconvert[eval_idx][n] for n in path[istep]['nidxs']]\n print('\\n[ Step %d/%d ] %d nodes / %d expandable links' % (istep+1, n_steps, len(path[istep]['nidxs']),\n len(path[istep]['links'])))\n\n # Predict\n predictions = self.clf_all_predict(nidx_target=nidxs, data=self.datas[blend_idx])\n\n # Baseline\n y_bl_pred = self.bl_predict(n_samples=len(nidxs), data=self.datas[blend_idx])\n predictions['ybl'] = y_bl_pred\n\n # Predict with clf-opt if trained\n if self.clf_opt_trained:\n X = self._construct_clf_opt_X(predictions)\n predictions['yopt'] = self._predict_proba(self.clf_opt, X)\n\n # Fill (fill==True) what clf-opt did not predict with the joint solutions from base classifiers\n if fill:\n coverage0 = self.calc_coverage(predictions['yopt'])\n filling_coverage = 0.\n if coverage0 < 1.:\n i_missing = np.invert(np.any(self._round(predictions['yopt']), axis=1))\n y_fill = self._y_merge(predictions=predictions, i=i_missing)\n filling_coverage = self.calc_coverage(y_fill) * len(y_fill) / len(i_missing)\n predictions['yopt'][i_missing] = y_fill\n predictions['fill'] = filling_coverage\n\n if self.verbose:\n coverage1 = self.calc_coverage(predictions['yopt'])\n print('\\n[ clf-opt ]\\n no-fill coverage: {:.1%}\\n filling: {:.1%}\\n filled coverage: {:.1%}\\n'.format(coverage0, filling_coverage, coverage1))\n\n # Show scores\n self._print_eval(predictions=predictions, to_eval=to_eval)\n\n if (istep + 1) < n_steps and path[istep]['links']:\n\n new_links = list(path[istep]['links'])\n\n # Set training data index to the blended data\n self.train_idx = blend_idx\n\n # find expand features with evaluating data then set features in the blended data\n if self.verbose:\n print('\\n[ Expanding ] Evaluating %d links' % len(new_links))\n r = self.datas[blend_idx].eval_lidxs(lidxs=[self.datas[blend_idx].link2lidx[l] for l in new_links], nidxs=nidxs_train_blend)\n accepted_links = [new_links[i] for i, b in enumerate(r) if b]\n if self.verbose:\n print('[ Expanding ] Accepting %d links' % len(accepted_links))\n n = len(self.datas[blend_idx].link2featidx)\n self.datas[blend_idx].link2featidx.update({link: n + i for i, link in enumerate(accepted_links)})\n if accepted_links and self.verbose:\n print('[ Expanding ] Expanded features %d -> %d' % (n, len(self.datas[blend_idx].link2featidx)))\n\n # update labels with predictions in the blended data\n if self.verbose:\n print('[ Expanding ] Updating labels')\n if 'yopt' in predictions:\n yp = self._round(predictions['yopt'])\n else:\n yp = self._y_merge(predictions=predictions)\n nidxs_conv = nidxs.copy()\n labels0 = self.datas[blend_idx].node_labels.copy()\n self.datas[blend_idx].update_labels(nidxs=nidxs_conv, y=yp)\n\n # Compile all training nodes, which include nodes previously trained and now training in expansion\n if nidxs_train is not None:\n nidxs_conv += nidxs_train_blend\n\n # retrain model with predictions and features from this expansion\n if self.verbose:\n print('[ Expanding ] Training on expanded network')\n self._train_clfs(train_nidxs=nidxs_conv)\n\n # reset training data index and labels\n self.train_idx = train_idx0\n self.datas[blend_idx].node_labels = labels0.copy()\n\n # Remove blended data\n self.datas.pop(blend_idx)\n\n # Restore base-classifiers\n self.restore_clfs(clf_pkg)\n\n # Restore original multilayer state\n self.train_multilayers = train_multilayers0\n\n # Node indices\n predictions['nidxs'] = np.array([i for j in path for i in j['nidxs']])\n\n return predictions","def choose_to_stop_early(self):\n # return self.cumulated_num_tests > 10 # Limit to make 10 predictions\n # return np.random.rand() < self.early_stop_proba\n batch_size = 30 # See ingestion program: D_train.init(batch_size=30, repeat=True)\n num_examples = self.metadata_.size()\n num_epochs = self.cumulated_num_steps * batch_size / num_examples\n return num_epochs > self.num_epochs_we_want_to_train # Train for certain number of epochs then stop","def cross_validate(all_tetrode_data, target, tetrode_ids, tetrode_units, verbose=True):\n kf = StratifiedKFold(n_splits=10)\n y_true = np.zeros(target.shape)\n y_hat = np.zeros(target.shape)\n i = 0\n\n for train_index, test_index in kf.split(np.zeros(target.shape[0]), target.argmax(axis=-1)):\n X_train, X_test = select_data(all_tetrode_data, train_index), select_data(all_tetrode_data, test_index)\n y_train, y_test = target[train_index, :], target[test_index, :]\n\n model = build_tetrode_model(tetrode_ids, tetrode_units)\n checkpointer = ModelCheckpoint('temp_model.h5',\n verbose=0, save_best_only=True)\n hist = model.fit(X_train, y_train,\n nb_epoch=200, batch_size=20,\n validation_data=(X_test, y_test),\n callbacks=[checkpointer], verbose=0)\n best_model = load_model('temp_model.h5')\n\n n = y_test.shape[0]\n y_true[i:(i + n), :] = y_test\n y_hat[i:(i + n), :] = best_model.predict(X_test)\n i += n\n\n if verbose:\n accuracy = max(hist.history['val_acc'])\n print('Current fold validation accuracy: {acc}'.format(acc=accuracy))\n\n return y_true, y_hat","def load_own_target():\n data = Data()\n target = data.get_label_col()\n return target.values","def load_folds(options=None, df=None):\n if df is not None and 'fold' in df.columns:\n i_train = df.query(\"fold != 'test'\").index.to_numpy()\n i_test = df.query(\"fold == 'test'\").index.to_numpy()\n return [(i_train, i_test)]\n print('No folds specified in CSV file')\n\n if options.folds == 'weak':\n return save_weak_folds(df)\n return save_folds(df)","def test(self) -> tf.contrib.data.Dataset:\n return self.__test_dataset","def log_eval(self, epoch, dataset_name):\n pass","def get_train_validation_test_split(dframe):\n skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2)\n result = next(skf.split(dframe, get_label(dframe)), None)\n\n train_validation = dframe.iloc[result[0]]\n test = dframe.iloc[result[1]]\n\n result = next(skf.split(train_validation, get_label(train_validation)), None)\n\n train = train_validation.iloc[result[0]]\n validation = train_validation.iloc[result[1]]\n return train, validation, test","def generate_validation_batch(self):\n assert self.validation_dataset is not None\n assert self.data_tags is not None\n \n # Sample indices and get data\n index_array = np.random.choice(self.num_validation_samples, self.p.trainer.batch_size)\n return self.get_data_from_indices(self.validation_dataset, index_array)","def _get_lr(self, initial_lr, step, epoch):\n return initial_lr","def test_train_spl(data, testsize):\n test = data.tail(testsize)\n train = data.head(data.shape[0] - testsize)\n return test, train","def run(self, orig_target_df):\n\n # For each fold\n for fold_idx, (fold_training_set_df, fold_testing_set_df, fold_target_df, fold_truth_df) in enumerate(self._generate_validation_fold()):\n train_test_date_split = fold_training_set_df[\"date\"].max()\n eval_start_date = train_test_date_split - timedelta(days = self.test_nb_days)\n date_to_predict = train_test_date_split + timedelta(days = 1)\n print(\"Warning: date_to_predict offset should be computed dynamically. Currently fixed to 1.\")\n\n # For each prediction method\n for process, process_name in zip(self.process_lst, self.process_names_lst):\n print(\"Running validation for process:\", process_name, \"on fold:\", fold_idx, \"...\")\n\n # Train the model\n with open(self.data_cache_path_str + \"data_bkp.pkl\", \"wb\") as f:\n pickle.dump((fold_training_set_df, fold_testing_set_df, fold_target_df, fold_truth_df), f)\n\n y_train = fold_target_df[\"demand\"].reset_index(drop = True)\n model = process(train_test_date_split, eval_start_date)\n model.fit(fold_training_set_df, y_train)\n\n # Generate predictions for validation set\n preds = model.predict(fold_testing_set_df, date_to_predict)\n preds[\"demand\"] = (orig_target_df[\"shifted_demand\"] + preds[\"demand\"]).apply(np.expm1)\n\n # Score the predictions\n preds2 = preds.copy()\n preds2.columns = [\"id\", \"date\", \"preds\"]\n preds_rmse_by_date_df = preds2.merge(fold_truth_df, how = \"left\", on = [\"id\", \"date\"])\n preds_rmse_by_date_df = preds_rmse_by_date_df[[\"date\", \"preds\", \"demand\"]].groupby(\"date\").apply(lambda x: self._rmse(x[\"demand\"], x[\"preds\"])).reset_index()\n preds_rmse_by_date_df.columns = [\"date\", \"preds_rmse\"]\n\n best_preds_piv = preds[[\"id\", \"date\", \"demand\"]].pivot(index = \"id\", columns = \"date\", values = \"demand\").reset_index()\n truth_piv = fold_truth_df[[\"id\", \"date\", \"demand\"]].pivot(index = \"id\", columns = \"date\", values = \"demand\").reset_index()\n truth_piv.set_index(\"id\", inplace = True)\n best_preds_piv.set_index(\"id\", inplace = True)\n best_preds_piv.columns = [\"F\" + str(i) for i in range(1, 29)]\n truth_piv.columns = [\"F\" + str(i) for i in range(1, 29)]\n validation_WRMSSE = round(model.evaluator.wrmsse(best_preds_piv, truth_piv, score_only = True), 6)\n\n # Save result for later use\n self.scores[process_name].append((fold_idx, preds_rmse_by_date_df, validation_WRMSSE))\n \n if self.verbose == True: \n print(process_name, \"had a score of\", validation_WRMSSE, \"on validation period\", fold_testing_set_df[\"date\"].min(), \"to\", fold_testing_set_df[\"date\"].max())\n\n metrics_lst = []\n for process_name, content in self.scores.items():\n for fold_idx, preds_rmse_by_date_df, validation_WRMSSE in content:\n preds_rmse_by_date_df[\"process_name\"] = process_name\n preds_rmse_by_date_df[\"fold_idx\"] = fold_idx\n preds_rmse_by_date_df[\"WRMSSE\"] = validation_WRMSSE\n metrics_lst.append(preds_rmse_by_date_df)\n\n metrics_df = pd.concat(metrics_lst, axis = 0)\n metrics_df.set_index(\"date\", inplace = True)\n\n return metrics_df","def systtest_fit_extract(self, fit_data, datakey, labels):\n # Find which hypothesis is the best fit.\n h0_fit = fit_data['h0_fit_to_%s'%datakey]\n h1_fit = fit_data['h1_fit_to_%s'%datakey]\n if h0_fit['metric_val'] > h1_fit['metric_val']:\n bestfit = 'h1'\n altfit = 'h0'\n else:\n bestfit = 'h0'\n altfit = 'h1'\n # Extract the relevant fits\n best_to_alt_key = '%s_fit_to_%s_fid'%(bestfit,altfit)\n best_to_alt_fit = fit_data[best_to_alt_key]['fid_asimov']\n relevant_fit_data = {}\n relevant_fit_data['best_to_alt_fit'] = best_to_alt_fit\n relevant_fit_data['alt_to_best_fit'] = \\\n fit_data['%s_fit_to_%s'%(altfit,datakey)]\n relevant_fit_data['best_to_best_fit'] = \\\n fit_data['%s_fit_to_%s'%(bestfit,datakey)]\n # Since this is an MC study in Asimov, this _should_ also be\n # the injected truth. But, it is possible that if a\n # systematically wrong hypothesis has been injected that\n # this is NOT the case.\n truth_recovered = labels['%s_name'%bestfit] in labels['data_name'] or \\\n labels['data_name'] in labels['%s_name'%bestfit]\n relevant_fit_data['truth_recovered'] = truth_recovered\n return relevant_fit_data","def test_invalid_target(self):\n y_valid = np.random.randint(2, size=100)\n y_invalid = np.random.uniform(size=100)\n\n oz = ClassBalance()\n\n with pytest.raises(YellowbrickValueError):\n oz.fit(y_invalid)\n\n with pytest.raises(YellowbrickValueError):\n oz.fit(y_valid, y_invalid)","def get_inputs_test():\n x = tf.constant(extract_pandas_data(x_test))\n y = tf.constant(y_test.values)\n return x, y","def _get_training_data(self) -> tuple:\n\n training_data = self._data.loc[self._data.target == 'train'].drop('target', axis=1)\n y = training_data.y_label.to_numpy()\n X = training_data.drop('y_label', axis=1).to_numpy()\n\n return X, y","def fold_data(fold, datas):\n training, validation = fold\n folded = {}\n\n # fold Xtrain\n if datas.get('Xtrain') is not None:\n folded['Xtrain'] = datas['Xtrain'][training, :]\n folded['Xtest'] = datas['Xtrain'][validation, :]\n\n # fold Ktrain\n if datas.get('Ktrain') is not None:\n folded['Ktrain'] = datas['Ktrain'][training, :][:, training]\n folded['Ktest'] = datas['Ktrain'][validation, ][:, training]\n # if datas.get('Ktest') is not None:\n # folded['Ktest'] = datas['Ktest'][:, validation]\n # else:\n # folded['Ktest'] = folded['Xtest'].dot(folded['Xtrain'].T)\n\n # fold Ytrain\n if datas.get('Ytrain') is not None:\n folded['Ytrain'] = datas['Ytrain'][training, :]\n folded['Ytest'] = datas['Ytrain'][validation, :]\n\n return folded","def failover_target(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"failover_target\")","def build_newstest_finetune(self):\n # Note that this function is purposefully similar to build_newscomment_only\n # The two datasets have very similar structure and it would just be more\n # confusing to refactor code, creating multiple overlapping paths.\n logging.info('Building newstest finetune dataset')\n logging.info(self.configs[NEWSTEST])\n builder = tfds.builder(WMT_BASE_DATASET_NAME,\n config=self.configs[NEWSTEST],\n data_dir=self.data_dir)\n self.default_builder_obj = builder\n shard_spec = self.build_shard_spec()\n logging.info('Training on TFDS dataset %s with split %s',\n WMT_BASE_DATASET_NAME, 'train' + shard_spec)\n train_data = builder.as_dataset(split='train' + shard_spec,\n shuffle_files=self.shuffle_train_files)\n eval_data = self.default_eval_builder(builder, shard_spec)\n return train_data, eval_data","def Experiment(self, default=None):\n return self.data.get('experiment', default)","def get_train_index():\n data_size = (NUM_CLASS - 1) * NUM_DATA_PER_CLASS\n return np.array([i for i in range(0, data_size)])","def get_checkpoint():\n if ((FLAGS.run_dir or FLAGS.checkpoint_file) and\n FLAGS.bundle_file and not should_save_generator_bundle()):\n raise sequence_generator.SequenceGeneratorException(\n 'Cannot specify both bundle_file and run_dir or checkpoint_file')\n if FLAGS.run_dir:\n train_dir = os.path.join(os.path.expanduser(FLAGS.run_dir), 'train')\n return train_dir\n elif FLAGS.checkpoint_file:\n return os.path.expanduser(FLAGS.checkpoint_file)\n else:\n return None","def target_dummy(config: Configuration, seed: int) -> int:\n return seed","def run(fold=None, resize=False, resume_training=False, model_path=None):\n experiment_tag = \"000_009\"\n if fold:\n experiment_id = f\"{experiment_tag}_{fold}\"\n else:\n experiment_id = experiment_tag\n # parameters\n parameters = {}\n for i in ConfigEnum:\n if i.name == 'optimizer_params':\n parameters['optimizer_params'] = i.value[ConfigEnum.optimizer_type.value]\n elif i.name == 'scheduler_params':\n parameters['scheduler_params'] = i.value[ConfigEnum.scheduler_type.value]\n elif i.name == 'stopping_criteria_params':\n parameters['stopping_criteria_params'] = i.value[ConfigEnum.training_stopping_criteria.value]\n else:\n parameters[i.name] = i.value\n \n parameters['experiment_id'] = experiment_id\n parameters['experiment_tag'] = experiment_tag\n # initialize Neptune\n neptune.init(project_qualified_name='aravind/kaggle-ranzcr')\n neptune.create_experiment(f\"{experiment_id}\", params=parameters)\n neptune.append_tag(experiment_tag)\n neptune.append_tag(parameters['optimizer_type'])\n neptune.append_tag(parameters['scheduler_type'])\n neptune.append_tag(parameters['training_stopping_criteria'])\n\n if os.path.isfile(os.path.join(path_train_folds_dir, 'train_folds.csv')):\n train = pd.read_csv(os.path.join(path_train_folds_dir, 'train_folds.csv'))\n print(\"train folds csv read from disk\")\n else:\n train = create_folds()\n train.to_csv(os.path.join(path_train_folds_dir, 'train_folds.csv'), index=False)\n print(\"train folds csv saved to disk for reuse\")\n\n if resize:\n resize_image_batch(path_train_dir, path_resized_train_image_dir, IMAGE_SIZE)\n\n # do not split the data into CV folds if train_on_all_data fold is True\n # create dummy validation data instead\n if ConfigEnum.train_on_all_data.value == True:\n valid = pd.DataFrame(columns=train.columns)\n else:\n valid = train.loc[train.kfold == fold].reset_index(drop=True)\n\n train = train.loc[train.kfold != fold].reset_index(drop=True)\n\n # image path for torch dataset\n path_train_images = [os.path.join(path_resized_train_image_dir, i + \".jpg\") for i in train.StudyInstanceUID.values]\n path_valid_images = [os.path.join(path_resized_train_image_dir, i + \".jpg\") for i in valid.StudyInstanceUID.values]\n # test images in the order of submission file\n submission_file = pd.read_csv(path_sample_submission_file)\n path_test_images = [os.path.join(path_resized_test_image_dir, i + \".jpg\") for i in submission_file.StudyInstanceUID.values]\n\n # targets values for torch dataset\n targets_train = train[target_cols].values\n targets_valid = valid[target_cols].values\n\n print(f\"number of train images={len(path_train_images)}\")\n print(f\"number of validation images={len(path_valid_images)}\")\n print(f\"train data size={train.shape}\")\n print(f\"valid data size={valid.shape}\")\n\n train_augmentation = A.Compose([\n A.CLAHE(p=1),\n A.HorizontalFlip(p=0.5),\n A.ShiftScaleRotate(p=0.5, rotate_limit=90, scale_limit=0.2),\n A.Normalize(mean=[0.485, 0.456, 0.406], \n std=[0.229, 0.224, 0.225], \n max_pixel_value=255.0, \n always_apply=True,\n ),\n ToTensorV2(p=1.0),\n ]\n )\n\n\n valid_augmentation = A.Compose([\n A.CLAHE(p=1),\n A.Normalize(\n mean=(0.485, 0.456, 0.406), \n std=(0.229, 0.224, 0.225), \n max_pixel_value=255.0, \n always_apply=True,\n ),\n ToTensorV2(),\n ])\n\n test_augmentation = A.Compose([\n A.CLAHE(p=1),\n A.Normalize(\n mean=(0.485, 0.456, 0.406), \n std=(0.229, 0.224, 0.225), \n max_pixel_value=255.0, \n always_apply=True,\n ),\n ToTensorV2(),\n ])\n\n train_dataset = ImageDataset(\n path_train_images, \n targets_train, \n augmentations=train_augmentation, \n backend=ConfigEnum.image_backend.value, \n channel_first=True, \n grayscale=True, \n grayscale_as_rgb=True,\n )\n\n valid_dataset = ImageDataset(\n path_valid_images, \n targets_valid, \n augmentations=valid_augmentation, \n backend=ConfigEnum.image_backend.value, \n channel_first=True, \n grayscale=True, \n grayscale_as_rgb=True,\n )\n\n test_dataset = ImageDataset(\n path_test_images, \n None, \n augmentations=test_augmentation, \n backend=ConfigEnum.image_backend.value, \n channel_first=True, \n grayscale=True, \n grayscale_as_rgb=True,\n )\n\n # preproccess and distribute all necessary params to respective modules\n if resume_training:\n model = EfficientNetModel(pretrained=False, backbone=ConfigEnum.model_backbone.value)\n else:\n model = EfficientNetModel(pretrained=True, backbone=ConfigEnum.model_backbone.value)\n\n data_module = DataModule(train_dataset, valid_dataset, test_dataset)\n trainer = Trainer(model, data_module, \n experiment_id=parameters['experiment_id'],\n experiment_tag=parameters['experiment_tag'],\n image_size=ConfigEnum.image_size.value,\n device=ConfigEnum.device.value,\n fp16=ConfigEnum.fp16.value,\n accumulate_grad_steps=ConfigEnum.accumulate_grad_steps.value,\n seed=ConfigEnum.seed.value,\n train_batch_size=ConfigEnum.train_batch_size.value,\n valid_batch_size=ConfigEnum.valid_batch_size.value,\n test_batch_size=ConfigEnum.test_batch_size.value, \n dataloader_num_workers=ConfigEnum.dataloader_num_workers.value,\n train_dataloader_shuffle=ConfigEnum.train_dataloader_shuffle.value,\n optimizer_type=ConfigEnum.optimizer_type.value,\n optimizer_params=parameters['optimizer_params'],\n scheduler_type=ConfigEnum.scheduler_type.value, \n scheduler_params=parameters['scheduler_params'],\n step_scheduler_after=ConfigEnum.step_scheduler_after.value,\n step_scheduler_metric=ConfigEnum.step_scheduler_metric.value,\n compute_train_loss_after=ConfigEnum.compute_train_loss_after.value,\n compute_train_metric_after=ConfigEnum.compute_train_metric_after.value,\n compute_valid_loss_after=ConfigEnum.compute_valid_loss_after.value,\n compute_valid_metric_after=ConfigEnum.compute_valid_metric_after.value,\n training_stopping_criteria=ConfigEnum.training_stopping_criteria.value,\n stopping_criteria_params =parameters['stopping_criteria_params'],\n max_epoch=ConfigEnum.max_epoch.value,\n train_on_all_data=ConfigEnum.train_on_all_data.value,\n validate_after=ConfigEnum.validate_after.value,\n validation_steps=ConfigEnum.validation_steps.value,\n run_lr_range_test=ConfigEnum.run_lr_range_test.value,\n sleep_in_epochs=ConfigEnum.sleep_in_epochs.value,\n sleep_time=ConfigEnum.sleep_time.value,\n checkpoint_epochs=ConfigEnum.checkpoint_epochs.value,\n )\n\n\n print(\"=============PARAMS=================\")\n print(f\"experiment_id={parameters['experiment_id']}\")\n print(f\"experiment_tag={parameters['experiment_tag']}\")\n print(f\"image_size={ConfigEnum.image_size.value}\")\n print(f\"device={ConfigEnum.device.value}\")\n print(f\"fp16={ConfigEnum.fp16.value}\")\n print(f\"accumulate_grad_steps={ConfigEnum.accumulate_grad_steps.value}\")\n print(f\"seed={ConfigEnum.seed.value}\")\n print(f\"train_batch_size={ConfigEnum.train_batch_size.value}\")\n print(f\"valid_batch_size={ConfigEnum.valid_batch_size.value}\")\n print(f\"test_batch_size={ConfigEnum.test_batch_size.value}\") \n print(f\"dataloader_num_workers={ConfigEnum.dataloader_num_workers.value}\")\n print(f\"train_dataloader_shuffle={ConfigEnum.train_dataloader_shuffle.value}\")\n print(f\"optimizer_type={ConfigEnum.optimizer_type.value}\")\n print(f\"optimizer_params={parameters['optimizer_params']}\")\n print(f\"scheduler_type={ConfigEnum.scheduler_type.value}\") \n print(f\"scheduler_params={parameters['scheduler_params']}\")\n print(f\"step_scheduler_after={ConfigEnum.step_scheduler_after.value}\")\n print(f\"step_scheduler_metric={ConfigEnum.step_scheduler_metric.value}\")\n print(f\"compute_train_loss_after={ConfigEnum.compute_train_loss_after.value}\")\n print(f\"compute_train_metric_after={ConfigEnum.compute_train_metric_after.value}\")\n print(f\"compute_valid_loss_after={ConfigEnum.compute_valid_loss_after.value}\")\n print(f\"compute_valid_metric_after={ConfigEnum.compute_valid_metric_after.value}\")\n print(f\"training_stopping_criteria={ConfigEnum.training_stopping_criteria.value}\")\n print(f\"stopping_criteria_params ={parameters['stopping_criteria_params']}\")\n print(f\"max_epoch={ConfigEnum.max_epoch.value}\")\n print(f\"train_on_all_data={ConfigEnum.train_on_all_data.value}\")\n print(f\"validate_after={ConfigEnum.validate_after.value}\")\n print(f\"validation_steps={ConfigEnum.validation_steps.value}\")\n print(f\"run_lr_range_test={ConfigEnum.run_lr_range_test.value}\")\n print(f\"sleep_in_epochs={ConfigEnum.sleep_in_epochs.value}\")\n print(f\"sleep_time={ConfigEnum.sleep_time.value}\")\n print(f\"checkpoint_epochs={ConfigEnum.checkpoint_epochs.value}\")\n \n # train or resume training logic\n if resume_training:\n trainer.resume_training(model_path)\n else:\n trainer.fit()","def early_stopping(self, epoch):\n if len(self.test_res['score']) == 0:\n return -1, False\n if self.test_res['score'][-1] > self.best_score: # improvement\n self.best_score = self.test_res['score'][-1]\n self.cur_patience = 0\n else:\n self.cur_patience += 1\n\n if self.max_patience == self.cur_patience: # early stop must happen\n best_epoch = epoch - self.max_patience\n return best_epoch, True\n else:\n return epoch, False","def get_mnist_data(batch=128):\n \n def transformer(data, label):\n data = data.flatten().expand_dims(0).astype(np.float32)/255\n data = data-0.13/0.31\n label = label.astype(np.float32)\n return data, label\n\n train_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=True, transform=transformer)\n validation_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=False, transform=transformer)\n train_dataloader = gluon.data.DataLoader(train_dataset, batch_size=batch, last_batch='keep',shuffle=True)\n validation_dataloader = gluon.data.DataLoader(validation_dataset, batch_size=batch, last_batch='keep')\n \n return train_dataloader, validation_dataloader","def check_training_samples(self):\n\n yidx = np.sum(self.datas[self.train_idx].gen_labels(), axis=0) < self.kfold_cv\n if np.any(yidx):\n xlist = ','.join(np.array(self.datas[self.train_idx].labels)[yidx])\n print('\\n *** WARNING ***\\n There are labels with very few samples: %s' % xlist)\n print(' If encounter chaotic errors, consider excluding these labels using --excludeloc %s\\n' % xlist)\n\n return","def experiment(\n self,\n dataset: Union[str, dict, pd.DataFrame] = None,\n training_set: Union[str, dict, pd.DataFrame] = None,\n validation_set: Union[str, dict, pd.DataFrame] = None,\n test_set: Union[str, dict, pd.DataFrame] = None,\n training_set_metadata: Union[str, dict] = None,\n data_format: str = None,\n experiment_name: str = \"experiment\",\n model_name: str = \"run\",\n model_load_path: str = None,\n model_resume_path: str = None,\n eval_split: str = TEST,\n skip_save_training_description: bool = False,\n skip_save_training_statistics: bool = False,\n skip_save_model: bool = False,\n skip_save_progress: bool = False,\n skip_save_log: bool = False,\n skip_save_processed_input: bool = False,\n skip_save_unprocessed_output: bool = False,\n skip_save_predictions: bool = False,\n skip_save_eval_stats: bool = False,\n skip_collect_predictions: bool = False,\n skip_collect_overall_stats: bool = False,\n output_directory: str = \"results\",\n random_seed: int = default_random_seed,\n **kwargs,\n ) -> Tuple[Optional[dict], TrainingStats, PreprocessedDataset, str]:\n if self._user_config.get(HYPEROPT):\n print_boxed(\"WARNING\")\n logger.warning(HYPEROPT_WARNING)\n\n (train_stats, preprocessed_data, output_directory) = self.train(\n dataset=dataset,\n training_set=training_set,\n validation_set=validation_set,\n test_set=test_set,\n training_set_metadata=training_set_metadata,\n data_format=data_format,\n experiment_name=experiment_name,\n model_name=model_name,\n model_load_path=model_load_path,\n model_resume_path=model_resume_path,\n skip_save_training_description=skip_save_training_description,\n skip_save_training_statistics=skip_save_training_statistics,\n skip_save_model=skip_save_model,\n skip_save_progress=skip_save_progress,\n skip_save_log=skip_save_log,\n skip_save_processed_input=skip_save_processed_input,\n skip_save_unprocessed_output=skip_save_unprocessed_output,\n output_directory=output_directory,\n random_seed=random_seed,\n )\n\n (training_set, validation_set, test_set, training_set_metadata) = preprocessed_data\n\n eval_set = validation_set\n if eval_split == TRAINING:\n eval_set = training_set\n elif eval_split == VALIDATION:\n eval_set = validation_set\n elif eval_split == TEST:\n eval_set = test_set\n else:\n logger.warning(f\"Eval split {eval_split} not supported. \" f\"Using validation set instead\")\n\n if eval_set is not None:\n trainer_dict = self.config_obj.trainer.to_dict()\n batch_size = trainer_dict.get(EVAL_BATCH_SIZE, trainer_dict.get(BATCH_SIZE, None))\n\n # predict\n try:\n eval_stats, _, _ = self.evaluate(\n eval_set,\n data_format=data_format,\n batch_size=batch_size,\n output_directory=output_directory,\n skip_save_unprocessed_output=skip_save_unprocessed_output,\n skip_save_predictions=skip_save_predictions,\n skip_save_eval_stats=skip_save_eval_stats,\n collect_predictions=not skip_collect_predictions,\n collect_overall_stats=not skip_collect_overall_stats,\n return_type=\"dict\",\n )\n except NotImplementedError:\n logger.warning(\n \"Skipping evaluation as the necessary methods are not \"\n \"supported. Full exception below:\\n\"\n f\"{traceback.format_exc()}\"\n )\n eval_stats = None\n else:\n logger.warning(f\"The evaluation set {eval_set} was not provided. \" f\"Skipping evaluation\")\n eval_stats = None\n\n return eval_stats, train_stats, preprocessed_data, output_directory","def get_inputs_test_S():\n x = tf.constant(extract_pandas_data(x_test_S))\n y = tf.constant(y_test_S.values)\n return x, y","def test_fit_returns_self(self):\n\n df = d.create_df_1()\n\n x = BaseTransformer(columns=\"a\")\n\n x_fitted = x.fit(df)\n\n assert x_fitted is x, \"Returned value from BaseTransformer.fit not as expected.\"","def get_iris_data():\n iris = datasets.load_iris()\n data = iris[\"data\"]\n target = iris[\"target\"]\n\n # Prepend the column of 1s for bias\n N, M = data.shape\n all_X = np.ones((N, M + 1))\n all_X[:, 1:] = data\n\n # Convert into one-hot vectors\n num_labels = len(np.unique(target))\n all_Y = np.eye(num_labels)[target]\n return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)","def get_validation_data(self):\n return self.unzip_batch(self.valid)","def trainValidationData(self, data, labels, history):\n data, labels = getValidationData(data, labels)\n validationLabelsAsInt = [np.where(r == 1)[0][0] for r in labels]\n sampleWeights = sampling.getSampleWeightArray(validationLabelsAsInt, self.classWeightDic)\n history = self.model.fit(data, labels,\n epochs=len(history.epoch),\n batch_size=configuration['mlp']['batchSize'],\n verbose=0,\n sample_weight=sampleWeights)\n return history","def fit_predict_single_fold(\n self, train: TabularDataset, valid: TabularDataset\n ) -> Tuple[TorchBasedLinearEstimator, np.ndarray]:\n if type(train) is PandasDataset:\n train = train.to_numpy()\n valid = valid.to_numpy()\n\n model = self._infer_params()\n\n model.fit(\n train.data,\n train.target,\n train.weights,\n valid.data,\n valid.target,\n valid.weights,\n )\n\n val_pred = model.predict(valid.data)\n\n return model, val_pred","def make_predict_step(self):\n return self.make_eval_step()"],"string":"[\n \"def train_target(self):\\n return DatasetSentinel(\\\"train_target\\\", **self._dataset_sentinel_helper())\",\n \"def validation_input(self):\\n return DatasetSentinel(\\\"validation_input\\\", **self._dataset_sentinel_helper())\",\n \"def train_input(self):\\n return DatasetSentinel(\\\"train_input\\\", **self._dataset_sentinel_helper())\",\n \"def holdout_target(self):\\n return DatasetSentinel(\\n \\\"holdout_target\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def holdout_input(self):\\n return DatasetSentinel(\\n \\\"holdout_input\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def validation(self) -> tf.contrib.data.Dataset:\\n return self.__validation_dataset\",\n \"def training_target(training_data):\\n return training_data.status == \\\"DEFAULT\\\"\",\n \"def __get_x_y_from_training_validation(\\n logger, training, validation, predictors, target):\\n if training is not None:\\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\\n if validation is not None:\\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\\n return training_X, training_Y, validation_X, validation_Y\",\n \"def _dataset_sentinel_helper(self):\\n params = self.cross_experiment_key.parameters\\n return dict(\\n dataset_hash=params[\\\"train_dataset\\\"],\\n cv_type=params[\\\"cross_experiment_params\\\"].get(\\n \\\"cv_type\\\", params[\\\"cross_experiment_params\\\"].get(\\\"cross_validation_type\\\", None)\\n ),\\n global_random_seed=params[\\\"cross_experiment_params\\\"][\\\"global_random_seed\\\"],\\n random_seeds=params[\\\"cross_experiment_params\\\"][\\\"random_seeds\\\"],\\n )\",\n \"def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\\n dataset = input_data.Data(Xtrain_ul,\\n Xtrain_l,\\n Xtest,\\n ytrain_ul,\\n ytrain_l,\\n ytest,\\n num_labeled, \\n batch_size, \\n shuffle=True)\\n return dataset.next_batch()\",\n \"def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\\n i = offset_steps\\n lval = splitsize\\n if not do_offset:\\n i_val = allind[:lval]\\n i_train = allind[lval:]\\n else:\\n i_val = allind[i * lval:(i + 1) * lval]\\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\\n if len(i_val) <= 0:\\n print(\\\"Warning: #Validation data is 0, take 1 training sample instead\\\")\\n i_val = i_train[:1]\\n\\n return i_train, i_val\",\n \"def test_early_stopping_no_val_step(tmpdir):\\n\\n class CurrentModel(EvalModelTemplate):\\n def training_step(self, *args, **kwargs):\\n output = super().training_step(*args, **kwargs)\\n output.update({'my_train_metric': output['loss']}) # could be anything else\\n return output\\n\\n model = CurrentModel()\\n model.validation_step = None\\n model.val_dataloader = None\\n\\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\\n trainer = Trainer(\\n default_root_dir=tmpdir,\\n early_stop_callback=stopping,\\n overfit_pct=0.20,\\n max_epochs=5,\\n )\\n result = trainer.fit(model)\\n\\n assert result == 1, 'training failed to complete'\\n assert trainer.current_epoch < trainer.max_epochs\",\n \"def get_train_input(self, prev, i):\\n return self.get_eval_input(prev, i)\",\n \"def get_train_input(self, prev, i):\\n return self.get_eval_input(prev, i)\",\n \"def get_init_fn():\\n checkpoint_exclude_scopes = [\\\"InceptionV1/Logits\\\", \\\"InceptionV1/AuxLogits\\\"]\\n\\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n return slim.assign_from_checkpoint_fn(\\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\\n variables_to_restore)\",\n \"def _generate_validation_fold(self):\\n\\n for offset in range(self.nb_folds):\\n # Load all the data from cache (do this to save memory)\\n with open(self.data_cache_path_str + \\\"data_cache.pkl\\\", \\\"rb\\\") as f:\\n data_df, target_df = pickle.load(f)\\n\\n # Generate train and test sets\\n data_dates_lst = data_df[\\\"date\\\"].drop_duplicates().sort_values().tolist()\\n train_start_day = len(data_dates_lst) - ((self.nb_folds - offset) * self.test_nb_days + self.train_nb_days)\\n train_end_day = train_start_day + self.train_nb_days\\n test_start_day = train_end_day\\n test_end_day = test_start_day + self.test_nb_days\\n\\n train_dates_lst = data_dates_lst[train_start_day:train_end_day]\\n test_dates_lst = data_dates_lst[test_start_day:test_end_day]\\n\\n # Generate train and test labels\\n training_set_df = data_df.loc[data_df[\\\"date\\\"].isin(train_dates_lst)].reset_index(drop = True)\\n testing_set_df = data_df.loc[data_df[\\\"date\\\"].isin(test_dates_lst)].reset_index(drop = True)\\n new_target_df = target_df.loc[data_df[\\\"date\\\"].isin(train_dates_lst)].reset_index(drop = True)\\n truth_df = target_df.loc[data_df[\\\"date\\\"].isin(test_dates_lst)].reset_index(drop = True)\\n\\n # Reduce memory usage\\n del data_df, target_df\\n gc.collect()\\n\\n # Return result\\n yield (training_set_df, testing_set_df, new_target_df, truth_df)\",\n \"def test_train_test_split_uni_exo(load_uni_exo_data_target):\\n data, target = load_uni_exo_data_target\\n\\n ####################################\\n #### Continuous fh without Gaps ####\\n ####################################\\n\\n #### Integer fh ----\\n exp = TSForecastingExperiment()\\n fh = 12\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test.index == data.iloc[-fh:].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(1, 10) # 9 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [1, 2, 3, 4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #################################\\n #### Continuous fh with Gaps ####\\n #################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(7, 13) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n ####################################\\n #### Discontinuous fh with Gaps ####\\n ####################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6, 10, 11, 12]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\",\n \"def _kfold_cross_val(self, training_elms: np.ndarray) -> None:\\n kf = model_selection.KFold(\\n n_splits=config.folds, shuffle=True, random_state=config.seed\\n )\\n self.df[\\\"elm_events\\\"] = training_elms\\n self.df[\\\"fold\\\"] = -1\\n for f_, (_, valid_idx) in enumerate(kf.split(X=training_elms)):\\n self.df.loc[valid_idx, \\\"fold\\\"] = f_\",\n \"def get_eval_input(self, prev, i):\\n del prev, i\\n # The attention_decoder will fetch image features from the net, no need for\\n # extra inputs.\\n return self._zero_label\",\n \"def get_validation_batch(self, batch_size):\\n if batch_size > len(self.val_indices):\\n return self.data_handler.slice_data(self.val_indices)\\n else:\\n return self.data_handler.slice_data(list(np.random.choice(self.val_indices, size=batch_size)))\",\n \"def sample_train_data(dataset ,target,data_len, resp = True ):\\r\\n np.random.seed(222)\\r\\n ixes = np.random.choice(dataset.index, data_len, replace = False)\\r\\n print(ixes)\\r\\n under_df = dataset.iloc[ixes]\\r\\n if resp==True:\\r\\n under_target = target.iloc[ixes]\\r\\n return under_df, under_target\\r\\n else:\\r\\n return under_df\",\n \"def get_train(self, preprocess=False):\\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')\",\n \"def load_input(io_config):\\n\\n fold = io_config['fold']\\n\\n train_data_fnames = io_config['file']['train_data']\\n\\n train_fname = [t[1] for t in train_data_fnames\\n if t[0] == fold][0]\\n\\n return train_fname\",\n \"def cross_validation_datasets(self, fold):\\n if fold > len(self): fold = len(self) / 2\\n stratified = self.stratified_bunches(fold)\\n datasets = []\\n for index in range(len(stratified)):\\n gold = GoldInstances(training_as_gold(stratified[index]))\\n rest = flatten(stratified[:index]) + flatten(stratified[index + 1:])\\n training = TrainingInstances(rest)\\n datasets.append((training, gold))\\n return datasets\",\n \"def _partition_elms(\\n self, max_elms: int = None, fold: int = None\\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n # get ELM indices from datafile\\n elm_index, _ = self._read_file()\\n\\n # limit the data according to the max number of events passed\\n if max_elms is not None and max_elms != -1:\\n LOGGER.info(f\\\"Limiting data read to {max_elms} events.\\\")\\n n_elms = max_elms\\n else:\\n n_elms = len(elm_index)\\n\\n # split the data into train, validation and test sets\\n training_elms, test_elms = model_selection.train_test_split(\\n elm_index[:n_elms],\\n test_size=self.fraction_test,\\n shuffle=True,\\n random_state=config.seed,\\n )\\n\\n # kfold cross validation\\n if self.kfold and fold is None:\\n raise Exception(\\n f\\\"K-fold cross validation is passed but fold index in range [0, {config.folds}) is not specified.\\\"\\n )\\n\\n if self.kfold:\\n LOGGER.info(\\\"Using K-fold cross validation\\\")\\n self._kfold_cross_val(training_elms)\\n training_elms = self.df[self.df[\\\"fold\\\"] != fold][\\\"elm_events\\\"]\\n validation_elms = self.df[self.df[\\\"fold\\\"] == fold][\\\"elm_events\\\"]\\n else:\\n LOGGER.info(\\n \\\"Creating training and validation datasets by simple splitting\\\"\\n )\\n training_elms, validation_elms = model_selection.train_test_split(\\n training_elms, test_size=self.fraction_validate\\n )\\n LOGGER.info(f\\\"Number of training ELM events: {training_elms.size}\\\")\\n LOGGER.info(f\\\"Number of validation ELM events: {validation_elms.size}\\\")\\n LOGGER.info(f\\\"Number of test ELM events: {test_elms.size}\\\")\\n\\n return training_elms, validation_elms, test_elms\",\n \"def _get_training_dataset(self):\\n\\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))\",\n \"def example(self):\\n result = getattr(self, '_example', None)\\n if result is None:\\n # No example batch was found, so get one from the `.train` dataset\\n result = next(iter(self.train))\\n # And cache it for next time\\n self._example = result\\n return result\",\n \"def _get_init_fn():\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\\n\\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)\",\n \"def keep_cross_validation_fold_assignment(self):\\n return self._parms.get(\\\"keep_cross_validation_fold_assignment\\\")\",\n \"def get_target_encoder(self, train: NumpyOrPandas) -> Optional[type]:\\n target_encoder = None\\n if train.folds is not None:\\n if train.task.name in [\\\"binary\\\", \\\"reg\\\"]:\\n target_encoder = TargetEncoder\\n else:\\n n_classes = train.target.max() + 1\\n if n_classes <= self.multiclass_te_co:\\n target_encoder = MultiClassTargetEncoder\\n\\n return target_encoder\",\n \"def get_init_fn():\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n variables_to_restore.append(var)\\n\\n checkpoint_path = tf.train.latest_checkpoint(\\\"./base_checkpoint\\\")\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=False)\",\n \"def get_test(self, preprocess=False):\\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')\",\n \"def get_train_input(self, prev, i):\\n pass\",\n \"def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):\\n\\n epoch = K.eval(self._ckpt_saved_epoch)\\n if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0:\\n # The most recently saved epoch is one epoch prior to the epoch it\\n # failed at, so return the value of 'self._ckpt_saved_epoch' plus one.\\n return epoch + 1\\n return initial_epoch\",\n \"def prepare_train_validation(self) -> Tuple:\\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\\n\\n Xt = self.dataset.prepare_input_samples(Xt)\\n Yt = self.dataset.prepare_output_samples(Yt)\\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\\n traindataset = traindataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n Xv = self.dataset.prepare_input_samples(Xv)\\n Yv = self.dataset.prepare_output_samples(Yv)\\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\\n validdataset = validdataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n return traindataset, validdataset\",\n \"def _evaluate_during_fit(self, test_loader, epoch):\",\n \"def validation_step(self, *args: Any, **kwargs: Any) -> None:\\n batch = args[0]\\n batch_idx = args[1]\\n x = batch[\\\"image\\\"]\\n y = batch[\\\"mask\\\"]\\n y_hat = self(x)\\n y_hat_hard = y_hat.argmax(dim=1)\\n\\n loss = self.loss(y_hat, y)\\n\\n self.log(\\\"val_loss\\\", loss, on_step=False, on_epoch=True)\\n self.val_metrics(y_hat_hard, y)\\n\\n if (\\n batch_idx < 10\\n and hasattr(self.trainer, \\\"datamodule\\\")\\n and self.logger\\n and hasattr(self.logger, \\\"experiment\\\")\\n and hasattr(self.logger.experiment, \\\"add_figure\\\")\\n ):\\n try:\\n datamodule = self.trainer.datamodule\\n batch[\\\"prediction\\\"] = y_hat_hard\\n for key in [\\\"image\\\", \\\"mask\\\", \\\"prediction\\\"]:\\n batch[key] = batch[key].cpu()\\n sample = unbind_samples(batch)[0]\\n fig = datamodule.plot(sample)\\n summary_writer = self.logger.experiment\\n summary_writer.add_figure(\\n f\\\"image/{batch_idx}\\\", fig, global_step=self.global_step\\n )\\n plt.close()\\n except ValueError:\\n pass\",\n \"def get_train_inputs(self, example):\\n return example\",\n \"def from_checkpoint(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n ds_iter: Optional[tf.data.Iterator] = None,\\n init_rng: Optional[jnp.ndarray] = None\\n ) -> Optional[train_state_lib.TrainState]:\\n train_states = list(\\n self.from_checkpoints(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng))\\n if len(train_states) > 1:\\n raise ValueError(\\n f'Expected at most 1 checkpoint but got {len(train_states)} for '\\n f'config(s): {ckpt_cfgs}')\\n return (train_states[0]) if train_states else None\",\n \"def predict_single_fold(self, model: TorchBasedLinearEstimator, dataset: TabularDataset) -> np.ndarray:\\n pred = model.predict(dataset.data)\\n\\n return pred\",\n \"def get_infer_fn(infer_step: InferStepCallable, batch_size: int,\\n train_state_axes: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner):\\n infer_step = partitioner.partition(\\n infer_step,\\n in_axis_resources=(train_state_axes.params, PartitionSpec('data',)),\\n out_axis_resources=PartitionSpec('data',))\\n\\n data_layout = partitioner.get_data_layout(batch_size)\\n shard_id = data_layout.shard_id\\n num_shards = data_layout.num_shards\\n\\n per_shard_batch_size = batch_size // num_shards\\n\\n def infer_fn(ds: tf.data.Dataset, train_state: train_state_lib.TrainState):\\n ds_shapes = jax.tree_map(lambda x: jnp.array(x.shape), ds.element_spec)\\n multihost_utils.assert_same(\\n ds_shapes, 'Dataset element shapes do not agree across hosts. '\\n 'This could be an indication that the dataset is nondeterministic.')\\n try:\\n dataset_remainder = len(ds) % batch_size # pytype:disable=wrong-arg-types\\n logging.info('length of dataset = %s', len(ds))\\n except TypeError as e:\\n if str(e) == 'dataset length is unknown.':\\n logging.warning(\\n 'The following error is likely due to the use of TensorFlow v1 in '\\n 'your dataset pipeline. Verify you are not importing from '\\n '`tf.compat.v1` as part of your pipeline.')\\n raise e\\n\\n if dataset_remainder:\\n dataset_pad_amt = batch_size - dataset_remainder\\n logging.info(\\n 'Padding infer dataset with %d examples for even per-replica shards.',\\n dataset_pad_amt)\\n # Pad with the first example using an index of -1 so seqio will ignore.\\n pad_ds = ds.take(1).map(lambda i, x: (np.int64(-1), x)).repeat(\\n dataset_pad_amt)\\n ds = ds.concatenate(pad_ds)\\n\\n # Shard the infer dataset across replica sets.\\n sharded_ds = ds.shard(num_shards, shard_id).batch(\\n per_shard_batch_size, drop_remainder=True)\\n multihost_utils.assert_same(\\n jnp.array(len(sharded_ds)),\\n 'Dataset lengths do not agree across hosts.')\\n\\n logging.info(\\n 'The infer dataset is sharded into %d shards with per-shard '\\n 'batch size of %d', num_shards, per_shard_batch_size)\\n\\n # Run inference for each replica set.\\n batched_results, all_indices = [], []\\n for index, infer_batch in sharded_ds.as_numpy_iterator():\\n # Run fast inference on batch.\\n # [B, ...] -> [B, ...]\\n batch_result = infer_step(train_state.params, infer_batch)\\n logging.info('Inference of batch %s done.', index)\\n # Issue asynchronous copy request which serves as prefetching to the host.\\n # The result value is synchronized with host_allgather in the loop below.\\n try:\\n jax.tree_map(lambda x: x.copy_to_host_async(), batch_result)\\n except AttributeError:\\n # Similar to jax.device_get, we skip transfers for non DeviceArrays.\\n pass\\n batched_results.append(batch_result)\\n all_indices.append(index)\\n logging.info('Inference of all batches done.')\\n all_inferences = []\\n for batch_result in batched_results:\\n # [B, ...] -> [H, B, ...]\\n batch_result = multihost_utils.host_allgather(\\n batch_result, num_shards, shard_id,\\n data_layout.is_first_host_in_replica_set)\\n all_inferences.append(batch_result)\\n\\n # List[H, B, ...] -> List[B, H, ...]\\n all_inferences = jax.tree_map(lambda x: np.moveaxis(x, 0, 1),\\n all_inferences)\\n\\n # List[B, H, ...] -> [B * batch_count, H, ...]\\n all_inferences = jax.tree_multimap(lambda *args: np.concatenate(args),\\n *all_inferences)\\n # List[B] -> [B * batch_count]\\n all_indices = np.concatenate(all_indices)\\n # Collect all batches from across hosts.\\n # [B * batch_count] -> [H, B * batch_count]\\n all_indices = multihost_utils.host_allgather(\\n all_indices, num_shards, shard_id,\\n data_layout.is_first_host_in_replica_set)\\n # [H, B * batch_count] -> [B * batch_count, H]\\n all_indices = np.transpose(all_indices)\\n all_inferences, all_indices = _deshard_and_remove_padding(\\n all_inferences, all_indices)\\n\\n # Translate [B, ...] -> List[...] by flattening inferences making sure to\\n # preserve structure of individual elements (inferences are not assumed to\\n # be simple np.array). Finally, zip inferences with corresponding indices\\n # and convert leaf np.arrays into lists.\\n all_inferences, struct = jax.tree_flatten(all_inferences)\\n all_inferences = map(\\n functools.partial(jax.tree_unflatten, struct), zip(*all_inferences))\\n indices_and_outputs = list(zip(all_indices, all_inferences))\\n indices_and_outputs = jax.tree_map(lambda x: np.array(x).tolist(),\\n indices_and_outputs)\\n return indices_and_outputs\\n\\n return infer_fn\",\n \"def _get__target(self):\\n if self._saved_target is None:\\n self._saved_target = self._metadata.get_callable()()\\n \\n return self._saved_target\",\n \"def get_train_input(self, prev, i):\\n if i == 0:\\n return self._zero_label\\n else:\\n # TODO(gorban): update to gradually introduce gt labels.\\n return self._labels_one_hot[:, i - 1, :]\",\n \"def get_init_fn():\\n\\tif train_config['checkpoint_path'] is None:\\n\\t\\treturn None\\n\\t# Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n\\t# ignoring the checkpoint anyway.\\n\\tif tf.train.latest_checkpoint(train_config['checkpoint_path']):\\n\\t\\ttf.logging.info(\\n\\t\\t\\t'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n\\t\\t\\t% train_config['checkpoint_path'])\\n\\t\\treturn None\\n\\n\\texclusions = []\\n\\tif train_config['checkpoint_exclude_scopes']:\\n\\t\\texclusions = [scope.strip()\\n\\t\\t for scope in train_config['checkpoint_exclude_scopes'].split(',')]\\n\\n\\t\\tvariable_to_restore = []\\n\\t\\tfor var in slim.get_model_variables():\\n\\t\\t\\texcluded = False\\n\\t\\t\\tfor exclusion in exclusions:\\n\\t\\t\\t\\tif var.op.name.startswith(exclusion):\\n\\t\\t\\t\\t\\texcluded = True\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tif not excluded:\\n\\t\\t\\t\\tvariable_to_restore.append(var)\\n\\n\\t\\tif tf.gfile.IsDirectory(train_config['checkpoint_path']):\\n\\t\\t\\tcheckpoint_path = tf.train.latest_checkpoint(train_config['checkpoint_path'])\\n\\t\\telse:\\n\\t\\t\\tcheckpoint_path = train_config['checkpoint_path']\\n\\n\\t\\ttf.logging.info('Fune-tuning from %s' % checkpoint_path)\\n\\n\\t\\treturn slim.assign_from_checkpoint_fn(\\n\\t\\t\\tcheckpoint_path,\\n\\t\\t\\tvariable_to_restore,\\n\\t\\t\\tignore_missing_vars=train_config['ignore_missing_vars'])\",\n \"def eval_step(data_laoder, model, device, fold):\\n\\tmodel.eval()\\n\\ttrue_labels, pred_labels = [], []\\n\\tfor inputs, input_lens, labels in tqdm.tqdm(data_laoder, ncols=100, desc=f\\\"eval--- F: {fold} -- E: n\\\"):\\n\\t\\tinputs = inputs.to(device)\\n\\t\\tlabels = labels.to(device)\\n\\t\\t#input_lens = input_lens.to(device)\\n\\n\\t\\tpreds = model(inputs, input_lens)\\n\\n\\t\\ttrue_labels.extend(labels.detach().squeeze().cpu().numpy().tolist())\\n\\t\\tpred_labels.extend(preds.detach().squeeze().cpu().numpy().tolist())\\n\\n\\treturn true_labels, pred_labels\",\n \"def get_dataset(self):\\n\\n trainset = datasets.MNIST('datasets/MNIST/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.MNIST('datasets/MNIST/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def create_master(exp, var, data=None, new_fields=[]):\\n\\n if isinstance(var, basestring):\\n assert data is not None\\n data_dict = data\\n new_fields.append(var)\\n else:\\n # see if it's a Var, and access metadata from the associated\\n # Experiment\\n if data is None:\\n data_dict = var.data\\n else:\\n data_dict = data\\n new_fields.append(var.varname)\\n new_fields.extend(var.oldvar)\\n\\n all_case_vals = exp.all_case_vals()\\n\\n # Post-process the case inspection a bit:\\n # 1) Promote any single-value case to a list with one entry\\n for i, case_vals in enumerate(all_case_vals):\\n if isinstance(case_vals, str):\\n all_case_vals[i] = list(case_vals)\\n\\n # 2) Make sure they're all still in the data dictionary. This is\\n # circular but a necessary sanity check\\n for case_bits in product(*all_case_vals):\\n assert case_bits in data_dict\\n\\n # Discover the type of the data passed into this method. If\\n # it's an xarray type, we'll preserve that. If it's an iris type,\\n # then we need to crash for now.\\n first_case = next(exp.all_cases())\\n\\n proto = data_dict[first_case]\\n if isinstance(proto, Dataset):\\n return _master_dataset(exp, data_dict, new_fields)\\n elif isinstance(proto, DataArray):\\n return _master_dataarray(exp, data_dict)\\n # elif isinstance(proto, Cube):\\n # raise NotImplementedError(\\\"Cube handling not yet implemented\\\")\\n else:\\n raise ValueError(\\\"Data must be an xarray type\\\")\",\n \"def MNIST_experiment():\\n tsetlin_machine = TsetlinMachine(number_clauses=1000,\\n number_action_states=1000,\\n precision=3.0,\\n threshold=10)\\n\\n X, y, val_X, val_y = MNIST()\\n\\n tsetlin_machine.fit(X, y, val_X, val_y, 300)\\n print('Final training accuracy:', tsetlin_machine.accuracy(X, y))\\n print('Final validation accuracy:', tsetlin_machine.accuracy(val_X, val_y))\",\n \"def _next_train_batch(self, batch_size=128, replace=False):\\n mask = np.random.choice(self.train_data.shape[0], batch_size, replace=replace)\\n return self.train_data[mask], self.train_label[mask]\",\n \"def train(\\n self, training_data: Dataset, validation_data: Optional[Dataset] = None\\n ) -> Predictor:\\n raise NotImplementedError\",\n \"def _get_init_fn():\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n if tf.train.latest_checkpoint(FLAGS.train_dir):\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % FLAGS.train_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n # TODO(sguada) variables.filter_variables()\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def _get_init_fn():\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n if tf.train.latest_checkpoint(FLAGS.train_dir):\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % FLAGS.train_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n # TODO(sguada) variables.filter_variables()\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def test_train_with_early_stopping(task_dataset, mocker):\\n wandb_log = mocker.patch.object(wandb, 'log')\\n\\n early_stopping = learning.EarlyStopping(patience=PATIENCE)\\n # Cannot possible go lower! So we should stop after PATIENCE steps.\\n early_stopping(float('-inf'))\\n\\n probe = nn.Linear(N_DIMS_PER_REP, N_UNIQUE_FEATS)\\n before = probe.weight.data.clone()\\n\\n learning.train(probe,\\n task_dataset,\\n epochs=EPOCHS,\\n stopper=early_stopping,\\n also_log_to_wandb=True)\\n after = probe.weight.data\\n\\n assert not before.equal(after)\\n assert wandb_log.call_args_list == [\\n mocker.call({'train loss': mocker.ANY}),\\n ] * (PATIENCE + 1)\",\n \"def test_no_fit(self, example_dataset):\\n\\n transformer = PreprocessFeatures()\\n\\n with pytest.raises(NotFittedError):\\n transformer.transform(example_dataset)\",\n \"def get_input(self, prev, i):\\n if self.is_training():\\n return self.get_train_input(prev, i)\\n else:\\n return self.get_eval_input(prev, i)\",\n \"def all2one(df, val_size=2, seed=0xBadCafe):\\n folds = sorted(df.fold.unique())\\n split = []\\n for f in folds:\\n idx_b = df[df.fold != f].index.tolist()\\n test_ids = df[df.fold == f].index.tolist()\\n train_ids, val_ids = train_test_split(idx_b, test_size=val_size, random_state=seed)\\n split.append([train_ids, val_ids, test_ids])\\n return split\",\n \"def ae_finetuning(decoder, train_loader, valid_loader, criterion, optimizer, resume, options):\\n from tensorboardX import SummaryWriter\\n\\n log_dir = os.path.join(options.output_dir, 'log_dir', 'fold_' + str(options.split), 'ConvAutoencoder')\\n visualization_path = os.path.join(options.output_dir, 'visualize', 'fold_' + str(options.split))\\n best_model_dir = os.path.join(options.output_dir, 'best_model_dir', 'fold_' + str(options.split), 'ConvAutoencoder')\\n filename = os.path.join(log_dir, 'training.tsv')\\n\\n if not resume:\\n check_and_clean(best_model_dir)\\n check_and_clean(visualization_path)\\n check_and_clean(log_dir)\\n columns = ['epoch', 'iteration', 'loss_train', 'mean_loss_train', 'loss_valid', 'mean_loss_valid']\\n results_df = pd.DataFrame(columns=columns)\\n with open(filename, 'w') as f:\\n results_df.to_csv(f, index=False, sep='\\\\t')\\n options.beginning_epoch = 0\\n\\n else:\\n if not os.path.exists(filename):\\n raise ValueError('The training.tsv file of the resumed experiment does not exist.')\\n truncated_tsv = pd.read_csv(filename, sep='\\\\t')\\n truncated_tsv.set_index(['epoch', 'iteration'], inplace=True)\\n truncated_tsv.drop(options.beginning_epoch, level=0, inplace=True)\\n truncated_tsv.to_csv(filename, index=True, sep='\\\\t')\\n\\n # Create writers\\n writer_train = SummaryWriter(os.path.join(log_dir, 'train'))\\n writer_valid = SummaryWriter(os.path.join(log_dir, 'valid'))\\n\\n decoder.train()\\n first_visu = True\\n print(decoder)\\n\\n if options.gpu:\\n decoder.cuda()\\n\\n # Initialize variables\\n best_loss_valid = np.inf\\n\\n early_stopping = EarlyStopping('min', min_delta=options.tolerance, patience=options.patience)\\n loss_valid = None\\n epoch = options.beginning_epoch\\n\\n print(\\\"Beginning training\\\")\\n while epoch < options.epochs and not early_stopping.step(loss_valid):\\n print(\\\"At %d-th epoch.\\\" % epoch)\\n\\n decoder.zero_grad()\\n evaluation_flag = True\\n step_flag = True\\n for i, data in enumerate(train_loader):\\n if options.gpu:\\n imgs = data['image'].cuda()\\n else:\\n imgs = data['image']\\n\\n train_output = decoder(imgs)\\n loss = criterion(train_output, imgs)\\n loss.backward()\\n\\n del imgs, train_output\\n\\n if (i+1) % options.accumulation_steps == 0:\\n step_flag = False\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # Evaluate the decoder only when no gradients are accumulated\\n if (i+1) % options.evaluation_steps == 0:\\n evaluation_flag = False\\n print('Iteration %d' % i)\\n loss_train = test_ae(decoder, train_loader, options.gpu, criterion)\\n mean_loss_train = loss_train / (len(train_loader) * train_loader.batch_size)\\n\\n loss_valid = test_ae(decoder, valid_loader, options.gpu, criterion)\\n mean_loss_valid = loss_valid / (len(valid_loader) * valid_loader.batch_size)\\n decoder.train()\\n\\n writer_train.add_scalar('loss', mean_loss_train, i + epoch * len(train_loader))\\n writer_valid.add_scalar('loss', mean_loss_valid, i + epoch * len(train_loader))\\n print(\\\"Scan level validation loss is %f at the end of iteration %d\\\" % (loss_valid, i))\\n row = np.array([epoch, i, loss_train, mean_loss_train, loss_valid, mean_loss_valid]).reshape(1, -1)\\n row_df = pd.DataFrame(row, columns=columns)\\n with open(filename, 'a') as f:\\n row_df.to_csv(f, header=False, index=False, sep='\\\\t')\\n\\n # If no step has been performed, raise Exception\\n if step_flag:\\n raise Exception('The model has not been updated once in the epoch. The accumulation step may be too large.')\\n\\n # If no evaluation has been performed, warn the user\\n if evaluation_flag:\\n warnings.warn('Your evaluation steps are too big compared to the size of the dataset.'\\n 'The model is evaluated only once at the end of the epoch')\\n\\n # Always test the results and save them once at the end of the epoch\\n print('Last checkpoint at the end of the epoch %d' % epoch)\\n\\n loss_train = test_ae(decoder, train_loader, options.gpu, criterion)\\n mean_loss_train = loss_train / (len(train_loader) * train_loader.batch_size)\\n\\n loss_valid = test_ae(decoder, valid_loader, options.gpu, criterion)\\n mean_loss_valid = loss_valid / (len(valid_loader) * valid_loader.batch_size)\\n decoder.train()\\n\\n writer_train.add_scalar('loss', mean_loss_train, i + epoch * len(train_loader))\\n writer_valid.add_scalar('loss', mean_loss_valid, i + epoch * len(train_loader))\\n print(\\\"Scan level validation loss is %f at the end of iteration %d\\\" % (loss_valid, i))\\n\\n row = np.array([epoch, i, loss_train, mean_loss_train, loss_valid, mean_loss_valid]).reshape(1, -1)\\n row_df = pd.DataFrame(row, columns=columns)\\n with open(filename, 'a') as f:\\n row_df.to_csv(f, header=False, index=False, sep='\\\\t')\\n\\n is_best = loss_valid < best_loss_valid\\n best_loss_valid = min(best_loss_valid, loss_valid)\\n # Always save the model at the end of the epoch and update best model\\n save_checkpoint({'model': decoder.state_dict(),\\n 'iteration': i,\\n 'epoch': epoch,\\n 'loss_valid': loss_valid},\\n False, is_best,\\n best_model_dir)\\n # Save optimizer state_dict to be able to reload\\n save_checkpoint({'optimizer': optimizer.state_dict(),\\n 'epoch': epoch,\\n 'name': options.optimizer,\\n },\\n False, False,\\n best_model_dir,\\n filename='optimizer.pth.tar')\\n\\n if epoch % 10 == 0:\\n visualize_subject(decoder, train_loader, visualization_path, options, epoch=epoch, save_input=first_visu)\\n first_visu = False\\n\\n epoch += 1\\n\\n visualize_subject(decoder, train_loader, visualization_path, options, epoch=epoch, save_input=first_visu)\",\n \"def test_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,\\n with_preprocessor):\\n input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)\\n estimator = clone(estimator)\\n estimator.set_params(preprocessor=preprocessor)\\n set_random_state(estimator)\\n with pytest.raises(NotFittedError):\\n estimator.predict(input_data)\",\n \"def _get_init_fn(checkpoints_dir_y):\\n if FLAGS.checkpoint_path is None:\\n return None\\n\\n # Warn the user if a checkpoint exists in the train_dir. Then we'll be\\n # ignoring the checkpoint anyway.\\n # checkpoints_dir_y = namings.generate_checkpoints_dir(FLAGS, image_size)\\n if tf.train.latest_checkpoint(checkpoints_dir_y): #FLAGS.checkpoints_dir): # YY\\n tf.logging.info(\\n 'Ignoring --checkpoint_path because a checkpoint already exists in %s'\\n % checkpoints_dir_y) # YY: FLAGS.checkpoints_dir)\\n return None\\n\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from %s' % checkpoint_path)\\n\\n return slim.assign_from_checkpoint_fn(\\n checkpoint_path,\\n variables_to_restore,\\n ignore_missing_vars=FLAGS.ignore_missing_vars)\",\n \"def getLabeledXYonly(self, trainingData):\\r\\n labeledData, unlabeledData = trainingData\\r\\n return labeledData\",\n \"def get_fit_intercept(self):\\n return self.fit_intercept\",\n \"def target(self, example):\\n return example[self.target_index]\",\n \"def from_checkpoint_or_scratch(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n init_rng: Array,\\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\\n return (self.from_checkpoint(ckpt_cfgs, ds_iter=ds_iter, init_rng=init_rng)\\n or self.from_scratch(init_rng))\",\n \"def get_dataset(self):\\n trainset = datasets.KMNIST('datasets/KMNIST/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.KMNIST('datasets/KMNIST/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def imit_test(self, epoch, best): \\n a_loss, t_loss = 0., 0.\\n data_valid_iter = batch_iter(self.data_valid[0], self.data_valid[1], self.data_valid[2], self.data_valid[3])\\n for i, data in enumerate(data_valid_iter):\\n loss_a, loss_t = self.user_loop(data)\\n a_loss += loss_a.item()\\n t_loss += loss_t.item()\\n \\n a_loss /= i\\n t_loss /= i\\n logging.debug('<<user simulator>> validation, epoch {}, loss_a:{}, loss_t:{}'.format(epoch, a_loss, t_loss))\\n loss = a_loss + t_loss\\n if loss < best:\\n logging.info('<<user simulator>> best model saved')\\n best = loss\\n self.save(self.save_dir, 'best')\\n \\n a_loss, t_loss = 0., 0.\\n data_test_iter = batch_iter(self.data_test[0], self.data_test[1], self.data_test[2], self.data_test[3])\\n for i, data in enumerate(data_test_iter):\\n loss_a, loss_t = self.user_loop(data)\\n a_loss += loss_a.item()\\n t_loss += loss_t.item()\\n \\n a_loss /= i\\n t_loss /= i\\n logging.debug('<<user simulator>> test, epoch {}, loss_a:{}, loss_t:{}'.format(epoch, a_loss, t_loss))\\n return best\",\n \"def clfs_predict(self, nidxs_target, data=None, to_eval=False, fill=True, nidxs_train=None, eval_idx=None):\\n\\n if data is None:\\n data = self.datas[self.train_idx]\\n train_idx0 = self.train_idx\\n\\n if eval_idx is None:\\n for i, d in enumerate(self.datas):\\n if i != self.train_idx and data.p_data == d.p_data:\\n eval_idx = i\\n break\\n\\n # Still doesn't find the right data\\n if eval_idx is None:\\n print('\\\\n { Cannot find eval data }\\\\n')\\n return None\\n\\n # Combine training and testing data into layers of new Data object used to retrain clf-net during expansion\\n # The training and test data will be in different layers\\n # This is so training data is not lost during expansive training iterations\\n self.datas.append(self.blend_data(idxs=list({train_idx0, eval_idx})))\\n blend_idx = len(self.datas) - 1\\n if nidxs_train is not None:\\n nidxs_train_blend = [self.datas[blend_idx].nidxconvert[train_idx0][n] for n in nidxs_train]\\n else:\\n nidxs_train_blend = [self.datas[blend_idx].nidxconvert[train_idx0][n] for n in self.datas[train_idx0].nidx_train]\\n\\n # Compute expansion path\\n path = data.recruit(nidxs_remain=set(nidxs_target))\\n n_steps = len(path)\\n print('\\\\n { Network expansion path contains %d steps }' % n_steps)\\n\\n # Retain original base-classifiers\\n clf_pkg = self.archive_clfs()\\n\\n # Retain original multilayer state and change to train multilayers\\n train_multilayers0 = self.train_multilayers\\n self.train_multilayers = True\\n\\n nidxs = []\\n predictions = None\\n for istep in range(n_steps):\\n\\n nidxs += [self.datas[blend_idx].nidxconvert[eval_idx][n] for n in path[istep]['nidxs']]\\n print('\\\\n[ Step %d/%d ] %d nodes / %d expandable links' % (istep+1, n_steps, len(path[istep]['nidxs']),\\n len(path[istep]['links'])))\\n\\n # Predict\\n predictions = self.clf_all_predict(nidx_target=nidxs, data=self.datas[blend_idx])\\n\\n # Baseline\\n y_bl_pred = self.bl_predict(n_samples=len(nidxs), data=self.datas[blend_idx])\\n predictions['ybl'] = y_bl_pred\\n\\n # Predict with clf-opt if trained\\n if self.clf_opt_trained:\\n X = self._construct_clf_opt_X(predictions)\\n predictions['yopt'] = self._predict_proba(self.clf_opt, X)\\n\\n # Fill (fill==True) what clf-opt did not predict with the joint solutions from base classifiers\\n if fill:\\n coverage0 = self.calc_coverage(predictions['yopt'])\\n filling_coverage = 0.\\n if coverage0 < 1.:\\n i_missing = np.invert(np.any(self._round(predictions['yopt']), axis=1))\\n y_fill = self._y_merge(predictions=predictions, i=i_missing)\\n filling_coverage = self.calc_coverage(y_fill) * len(y_fill) / len(i_missing)\\n predictions['yopt'][i_missing] = y_fill\\n predictions['fill'] = filling_coverage\\n\\n if self.verbose:\\n coverage1 = self.calc_coverage(predictions['yopt'])\\n print('\\\\n[ clf-opt ]\\\\n no-fill coverage: {:.1%}\\\\n filling: {:.1%}\\\\n filled coverage: {:.1%}\\\\n'.format(coverage0, filling_coverage, coverage1))\\n\\n # Show scores\\n self._print_eval(predictions=predictions, to_eval=to_eval)\\n\\n if (istep + 1) < n_steps and path[istep]['links']:\\n\\n new_links = list(path[istep]['links'])\\n\\n # Set training data index to the blended data\\n self.train_idx = blend_idx\\n\\n # find expand features with evaluating data then set features in the blended data\\n if self.verbose:\\n print('\\\\n[ Expanding ] Evaluating %d links' % len(new_links))\\n r = self.datas[blend_idx].eval_lidxs(lidxs=[self.datas[blend_idx].link2lidx[l] for l in new_links], nidxs=nidxs_train_blend)\\n accepted_links = [new_links[i] for i, b in enumerate(r) if b]\\n if self.verbose:\\n print('[ Expanding ] Accepting %d links' % len(accepted_links))\\n n = len(self.datas[blend_idx].link2featidx)\\n self.datas[blend_idx].link2featidx.update({link: n + i for i, link in enumerate(accepted_links)})\\n if accepted_links and self.verbose:\\n print('[ Expanding ] Expanded features %d -> %d' % (n, len(self.datas[blend_idx].link2featidx)))\\n\\n # update labels with predictions in the blended data\\n if self.verbose:\\n print('[ Expanding ] Updating labels')\\n if 'yopt' in predictions:\\n yp = self._round(predictions['yopt'])\\n else:\\n yp = self._y_merge(predictions=predictions)\\n nidxs_conv = nidxs.copy()\\n labels0 = self.datas[blend_idx].node_labels.copy()\\n self.datas[blend_idx].update_labels(nidxs=nidxs_conv, y=yp)\\n\\n # Compile all training nodes, which include nodes previously trained and now training in expansion\\n if nidxs_train is not None:\\n nidxs_conv += nidxs_train_blend\\n\\n # retrain model with predictions and features from this expansion\\n if self.verbose:\\n print('[ Expanding ] Training on expanded network')\\n self._train_clfs(train_nidxs=nidxs_conv)\\n\\n # reset training data index and labels\\n self.train_idx = train_idx0\\n self.datas[blend_idx].node_labels = labels0.copy()\\n\\n # Remove blended data\\n self.datas.pop(blend_idx)\\n\\n # Restore base-classifiers\\n self.restore_clfs(clf_pkg)\\n\\n # Restore original multilayer state\\n self.train_multilayers = train_multilayers0\\n\\n # Node indices\\n predictions['nidxs'] = np.array([i for j in path for i in j['nidxs']])\\n\\n return predictions\",\n \"def choose_to_stop_early(self):\\n # return self.cumulated_num_tests > 10 # Limit to make 10 predictions\\n # return np.random.rand() < self.early_stop_proba\\n batch_size = 30 # See ingestion program: D_train.init(batch_size=30, repeat=True)\\n num_examples = self.metadata_.size()\\n num_epochs = self.cumulated_num_steps * batch_size / num_examples\\n return num_epochs > self.num_epochs_we_want_to_train # Train for certain number of epochs then stop\",\n \"def cross_validate(all_tetrode_data, target, tetrode_ids, tetrode_units, verbose=True):\\n kf = StratifiedKFold(n_splits=10)\\n y_true = np.zeros(target.shape)\\n y_hat = np.zeros(target.shape)\\n i = 0\\n\\n for train_index, test_index in kf.split(np.zeros(target.shape[0]), target.argmax(axis=-1)):\\n X_train, X_test = select_data(all_tetrode_data, train_index), select_data(all_tetrode_data, test_index)\\n y_train, y_test = target[train_index, :], target[test_index, :]\\n\\n model = build_tetrode_model(tetrode_ids, tetrode_units)\\n checkpointer = ModelCheckpoint('temp_model.h5',\\n verbose=0, save_best_only=True)\\n hist = model.fit(X_train, y_train,\\n nb_epoch=200, batch_size=20,\\n validation_data=(X_test, y_test),\\n callbacks=[checkpointer], verbose=0)\\n best_model = load_model('temp_model.h5')\\n\\n n = y_test.shape[0]\\n y_true[i:(i + n), :] = y_test\\n y_hat[i:(i + n), :] = best_model.predict(X_test)\\n i += n\\n\\n if verbose:\\n accuracy = max(hist.history['val_acc'])\\n print('Current fold validation accuracy: {acc}'.format(acc=accuracy))\\n\\n return y_true, y_hat\",\n \"def load_own_target():\\n data = Data()\\n target = data.get_label_col()\\n return target.values\",\n \"def load_folds(options=None, df=None):\\n if df is not None and 'fold' in df.columns:\\n i_train = df.query(\\\"fold != 'test'\\\").index.to_numpy()\\n i_test = df.query(\\\"fold == 'test'\\\").index.to_numpy()\\n return [(i_train, i_test)]\\n print('No folds specified in CSV file')\\n\\n if options.folds == 'weak':\\n return save_weak_folds(df)\\n return save_folds(df)\",\n \"def test(self) -> tf.contrib.data.Dataset:\\n return self.__test_dataset\",\n \"def log_eval(self, epoch, dataset_name):\\n pass\",\n \"def get_train_validation_test_split(dframe):\\n skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2)\\n result = next(skf.split(dframe, get_label(dframe)), None)\\n\\n train_validation = dframe.iloc[result[0]]\\n test = dframe.iloc[result[1]]\\n\\n result = next(skf.split(train_validation, get_label(train_validation)), None)\\n\\n train = train_validation.iloc[result[0]]\\n validation = train_validation.iloc[result[1]]\\n return train, validation, test\",\n \"def generate_validation_batch(self):\\n assert self.validation_dataset is not None\\n assert self.data_tags is not None\\n \\n # Sample indices and get data\\n index_array = np.random.choice(self.num_validation_samples, self.p.trainer.batch_size)\\n return self.get_data_from_indices(self.validation_dataset, index_array)\",\n \"def _get_lr(self, initial_lr, step, epoch):\\n return initial_lr\",\n \"def test_train_spl(data, testsize):\\n test = data.tail(testsize)\\n train = data.head(data.shape[0] - testsize)\\n return test, train\",\n \"def run(self, orig_target_df):\\n\\n # For each fold\\n for fold_idx, (fold_training_set_df, fold_testing_set_df, fold_target_df, fold_truth_df) in enumerate(self._generate_validation_fold()):\\n train_test_date_split = fold_training_set_df[\\\"date\\\"].max()\\n eval_start_date = train_test_date_split - timedelta(days = self.test_nb_days)\\n date_to_predict = train_test_date_split + timedelta(days = 1)\\n print(\\\"Warning: date_to_predict offset should be computed dynamically. Currently fixed to 1.\\\")\\n\\n # For each prediction method\\n for process, process_name in zip(self.process_lst, self.process_names_lst):\\n print(\\\"Running validation for process:\\\", process_name, \\\"on fold:\\\", fold_idx, \\\"...\\\")\\n\\n # Train the model\\n with open(self.data_cache_path_str + \\\"data_bkp.pkl\\\", \\\"wb\\\") as f:\\n pickle.dump((fold_training_set_df, fold_testing_set_df, fold_target_df, fold_truth_df), f)\\n\\n y_train = fold_target_df[\\\"demand\\\"].reset_index(drop = True)\\n model = process(train_test_date_split, eval_start_date)\\n model.fit(fold_training_set_df, y_train)\\n\\n # Generate predictions for validation set\\n preds = model.predict(fold_testing_set_df, date_to_predict)\\n preds[\\\"demand\\\"] = (orig_target_df[\\\"shifted_demand\\\"] + preds[\\\"demand\\\"]).apply(np.expm1)\\n\\n # Score the predictions\\n preds2 = preds.copy()\\n preds2.columns = [\\\"id\\\", \\\"date\\\", \\\"preds\\\"]\\n preds_rmse_by_date_df = preds2.merge(fold_truth_df, how = \\\"left\\\", on = [\\\"id\\\", \\\"date\\\"])\\n preds_rmse_by_date_df = preds_rmse_by_date_df[[\\\"date\\\", \\\"preds\\\", \\\"demand\\\"]].groupby(\\\"date\\\").apply(lambda x: self._rmse(x[\\\"demand\\\"], x[\\\"preds\\\"])).reset_index()\\n preds_rmse_by_date_df.columns = [\\\"date\\\", \\\"preds_rmse\\\"]\\n\\n best_preds_piv = preds[[\\\"id\\\", \\\"date\\\", \\\"demand\\\"]].pivot(index = \\\"id\\\", columns = \\\"date\\\", values = \\\"demand\\\").reset_index()\\n truth_piv = fold_truth_df[[\\\"id\\\", \\\"date\\\", \\\"demand\\\"]].pivot(index = \\\"id\\\", columns = \\\"date\\\", values = \\\"demand\\\").reset_index()\\n truth_piv.set_index(\\\"id\\\", inplace = True)\\n best_preds_piv.set_index(\\\"id\\\", inplace = True)\\n best_preds_piv.columns = [\\\"F\\\" + str(i) for i in range(1, 29)]\\n truth_piv.columns = [\\\"F\\\" + str(i) for i in range(1, 29)]\\n validation_WRMSSE = round(model.evaluator.wrmsse(best_preds_piv, truth_piv, score_only = True), 6)\\n\\n # Save result for later use\\n self.scores[process_name].append((fold_idx, preds_rmse_by_date_df, validation_WRMSSE))\\n \\n if self.verbose == True: \\n print(process_name, \\\"had a score of\\\", validation_WRMSSE, \\\"on validation period\\\", fold_testing_set_df[\\\"date\\\"].min(), \\\"to\\\", fold_testing_set_df[\\\"date\\\"].max())\\n\\n metrics_lst = []\\n for process_name, content in self.scores.items():\\n for fold_idx, preds_rmse_by_date_df, validation_WRMSSE in content:\\n preds_rmse_by_date_df[\\\"process_name\\\"] = process_name\\n preds_rmse_by_date_df[\\\"fold_idx\\\"] = fold_idx\\n preds_rmse_by_date_df[\\\"WRMSSE\\\"] = validation_WRMSSE\\n metrics_lst.append(preds_rmse_by_date_df)\\n\\n metrics_df = pd.concat(metrics_lst, axis = 0)\\n metrics_df.set_index(\\\"date\\\", inplace = True)\\n\\n return metrics_df\",\n \"def systtest_fit_extract(self, fit_data, datakey, labels):\\n # Find which hypothesis is the best fit.\\n h0_fit = fit_data['h0_fit_to_%s'%datakey]\\n h1_fit = fit_data['h1_fit_to_%s'%datakey]\\n if h0_fit['metric_val'] > h1_fit['metric_val']:\\n bestfit = 'h1'\\n altfit = 'h0'\\n else:\\n bestfit = 'h0'\\n altfit = 'h1'\\n # Extract the relevant fits\\n best_to_alt_key = '%s_fit_to_%s_fid'%(bestfit,altfit)\\n best_to_alt_fit = fit_data[best_to_alt_key]['fid_asimov']\\n relevant_fit_data = {}\\n relevant_fit_data['best_to_alt_fit'] = best_to_alt_fit\\n relevant_fit_data['alt_to_best_fit'] = \\\\\\n fit_data['%s_fit_to_%s'%(altfit,datakey)]\\n relevant_fit_data['best_to_best_fit'] = \\\\\\n fit_data['%s_fit_to_%s'%(bestfit,datakey)]\\n # Since this is an MC study in Asimov, this _should_ also be\\n # the injected truth. But, it is possible that if a\\n # systematically wrong hypothesis has been injected that\\n # this is NOT the case.\\n truth_recovered = labels['%s_name'%bestfit] in labels['data_name'] or \\\\\\n labels['data_name'] in labels['%s_name'%bestfit]\\n relevant_fit_data['truth_recovered'] = truth_recovered\\n return relevant_fit_data\",\n \"def test_invalid_target(self):\\n y_valid = np.random.randint(2, size=100)\\n y_invalid = np.random.uniform(size=100)\\n\\n oz = ClassBalance()\\n\\n with pytest.raises(YellowbrickValueError):\\n oz.fit(y_invalid)\\n\\n with pytest.raises(YellowbrickValueError):\\n oz.fit(y_valid, y_invalid)\",\n \"def get_inputs_test():\\n x = tf.constant(extract_pandas_data(x_test))\\n y = tf.constant(y_test.values)\\n return x, y\",\n \"def _get_training_data(self) -> tuple:\\n\\n training_data = self._data.loc[self._data.target == 'train'].drop('target', axis=1)\\n y = training_data.y_label.to_numpy()\\n X = training_data.drop('y_label', axis=1).to_numpy()\\n\\n return X, y\",\n \"def fold_data(fold, datas):\\n training, validation = fold\\n folded = {}\\n\\n # fold Xtrain\\n if datas.get('Xtrain') is not None:\\n folded['Xtrain'] = datas['Xtrain'][training, :]\\n folded['Xtest'] = datas['Xtrain'][validation, :]\\n\\n # fold Ktrain\\n if datas.get('Ktrain') is not None:\\n folded['Ktrain'] = datas['Ktrain'][training, :][:, training]\\n folded['Ktest'] = datas['Ktrain'][validation, ][:, training]\\n # if datas.get('Ktest') is not None:\\n # folded['Ktest'] = datas['Ktest'][:, validation]\\n # else:\\n # folded['Ktest'] = folded['Xtest'].dot(folded['Xtrain'].T)\\n\\n # fold Ytrain\\n if datas.get('Ytrain') is not None:\\n folded['Ytrain'] = datas['Ytrain'][training, :]\\n folded['Ytest'] = datas['Ytrain'][validation, :]\\n\\n return folded\",\n \"def failover_target(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"failover_target\\\")\",\n \"def build_newstest_finetune(self):\\n # Note that this function is purposefully similar to build_newscomment_only\\n # The two datasets have very similar structure and it would just be more\\n # confusing to refactor code, creating multiple overlapping paths.\\n logging.info('Building newstest finetune dataset')\\n logging.info(self.configs[NEWSTEST])\\n builder = tfds.builder(WMT_BASE_DATASET_NAME,\\n config=self.configs[NEWSTEST],\\n data_dir=self.data_dir)\\n self.default_builder_obj = builder\\n shard_spec = self.build_shard_spec()\\n logging.info('Training on TFDS dataset %s with split %s',\\n WMT_BASE_DATASET_NAME, 'train' + shard_spec)\\n train_data = builder.as_dataset(split='train' + shard_spec,\\n shuffle_files=self.shuffle_train_files)\\n eval_data = self.default_eval_builder(builder, shard_spec)\\n return train_data, eval_data\",\n \"def Experiment(self, default=None):\\n return self.data.get('experiment', default)\",\n \"def get_train_index():\\n data_size = (NUM_CLASS - 1) * NUM_DATA_PER_CLASS\\n return np.array([i for i in range(0, data_size)])\",\n \"def get_checkpoint():\\n if ((FLAGS.run_dir or FLAGS.checkpoint_file) and\\n FLAGS.bundle_file and not should_save_generator_bundle()):\\n raise sequence_generator.SequenceGeneratorException(\\n 'Cannot specify both bundle_file and run_dir or checkpoint_file')\\n if FLAGS.run_dir:\\n train_dir = os.path.join(os.path.expanduser(FLAGS.run_dir), 'train')\\n return train_dir\\n elif FLAGS.checkpoint_file:\\n return os.path.expanduser(FLAGS.checkpoint_file)\\n else:\\n return None\",\n \"def target_dummy(config: Configuration, seed: int) -> int:\\n return seed\",\n \"def run(fold=None, resize=False, resume_training=False, model_path=None):\\n experiment_tag = \\\"000_009\\\"\\n if fold:\\n experiment_id = f\\\"{experiment_tag}_{fold}\\\"\\n else:\\n experiment_id = experiment_tag\\n # parameters\\n parameters = {}\\n for i in ConfigEnum:\\n if i.name == 'optimizer_params':\\n parameters['optimizer_params'] = i.value[ConfigEnum.optimizer_type.value]\\n elif i.name == 'scheduler_params':\\n parameters['scheduler_params'] = i.value[ConfigEnum.scheduler_type.value]\\n elif i.name == 'stopping_criteria_params':\\n parameters['stopping_criteria_params'] = i.value[ConfigEnum.training_stopping_criteria.value]\\n else:\\n parameters[i.name] = i.value\\n \\n parameters['experiment_id'] = experiment_id\\n parameters['experiment_tag'] = experiment_tag\\n # initialize Neptune\\n neptune.init(project_qualified_name='aravind/kaggle-ranzcr')\\n neptune.create_experiment(f\\\"{experiment_id}\\\", params=parameters)\\n neptune.append_tag(experiment_tag)\\n neptune.append_tag(parameters['optimizer_type'])\\n neptune.append_tag(parameters['scheduler_type'])\\n neptune.append_tag(parameters['training_stopping_criteria'])\\n\\n if os.path.isfile(os.path.join(path_train_folds_dir, 'train_folds.csv')):\\n train = pd.read_csv(os.path.join(path_train_folds_dir, 'train_folds.csv'))\\n print(\\\"train folds csv read from disk\\\")\\n else:\\n train = create_folds()\\n train.to_csv(os.path.join(path_train_folds_dir, 'train_folds.csv'), index=False)\\n print(\\\"train folds csv saved to disk for reuse\\\")\\n\\n if resize:\\n resize_image_batch(path_train_dir, path_resized_train_image_dir, IMAGE_SIZE)\\n\\n # do not split the data into CV folds if train_on_all_data fold is True\\n # create dummy validation data instead\\n if ConfigEnum.train_on_all_data.value == True:\\n valid = pd.DataFrame(columns=train.columns)\\n else:\\n valid = train.loc[train.kfold == fold].reset_index(drop=True)\\n\\n train = train.loc[train.kfold != fold].reset_index(drop=True)\\n\\n # image path for torch dataset\\n path_train_images = [os.path.join(path_resized_train_image_dir, i + \\\".jpg\\\") for i in train.StudyInstanceUID.values]\\n path_valid_images = [os.path.join(path_resized_train_image_dir, i + \\\".jpg\\\") for i in valid.StudyInstanceUID.values]\\n # test images in the order of submission file\\n submission_file = pd.read_csv(path_sample_submission_file)\\n path_test_images = [os.path.join(path_resized_test_image_dir, i + \\\".jpg\\\") for i in submission_file.StudyInstanceUID.values]\\n\\n # targets values for torch dataset\\n targets_train = train[target_cols].values\\n targets_valid = valid[target_cols].values\\n\\n print(f\\\"number of train images={len(path_train_images)}\\\")\\n print(f\\\"number of validation images={len(path_valid_images)}\\\")\\n print(f\\\"train data size={train.shape}\\\")\\n print(f\\\"valid data size={valid.shape}\\\")\\n\\n train_augmentation = A.Compose([\\n A.CLAHE(p=1),\\n A.HorizontalFlip(p=0.5),\\n A.ShiftScaleRotate(p=0.5, rotate_limit=90, scale_limit=0.2),\\n A.Normalize(mean=[0.485, 0.456, 0.406], \\n std=[0.229, 0.224, 0.225], \\n max_pixel_value=255.0, \\n always_apply=True,\\n ),\\n ToTensorV2(p=1.0),\\n ]\\n )\\n\\n\\n valid_augmentation = A.Compose([\\n A.CLAHE(p=1),\\n A.Normalize(\\n mean=(0.485, 0.456, 0.406), \\n std=(0.229, 0.224, 0.225), \\n max_pixel_value=255.0, \\n always_apply=True,\\n ),\\n ToTensorV2(),\\n ])\\n\\n test_augmentation = A.Compose([\\n A.CLAHE(p=1),\\n A.Normalize(\\n mean=(0.485, 0.456, 0.406), \\n std=(0.229, 0.224, 0.225), \\n max_pixel_value=255.0, \\n always_apply=True,\\n ),\\n ToTensorV2(),\\n ])\\n\\n train_dataset = ImageDataset(\\n path_train_images, \\n targets_train, \\n augmentations=train_augmentation, \\n backend=ConfigEnum.image_backend.value, \\n channel_first=True, \\n grayscale=True, \\n grayscale_as_rgb=True,\\n )\\n\\n valid_dataset = ImageDataset(\\n path_valid_images, \\n targets_valid, \\n augmentations=valid_augmentation, \\n backend=ConfigEnum.image_backend.value, \\n channel_first=True, \\n grayscale=True, \\n grayscale_as_rgb=True,\\n )\\n\\n test_dataset = ImageDataset(\\n path_test_images, \\n None, \\n augmentations=test_augmentation, \\n backend=ConfigEnum.image_backend.value, \\n channel_first=True, \\n grayscale=True, \\n grayscale_as_rgb=True,\\n )\\n\\n # preproccess and distribute all necessary params to respective modules\\n if resume_training:\\n model = EfficientNetModel(pretrained=False, backbone=ConfigEnum.model_backbone.value)\\n else:\\n model = EfficientNetModel(pretrained=True, backbone=ConfigEnum.model_backbone.value)\\n\\n data_module = DataModule(train_dataset, valid_dataset, test_dataset)\\n trainer = Trainer(model, data_module, \\n experiment_id=parameters['experiment_id'],\\n experiment_tag=parameters['experiment_tag'],\\n image_size=ConfigEnum.image_size.value,\\n device=ConfigEnum.device.value,\\n fp16=ConfigEnum.fp16.value,\\n accumulate_grad_steps=ConfigEnum.accumulate_grad_steps.value,\\n seed=ConfigEnum.seed.value,\\n train_batch_size=ConfigEnum.train_batch_size.value,\\n valid_batch_size=ConfigEnum.valid_batch_size.value,\\n test_batch_size=ConfigEnum.test_batch_size.value, \\n dataloader_num_workers=ConfigEnum.dataloader_num_workers.value,\\n train_dataloader_shuffle=ConfigEnum.train_dataloader_shuffle.value,\\n optimizer_type=ConfigEnum.optimizer_type.value,\\n optimizer_params=parameters['optimizer_params'],\\n scheduler_type=ConfigEnum.scheduler_type.value, \\n scheduler_params=parameters['scheduler_params'],\\n step_scheduler_after=ConfigEnum.step_scheduler_after.value,\\n step_scheduler_metric=ConfigEnum.step_scheduler_metric.value,\\n compute_train_loss_after=ConfigEnum.compute_train_loss_after.value,\\n compute_train_metric_after=ConfigEnum.compute_train_metric_after.value,\\n compute_valid_loss_after=ConfigEnum.compute_valid_loss_after.value,\\n compute_valid_metric_after=ConfigEnum.compute_valid_metric_after.value,\\n training_stopping_criteria=ConfigEnum.training_stopping_criteria.value,\\n stopping_criteria_params =parameters['stopping_criteria_params'],\\n max_epoch=ConfigEnum.max_epoch.value,\\n train_on_all_data=ConfigEnum.train_on_all_data.value,\\n validate_after=ConfigEnum.validate_after.value,\\n validation_steps=ConfigEnum.validation_steps.value,\\n run_lr_range_test=ConfigEnum.run_lr_range_test.value,\\n sleep_in_epochs=ConfigEnum.sleep_in_epochs.value,\\n sleep_time=ConfigEnum.sleep_time.value,\\n checkpoint_epochs=ConfigEnum.checkpoint_epochs.value,\\n )\\n\\n\\n print(\\\"=============PARAMS=================\\\")\\n print(f\\\"experiment_id={parameters['experiment_id']}\\\")\\n print(f\\\"experiment_tag={parameters['experiment_tag']}\\\")\\n print(f\\\"image_size={ConfigEnum.image_size.value}\\\")\\n print(f\\\"device={ConfigEnum.device.value}\\\")\\n print(f\\\"fp16={ConfigEnum.fp16.value}\\\")\\n print(f\\\"accumulate_grad_steps={ConfigEnum.accumulate_grad_steps.value}\\\")\\n print(f\\\"seed={ConfigEnum.seed.value}\\\")\\n print(f\\\"train_batch_size={ConfigEnum.train_batch_size.value}\\\")\\n print(f\\\"valid_batch_size={ConfigEnum.valid_batch_size.value}\\\")\\n print(f\\\"test_batch_size={ConfigEnum.test_batch_size.value}\\\") \\n print(f\\\"dataloader_num_workers={ConfigEnum.dataloader_num_workers.value}\\\")\\n print(f\\\"train_dataloader_shuffle={ConfigEnum.train_dataloader_shuffle.value}\\\")\\n print(f\\\"optimizer_type={ConfigEnum.optimizer_type.value}\\\")\\n print(f\\\"optimizer_params={parameters['optimizer_params']}\\\")\\n print(f\\\"scheduler_type={ConfigEnum.scheduler_type.value}\\\") \\n print(f\\\"scheduler_params={parameters['scheduler_params']}\\\")\\n print(f\\\"step_scheduler_after={ConfigEnum.step_scheduler_after.value}\\\")\\n print(f\\\"step_scheduler_metric={ConfigEnum.step_scheduler_metric.value}\\\")\\n print(f\\\"compute_train_loss_after={ConfigEnum.compute_train_loss_after.value}\\\")\\n print(f\\\"compute_train_metric_after={ConfigEnum.compute_train_metric_after.value}\\\")\\n print(f\\\"compute_valid_loss_after={ConfigEnum.compute_valid_loss_after.value}\\\")\\n print(f\\\"compute_valid_metric_after={ConfigEnum.compute_valid_metric_after.value}\\\")\\n print(f\\\"training_stopping_criteria={ConfigEnum.training_stopping_criteria.value}\\\")\\n print(f\\\"stopping_criteria_params ={parameters['stopping_criteria_params']}\\\")\\n print(f\\\"max_epoch={ConfigEnum.max_epoch.value}\\\")\\n print(f\\\"train_on_all_data={ConfigEnum.train_on_all_data.value}\\\")\\n print(f\\\"validate_after={ConfigEnum.validate_after.value}\\\")\\n print(f\\\"validation_steps={ConfigEnum.validation_steps.value}\\\")\\n print(f\\\"run_lr_range_test={ConfigEnum.run_lr_range_test.value}\\\")\\n print(f\\\"sleep_in_epochs={ConfigEnum.sleep_in_epochs.value}\\\")\\n print(f\\\"sleep_time={ConfigEnum.sleep_time.value}\\\")\\n print(f\\\"checkpoint_epochs={ConfigEnum.checkpoint_epochs.value}\\\")\\n \\n # train or resume training logic\\n if resume_training:\\n trainer.resume_training(model_path)\\n else:\\n trainer.fit()\",\n \"def early_stopping(self, epoch):\\n if len(self.test_res['score']) == 0:\\n return -1, False\\n if self.test_res['score'][-1] > self.best_score: # improvement\\n self.best_score = self.test_res['score'][-1]\\n self.cur_patience = 0\\n else:\\n self.cur_patience += 1\\n\\n if self.max_patience == self.cur_patience: # early stop must happen\\n best_epoch = epoch - self.max_patience\\n return best_epoch, True\\n else:\\n return epoch, False\",\n \"def get_mnist_data(batch=128):\\n \\n def transformer(data, label):\\n data = data.flatten().expand_dims(0).astype(np.float32)/255\\n data = data-0.13/0.31\\n label = label.astype(np.float32)\\n return data, label\\n\\n train_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=True, transform=transformer)\\n validation_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=False, transform=transformer)\\n train_dataloader = gluon.data.DataLoader(train_dataset, batch_size=batch, last_batch='keep',shuffle=True)\\n validation_dataloader = gluon.data.DataLoader(validation_dataset, batch_size=batch, last_batch='keep')\\n \\n return train_dataloader, validation_dataloader\",\n \"def check_training_samples(self):\\n\\n yidx = np.sum(self.datas[self.train_idx].gen_labels(), axis=0) < self.kfold_cv\\n if np.any(yidx):\\n xlist = ','.join(np.array(self.datas[self.train_idx].labels)[yidx])\\n print('\\\\n *** WARNING ***\\\\n There are labels with very few samples: %s' % xlist)\\n print(' If encounter chaotic errors, consider excluding these labels using --excludeloc %s\\\\n' % xlist)\\n\\n return\",\n \"def experiment(\\n self,\\n dataset: Union[str, dict, pd.DataFrame] = None,\\n training_set: Union[str, dict, pd.DataFrame] = None,\\n validation_set: Union[str, dict, pd.DataFrame] = None,\\n test_set: Union[str, dict, pd.DataFrame] = None,\\n training_set_metadata: Union[str, dict] = None,\\n data_format: str = None,\\n experiment_name: str = \\\"experiment\\\",\\n model_name: str = \\\"run\\\",\\n model_load_path: str = None,\\n model_resume_path: str = None,\\n eval_split: str = TEST,\\n skip_save_training_description: bool = False,\\n skip_save_training_statistics: bool = False,\\n skip_save_model: bool = False,\\n skip_save_progress: bool = False,\\n skip_save_log: bool = False,\\n skip_save_processed_input: bool = False,\\n skip_save_unprocessed_output: bool = False,\\n skip_save_predictions: bool = False,\\n skip_save_eval_stats: bool = False,\\n skip_collect_predictions: bool = False,\\n skip_collect_overall_stats: bool = False,\\n output_directory: str = \\\"results\\\",\\n random_seed: int = default_random_seed,\\n **kwargs,\\n ) -> Tuple[Optional[dict], TrainingStats, PreprocessedDataset, str]:\\n if self._user_config.get(HYPEROPT):\\n print_boxed(\\\"WARNING\\\")\\n logger.warning(HYPEROPT_WARNING)\\n\\n (train_stats, preprocessed_data, output_directory) = self.train(\\n dataset=dataset,\\n training_set=training_set,\\n validation_set=validation_set,\\n test_set=test_set,\\n training_set_metadata=training_set_metadata,\\n data_format=data_format,\\n experiment_name=experiment_name,\\n model_name=model_name,\\n model_load_path=model_load_path,\\n model_resume_path=model_resume_path,\\n skip_save_training_description=skip_save_training_description,\\n skip_save_training_statistics=skip_save_training_statistics,\\n skip_save_model=skip_save_model,\\n skip_save_progress=skip_save_progress,\\n skip_save_log=skip_save_log,\\n skip_save_processed_input=skip_save_processed_input,\\n skip_save_unprocessed_output=skip_save_unprocessed_output,\\n output_directory=output_directory,\\n random_seed=random_seed,\\n )\\n\\n (training_set, validation_set, test_set, training_set_metadata) = preprocessed_data\\n\\n eval_set = validation_set\\n if eval_split == TRAINING:\\n eval_set = training_set\\n elif eval_split == VALIDATION:\\n eval_set = validation_set\\n elif eval_split == TEST:\\n eval_set = test_set\\n else:\\n logger.warning(f\\\"Eval split {eval_split} not supported. \\\" f\\\"Using validation set instead\\\")\\n\\n if eval_set is not None:\\n trainer_dict = self.config_obj.trainer.to_dict()\\n batch_size = trainer_dict.get(EVAL_BATCH_SIZE, trainer_dict.get(BATCH_SIZE, None))\\n\\n # predict\\n try:\\n eval_stats, _, _ = self.evaluate(\\n eval_set,\\n data_format=data_format,\\n batch_size=batch_size,\\n output_directory=output_directory,\\n skip_save_unprocessed_output=skip_save_unprocessed_output,\\n skip_save_predictions=skip_save_predictions,\\n skip_save_eval_stats=skip_save_eval_stats,\\n collect_predictions=not skip_collect_predictions,\\n collect_overall_stats=not skip_collect_overall_stats,\\n return_type=\\\"dict\\\",\\n )\\n except NotImplementedError:\\n logger.warning(\\n \\\"Skipping evaluation as the necessary methods are not \\\"\\n \\\"supported. Full exception below:\\\\n\\\"\\n f\\\"{traceback.format_exc()}\\\"\\n )\\n eval_stats = None\\n else:\\n logger.warning(f\\\"The evaluation set {eval_set} was not provided. \\\" f\\\"Skipping evaluation\\\")\\n eval_stats = None\\n\\n return eval_stats, train_stats, preprocessed_data, output_directory\",\n \"def get_inputs_test_S():\\n x = tf.constant(extract_pandas_data(x_test_S))\\n y = tf.constant(y_test_S.values)\\n return x, y\",\n \"def test_fit_returns_self(self):\\n\\n df = d.create_df_1()\\n\\n x = BaseTransformer(columns=\\\"a\\\")\\n\\n x_fitted = x.fit(df)\\n\\n assert x_fitted is x, \\\"Returned value from BaseTransformer.fit not as expected.\\\"\",\n \"def get_iris_data():\\n iris = datasets.load_iris()\\n data = iris[\\\"data\\\"]\\n target = iris[\\\"target\\\"]\\n\\n # Prepend the column of 1s for bias\\n N, M = data.shape\\n all_X = np.ones((N, M + 1))\\n all_X[:, 1:] = data\\n\\n # Convert into one-hot vectors\\n num_labels = len(np.unique(target))\\n all_Y = np.eye(num_labels)[target]\\n return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)\",\n \"def get_validation_data(self):\\n return self.unzip_batch(self.valid)\",\n \"def trainValidationData(self, data, labels, history):\\n data, labels = getValidationData(data, labels)\\n validationLabelsAsInt = [np.where(r == 1)[0][0] for r in labels]\\n sampleWeights = sampling.getSampleWeightArray(validationLabelsAsInt, self.classWeightDic)\\n history = self.model.fit(data, labels,\\n epochs=len(history.epoch),\\n batch_size=configuration['mlp']['batchSize'],\\n verbose=0,\\n sample_weight=sampleWeights)\\n return history\",\n \"def fit_predict_single_fold(\\n self, train: TabularDataset, valid: TabularDataset\\n ) -> Tuple[TorchBasedLinearEstimator, np.ndarray]:\\n if type(train) is PandasDataset:\\n train = train.to_numpy()\\n valid = valid.to_numpy()\\n\\n model = self._infer_params()\\n\\n model.fit(\\n train.data,\\n train.target,\\n train.weights,\\n valid.data,\\n valid.target,\\n valid.weights,\\n )\\n\\n val_pred = model.predict(valid.data)\\n\\n return model, val_pred\",\n \"def make_predict_step(self):\\n return self.make_eval_step()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7272191","0.7040783","0.65106875","0.6453621","0.58669484","0.52230227","0.52210194","0.5189654","0.5189253","0.5166722","0.50729924","0.5023478","0.4998633","0.4998633","0.49692518","0.4958584","0.4885863","0.4881906","0.48708826","0.4870653","0.48660624","0.48495704","0.48440486","0.48257494","0.480864","0.47701162","0.47636676","0.47615838","0.47511327","0.47497708","0.47414285","0.47403523","0.4739507","0.47290415","0.4701116","0.46914494","0.4677682","0.46770254","0.4627903","0.46142966","0.4612329","0.46086684","0.46055785","0.4599509","0.45974687","0.45833284","0.45827937","0.45798922","0.4576396","0.4575661","0.45709154","0.4570837","0.45692128","0.45601588","0.45575684","0.45533592","0.455307","0.4551651","0.45451736","0.45421764","0.45331877","0.4529609","0.45221278","0.45151404","0.45034054","0.4503158","0.4501761","0.44963032","0.44910365","0.4489903","0.44865862","0.44864804","0.44798476","0.4478702","0.4476208","0.44759068","0.44718558","0.4468614","0.4468472","0.44621304","0.44600093","0.4455646","0.44522107","0.4452052","0.44512114","0.4448824","0.44465822","0.44422793","0.44371113","0.44288006","0.44272083","0.4415814","0.4415468","0.44139796","0.4400422","0.43850943","0.43814334","0.43814015","0.43729913","0.43723047"],"string":"[\n \"0.7272191\",\n \"0.7040783\",\n \"0.65106875\",\n \"0.6453621\",\n \"0.58669484\",\n \"0.52230227\",\n \"0.52210194\",\n \"0.5189654\",\n \"0.5189253\",\n \"0.5166722\",\n \"0.50729924\",\n \"0.5023478\",\n \"0.4998633\",\n \"0.4998633\",\n \"0.49692518\",\n \"0.4958584\",\n \"0.4885863\",\n \"0.4881906\",\n \"0.48708826\",\n \"0.4870653\",\n \"0.48660624\",\n \"0.48495704\",\n \"0.48440486\",\n \"0.48257494\",\n \"0.480864\",\n \"0.47701162\",\n \"0.47636676\",\n \"0.47615838\",\n \"0.47511327\",\n \"0.47497708\",\n \"0.47414285\",\n \"0.47403523\",\n \"0.4739507\",\n \"0.47290415\",\n \"0.4701116\",\n \"0.46914494\",\n \"0.4677682\",\n \"0.46770254\",\n \"0.4627903\",\n \"0.46142966\",\n \"0.4612329\",\n \"0.46086684\",\n \"0.46055785\",\n \"0.4599509\",\n \"0.45974687\",\n \"0.45833284\",\n \"0.45827937\",\n \"0.45798922\",\n \"0.4576396\",\n \"0.4575661\",\n \"0.45709154\",\n \"0.4570837\",\n \"0.45692128\",\n \"0.45601588\",\n \"0.45575684\",\n \"0.45533592\",\n \"0.455307\",\n \"0.4551651\",\n \"0.45451736\",\n \"0.45421764\",\n \"0.45331877\",\n \"0.4529609\",\n \"0.45221278\",\n \"0.45151404\",\n \"0.45034054\",\n \"0.4503158\",\n \"0.4501761\",\n \"0.44963032\",\n \"0.44910365\",\n \"0.4489903\",\n \"0.44865862\",\n \"0.44864804\",\n \"0.44798476\",\n \"0.4478702\",\n \"0.4476208\",\n \"0.44759068\",\n \"0.44718558\",\n \"0.4468614\",\n \"0.4468472\",\n \"0.44621304\",\n \"0.44600093\",\n \"0.4455646\",\n \"0.44522107\",\n \"0.4452052\",\n \"0.44512114\",\n \"0.4448824\",\n \"0.44465822\",\n \"0.44422793\",\n \"0.44371113\",\n \"0.44288006\",\n \"0.44272083\",\n \"0.4415814\",\n \"0.4415468\",\n \"0.44139796\",\n \"0.4400422\",\n \"0.43850943\",\n \"0.43814334\",\n \"0.43814015\",\n \"0.43729913\",\n \"0.43723047\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80776197"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18480,"cells":{"query":{"kind":"string","value":"Get a `DatasetSentinel` representing an Experiment's `holdout_input_data` Returns"},"document":{"kind":"string","value":"def holdout_input(self):\n return DatasetSentinel(\n \"holdout_input\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def holdout_target(self):\n return DatasetSentinel(\n \"holdout_target\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())","def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())","def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())","def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())","def _dataset_sentinel_helper(self):\n params = self.cross_experiment_key.parameters\n return dict(\n dataset_hash=params[\"train_dataset\"],\n cv_type=params[\"cross_experiment_params\"].get(\n \"cv_type\", params[\"cross_experiment_params\"].get(\"cross_validation_type\", None)\n ),\n global_random_seed=params[\"cross_experiment_params\"][\"global_random_seed\"],\n random_seeds=params[\"cross_experiment_params\"][\"random_seeds\"],\n )","def test_additional_data(self):\n task = SentinelHubInputTask(\n bands_feature=(FeatureType.DATA, 'BANDS'),\n bands=['B01', 'B02', 'B05'],\n additional_data=[\n (FeatureType.MASK, 'dataMask', 'IS_DATA'),\n (FeatureType.MASK, 'CLM'),\n (FeatureType.MASK, 'SCL'),\n (FeatureType.MASK, 'SNW'),\n (FeatureType.MASK, 'CLD'),\n (FeatureType.DATA, 'CLP'),\n (FeatureType.DATA, 'viewAzimuthMean', 'view_azimuth_mean'),\n (FeatureType.DATA, 'sunAzimuthAngles'),\n (FeatureType.DATA, 'sunZenithAngles')\n ],\n size=self.size,\n maxcc=self.maxcc,\n time_difference=self.time_difference,\n data_collection=DataCollection.SENTINEL2_L2A,\n max_threads=self.max_threads\n )\n\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\n\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\n is_data = eopatch[(FeatureType.MASK, 'IS_DATA')]\n clm = eopatch[(FeatureType.MASK, 'CLM')]\n scl = eopatch[(FeatureType.MASK, 'SCL')]\n snw = eopatch[(FeatureType.MASK, 'SNW')]\n cld = eopatch[(FeatureType.MASK, 'CLD')]\n clp = eopatch[(FeatureType.DATA, 'CLP')]\n view_azimuth_mean = eopatch[(FeatureType.DATA, 'view_azimuth_mean')]\n sun_azimuth_angles = eopatch[(FeatureType.DATA, 'sunAzimuthAngles')]\n sun_zenith_angles = eopatch[(FeatureType.DATA, 'sunZenithAngles')]\n\n self.assertTrue(np.allclose(array_stats(bands), [0.027, 0.0243, 0.0162]))\n\n width, height = self.size\n self.assertTrue(bands.shape == (4, height, width, 3))\n self.assertTrue(is_data.shape == (4, height, width, 1))\n self.assertTrue(is_data.dtype == bool)\n self.assertTrue(clm.shape == (4, height, width, 1))\n self.assertTrue(clm.dtype == np.uint8)\n self.assertTrue(scl.shape == (4, height, width, 1))\n self.assertTrue(snw.shape == (4, height, width, 1))\n self.assertTrue(cld.shape == (4, height, width, 1))\n self.assertTrue(clp.shape == (4, height, width, 1))\n self.assertTrue(view_azimuth_mean.shape == (4, height, width, 1))\n self.assertTrue(sun_azimuth_angles.shape == (4, height, width, 1))\n self.assertTrue(sun_zenith_angles.shape == (4, height, width, 1))\n self.assertTrue(len(eopatch.timestamp) == 4)","def get_dataset_sampler(self):\n return None","def get_interest_variable(\n in_dataset, sensor_var, date_col, hr_col, numeric_var, target_sensor=\"A620\"\n):\n dataset_pproc = in_dataset.loc[\n in_dataset[sensor_var] == target_sensor, [date_col, hr_col] + [numeric_var]\n ]\n hrs_str = dataset_pproc[hr_col].to_string()\n dates_str = dataset_pproc[date_col]\n\n dataset_pproc[date_col] = pd.to_datetime(dataset_pproc[date_col])\n dataset_pproc.set_index([date_col, hr_col], inplace=True)\n dataset_pproc.fillna(method=\"ffill\", inplace=True)\n dataset_pproc.interpolate(method=\"linear\", axis=0)\n\n return dataset_pproc","def _create_dataset(source=''):\n return ExperimentalDataset()","def get_train_inputs(self, example):\n return example","def get_data_for_semi_supervised(self,percent_dropout,num_dropout_corruptions_per_point):\n\n\t\t\"\"\"Select the corrupted data if applicable, and otherwise the original training data\"\"\"\n\t\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=True)","def input(self):\n try:\n return self.inputs[-1]\n except IndexError:\n pass\n raise ValueError(\"The sample method has not been called\")","def get_input_data(sample):\n with checkpoints.query_portal.get(sample=sample).output[0].open() as f:\n data = json.read(f)\n return data","def get_dataset(dataset_name):\n if dataset_name == \"Iris\":\n data = datasets.load_iris()\n elif dataset_name == \"Breast Cancer\":\n data = datasets.load_breast_cancer()\n else:\n data = datasets.load_wine()\n\n X = data.data\n y = data.target\n return X, y","def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\n dataset = input_data.Data(Xtrain_ul,\n Xtrain_l,\n Xtest,\n ytrain_ul,\n ytrain_l,\n ytest,\n num_labeled, \n batch_size, \n shuffle=True)\n return dataset.next_batch()","def _input_fn(input_pipeline_context=None):\n return _create_dataset(options, is_training, input_pipeline_context)","def get_dataset(args):\n\n if args['experiment']['dataset'] == Dataset.mindsets:\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\n nb_questions=args['dataset']['nb_questions'],\n nb_useless=args['dataset']['nb_useless'],\n noise=args['dataset']['noise'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\n nb_features=args['dataset']['nb_features'],\n nb_mindsets=args['dataset']['nb_mindsets'],\n centers=args['dataset']['centers'],\n range_answers=args['dataset']['range_answers'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.retinal:\n xs, ys = load_RETINAL(root_path=args['root_dir'],\n nb_bins=args['dataset']['nb_bins'],\n max_idx=args['dataset']['max_idx'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.moons:\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\n noise=args['dataset']['noise'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.SBM:\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\n p_in=args['dataset']['p'],\n p_out=args['dataset']['q'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\n centers=args['dataset']['blob_centers'],\n n_features=args['dataset']['blob_centers'],\n cluster_std=args['dataset']['blob_variances'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.LFR:\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\n tau1=args['dataset']['tau1'],\n tau2=args['dataset']['tau2'],\n mu=args['dataset']['mu'],\n average_degree=args['dataset']['average_degree'],\n min_community=args['dataset']['min_community'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.wave:\n df = pd.read_csv('datasets/waveform.csv')\n xs = df[df.columns[:-1]].to_numpy()\n ys = df[df.columns[-1]].to_numpy()\n\n return Data(xs=xs, ys=ys)\n\n raise ValueError('Wrong name for a dataset')","def fixture_input_data(wrapper_arguments):\n n_features, classes = wrapper_arguments\n num_data = 50\n data = np.random.rand(num_data, n_features)\n labels = np.random.choice(classes, size=num_data)\n\n return data, labels","def readMNISTData():\n mnist = input_data.read_data_sets(\"MNIST_data\",one_hot=True) \n return mnist","def get_seeds_null(samples,signal):\n theta_seeds={}\n if selected is None:\n SRlist = range(self.N_SR)\n theta_samples = samples[:,0,self.N_SR:].T\n else:\n SRlist = [selected] \n theta_samples = samples[:,0,1].T\n for i,x in zip(SRlist,theta_samples):\n theta_MLE = x\n theta_seeds['theta_{0}'.format(i)] = theta_MLE\n return theta_seeds","def tempfeeder_exp():\n global _tempfeeder_exp\n if _tempfeeder_exp is None:\n _tempfeeder_exp = UserLoads_Experiment(DATA_WITHOUT_DUPES_PATH)\n return _tempfeeder_exp","def iris():\n return IrisDataset()","def find_input_dataset(stage, rconfig, data_type=None):\n\n # Use the stage-to-data mapping to find the data_type if none was handed in\n if data_type is None:\n data_type = DOCUMENT_PROCESSING_IO[stage]['in']\n # Get all data sets D for input name\n dirname = os.path.join(rconfig.target_path, 'data', data_type)\n datasets1 = [ds for ds in os.listdir(dirname) if ds.isdigit()]\n datasets2 = [DataSet(stage, data_type, rconfig, ds) for ds in datasets1]\n # Filer the datasets making sure that d.trace + d.head matches\n # rconfig.pipeline(txt).trace\n datasets3 = [ds for ds in datasets2 if ds.input_matches_global_config()]\n # If there is one result, return it, otherwise write a warning and exit\n if len(datasets3) == 1:\n return datasets3[0]\n elif len(datasets3) > 1:\n print \"WARNING, more than one approriate training set:\"\n for ds in datasets3:\n print ' ', ds\n sys.exit(\"Exiting...\")\n elif len(datasets3) == 0:\n print \"WARNING: no datasets available to meet input requirements\"\n sys.exit(\"Exiting...\")","def sample_train_data(dataset ,target,data_len, resp = True ):\r\n np.random.seed(222)\r\n ixes = np.random.choice(dataset.index, data_len, replace = False)\r\n print(ixes)\r\n under_df = dataset.iloc[ixes]\r\n if resp==True:\r\n under_target = target.iloc[ixes]\r\n return under_df, under_target\r\n else:\r\n return under_df","def default_dataset(self):\n return self.get_by_index(self._default_index)","def get_data_only_random_dropout(self,percent_dropout,num_dropout_corruptions_per_point):\n\n\t\t\"\"\"Select the corrupted data if applicable, and otherwise the original training data\"\"\"\n\t\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=False)","def get_datasetID(self):\n\t\treturn self.prDoc['inputs']['data'][0]['datasetID']","def GetLabelInput(self) -> \"itkImageSS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS3ISS3_GetLabelInput(self)","def GetInput(self) -> \"itkHistogramD const *\":\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDF_GetInput(self)","def noise_input_fn(params):\n batch_size = params['batch_size']\n noise_dim = params['noise_dim']\n # Use constant seed to obtain same noise\n np.random.seed(0)\n noise_dataset = tf.data.Dataset.from_tensors(\n {'random_noise': tf.constant(\n np.random.randn(batch_size, noise_dim), dtype=tf.float32)\n })\n noise = noise_dataset.make_one_shot_iterator().get_next()\n tf.logging.debug('Noise input %s', noise)\n return noise_dataset","def dataset_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"dataset_id\")","def GetLabelInput(self) -> \"itkImageSS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS3ISS3_GetLabelInput(self)","def GetLabelInput(self) -> \"itkImageSS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF3ISS3_GetLabelInput(self)","def _fetch_input():\n try:\n simplecount_input = inputtools.fetch_data()\n return simplecount_input\n except:\n raise","def get_expo_data(\n data: Union[pd.Series, pd.DataFrame],\n target_column: str = \"close\",\n trend: str = \"A\",\n seasonal: str = \"A\",\n seasonal_periods: int = 7,\n dampen: str = \"F\",\n n_predict: int = 5,\n start_window: float = 0.85,\n forecast_horizon: int = 5,\n metric: str = \"mape\",\n) -> Tuple[\n List[TimeSeries],\n List[TimeSeries],\n List[TimeSeries],\n Optional[Union[float, ndarray]],\n ExponentialSmoothing,\n]:\n\n use_scalers = False\n _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers)\n\n if trend == \"M\":\n trend_model = ModelMode.MULTIPLICATIVE\n elif trend == \"N\":\n trend_model = ModelMode.NONE\n else: # Default\n trend_model = ModelMode.ADDITIVE\n\n if seasonal == \"M\":\n seasonal_model = SeasonalityMode.MULTIPLICATIVE\n elif seasonal == \"N\":\n seasonal_model = SeasonalityMode.NONE\n else: # Default\n seasonal_model = SeasonalityMode.ADDITIVE\n\n damped = True\n if dampen == \"F\":\n damped = False\n\n # Model Init\n model_es = ExponentialSmoothing(\n trend=trend_model,\n seasonal=seasonal_model,\n seasonal_periods=int(seasonal_periods),\n damped=damped,\n random_state=42,\n )\n\n try:\n # Historical backtesting\n historical_fcast_es = model_es.historical_forecasts(\n ticker_series, # backtest on entire ts\n start=float(start_window),\n forecast_horizon=int(forecast_horizon),\n verbose=True,\n )\n except Exception as e: # noqa\n error = str(e)\n # lets translate this to something everyone understands\n if \"with`overlap_end` set to `False`.\" in error:\n console.print(\n \"[red]Dataset too small.[/red]\"\n \"[red] Please increase size to at least 100 data points.[/red]\"\n )\n else:\n console.print(f\"[red]{error}[/red]\")\n return [], [], [], None, None\n\n # train new model on entire timeseries to provide best current forecast\n best_model = ExponentialSmoothing(\n trend=trend_model,\n seasonal=seasonal_model,\n seasonal_periods=int(seasonal_periods),\n damped=damped,\n random_state=42,\n )\n\n # we have the historical fcast, now lets train on entire set and predict.\n best_model.fit(ticker_series)\n probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)\n\n if metric == \"rmse\":\n precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"mse\":\n precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"mape\":\n precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"smape\":\n precision = smape(actual_series=ticker_series, pred_series=historical_fcast_es)\n\n console.print(\n f\"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \\n\"\n )\n\n return (\n ticker_series,\n historical_fcast_es,\n probabilistic_forecast,\n precision,\n best_model,\n )","def get_train(self, preprocess=False):\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')","def get_sase(self, datain, points=None): \n try: #try to average over and array input\n\t\t\tdataout = np.mean(datain[-(points):])\n\t\t\tsigma = np.std( datain[-(points):])\n except: #if average fails use the scaler input\n print \"Detector is not a waveform PV, using scalar value\"\n dataout = datain\n sigma = -1\n return dataout, sigma","def holdout(self, X, y):\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size = 0.3, random_state = 0)\n\n return X_train, X_test, y_train, y_test","def get_imp_data_object(evt, src):\n o = evt.get(_psana.Imp.ElementV1, src)\n if o is not None: return o\n\n return None","def setup_no_data_values(input_dataset, options):\n in_nodata = []\n if options.srcnodata:\n nds = list(map(float, options.srcnodata.split(',')))\n if len(nds) < input_dataset.RasterCount:\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\n else:\n in_nodata = nds\n else:\n for i in range(1, input_dataset.RasterCount+1):\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\n if raster_no_data is not None:\n in_nodata.append(raster_no_data)\n\n if options.verbose:\n print(\"NODATA: %s\" % in_nodata)\n\n return in_nodata","def get_data(self):\n if self.config['model'] == 'vggnet':\n if self.is_training:\n return self.data.shuffle(self.shuffle).batch(self.batch_size)\n elif self.is_testing:\n return self.data.batch(self.batch_size)\n elif not self.is_testing and not self.is_training:\n return self.data.batch(self.batch_size)\n else:\n raise NotImplementedError('In dataset.py: default input not specified for this model!')","def GetLabelInput(self) -> \"itkImageUS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS3IUS3_GetLabelInput(self)","def kinesis_firehose_input(self) -> Optional[pulumi.Input['ApplicationApplicationConfigurationSqlApplicationConfigurationInputKinesisFirehoseInputArgs']]:\n return pulumi.get(self, \"kinesis_firehose_input\")","def get_dataset(self):\n return","def get_input_datasets(cfg):\n input_data = mlr.get_input_data(cfg,\n pattern=cfg.get('pattern'),\n ignore=cfg.get('ignore'))\n tags = list(group_metadata(input_data, 'tag').keys())\n if len(tags) != 1:\n raise ValueError(\n f\"Expected unique 'tag' for all input datasets, got {len(tags):d} \"\n f\"different ones ({tags})\")\n return input_data","def import_data(seed: object = 42) -> object:\n\n # Read input data\n df = pd.read_csv(\"x_train_gr_smpl.csv\").astype(int)\n\n # label data-frame rows based on sample data\n for x in range(10):\n index = ~pd.read_csv(\"y_train_smpl_%s.csv\" % x, squeeze=True).astype(bool) # reversed flags (~)\n df.loc[index, 'label'] = str(x)\n\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\n output_data_ordered = df.iloc[:, 2304].to_numpy()\n\n # Randomise instance order (forcing the same result each time)\n np.random.seed(seed)\n permutation = np.random.permutation(df.shape[0])\n\n # Create base input and output arrays\n input_data = input_data_ordered[permutation]\n output_data = output_data_ordered[permutation]\n\n return input_data, output_data, df, input_data_ordered, output_data_ordered","def get_data(dataset, **kwargs):\n age_df = load_age_sample_from_mcmc_chains(dataset, **kwargs)\n hr_df = load_hr()\n age_df, hr_df = clean_data(age_df, hr_df)\n snids = age_df.index.unique().tolist()\n return snids, age_df, hr_df","def ins_ii(self):\n return self._ins_ii","def GetInput(self) -> \"itkHistogramD const *\":\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDSS_GetInput(self)","def get_input_data_folder(self):\n return self.config['input_folder_path']","def input_fn(self, ctx=None):\n sup_dataset = self.supervised_input.make_parsed_dataset(ctx)\n unsup_dataset = self.unsupervised_input.make_parsed_dataset(ctx)\n\n dataset = tf.data.Dataset.zip((sup_dataset, unsup_dataset))\n dataset = dataset.map(_combine_sup_unsup_datasets)\n\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n\n options = tf.data.Options()\n options.experimental_deterministic = False\n dataset = dataset.with_options(options)\n\n return dataset","def input_example(self, max_batch=1, max_dim=8192):\n device = next(self.parameters()).device\n input_example = torch.randn(max_batch, self._feat_in, max_dim, device=device)\n lens = torch.full(size=(input_example.shape[0],), fill_value=max_dim, device=device)\n return tuple([input_example, lens])","def _getInputValue(self, name, target = ''):\n value = self._getInputValue(name, target)\n return value.getData() if value else None","def GetLabelInput(self) -> \"itkImageSS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC3ISS3_GetLabelInput(self)","def GetLabelInput(self) -> \"itkImageUS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF3IUS3_GetLabelInput(self)","def inf(self, data):\n self.set_data(data)\n self.infer_post_and_update_samples()","def getInputVector(self, data = None):\n\t\treturn self.loader.getInputVector(data)","def GetLabelInput(self) -> \"itkImageUS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS3IUS3_GetLabelInput(self)","def GetLabelInput(self) -> \"itkImageSS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2ISS2_GetLabelInput(self)","def ingestData(self, **kw):\n portal = self.getPortalObject()\n # in http post, parameter is ignored in url but is inside request body\n query = urlparse.parse_qs(self.REQUEST.get('QUERY_STRING'))\n\n ingestion_policy = getattr(portal.portal_ingestion_policies, query['ingestion_policy'][0], None)\n if ingestion_policy is None:\n raise NotFound\n return ingestion_policy.ingest(**kw)","def GetLabelInput(self) -> \"itkImageSS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2ISS2_GetLabelInput(self)","def eye_during_period(self, time_period, alias):\n\t\twith pd.get_store(self.input_object) as h5_file:\n\t\t\tperiod_block_nr = self.sample_in_block(sample = time_period[0], block_table = h5_file['%s/blocks'%alias])\n\t\t\teye = h5_file['%s/blocks'%alias]['eye_recorded'][period_block_nr]\n\t\treturn eye","def get_health_data_and_ingest_into_sentinel(self):\n self.pull_and_push_the_snapshot_data(\n HEALTH_ENDPOINT, self.health_table_name, fields=MODIFIED_FIELDS\n )","def GetLabelInput(self) -> \"itkImageSS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2ISS2_GetLabelInput(self)","def _save_stdin(self, stdin):\n\t\tself.temp_dir = TemporaryDirectory()\n\t\tfile_path = os.path.join(self.temp_dir.name, 'dataset')\n\n\t\ttry:\n\t\t\twith open(file_path, 'w') as f:\n\t\t\t\tfor line in stdin:\n\t\t\t\t\tf.write(line)\n\t\texcept TypeError:\n\t\t\tself.temp_dir.cleanup()\n\t\t\traise ValueError('Could not read stdin')\n\n\t\treturn file_path","def GetInput(self) -> \"itkHistogramD const *\":\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDUS_GetInput(self)","def predict(self, data_in):\n pass","def GetLabelInput(self) -> \"itkImageUS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2IUS2_GetLabelInput(self)","def import_data(inp, picks=None, array_keys={'X': 'X', 'y': 'y'}):\n if isinstance(inp, (mne.epochs.EpochsFIF, mne.epochs.BaseEpochs)):\n print('processing epochs')\n # if isinstance(picks, dict):\n # picks = mne.pick_types(inp.info, include=picks)\n inp.load_data()\n data = inp.get_data()\n events = inp.events[:, 2]\n if isinstance(picks, dict):\n print(\"Converting picks\")\n picks = mne.pick_types(inp.info, **picks)\n\n elif isinstance(inp, tuple) and len(inp) == 2:\n print('importing from tuple')\n data, events = inp\n\n elif isinstance(inp, str):\n # TODO: ADD CASE FOR RAW FILE\n fname = inp\n if fname[-3:] == 'fif':\n epochs = mne.epochs.read_epochs(fname, preload=True,\n verbose='CRITICAL')\n print(np.unique(epochs.events[:, 2]))\n events = epochs.events[:, 2]\n epochs.crop(tmin=-1., tmax=1.)\n data = epochs.get_data()\n if isinstance(picks, dict):\n print(\"Converting picks\")\n picks = mne.pick_types(epochs.info, **picks)\n\n else:\n if fname[-3:] == 'mat':\n datafile = sio.loadmat(fname)\n\n if fname[-3:] == 'npz':\n print('Importing from npz')\n datafile = np.load(fname)\n\n data = datafile[array_keys['X']]\n events = datafile[array_keys['y']]\n print('Extracting target variables from {}'\n .format(array_keys['y']))\n else:\n print(\"Dataset not found\")\n return None, None\n\n data = data.astype(np.float32)\n\n # Make sure that X is 3d here\n while data.ndim < 3:\n # (x, ) -> (1, 1, x)\n # (x, y) -> (1, x, y)\n data = np.expand_dims(data, 0)\n\n if isinstance(picks, (np.ndarray, list, tuple)):\n picks = np.asarray(picks)\n if np.any(data.shape[1] <= picks):\n raise ValueError(\"Invalid picks {} for n_channels {} \".format(\n max(len(picks), max(picks)), data.shape[1]))\n data = data[:, picks, :]\n\n return data, events","def get_test(self, preprocess=False):\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')","def in_(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"in_\")","def pick_initial_sample(self):\n x = np.atleast_1d(self.init_sample_func())\n return 0, x","def __get_isin(instrument):\n return instrument['isin']","def get_init_fn():\n checkpoint_exclude_scopes = [\"InceptionV1/Logits\", \"InceptionV1/AuxLogits\"]\n\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n return slim.assign_from_checkpoint_fn(\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n variables_to_restore)","def get_Ei_point(self, source_point, target_point):\n Ei = 0.0\n source_patch_mask = self.source_patch_masks[source_point]\n source_patch = self.source_patches[source_point]\n source_patch_unknown_overlap_mask = np.bitwise_and(source_patch_mask, self.unknown_mask)\n target_patch_mask = self.target_patch_masks[target_point]\n # try:\n if source_patch_unknown_overlap_mask.any():\n test_img = self.img.copy()\n test_img[target_patch_mask] = source_patch\n Ei = self.get_norm_ssd(self.img, source_patch)\n return Ei","def soleDataPoint(self):\n dps = self.datapoints()\n if dps:\n return dps[0]","def get_dataset(dataset_name):\n if dataset_name == \"Iris\":\n data = datasets.load_iris()\n\n elif dataset_name == \"Breast Cancer\":\n data = datasets.load_breast_cancer()\n\n elif dataset_name == \"Wine Dataset\":\n data = datasets.load_wine()\n\n elif dataset_name == \"MNIST\":\n data = datasets.load_digits()\n\n #elif dataset_name == \"Boston Housing Price\":\n # data = datasets.load_boston()\n\n X = data.data\n y = data.target\n\n return X, y","def get_input_data(galaxyemail, galaxypass, server):\n gi = GalaxyInstance(url=server, email=galaxyemail, password=galaxypass)\n history_id = get_history_id(galaxyemail, galaxypass, server)\n hist_contents = gi.histories.show_history(history_id, contents=True)\n inputs = []\n datacount = 0\n datasets = [dataset for dataset in hist_contents if not dataset['deleted']]\n for dataset in datasets:\n inputs.append(dataset['id'])\n datacount += 1\n return inputs, datacount","def get_dummy_input(dataset=None, device=None, input_shape=None):\n def create_single(shape):\n t = torch.randn(shape)\n if device:\n t = t.to(device)\n return t\n\n def create_recurse(shape):\n if all(isinstance(x, int) for x in shape):\n return create_single(shape)\n return tuple(create_recurse(s) for s in shape)\n\n input_shape = input_shape or dataset.shape\n return create_recurse(input_shape)","def get_input_id(self):\n return self.net.inputs[0]","def get_eval_input(self, prev, i):\n del prev, i\n # The attention_decoder will fetch image features from the net, no need for\n # extra inputs.\n return self._zero_label","def _get_data_for_tests():\n X = np.random.randn(100, input_dim)\n Y = np.random.randn(100, output_dim)\n X_new = np.random.randn(100, input_dim)\n return X, X_new, Y","def define_holdout_set(\n train_set: pd.DataFrame,\n holdout_set: Union[pd.DataFrame, callable, str, None],\n target_column: Union[str, List[str]],\n) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:\n #################### Update `holdout_set` ####################\n if callable(holdout_set):\n train_set, holdout_set = holdout_set(train_set, target_column)\n elif isinstance(holdout_set, str):\n holdout_set = pd.read_csv(holdout_set)\n #################### Validate `holdout_set` ####################\n try:\n if holdout_set is None or np.array_equal(train_set.columns, holdout_set.columns):\n return train_set, holdout_set\n except AttributeError:\n raise TypeError(f\"holdout_set must be None, DataFrame, callable, or str, not {holdout_set}\")\n raise ValueError(f\"Mismatched columns\\n{train_set.columns}\\n!=\\n{holdout_set.columns}\")","def getDataset(self, train=True):\n \n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") \n \n if self.dataset == \"ELLIPSE\":\n a = np.array([[0,1.0],[1.0,2.0]]) \n b = a*0.5 \n myE = el.ellipse(device, 500, 100, a, b) \n if train == True:\n return myE.create_dataset(myE.examples)\n return myE.create_dataset(myE.valid) \n \n if self.dataset == \"SWISS\": \n myS = sw.SwissRoll(device, 500, 0.2) \n if train == True:\n return myS.create_dataset(myS.examples)\n return myS.create_dataset(myS.valid)\n \n \n #open file\n myFile = h5py.File(self.dataString, 'r', self.driver)\n \n if train == True: \n inputString = \"train_inputs\"\n labelsString = \"train_labels\"\n \n else:\n inputString = \"test_inputs\"\n labelsString = \"test_labels\"\n \n #get hdf5 datsets\n features = myFile.get(inputString)\n labels = myFile.get(labelsString)\n \n #convert to tensors\n features = torch.from_numpy(np.array(features))\n labels = torch.from_numpy(np.array(labels))\n \n #close file to ensure dataset is in memory\n myFile.close()\n \n #conver to correct datatypes\n features = features.float()\n \n if self.conv_sg == False:\n labels = labels.long() \n \n dataset = torch.utils.data.TensorDataset(features, labels)\n \n return dataset","def get_inputs_train():\n x = tf.constant(extract_pandas_data(x_train))\n y = tf.constant(y_train.values)\n return x, y","def predict(self, X=None):\n X = X if X is not None else self.X\n self.outliers = pd.Series(\n self.outlier_detector.predict(X.fillna(0)) == -1,\n index=X.index)\n # TODO: Since you can run this on self.X OR new X, then \"self.outliers\"\n # can change and not be consistent....... this is a problem\n return self.outliers","def get_dataset(self) -> datasets.OpenMLDataset:\n return datasets.get_dataset(self.dataset_id)","def GetLabelInput(self) -> \"itkImageUS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUS2_GetLabelInput(self)","def get_inputs_test_S():\n x = tf.constant(extract_pandas_data(x_test_S))\n y = tf.constant(y_test_S.values)\n return x, y","def GetLabelInput(self) -> \"itkImageUS3 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC3IUS3_GetLabelInput(self)","def backup_dataset(outfile=None):\n return backup_es(Dataset, outfile=outfile)","def GetInputNarrowBand(self) -> \"itkVectorContainerUILSNF3_Pointer\":\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF3_GetInputNarrowBand(self)","async def get_input_entity(self):\n # We don't actually have an API call we can make yet\n # to get more info, but keep this method for consistency.\n return self.input_entity","def GetLabelInput(self) -> \"itkImageUS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2IUS2_GetLabelInput(self)","def GetLabelInput(self) -> \"itkImageSS2 const *\":\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC2ISS2_GetLabelInput(self)","def get_input_data(self, name='0'):\n return data","def get_input_center_layer(dataset: str) -> torch.nn.Module:\n if dataset == \"imagenet\":\n return InputCenterLayer(_IMAGENET_MEAN)\n elif dataset == \"cifar10\":\n return InputCenterLayer(_CIFAR10_MEAN)","def get_corrupted_input(self, input):\n return self.theano_rng.binomial(size=input.shape, n=1,\n p=1 - self.corrupt_input_l,\n dtype=theano.config.floatX) * input","def sample(self):\n\n\t\t# get newest sample\n\t\ts = self.eyetribe.sample()\n\t\t\n\t\t# invalid data\n\t\tif s == (None,None):\n\t\t\treturn (-1,-1)\n\t\t\n\t\t# check if the new sample is the same as the previous\n\t\tif s != self.prevsample:\n\t\t\t# update the current sample\n\t\t\tself.prevsample = copy.copy(s)\n\t\t\n\t\treturn self.prevsample"],"string":"[\n \"def holdout_target(self):\\n return DatasetSentinel(\\n \\\"holdout_target\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def train_input(self):\\n return DatasetSentinel(\\\"train_input\\\", **self._dataset_sentinel_helper())\",\n \"def validation_input(self):\\n return DatasetSentinel(\\\"validation_input\\\", **self._dataset_sentinel_helper())\",\n \"def train_target(self):\\n return DatasetSentinel(\\\"train_target\\\", **self._dataset_sentinel_helper())\",\n \"def validation_target(self):\\n return DatasetSentinel(\\\"validation_target\\\", **self._dataset_sentinel_helper())\",\n \"def _dataset_sentinel_helper(self):\\n params = self.cross_experiment_key.parameters\\n return dict(\\n dataset_hash=params[\\\"train_dataset\\\"],\\n cv_type=params[\\\"cross_experiment_params\\\"].get(\\n \\\"cv_type\\\", params[\\\"cross_experiment_params\\\"].get(\\\"cross_validation_type\\\", None)\\n ),\\n global_random_seed=params[\\\"cross_experiment_params\\\"][\\\"global_random_seed\\\"],\\n random_seeds=params[\\\"cross_experiment_params\\\"][\\\"random_seeds\\\"],\\n )\",\n \"def test_additional_data(self):\\n task = SentinelHubInputTask(\\n bands_feature=(FeatureType.DATA, 'BANDS'),\\n bands=['B01', 'B02', 'B05'],\\n additional_data=[\\n (FeatureType.MASK, 'dataMask', 'IS_DATA'),\\n (FeatureType.MASK, 'CLM'),\\n (FeatureType.MASK, 'SCL'),\\n (FeatureType.MASK, 'SNW'),\\n (FeatureType.MASK, 'CLD'),\\n (FeatureType.DATA, 'CLP'),\\n (FeatureType.DATA, 'viewAzimuthMean', 'view_azimuth_mean'),\\n (FeatureType.DATA, 'sunAzimuthAngles'),\\n (FeatureType.DATA, 'sunZenithAngles')\\n ],\\n size=self.size,\\n maxcc=self.maxcc,\\n time_difference=self.time_difference,\\n data_collection=DataCollection.SENTINEL2_L2A,\\n max_threads=self.max_threads\\n )\\n\\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\\n\\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\\n is_data = eopatch[(FeatureType.MASK, 'IS_DATA')]\\n clm = eopatch[(FeatureType.MASK, 'CLM')]\\n scl = eopatch[(FeatureType.MASK, 'SCL')]\\n snw = eopatch[(FeatureType.MASK, 'SNW')]\\n cld = eopatch[(FeatureType.MASK, 'CLD')]\\n clp = eopatch[(FeatureType.DATA, 'CLP')]\\n view_azimuth_mean = eopatch[(FeatureType.DATA, 'view_azimuth_mean')]\\n sun_azimuth_angles = eopatch[(FeatureType.DATA, 'sunAzimuthAngles')]\\n sun_zenith_angles = eopatch[(FeatureType.DATA, 'sunZenithAngles')]\\n\\n self.assertTrue(np.allclose(array_stats(bands), [0.027, 0.0243, 0.0162]))\\n\\n width, height = self.size\\n self.assertTrue(bands.shape == (4, height, width, 3))\\n self.assertTrue(is_data.shape == (4, height, width, 1))\\n self.assertTrue(is_data.dtype == bool)\\n self.assertTrue(clm.shape == (4, height, width, 1))\\n self.assertTrue(clm.dtype == np.uint8)\\n self.assertTrue(scl.shape == (4, height, width, 1))\\n self.assertTrue(snw.shape == (4, height, width, 1))\\n self.assertTrue(cld.shape == (4, height, width, 1))\\n self.assertTrue(clp.shape == (4, height, width, 1))\\n self.assertTrue(view_azimuth_mean.shape == (4, height, width, 1))\\n self.assertTrue(sun_azimuth_angles.shape == (4, height, width, 1))\\n self.assertTrue(sun_zenith_angles.shape == (4, height, width, 1))\\n self.assertTrue(len(eopatch.timestamp) == 4)\",\n \"def get_dataset_sampler(self):\\n return None\",\n \"def get_interest_variable(\\n in_dataset, sensor_var, date_col, hr_col, numeric_var, target_sensor=\\\"A620\\\"\\n):\\n dataset_pproc = in_dataset.loc[\\n in_dataset[sensor_var] == target_sensor, [date_col, hr_col] + [numeric_var]\\n ]\\n hrs_str = dataset_pproc[hr_col].to_string()\\n dates_str = dataset_pproc[date_col]\\n\\n dataset_pproc[date_col] = pd.to_datetime(dataset_pproc[date_col])\\n dataset_pproc.set_index([date_col, hr_col], inplace=True)\\n dataset_pproc.fillna(method=\\\"ffill\\\", inplace=True)\\n dataset_pproc.interpolate(method=\\\"linear\\\", axis=0)\\n\\n return dataset_pproc\",\n \"def _create_dataset(source=''):\\n return ExperimentalDataset()\",\n \"def get_train_inputs(self, example):\\n return example\",\n \"def get_data_for_semi_supervised(self,percent_dropout,num_dropout_corruptions_per_point):\\n\\n\\t\\t\\\"\\\"\\\"Select the corrupted data if applicable, and otherwise the original training data\\\"\\\"\\\"\\n\\t\\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=True)\",\n \"def input(self):\\n try:\\n return self.inputs[-1]\\n except IndexError:\\n pass\\n raise ValueError(\\\"The sample method has not been called\\\")\",\n \"def get_input_data(sample):\\n with checkpoints.query_portal.get(sample=sample).output[0].open() as f:\\n data = json.read(f)\\n return data\",\n \"def get_dataset(dataset_name):\\n if dataset_name == \\\"Iris\\\":\\n data = datasets.load_iris()\\n elif dataset_name == \\\"Breast Cancer\\\":\\n data = datasets.load_breast_cancer()\\n else:\\n data = datasets.load_wine()\\n\\n X = data.data\\n y = data.target\\n return X, y\",\n \"def get_input_fn_training(Xtrain_ul, Xtrain_l, Xtest, ytrain_ul, ytrain_l, ytest, batch_size, num_labeled):\\n dataset = input_data.Data(Xtrain_ul,\\n Xtrain_l,\\n Xtest,\\n ytrain_ul,\\n ytrain_l,\\n ytest,\\n num_labeled, \\n batch_size, \\n shuffle=True)\\n return dataset.next_batch()\",\n \"def _input_fn(input_pipeline_context=None):\\n return _create_dataset(options, is_training, input_pipeline_context)\",\n \"def get_dataset(args):\\n\\n if args['experiment']['dataset'] == Dataset.mindsets:\\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\\n nb_questions=args['dataset']['nb_questions'],\\n nb_useless=args['dataset']['nb_useless'],\\n noise=args['dataset']['noise'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\\n nb_features=args['dataset']['nb_features'],\\n nb_mindsets=args['dataset']['nb_mindsets'],\\n centers=args['dataset']['centers'],\\n range_answers=args['dataset']['range_answers'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.retinal:\\n xs, ys = load_RETINAL(root_path=args['root_dir'],\\n nb_bins=args['dataset']['nb_bins'],\\n max_idx=args['dataset']['max_idx'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.moons:\\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\\n noise=args['dataset']['noise'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.SBM:\\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\\n p_in=args['dataset']['p'],\\n p_out=args['dataset']['q'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\\n centers=args['dataset']['blob_centers'],\\n n_features=args['dataset']['blob_centers'],\\n cluster_std=args['dataset']['blob_variances'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.LFR:\\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\\n tau1=args['dataset']['tau1'],\\n tau2=args['dataset']['tau2'],\\n mu=args['dataset']['mu'],\\n average_degree=args['dataset']['average_degree'],\\n min_community=args['dataset']['min_community'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.wave:\\n df = pd.read_csv('datasets/waveform.csv')\\n xs = df[df.columns[:-1]].to_numpy()\\n ys = df[df.columns[-1]].to_numpy()\\n\\n return Data(xs=xs, ys=ys)\\n\\n raise ValueError('Wrong name for a dataset')\",\n \"def fixture_input_data(wrapper_arguments):\\n n_features, classes = wrapper_arguments\\n num_data = 50\\n data = np.random.rand(num_data, n_features)\\n labels = np.random.choice(classes, size=num_data)\\n\\n return data, labels\",\n \"def readMNISTData():\\n mnist = input_data.read_data_sets(\\\"MNIST_data\\\",one_hot=True) \\n return mnist\",\n \"def get_seeds_null(samples,signal):\\n theta_seeds={}\\n if selected is None:\\n SRlist = range(self.N_SR)\\n theta_samples = samples[:,0,self.N_SR:].T\\n else:\\n SRlist = [selected] \\n theta_samples = samples[:,0,1].T\\n for i,x in zip(SRlist,theta_samples):\\n theta_MLE = x\\n theta_seeds['theta_{0}'.format(i)] = theta_MLE\\n return theta_seeds\",\n \"def tempfeeder_exp():\\n global _tempfeeder_exp\\n if _tempfeeder_exp is None:\\n _tempfeeder_exp = UserLoads_Experiment(DATA_WITHOUT_DUPES_PATH)\\n return _tempfeeder_exp\",\n \"def iris():\\n return IrisDataset()\",\n \"def find_input_dataset(stage, rconfig, data_type=None):\\n\\n # Use the stage-to-data mapping to find the data_type if none was handed in\\n if data_type is None:\\n data_type = DOCUMENT_PROCESSING_IO[stage]['in']\\n # Get all data sets D for input name\\n dirname = os.path.join(rconfig.target_path, 'data', data_type)\\n datasets1 = [ds for ds in os.listdir(dirname) if ds.isdigit()]\\n datasets2 = [DataSet(stage, data_type, rconfig, ds) for ds in datasets1]\\n # Filer the datasets making sure that d.trace + d.head matches\\n # rconfig.pipeline(txt).trace\\n datasets3 = [ds for ds in datasets2 if ds.input_matches_global_config()]\\n # If there is one result, return it, otherwise write a warning and exit\\n if len(datasets3) == 1:\\n return datasets3[0]\\n elif len(datasets3) > 1:\\n print \\\"WARNING, more than one approriate training set:\\\"\\n for ds in datasets3:\\n print ' ', ds\\n sys.exit(\\\"Exiting...\\\")\\n elif len(datasets3) == 0:\\n print \\\"WARNING: no datasets available to meet input requirements\\\"\\n sys.exit(\\\"Exiting...\\\")\",\n \"def sample_train_data(dataset ,target,data_len, resp = True ):\\r\\n np.random.seed(222)\\r\\n ixes = np.random.choice(dataset.index, data_len, replace = False)\\r\\n print(ixes)\\r\\n under_df = dataset.iloc[ixes]\\r\\n if resp==True:\\r\\n under_target = target.iloc[ixes]\\r\\n return under_df, under_target\\r\\n else:\\r\\n return under_df\",\n \"def default_dataset(self):\\n return self.get_by_index(self._default_index)\",\n \"def get_data_only_random_dropout(self,percent_dropout,num_dropout_corruptions_per_point):\\n\\n\\t\\t\\\"\\\"\\\"Select the corrupted data if applicable, and otherwise the original training data\\\"\\\"\\\"\\n\\t\\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=False)\",\n \"def get_datasetID(self):\\n\\t\\treturn self.prDoc['inputs']['data'][0]['datasetID']\",\n \"def GetLabelInput(self) -> \\\"itkImageSS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS3ISS3_GetLabelInput(self)\",\n \"def GetInput(self) -> \\\"itkHistogramD const *\\\":\\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDF_GetInput(self)\",\n \"def noise_input_fn(params):\\n batch_size = params['batch_size']\\n noise_dim = params['noise_dim']\\n # Use constant seed to obtain same noise\\n np.random.seed(0)\\n noise_dataset = tf.data.Dataset.from_tensors(\\n {'random_noise': tf.constant(\\n np.random.randn(batch_size, noise_dim), dtype=tf.float32)\\n })\\n noise = noise_dataset.make_one_shot_iterator().get_next()\\n tf.logging.debug('Noise input %s', noise)\\n return noise_dataset\",\n \"def dataset_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"dataset_id\\\")\",\n \"def GetLabelInput(self) -> \\\"itkImageSS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS3ISS3_GetLabelInput(self)\",\n \"def GetLabelInput(self) -> \\\"itkImageSS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF3ISS3_GetLabelInput(self)\",\n \"def _fetch_input():\\n try:\\n simplecount_input = inputtools.fetch_data()\\n return simplecount_input\\n except:\\n raise\",\n \"def get_expo_data(\\n data: Union[pd.Series, pd.DataFrame],\\n target_column: str = \\\"close\\\",\\n trend: str = \\\"A\\\",\\n seasonal: str = \\\"A\\\",\\n seasonal_periods: int = 7,\\n dampen: str = \\\"F\\\",\\n n_predict: int = 5,\\n start_window: float = 0.85,\\n forecast_horizon: int = 5,\\n metric: str = \\\"mape\\\",\\n) -> Tuple[\\n List[TimeSeries],\\n List[TimeSeries],\\n List[TimeSeries],\\n Optional[Union[float, ndarray]],\\n ExponentialSmoothing,\\n]:\\n\\n use_scalers = False\\n _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers)\\n\\n if trend == \\\"M\\\":\\n trend_model = ModelMode.MULTIPLICATIVE\\n elif trend == \\\"N\\\":\\n trend_model = ModelMode.NONE\\n else: # Default\\n trend_model = ModelMode.ADDITIVE\\n\\n if seasonal == \\\"M\\\":\\n seasonal_model = SeasonalityMode.MULTIPLICATIVE\\n elif seasonal == \\\"N\\\":\\n seasonal_model = SeasonalityMode.NONE\\n else: # Default\\n seasonal_model = SeasonalityMode.ADDITIVE\\n\\n damped = True\\n if dampen == \\\"F\\\":\\n damped = False\\n\\n # Model Init\\n model_es = ExponentialSmoothing(\\n trend=trend_model,\\n seasonal=seasonal_model,\\n seasonal_periods=int(seasonal_periods),\\n damped=damped,\\n random_state=42,\\n )\\n\\n try:\\n # Historical backtesting\\n historical_fcast_es = model_es.historical_forecasts(\\n ticker_series, # backtest on entire ts\\n start=float(start_window),\\n forecast_horizon=int(forecast_horizon),\\n verbose=True,\\n )\\n except Exception as e: # noqa\\n error = str(e)\\n # lets translate this to something everyone understands\\n if \\\"with`overlap_end` set to `False`.\\\" in error:\\n console.print(\\n \\\"[red]Dataset too small.[/red]\\\"\\n \\\"[red] Please increase size to at least 100 data points.[/red]\\\"\\n )\\n else:\\n console.print(f\\\"[red]{error}[/red]\\\")\\n return [], [], [], None, None\\n\\n # train new model on entire timeseries to provide best current forecast\\n best_model = ExponentialSmoothing(\\n trend=trend_model,\\n seasonal=seasonal_model,\\n seasonal_periods=int(seasonal_periods),\\n damped=damped,\\n random_state=42,\\n )\\n\\n # we have the historical fcast, now lets train on entire set and predict.\\n best_model.fit(ticker_series)\\n probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)\\n\\n if metric == \\\"rmse\\\":\\n precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"mse\\\":\\n precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"mape\\\":\\n precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"smape\\\":\\n precision = smape(actual_series=ticker_series, pred_series=historical_fcast_es)\\n\\n console.print(\\n f\\\"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \\\\n\\\"\\n )\\n\\n return (\\n ticker_series,\\n historical_fcast_es,\\n probabilistic_forecast,\\n precision,\\n best_model,\\n )\",\n \"def get_train(self, preprocess=False):\\n return self._dataset('train', self._directory, 'sharded_train_0shifted_affnist.tfrecords')\",\n \"def get_sase(self, datain, points=None): \\n try: #try to average over and array input\\n\\t\\t\\tdataout = np.mean(datain[-(points):])\\n\\t\\t\\tsigma = np.std( datain[-(points):])\\n except: #if average fails use the scaler input\\n print \\\"Detector is not a waveform PV, using scalar value\\\"\\n dataout = datain\\n sigma = -1\\n return dataout, sigma\",\n \"def holdout(self, X, y):\\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\\n X_train, X_test, y_train, y_test = train_test_split(\\n X, y, test_size = 0.3, random_state = 0)\\n\\n return X_train, X_test, y_train, y_test\",\n \"def get_imp_data_object(evt, src):\\n o = evt.get(_psana.Imp.ElementV1, src)\\n if o is not None: return o\\n\\n return None\",\n \"def setup_no_data_values(input_dataset, options):\\n in_nodata = []\\n if options.srcnodata:\\n nds = list(map(float, options.srcnodata.split(',')))\\n if len(nds) < input_dataset.RasterCount:\\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\\n else:\\n in_nodata = nds\\n else:\\n for i in range(1, input_dataset.RasterCount+1):\\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\\n if raster_no_data is not None:\\n in_nodata.append(raster_no_data)\\n\\n if options.verbose:\\n print(\\\"NODATA: %s\\\" % in_nodata)\\n\\n return in_nodata\",\n \"def get_data(self):\\n if self.config['model'] == 'vggnet':\\n if self.is_training:\\n return self.data.shuffle(self.shuffle).batch(self.batch_size)\\n elif self.is_testing:\\n return self.data.batch(self.batch_size)\\n elif not self.is_testing and not self.is_training:\\n return self.data.batch(self.batch_size)\\n else:\\n raise NotImplementedError('In dataset.py: default input not specified for this model!')\",\n \"def GetLabelInput(self) -> \\\"itkImageUS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS3IUS3_GetLabelInput(self)\",\n \"def kinesis_firehose_input(self) -> Optional[pulumi.Input['ApplicationApplicationConfigurationSqlApplicationConfigurationInputKinesisFirehoseInputArgs']]:\\n return pulumi.get(self, \\\"kinesis_firehose_input\\\")\",\n \"def get_dataset(self):\\n return\",\n \"def get_input_datasets(cfg):\\n input_data = mlr.get_input_data(cfg,\\n pattern=cfg.get('pattern'),\\n ignore=cfg.get('ignore'))\\n tags = list(group_metadata(input_data, 'tag').keys())\\n if len(tags) != 1:\\n raise ValueError(\\n f\\\"Expected unique 'tag' for all input datasets, got {len(tags):d} \\\"\\n f\\\"different ones ({tags})\\\")\\n return input_data\",\n \"def import_data(seed: object = 42) -> object:\\n\\n # Read input data\\n df = pd.read_csv(\\\"x_train_gr_smpl.csv\\\").astype(int)\\n\\n # label data-frame rows based on sample data\\n for x in range(10):\\n index = ~pd.read_csv(\\\"y_train_smpl_%s.csv\\\" % x, squeeze=True).astype(bool) # reversed flags (~)\\n df.loc[index, 'label'] = str(x)\\n\\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\\n output_data_ordered = df.iloc[:, 2304].to_numpy()\\n\\n # Randomise instance order (forcing the same result each time)\\n np.random.seed(seed)\\n permutation = np.random.permutation(df.shape[0])\\n\\n # Create base input and output arrays\\n input_data = input_data_ordered[permutation]\\n output_data = output_data_ordered[permutation]\\n\\n return input_data, output_data, df, input_data_ordered, output_data_ordered\",\n \"def get_data(dataset, **kwargs):\\n age_df = load_age_sample_from_mcmc_chains(dataset, **kwargs)\\n hr_df = load_hr()\\n age_df, hr_df = clean_data(age_df, hr_df)\\n snids = age_df.index.unique().tolist()\\n return snids, age_df, hr_df\",\n \"def ins_ii(self):\\n return self._ins_ii\",\n \"def GetInput(self) -> \\\"itkHistogramD const *\\\":\\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDSS_GetInput(self)\",\n \"def get_input_data_folder(self):\\n return self.config['input_folder_path']\",\n \"def input_fn(self, ctx=None):\\n sup_dataset = self.supervised_input.make_parsed_dataset(ctx)\\n unsup_dataset = self.unsupervised_input.make_parsed_dataset(ctx)\\n\\n dataset = tf.data.Dataset.zip((sup_dataset, unsup_dataset))\\n dataset = dataset.map(_combine_sup_unsup_datasets)\\n\\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\\n\\n options = tf.data.Options()\\n options.experimental_deterministic = False\\n dataset = dataset.with_options(options)\\n\\n return dataset\",\n \"def input_example(self, max_batch=1, max_dim=8192):\\n device = next(self.parameters()).device\\n input_example = torch.randn(max_batch, self._feat_in, max_dim, device=device)\\n lens = torch.full(size=(input_example.shape[0],), fill_value=max_dim, device=device)\\n return tuple([input_example, lens])\",\n \"def _getInputValue(self, name, target = ''):\\n value = self._getInputValue(name, target)\\n return value.getData() if value else None\",\n \"def GetLabelInput(self) -> \\\"itkImageSS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC3ISS3_GetLabelInput(self)\",\n \"def GetLabelInput(self) -> \\\"itkImageUS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF3IUS3_GetLabelInput(self)\",\n \"def inf(self, data):\\n self.set_data(data)\\n self.infer_post_and_update_samples()\",\n \"def getInputVector(self, data = None):\\n\\t\\treturn self.loader.getInputVector(data)\",\n \"def GetLabelInput(self) -> \\\"itkImageUS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS3IUS3_GetLabelInput(self)\",\n \"def GetLabelInput(self) -> \\\"itkImageSS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2ISS2_GetLabelInput(self)\",\n \"def ingestData(self, **kw):\\n portal = self.getPortalObject()\\n # in http post, parameter is ignored in url but is inside request body\\n query = urlparse.parse_qs(self.REQUEST.get('QUERY_STRING'))\\n\\n ingestion_policy = getattr(portal.portal_ingestion_policies, query['ingestion_policy'][0], None)\\n if ingestion_policy is None:\\n raise NotFound\\n return ingestion_policy.ingest(**kw)\",\n \"def GetLabelInput(self) -> \\\"itkImageSS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2ISS2_GetLabelInput(self)\",\n \"def eye_during_period(self, time_period, alias):\\n\\t\\twith pd.get_store(self.input_object) as h5_file:\\n\\t\\t\\tperiod_block_nr = self.sample_in_block(sample = time_period[0], block_table = h5_file['%s/blocks'%alias])\\n\\t\\t\\teye = h5_file['%s/blocks'%alias]['eye_recorded'][period_block_nr]\\n\\t\\treturn eye\",\n \"def get_health_data_and_ingest_into_sentinel(self):\\n self.pull_and_push_the_snapshot_data(\\n HEALTH_ENDPOINT, self.health_table_name, fields=MODIFIED_FIELDS\\n )\",\n \"def GetLabelInput(self) -> \\\"itkImageSS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2ISS2_GetLabelInput(self)\",\n \"def _save_stdin(self, stdin):\\n\\t\\tself.temp_dir = TemporaryDirectory()\\n\\t\\tfile_path = os.path.join(self.temp_dir.name, 'dataset')\\n\\n\\t\\ttry:\\n\\t\\t\\twith open(file_path, 'w') as f:\\n\\t\\t\\t\\tfor line in stdin:\\n\\t\\t\\t\\t\\tf.write(line)\\n\\t\\texcept TypeError:\\n\\t\\t\\tself.temp_dir.cleanup()\\n\\t\\t\\traise ValueError('Could not read stdin')\\n\\n\\t\\treturn file_path\",\n \"def GetInput(self) -> \\\"itkHistogramD const *\\\":\\n return _itkHistogramThresholdCalculatorPython.itkHistogramThresholdCalculatorHDUS_GetInput(self)\",\n \"def predict(self, data_in):\\n pass\",\n \"def GetLabelInput(self) -> \\\"itkImageUS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterISS2IUS2_GetLabelInput(self)\",\n \"def import_data(inp, picks=None, array_keys={'X': 'X', 'y': 'y'}):\\n if isinstance(inp, (mne.epochs.EpochsFIF, mne.epochs.BaseEpochs)):\\n print('processing epochs')\\n # if isinstance(picks, dict):\\n # picks = mne.pick_types(inp.info, include=picks)\\n inp.load_data()\\n data = inp.get_data()\\n events = inp.events[:, 2]\\n if isinstance(picks, dict):\\n print(\\\"Converting picks\\\")\\n picks = mne.pick_types(inp.info, **picks)\\n\\n elif isinstance(inp, tuple) and len(inp) == 2:\\n print('importing from tuple')\\n data, events = inp\\n\\n elif isinstance(inp, str):\\n # TODO: ADD CASE FOR RAW FILE\\n fname = inp\\n if fname[-3:] == 'fif':\\n epochs = mne.epochs.read_epochs(fname, preload=True,\\n verbose='CRITICAL')\\n print(np.unique(epochs.events[:, 2]))\\n events = epochs.events[:, 2]\\n epochs.crop(tmin=-1., tmax=1.)\\n data = epochs.get_data()\\n if isinstance(picks, dict):\\n print(\\\"Converting picks\\\")\\n picks = mne.pick_types(epochs.info, **picks)\\n\\n else:\\n if fname[-3:] == 'mat':\\n datafile = sio.loadmat(fname)\\n\\n if fname[-3:] == 'npz':\\n print('Importing from npz')\\n datafile = np.load(fname)\\n\\n data = datafile[array_keys['X']]\\n events = datafile[array_keys['y']]\\n print('Extracting target variables from {}'\\n .format(array_keys['y']))\\n else:\\n print(\\\"Dataset not found\\\")\\n return None, None\\n\\n data = data.astype(np.float32)\\n\\n # Make sure that X is 3d here\\n while data.ndim < 3:\\n # (x, ) -> (1, 1, x)\\n # (x, y) -> (1, x, y)\\n data = np.expand_dims(data, 0)\\n\\n if isinstance(picks, (np.ndarray, list, tuple)):\\n picks = np.asarray(picks)\\n if np.any(data.shape[1] <= picks):\\n raise ValueError(\\\"Invalid picks {} for n_channels {} \\\".format(\\n max(len(picks), max(picks)), data.shape[1]))\\n data = data[:, picks, :]\\n\\n return data, events\",\n \"def get_test(self, preprocess=False):\\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')\",\n \"def in_(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"in_\\\")\",\n \"def pick_initial_sample(self):\\n x = np.atleast_1d(self.init_sample_func())\\n return 0, x\",\n \"def __get_isin(instrument):\\n return instrument['isin']\",\n \"def get_init_fn():\\n checkpoint_exclude_scopes = [\\\"InceptionV1/Logits\\\", \\\"InceptionV1/AuxLogits\\\"]\\n\\n exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n return slim.assign_from_checkpoint_fn(\\n os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\\n variables_to_restore)\",\n \"def get_Ei_point(self, source_point, target_point):\\n Ei = 0.0\\n source_patch_mask = self.source_patch_masks[source_point]\\n source_patch = self.source_patches[source_point]\\n source_patch_unknown_overlap_mask = np.bitwise_and(source_patch_mask, self.unknown_mask)\\n target_patch_mask = self.target_patch_masks[target_point]\\n # try:\\n if source_patch_unknown_overlap_mask.any():\\n test_img = self.img.copy()\\n test_img[target_patch_mask] = source_patch\\n Ei = self.get_norm_ssd(self.img, source_patch)\\n return Ei\",\n \"def soleDataPoint(self):\\n dps = self.datapoints()\\n if dps:\\n return dps[0]\",\n \"def get_dataset(dataset_name):\\n if dataset_name == \\\"Iris\\\":\\n data = datasets.load_iris()\\n\\n elif dataset_name == \\\"Breast Cancer\\\":\\n data = datasets.load_breast_cancer()\\n\\n elif dataset_name == \\\"Wine Dataset\\\":\\n data = datasets.load_wine()\\n\\n elif dataset_name == \\\"MNIST\\\":\\n data = datasets.load_digits()\\n\\n #elif dataset_name == \\\"Boston Housing Price\\\":\\n # data = datasets.load_boston()\\n\\n X = data.data\\n y = data.target\\n\\n return X, y\",\n \"def get_input_data(galaxyemail, galaxypass, server):\\n gi = GalaxyInstance(url=server, email=galaxyemail, password=galaxypass)\\n history_id = get_history_id(galaxyemail, galaxypass, server)\\n hist_contents = gi.histories.show_history(history_id, contents=True)\\n inputs = []\\n datacount = 0\\n datasets = [dataset for dataset in hist_contents if not dataset['deleted']]\\n for dataset in datasets:\\n inputs.append(dataset['id'])\\n datacount += 1\\n return inputs, datacount\",\n \"def get_dummy_input(dataset=None, device=None, input_shape=None):\\n def create_single(shape):\\n t = torch.randn(shape)\\n if device:\\n t = t.to(device)\\n return t\\n\\n def create_recurse(shape):\\n if all(isinstance(x, int) for x in shape):\\n return create_single(shape)\\n return tuple(create_recurse(s) for s in shape)\\n\\n input_shape = input_shape or dataset.shape\\n return create_recurse(input_shape)\",\n \"def get_input_id(self):\\n return self.net.inputs[0]\",\n \"def get_eval_input(self, prev, i):\\n del prev, i\\n # The attention_decoder will fetch image features from the net, no need for\\n # extra inputs.\\n return self._zero_label\",\n \"def _get_data_for_tests():\\n X = np.random.randn(100, input_dim)\\n Y = np.random.randn(100, output_dim)\\n X_new = np.random.randn(100, input_dim)\\n return X, X_new, Y\",\n \"def define_holdout_set(\\n train_set: pd.DataFrame,\\n holdout_set: Union[pd.DataFrame, callable, str, None],\\n target_column: Union[str, List[str]],\\n) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:\\n #################### Update `holdout_set` ####################\\n if callable(holdout_set):\\n train_set, holdout_set = holdout_set(train_set, target_column)\\n elif isinstance(holdout_set, str):\\n holdout_set = pd.read_csv(holdout_set)\\n #################### Validate `holdout_set` ####################\\n try:\\n if holdout_set is None or np.array_equal(train_set.columns, holdout_set.columns):\\n return train_set, holdout_set\\n except AttributeError:\\n raise TypeError(f\\\"holdout_set must be None, DataFrame, callable, or str, not {holdout_set}\\\")\\n raise ValueError(f\\\"Mismatched columns\\\\n{train_set.columns}\\\\n!=\\\\n{holdout_set.columns}\\\")\",\n \"def getDataset(self, train=True):\\n \\n device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\") \\n \\n if self.dataset == \\\"ELLIPSE\\\":\\n a = np.array([[0,1.0],[1.0,2.0]]) \\n b = a*0.5 \\n myE = el.ellipse(device, 500, 100, a, b) \\n if train == True:\\n return myE.create_dataset(myE.examples)\\n return myE.create_dataset(myE.valid) \\n \\n if self.dataset == \\\"SWISS\\\": \\n myS = sw.SwissRoll(device, 500, 0.2) \\n if train == True:\\n return myS.create_dataset(myS.examples)\\n return myS.create_dataset(myS.valid)\\n \\n \\n #open file\\n myFile = h5py.File(self.dataString, 'r', self.driver)\\n \\n if train == True: \\n inputString = \\\"train_inputs\\\"\\n labelsString = \\\"train_labels\\\"\\n \\n else:\\n inputString = \\\"test_inputs\\\"\\n labelsString = \\\"test_labels\\\"\\n \\n #get hdf5 datsets\\n features = myFile.get(inputString)\\n labels = myFile.get(labelsString)\\n \\n #convert to tensors\\n features = torch.from_numpy(np.array(features))\\n labels = torch.from_numpy(np.array(labels))\\n \\n #close file to ensure dataset is in memory\\n myFile.close()\\n \\n #conver to correct datatypes\\n features = features.float()\\n \\n if self.conv_sg == False:\\n labels = labels.long() \\n \\n dataset = torch.utils.data.TensorDataset(features, labels)\\n \\n return dataset\",\n \"def get_inputs_train():\\n x = tf.constant(extract_pandas_data(x_train))\\n y = tf.constant(y_train.values)\\n return x, y\",\n \"def predict(self, X=None):\\n X = X if X is not None else self.X\\n self.outliers = pd.Series(\\n self.outlier_detector.predict(X.fillna(0)) == -1,\\n index=X.index)\\n # TODO: Since you can run this on self.X OR new X, then \\\"self.outliers\\\"\\n # can change and not be consistent....... this is a problem\\n return self.outliers\",\n \"def get_dataset(self) -> datasets.OpenMLDataset:\\n return datasets.get_dataset(self.dataset_id)\",\n \"def GetLabelInput(self) -> \\\"itkImageUS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIF2IUS2_GetLabelInput(self)\",\n \"def get_inputs_test_S():\\n x = tf.constant(extract_pandas_data(x_test_S))\\n y = tf.constant(y_test_S.values)\\n return x, y\",\n \"def GetLabelInput(self) -> \\\"itkImageUS3 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC3IUS3_GetLabelInput(self)\",\n \"def backup_dataset(outfile=None):\\n return backup_es(Dataset, outfile=outfile)\",\n \"def GetInputNarrowBand(self) -> \\\"itkVectorContainerUILSNF3_Pointer\\\":\\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF3_GetInputNarrowBand(self)\",\n \"async def get_input_entity(self):\\n # We don't actually have an API call we can make yet\\n # to get more info, but keep this method for consistency.\\n return self.input_entity\",\n \"def GetLabelInput(self) -> \\\"itkImageUS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUS2IUS2_GetLabelInput(self)\",\n \"def GetLabelInput(self) -> \\\"itkImageSS2 const *\\\":\\n return _itkLabelStatisticsImageFilterPython.itkLabelStatisticsImageFilterIUC2ISS2_GetLabelInput(self)\",\n \"def get_input_data(self, name='0'):\\n return data\",\n \"def get_input_center_layer(dataset: str) -> torch.nn.Module:\\n if dataset == \\\"imagenet\\\":\\n return InputCenterLayer(_IMAGENET_MEAN)\\n elif dataset == \\\"cifar10\\\":\\n return InputCenterLayer(_CIFAR10_MEAN)\",\n \"def get_corrupted_input(self, input):\\n return self.theano_rng.binomial(size=input.shape, n=1,\\n p=1 - self.corrupt_input_l,\\n dtype=theano.config.floatX) * input\",\n \"def sample(self):\\n\\n\\t\\t# get newest sample\\n\\t\\ts = self.eyetribe.sample()\\n\\t\\t\\n\\t\\t# invalid data\\n\\t\\tif s == (None,None):\\n\\t\\t\\treturn (-1,-1)\\n\\t\\t\\n\\t\\t# check if the new sample is the same as the previous\\n\\t\\tif s != self.prevsample:\\n\\t\\t\\t# update the current sample\\n\\t\\t\\tself.prevsample = copy.copy(s)\\n\\t\\t\\n\\t\\treturn self.prevsample\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7605484","0.70850253","0.65761745","0.62563837","0.58847857","0.5119277","0.49333444","0.48865077","0.48654017","0.48275244","0.48057696","0.47976065","0.47965127","0.4744823","0.47400033","0.47386026","0.47028476","0.4699324","0.46962723","0.4694681","0.46845272","0.46641162","0.46598667","0.46492717","0.46456861","0.4645556","0.46335387","0.46245515","0.46165693","0.461459","0.461439","0.46130466","0.46128196","0.4610764","0.46075165","0.46043193","0.4584948","0.45838737","0.4569149","0.45654538","0.45643","0.45507893","0.45485458","0.45417216","0.45368475","0.45350093","0.4530957","0.45291075","0.45248145","0.45246196","0.4519399","0.45130503","0.45126024","0.4510889","0.44974124","0.44796932","0.44781756","0.44774723","0.4476001","0.44735888","0.4464816","0.44639954","0.4463351","0.4456397","0.4453274","0.4451095","0.4441693","0.44325483","0.44311145","0.4430333","0.442166","0.44215325","0.44159955","0.4395098","0.43874243","0.4379003","0.43789968","0.43776187","0.43764877","0.4375201","0.43641266","0.43633702","0.4362867","0.43616197","0.43615752","0.43611068","0.43610704","0.43569353","0.4356529","0.43537357","0.43503323","0.43498182","0.4347748","0.43435103","0.4342577","0.43368292","0.43335044","0.43320224","0.43309674","0.43257582"],"string":"[\n \"0.7605484\",\n \"0.70850253\",\n \"0.65761745\",\n \"0.62563837\",\n \"0.58847857\",\n \"0.5119277\",\n \"0.49333444\",\n \"0.48865077\",\n \"0.48654017\",\n \"0.48275244\",\n \"0.48057696\",\n \"0.47976065\",\n \"0.47965127\",\n \"0.4744823\",\n \"0.47400033\",\n \"0.47386026\",\n \"0.47028476\",\n \"0.4699324\",\n \"0.46962723\",\n \"0.4694681\",\n \"0.46845272\",\n \"0.46641162\",\n \"0.46598667\",\n \"0.46492717\",\n \"0.46456861\",\n \"0.4645556\",\n \"0.46335387\",\n \"0.46245515\",\n \"0.46165693\",\n \"0.461459\",\n \"0.461439\",\n \"0.46130466\",\n \"0.46128196\",\n \"0.4610764\",\n \"0.46075165\",\n \"0.46043193\",\n \"0.4584948\",\n \"0.45838737\",\n \"0.4569149\",\n \"0.45654538\",\n \"0.45643\",\n \"0.45507893\",\n \"0.45485458\",\n \"0.45417216\",\n \"0.45368475\",\n \"0.45350093\",\n \"0.4530957\",\n \"0.45291075\",\n \"0.45248145\",\n \"0.45246196\",\n \"0.4519399\",\n \"0.45130503\",\n \"0.45126024\",\n \"0.4510889\",\n \"0.44974124\",\n \"0.44796932\",\n \"0.44781756\",\n \"0.44774723\",\n \"0.4476001\",\n \"0.44735888\",\n \"0.4464816\",\n \"0.44639954\",\n \"0.4463351\",\n \"0.4456397\",\n \"0.4453274\",\n \"0.4451095\",\n \"0.4441693\",\n \"0.44325483\",\n \"0.44311145\",\n \"0.4430333\",\n \"0.442166\",\n \"0.44215325\",\n \"0.44159955\",\n \"0.4395098\",\n \"0.43874243\",\n \"0.4379003\",\n \"0.43789968\",\n \"0.43776187\",\n \"0.43764877\",\n \"0.4375201\",\n \"0.43641266\",\n \"0.43633702\",\n \"0.4362867\",\n \"0.43616197\",\n \"0.43615752\",\n \"0.43611068\",\n \"0.43610704\",\n \"0.43569353\",\n \"0.4356529\",\n \"0.43537357\",\n \"0.43503323\",\n \"0.43498182\",\n \"0.4347748\",\n \"0.43435103\",\n \"0.4342577\",\n \"0.43368292\",\n \"0.43335044\",\n \"0.43320224\",\n \"0.43309674\",\n \"0.43257582\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8473368"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18481,"cells":{"query":{"kind":"string","value":"Get a `DatasetSentinel` representing an Experiment's `holdout_target_data` Returns"},"document":{"kind":"string","value":"def holdout_target(self):\n return DatasetSentinel(\n \"holdout_target\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def holdout_input(self):\n return DatasetSentinel(\n \"holdout_input\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )","def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())","def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())","def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())","def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())","def sample_train_data(dataset ,target,data_len, resp = True ):\r\n np.random.seed(222)\r\n ixes = np.random.choice(dataset.index, data_len, replace = False)\r\n print(ixes)\r\n under_df = dataset.iloc[ixes]\r\n if resp==True:\r\n under_target = target.iloc[ixes]\r\n return under_df, under_target\r\n else:\r\n return under_df","def get_dataset_sampler(self):\n return None","def load_own_target():\n data = Data()\n target = data.get_label_col()\n return target.values","def holdout(self, X, y):\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size = 0.3, random_state = 0)\n\n return X_train, X_test, y_train, y_test","def _dataset_sentinel_helper(self):\n params = self.cross_experiment_key.parameters\n return dict(\n dataset_hash=params[\"train_dataset\"],\n cv_type=params[\"cross_experiment_params\"].get(\n \"cv_type\", params[\"cross_experiment_params\"].get(\"cross_validation_type\", None)\n ),\n global_random_seed=params[\"cross_experiment_params\"][\"global_random_seed\"],\n random_seeds=params[\"cross_experiment_params\"][\"random_seeds\"],\n )","def _create_dataset(source=''):\n return ExperimentalDataset()","def get_data_only_random_dropout(self,percent_dropout,num_dropout_corruptions_per_point):\n\n\t\t\"\"\"Select the corrupted data if applicable, and otherwise the original training data\"\"\"\n\t\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=False)","def get_dataset(dataset_name):\n if dataset_name == \"Iris\":\n data = datasets.load_iris()\n elif dataset_name == \"Breast Cancer\":\n data = datasets.load_breast_cancer()\n else:\n data = datasets.load_wine()\n\n X = data.data\n y = data.target\n return X, y","def discard_none_targets(dataset):\r\n indices = []\r\n for (ii,sample) in enumerate(dataset):\r\n target = sample[1]\r\n if target is not None:\r\n indices.append(ii)\r\n\r\n return Subset(dataset,indices)","def get_data_for_semi_supervised(self,percent_dropout,num_dropout_corruptions_per_point):\n\n\t\t\"\"\"Select the corrupted data if applicable, and otherwise the original training data\"\"\"\n\t\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=True)","def target(self):\n return self.data.target","def target(self, example):\n return example[self.target_index]","def default_dataset(self):\n return self.get_by_index(self._default_index)","def get_expo_data(\n data: Union[pd.Series, pd.DataFrame],\n target_column: str = \"close\",\n trend: str = \"A\",\n seasonal: str = \"A\",\n seasonal_periods: int = 7,\n dampen: str = \"F\",\n n_predict: int = 5,\n start_window: float = 0.85,\n forecast_horizon: int = 5,\n metric: str = \"mape\",\n) -> Tuple[\n List[TimeSeries],\n List[TimeSeries],\n List[TimeSeries],\n Optional[Union[float, ndarray]],\n ExponentialSmoothing,\n]:\n\n use_scalers = False\n _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers)\n\n if trend == \"M\":\n trend_model = ModelMode.MULTIPLICATIVE\n elif trend == \"N\":\n trend_model = ModelMode.NONE\n else: # Default\n trend_model = ModelMode.ADDITIVE\n\n if seasonal == \"M\":\n seasonal_model = SeasonalityMode.MULTIPLICATIVE\n elif seasonal == \"N\":\n seasonal_model = SeasonalityMode.NONE\n else: # Default\n seasonal_model = SeasonalityMode.ADDITIVE\n\n damped = True\n if dampen == \"F\":\n damped = False\n\n # Model Init\n model_es = ExponentialSmoothing(\n trend=trend_model,\n seasonal=seasonal_model,\n seasonal_periods=int(seasonal_periods),\n damped=damped,\n random_state=42,\n )\n\n try:\n # Historical backtesting\n historical_fcast_es = model_es.historical_forecasts(\n ticker_series, # backtest on entire ts\n start=float(start_window),\n forecast_horizon=int(forecast_horizon),\n verbose=True,\n )\n except Exception as e: # noqa\n error = str(e)\n # lets translate this to something everyone understands\n if \"with`overlap_end` set to `False`.\" in error:\n console.print(\n \"[red]Dataset too small.[/red]\"\n \"[red] Please increase size to at least 100 data points.[/red]\"\n )\n else:\n console.print(f\"[red]{error}[/red]\")\n return [], [], [], None, None\n\n # train new model on entire timeseries to provide best current forecast\n best_model = ExponentialSmoothing(\n trend=trend_model,\n seasonal=seasonal_model,\n seasonal_periods=int(seasonal_periods),\n damped=damped,\n random_state=42,\n )\n\n # we have the historical fcast, now lets train on entire set and predict.\n best_model.fit(ticker_series)\n probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)\n\n if metric == \"rmse\":\n precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"mse\":\n precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"mape\":\n precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)\n elif metric == \"smape\":\n precision = smape(actual_series=ticker_series, pred_series=historical_fcast_es)\n\n console.print(\n f\"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \\n\"\n )\n\n return (\n ticker_series,\n historical_fcast_es,\n probabilistic_forecast,\n precision,\n best_model,\n )","def define_holdout_set(\n train_set: pd.DataFrame,\n holdout_set: Union[pd.DataFrame, callable, str, None],\n target_column: Union[str, List[str]],\n) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:\n #################### Update `holdout_set` ####################\n if callable(holdout_set):\n train_set, holdout_set = holdout_set(train_set, target_column)\n elif isinstance(holdout_set, str):\n holdout_set = pd.read_csv(holdout_set)\n #################### Validate `holdout_set` ####################\n try:\n if holdout_set is None or np.array_equal(train_set.columns, holdout_set.columns):\n return train_set, holdout_set\n except AttributeError:\n raise TypeError(f\"holdout_set must be None, DataFrame, callable, or str, not {holdout_set}\")\n raise ValueError(f\"Mismatched columns\\n{train_set.columns}\\n!=\\n{holdout_set.columns}\")","def get_interest_variable(\n in_dataset, sensor_var, date_col, hr_col, numeric_var, target_sensor=\"A620\"\n):\n dataset_pproc = in_dataset.loc[\n in_dataset[sensor_var] == target_sensor, [date_col, hr_col] + [numeric_var]\n ]\n hrs_str = dataset_pproc[hr_col].to_string()\n dates_str = dataset_pproc[date_col]\n\n dataset_pproc[date_col] = pd.to_datetime(dataset_pproc[date_col])\n dataset_pproc.set_index([date_col, hr_col], inplace=True)\n dataset_pproc.fillna(method=\"ffill\", inplace=True)\n dataset_pproc.interpolate(method=\"linear\", axis=0)\n\n return dataset_pproc","def get_dataset(self):\n return","def backup_dataset(outfile=None):\n return backup_es(Dataset, outfile=outfile)","def target_dummy(config: Configuration, seed: int) -> int:\n return seed","def test_additional_data(self):\n task = SentinelHubInputTask(\n bands_feature=(FeatureType.DATA, 'BANDS'),\n bands=['B01', 'B02', 'B05'],\n additional_data=[\n (FeatureType.MASK, 'dataMask', 'IS_DATA'),\n (FeatureType.MASK, 'CLM'),\n (FeatureType.MASK, 'SCL'),\n (FeatureType.MASK, 'SNW'),\n (FeatureType.MASK, 'CLD'),\n (FeatureType.DATA, 'CLP'),\n (FeatureType.DATA, 'viewAzimuthMean', 'view_azimuth_mean'),\n (FeatureType.DATA, 'sunAzimuthAngles'),\n (FeatureType.DATA, 'sunZenithAngles')\n ],\n size=self.size,\n maxcc=self.maxcc,\n time_difference=self.time_difference,\n data_collection=DataCollection.SENTINEL2_L2A,\n max_threads=self.max_threads\n )\n\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\n\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\n is_data = eopatch[(FeatureType.MASK, 'IS_DATA')]\n clm = eopatch[(FeatureType.MASK, 'CLM')]\n scl = eopatch[(FeatureType.MASK, 'SCL')]\n snw = eopatch[(FeatureType.MASK, 'SNW')]\n cld = eopatch[(FeatureType.MASK, 'CLD')]\n clp = eopatch[(FeatureType.DATA, 'CLP')]\n view_azimuth_mean = eopatch[(FeatureType.DATA, 'view_azimuth_mean')]\n sun_azimuth_angles = eopatch[(FeatureType.DATA, 'sunAzimuthAngles')]\n sun_zenith_angles = eopatch[(FeatureType.DATA, 'sunZenithAngles')]\n\n self.assertTrue(np.allclose(array_stats(bands), [0.027, 0.0243, 0.0162]))\n\n width, height = self.size\n self.assertTrue(bands.shape == (4, height, width, 3))\n self.assertTrue(is_data.shape == (4, height, width, 1))\n self.assertTrue(is_data.dtype == bool)\n self.assertTrue(clm.shape == (4, height, width, 1))\n self.assertTrue(clm.dtype == np.uint8)\n self.assertTrue(scl.shape == (4, height, width, 1))\n self.assertTrue(snw.shape == (4, height, width, 1))\n self.assertTrue(cld.shape == (4, height, width, 1))\n self.assertTrue(clp.shape == (4, height, width, 1))\n self.assertTrue(view_azimuth_mean.shape == (4, height, width, 1))\n self.assertTrue(sun_azimuth_angles.shape == (4, height, width, 1))\n self.assertTrue(sun_zenith_angles.shape == (4, height, width, 1))\n self.assertTrue(len(eopatch.timestamp) == 4)","def target_temperature(self):\n return self._boiler.setpoint","def eye_during_period(self, time_period, alias):\n\t\twith pd.get_store(self.input_object) as h5_file:\n\t\t\tperiod_block_nr = self.sample_in_block(sample = time_period[0], block_table = h5_file['%s/blocks'%alias])\n\t\t\teye = h5_file['%s/blocks'%alias]['eye_recorded'][period_block_nr]\n\t\treturn eye","def get_dataset(args):\n\n if args['experiment']['dataset'] == Dataset.mindsets:\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\n nb_questions=args['dataset']['nb_questions'],\n nb_useless=args['dataset']['nb_useless'],\n noise=args['dataset']['noise'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\n nb_features=args['dataset']['nb_features'],\n nb_mindsets=args['dataset']['nb_mindsets'],\n centers=args['dataset']['centers'],\n range_answers=args['dataset']['range_answers'],\n seed=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys, cs=cs)\n\n if args['experiment']['dataset'] == Dataset.retinal:\n xs, ys = load_RETINAL(root_path=args['root_dir'],\n nb_bins=args['dataset']['nb_bins'],\n max_idx=args['dataset']['max_idx'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.moons:\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\n noise=args['dataset']['noise'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.SBM:\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\n p_in=args['dataset']['p'],\n p_out=args['dataset']['q'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\n centers=args['dataset']['blob_centers'],\n n_features=args['dataset']['blob_centers'],\n cluster_std=args['dataset']['blob_variances'],\n random_state=args['experiment']['seed'])\n\n return Data(xs=xs, ys=ys)\n\n if args['experiment']['dataset'] == Dataset.LFR:\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\n tau1=args['dataset']['tau1'],\n tau2=args['dataset']['tau2'],\n mu=args['dataset']['mu'],\n average_degree=args['dataset']['average_degree'],\n min_community=args['dataset']['min_community'],\n seed=args['experiment']['seed'])\n\n return Data(ys=ys, A=A, G=G)\n\n if args['experiment']['dataset'] == Dataset.wave:\n df = pd.read_csv('datasets/waveform.csv')\n xs = df[df.columns[:-1]].to_numpy()\n ys = df[df.columns[-1]].to_numpy()\n\n return Data(xs=xs, ys=ys)\n\n raise ValueError('Wrong name for a dataset')","def get_target_market():\n return choice(GraphParameters.TARGET_MARKETS)","def _get__target(self):\n if self._saved_target is None:\n self._saved_target = self._metadata.get_callable()()\n \n return self._saved_target","def load_dataset(label, max_deltaR=None):\n\n # Files should be located in the datasets directory\n particles = np.load(\"datasets/PARTICLES_\"+label+\".npy\", allow_pickle=True)\n\n with open(\"datasets/DROP_\"+label, 'rb') as pickle_file:\n water = pickle.load(pickle_file)\n\n with open(\"datasets/INTERACTIONS_\"+label, 'rb') as pickle_file:\n interactions = pickle.load(pickle_file)\n\n samples = particles_to_samples(particles)\n\n if max_deltaR is not None:\n whr = water.deltaR.flatten() < max_deltaR\n samples = samples[whr]\n water.deltaR = water.deltaR[whr]\n water.theta = water.theta[whr]\n\n return samples, water, interactions","def get_target_entity(self):\n return None","def get_dataset(self) -> datasets.OpenMLDataset:\n return datasets.get_dataset(self.dataset_id)","def tempfeeder_exp():\n global _tempfeeder_exp\n if _tempfeeder_exp is None:\n _tempfeeder_exp = UserLoads_Experiment(DATA_WITHOUT_DUPES_PATH)\n return _tempfeeder_exp","def get_health_data_and_ingest_into_sentinel(self):\n self.pull_and_push_the_snapshot_data(\n HEALTH_ENDPOINT, self.health_table_name, fields=MODIFIED_FIELDS\n )","def get_dataset(self):\n\n trainset = datasets.SVHN('datasets/SVHN/train/', split='train', transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.SVHN('datasets/SVHN/test/', split='test', transform=self.val_transforms,\n target_transform=None, download=True)\n extraset = datasets.SVHN('datasets/SVHN/extra', split='extra', transform=self.train_transforms,\n target_transform=None, download=True)\n\n trainset = torch.utils.data.ConcatDataset([trainset, extraset])\n\n return trainset, valset","def predict(\n self,\n targetSeries,\n exogenousSeries=None\n ):\n pass","def oversized_data_oss_target_arn(self) -> str:\n return pulumi.get(self, \"oversized_data_oss_target_arn\")","def oversized_data_oss_target_arn(self) -> str:\n return pulumi.get(self, \"oversized_data_oss_target_arn\")","def get_target(model, input_data, input_size=None):\n prediction, target = query_model(model, input_data, input_size)\n return target","def storage_target(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"storage_target\")","def get_seeds_null(samples,signal):\n theta_seeds={}\n if selected is None:\n SRlist = range(self.N_SR)\n theta_samples = samples[:,0,self.N_SR:].T\n else:\n SRlist = [selected] \n theta_samples = samples[:,0,1].T\n for i,x in zip(SRlist,theta_samples):\n theta_MLE = x\n theta_seeds['theta_{0}'.format(i)] = theta_MLE\n return theta_seeds","def _target(self, data):\n index = self._get_index(data)\n value = data[index]\n return index, abs(value - data.mean())","def __get_x_y_from_data(\n logger, df, predictors, target):\n if df is not None:\n df_X = df[predictors]\n df_Y = df[target]\n return df_X, df_Y","def get_feat_and_target(df, target):\n x = df.drop(target, axis = 1)\n y = df[target]\n return x, y","def target(self) -> pulumi.Input[float]:\n return pulumi.get(self, \"target\")","def target(self) -> pulumi.Input[float]:\n return pulumi.get(self, \"target\")","def get_data(dataset, **kwargs):\n age_df = load_age_sample_from_mcmc_chains(dataset, **kwargs)\n hr_df = load_hr()\n age_df, hr_df = clean_data(age_df, hr_df)\n snids = age_df.index.unique().tolist()\n return snids, age_df, hr_df","def _get_prediction_data(self) -> pd.DataFrame:\n\n prediction_data = self._data.loc[self._data.target == 'predict'].drop('target', axis=1)\n prediction_data = prediction_data.drop('y_label', axis=1)\n return prediction_data","def target(self) -> np.ndarray:\n return self._dist['target']","def get_test(self, preprocess=False):\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')","def get_dataset(dataset_name):\n if dataset_name == \"Iris\":\n data = datasets.load_iris()\n\n elif dataset_name == \"Breast Cancer\":\n data = datasets.load_breast_cancer()\n\n elif dataset_name == \"Wine Dataset\":\n data = datasets.load_wine()\n\n elif dataset_name == \"MNIST\":\n data = datasets.load_digits()\n\n #elif dataset_name == \"Boston Housing Price\":\n # data = datasets.load_boston()\n\n X = data.data\n y = data.target\n\n return X, y","def iris():\n return IrisDataset()","def get_iris_data():\n iris = datasets.load_iris()\n data = iris[\"data\"]\n target = iris[\"target\"]\n\n # Prepend the column of 1s for bias\n N, M = data.shape\n all_X = np.ones((N, M + 1))\n all_X[:, 1:] = data\n\n # Convert into one-hot vectors\n num_labels = len(np.unique(target))\n all_Y = np.eye(num_labels)[target]\n return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)","def get_dataset(self):\n\n trainset = datasets.MNIST('datasets/MNIST/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.MNIST('datasets/MNIST/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def generate_data(data, samples, targeted=True, start=0, inception=True):\n \n assert (targeted==True and start==0 and inception==True)\n \n \n inputs = []\n targets = []\n \n '''\n for i in range(samples):\n if targeted:\n if inception:\n seq = random.sample(range(1,1001), 10)\n else:\n seq = range(data.test_labels.shape[1])\n\n for j in seq:\n if (j == np.argmax(data.test_labels[start+i])) and (inception == False):\n continue\n inputs.append(data.test_data[start+i])\n targets.append(np.eye(data.test_labels.shape[1])[j])\n else:\n inputs.append(data.test_data[start+i])\n targets.append(data.test_labels[start+i])\n\n inputs = np.array(inputs)\n targets = np.array(targets)\n '''\n\n return inputs, targets","def prepare_data():\n #data, label = load_ta_data(), load_ta_target()\n data, label = load_own_data(), load_own_target()\n tra_x, tst_x = split_samples(data)\n tra_y, tst_y = split_samples(label)\n return (tra_x, tst_x, tra_y, tst_y)","def soleDataPoint(self):\n dps = self.datapoints()\n if dps:\n return dps[0]","def _target(self, data):\n relative_values = abs(data - data.mean())\n index = relative_values.idxmax()\n value = relative_values[index]\n return index, value","def temp_holdout(y_panel_df, val_periods):\n val = y_panel_df.groupby('unique_id').tail(val_periods)\n train = y_panel_df.groupby('unique_id').apply(lambda df: df.head(-val_periods)).reset_index(drop=True)\n\n return train, val","def get_data(datadir):\n return sklearn.datasets.fetch_california_housing(\n datadir,\n return_X_y=True)","def get_return_operand_data(tensor, target_location):\n if tensor is None: # potentially for s\n return tensor\n if target_location == 'cpu':\n return tensor.to('cpu')\n else: # already on device\n return tensor.tensor","def generate_data(data, model, samples, targeted=True, target_num=9, start=0, inception=False, seed=3, handpick=False ):\n random.seed(seed)\n inputs = []\n targets = []\n labels = []\n true_ids = []\n sample_set = []\n\n data_d = data.test_data\n labels_d = data.test_labels\n\n if handpick:\n if inception:\n deck = list(range(0, 1500))\n else:\n deck = list(range(0, 10000))\n random.shuffle(deck)\n print('Handpicking')\n\n while (len(sample_set) < samples):\n rand_int = deck.pop()\n pred = model.model.predict(data_d[rand_int:rand_int + 1])\n\n if inception:\n pred = np.reshape(pred, (labels_d[0:1].shape))\n\n if (np.argmax(pred, 1) == np.argmax(labels_d[rand_int:rand_int + 1], 1)):\n sample_set.append(rand_int)\n print('Handpicked')\n else:\n sample_set = random.sample(range(0, 10000), samples)\n\n for i in sample_set:\n if targeted:\n if inception:\n seq = random.sample(range(1, 1001), target_num)\n else:\n seq = range(labels_d.shape[1])\n\n for j in seq:\n if (j == np.argmax(labels_d[start + i])) and (inception == False):\n continue\n inputs.append(data_d[start + i])\n targets.append(np.eye(labels_d.shape[1])[j])\n labels.append(labels_d[start + i])\n true_ids.append(start + i)\n else:\n inputs.append(data_d[start + i])\n targets.append(labels_d[start + i])\n labels.append(labels_d[start + i])\n true_ids.append(start + i)\n\n inputs = np.array(inputs)\n targets = np.array(targets)\n labels = np.array(labels)\n true_ids = np.array(true_ids)\n return inputs, targets, labels, true_ids","def load_data():\n \n data = datasets.load_iris()\n df = pd.DataFrame(data.data, columns = data.feature_names)\n df2 = pd.DataFrame(data.target, columns = [\"target\"])\n\n return df.join(df2)","def test_single_null(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dset = f.create_dataset('x', (1,), dtype='i1')\n out = dset[()]\n assert isinstance(out, np.ndarray)\n assert out.shape == (1,)","def target_temperature_low(self):\n if self._client.mode == self._client.MODE_AUTO:\n return self._client.heattemp\n return None","def source_exercise_target(self, node):\n std_domain = self.builder.env.domains['std']\n figtype = std_domain.get_enumerable_node_type(node.parent)\n assert figtype == 'solution'\n\n fig_id = node.parent['ids'][0]\n\n # sort out the label\n exercise_label = node.parent.attributes['exercise']\n\n names = node.parent['names']\n assert len(names) == 1\n assert names[0].startswith('sol:')\n\n # get exercise id\n assert fig_id.startswith('sol-')\n exercise_id = 'ex-{}'.format(fig_id[4:])\n assert exercise_id == nodes.make_id(exercise_label)\n\n # because the exercise may be in a different document, we go global\n all_labels = std_domain.data['labels']\n assert exercise_label in all_labels\n\n # track down the document and identifier\n exercise_source_docname = all_labels[exercise_label][0]\n fig_identifiers = self.builder.env.toc_fignumbers\n assert exercise_source_docname in fig_identifiers\n assert 'exercise' in fig_identifiers[exercise_source_docname]\n ex_docname_map = fig_identifiers[exercise_source_docname]['exercise']\n assert exercise_id in ex_docname_map\n\n fignumber = ex_docname_map[exercise_id]\n\n return exercise_source_docname, exercise_id, fignumber","def _get_dataset_bin(self):\n df_raw = pd.read_csv(self.path_bin)\n # kick all features we don't want\n features = get_features_from_questions(self.path_questions, remove_cond=True)\n features.append(self.target)\n df = df_raw[features]\n return df","def source(self) -> DatasetSource:\n return self._source","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def target(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target\")","def get_replica_bottom(self):\n return self.dummy_row_insts[0].uy()","def get_halo(self, index):\n try:\n halo = self.data.loc[index]\n except KeyError:\n raise IndexError(\n \"Halo id %d not found in %s\" % (index, self.filename)\n )\n return halo","def get_halo(self, index):\n try:\n halo = self.data.loc[index]\n except KeyError:\n raise IndexError(\n \"Halo id %d not found in %s\" % (index, self.filename)\n )\n return halo","def Experiment(self, default=None):\n return self.data.get('experiment', default)","def get_Ei_point(self, source_point, target_point):\n Ei = 0.0\n source_patch_mask = self.source_patch_masks[source_point]\n source_patch = self.source_patches[source_point]\n source_patch_unknown_overlap_mask = np.bitwise_and(source_patch_mask, self.unknown_mask)\n target_patch_mask = self.target_patch_masks[target_point]\n # try:\n if source_patch_unknown_overlap_mask.any():\n test_img = self.img.copy()\n test_img[target_patch_mask] = source_patch\n Ei = self.get_norm_ssd(self.img, source_patch)\n return Ei","def get_dataset(self):\n trainset = datasets.KMNIST('datasets/KMNIST/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.KMNIST('datasets/KMNIST/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def X_test(self):\n return self.test.drop(self.target_param, axis=1)","def get_minlplib_instancedata(target_filename=None):\n if target_filename is None:\n target_filename = os.path.join(os.getcwd(), 'minlplib', 'instancedata.csv')\n download_dir = os.path.dirname(target_filename)\n\n if os.path.exists(target_filename):\n raise ValueError('A file named {filename} already exists.'.format(filename=target_filename))\n \n if not os.path.exists(download_dir):\n os.makedirs(download_dir)\n\n downloader = download.FileDownloader()\n downloader.set_destination_filename(target_filename)\n downloader.get_text_file('http://www.minlplib.org/instancedata.csv')","def X_train(self):\n return self.train.drop(self.target_param, axis=1)","def target_set_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"target_set_id\")","def backward_sample(self, target):\n return self.flow.bijector.inverse(target)","def _get_target_data_generators(self, ephase, list_iterstore, dict_iterstore):\n X1_gen = None\n X2_gen = None\n if (ephase == NNModelPhase.PREDICT or ephase == NNModelPhase.TEST):\n # Iterstore for dbparam1, TE_OUT\n X1_gen = list_iterstore[0].setdefault(Dataset.TE_OUT, None)\n\n return X1_gen, X2_gen","def get_spike_data():\n neuron_spikes = []\n first = 0\n last = 0\n for i, file in enumerate(os.listdir(\"\"\"/Users/markusekvall/Desktop/\n final_entropy_model/Spike_trains\"\"\")):\n if file.endswith(\".mat\"):\n if file[0] == \".\":\n continue\n else:\n x = sio.loadmat(os.path.join(\"./Spike_trains\", file))\n cluster_class = x[\"cluster_class\"]\n # Set to ==2 if you want the second cluster\n idx = [cluster_class[:, 0] == 1]\n spike = cluster_class[idx[0], 1]\n if np.size(spike) != 0:\n if max(spike) > last:\n last = max(spike)\n if min(spike) > last:\n first = min(spike)\n neuron_spikes.append(spike)\n return neuron_spikes, first, last","def target_temperature_low(self):\n return self._device.setpoint_heat","def test_train_test_split_uni_exo(load_uni_exo_data_target):\n data, target = load_uni_exo_data_target\n\n ####################################\n #### Continuous fh without Gaps ####\n ####################################\n\n #### Integer fh ----\n exp = TSForecastingExperiment()\n fh = 12\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test.index == data.iloc[-fh:].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(1, 10) # 9 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [1, 2, 3, 4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #################################\n #### Continuous fh with Gaps ####\n #################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(7, 13) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n ####################################\n #### Discontinuous fh with Gaps ####\n ####################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6, 10, 11, 12]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)","def _get_data_for_tests():\n X = np.random.randn(100, input_dim)\n Y = np.random.randn(100, output_dim)\n X_new = np.random.randn(100, input_dim)\n return X, X_new, Y","def get_lindark(self, instrument, detector):\n files = self.lindark_list[instrument][detector]\n if len(files) == 1:\n return files[0]\n elif len(files) > 1:\n rand_index = np.random.randint(0, len(files) - 1)\n return files[rand_index]\n else:\n return None","def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \r\n \r\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\r\n \r\n if dropnan==False:\r\n series.replace(pd.np.nan,0,inplace=True)\r\n \r\n # Dropping other variables:\r\n if dropVars:\r\n index = list(np.arange(series.shape[1]-2,\r\n series.shape[1]-len(predictors)-2,\r\n -1))\r\n \r\n labels = [item for idx,item in enumerate(series.columns) \r\n if idx in index]\r\n \r\n #print(\"Eliminando variáveis: {}\".format(labels))\r\n series.drop(labels,axis=1,inplace=True) \r\n \r\n return series","def target_temperature(self):\n return self.atag.dhw_target_temperature","def _get_training_dataset(self):\n\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))","def readMNISTData():\n mnist = input_data.read_data_sets(\"MNIST_data\",one_hot=True) \n return mnist","def sample(self):\n\n\t\t# get newest sample\n\t\ts = self.eyetribe.sample()\n\t\t\n\t\t# invalid data\n\t\tif s == (None,None):\n\t\t\treturn (-1,-1)\n\t\t\n\t\t# check if the new sample is the same as the previous\n\t\tif s != self.prevsample:\n\t\t\t# update the current sample\n\t\t\tself.prevsample = copy.copy(s)\n\t\t\n\t\treturn self.prevsample","def get_datasetID(self):\n\t\treturn self.prDoc['inputs']['data'][0]['datasetID']"],"string":"[\n \"def holdout_input(self):\\n return DatasetSentinel(\\n \\\"holdout_input\\\", self.cross_experiment_key.parameters[\\\"holdout_dataset\\\"]\\n )\",\n \"def train_target(self):\\n return DatasetSentinel(\\\"train_target\\\", **self._dataset_sentinel_helper())\",\n \"def validation_target(self):\\n return DatasetSentinel(\\\"validation_target\\\", **self._dataset_sentinel_helper())\",\n \"def train_input(self):\\n return DatasetSentinel(\\\"train_input\\\", **self._dataset_sentinel_helper())\",\n \"def validation_input(self):\\n return DatasetSentinel(\\\"validation_input\\\", **self._dataset_sentinel_helper())\",\n \"def sample_train_data(dataset ,target,data_len, resp = True ):\\r\\n np.random.seed(222)\\r\\n ixes = np.random.choice(dataset.index, data_len, replace = False)\\r\\n print(ixes)\\r\\n under_df = dataset.iloc[ixes]\\r\\n if resp==True:\\r\\n under_target = target.iloc[ixes]\\r\\n return under_df, under_target\\r\\n else:\\r\\n return under_df\",\n \"def get_dataset_sampler(self):\\n return None\",\n \"def load_own_target():\\n data = Data()\\n target = data.get_label_col()\\n return target.values\",\n \"def holdout(self, X, y):\\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\\n X_train, X_test, y_train, y_test = train_test_split(\\n X, y, test_size = 0.3, random_state = 0)\\n\\n return X_train, X_test, y_train, y_test\",\n \"def _dataset_sentinel_helper(self):\\n params = self.cross_experiment_key.parameters\\n return dict(\\n dataset_hash=params[\\\"train_dataset\\\"],\\n cv_type=params[\\\"cross_experiment_params\\\"].get(\\n \\\"cv_type\\\", params[\\\"cross_experiment_params\\\"].get(\\\"cross_validation_type\\\", None)\\n ),\\n global_random_seed=params[\\\"cross_experiment_params\\\"][\\\"global_random_seed\\\"],\\n random_seeds=params[\\\"cross_experiment_params\\\"][\\\"random_seeds\\\"],\\n )\",\n \"def _create_dataset(source=''):\\n return ExperimentalDataset()\",\n \"def get_data_only_random_dropout(self,percent_dropout,num_dropout_corruptions_per_point):\\n\\n\\t\\t\\\"\\\"\\\"Select the corrupted data if applicable, and otherwise the original training data\\\"\\\"\\\"\\n\\t\\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=False)\",\n \"def get_dataset(dataset_name):\\n if dataset_name == \\\"Iris\\\":\\n data = datasets.load_iris()\\n elif dataset_name == \\\"Breast Cancer\\\":\\n data = datasets.load_breast_cancer()\\n else:\\n data = datasets.load_wine()\\n\\n X = data.data\\n y = data.target\\n return X, y\",\n \"def discard_none_targets(dataset):\\r\\n indices = []\\r\\n for (ii,sample) in enumerate(dataset):\\r\\n target = sample[1]\\r\\n if target is not None:\\r\\n indices.append(ii)\\r\\n\\r\\n return Subset(dataset,indices)\",\n \"def get_data_for_semi_supervised(self,percent_dropout,num_dropout_corruptions_per_point):\\n\\n\\t\\t\\\"\\\"\\\"Select the corrupted data if applicable, and otherwise the original training data\\\"\\\"\\\"\\n\\t\\treturn self.__get_data__(percent_dropout,num_dropout_corruptions_per_point,bool_targetted_dropout=True)\",\n \"def target(self):\\n return self.data.target\",\n \"def target(self, example):\\n return example[self.target_index]\",\n \"def default_dataset(self):\\n return self.get_by_index(self._default_index)\",\n \"def get_expo_data(\\n data: Union[pd.Series, pd.DataFrame],\\n target_column: str = \\\"close\\\",\\n trend: str = \\\"A\\\",\\n seasonal: str = \\\"A\\\",\\n seasonal_periods: int = 7,\\n dampen: str = \\\"F\\\",\\n n_predict: int = 5,\\n start_window: float = 0.85,\\n forecast_horizon: int = 5,\\n metric: str = \\\"mape\\\",\\n) -> Tuple[\\n List[TimeSeries],\\n List[TimeSeries],\\n List[TimeSeries],\\n Optional[Union[float, ndarray]],\\n ExponentialSmoothing,\\n]:\\n\\n use_scalers = False\\n _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers)\\n\\n if trend == \\\"M\\\":\\n trend_model = ModelMode.MULTIPLICATIVE\\n elif trend == \\\"N\\\":\\n trend_model = ModelMode.NONE\\n else: # Default\\n trend_model = ModelMode.ADDITIVE\\n\\n if seasonal == \\\"M\\\":\\n seasonal_model = SeasonalityMode.MULTIPLICATIVE\\n elif seasonal == \\\"N\\\":\\n seasonal_model = SeasonalityMode.NONE\\n else: # Default\\n seasonal_model = SeasonalityMode.ADDITIVE\\n\\n damped = True\\n if dampen == \\\"F\\\":\\n damped = False\\n\\n # Model Init\\n model_es = ExponentialSmoothing(\\n trend=trend_model,\\n seasonal=seasonal_model,\\n seasonal_periods=int(seasonal_periods),\\n damped=damped,\\n random_state=42,\\n )\\n\\n try:\\n # Historical backtesting\\n historical_fcast_es = model_es.historical_forecasts(\\n ticker_series, # backtest on entire ts\\n start=float(start_window),\\n forecast_horizon=int(forecast_horizon),\\n verbose=True,\\n )\\n except Exception as e: # noqa\\n error = str(e)\\n # lets translate this to something everyone understands\\n if \\\"with`overlap_end` set to `False`.\\\" in error:\\n console.print(\\n \\\"[red]Dataset too small.[/red]\\\"\\n \\\"[red] Please increase size to at least 100 data points.[/red]\\\"\\n )\\n else:\\n console.print(f\\\"[red]{error}[/red]\\\")\\n return [], [], [], None, None\\n\\n # train new model on entire timeseries to provide best current forecast\\n best_model = ExponentialSmoothing(\\n trend=trend_model,\\n seasonal=seasonal_model,\\n seasonal_periods=int(seasonal_periods),\\n damped=damped,\\n random_state=42,\\n )\\n\\n # we have the historical fcast, now lets train on entire set and predict.\\n best_model.fit(ticker_series)\\n probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)\\n\\n if metric == \\\"rmse\\\":\\n precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"mse\\\":\\n precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"mape\\\":\\n precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)\\n elif metric == \\\"smape\\\":\\n precision = smape(actual_series=ticker_series, pred_series=historical_fcast_es)\\n\\n console.print(\\n f\\\"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \\\\n\\\"\\n )\\n\\n return (\\n ticker_series,\\n historical_fcast_es,\\n probabilistic_forecast,\\n precision,\\n best_model,\\n )\",\n \"def define_holdout_set(\\n train_set: pd.DataFrame,\\n holdout_set: Union[pd.DataFrame, callable, str, None],\\n target_column: Union[str, List[str]],\\n) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:\\n #################### Update `holdout_set` ####################\\n if callable(holdout_set):\\n train_set, holdout_set = holdout_set(train_set, target_column)\\n elif isinstance(holdout_set, str):\\n holdout_set = pd.read_csv(holdout_set)\\n #################### Validate `holdout_set` ####################\\n try:\\n if holdout_set is None or np.array_equal(train_set.columns, holdout_set.columns):\\n return train_set, holdout_set\\n except AttributeError:\\n raise TypeError(f\\\"holdout_set must be None, DataFrame, callable, or str, not {holdout_set}\\\")\\n raise ValueError(f\\\"Mismatched columns\\\\n{train_set.columns}\\\\n!=\\\\n{holdout_set.columns}\\\")\",\n \"def get_interest_variable(\\n in_dataset, sensor_var, date_col, hr_col, numeric_var, target_sensor=\\\"A620\\\"\\n):\\n dataset_pproc = in_dataset.loc[\\n in_dataset[sensor_var] == target_sensor, [date_col, hr_col] + [numeric_var]\\n ]\\n hrs_str = dataset_pproc[hr_col].to_string()\\n dates_str = dataset_pproc[date_col]\\n\\n dataset_pproc[date_col] = pd.to_datetime(dataset_pproc[date_col])\\n dataset_pproc.set_index([date_col, hr_col], inplace=True)\\n dataset_pproc.fillna(method=\\\"ffill\\\", inplace=True)\\n dataset_pproc.interpolate(method=\\\"linear\\\", axis=0)\\n\\n return dataset_pproc\",\n \"def get_dataset(self):\\n return\",\n \"def backup_dataset(outfile=None):\\n return backup_es(Dataset, outfile=outfile)\",\n \"def target_dummy(config: Configuration, seed: int) -> int:\\n return seed\",\n \"def test_additional_data(self):\\n task = SentinelHubInputTask(\\n bands_feature=(FeatureType.DATA, 'BANDS'),\\n bands=['B01', 'B02', 'B05'],\\n additional_data=[\\n (FeatureType.MASK, 'dataMask', 'IS_DATA'),\\n (FeatureType.MASK, 'CLM'),\\n (FeatureType.MASK, 'SCL'),\\n (FeatureType.MASK, 'SNW'),\\n (FeatureType.MASK, 'CLD'),\\n (FeatureType.DATA, 'CLP'),\\n (FeatureType.DATA, 'viewAzimuthMean', 'view_azimuth_mean'),\\n (FeatureType.DATA, 'sunAzimuthAngles'),\\n (FeatureType.DATA, 'sunZenithAngles')\\n ],\\n size=self.size,\\n maxcc=self.maxcc,\\n time_difference=self.time_difference,\\n data_collection=DataCollection.SENTINEL2_L2A,\\n max_threads=self.max_threads\\n )\\n\\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\\n\\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\\n is_data = eopatch[(FeatureType.MASK, 'IS_DATA')]\\n clm = eopatch[(FeatureType.MASK, 'CLM')]\\n scl = eopatch[(FeatureType.MASK, 'SCL')]\\n snw = eopatch[(FeatureType.MASK, 'SNW')]\\n cld = eopatch[(FeatureType.MASK, 'CLD')]\\n clp = eopatch[(FeatureType.DATA, 'CLP')]\\n view_azimuth_mean = eopatch[(FeatureType.DATA, 'view_azimuth_mean')]\\n sun_azimuth_angles = eopatch[(FeatureType.DATA, 'sunAzimuthAngles')]\\n sun_zenith_angles = eopatch[(FeatureType.DATA, 'sunZenithAngles')]\\n\\n self.assertTrue(np.allclose(array_stats(bands), [0.027, 0.0243, 0.0162]))\\n\\n width, height = self.size\\n self.assertTrue(bands.shape == (4, height, width, 3))\\n self.assertTrue(is_data.shape == (4, height, width, 1))\\n self.assertTrue(is_data.dtype == bool)\\n self.assertTrue(clm.shape == (4, height, width, 1))\\n self.assertTrue(clm.dtype == np.uint8)\\n self.assertTrue(scl.shape == (4, height, width, 1))\\n self.assertTrue(snw.shape == (4, height, width, 1))\\n self.assertTrue(cld.shape == (4, height, width, 1))\\n self.assertTrue(clp.shape == (4, height, width, 1))\\n self.assertTrue(view_azimuth_mean.shape == (4, height, width, 1))\\n self.assertTrue(sun_azimuth_angles.shape == (4, height, width, 1))\\n self.assertTrue(sun_zenith_angles.shape == (4, height, width, 1))\\n self.assertTrue(len(eopatch.timestamp) == 4)\",\n \"def target_temperature(self):\\n return self._boiler.setpoint\",\n \"def eye_during_period(self, time_period, alias):\\n\\t\\twith pd.get_store(self.input_object) as h5_file:\\n\\t\\t\\tperiod_block_nr = self.sample_in_block(sample = time_period[0], block_table = h5_file['%s/blocks'%alias])\\n\\t\\t\\teye = h5_file['%s/blocks'%alias]['eye_recorded'][period_block_nr]\\n\\t\\treturn eye\",\n \"def get_dataset(args):\\n\\n if args['experiment']['dataset'] == Dataset.mindsets:\\n xs, ys, cs = make_mindsets(mindset_sizes=args['dataset']['mindset_sizes'],\\n nb_questions=args['dataset']['nb_questions'],\\n nb_useless=args['dataset']['nb_useless'],\\n noise=args['dataset']['noise'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.questionnaire_likert:\\n xs, ys, cs = make_likert_questionnaire(nb_samples=args['dataset']['nb_samples'],\\n nb_features=args['dataset']['nb_features'],\\n nb_mindsets=args['dataset']['nb_mindsets'],\\n centers=args['dataset']['centers'],\\n range_answers=args['dataset']['range_answers'],\\n seed=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys, cs=cs)\\n\\n if args['experiment']['dataset'] == Dataset.retinal:\\n xs, ys = load_RETINAL(root_path=args['root_dir'],\\n nb_bins=args['dataset']['nb_bins'],\\n max_idx=args['dataset']['max_idx'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.moons:\\n xs, ys = make_moons(n_samples=args['dataset']['n_samples'],\\n noise=args['dataset']['noise'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.breast_cancer_wisconsin:\\n xs, ys = load_CANCER(args['dataset']['nb_bins'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.SBM:\\n A, ys, G = load_SBM(block_sizes=args['dataset']['block_sizes'],\\n p_in=args['dataset']['p'],\\n p_out=args['dataset']['q'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.gaussian_mixture:\\n xs, ys = make_blobs(n_samples=args['dataset']['blob_sizes'],\\n centers=args['dataset']['blob_centers'],\\n n_features=args['dataset']['blob_centers'],\\n cluster_std=args['dataset']['blob_variances'],\\n random_state=args['experiment']['seed'])\\n\\n return Data(xs=xs, ys=ys)\\n\\n if args['experiment']['dataset'] == Dataset.LFR:\\n A, ys, G = load_LFR(nb_nodes=args['dataset']['nb_nodes'],\\n tau1=args['dataset']['tau1'],\\n tau2=args['dataset']['tau2'],\\n mu=args['dataset']['mu'],\\n average_degree=args['dataset']['average_degree'],\\n min_community=args['dataset']['min_community'],\\n seed=args['experiment']['seed'])\\n\\n return Data(ys=ys, A=A, G=G)\\n\\n if args['experiment']['dataset'] == Dataset.wave:\\n df = pd.read_csv('datasets/waveform.csv')\\n xs = df[df.columns[:-1]].to_numpy()\\n ys = df[df.columns[-1]].to_numpy()\\n\\n return Data(xs=xs, ys=ys)\\n\\n raise ValueError('Wrong name for a dataset')\",\n \"def get_target_market():\\n return choice(GraphParameters.TARGET_MARKETS)\",\n \"def _get__target(self):\\n if self._saved_target is None:\\n self._saved_target = self._metadata.get_callable()()\\n \\n return self._saved_target\",\n \"def load_dataset(label, max_deltaR=None):\\n\\n # Files should be located in the datasets directory\\n particles = np.load(\\\"datasets/PARTICLES_\\\"+label+\\\".npy\\\", allow_pickle=True)\\n\\n with open(\\\"datasets/DROP_\\\"+label, 'rb') as pickle_file:\\n water = pickle.load(pickle_file)\\n\\n with open(\\\"datasets/INTERACTIONS_\\\"+label, 'rb') as pickle_file:\\n interactions = pickle.load(pickle_file)\\n\\n samples = particles_to_samples(particles)\\n\\n if max_deltaR is not None:\\n whr = water.deltaR.flatten() < max_deltaR\\n samples = samples[whr]\\n water.deltaR = water.deltaR[whr]\\n water.theta = water.theta[whr]\\n\\n return samples, water, interactions\",\n \"def get_target_entity(self):\\n return None\",\n \"def get_dataset(self) -> datasets.OpenMLDataset:\\n return datasets.get_dataset(self.dataset_id)\",\n \"def tempfeeder_exp():\\n global _tempfeeder_exp\\n if _tempfeeder_exp is None:\\n _tempfeeder_exp = UserLoads_Experiment(DATA_WITHOUT_DUPES_PATH)\\n return _tempfeeder_exp\",\n \"def get_health_data_and_ingest_into_sentinel(self):\\n self.pull_and_push_the_snapshot_data(\\n HEALTH_ENDPOINT, self.health_table_name, fields=MODIFIED_FIELDS\\n )\",\n \"def get_dataset(self):\\n\\n trainset = datasets.SVHN('datasets/SVHN/train/', split='train', transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.SVHN('datasets/SVHN/test/', split='test', transform=self.val_transforms,\\n target_transform=None, download=True)\\n extraset = datasets.SVHN('datasets/SVHN/extra', split='extra', transform=self.train_transforms,\\n target_transform=None, download=True)\\n\\n trainset = torch.utils.data.ConcatDataset([trainset, extraset])\\n\\n return trainset, valset\",\n \"def predict(\\n self,\\n targetSeries,\\n exogenousSeries=None\\n ):\\n pass\",\n \"def oversized_data_oss_target_arn(self) -> str:\\n return pulumi.get(self, \\\"oversized_data_oss_target_arn\\\")\",\n \"def oversized_data_oss_target_arn(self) -> str:\\n return pulumi.get(self, \\\"oversized_data_oss_target_arn\\\")\",\n \"def get_target(model, input_data, input_size=None):\\n prediction, target = query_model(model, input_data, input_size)\\n return target\",\n \"def storage_target(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"storage_target\\\")\",\n \"def get_seeds_null(samples,signal):\\n theta_seeds={}\\n if selected is None:\\n SRlist = range(self.N_SR)\\n theta_samples = samples[:,0,self.N_SR:].T\\n else:\\n SRlist = [selected] \\n theta_samples = samples[:,0,1].T\\n for i,x in zip(SRlist,theta_samples):\\n theta_MLE = x\\n theta_seeds['theta_{0}'.format(i)] = theta_MLE\\n return theta_seeds\",\n \"def _target(self, data):\\n index = self._get_index(data)\\n value = data[index]\\n return index, abs(value - data.mean())\",\n \"def __get_x_y_from_data(\\n logger, df, predictors, target):\\n if df is not None:\\n df_X = df[predictors]\\n df_Y = df[target]\\n return df_X, df_Y\",\n \"def get_feat_and_target(df, target):\\n x = df.drop(target, axis = 1)\\n y = df[target]\\n return x, y\",\n \"def target(self) -> pulumi.Input[float]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> pulumi.Input[float]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def get_data(dataset, **kwargs):\\n age_df = load_age_sample_from_mcmc_chains(dataset, **kwargs)\\n hr_df = load_hr()\\n age_df, hr_df = clean_data(age_df, hr_df)\\n snids = age_df.index.unique().tolist()\\n return snids, age_df, hr_df\",\n \"def _get_prediction_data(self) -> pd.DataFrame:\\n\\n prediction_data = self._data.loc[self._data.target == 'predict'].drop('target', axis=1)\\n prediction_data = prediction_data.drop('y_label', axis=1)\\n return prediction_data\",\n \"def target(self) -> np.ndarray:\\n return self._dist['target']\",\n \"def get_test(self, preprocess=False):\\n return self._dataset('test', self._directory, 'sharded_test_0shifted_affnist.tfrecords')\",\n \"def get_dataset(dataset_name):\\n if dataset_name == \\\"Iris\\\":\\n data = datasets.load_iris()\\n\\n elif dataset_name == \\\"Breast Cancer\\\":\\n data = datasets.load_breast_cancer()\\n\\n elif dataset_name == \\\"Wine Dataset\\\":\\n data = datasets.load_wine()\\n\\n elif dataset_name == \\\"MNIST\\\":\\n data = datasets.load_digits()\\n\\n #elif dataset_name == \\\"Boston Housing Price\\\":\\n # data = datasets.load_boston()\\n\\n X = data.data\\n y = data.target\\n\\n return X, y\",\n \"def iris():\\n return IrisDataset()\",\n \"def get_iris_data():\\n iris = datasets.load_iris()\\n data = iris[\\\"data\\\"]\\n target = iris[\\\"target\\\"]\\n\\n # Prepend the column of 1s for bias\\n N, M = data.shape\\n all_X = np.ones((N, M + 1))\\n all_X[:, 1:] = data\\n\\n # Convert into one-hot vectors\\n num_labels = len(np.unique(target))\\n all_Y = np.eye(num_labels)[target]\\n return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)\",\n \"def get_dataset(self):\\n\\n trainset = datasets.MNIST('datasets/MNIST/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.MNIST('datasets/MNIST/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def generate_data(data, samples, targeted=True, start=0, inception=True):\\n \\n assert (targeted==True and start==0 and inception==True)\\n \\n \\n inputs = []\\n targets = []\\n \\n '''\\n for i in range(samples):\\n if targeted:\\n if inception:\\n seq = random.sample(range(1,1001), 10)\\n else:\\n seq = range(data.test_labels.shape[1])\\n\\n for j in seq:\\n if (j == np.argmax(data.test_labels[start+i])) and (inception == False):\\n continue\\n inputs.append(data.test_data[start+i])\\n targets.append(np.eye(data.test_labels.shape[1])[j])\\n else:\\n inputs.append(data.test_data[start+i])\\n targets.append(data.test_labels[start+i])\\n\\n inputs = np.array(inputs)\\n targets = np.array(targets)\\n '''\\n\\n return inputs, targets\",\n \"def prepare_data():\\n #data, label = load_ta_data(), load_ta_target()\\n data, label = load_own_data(), load_own_target()\\n tra_x, tst_x = split_samples(data)\\n tra_y, tst_y = split_samples(label)\\n return (tra_x, tst_x, tra_y, tst_y)\",\n \"def soleDataPoint(self):\\n dps = self.datapoints()\\n if dps:\\n return dps[0]\",\n \"def _target(self, data):\\n relative_values = abs(data - data.mean())\\n index = relative_values.idxmax()\\n value = relative_values[index]\\n return index, value\",\n \"def temp_holdout(y_panel_df, val_periods):\\n val = y_panel_df.groupby('unique_id').tail(val_periods)\\n train = y_panel_df.groupby('unique_id').apply(lambda df: df.head(-val_periods)).reset_index(drop=True)\\n\\n return train, val\",\n \"def get_data(datadir):\\n return sklearn.datasets.fetch_california_housing(\\n datadir,\\n return_X_y=True)\",\n \"def get_return_operand_data(tensor, target_location):\\n if tensor is None: # potentially for s\\n return tensor\\n if target_location == 'cpu':\\n return tensor.to('cpu')\\n else: # already on device\\n return tensor.tensor\",\n \"def generate_data(data, model, samples, targeted=True, target_num=9, start=0, inception=False, seed=3, handpick=False ):\\n random.seed(seed)\\n inputs = []\\n targets = []\\n labels = []\\n true_ids = []\\n sample_set = []\\n\\n data_d = data.test_data\\n labels_d = data.test_labels\\n\\n if handpick:\\n if inception:\\n deck = list(range(0, 1500))\\n else:\\n deck = list(range(0, 10000))\\n random.shuffle(deck)\\n print('Handpicking')\\n\\n while (len(sample_set) < samples):\\n rand_int = deck.pop()\\n pred = model.model.predict(data_d[rand_int:rand_int + 1])\\n\\n if inception:\\n pred = np.reshape(pred, (labels_d[0:1].shape))\\n\\n if (np.argmax(pred, 1) == np.argmax(labels_d[rand_int:rand_int + 1], 1)):\\n sample_set.append(rand_int)\\n print('Handpicked')\\n else:\\n sample_set = random.sample(range(0, 10000), samples)\\n\\n for i in sample_set:\\n if targeted:\\n if inception:\\n seq = random.sample(range(1, 1001), target_num)\\n else:\\n seq = range(labels_d.shape[1])\\n\\n for j in seq:\\n if (j == np.argmax(labels_d[start + i])) and (inception == False):\\n continue\\n inputs.append(data_d[start + i])\\n targets.append(np.eye(labels_d.shape[1])[j])\\n labels.append(labels_d[start + i])\\n true_ids.append(start + i)\\n else:\\n inputs.append(data_d[start + i])\\n targets.append(labels_d[start + i])\\n labels.append(labels_d[start + i])\\n true_ids.append(start + i)\\n\\n inputs = np.array(inputs)\\n targets = np.array(targets)\\n labels = np.array(labels)\\n true_ids = np.array(true_ids)\\n return inputs, targets, labels, true_ids\",\n \"def load_data():\\n \\n data = datasets.load_iris()\\n df = pd.DataFrame(data.data, columns = data.feature_names)\\n df2 = pd.DataFrame(data.target, columns = [\\\"target\\\"])\\n\\n return df.join(df2)\",\n \"def test_single_null(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dset = f.create_dataset('x', (1,), dtype='i1')\\n out = dset[()]\\n assert isinstance(out, np.ndarray)\\n assert out.shape == (1,)\",\n \"def target_temperature_low(self):\\n if self._client.mode == self._client.MODE_AUTO:\\n return self._client.heattemp\\n return None\",\n \"def source_exercise_target(self, node):\\n std_domain = self.builder.env.domains['std']\\n figtype = std_domain.get_enumerable_node_type(node.parent)\\n assert figtype == 'solution'\\n\\n fig_id = node.parent['ids'][0]\\n\\n # sort out the label\\n exercise_label = node.parent.attributes['exercise']\\n\\n names = node.parent['names']\\n assert len(names) == 1\\n assert names[0].startswith('sol:')\\n\\n # get exercise id\\n assert fig_id.startswith('sol-')\\n exercise_id = 'ex-{}'.format(fig_id[4:])\\n assert exercise_id == nodes.make_id(exercise_label)\\n\\n # because the exercise may be in a different document, we go global\\n all_labels = std_domain.data['labels']\\n assert exercise_label in all_labels\\n\\n # track down the document and identifier\\n exercise_source_docname = all_labels[exercise_label][0]\\n fig_identifiers = self.builder.env.toc_fignumbers\\n assert exercise_source_docname in fig_identifiers\\n assert 'exercise' in fig_identifiers[exercise_source_docname]\\n ex_docname_map = fig_identifiers[exercise_source_docname]['exercise']\\n assert exercise_id in ex_docname_map\\n\\n fignumber = ex_docname_map[exercise_id]\\n\\n return exercise_source_docname, exercise_id, fignumber\",\n \"def _get_dataset_bin(self):\\n df_raw = pd.read_csv(self.path_bin)\\n # kick all features we don't want\\n features = get_features_from_questions(self.path_questions, remove_cond=True)\\n features.append(self.target)\\n df = df_raw[features]\\n return df\",\n \"def source(self) -> DatasetSource:\\n return self._source\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def get_replica_bottom(self):\\n return self.dummy_row_insts[0].uy()\",\n \"def get_halo(self, index):\\n try:\\n halo = self.data.loc[index]\\n except KeyError:\\n raise IndexError(\\n \\\"Halo id %d not found in %s\\\" % (index, self.filename)\\n )\\n return halo\",\n \"def get_halo(self, index):\\n try:\\n halo = self.data.loc[index]\\n except KeyError:\\n raise IndexError(\\n \\\"Halo id %d not found in %s\\\" % (index, self.filename)\\n )\\n return halo\",\n \"def Experiment(self, default=None):\\n return self.data.get('experiment', default)\",\n \"def get_Ei_point(self, source_point, target_point):\\n Ei = 0.0\\n source_patch_mask = self.source_patch_masks[source_point]\\n source_patch = self.source_patches[source_point]\\n source_patch_unknown_overlap_mask = np.bitwise_and(source_patch_mask, self.unknown_mask)\\n target_patch_mask = self.target_patch_masks[target_point]\\n # try:\\n if source_patch_unknown_overlap_mask.any():\\n test_img = self.img.copy()\\n test_img[target_patch_mask] = source_patch\\n Ei = self.get_norm_ssd(self.img, source_patch)\\n return Ei\",\n \"def get_dataset(self):\\n trainset = datasets.KMNIST('datasets/KMNIST/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.KMNIST('datasets/KMNIST/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def X_test(self):\\n return self.test.drop(self.target_param, axis=1)\",\n \"def get_minlplib_instancedata(target_filename=None):\\n if target_filename is None:\\n target_filename = os.path.join(os.getcwd(), 'minlplib', 'instancedata.csv')\\n download_dir = os.path.dirname(target_filename)\\n\\n if os.path.exists(target_filename):\\n raise ValueError('A file named {filename} already exists.'.format(filename=target_filename))\\n \\n if not os.path.exists(download_dir):\\n os.makedirs(download_dir)\\n\\n downloader = download.FileDownloader()\\n downloader.set_destination_filename(target_filename)\\n downloader.get_text_file('http://www.minlplib.org/instancedata.csv')\",\n \"def X_train(self):\\n return self.train.drop(self.target_param, axis=1)\",\n \"def target_set_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"target_set_id\\\")\",\n \"def backward_sample(self, target):\\n return self.flow.bijector.inverse(target)\",\n \"def _get_target_data_generators(self, ephase, list_iterstore, dict_iterstore):\\n X1_gen = None\\n X2_gen = None\\n if (ephase == NNModelPhase.PREDICT or ephase == NNModelPhase.TEST):\\n # Iterstore for dbparam1, TE_OUT\\n X1_gen = list_iterstore[0].setdefault(Dataset.TE_OUT, None)\\n\\n return X1_gen, X2_gen\",\n \"def get_spike_data():\\n neuron_spikes = []\\n first = 0\\n last = 0\\n for i, file in enumerate(os.listdir(\\\"\\\"\\\"/Users/markusekvall/Desktop/\\n final_entropy_model/Spike_trains\\\"\\\"\\\")):\\n if file.endswith(\\\".mat\\\"):\\n if file[0] == \\\".\\\":\\n continue\\n else:\\n x = sio.loadmat(os.path.join(\\\"./Spike_trains\\\", file))\\n cluster_class = x[\\\"cluster_class\\\"]\\n # Set to ==2 if you want the second cluster\\n idx = [cluster_class[:, 0] == 1]\\n spike = cluster_class[idx[0], 1]\\n if np.size(spike) != 0:\\n if max(spike) > last:\\n last = max(spike)\\n if min(spike) > last:\\n first = min(spike)\\n neuron_spikes.append(spike)\\n return neuron_spikes, first, last\",\n \"def target_temperature_low(self):\\n return self._device.setpoint_heat\",\n \"def test_train_test_split_uni_exo(load_uni_exo_data_target):\\n data, target = load_uni_exo_data_target\\n\\n ####################################\\n #### Continuous fh without Gaps ####\\n ####################################\\n\\n #### Integer fh ----\\n exp = TSForecastingExperiment()\\n fh = 12\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test.index == data.iloc[-fh:].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(1, 10) # 9 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [1, 2, 3, 4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #################################\\n #### Continuous fh with Gaps ####\\n #################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(7, 13) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n ####################################\\n #### Discontinuous fh with Gaps ####\\n ####################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6, 10, 11, 12]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\",\n \"def _get_data_for_tests():\\n X = np.random.randn(100, input_dim)\\n Y = np.random.randn(100, output_dim)\\n X_new = np.random.randn(100, input_dim)\\n return X, X_new, Y\",\n \"def get_lindark(self, instrument, detector):\\n files = self.lindark_list[instrument][detector]\\n if len(files) == 1:\\n return files[0]\\n elif len(files) > 1:\\n rand_index = np.random.randint(0, len(files) - 1)\\n return files[rand_index]\\n else:\\n return None\",\n \"def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \\r\\n \\r\\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\\r\\n \\r\\n if dropnan==False:\\r\\n series.replace(pd.np.nan,0,inplace=True)\\r\\n \\r\\n # Dropping other variables:\\r\\n if dropVars:\\r\\n index = list(np.arange(series.shape[1]-2,\\r\\n series.shape[1]-len(predictors)-2,\\r\\n -1))\\r\\n \\r\\n labels = [item for idx,item in enumerate(series.columns) \\r\\n if idx in index]\\r\\n \\r\\n #print(\\\"Eliminando variáveis: {}\\\".format(labels))\\r\\n series.drop(labels,axis=1,inplace=True) \\r\\n \\r\\n return series\",\n \"def target_temperature(self):\\n return self.atag.dhw_target_temperature\",\n \"def _get_training_dataset(self):\\n\\n return tf.data.Dataset.zip((self.conditioned_dataset, self.dataset))\",\n \"def readMNISTData():\\n mnist = input_data.read_data_sets(\\\"MNIST_data\\\",one_hot=True) \\n return mnist\",\n \"def sample(self):\\n\\n\\t\\t# get newest sample\\n\\t\\ts = self.eyetribe.sample()\\n\\t\\t\\n\\t\\t# invalid data\\n\\t\\tif s == (None,None):\\n\\t\\t\\treturn (-1,-1)\\n\\t\\t\\n\\t\\t# check if the new sample is the same as the previous\\n\\t\\tif s != self.prevsample:\\n\\t\\t\\t# update the current sample\\n\\t\\t\\tself.prevsample = copy.copy(s)\\n\\t\\t\\n\\t\\treturn self.prevsample\",\n \"def get_datasetID(self):\\n\\t\\treturn self.prDoc['inputs']['data'][0]['datasetID']\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7770308","0.7121358","0.64517236","0.6015566","0.54749423","0.5369748","0.5230045","0.52188426","0.5049947","0.49974164","0.49770722","0.49149367","0.48960745","0.48660335","0.4858429","0.47984567","0.47667906","0.47544086","0.47335964","0.47325793","0.46791846","0.4668791","0.46662125","0.46469977","0.46438655","0.4628731","0.46123934","0.460944","0.4598078","0.45970562","0.45901757","0.4585056","0.45438892","0.45357984","0.45345905","0.45211202","0.45140848","0.4506973","0.4506973","0.45047247","0.44956857","0.44907573","0.44827965","0.447413","0.44597465","0.44531342","0.44531342","0.44477782","0.44348568","0.4417495","0.4415941","0.44123286","0.4410562","0.44057","0.44017914","0.43900174","0.437544","0.43737555","0.43686855","0.43638825","0.43597183","0.43583703","0.43583605","0.4357378","0.43565822","0.43537807","0.43537575","0.43530893","0.43522254","0.43514997","0.43514997","0.43514997","0.43514997","0.43514997","0.43514997","0.43514997","0.43514997","0.43499583","0.4348487","0.4348487","0.43473965","0.43463838","0.4337903","0.43356016","0.43247035","0.43246782","0.4317414","0.4316694","0.43159518","0.4314955","0.43138444","0.43010524","0.42961842","0.4292327","0.42903158","0.42867243","0.42838496","0.42785677","0.42751104","0.4273477"],"string":"[\n \"0.7770308\",\n \"0.7121358\",\n \"0.64517236\",\n \"0.6015566\",\n \"0.54749423\",\n \"0.5369748\",\n \"0.5230045\",\n \"0.52188426\",\n \"0.5049947\",\n \"0.49974164\",\n \"0.49770722\",\n \"0.49149367\",\n \"0.48960745\",\n \"0.48660335\",\n \"0.4858429\",\n \"0.47984567\",\n \"0.47667906\",\n \"0.47544086\",\n \"0.47335964\",\n \"0.47325793\",\n \"0.46791846\",\n \"0.4668791\",\n \"0.46662125\",\n \"0.46469977\",\n \"0.46438655\",\n \"0.4628731\",\n \"0.46123934\",\n \"0.460944\",\n \"0.4598078\",\n \"0.45970562\",\n \"0.45901757\",\n \"0.4585056\",\n \"0.45438892\",\n \"0.45357984\",\n \"0.45345905\",\n \"0.45211202\",\n \"0.45140848\",\n \"0.4506973\",\n \"0.4506973\",\n \"0.45047247\",\n \"0.44956857\",\n \"0.44907573\",\n \"0.44827965\",\n \"0.447413\",\n \"0.44597465\",\n \"0.44531342\",\n \"0.44531342\",\n \"0.44477782\",\n \"0.44348568\",\n \"0.4417495\",\n \"0.4415941\",\n \"0.44123286\",\n \"0.4410562\",\n \"0.44057\",\n \"0.44017914\",\n \"0.43900174\",\n \"0.437544\",\n \"0.43737555\",\n \"0.43686855\",\n \"0.43638825\",\n \"0.43597183\",\n \"0.43583703\",\n \"0.43583605\",\n \"0.4357378\",\n \"0.43565822\",\n \"0.43537807\",\n \"0.43537575\",\n \"0.43530893\",\n \"0.43522254\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43514997\",\n \"0.43499583\",\n \"0.4348487\",\n \"0.4348487\",\n \"0.43473965\",\n \"0.43463838\",\n \"0.4337903\",\n \"0.43356016\",\n \"0.43247035\",\n \"0.43246782\",\n \"0.4317414\",\n \"0.4316694\",\n \"0.43159518\",\n \"0.4314955\",\n \"0.43138444\",\n \"0.43010524\",\n \"0.42961842\",\n \"0.4292327\",\n \"0.42903158\",\n \"0.42867243\",\n \"0.42838496\",\n \"0.42785677\",\n \"0.42751104\",\n \"0.4273477\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84920114"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18482,"cells":{"query":{"kind":"string","value":"Helper method for retrieving train/validation sentinel parameters"},"document":{"kind":"string","value":"def _dataset_sentinel_helper(self):\n params = self.cross_experiment_key.parameters\n return dict(\n dataset_hash=params[\"train_dataset\"],\n cv_type=params[\"cross_experiment_params\"].get(\n \"cv_type\", params[\"cross_experiment_params\"].get(\"cross_validation_type\", None)\n ),\n global_random_seed=params[\"cross_experiment_params\"][\"global_random_seed\"],\n random_seeds=params[\"cross_experiment_params\"][\"random_seeds\"],\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def default_training_params():\n N_EPOCHS = 100\n BATCH_SIZE = 64\n EPSILON = 0.0001\n return N_EPOCHS, BATCH_SIZE, EPSILON","def set_training_params(use_defaults):\n if use_defaults:\n n_epochs, batch_size, epsilon = default_training_params()\n return n_epochs, batch_size, epsilon\n\n print (\"Select number of epochs to train (default 100):\")\n n_epochs = int(input())\n print (\"Select batch size (default 64):\")\n batch_size = int(input())\n print (\"Select learning rate (default 0.0001):\")\n epsilon = float(input())\n return n_epochs, batch_size, epsilon","def feed_training_args(self):\n return {}","def getParams(self):\n return self.trainError, self.trainAcc, self.w","def getDefaultParameterValues(self):\r\n dct = {}\r\n self.initializeRoadRunnerModel()\r\n self.roadrunnerModel.reset()\r\n for parameterName in self.parametersToFit:\r\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\r\n return dct","def init_params_on_input(self, train_valid_iterator: TrainValidIterator) -> dict:\n suggested_params = copy(self.default_params)\n task = train_valid_iterator.train.task\n\n assert \"sklearn\" in task.losses, \"Sklearn loss should be defined\"\n\n if task.name == \"reg\":\n # suggested_params['cs'] = list(map(lambda x: 1 / (2 * x), suggested_params['cs']))\n suggested_params[\"cs\"] = [1 / (2 * i) for i in suggested_params[\"cs\"]]\n\n return suggested_params","def add_train_val_arguments(self):\n self.add_train_arguments()\n self.add_val_arguments()","def required_parameters(self):\n return ['seed', 'run_params']","def _training_params(self):\n if isinstance(\n self.lyapunov_hybrid_system.system,\n feedback_system.FeedbackSystem) and self.search_controller:\n # For a feedback system, we train both the Lyapunov network\n # parameters and the controller network parameters.\n training_params = list(\n self.lyapunov_hybrid_system.lyapunov_relu.parameters(\n )) + self.lyapunov_hybrid_system.system.controller_variables(\n ) + self.R_options.variables()\n else:\n training_params = \\\n list(self.lyapunov_hybrid_system.lyapunov_relu.parameters()) +\\\n self.R_options.variables()\n return training_params","def _initial_setup(self, **train_kwargs):\n super(NetworkValidationBase, self)._initial_setup(**train_kwargs)","def input_config():\n run_dir = 'runs/ODEMnistClassification/8'\n epoch = 'latest'\n device = 'cpu'\n min_end_time = 10\n max_end_time = 100\n tol = 1e-3","def test_text_classifier_get_params(self):\n pass","def _check_model_params(self):\n enc_type = self.configs['encoder_type']\n if enc_type not in ['uni', 'bi', 'gnmt']:\n raise ValueError(\"encoder type must be one of ['uni', 'bi', 'gnmt'].\")\n\n attn = self.configs['attention']\n if attn not in ['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau']:\n raise ValueError(\n \"attention must be one of \"\n \"['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau'].\")\n\n num_enc_layers = self.configs['num_encoder_layers']\n num_dec_layers = self.configs['num_decoder_layers']\n if not num_enc_layers or not num_dec_layers:\n raise ValueError(\n \"num_encoder_layers and num_decoder_layers must be greater than 0.\")\n if num_enc_layers != num_dec_layers:\n self.configs['pass_hidden_state'] = False\n\n if enc_type == \"bi\" and num_enc_layers % 2 != 0:\n raise ValueError(\n \"num_encoder_layers must be even when encoder_type is %s.\" % enc_type)\n\n attn_arch = self.configs.get('attention_architecture', None)\n if attn_arch in [\"gnmt\"] and num_enc_layers < 2:\n raise ValueError(\"For gnmt attention architecture, \"\n \"num_encoder_layers: %d should be >= 2.\" %\n num_enc_layers)\n\n infer_mode = self.configs['infer_mode']\n beam_width = self.configs.get(\"beam_width\", 0)\n if infer_mode == \"beam_search\" and beam_width <= 0:\n raise ValueError(\"beam_width must be > 0 if infer_mode is `beam_search`.\")\n\n sample_temp = self.configs.get(\"sampling_temperature\", 0.0)\n if infer_mode == \"sample\" and sample_temp <= 0.0:\n raise ValueError(\n \"sampling_temperature must greater than 0.0 using sample decode.\")\n\n subword_option = self.configs['subword_option']\n if subword_option not in ['', 'bpe', 'spm']:\n raise ValueError(\"subword_option must be one of ['','bpe','spm']\")\n\n num_enc_residual_layers = 0\n num_dec_residual_layers = 0\n if self.configs['residual']:\n if num_enc_layers > 1:\n num_enc_residual_layers = num_enc_layers - 1\n if num_dec_layers > 1:\n num_dec_residual_layers = num_dec_layers - 1\n\n if enc_type == \"gnmt\":\n num_enc_residual_layers = num_enc_layers - 2\n if num_enc_layers == num_dec_layers:\n num_dec_residual_layers = num_enc_residual_layers\n\n self.configs['num_encoder_residual_layers'] = num_enc_residual_layers\n self.configs['num_decoder_residual_layers'] = num_dec_residual_layers","def experiment_params():\n exp = {\n 'lr': [1e-3],\n 'loss_function': ['cce'],\n 'optimizer': ['nadam'],\n 'dataset': [\n # 'curv_contour_length_9',\n 'curv_contour_length_14',\n # 'curv_baseline',\n ]\n }\n exp['data_augmentations'] = [\n [\n 'grayscale',\n 'left_right',\n 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'resize',\n # 'per_image_standardization',\n 'zero_one'\n ]]\n exp['val_augmentations'] = exp['data_augmentations']\n exp['batch_size'] = 32 # Train/val batch size.\n exp['epochs'] = 16\n exp['exp_name'] = 'hgru_bn_pathfinder_14'\n exp['model_name'] = 'hgru'\n # exp['clip_gradients'] = 7.\n exp['save_weights'] = True\n exp['validation_iters'] = 1000\n exp['num_validation_evals'] = 50\n exp['shuffle_val'] = True # Shuffle val data.\n exp['shuffle_train'] = True\n return exp","def __init__(self, \n training_epochs):\n self.training_epochs = training_epochs","def _default_parameters():\n\n return {\n 'opt': 'adadelta',\n 'activation_function': 'softmax',\n 'lr': 0.0001,\n 'decay': 1e-6,\n 'loss': 'categorical_crossentropy',\n 'batch_size': 32,\n 'nb_epoch': 20,\n 'shuffle': True,\n 'momentum': 0.9,\n 'nesterov': True,\n 'rho': 0.95,\n 'epsilon': 1e-08,\n 'beta_1': 0.9,\n 'beta_2': 0.999,\n 'horizontal_flip': False,\n 'im_size': 240,#256,\n 'dense_layer': 1024,\n 'nb_classes': 10,\n 'nb_channels': 3,\n 'dropout': 0.5,\n 'metrics': ['accuracy'],\n 'volume': None,\n 'input_size': 25,\n 'temporal': False,\n 'input_dim': 512,\n 'nb_frames': 60,\n 'stride': 16,\n 'nb_hidden':512,\n 'lstm': False\n\n }","def parameters(self):\n return self.trainer_parameters","def parameters(self):\n return self.trainer_parameters","def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())","def _get_current_training_params(self):\n params = {}\n params[\"lyap_relu_params\"] = copy.deepcopy(\n self.lyapunov_hybrid_system.lyapunov_relu.state_dict())\n if not self.R_options.fixed_R:\n params[\"R_params\"] = self.R_options._variables.clone()\n if isinstance(self.lyapunov_hybrid_system.system,\n feedback_system.FeedbackSystem):\n params[\"controller_params\"] = copy.deepcopy(\n self.lyapunov_hybrid_system.system.controller_network.\n state_dict())\n return params","def __get_x_y_from_training_validation(\n logger, training, validation, predictors, target):\n if training is not None:\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\n if validation is not None:\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\n return training_X, training_Y, validation_X, validation_Y","def training_info(self):\n pass","def _default_params(self) -> dict[str, Any]:\n return {\n \"max_tokens\": self.max_tokens,\n \"temperature\": self.temperature,\n \"top_p\": self.top_p,\n \"logprobs\": self.logprobs,\n \"echo\": self.echo,\n \"stop_sequences\": self.stop_sequences,\n \"repeat_penalty\": self.repeat_penalty,\n \"top_k\": self.top_k,\n \"n_threads\": self.n_threads,\n \"n_ctx\": self.n_ctx,\n \"n_gpu_layers\": self.n_gpu_layers,\n \"n_gqa\": self.n_gqa if self.n_gqa else None,\n \"n_parts\": self.n_parts,\n \"seed\": self.seed,\n \"f16_kv\": self.f16_kv,\n \"logits_all\": self.logits_all,\n \"vocab_only\": self.vocab_only,\n \"use_mlock\": self.use_mlock,\n \"n_batch\": self.n_batch,\n \"last_n_tokens_size\": self.last_n_tokens_size,\n \"streaming\": self.streaming,\n }","def default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n params['frequency'] = 1\n params['seed'] = 0\n params['save'] = None\n params['load'] = None\n params['plot'] = None\n\n return params","def _initialize_early_stopping(self):\n try:\n self.x_valid = self.early_stopping_params['x_valid']\n if type(self.x_valid) != np.ndarray:\n msg = 'Invalid type for input x_valid. Expected ndarray, got {}'.format(type(self.x_valid))\n logger.error(msg)\n raise ValueError(msg)\n\n if self.x_valid.ndim <= 1 or (np.shape(self.x_valid)[1]) != 3:\n msg = 'Invalid size for input x_valid. Expected (n,3): got {}'.format(np.shape(self.x_valid))\n logger.error(msg)\n raise ValueError(msg)\n self.x_valid = to_idx(self.x_valid, ent_to_idx=self.ent_to_idx, rel_to_idx=self.rel_to_idx)\n\n except KeyError:\n msg = 'x_valid must be passed for early fitting.'\n logger.error(msg)\n raise KeyError(msg)\n\n self.early_stopping_criteria = self.early_stopping_params.get('criteria', DEFAULT_CRITERIA_EARLY_STOPPING)\n if self.early_stopping_criteria not in ['hits10', 'hits1', 'hits3', 'mrr']:\n msg = 'Unsupported early stopping criteria.'\n logger.error(msg)\n raise ValueError(msg)\n \n self.eval_config['corruption_entities'] = self.early_stopping_params.get('corruption_entities', \n DEFAULT_CORRUPTION_ENTITIES)\n self.eval_config['corrupt_side'] = self.early_stopping_params.get('corrupt_side', DEFAULT_CORRUPT_SIDE)\n\n self.early_stopping_best_value = INITIAL_EARLY_STOPPING_CRITERIA_VALUE\n self.early_stopping_stop_counter = 0\n try:\n x_filter = self.early_stopping_params['x_filter']\n x_filter = to_idx(x_filter, ent_to_idx=self.ent_to_idx, rel_to_idx=self.rel_to_idx)\n self.set_filter_for_eval(x_filter)\n except KeyError:\n logger.debug('x_filter not found in early_stopping_params.')\n pass\n\n self._initialize_eval_graph()","def experiment_params():\n exp = {\n 'lr': [1e-3],\n 'loss_function': ['cce'],\n 'optimizer': ['nadam'],\n 'dataset': [\n # 'curv_contour_length_9',\n 'cluttered_nist_ix1',\n # 'curv_baseline',\n ]\n }\n exp['data_augmentations'] = [\n [\n 'grayscale',\n 'center_crop',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n exp['val_augmentations'] = [\n [\n 'grayscale',\n 'center_crop',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n exp['batch_size'] = 32 # Train/val batch size.\n exp['epochs'] = 4\n exp['model_name'] = 'unet'\n exp['exp_name'] = exp['model_name'] + '_' + exp['dataset'][0]\n exp['save_weights'] = True\n exp['validation_iters'] = 1000\n exp['num_validation_evals'] = 200\n exp['shuffle_val'] = True # Shuffle val data.\n exp['shuffle_train'] = True\n return exp","def set_hyperparams(use_defaults):\n if use_defaults:\n n_neurons, n_hidden, n_steps, k_prob = default_hyperparams()\n return n_neurons, n_hidden, n_steps, k_prob\n\n print (\"Select number of neurons in recurrent layer (default \" +\n \"100):\")\n n_neurons = int(input())\n print (\"Select number of hidden neurons in fully connected \" +\n \"layer (default 100):\")\n n_hidden = int(input())\n print (\"Select n_steps; the max number of words to be read \" +\n \"from each abstract (default 50):\")\n n_steps = int(input())\n print (\"Select k_prob; the dropout probability (default 0.5):\")\n k_prob = float(input())\n\n return n_neurons, n_hidden, n_steps, k_prob","def _get_fitted_params(self):\n return {}","def train_with_validation_provided(self, features, labels, validation_features, validation_labels):\n pass","def resetParams(self):\n self.prediction = cons.init_pred # Classifier payoff - initialized to a constant initial payoff value\n self.error = cons.init_err # Classifier error - initialized to a constant initial error value\n self.fitness = cons.init_fit # Classifier fitness - initialized to a constant initial fitness value","def _save_trained_params(self):\n self.trained_model_params = self.sess_train.run([self.ent_emb, self.rel_emb])","def test_KNN_test_parameter(params, X_train, X_test, y_train, y_test):","def get_parameters(self):\n params = {\"train_frac\": self.train_frac, \"split_alg\": self.split_alg,\n \"nw_name\": self._nw_name, \"split_id\": self.split_id}\n return params","def _get_trainable_params(model):\n trainable_params = []\n for x in model.parameters():\n if x.requires_grad:\n trainable_params.append(x)\n return trainable_params","def train(self, x={}, **kwargs):\n return 0","def _init_node_parm(self, key):\n wf_net_conf = WorkFlowNetConfML(key)\n self.model_path = wf_net_conf.model_path\n self.ml_class = wf_net_conf.ml_class\n self.config = wf_net_conf.config\n self.batch_size = 10000\n self.model_type = wf_net_conf.model_type\n\n #Todo 어떻게 꺼내는지 승우씨한테 물어볼것\n _wf_data_conf = wf_data_conf(key.split('_')[0]+'_'+key.split('_')[1]+'_'+'dataconf_node')\n self.data_conf = _wf_data_conf.conf\n self.label = _wf_data_conf.label\n self.cell_feature = _wf_data_conf.cell_feature\n self.cross_cell = _wf_data_conf.cross_cell\n self.extend_cell_feature = _wf_data_conf.extend_cell_feature\n self.label_values = _wf_data_conf.label_values\n\n _wf_data_node = wf_data_node(key.split('_')[0] + '_' + key.split('_')[1] + '_' + 'data_node')\n self.multi_read_flag = _wf_data_node.multi_node_flag\n self.predict_path = _wf_data_node.predict_path","def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())","def create_training(logits):\r\n \r\n\r\n return train_op, loss, label_ph","def parameters(self) -> Dict[str, Any]:\n return self.trainer_parameters","def get_inner_training_args(self, eval=False):\n inner_training_args = TrainArgs(\n min_steps=self.num_inner_steps if not eval else self.num_inner_steps_eval,\n max_steps=self.num_inner_steps if not eval else self.num_inner_steps_eval,\n log_every_n_steps=self.log_every_n_steps,\n weights_summary=None, # Do not show model summary\n progress_bar_refresh_rate=0 # Do not show training progress bar\n )\n if torch.cuda.is_available():\n inner_training_args.kwargs['gpus'] = 1\n return inner_training_args","def get_parameters():\n \n tf.set_random_seed(1) #so that your \"random\" numbers match ours\n sess = tf.Session() \n #First let's load meta graph and restore weights\n saver = tf.train.import_meta_graph(ckpt_dir + '/trained_model.meta')\n saver.restore(sess, tf.train.latest_checkpoint(ckpt_dir))\n\n #print all tensor name\n #print([n.name for n in graph.as_graph_def().node]) \n #extract parameters from saved session\n W1 = sess.run(\"W1:0\")\n b1 = sess.run(\"b1:0\")\n W2 = sess.run(\"W2:0\")\n b2 = sess.run(\"b2:0\")\n W3 = sess.run(\"W3:0\")\n b3 = sess.run(\"b3:0\") \n\n parameters = {\"W1\": W1,\n \"b1\": b1,\n \"W2\": W2,\n \"b2\": b2,\n \"W3\": W3,\n \"b3\": b3}\n \n return parameters, sess","def _check_restore_parameters(sess, saver):\r\n ckpt = tf.train.get_checkpoint_state(os.path.dirname( 'final_model/'))\r\n if ckpt and ckpt.model_checkpoint_path:\r\n print(\"Loading parameters for the Chatbot -> {}\".format(ckpt.model_checkpoint_path))\r\n saver.restore(sess, ckpt.model_checkpoint_path)\r\n\r\n else:\r\n print(\"Initializing fresh parameters for the Chatbot\")","def test_fetch_model_parameters(self):\n resp = self.app.get('/api/v1/model-parameters?model-name=HemlockRandomForestClassifier')\n data = json.loads(resp.data)\n self.assertTrue('n_estimators' in data.get('parameters'))","def __init__(self, inp: VarFeeder, features: Iterable[tf.Tensor], n_pages: int, mode: ModelMode, n_epoch=None,\n batch_size=127, runs_in_burst=1, verbose=True, predict_window=60, train_window=500,\n train_completeness_threshold=1, predict_completeness_threshold=1, back_offset=0,\n train_skip_first=0, rand_seed=None):\n self.n_pages = n_pages\n self.inp = inp\n self.batch_size = batch_size\n self.rand_seed = rand_seed\n self.back_offset = back_offset\n if verbose:\n print(\"Mode:%s, data days:%d, Data start:%s, data end:%s, features end:%s \" % (\n mode, inp.data_days, inp.data_start, inp.data_end, inp.features_end))\n\n if mode == ModelMode.TRAIN:\n # reserve predict_window at the end for validation\n assert inp.data_days - predict_window > predict_window + train_window, \\\n \"Predict+train window length (+predict window for validation) is larger than total number of days in dataset\"\n self.start_offset = train_skip_first\n elif mode == ModelMode.TRAIN_SKIP_PREDICT:\n assert inp.data_days >= predict_window + train_window, \"Predict+train window length is larger than total number of days in dataset\"\n self.start_offset = train_skip_first\n elif mode == ModelMode.EVAL or mode == ModelMode.PREDICT:\n self.start_offset = inp.data_days - train_window - back_offset\n if verbose:\n train_start = inp.data_start + pd.Timedelta(self.start_offset, 'D')\n eval_start = train_start + pd.Timedelta(train_window, 'D')\n end = eval_start + pd.Timedelta(predict_window - 1, 'D')\n print(\"Train start %s, predict start %s, end %s\" % (train_start, eval_start, end))\n assert self.start_offset >= 0\n\n self.train_window = train_window\n self.predict_window = predict_window\n self.attn_window = train_window - predict_window + 1\n self.max_train_empty = int(round(train_window * (1 - train_completeness_threshold)))\n self.max_predict_empty = int(round(predict_window * (1 - predict_completeness_threshold)))\n self.mode = mode\n self.verbose = verbose\n\n # Reserve more processing threads for eval/predict because of larger batches\n num_threads = 3 if mode == ModelMode.TRAIN else 6\n\n # Choose right cutter function for current ModelMode\n cutter = {ModelMode.TRAIN: self.cut_train,\n ModelMode.TRAIN_SKIP_PREDICT: self.cut_train_skip_predict,\n ModelMode.EVAL: self.cut_eval,\n ModelMode.PREDICT: self.cut_eval}\n # Create dataset, transform features and assemble batches\n root_ds = tf.data.Dataset.from_tensor_slices(tuple(features)).repeat(n_epoch)\n batch = (root_ds\n .map(cutter[mode])\n .filter(self.reject_filter)\n .map(self.make_features, num_parallel_calls=num_threads)\n .batch(batch_size)\n .prefetch(runs_in_burst * 2)\n )\n\n self.iterator = batch.make_initializable_iterator()\n it_tensors = self.iterator.get_next()\n\n # Assign all tensors to class variables\n self.true_x, self.time_x, self.norm_x, self.lagged_x, self.true_y, self.time_y, self.norm_y, self.norm_mean, \\\n self.norm_std, self.ucdoc_features, self.page_ix = it_tensors\n\n self.encoder_features_depth = self.time_x.shape[2].value","def _training_vars(self):\n self.vars = dict()\n # Temperature params\n self.vars['TInit'] = -1\n self.vars['TDecayRate'] = 0.05\n # Bowl params\n self.vars['q_init'] = 16.58 # initial strength for the bowl\n self.vars['q_max'] = 150.\n #self.vars['q_rate'] = 10.\n # Check if we can improve learning, adjusting this value\n self.vars['bowl_center'] = 0.4\n self.vars['bowl_strength'] = None\n self.vars['beta_min_offset'] = 2\n # Time step params\n self.vars['max_dt'] = 0.01\n self.vars['min_dt'] = 0.0005\n self.vars['dt'] = 0.009\n # Training traces\n self.vars['prev_s'] = None\n self.vars['Harmony_trace'] = None\n self.vars['speed_trace'] = None\n self.vars['ema_trace'] = None\n self.vars['lambda_trace'] = None\n self.vars['temp_trace'] = None\n self.vars['TP_trace'] = None\n self.vars['TPnum_trace'] = None\n self.vars['TP_h_trace'] = None\n self.vars['TP_dist_trace'] = None\n self.vars['S_trace'] = None\n\n if self.custom_settings is not None:\n for key, value in self.custom_settings.items():\n if key in self.vars:\n self.vars[key] = value","def get_fixed_params():\n fixed_params = {\n 'total_time_steps': 8 * 24,\n 'num_encoder_steps': 7 * 24,\n 'num_epochs': 100,\n 'early_stopping_patience': 5,\n 'multiprocessing_workers': 5\n }\n return fixed_params","def set_params(self, params: Dict):\n\n if params['training_instances'] is not None:\n self.training_instances = params['training_instances']\n if params['n'] is not None:\n self.n = params['n']\n if params['lda'] is not None:\n self.lda = params['lda']\n if params['verbose'] is not None:\n self.verbose = params['verbose']\n\n self.num_features = self.training_instances[0].get_feature_count()\n self.w = None\n self.b = None","def read_test_parameters():\n DBHelperRaw.get_test_data_bunch() # return DBHelper.test_exposure_sequences \n return DBHelperRaw.test_exposure_sequences","def define_tr_val(self, inputs):\n ####\n #print(\"Hayden edit: parameters: \" + str(parameters))\n #print(\"Hayden edit: fixed parameters: \" + str(self.fixed_parameters))\n #print(\"Hayden edit: free parameters: \" + str(self.free_parameters))\n ####\n \n start_index, random_seed = inputs[\"start_index\"], inputs[\"random_seed\"]\n train_stop_index = start_index + self.train_length\n validate_stop_index = train_stop_index + self.validate_length\n \n # Get samples\n if self.ODE_order:\n train_y = None\n validate_y = None\n else:\n train_y = self.y[start_index: train_stop_index]\n validate_y = self.y[train_stop_index: validate_stop_index]\n \n\n if not self.x is None:\n train_x = self.x[start_index: train_stop_index]\n validate_x = self.x[train_stop_index: validate_stop_index]\n \n else:\n train_x = None\n validate_x = None\n\n return {\"tr_x\" : train_x, \"tr_y\": train_y, \"val_x\": validate_x, \"val_y\" : validate_y }","def test_training(self):\n\t\tpass","def __validate__(self):\n if self.train:\n assert self.random is not None","def _get_lstm_parameters(self, X_train):\n # Add one extra element for the special symbol zero\n self.max_n_span_types = np.max(X_train) + 1\n self.trace_size = max(len(i) for i in X_train)","def _evaluate_model_parameters(self, session):\n logger.info('There are no model specific operation evaluation!')","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def training_config(**overrides):\n config = base_training_config()\n _override(config, overrides)\n return tf.contrib.training.HParams(**config)","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def prepare_train_validation(self) -> Tuple:\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\n\n Xt = self.dataset.prepare_input_samples(Xt)\n Yt = self.dataset.prepare_output_samples(Yt)\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\n traindataset = traindataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n Xv = self.dataset.prepare_input_samples(Xv)\n Yv = self.dataset.prepare_output_samples(Yv)\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\n validdataset = validdataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n return traindataset, validdataset","def test_get_params():\n model = BERTopic()\n params = model.get_params()\n assert not params[\"embedding_model\"]\n assert not params[\"low_memory\"]\n assert not params[\"nr_topics\"]\n assert params[\"n_gram_range\"] == (1, 1)\n assert params[\"min_topic_size\"] == 10\n assert params[\"language\"] == 'english'","def input_fn(params, is_training):\n features = tf.constant(0)\n labels = tf.constant(0)\n\n return features, labels","def Params(cls):\n p = super(LmInput, cls).Params()\n p.Define('fixed_input_shape', False, 'Fixed input shape or not.')\n p.tokenizer = tokenizers.AsciiTokenizer.Params()\n return p","def params_for_checkpoint(self):\n # We need real int types, not numpy.int64, not recognized by json\n # dumps.\n params = {\n 'nb_features_in': int(self.nb_features_in),\n 'nb_features_out': int(self.nb_features_out),\n 'key': self.key\n }\n return params","def test_text_classifier_set_params(self):\n pass","def get_params(self):\n return {'threshold': self.threshold,\n 'subsample': self.subsample,\n 'estimator': self.estimator,\n 'n_folds': self.n_folds,\n 'stratify': self.stratify,\n 'random_state': self.random_state,\n 'n_jobs': self.n_jobs}","def _reset_parameters(self):\n if self.cfg.initial_forget_bias is not None:\n self.hindcast_lstm.bias_hh_l0.data[self.cfg.hidden_size:2 * self.cfg.hidden_size] = self.cfg.initial_forget_bias","def test_intent_classifier_get_params(self):\n pass","def nms_params(self):\n return [0, self.num_classes]","def nms_params(self):\n return [0, self.num_classes]","def training_opts(self):\n return self._training_opts","def training_step(self, batch):\n return {}","def feed_dict(training_data=True):\n if training_data:\n xs, ys = mnist.train.next_batch(batch_size)\n kp = keep_prob\n else:\n xs, ys = mnist.validation.images, mnist.validation.labels\n kp = 1.0\n return {x: xs, t: ys, k: kp}","def _starting_hparams():\n hparams = contrib_training.HParams()\n hparams.add_hparam('batch_style', 'bucket')\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\n hparams.add_hparam('learning_rate', 0.0005)\n hparams.add_hparam('lr_decay_rate', .997)\n hparams.add_hparam('lr_decay_steps', 1000)\n hparams.add_hparam('lr_warmup_steps', 3000)\n hparams.add_hparam('model_type', 'cnn')\n hparams.add_hparam('resnet_bottleneck_factor', 0.5)\n hparams.add_hparam('decision_threshold', 0.5)\n hparams.add_hparam('denominator_power', 1.0) # Standard mean-pooling.\n return hparams","def fit_config(rnd: int) -> Dict[str, str]:\n config = {\n \"epoch_global\": str(rnd),\n \"epochs\": str(1),\n \"batch_size\": str(32),\n \"learning_rate\": str(0.001),\n }\n return config","def _initialize_parameters(self):\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k * 2],\n initializer=self.initializer)\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k * 2],\n initializer=self.initializer)","def fixture_wrapper_arguments():\n n_features = 9\n classes = [\"a\", \"b\", \"c\"]\n\n return n_features, classes","def __init__(self, epochs, **kwargs):\n if epochs == -1:\n self.layer_ls = []\n self.thick_ls = []\n self.max_ret_ls = []\n self.layer_ls = [0] * epochs\n self.thick_ls = [0] * epochs\n self.max_ret_ls = [0] * epochs\n self.kwargs = kwargs\n self.current_e = 0","def test_early_stopping_no_val_step(tmpdir):\n\n class CurrentModel(EvalModelTemplate):\n def training_step(self, *args, **kwargs):\n output = super().training_step(*args, **kwargs)\n output.update({'my_train_metric': output['loss']}) # could be anything else\n return output\n\n model = CurrentModel()\n model.validation_step = None\n model.val_dataloader = None\n\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\n trainer = Trainer(\n default_root_dir=tmpdir,\n early_stop_callback=stopping,\n overfit_pct=0.20,\n max_epochs=5,\n )\n result = trainer.fit(model)\n\n assert result == 1, 'training failed to complete'\n assert trainer.current_epoch < trainer.max_epochs","def get_init_params(self):\n return PgInput.get_init_params(self) + [EnstoreTapeDriveInput.VERSION_ATTRIBUTE,\n 'InputMinInterval', 'InputDelay', 'CheckpointRollback']","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 0.3\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def _validate_parameters(self, epochs, log_interval):\n\n if not epochs > 0:\n msg = (\n \"The number of training epochs = {} should be strictly\"\n \" positive.\"\n )\n self.logger.error(msg.format(epochs))\n raise ValueError(msg.format(epochs))\n\n if not log_interval > 0:\n msg = (\n \"The number of batches to wait before printting the\"\n \" training status should be strictly positive, but got {}\"\n \" instead.\"\n )\n self.logger.error(msg.format(log_interval))\n raise ValueError(msg.format(log_interval))\n\n if not 0 < self.shrinkage_rate <= 1:\n msg = (\n \"The shrinkage rate should be in the range (0, 1], but got\"\n \" {} instead.\"\n )\n self.logger.error(msg.format(self.shrinkage_rate))\n raise ValueError(msg.format(self.shrinkage_rate))","def _initialize_parameters(self):\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k],\n initializer=self.initializer)\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k],\n initializer=self.initializer)","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 1e-7\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def _prep_data(self, x_train):\n batch_size = x_train.shape[0]\n x_neg = self.encoder.predict_on_batch(x_train)\n y_neg = np.zeros(batch_size)\n x_pos = self.std_dev * np.random.randn(batch_size,\n self.latent_dim) + self.mu\n y_pos = np.ones(batch_size)\n if self.joint_train:\n y_train = [x_train, y_pos]\n else:\n y_train = x_train\n\n return (x_neg, y_neg, x_pos, y_pos, y_train)","def __init__(self, x_train, model):\n self.x_train = x_train\n self.model = model","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def prepare_data(self, *args, **kwargs) -> None:\n if not (self.root / \"train\").exists():\n raise ValueError(\n f\"Please download and set ImageNet-1k train data under {self.root}.\"\n )\n if not (self.root / \"val\").exists():\n raise ValueError(\n f\"Please download and set ImageNet-1k val data under {self.root}.\"\n )","def __init__(self):\n # Number of examples per epoch of training data.\n self.num_examples_per_epoch = None \n\n # Optimizer for training the model.\n self.optimizer = \"SGD\" #default \"SGD\"\n\n # Learning rate for the initial phase of training.\n self.initial_learning_rate = 2.0 # default 2.0\n self.learning_rate_decay_factor = 0.8\n self.num_epochs_per_decay = 4 #default 8\n\n # If not None, clip gradients to this value.\n self.clip_gradients = 5.0\n\n # How many model checkpoints to keep.\n self.max_checkpoints_to_keep = 2","def stopping_criteria(self):\n stop_training = False\n if self.training_stopping_criteria == \"early_stopping\":\n stop_training = self.early_stopping()\n elif self.training_stopping_criteria == 'SGDR_ensemble':\n T_0 = self.scheduler_params['T_0']\n if self.step_scheduler_after == 'batch':\n T_0 = T_0/self.num_train_iterations\n print(f\"sgdr T_0={T_0}\")\n\n T_mult = self.scheduler_params['T_mult']\n N = self.stopping_criteria_params['N']\n M = self.stopping_criteria_params['M']\n possible_epoch = [T_0*math.pow(T_mult, i) for i in range(N)] \n possible_epoch = np.cumsum(possible_epoch)\n snapshot_epochs = possible_epoch[-M:]\n print(snapshot_epochs)\n\n if self.current_epoch in snapshot_epochs:\n print(\"warm restarted learning rate\")\n print(f\"saving model={self.checkpoint_snapshot} out of {M}\")\n model_path = os.path.join(path_checkpoints_dir, \n f\"{self.experiment_tag}_snapshot_{self.checkpoint_snapshot}_epoch_{self.current_epoch}.pth\")\n self.checkpoint_snapshot += 1\n self.save_checkpoint(model_path=model_path)\n\n if self.current_epoch == possible_epoch[-1]:\n stop_training = True\n\n elif self.training_stopping_criteria == \"max_epoch\":\n if self.current_epoch == self.stopping_criteria_params['max_epoch']: \n stop_training = True\n self.save_checkpoint()\n \n return stop_training","def get_optional_params():\n return {\n 'regularizer': None, # any valid TensorFlow regularizer\n 'regularizer_params': dict,\n 'initializer': None, # any valid TensorFlow initializer\n 'initializer_params': dict,\n 'dtype': [tf.float32, tf.float16, 'mixed'],\n }","def _get_parameters(self):\n return None","def make_train_state(save_path, early_stopping_patience):\n return {'stop_early': False,\n 'early_stopping_step': 0,\n 'early_stopping_patience': early_stopping_patience,\n 'steps': 0,\n 'min_val_loss': float('Inf'),\n 'train_loss': [],\n 'val_loss': [],\n 'save_path': save_path}","def _get_init_fn():\n exclusions = []\n if FLAGS.checkpoint_exclude_scopes:\n exclusions = [scope.strip()\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\n\n variables_to_restore = []\n for var in slim.get_model_variables():\n excluded = False\n for exclusion in exclusions:\n if var.op.name.startswith(exclusion):\n excluded = True\n break\n if not excluded:\n variables_to_restore.append(var)\n\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\n else:\n checkpoint_path = FLAGS.checkpoint_path\n\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\n\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)","def Params(cls):\n p = super().Params()\n p.Define('train_task', None, 'Underlying task')\n p.Define('decode_task', None, 'Underlying task')\n p.Define('train_dataset_name', None, '')\n p.Define('decode_dataset_name', None, '')\n p.Define('train_steps_per_loop', 0, '')\n p.Define('decode_steps_per_loop', 0, '')\n return p","def get_params(self):","def get_meta_training_args(self, frac_done: float):\n return TrainArgs(\n meta_learning_rate=frac_done * self.meta_learning_rate_final + \\\n (1 - frac_done) * self.meta_learning_rate_initial\n )","def train(self)->None:"],"string":"[\n \"def default_training_params():\\n N_EPOCHS = 100\\n BATCH_SIZE = 64\\n EPSILON = 0.0001\\n return N_EPOCHS, BATCH_SIZE, EPSILON\",\n \"def set_training_params(use_defaults):\\n if use_defaults:\\n n_epochs, batch_size, epsilon = default_training_params()\\n return n_epochs, batch_size, epsilon\\n\\n print (\\\"Select number of epochs to train (default 100):\\\")\\n n_epochs = int(input())\\n print (\\\"Select batch size (default 64):\\\")\\n batch_size = int(input())\\n print (\\\"Select learning rate (default 0.0001):\\\")\\n epsilon = float(input())\\n return n_epochs, batch_size, epsilon\",\n \"def feed_training_args(self):\\n return {}\",\n \"def getParams(self):\\n return self.trainError, self.trainAcc, self.w\",\n \"def getDefaultParameterValues(self):\\r\\n dct = {}\\r\\n self.initializeRoadRunnerModel()\\r\\n self.roadrunnerModel.reset()\\r\\n for parameterName in self.parametersToFit:\\r\\n dct[parameterName] = self.roadrunnerModel.model[parameterName]\\r\\n return dct\",\n \"def init_params_on_input(self, train_valid_iterator: TrainValidIterator) -> dict:\\n suggested_params = copy(self.default_params)\\n task = train_valid_iterator.train.task\\n\\n assert \\\"sklearn\\\" in task.losses, \\\"Sklearn loss should be defined\\\"\\n\\n if task.name == \\\"reg\\\":\\n # suggested_params['cs'] = list(map(lambda x: 1 / (2 * x), suggested_params['cs']))\\n suggested_params[\\\"cs\\\"] = [1 / (2 * i) for i in suggested_params[\\\"cs\\\"]]\\n\\n return suggested_params\",\n \"def add_train_val_arguments(self):\\n self.add_train_arguments()\\n self.add_val_arguments()\",\n \"def required_parameters(self):\\n return ['seed', 'run_params']\",\n \"def _training_params(self):\\n if isinstance(\\n self.lyapunov_hybrid_system.system,\\n feedback_system.FeedbackSystem) and self.search_controller:\\n # For a feedback system, we train both the Lyapunov network\\n # parameters and the controller network parameters.\\n training_params = list(\\n self.lyapunov_hybrid_system.lyapunov_relu.parameters(\\n )) + self.lyapunov_hybrid_system.system.controller_variables(\\n ) + self.R_options.variables()\\n else:\\n training_params = \\\\\\n list(self.lyapunov_hybrid_system.lyapunov_relu.parameters()) +\\\\\\n self.R_options.variables()\\n return training_params\",\n \"def _initial_setup(self, **train_kwargs):\\n super(NetworkValidationBase, self)._initial_setup(**train_kwargs)\",\n \"def input_config():\\n run_dir = 'runs/ODEMnistClassification/8'\\n epoch = 'latest'\\n device = 'cpu'\\n min_end_time = 10\\n max_end_time = 100\\n tol = 1e-3\",\n \"def test_text_classifier_get_params(self):\\n pass\",\n \"def _check_model_params(self):\\n enc_type = self.configs['encoder_type']\\n if enc_type not in ['uni', 'bi', 'gnmt']:\\n raise ValueError(\\\"encoder type must be one of ['uni', 'bi', 'gnmt'].\\\")\\n\\n attn = self.configs['attention']\\n if attn not in ['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau']:\\n raise ValueError(\\n \\\"attention must be one of \\\"\\n \\\"['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau'].\\\")\\n\\n num_enc_layers = self.configs['num_encoder_layers']\\n num_dec_layers = self.configs['num_decoder_layers']\\n if not num_enc_layers or not num_dec_layers:\\n raise ValueError(\\n \\\"num_encoder_layers and num_decoder_layers must be greater than 0.\\\")\\n if num_enc_layers != num_dec_layers:\\n self.configs['pass_hidden_state'] = False\\n\\n if enc_type == \\\"bi\\\" and num_enc_layers % 2 != 0:\\n raise ValueError(\\n \\\"num_encoder_layers must be even when encoder_type is %s.\\\" % enc_type)\\n\\n attn_arch = self.configs.get('attention_architecture', None)\\n if attn_arch in [\\\"gnmt\\\"] and num_enc_layers < 2:\\n raise ValueError(\\\"For gnmt attention architecture, \\\"\\n \\\"num_encoder_layers: %d should be >= 2.\\\" %\\n num_enc_layers)\\n\\n infer_mode = self.configs['infer_mode']\\n beam_width = self.configs.get(\\\"beam_width\\\", 0)\\n if infer_mode == \\\"beam_search\\\" and beam_width <= 0:\\n raise ValueError(\\\"beam_width must be > 0 if infer_mode is `beam_search`.\\\")\\n\\n sample_temp = self.configs.get(\\\"sampling_temperature\\\", 0.0)\\n if infer_mode == \\\"sample\\\" and sample_temp <= 0.0:\\n raise ValueError(\\n \\\"sampling_temperature must greater than 0.0 using sample decode.\\\")\\n\\n subword_option = self.configs['subword_option']\\n if subword_option not in ['', 'bpe', 'spm']:\\n raise ValueError(\\\"subword_option must be one of ['','bpe','spm']\\\")\\n\\n num_enc_residual_layers = 0\\n num_dec_residual_layers = 0\\n if self.configs['residual']:\\n if num_enc_layers > 1:\\n num_enc_residual_layers = num_enc_layers - 1\\n if num_dec_layers > 1:\\n num_dec_residual_layers = num_dec_layers - 1\\n\\n if enc_type == \\\"gnmt\\\":\\n num_enc_residual_layers = num_enc_layers - 2\\n if num_enc_layers == num_dec_layers:\\n num_dec_residual_layers = num_enc_residual_layers\\n\\n self.configs['num_encoder_residual_layers'] = num_enc_residual_layers\\n self.configs['num_decoder_residual_layers'] = num_dec_residual_layers\",\n \"def experiment_params():\\n exp = {\\n 'lr': [1e-3],\\n 'loss_function': ['cce'],\\n 'optimizer': ['nadam'],\\n 'dataset': [\\n # 'curv_contour_length_9',\\n 'curv_contour_length_14',\\n # 'curv_baseline',\\n ]\\n }\\n exp['data_augmentations'] = [\\n [\\n 'grayscale',\\n 'left_right',\\n 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'resize',\\n # 'per_image_standardization',\\n 'zero_one'\\n ]]\\n exp['val_augmentations'] = exp['data_augmentations']\\n exp['batch_size'] = 32 # Train/val batch size.\\n exp['epochs'] = 16\\n exp['exp_name'] = 'hgru_bn_pathfinder_14'\\n exp['model_name'] = 'hgru'\\n # exp['clip_gradients'] = 7.\\n exp['save_weights'] = True\\n exp['validation_iters'] = 1000\\n exp['num_validation_evals'] = 50\\n exp['shuffle_val'] = True # Shuffle val data.\\n exp['shuffle_train'] = True\\n return exp\",\n \"def __init__(self, \\n training_epochs):\\n self.training_epochs = training_epochs\",\n \"def _default_parameters():\\n\\n return {\\n 'opt': 'adadelta',\\n 'activation_function': 'softmax',\\n 'lr': 0.0001,\\n 'decay': 1e-6,\\n 'loss': 'categorical_crossentropy',\\n 'batch_size': 32,\\n 'nb_epoch': 20,\\n 'shuffle': True,\\n 'momentum': 0.9,\\n 'nesterov': True,\\n 'rho': 0.95,\\n 'epsilon': 1e-08,\\n 'beta_1': 0.9,\\n 'beta_2': 0.999,\\n 'horizontal_flip': False,\\n 'im_size': 240,#256,\\n 'dense_layer': 1024,\\n 'nb_classes': 10,\\n 'nb_channels': 3,\\n 'dropout': 0.5,\\n 'metrics': ['accuracy'],\\n 'volume': None,\\n 'input_size': 25,\\n 'temporal': False,\\n 'input_dim': 512,\\n 'nb_frames': 60,\\n 'stride': 16,\\n 'nb_hidden':512,\\n 'lstm': False\\n\\n }\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def validation_input(self):\\n return DatasetSentinel(\\\"validation_input\\\", **self._dataset_sentinel_helper())\",\n \"def _get_current_training_params(self):\\n params = {}\\n params[\\\"lyap_relu_params\\\"] = copy.deepcopy(\\n self.lyapunov_hybrid_system.lyapunov_relu.state_dict())\\n if not self.R_options.fixed_R:\\n params[\\\"R_params\\\"] = self.R_options._variables.clone()\\n if isinstance(self.lyapunov_hybrid_system.system,\\n feedback_system.FeedbackSystem):\\n params[\\\"controller_params\\\"] = copy.deepcopy(\\n self.lyapunov_hybrid_system.system.controller_network.\\n state_dict())\\n return params\",\n \"def __get_x_y_from_training_validation(\\n logger, training, validation, predictors, target):\\n if training is not None:\\n training_X, training_Y = __get_x_y_from_data(logger, training, predictors, target)\\n if validation is not None:\\n validation_X, validation_Y = __get_x_y_from_data(logger, validation, predictors, target)\\n return training_X, training_Y, validation_X, validation_Y\",\n \"def training_info(self):\\n pass\",\n \"def _default_params(self) -> dict[str, Any]:\\n return {\\n \\\"max_tokens\\\": self.max_tokens,\\n \\\"temperature\\\": self.temperature,\\n \\\"top_p\\\": self.top_p,\\n \\\"logprobs\\\": self.logprobs,\\n \\\"echo\\\": self.echo,\\n \\\"stop_sequences\\\": self.stop_sequences,\\n \\\"repeat_penalty\\\": self.repeat_penalty,\\n \\\"top_k\\\": self.top_k,\\n \\\"n_threads\\\": self.n_threads,\\n \\\"n_ctx\\\": self.n_ctx,\\n \\\"n_gpu_layers\\\": self.n_gpu_layers,\\n \\\"n_gqa\\\": self.n_gqa if self.n_gqa else None,\\n \\\"n_parts\\\": self.n_parts,\\n \\\"seed\\\": self.seed,\\n \\\"f16_kv\\\": self.f16_kv,\\n \\\"logits_all\\\": self.logits_all,\\n \\\"vocab_only\\\": self.vocab_only,\\n \\\"use_mlock\\\": self.use_mlock,\\n \\\"n_batch\\\": self.n_batch,\\n \\\"last_n_tokens_size\\\": self.last_n_tokens_size,\\n \\\"streaming\\\": self.streaming,\\n }\",\n \"def default_params():\\n params = {}\\n params['dataset'] = 'adult'\\n params['engines'] = ['MD','RDA']\\n params['iters'] = 10000\\n params['epsilon'] = 1.0\\n params['delta'] = 0.0\\n params['bounded'] = True\\n params['frequency'] = 1\\n params['seed'] = 0\\n params['save'] = None\\n params['load'] = None\\n params['plot'] = None\\n\\n return params\",\n \"def _initialize_early_stopping(self):\\n try:\\n self.x_valid = self.early_stopping_params['x_valid']\\n if type(self.x_valid) != np.ndarray:\\n msg = 'Invalid type for input x_valid. Expected ndarray, got {}'.format(type(self.x_valid))\\n logger.error(msg)\\n raise ValueError(msg)\\n\\n if self.x_valid.ndim <= 1 or (np.shape(self.x_valid)[1]) != 3:\\n msg = 'Invalid size for input x_valid. Expected (n,3): got {}'.format(np.shape(self.x_valid))\\n logger.error(msg)\\n raise ValueError(msg)\\n self.x_valid = to_idx(self.x_valid, ent_to_idx=self.ent_to_idx, rel_to_idx=self.rel_to_idx)\\n\\n except KeyError:\\n msg = 'x_valid must be passed for early fitting.'\\n logger.error(msg)\\n raise KeyError(msg)\\n\\n self.early_stopping_criteria = self.early_stopping_params.get('criteria', DEFAULT_CRITERIA_EARLY_STOPPING)\\n if self.early_stopping_criteria not in ['hits10', 'hits1', 'hits3', 'mrr']:\\n msg = 'Unsupported early stopping criteria.'\\n logger.error(msg)\\n raise ValueError(msg)\\n \\n self.eval_config['corruption_entities'] = self.early_stopping_params.get('corruption_entities', \\n DEFAULT_CORRUPTION_ENTITIES)\\n self.eval_config['corrupt_side'] = self.early_stopping_params.get('corrupt_side', DEFAULT_CORRUPT_SIDE)\\n\\n self.early_stopping_best_value = INITIAL_EARLY_STOPPING_CRITERIA_VALUE\\n self.early_stopping_stop_counter = 0\\n try:\\n x_filter = self.early_stopping_params['x_filter']\\n x_filter = to_idx(x_filter, ent_to_idx=self.ent_to_idx, rel_to_idx=self.rel_to_idx)\\n self.set_filter_for_eval(x_filter)\\n except KeyError:\\n logger.debug('x_filter not found in early_stopping_params.')\\n pass\\n\\n self._initialize_eval_graph()\",\n \"def experiment_params():\\n exp = {\\n 'lr': [1e-3],\\n 'loss_function': ['cce'],\\n 'optimizer': ['nadam'],\\n 'dataset': [\\n # 'curv_contour_length_9',\\n 'cluttered_nist_ix1',\\n # 'curv_baseline',\\n ]\\n }\\n exp['data_augmentations'] = [\\n [\\n 'grayscale',\\n 'center_crop',\\n # 'left_right',\\n # 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'zero_one'\\n ]]\\n exp['val_augmentations'] = [\\n [\\n 'grayscale',\\n 'center_crop',\\n # 'left_right',\\n # 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'zero_one'\\n ]]\\n exp['batch_size'] = 32 # Train/val batch size.\\n exp['epochs'] = 4\\n exp['model_name'] = 'unet'\\n exp['exp_name'] = exp['model_name'] + '_' + exp['dataset'][0]\\n exp['save_weights'] = True\\n exp['validation_iters'] = 1000\\n exp['num_validation_evals'] = 200\\n exp['shuffle_val'] = True # Shuffle val data.\\n exp['shuffle_train'] = True\\n return exp\",\n \"def set_hyperparams(use_defaults):\\n if use_defaults:\\n n_neurons, n_hidden, n_steps, k_prob = default_hyperparams()\\n return n_neurons, n_hidden, n_steps, k_prob\\n\\n print (\\\"Select number of neurons in recurrent layer (default \\\" +\\n \\\"100):\\\")\\n n_neurons = int(input())\\n print (\\\"Select number of hidden neurons in fully connected \\\" +\\n \\\"layer (default 100):\\\")\\n n_hidden = int(input())\\n print (\\\"Select n_steps; the max number of words to be read \\\" +\\n \\\"from each abstract (default 50):\\\")\\n n_steps = int(input())\\n print (\\\"Select k_prob; the dropout probability (default 0.5):\\\")\\n k_prob = float(input())\\n\\n return n_neurons, n_hidden, n_steps, k_prob\",\n \"def _get_fitted_params(self):\\n return {}\",\n \"def train_with_validation_provided(self, features, labels, validation_features, validation_labels):\\n pass\",\n \"def resetParams(self):\\n self.prediction = cons.init_pred # Classifier payoff - initialized to a constant initial payoff value\\n self.error = cons.init_err # Classifier error - initialized to a constant initial error value\\n self.fitness = cons.init_fit # Classifier fitness - initialized to a constant initial fitness value\",\n \"def _save_trained_params(self):\\n self.trained_model_params = self.sess_train.run([self.ent_emb, self.rel_emb])\",\n \"def test_KNN_test_parameter(params, X_train, X_test, y_train, y_test):\",\n \"def get_parameters(self):\\n params = {\\\"train_frac\\\": self.train_frac, \\\"split_alg\\\": self.split_alg,\\n \\\"nw_name\\\": self._nw_name, \\\"split_id\\\": self.split_id}\\n return params\",\n \"def _get_trainable_params(model):\\n trainable_params = []\\n for x in model.parameters():\\n if x.requires_grad:\\n trainable_params.append(x)\\n return trainable_params\",\n \"def train(self, x={}, **kwargs):\\n return 0\",\n \"def _init_node_parm(self, key):\\n wf_net_conf = WorkFlowNetConfML(key)\\n self.model_path = wf_net_conf.model_path\\n self.ml_class = wf_net_conf.ml_class\\n self.config = wf_net_conf.config\\n self.batch_size = 10000\\n self.model_type = wf_net_conf.model_type\\n\\n #Todo 어떻게 꺼내는지 승우씨한테 물어볼것\\n _wf_data_conf = wf_data_conf(key.split('_')[0]+'_'+key.split('_')[1]+'_'+'dataconf_node')\\n self.data_conf = _wf_data_conf.conf\\n self.label = _wf_data_conf.label\\n self.cell_feature = _wf_data_conf.cell_feature\\n self.cross_cell = _wf_data_conf.cross_cell\\n self.extend_cell_feature = _wf_data_conf.extend_cell_feature\\n self.label_values = _wf_data_conf.label_values\\n\\n _wf_data_node = wf_data_node(key.split('_')[0] + '_' + key.split('_')[1] + '_' + 'data_node')\\n self.multi_read_flag = _wf_data_node.multi_node_flag\\n self.predict_path = _wf_data_node.predict_path\",\n \"def train_input(self):\\n return DatasetSentinel(\\\"train_input\\\", **self._dataset_sentinel_helper())\",\n \"def create_training(logits):\\r\\n \\r\\n\\r\\n return train_op, loss, label_ph\",\n \"def parameters(self) -> Dict[str, Any]:\\n return self.trainer_parameters\",\n \"def get_inner_training_args(self, eval=False):\\n inner_training_args = TrainArgs(\\n min_steps=self.num_inner_steps if not eval else self.num_inner_steps_eval,\\n max_steps=self.num_inner_steps if not eval else self.num_inner_steps_eval,\\n log_every_n_steps=self.log_every_n_steps,\\n weights_summary=None, # Do not show model summary\\n progress_bar_refresh_rate=0 # Do not show training progress bar\\n )\\n if torch.cuda.is_available():\\n inner_training_args.kwargs['gpus'] = 1\\n return inner_training_args\",\n \"def get_parameters():\\n \\n tf.set_random_seed(1) #so that your \\\"random\\\" numbers match ours\\n sess = tf.Session() \\n #First let's load meta graph and restore weights\\n saver = tf.train.import_meta_graph(ckpt_dir + '/trained_model.meta')\\n saver.restore(sess, tf.train.latest_checkpoint(ckpt_dir))\\n\\n #print all tensor name\\n #print([n.name for n in graph.as_graph_def().node]) \\n #extract parameters from saved session\\n W1 = sess.run(\\\"W1:0\\\")\\n b1 = sess.run(\\\"b1:0\\\")\\n W2 = sess.run(\\\"W2:0\\\")\\n b2 = sess.run(\\\"b2:0\\\")\\n W3 = sess.run(\\\"W3:0\\\")\\n b3 = sess.run(\\\"b3:0\\\") \\n\\n parameters = {\\\"W1\\\": W1,\\n \\\"b1\\\": b1,\\n \\\"W2\\\": W2,\\n \\\"b2\\\": b2,\\n \\\"W3\\\": W3,\\n \\\"b3\\\": b3}\\n \\n return parameters, sess\",\n \"def _check_restore_parameters(sess, saver):\\r\\n ckpt = tf.train.get_checkpoint_state(os.path.dirname( 'final_model/'))\\r\\n if ckpt and ckpt.model_checkpoint_path:\\r\\n print(\\\"Loading parameters for the Chatbot -> {}\\\".format(ckpt.model_checkpoint_path))\\r\\n saver.restore(sess, ckpt.model_checkpoint_path)\\r\\n\\r\\n else:\\r\\n print(\\\"Initializing fresh parameters for the Chatbot\\\")\",\n \"def test_fetch_model_parameters(self):\\n resp = self.app.get('/api/v1/model-parameters?model-name=HemlockRandomForestClassifier')\\n data = json.loads(resp.data)\\n self.assertTrue('n_estimators' in data.get('parameters'))\",\n \"def __init__(self, inp: VarFeeder, features: Iterable[tf.Tensor], n_pages: int, mode: ModelMode, n_epoch=None,\\n batch_size=127, runs_in_burst=1, verbose=True, predict_window=60, train_window=500,\\n train_completeness_threshold=1, predict_completeness_threshold=1, back_offset=0,\\n train_skip_first=0, rand_seed=None):\\n self.n_pages = n_pages\\n self.inp = inp\\n self.batch_size = batch_size\\n self.rand_seed = rand_seed\\n self.back_offset = back_offset\\n if verbose:\\n print(\\\"Mode:%s, data days:%d, Data start:%s, data end:%s, features end:%s \\\" % (\\n mode, inp.data_days, inp.data_start, inp.data_end, inp.features_end))\\n\\n if mode == ModelMode.TRAIN:\\n # reserve predict_window at the end for validation\\n assert inp.data_days - predict_window > predict_window + train_window, \\\\\\n \\\"Predict+train window length (+predict window for validation) is larger than total number of days in dataset\\\"\\n self.start_offset = train_skip_first\\n elif mode == ModelMode.TRAIN_SKIP_PREDICT:\\n assert inp.data_days >= predict_window + train_window, \\\"Predict+train window length is larger than total number of days in dataset\\\"\\n self.start_offset = train_skip_first\\n elif mode == ModelMode.EVAL or mode == ModelMode.PREDICT:\\n self.start_offset = inp.data_days - train_window - back_offset\\n if verbose:\\n train_start = inp.data_start + pd.Timedelta(self.start_offset, 'D')\\n eval_start = train_start + pd.Timedelta(train_window, 'D')\\n end = eval_start + pd.Timedelta(predict_window - 1, 'D')\\n print(\\\"Train start %s, predict start %s, end %s\\\" % (train_start, eval_start, end))\\n assert self.start_offset >= 0\\n\\n self.train_window = train_window\\n self.predict_window = predict_window\\n self.attn_window = train_window - predict_window + 1\\n self.max_train_empty = int(round(train_window * (1 - train_completeness_threshold)))\\n self.max_predict_empty = int(round(predict_window * (1 - predict_completeness_threshold)))\\n self.mode = mode\\n self.verbose = verbose\\n\\n # Reserve more processing threads for eval/predict because of larger batches\\n num_threads = 3 if mode == ModelMode.TRAIN else 6\\n\\n # Choose right cutter function for current ModelMode\\n cutter = {ModelMode.TRAIN: self.cut_train,\\n ModelMode.TRAIN_SKIP_PREDICT: self.cut_train_skip_predict,\\n ModelMode.EVAL: self.cut_eval,\\n ModelMode.PREDICT: self.cut_eval}\\n # Create dataset, transform features and assemble batches\\n root_ds = tf.data.Dataset.from_tensor_slices(tuple(features)).repeat(n_epoch)\\n batch = (root_ds\\n .map(cutter[mode])\\n .filter(self.reject_filter)\\n .map(self.make_features, num_parallel_calls=num_threads)\\n .batch(batch_size)\\n .prefetch(runs_in_burst * 2)\\n )\\n\\n self.iterator = batch.make_initializable_iterator()\\n it_tensors = self.iterator.get_next()\\n\\n # Assign all tensors to class variables\\n self.true_x, self.time_x, self.norm_x, self.lagged_x, self.true_y, self.time_y, self.norm_y, self.norm_mean, \\\\\\n self.norm_std, self.ucdoc_features, self.page_ix = it_tensors\\n\\n self.encoder_features_depth = self.time_x.shape[2].value\",\n \"def _training_vars(self):\\n self.vars = dict()\\n # Temperature params\\n self.vars['TInit'] = -1\\n self.vars['TDecayRate'] = 0.05\\n # Bowl params\\n self.vars['q_init'] = 16.58 # initial strength for the bowl\\n self.vars['q_max'] = 150.\\n #self.vars['q_rate'] = 10.\\n # Check if we can improve learning, adjusting this value\\n self.vars['bowl_center'] = 0.4\\n self.vars['bowl_strength'] = None\\n self.vars['beta_min_offset'] = 2\\n # Time step params\\n self.vars['max_dt'] = 0.01\\n self.vars['min_dt'] = 0.0005\\n self.vars['dt'] = 0.009\\n # Training traces\\n self.vars['prev_s'] = None\\n self.vars['Harmony_trace'] = None\\n self.vars['speed_trace'] = None\\n self.vars['ema_trace'] = None\\n self.vars['lambda_trace'] = None\\n self.vars['temp_trace'] = None\\n self.vars['TP_trace'] = None\\n self.vars['TPnum_trace'] = None\\n self.vars['TP_h_trace'] = None\\n self.vars['TP_dist_trace'] = None\\n self.vars['S_trace'] = None\\n\\n if self.custom_settings is not None:\\n for key, value in self.custom_settings.items():\\n if key in self.vars:\\n self.vars[key] = value\",\n \"def get_fixed_params():\\n fixed_params = {\\n 'total_time_steps': 8 * 24,\\n 'num_encoder_steps': 7 * 24,\\n 'num_epochs': 100,\\n 'early_stopping_patience': 5,\\n 'multiprocessing_workers': 5\\n }\\n return fixed_params\",\n \"def set_params(self, params: Dict):\\n\\n if params['training_instances'] is not None:\\n self.training_instances = params['training_instances']\\n if params['n'] is not None:\\n self.n = params['n']\\n if params['lda'] is not None:\\n self.lda = params['lda']\\n if params['verbose'] is not None:\\n self.verbose = params['verbose']\\n\\n self.num_features = self.training_instances[0].get_feature_count()\\n self.w = None\\n self.b = None\",\n \"def read_test_parameters():\\n DBHelperRaw.get_test_data_bunch() # return DBHelper.test_exposure_sequences \\n return DBHelperRaw.test_exposure_sequences\",\n \"def define_tr_val(self, inputs):\\n ####\\n #print(\\\"Hayden edit: parameters: \\\" + str(parameters))\\n #print(\\\"Hayden edit: fixed parameters: \\\" + str(self.fixed_parameters))\\n #print(\\\"Hayden edit: free parameters: \\\" + str(self.free_parameters))\\n ####\\n \\n start_index, random_seed = inputs[\\\"start_index\\\"], inputs[\\\"random_seed\\\"]\\n train_stop_index = start_index + self.train_length\\n validate_stop_index = train_stop_index + self.validate_length\\n \\n # Get samples\\n if self.ODE_order:\\n train_y = None\\n validate_y = None\\n else:\\n train_y = self.y[start_index: train_stop_index]\\n validate_y = self.y[train_stop_index: validate_stop_index]\\n \\n\\n if not self.x is None:\\n train_x = self.x[start_index: train_stop_index]\\n validate_x = self.x[train_stop_index: validate_stop_index]\\n \\n else:\\n train_x = None\\n validate_x = None\\n\\n return {\\\"tr_x\\\" : train_x, \\\"tr_y\\\": train_y, \\\"val_x\\\": validate_x, \\\"val_y\\\" : validate_y }\",\n \"def test_training(self):\\n\\t\\tpass\",\n \"def __validate__(self):\\n if self.train:\\n assert self.random is not None\",\n \"def _get_lstm_parameters(self, X_train):\\n # Add one extra element for the special symbol zero\\n self.max_n_span_types = np.max(X_train) + 1\\n self.trace_size = max(len(i) for i in X_train)\",\n \"def _evaluate_model_parameters(self, session):\\n logger.info('There are no model specific operation evaluation!')\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def training_config(**overrides):\\n config = base_training_config()\\n _override(config, overrides)\\n return tf.contrib.training.HParams(**config)\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def prepare_train_validation(self) -> Tuple:\\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\\n\\n Xt = self.dataset.prepare_input_samples(Xt)\\n Yt = self.dataset.prepare_output_samples(Yt)\\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\\n traindataset = traindataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n Xv = self.dataset.prepare_input_samples(Xv)\\n Yv = self.dataset.prepare_output_samples(Yv)\\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\\n validdataset = validdataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n return traindataset, validdataset\",\n \"def test_get_params():\\n model = BERTopic()\\n params = model.get_params()\\n assert not params[\\\"embedding_model\\\"]\\n assert not params[\\\"low_memory\\\"]\\n assert not params[\\\"nr_topics\\\"]\\n assert params[\\\"n_gram_range\\\"] == (1, 1)\\n assert params[\\\"min_topic_size\\\"] == 10\\n assert params[\\\"language\\\"] == 'english'\",\n \"def input_fn(params, is_training):\\n features = tf.constant(0)\\n labels = tf.constant(0)\\n\\n return features, labels\",\n \"def Params(cls):\\n p = super(LmInput, cls).Params()\\n p.Define('fixed_input_shape', False, 'Fixed input shape or not.')\\n p.tokenizer = tokenizers.AsciiTokenizer.Params()\\n return p\",\n \"def params_for_checkpoint(self):\\n # We need real int types, not numpy.int64, not recognized by json\\n # dumps.\\n params = {\\n 'nb_features_in': int(self.nb_features_in),\\n 'nb_features_out': int(self.nb_features_out),\\n 'key': self.key\\n }\\n return params\",\n \"def test_text_classifier_set_params(self):\\n pass\",\n \"def get_params(self):\\n return {'threshold': self.threshold,\\n 'subsample': self.subsample,\\n 'estimator': self.estimator,\\n 'n_folds': self.n_folds,\\n 'stratify': self.stratify,\\n 'random_state': self.random_state,\\n 'n_jobs': self.n_jobs}\",\n \"def _reset_parameters(self):\\n if self.cfg.initial_forget_bias is not None:\\n self.hindcast_lstm.bias_hh_l0.data[self.cfg.hidden_size:2 * self.cfg.hidden_size] = self.cfg.initial_forget_bias\",\n \"def test_intent_classifier_get_params(self):\\n pass\",\n \"def nms_params(self):\\n return [0, self.num_classes]\",\n \"def nms_params(self):\\n return [0, self.num_classes]\",\n \"def training_opts(self):\\n return self._training_opts\",\n \"def training_step(self, batch):\\n return {}\",\n \"def feed_dict(training_data=True):\\n if training_data:\\n xs, ys = mnist.train.next_batch(batch_size)\\n kp = keep_prob\\n else:\\n xs, ys = mnist.validation.images, mnist.validation.labels\\n kp = 1.0\\n return {x: xs, t: ys, k: kp}\",\n \"def _starting_hparams():\\n hparams = contrib_training.HParams()\\n hparams.add_hparam('batch_style', 'bucket')\\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\\n hparams.add_hparam('learning_rate', 0.0005)\\n hparams.add_hparam('lr_decay_rate', .997)\\n hparams.add_hparam('lr_decay_steps', 1000)\\n hparams.add_hparam('lr_warmup_steps', 3000)\\n hparams.add_hparam('model_type', 'cnn')\\n hparams.add_hparam('resnet_bottleneck_factor', 0.5)\\n hparams.add_hparam('decision_threshold', 0.5)\\n hparams.add_hparam('denominator_power', 1.0) # Standard mean-pooling.\\n return hparams\",\n \"def fit_config(rnd: int) -> Dict[str, str]:\\n config = {\\n \\\"epoch_global\\\": str(rnd),\\n \\\"epochs\\\": str(1),\\n \\\"batch_size\\\": str(32),\\n \\\"learning_rate\\\": str(0.001),\\n }\\n return config\",\n \"def _initialize_parameters(self):\\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k * 2],\\n initializer=self.initializer)\\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k * 2],\\n initializer=self.initializer)\",\n \"def fixture_wrapper_arguments():\\n n_features = 9\\n classes = [\\\"a\\\", \\\"b\\\", \\\"c\\\"]\\n\\n return n_features, classes\",\n \"def __init__(self, epochs, **kwargs):\\n if epochs == -1:\\n self.layer_ls = []\\n self.thick_ls = []\\n self.max_ret_ls = []\\n self.layer_ls = [0] * epochs\\n self.thick_ls = [0] * epochs\\n self.max_ret_ls = [0] * epochs\\n self.kwargs = kwargs\\n self.current_e = 0\",\n \"def test_early_stopping_no_val_step(tmpdir):\\n\\n class CurrentModel(EvalModelTemplate):\\n def training_step(self, *args, **kwargs):\\n output = super().training_step(*args, **kwargs)\\n output.update({'my_train_metric': output['loss']}) # could be anything else\\n return output\\n\\n model = CurrentModel()\\n model.validation_step = None\\n model.val_dataloader = None\\n\\n stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)\\n trainer = Trainer(\\n default_root_dir=tmpdir,\\n early_stop_callback=stopping,\\n overfit_pct=0.20,\\n max_epochs=5,\\n )\\n result = trainer.fit(model)\\n\\n assert result == 1, 'training failed to complete'\\n assert trainer.current_epoch < trainer.max_epochs\",\n \"def get_init_params(self):\\n return PgInput.get_init_params(self) + [EnstoreTapeDriveInput.VERSION_ATTRIBUTE,\\n 'InputMinInterval', 'InputDelay', 'CheckpointRollback']\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 0.3\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def _validate_parameters(self, epochs, log_interval):\\n\\n if not epochs > 0:\\n msg = (\\n \\\"The number of training epochs = {} should be strictly\\\"\\n \\\" positive.\\\"\\n )\\n self.logger.error(msg.format(epochs))\\n raise ValueError(msg.format(epochs))\\n\\n if not log_interval > 0:\\n msg = (\\n \\\"The number of batches to wait before printting the\\\"\\n \\\" training status should be strictly positive, but got {}\\\"\\n \\\" instead.\\\"\\n )\\n self.logger.error(msg.format(log_interval))\\n raise ValueError(msg.format(log_interval))\\n\\n if not 0 < self.shrinkage_rate <= 1:\\n msg = (\\n \\\"The shrinkage rate should be in the range (0, 1], but got\\\"\\n \\\" {} instead.\\\"\\n )\\n self.logger.error(msg.format(self.shrinkage_rate))\\n raise ValueError(msg.format(self.shrinkage_rate))\",\n \"def _initialize_parameters(self):\\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k],\\n initializer=self.initializer)\\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k],\\n initializer=self.initializer)\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 1e-7\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def _prep_data(self, x_train):\\n batch_size = x_train.shape[0]\\n x_neg = self.encoder.predict_on_batch(x_train)\\n y_neg = np.zeros(batch_size)\\n x_pos = self.std_dev * np.random.randn(batch_size,\\n self.latent_dim) + self.mu\\n y_pos = np.ones(batch_size)\\n if self.joint_train:\\n y_train = [x_train, y_pos]\\n else:\\n y_train = x_train\\n\\n return (x_neg, y_neg, x_pos, y_pos, y_train)\",\n \"def __init__(self, x_train, model):\\n self.x_train = x_train\\n self.model = model\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def prepare_data(self, *args, **kwargs) -> None:\\n if not (self.root / \\\"train\\\").exists():\\n raise ValueError(\\n f\\\"Please download and set ImageNet-1k train data under {self.root}.\\\"\\n )\\n if not (self.root / \\\"val\\\").exists():\\n raise ValueError(\\n f\\\"Please download and set ImageNet-1k val data under {self.root}.\\\"\\n )\",\n \"def __init__(self):\\n # Number of examples per epoch of training data.\\n self.num_examples_per_epoch = None \\n\\n # Optimizer for training the model.\\n self.optimizer = \\\"SGD\\\" #default \\\"SGD\\\"\\n\\n # Learning rate for the initial phase of training.\\n self.initial_learning_rate = 2.0 # default 2.0\\n self.learning_rate_decay_factor = 0.8\\n self.num_epochs_per_decay = 4 #default 8\\n\\n # If not None, clip gradients to this value.\\n self.clip_gradients = 5.0\\n\\n # How many model checkpoints to keep.\\n self.max_checkpoints_to_keep = 2\",\n \"def stopping_criteria(self):\\n stop_training = False\\n if self.training_stopping_criteria == \\\"early_stopping\\\":\\n stop_training = self.early_stopping()\\n elif self.training_stopping_criteria == 'SGDR_ensemble':\\n T_0 = self.scheduler_params['T_0']\\n if self.step_scheduler_after == 'batch':\\n T_0 = T_0/self.num_train_iterations\\n print(f\\\"sgdr T_0={T_0}\\\")\\n\\n T_mult = self.scheduler_params['T_mult']\\n N = self.stopping_criteria_params['N']\\n M = self.stopping_criteria_params['M']\\n possible_epoch = [T_0*math.pow(T_mult, i) for i in range(N)] \\n possible_epoch = np.cumsum(possible_epoch)\\n snapshot_epochs = possible_epoch[-M:]\\n print(snapshot_epochs)\\n\\n if self.current_epoch in snapshot_epochs:\\n print(\\\"warm restarted learning rate\\\")\\n print(f\\\"saving model={self.checkpoint_snapshot} out of {M}\\\")\\n model_path = os.path.join(path_checkpoints_dir, \\n f\\\"{self.experiment_tag}_snapshot_{self.checkpoint_snapshot}_epoch_{self.current_epoch}.pth\\\")\\n self.checkpoint_snapshot += 1\\n self.save_checkpoint(model_path=model_path)\\n\\n if self.current_epoch == possible_epoch[-1]:\\n stop_training = True\\n\\n elif self.training_stopping_criteria == \\\"max_epoch\\\":\\n if self.current_epoch == self.stopping_criteria_params['max_epoch']: \\n stop_training = True\\n self.save_checkpoint()\\n \\n return stop_training\",\n \"def get_optional_params():\\n return {\\n 'regularizer': None, # any valid TensorFlow regularizer\\n 'regularizer_params': dict,\\n 'initializer': None, # any valid TensorFlow initializer\\n 'initializer_params': dict,\\n 'dtype': [tf.float32, tf.float16, 'mixed'],\\n }\",\n \"def _get_parameters(self):\\n return None\",\n \"def make_train_state(save_path, early_stopping_patience):\\n return {'stop_early': False,\\n 'early_stopping_step': 0,\\n 'early_stopping_patience': early_stopping_patience,\\n 'steps': 0,\\n 'min_val_loss': float('Inf'),\\n 'train_loss': [],\\n 'val_loss': [],\\n 'save_path': save_path}\",\n \"def _get_init_fn():\\n exclusions = []\\n if FLAGS.checkpoint_exclude_scopes:\\n exclusions = [scope.strip()\\n for scope in FLAGS.checkpoint_exclude_scopes.split(',')]\\n\\n variables_to_restore = []\\n for var in slim.get_model_variables():\\n excluded = False\\n for exclusion in exclusions:\\n if var.op.name.startswith(exclusion):\\n excluded = True\\n break\\n if not excluded:\\n variables_to_restore.append(var)\\n\\n if tf.gfile.IsDirectory(FLAGS.checkpoint_path):\\n checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)\\n else:\\n checkpoint_path = FLAGS.checkpoint_path\\n\\n tf.logging.info('Fine-tuning from {}'.format(checkpoint_path))\\n\\n return slim.assign_from_checkpoint_fn(checkpoint_path, variables_to_restore)\",\n \"def Params(cls):\\n p = super().Params()\\n p.Define('train_task', None, 'Underlying task')\\n p.Define('decode_task', None, 'Underlying task')\\n p.Define('train_dataset_name', None, '')\\n p.Define('decode_dataset_name', None, '')\\n p.Define('train_steps_per_loop', 0, '')\\n p.Define('decode_steps_per_loop', 0, '')\\n return p\",\n \"def get_params(self):\",\n \"def get_meta_training_args(self, frac_done: float):\\n return TrainArgs(\\n meta_learning_rate=frac_done * self.meta_learning_rate_final + \\\\\\n (1 - frac_done) * self.meta_learning_rate_initial\\n )\",\n \"def train(self)->None:\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64764184","0.6429467","0.6267135","0.6226991","0.59285235","0.5904875","0.59016514","0.57753325","0.5774506","0.55555415","0.55554277","0.5521391","0.5516484","0.5502349","0.5497063","0.54911923","0.54711896","0.54711896","0.5470737","0.54651886","0.5448864","0.544763","0.54425204","0.54381055","0.54339254","0.5432346","0.5431156","0.5426798","0.5425489","0.5409042","0.54089904","0.54079545","0.54032266","0.54006827","0.53900605","0.5383039","0.5374136","0.53697217","0.53609407","0.53607655","0.5358508","0.5356826","0.53306377","0.53301644","0.53298306","0.5326447","0.5323386","0.5318323","0.5317626","0.53141075","0.5294915","0.52897096","0.5264955","0.5264181","0.5264181","0.5264181","0.5264181","0.5264181","0.52636945","0.52596056","0.5256271","0.52505594","0.52503324","0.52501255","0.52373827","0.52301687","0.5226473","0.522572","0.5224667","0.52191025","0.52191025","0.52185714","0.52155256","0.52135384","0.5212376","0.5204572","0.5204257","0.5201557","0.5199908","0.5199761","0.5198154","0.51957047","0.5186573","0.51855034","0.51851904","0.51815414","0.5180346","0.51799726","0.51799726","0.51770616","0.51736504","0.51658064","0.51636094","0.5158001","0.51545566","0.5150501","0.51494825","0.51473427","0.5146245","0.51456136"],"string":"[\n \"0.64764184\",\n \"0.6429467\",\n \"0.6267135\",\n \"0.6226991\",\n \"0.59285235\",\n \"0.5904875\",\n \"0.59016514\",\n \"0.57753325\",\n \"0.5774506\",\n \"0.55555415\",\n \"0.55554277\",\n \"0.5521391\",\n \"0.5516484\",\n \"0.5502349\",\n \"0.5497063\",\n \"0.54911923\",\n \"0.54711896\",\n \"0.54711896\",\n \"0.5470737\",\n \"0.54651886\",\n \"0.5448864\",\n \"0.544763\",\n \"0.54425204\",\n \"0.54381055\",\n \"0.54339254\",\n \"0.5432346\",\n \"0.5431156\",\n \"0.5426798\",\n \"0.5425489\",\n \"0.5409042\",\n \"0.54089904\",\n \"0.54079545\",\n \"0.54032266\",\n \"0.54006827\",\n \"0.53900605\",\n \"0.5383039\",\n \"0.5374136\",\n \"0.53697217\",\n \"0.53609407\",\n \"0.53607655\",\n \"0.5358508\",\n \"0.5356826\",\n \"0.53306377\",\n \"0.53301644\",\n \"0.53298306\",\n \"0.5326447\",\n \"0.5323386\",\n \"0.5318323\",\n \"0.5317626\",\n \"0.53141075\",\n \"0.5294915\",\n \"0.52897096\",\n \"0.5264955\",\n \"0.5264181\",\n \"0.5264181\",\n \"0.5264181\",\n \"0.5264181\",\n \"0.5264181\",\n \"0.52636945\",\n \"0.52596056\",\n \"0.5256271\",\n \"0.52505594\",\n \"0.52503324\",\n \"0.52501255\",\n \"0.52373827\",\n \"0.52301687\",\n \"0.5226473\",\n \"0.522572\",\n \"0.5224667\",\n \"0.52191025\",\n \"0.52191025\",\n \"0.52185714\",\n \"0.52155256\",\n \"0.52135384\",\n \"0.5212376\",\n \"0.5204572\",\n \"0.5204257\",\n \"0.5201557\",\n \"0.5199908\",\n \"0.5199761\",\n \"0.5198154\",\n \"0.51957047\",\n \"0.5186573\",\n \"0.51855034\",\n \"0.51851904\",\n \"0.51815414\",\n \"0.5180346\",\n \"0.51799726\",\n \"0.51799726\",\n \"0.51770616\",\n \"0.51736504\",\n \"0.51658064\",\n \"0.51636094\",\n \"0.5158001\",\n \"0.51545566\",\n \"0.5150501\",\n \"0.51494825\",\n \"0.51473427\",\n \"0.5146245\",\n \"0.51456136\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5830468"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":18483,"cells":{"query":{"kind":"string","value":"Create `holdout_set` (if necessary) by loading a DataFrame from a .csv file, or by separating `train_set`, and return the updated (`train_set`, `holdout_set`) pair"},"document":{"kind":"string","value":"def define_holdout_set(\n train_set: pd.DataFrame,\n holdout_set: Union[pd.DataFrame, callable, str, None],\n target_column: Union[str, List[str]],\n) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:\n #################### Update `holdout_set` ####################\n if callable(holdout_set):\n train_set, holdout_set = holdout_set(train_set, target_column)\n elif isinstance(holdout_set, str):\n holdout_set = pd.read_csv(holdout_set)\n #################### Validate `holdout_set` ####################\n try:\n if holdout_set is None or np.array_equal(train_set.columns, holdout_set.columns):\n return train_set, holdout_set\n except AttributeError:\n raise TypeError(f\"holdout_set must be None, DataFrame, callable, or str, not {holdout_set}\")\n raise ValueError(f\"Mismatched columns\\n{train_set.columns}\\n!=\\n{holdout_set.columns}\")"},"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 create_train_set(addition_to_filename = ''):\n df = f.create_df()\n print('original df size is ', df.shape)\n print('original df columns ', df.columns)\n\n df = f.calculate_missing_prices_for_train_set(df)\n print('df size after averaging price ', df.shape)\n df = f.downcast_dtypes(df)\n df = f.add_lag(all_data_df = df, df_to_add_lag= df, number_of_months=3)\n df = f.add_days_stat(df)\n\n print(df.columns)\n\n # df = h.add_holidays(df)\n # print('df size with holidays ', df.shape)\n\n timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n pickle.dump(df, open(f\"{timestr}_{addition_to_filename}_train.pickle.dat\", \"wb\"))\n\n # save feature names for further use\n features_list = create_feature_names_list(df)\n pickle.dump(features_list, open(f\"{timestr}_{addition_to_filename}_features.pickle.dat\", \"wb\"))","def read_random_data_from_csv(\n file_name, training_set_size, unlabeled_set_size, holdout_set_size, validation_set_size):\n data = samp_file_to_arr(\n file_name, training_set_size + unlabeled_set_size + holdout_set_size + validation_set_size)\n y_raw = np.array([x[0] for x in data])\n x_all = np.array([x[1:] for x in data])\n # Now transform so that the lower label is -1, always. \n uq = np.unique(y_raw) # Assumed to be only two unique labels!\n y_all = np.zeros(len(y_raw))\n y_all[np.where(y_raw == uq[0])[0]] = -1\n y_all[np.where(y_raw == uq[1])[0]] = 1\n xtrhoval, x_unl, ytrhoval, y_unl = sklearn.model_selection.train_test_split(\n x_all, y_all, test_size=unlabeled_set_size)\n x_trho, x_validate, y_trte, y_validate = sklearn.model_selection.train_test_split(\n xtrhoval, ytrhoval, test_size=validation_set_size)\n x_train, x_out, y_train, y_out = sklearn.model_selection.train_test_split(\n x_trho, y_trte, test_size=holdout_set_size)\n return (x_train, y_train, x_unl, y_unl, x_out, y_out, x_validate, y_validate)","def get_train_data():\n # train set\n train = pd.read_csv(\"train.csv\")\n\n return train","def load_data():\n train = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n test = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )\n\n train = train.set_index('PassengerId')\n test = test.set_index('PassengerId')\n\n train = train.apply(preprocess, axis=1)\n test = test.apply(preprocess, axis=1)\n\n x_train = train.drop(['Survived'], axis=1)\n y_train = train['Survived']\n x_test = test\n return {'train': {'x': x_train, 'y': y_train},\n 'test': {'x': x_test},\n 'full_features': pd.concat([x_train, x_test])}","def learning_set_builders(self):\n # Transform in a dataframe:\n original_train_x = pd.DataFrame(self.original_train_x, columns=self.original_x_header)\n original_train_y = pd.DataFrame(self.original_train_y, columns=self.original_y_header)\n original_test_x = pd.DataFrame(self.original_test_x, columns=self.original_x_header)\n original_test_y = pd.DataFrame(self.original_test_y, columns=self.original_y_header)\n original_validation_x = pd.DataFrame(self.original_validation_x, columns=self.original_x_header)\n original_validation_y = pd.DataFrame(self.original_validation_y, columns=self.original_y_header)\n\n # Training set\n x, y = self.convertor(original_train_x, original_train_y)\n self.pairs_train_x = x.to_numpy()\n self.pairs_train_y = y.to_numpy()\n\n # Headers\n self.pairs_x_header = x.columns\n self.pairs_y_header = y.columns\n\n # Testing set\n x, y = self.convertor(original_test_x, original_test_y)\n self.pairs_test_x = x.to_numpy()\n self.pairs_test_y = y.to_numpy()\n\n # Validation set\n x, y = self.convertor(original_validation_x, original_validation_y)\n self.pairs_validation_x = x.to_numpy()\n self.pairs_validation_y = y.to_numpy()","def train_set(self):\n if self._trainset is None: # loads the data to memory once and when requested.\n trainset_raw = self.read_dataset(self._trainset_path)\n trainset_spacy = self.read_spacy_pickle(self._trainset_spacy_path)\n if trainset_raw is None and trainset_spacy is None:\n # This is for languages we never see (French)\n self._trainset = None\n else:\n self._trainset = pd.concat([trainset_raw, trainset_spacy], axis=1)\n\n self._trainset['language'] = self._language\n self._trainset['dataset_name'] = self._dataset_name\n\n return self._trainset","def createTrainTestSets():\n tweets = open(noDuplicatesFilename, 'r').read().splitlines()\n name_mapping = loadNameMapping()\n holdoutLocations = [u'Frederiksberg, Danmark', u'T\\xe5rnby, Danmark', u'Kolding, Danmark', u'T\\xe4by, Sverige', u'Kungsbacka, Sverige', u'Kristianstad, Sverige', u'Bod\\xf8, Norge', u'Kvinnherad, Norge', u'Ullensaker, Norge']\n testSetLocation = []\n rest = []\n for tweet in tweets:\n if stringToTweet(tweet).getFullName() in holdoutLocations:\n testSetLocation.append(tweet)\n else:\n rest.append(tweet)\n tweets = rest\n testIndex = int(round(len(tweets) * (1 - test_set_ratio)))\n random.seed(1)\n random.shuffle(tweets)\n trainSet = tweets[:testIndex]\n testSet = tweets[testIndex:]\n open(trainSetFilename, 'w').write('\\n'.join(trainSet))\n open(testSetNormalFilename, 'w').write('\\n'.join(testSet))\n open(testSetLocationFilename, 'w').write('\\n'.join(testSetLocation))\n print \"Wrote %d tweets to train set\" % len(trainSet)\n print \"Wrote %d tweets to normal test set\" % len(testSet)\n print \"Wrote %d tweets to location test set\" % len(testSetLocation)","def load_testset(filename):\n pickle_name = filename + \".pickle\"\n try:\n print(\"trying to load \" + filename + \" from pickle\")\n dataset = pickle.load(open(pickle_name, \"rb\"))\n except:\n with open(filename, 'r') as csv_file:\n print(\"no pickle exists. parsing file \" + filename)\n dataset = [DataPoint(item[0:], \" \")\n for item\n in csv.reader(csv_file, delimiter=',')]\n pickle.dump(dataset, open(pickle_name, \"wb\"))\n print(\"loaded \" + filename)\n return dataset","def load_data(self):\n with open('data/fordTrain.csv') as f:\n data = csv.reader(f, delimiter=',')\n train = [x for i, x in enumerate(data) if i > 0] \n # Extract features and target variable separately\n trainx = [x[3:] for x in train]\n trainy = [x[2] for x in train]\n\n with open('data/fordTest.csv') as f:\n data = csv.reader(f, delimiter=',')\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \n\n with open('data/Solution.csv') as f:\n data = csv.reader(f, delimiter=',')\n testy = [x[2] for i, x in enumerate(data) if i > 0] \n\n # Extract features and target variable, convert to numpy array\n trainx = np.asarray(trainx, dtype=np.float32)\n trainy = np.asarray(trainy, dtype=np.int8)\n testx = np.asarray(testx, dtype=np.float32)\n testy = np.asarray(testy, dtype=np.int8)\n\n # Return training and test sets\n trainSet = Dataset(trainx, trainy)\n testSet = Dataset(testx, testy)\n return trainSet, testSet","def load_data():\n data = gzip.open(\"mnist.pkl.gz\", \"rb\")\n train_set, valid_set, test_set = cPickle.load(data)\n data.close()\n\n # Combine validation and train folds to recreate the master 60k set.\n new_images = numpy.concatenate((train_set[0], valid_set[0]))\n new_labels = numpy.concatenate((train_set[1], valid_set[1]))\n\n train_set = (new_images, new_labels)\n \n return (train_set, test_set)","def load_train_test() -> Tuple[pd.DataFrame, pd.DataFrame]:\n df = load_data() \n #Sample\n train_df = df.sample(frac=0.8, random_state=42)\n test_df = df[~df.index.isin(train_df.index)]\n #Rescale\n train_mean = train_df.iloc[:, :-1].mean()\n train_std = train_df.iloc[:, :-1].std()\n train_df.iloc[:, :-1] = (train_df.iloc[:, :-1] - train_mean) / train_std\n test_df.iloc[:, :-1] = (test_df.iloc[:, :-1] - train_mean) / train_std\n return train_df, test_df","def get_dataset(filename, target_name, training_fraction):\n csv_reader = csv.DictReader(open(filename))\n attributes_names = [attribute_name for attribute_name in csv_reader.fieldnames\n if attribute_name != target_name]\n train_dataset = Dataset(attributes_names, target_name)\n test_dataset = Dataset(attributes_names, target_name)\n instances = []\n for row in csv_reader:\n instances.append(([row[attribute_name] for attribute_name in attributes_names],\n row[target_name]))\n all_instances_indexes = set(range(0, len(instances)))\n number_of_training_instances = int(len(all_instances_indexes) * training_fraction)\n training_instances_indexes = set(sample(all_instances_indexes, number_of_training_instances))\n test_instances_indexes = all_instances_indexes.difference(training_instances_indexes)\n for instance_index in training_instances_indexes:\n train_dataset.add_instance(instances[instance_index][0], instances[instance_index][1])\n for instance_index in test_instances_indexes:\n test_dataset.add_instance(instances[instance_index][0], instances[instance_index][1])\n return train_dataset, test_dataset","def create_sets(test, data, test_size=0.2, write=False):\n y_test = test['y_old']\n X_test = test.drop('y_old', 1)\n y_data = data['y_old']\n X_data = data.drop('y_old', 1)\n X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=test_size, random_state=123)\n if write:\n pickle.dump((X_train, X_val, y_train, y_val), open(obj_save_path+'train_val_df.p', 'wb'))\n #X_train, X_val, y_train, y_val = pickle.load(open(obj_save_path+'train_val_df.p', 'rb'))\n return X_train, y_train, X_val, y_val, X_test, y_test","def initSets(self):\n data_frame = pd.read_csv(self.train_file, header=None)\n data_frame = data_frame.drop(columns=self.drop_cols)\n features = data_frame.iloc[:, :-1].values\n labels = data_frame.iloc[:, -1].values\n if self.test_file is None:\n self.train_feat, self.test_feat, self.train_labl, self.test_labl = train_test_split(features, labels, test_size=self.test_size)\n else:\n data_frame = pd.read_csv(self.test_file, header=None)\n data_frame = data_frame.drop(columns=self.drop_cols)\n self.train_feat, _, self.train_labl, _ = train_test_split(features, labels, test_size=self.test_size)\n features = data_frame.iloc[:, :-1].values\n labels = data_frame.iloc[:, -1].values\n _, self.test_feat, _, self.test_labl = train_test_split(features, labels, test_size=self.test_size)\n # kfold = KFold(n_splits=3)\n # self.train_index, self.test_index = kfold.split(features,labels)","def time_split_dataset(df, train_start_date, train_end_date, holdout_end_date, date_col):\n\n train_set = df.copy()[\n (df[date_col] >= train_start_date) & (df[date_col] <= train_end_date)]\n\n test_set = df.copy()[\n (df[date_col] > train_end_date) & (df[date_col] <= holdout_end_date)]\n\n return train_set, test_set","def load_dataset_train():\n df_train = load_csv_file(\"31_train.csv\")\n df_train_target = load_csv_file(\"31_target_train.csv\")\n\n return df_train.values, df_train_target.values","def prepare_data(args):\n logger.info('Loading dataframe from %s' % args.newspath)\n df = pd.read_csv(args.newspath, encoding='gb18030')\n logger.info('Dataframe size: %d observations %d features after loaded' % (df.shape[0], df.shape[1]))\n\n # exclude rows with column source == NaN\n logger.info('Data cleansing...')\n df = df[~pd.isna(df['source'])]\n logger.info('Dataframe size: %d observations %d features after data cleansing' % (df.shape[0], df.shape[1]))\n\n # split the dataframe into training set and test set\n logger.info('Making training set & test set...')\n train_set, test_set = split_data(df)\n logger.info('Traning set size: %d' % train_set.shape[0])\n logger.info('Test set size: %d' % test_set.shape[0])\n\n # save the train set and test set to picke files\n logger.info('Save dataframes to files...')\n train_set.to_pickle(args.trainpath)\n test_set.to_pickle(args.testpath)","def create_train_valid_set(self):\n\n if not self.eq_train:\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level, self.train_weights, self.y_train,\n train_size=0.7, test_size=0.3\n )\n else:\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, w_train_eq, w_valid_eq, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level,\n self.train_weights, self.train_weights_eq, self.y_train,\n train_size=0.7, test_size=0.3\n )\n self.train_weights_eq = w_train_eq\n\n #NOTE: might need to re-equalise weights in each folds as sumW_sig != sumW_bkg anymroe!\n self.train_weights = train_w\n self.valid_weights = valid_w #validation weights should never be equalised weights!\n\n print 'creating validation dataset'\n self.X_train_high_level = X_train_high_level\n self.X_train_low_level = self.join_objects(X_train_low_level)\n\n self.X_valid_high_level = X_valid_high_level\n self.X_valid_low_level = self.join_objects(X_valid_low_level)\n print 'finished creating validation dataset'\n\n self.y_train = y_train\n self.y_valid = y_valid","def holdout(self, X, y):\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size = 0.3, random_state = 0)\n\n return X_train, X_test, y_train, y_test","def load_susy(trainsize=500, testsize=1000):\n filename = 'datasets/susysubset.csv'\n dataset = loadcsv(filename)\n trainset, testset = splitdataset(dataset, trainsize, testsize)\n return trainset, testset","def read(train_path, test_path, label_name):\n train_dataset = pd.read_csv(train_path)\n test_dataset = pd.read_csv(test_path)\n\n train_labels = train_dataset.pop(label_name)\n\n imputer = DataFrameImputer().fit(train_dataset)\n train_dataset = imputer.transform(train_dataset)\n test_dataset = imputer.transform(test_dataset)\n\n train_dataset = pd.get_dummies(train_dataset)\n test_dataset = pd.get_dummies(test_dataset)\n\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\n\n scaler = StandardScaler().fit(train_dataset)\n train_dataset = scaler.transform(train_dataset)\n test_dataset = scaler.transform(test_dataset)\n\n return train_dataset, train_labels, test_dataset","def create_sets():\n global train_x, train_y, val_x, val_y\n\n print('Creating sets')\n\n dataframe = pd.read_csv('LoggerBot.log', names=NAMES).sample(frac=1)\n inputs = dataframe.values[:,:-1].astype(np.float32)\n outputs = dataframe.values[:,-1].astype(np.int32)\n\n train_set_size = int(len(dataframe) * 0.7)\n train_x, train_y = inputs[:train_set_size], outputs[:train_set_size]\n val_x, val_y = inputs[train_set_size:], outputs[train_set_size:]","def import_data(seed: object = 42) -> object:\n\n # Read input data\n df = pd.read_csv(\"x_train_gr_smpl.csv\").astype(int)\n\n # label data-frame rows based on sample data\n for x in range(10):\n index = ~pd.read_csv(\"y_train_smpl_%s.csv\" % x, squeeze=True).astype(bool) # reversed flags (~)\n df.loc[index, 'label'] = str(x)\n\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\n output_data_ordered = df.iloc[:, 2304].to_numpy()\n\n # Randomise instance order (forcing the same result each time)\n np.random.seed(seed)\n permutation = np.random.permutation(df.shape[0])\n\n # Create base input and output arrays\n input_data = input_data_ordered[permutation]\n output_data = output_data_ordered[permutation]\n\n return input_data, output_data, df, input_data_ordered, output_data_ordered","def GetDataSet(filename: str):\n # Read csv files\n # Assign our data set name attribute to simplify our code\n df_train_set = pd.read_csv(filename + \"/train.csv\")\n df_train_set.name = \"train\"\n df_test_set = pd.read_csv(filename + \"/test.csv\")\n df_test_set.name = \"test\"\n # Combine train set and test set\n df_all = ConcatDF(df_train_set, df_test_set)\n df_all.name = \"all\"\n # Remember to return the train set and test set for the next step\n return df_train_set, df_test_set, df_all","def create_test_and_train_sets(input_file):\n \n input_df = pd.read_csv(input_file, sep=',', header=0)\n df_items = pd.DataFrame({'productId': input_df.productId.unique()})\n df_sorted_items = df_items.sort_values('productId').reset_index()\n pds_items = df_sorted_items.productId\n \n df_user_items = input_df.groupby(['userId', 'productId']).agg({'Expense': 'sum'})\n \n # create a list of (userId, productId, Expense) ratings, where userId and productId are 0-indexed\n current_u = -1\n ux = -1\n pv_ratings = []\n user_ux = []\n \n for timeonpg in df_user_items.itertuples():\n user = timeonpg[0][0]\n item = timeonpg[0][1]\n if user != current_u:\n user_ux.append(user)\n ux += 1\n current_u = user\n ix = pds_items.searchsorted(item)[0]\n pv_ratings.append((ux, ix, timeonpg[1]))\n\n # convert ratings list and user map to np array\n pv_ratings = np.asarray(pv_ratings)\n user_ux = np.asarray(user_ux)\n \n # create train and test coos matrixes\n tr_sparse, test_sparse = _create_sparse_train_and_test(pv_ratings, ux + 1, df_items.size)\n \n return user_ux, pds_items.as_matrix(), tr_sparse, test_sparse","def open_MRI_data(csv_path, train_set = 0.8, n_followups=5, normalize=True):\n\n data_df = pd.read_csv(csv_path)\n\n mri_col = data_df.columns.str.contains(\"SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16\")\n mri_col = data_df.columns[mri_col].values\n\n data_df = data_df.dropna(axis=0, subset=mri_col)\n\n # Select only the subjects with nfollowups\n # Code to only select 5 first appearances of each PTID\n ptid_list = np.unique(data_df[\"PTID\"])\n\n idx_to_drop = []\n for ptid in ptid_list:\n i_list = data_df.index[data_df['PTID'] == ptid].tolist()\n if len(i_list) < 5:\n idx_to_drop = idx_to_drop + i_list\n elif len(i_list) > 5:\n idx_to_drop = idx_to_drop + i_list[5:]\n\n data_final = data_df.drop(idx_to_drop)\n\n print(data_final.shape)\n\n # Normalize only features\n data_final.loc[:,mri_col] = data_final.loc[:,mri_col].apply(lambda x: (x-x.mean())/ x.std(), axis=0)\n\n # Divide between test and train\n from sklearn.model_selection import GroupShuffleSplit\n gss = GroupShuffleSplit(n_splits=1, test_size=1.0-train_set)\n train_dataset, test_dataset = next(gss.split(X=data_final, y=data_final.DX_bl.values, groups=data_final.PTID.values))\n\n df_train = data_final.iloc[train_dataset]\n df_test = data_final.iloc[test_dataset]\n\n df_train = df_train.reset_index(drop=True)\n df_test = df_test.reset_index(drop=True)\n\n # Return the features in the correct shape (Nsamples, timesteps, nfeatures)\n X_train = pandas_to_data_timeseries(df_train, mri_col)\n X_test = pandas_to_data_timeseries(df_test, mri_col)\n\n return X_train, X_test","def dataset()-> pd.DataFrame:\n old= pd.read_csv(DIRECT_OLD)\n new= pd.read_csv(DIRECT_NEW)\n return pd.concat([old, new])","def prep_data(df):\n y = df.target\n X = df.drop([\"target\"], axis=1)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n return X_train, X_test, y_train, y_test","def data_prep(data, y, dropna=False):\n\n rand_state = 10 # Setting random state for later cv\n df = pd.read_pickle(data) # Reading in data\n if dropna is True:\n df.dropna(axis=0, inplace = True)\n else:\n pass\n X = df.drop(y, axis=1) # Assigning the feature space to X\n y = df[y] # Class labels to predict\n\n return X, y, rand_state","def get_train_csv(self):\n try:\n self.train_article = pd.read_csv(constants.DATA_DIR / 'knn_article_tags.csv')\n except FileNotFoundError:\n train = pd.Series([])\n for csv_file in os.listdir(constants.CLEAN_DIR):\n if csv_file in self.article_feat_csvs:\n df = pd.read_csv(constants.CLEAN_DIR / csv_file)\n feat = csv_file[8:-4]\n g = df.dropna(axis=0).groupby(\"id\")[self.tag_ref[feat]]\n if train.empty:\n train = g.apply(lambda x: list(x.astype(str).str.lower()))\n else:\n g = g.apply(lambda x: list(x.astype(str).str.lower()))\n train = train.combine(g, lambda x1, x2: list(set(x1+x2)), fill_value=[])\n\n train = pd.DataFrame({'id':train.index, 'tags':train.values})\n train.to_csv(constants.DATA_DIR / 'knn_article_tags.csv', header=True)\n self.train_article = train\n\n try:\n self.train_image = pd.read_csv(constants.DATA_DIR / 'knn_image_tags.csv')\n except FileNotFoundError:\n train = pd.Series([])\n for csv_file in os.listdir(constants.CLEAN_DIR):\n if csv_file in self.image_feat_csvs:\n df = pd.read_csv(constants.CLEAN_DIR / csv_file)\n feat = csv_file[6:-4]\n g = df.dropna(axis=0).groupby(\"id\")[self.tag_ref[feat]]\n if train.empty:\n train = g.apply(lambda x: list(x.astype(str).str.lower()))\n else:\n g = g.apply(lambda x: list(x.astype(str).str.lower()))\n train = train.combine(g, lambda x1, x2: list(set(x1+x2)), fill_value=[])\n\n train = pd.DataFrame({'id':train.index, 'tags':train.values})\n train.to_csv(constants.DATA_DIR / 'knn_image_tags.csv', header=True)\n self.train_image = train","def get_training_and_testing_sets(data, Y):\r\n data = pd.concat([data, Y], axis=1)\r\n x,y=data.shape\r\n train_X_sub1=data[0:x//6]\r\n dev_X_sub1 = data[x//6:x//6 + x//12]\r\n test_X_sub1 = data[x//6 + x//12:x//3]\r\n\r\n train_X_sub2 = data[x//3:x//3+x//6]\r\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\r\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\r\n\r\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\r\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\r\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\r\n\r\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\r\n train_X =train_X.append(train_X_sub3,ignore_index = True)\r\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\r\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\r\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\r\n test_X = test_X.append(test_X_sub3,ignore_index = True)\r\n\r\n\r\n train_X = util.shuffle(train_X)\r\n train_X = train_X.reset_index(drop=True)\r\n\r\n dev_X = util.shuffle(dev_X)\r\n dev_X = dev_X.reset_index(drop=True)\r\n\r\n test_X = util.shuffle(test_X)\r\n test_X = test_X.reset_index(drop=True)\r\n\r\n train_X_final=train_X\r\n dev_X_final = dev_X\r\n test_X_final = test_X\r\n x, y = train_X_final.shape\r\n train_X = train_X_final.iloc[:, 0:y - 1]\r\n train_Y = train_X_final.iloc[:, y - 1]\r\n\r\n x, y = test_X_final.shape\r\n test_X = test_X_final.iloc[:, 0:y - 1]\r\n test_Y = test_X_final.iloc[:, y - 1]\r\n\r\n x, y = dev_X_final.shape\r\n dev_X = dev_X_final.iloc[:, 0:y - 1]\r\n dev_Y = dev_X_final.iloc[:, y - 1]\r\n\r\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y","def open_MRI_data_var(csv_path, train_set = 0.8, normalize=True):\n data_df = pd.read_csv(csv_path)\n\n mri_col = data_df.columns.str.contains(\"SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16\")\n mri_col = data_df.columns[mri_col].values\n\n data_df = data_df.dropna(axis=0, subset=mri_col)\n\n # Select only the subjects with nfollowups\n # Code to only select 5 first appearances of each PTID\n ptid_list = np.unique(data_df[\"PTID\"])\n\n idx_to_drop = []\n data_final = data_df.drop(idx_to_drop)\n\n # Divide between test and train\n from sklearn.model_selection import GroupShuffleSplit\n gss = GroupShuffleSplit(n_splits=1, test_size=1.0-train_set)\n train_dataset, test_dataset = next(gss.split(X=data_final, y=data_final.DX_bl.values, groups=data_final.PTID.values))\n\n df_train = data_final.iloc[train_dataset]\n df_test = data_final.iloc[test_dataset]\n\n df_train = df_train.reset_index(drop=True)\n df_test = df_test.reset_index(drop=True)\n\n # Return the features in the correct shape list of Tensors (timesteps, nfeatures)\n X_train = pandas_to_data_timeseries_var(df_train, mri_col)\n X_test = pandas_to_data_timeseries_var(df_test, mri_col)\n\n return X_train, X_test","def load_training_set():\n global training_set\n f = gzip.open('mnist.pkl.gz', 'rb')\n train, valid, test = cPickle.load(f)\n [training_set, training_labels] = train\n [validation_set, validation_labels] = valid\n [testing_set, testing_labels] = test\n training_set = np.concatenate((training_set, validation_set))\n f.close()\n np.random.shuffle(training_set)","def _prepare_ml_data(X, y, to_optimize=False):\n size_test = 1\n y_test = None\n if to_optimize:\n size_test = CONFIG.OPTIMIZE_PARAMS['size'] + 1\n y_test = y.iloc[-size_test:]\n X_train = X.iloc[:-size_test]\n y_train = y.iloc[:-size_test]\n X_test = X.iloc[-size_test:]\n return X_train, y_train, X_test, y_test","def import_training_data(target_col = 'label'):\n dir = os.path.dirname(os.path.dirname(__file__)) # go up one level to get root of this experiment\n path = os.path.join(dir, 'data','train.csv')\n utils_logr.info('Loading data from {} as pandas df'.format(path))\n df = pd.read_csv(path)\n y = df[target_col]\n df = df.drop(target_col, axis=1)\n return df, y","def load_occupancy_dataset(trainsize=500, testsize=1000):\n filename = 'datasets/numericsequence.csv'\n dataset = loadcsv(filename)\n trainset, testset = splitdataset(dataset, trainsize, testsize)\n return trainset, testset","def get_trainset(set_name=\"car\"):\n names_file = open(\"../../data/\"+set_str+\"/\"+set_name+\"_names.dat\", \"r\")\n data_file= open(\"../../data/\"+set_str+\"/\"+set_name+\"_data.dat\", \"r\")\n \n names_strs = names_file.readlines()\n v_index = 0\n a_index = 0\n for index,line in enumerate(names_strs):\n if line.find(\"| class values\") != -1:\n v_index = index\n elif line.find(\"| attributes\") != -1:\n a_index = index\n break\n class_values_strs = re.split(r\"\\W+\",names_strs[v_index+2].strip())\n attribute_namea_value_list = []\n for i in range(a_index, len(names_strs)):\n if re.search(r\"^\\w+:\\s+[\\w,\\s]+\\.\",names_strs[i]) != None:\n temp_strs = re.split(r\"\\W+\",names_strs[i][:-2])\n attribute_namea_value_list.append((temp_strs[0],temp_strs[1:]))\n# print class_values_strs\n# print attribute_namea_value_list\n train_set = []\n while True:\n temp_str = data_file.readline()[:-1]\n if temp_str == \"\":\n break\n values = temp_str.split(\",\")\n train_set.append((values[:-1], values[-1]))\n return class_values_strs,attribute_namea_value_list,train_set","def load_data(trainfile, testfile):\n raw_train = pd.read_csv(trainfile, header=None)\n raw_test = pd.read_csv(testfile, header=None)\n train = raw_train.values\n test = raw_test.values\n train_features = train[0::, 1::]\n train_label = train[::, 0]\n test_features = test[0::, 1::]\n test_label = test[::, 0]\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\n return train, train_label, \\\n cv, cv_label, \\\n test_features, test_label","def construct_train_set(tweet_pos, tweet_neg):\n tweet_pos['pred'] = 1\n tweet_neg['pred'] = 0\n tweet_pos.columns = ['tweet', 'pred']\n tweet_neg.columns = ['tweet', 'pred']\n all_tweets = tweet_neg.append(tweet_pos)\n tweet_TR = all_tweets.reset_index().drop(['index'], axis = 1)\n return tweet_TR","def load(train_file, test_file):\n print('\\nLoad the raw training and test set data...')\n y_train, tx_train, ids_train = load_csv_data(train_file)\n y_test, tx_test, ids_test = load_csv_data(test_file)\n print('\\n... finished.')\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test","def eccv_train_and_test_set(split_csv_filepath):\n df = pd.read_csv(split_csv_filepath, header=None)\n filenames = [os.path.join(os.path.dirname(split_csv_filepath), 'hotornot_face', _.replace('.bmp', '.jpg')) for _ in\n df.iloc[:, 0].tolist()]\n scores = df.iloc[:, 1].tolist()\n flags = df.iloc[:, 2].tolist()\n\n train_set = dict()\n test_set = dict()\n\n for i in range(len(flags)):\n if flags[i] == 'train':\n train_set[filenames[i]] = scores[i]\n elif flags[i] == 'test':\n test_set[filenames[i]] = scores[i]\n\n return train_set, test_set","def split_train_test(df_train, labels):\n n_train = np.shape(df_train)[0]\n X = {'train': [], 'holdout': []} # features\n Y = {'train': [], 'holdout': []} # labels\n p10 = int(0.1 * n_train)\n X['holdout'] = df_train.iloc[-p10:]\n Y['holdout'] = labels[-p10:]\n X['train'] = df_train.iloc[:(n_train - p10)]\n Y['train'] = labels[:(n_train - p10)]\n return X, Y","def validate_holdout(Xs, ys, X_holdout, y_holdout, non_bleeding_features, features_dir,\n scorer_type, feat_indices, result_string, learning_rate_string, features):\n # define folder to save the classifier and create it if not existing\n parent_folder = \"%s/data/fnc-1/mlp_models/\" % (path.dirname(path.dirname(path.abspath(__file__))))\n\n # create the new save folder\n save_folder = get_save_folder(parent_folder, scorer_type+\"_new\")\n\n # only pass a save folder if the classifier should be saved\n best_clf = esitmator_definitions.get_estimator(scorer_type, save_folder=save_folder)\n\n # stack all the feature vectors of all the folds\n X_train = np.vstack(tuple([Xs[i] for i in range(10)]))\n y_train = np.hstack(tuple([ys[i] for i in range(10)]))\n\n # concat non-bleeding features\n X_train, X_holdout, feat_indices_holdout = concat_non_bleeding_features(\n X_train, X_holdout,\n non_bleeding_features, features_dir, 'holdout')\n\n # test for oversampling: fits the current classifier, oversampled with a given\n # method and checks the score on the holdout set\n use_over_sampling = False\n if use_over_sampling == True:\n from imblearn.over_sampling import SMOTE\n kind = ['regular', 'borderline1', 'borderline2', 'svm']\n for m in kind:\n sm = SMOTE(kind=m)\n X_res, y_res = sm.fit_sample(X_train, y_train)\n best_clf.fit(X_res, y_res)\n y_predicted = best_clf.predict(X_holdout)\n predicted = [LABELS[int(a)] for a in y_predicted]\n actual = [LABELS[int(a)] for a in y_holdout]\n fold_score, _ = score_submission(actual, predicted)\n max_fold_score, _ = score_submission(actual, actual)\n score = fold_score / max_fold_score\n print(\"Score \" + m + \":\" + str(score))\n\n\n #Taken from Benjamins LSTM\n loss_monitor_file_dir = \"%s/data/fnc-1/model_results/loss_results/\" % (\n path.dirname(path.dirname(path.abspath(__file__))))\n loss_filename = loss_monitor_file_dir + str(datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M\")) + \".txt\"\n # fit the classifier\n if 'f_ext' in scorer_type:\n append_to_loss_monitor_file(\"\\n\\nFOLD holdout and classifier: \" + scorer_type + \"\\n\", loss_filename)\n append_to_loss_monitor_file(str(datetime.datetime.now()).split('.')[0], loss_filename)\n best_clf.fit(X_train, y_train, X_holdout, np.array(y_holdout), 'holdout', loss_filename)\n else:\n best_clf.fit(X_train, y_train)\n\n # predict labels\n y_predicted = best_clf.predict(X_holdout)\n predicted = [LABELS[int(a)] for a in y_predicted]\n actual = [LABELS[int(a)] for a in y_holdout]\n\n # calc FNC score\n fold_score, cm = score_submission(actual, predicted)\n max_fold_score, _ = score_submission(actual, actual)\n score = fold_score / max_fold_score\n\n # calc accuracy for related/unrelated and stances\n accuracy_stance = score_calculation.get_accuracy(y_predicted, y_holdout, stance=True)\n accuracy_related = score_calculation.get_accuracy(y_predicted, y_holdout, stance=False)\n f1_stance = score_calculation.get_f1score(y_predicted, y_holdout, stance=True)\n f1_related = score_calculation.get_f1score(y_predicted, y_holdout, stance=False)\n\n # prepare printout for final results of holdout set\n printout = printout_manager.get_holdout_printout(save_folder, accuracy_related, accuracy_stance, f1_related, f1_stance, score)\n printout += printout_manager.calculate_confusion_matrix(cm)\n print(printout) # print holdout results\n result_string += printout + \"\\n\"# add results to string that is going to be saved into a file\n\n result_file_folder = \"%s\" % (path.dirname(path.dirname(path.abspath(__file__))))\n printout_manager.save_file(result_string, result_file_folder + \"/fnc_results_holdout.txt\", \"a+\")\n\n #aligned printout for ablation:\n summary = printout_manager.get_holdout_ablation_printout(features, score,f1_stance,save_folder)\n printout_manager.save_file(summary, result_file_folder + \"/fnc_results_holdout_summary.txt\", \"a+\")\n\n # test saving and restoring model\n #filename = scorer_type + \".sav\"\n #save_model(best_clf, save_folder,filename)\n #load_clf = load_model(parent_folder + scorer_type + \"_new_0/\", filename) # the 0th folder should always exist\n #print_score_from_restored_model(load_clf, X_holdout, y_holdout)\n\n # add to special file that shows learning rate and loss of optimizer\n if isinstance(best_clf, MultiThreadingFeedForwardMLP):\n learning_rate_string += best_clf.get_learning_rates('holdout') + \"\\n\"\n\n # print feature importances\n if scorer_type == 'randomforest':\n result_file_folder = \"%s\" % (path.dirname(path.dirname(path.abspath(__file__))))\n importances = best_clf.feature_importances_\n std = np.std([tree.feature_importances_ for tree in best_clf.estimators_],\n axis=0)\n indices = np.argsort(importances)[::-1]\n feat_indices.append(feat_indices_holdout)\n\n feat_importance_string = str(feat_indices) + \"\\n\"\n for i in indices:\n feat_importance_string += str(i) + \";\" + str(importances[i]) + \";\" + str(std[i]) + \"\\n\"\n\n # save feature importances as file\n printout_manager.save_file(feat_importance_string, result_file_folder + \"/feat_importance_rf.txt\", \"a+\")\n\n return result_string, learning_rate_string","def make_data(input_filepath, output_filepath):\n\n df_train = pd.read_csv(input_filepath+'train_u6lujuX_CVtuZ9i.csv', index_col=0)\n df_test = pd.read_csv(input_filepath+'test_Y3wMUE5_7gLdaTN.csv', index_col=0)\n print('Sizes', df_train.shape, df_test.shape)\n print(\"Outcome dispersion:\\n\", df_train['Loan_Status'].value_counts())\n\n\n # recode and save outcome vector\n y = df_train['Loan_Status'].map({'N': 0, 'Y': 1})\n\n del df_train['Loan_Status']\n\n # all in one dataframe\n df = pd.concat([df_train, df_test])\n print(df.shape)\n\n from src.features.build_features import make_features\n df = make_features(df)\n\n # Divide data on train and test again and save\n data_train = df[df.index.isin(df_train.index)]\n data_test = df[df.index.isin(df_test.index)]\n print(data_train.shape, data_test.shape)\n\n data_tmp = data_train.copy()\n data_tmp['y'] = y\n\n\n data_tmp.to_csv(output_filepath + 'train_ready.csv', index=False)\n data_test.to_csv(output_filepath + 'test_ready.csv', index=False)\n id_test = pd.DataFrame(data=df_test.index, columns=['Loan_ID'])\n id_test.to_csv(output_filepath + 'id_test.csv', index=False)","def load_dataset():\n\n train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n\n test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n \n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n \n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes","def load_data():\n # Load data\n X_train = pd.read_csv(\n \"data/trimmed_merged_no_track_id_or_session_id.csv\"\n ) # load data from csv\n\n drop_columns = [\"mode\"] # drop unimportant columns\n X_train.drop(labels=drop_columns, axis=1, inplace=True)\n\n y_train = X_train[\"skip\"]\n X_train.drop(labels=\"skip\", axis=1, inplace=True) # ground truth from train_data\n\n # split data 60% train, 20% validation, 20% test\n X_train, X_val, y_train, y_val = train_test_split(\n X_train, y_train, test_size=0.2, random_state=1\n )\n X_train, X_test, y_train, y_test = train_test_split(\n X_train, y_train, test_size=0.25, random_state=1\n )\n\n print(\"X_train:\", len(X_train))\n print(\"y_train:\", len(y_train))\n print(\"X_val:\", len(X_val))\n print(\"y_val:\", len(y_val))\n print(\"X_test:\", len(X_test))\n print(\"y_test:\", len(y_test))\n\n return X_train, y_train, X_val, y_val, X_test, y_test","def make_training_set(ind_list, training_data): \n \n exp = training_data[ind_list[0]] \n X_train = exp[0]\n u_train = exp[1] \n\n for i in ind_list[1:]: \n exp = training_data[i]\n X_train = np.append(X_train, exp[0], axis=0)\n u_train = np.append(u_train, exp[1], axis=0)\n\n return X_train, u_train","def load(self, path=\"../data_set/final-train-dataset.csv\", shuffle=False,\n onlyLabelToUse=None, useOnlyBestIndicators=False, binary=False):\n data = []\n labels = []\n\n with open(path) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n skip = True\n\n for row in reader:\n if skip:\n skip = False\n continue\n\n entries = []\n\n for i in range(SAMPLES_OF_DATA_TO_LOOK_AT):\n # Get one time point's data, including indicators:\n if useOnlyBestIndicators:\n # entries.append(\n # [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n #\n entries.append(\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n j in [0, 1, 2, 5, 7]])\n else:\n entries.append(\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n j in range(INPUT_CHANNELS)])\n\n data.append(np.array(entries))\n\n if onlyLabelToUse is not None:\n if binary:\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse]) > 0.5)]\n else:\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse])]\n else:\n if binary:\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) > 0.5) for j in range(OUTPUT_CHANNELS)]\n else:\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) for j in range(OUTPUT_CHANNELS)]\n\n labels.append(label)\n\n if shuffle:\n indices = [i for i in range(len(data))]\n np.random.shuffle(indices)\n labels = np.array([labels[i] for i in indices])\n data = np.array([data[i] for i in indices])\n else:\n data = np.array(data)\n labels = np.array(labels)\n\n return data, labels","def get_dataset(self):\n\n trainset = datasets.SVHN('datasets/SVHN/train/', split='train', transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.SVHN('datasets/SVHN/test/', split='test', transform=self.val_transforms,\n target_transform=None, download=True)\n extraset = datasets.SVHN('datasets/SVHN/extra', split='extra', transform=self.train_transforms,\n target_transform=None, download=True)\n\n trainset = torch.utils.data.ConcatDataset([trainset, extraset])\n\n return trainset, valset","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n\n # split the data into 10% validation-set and 90% training set\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\n return raw_train, raw_valid, y_train, y_valid","def _load_training_and_test_sets(normalize):\n class_labels = []\n test_labels = []\n norm = None\n if normalize == True:\n norm = loading.get_normalize_vector()\n\n for i in range(0, 10):\n [training, test] = loading.load_number_set(i, 0.7, norm_vector=norm)\n labels = [str(i)] * training.shape[0]\n tlabels = [str(i)] * test.shape[0]\n if i == 0:\n train_points = training\n test_points = test\n else:\n train_points = np.concatenate((train_points, training), axis = 0)\n test_points = np.concatenate((test_points, test), axis = 0)\n class_labels.extend(labels)\n test_labels.extend(tlabels)\n\n return train_points, test_points, class_labels, test_labels","def build_full_trainset(self):\n\n return self.construct_trainset(self.raw_ratings)","def load_train_test_transactions(train_size=0.7):\n X, y = features_target_split()\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\n print('\\nTraining and testing data creation successful\\n')\n return X_train, X_test, y_train,y_test","def train_test(in_path, train_out_path, test_out_path):\n df = pd.read_csv(in_path, sep='|')\n rng = RandomState()\n\n train = df.sample(frac=0.8, random_state=rng)\n test = df.loc[~df.index.isin(train.index)]\n\n train.to_csv(train_out_path, sep='|', index = None, header=True)\n test.to_csv(test_out_path, sep='|', index = None, header=True)","def get_training_data():\n \n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\n return X, y","def fetch_training_df(df):\n\n gen_df = df.copy()\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\n return gen_df","def prepare_dataset(dataset, manifest):\n\n ## importation\n import pandas as pd\n\n ## craft output_filename\n output_filename = dataset.replace(\".csv\", \"_labeled.csv\")\n\n ## load dataset\n df_data = pd.read_csv(dataset)\n df_data = df_data.set_index('ID')\n\n ## load manifest\n df_cluster = pd.read_csv(manifest)\n\n ## merge\n result = df_data.join(df_cluster.set_index('ID'))\n\n ## drop columns conatining NA\n result = result.dropna(axis='columns')\n\n ## save dataset\n result.to_csv(output_filename)","def extract_validation_data_set_from_learning_data_set(nb_samples_per_classes: int, nb_class_names: int,\n learning_data_set: FeatureTable) -> FeatureTable:\n\n # Same algorithm than generate_training_base\n result = FeatureTable()\n class_names_found = list()\n nb_samples_per_class_found = list()\n nb_samples_required = (1 + nb_class_names) * nb_samples_per_classes\n\n while nb_samples_required != 0:\n individual_challenger = learning_data_set.pop_individual(\n random.randint(0, learning_data_set.size - 1))\n try:\n index = class_names_found.index(individual_challenger.label)\n except ValueError:\n class_names_found.append(individual_challenger.label)\n nb_samples_per_class_found.append(0)\n index = class_names_found.index(individual_challenger.label)\n\n if nb_samples_per_class_found[index] != nb_samples_per_classes:\n nb_samples_per_class_found[index] = nb_samples_per_class_found[index] + 1\n nb_samples_required = nb_samples_required - 1\n result.add_individual(individual_challenger)\n else:\n learning_data_set.add_individual(individual_challenger)\n return result","def get_train_and_test_sets(self,\n all_trades_df: pd.DataFrame,\n train_start: pd.Timestamp,\n train_end: pd.Timestamp,\n test_end: pd.Timestamp) -> pd.DataFrame:\n \n # training set\n feature_and_target_cutoff = train_end - pd.Timedelta('120D') # last 4 months of training set\n trades_features = all_trades_df[(all_trades_df['trade_date_dt'] >= train_start) & (all_trades_df['trade_date_dt'] < feature_and_target_cutoff)]\n trades_target = all_trades_df[(all_trades_df['trade_date_dt'] >= feature_and_target_cutoff) & (all_trades_df['trade_date_dt'] < train_end)]\n training_set = self.get_features_and_target(trades_features, trades_target)\n training_set['train_or_test'] = 'train'\n\n # test set\n trades_features = all_trades_df[(all_trades_df['trade_date_dt'] >= train_start) & (all_trades_df['trade_date_dt'] < train_end)]\n trades_target = all_trades_df[(all_trades_df['trade_date_dt'] >= train_end) & (all_trades_df['trade_date_dt'] < test_end)]\n test_set = self.get_features_and_target(trades_features, trades_target)\n test_set['train_or_test'] = 'test'\n \n customer_df = pd.concat([training_set, test_set], sort=False)\n customer_df.fillna(False, inplace=True)\n customer_df.index.name = 'sf_account_id'\n \n return customer_df","def housing_labels_(strat_train_set):\n logging.info(\"copy of dataset\")\n housing_labels = strat_train_set[\"median_house_value\"].copy()\n return housing_labels","def load_data(train=True):\n#test set\n start = time.clock()\n\n if train==False:\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\n df = pd.read_csv(dp)\n X = df.values.astype(np.float32)[:,1:]\n return X\n#train set\n else:\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\n df = pd.read_csv(dp)\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\n y = df.target\n y =y.apply(lambda X: int(X[-1])).values\n y = y.astype(np.int32)\n X, y = shuffle(X, y)\n #print(X.shape,y.shape)\n end = time.clock()\n print(end-start)\n\n return X,y","def house_prices_data() -> Tuple[\n pd.DataFrame, Datapoints, Datapoints, Datapoints, List[int]\n]:\n seed = 1515151555\n train_data_full = pd.read_csv(os.path.join(\"data\", \"train.csv\")).iloc[\n :, 1:\n ] # remove unused id column for training\n test_data_full = pd.read_csv(\n os.path.join(\"data\", \"test.csv\")\n ) # keep first id column for the later submission\n\n # Load transforms\n transforms = None\n with open(os.path.join(\"data\", \"transforms\"), \"r\") as f:\n transforms = json.load(f)\n transforms = [tuple(list_) for list_ in transforms]\n\n # Seed the train / val split\n np.random.seed(seed)\n train_data, eval_data = train_test_split_pandas(train_data_full)\n print(\n f\"\"\"Dataframe shapes (rows, cols):\ntrain {len(train_data), len(train_data.columns)},\neval {len(eval_data), len(eval_data.columns)},\ntest {len(test_data_full), len(test_data_full.columns)}.\"\"\"\n )\n (\n train_transformed,\n eval_transformed,\n test_transformed,\n cardinalities,\n ) = get_transformed_data(\n train_data=train_data,\n eval_data=eval_data,\n test_data=test_data_full,\n transforms=transforms,\n )\n print(f\"Baseline RMSE: {train_transformed.y.std():.3f}.\")\n return (\n test_data_full,\n train_transformed,\n eval_transformed,\n test_transformed,\n cardinalities,\n )","def prep_dataset(settings):\n train_dims = settings[\"train_dims\"]\n # Open HDF store. This is usually a soft link to our filtered dataset\n input_df, target_df, const = load_from_store(settings[\"dataset_path\"], columns=train_dims)\n\n try:\n del input_df[\"nions\"] # Delete leftover artifact from dataset split\n except KeyError:\n pass\n\n target_df = drop_outliers(target_df, settings)\n target_df = drop_nans(target_df)\n\n data_df = filter_input(input_df, target_df)\n del target_df, input_df\n data_df = convert_dtype(data_df, settings)\n\n return data_df","def loadSets(self, indir=\"\"):\n\n if indir==\"\":\n print(\"specify folder\")\n return -1\n\n self.train = pd.read_pickle(\"{}/train.pkl\".format(indir))\n self.valid = pd.read_pickle(\"{}/valid.pkl\".format(indir))\n self.test = pd.read_pickle(\"{}/test.pkl\".format(indir))\n\n print(\"sets loaded\")","def merge_data():\n\n\tconfig = Config()\n\tfilename_train, filename_test = \"../data/train.csv\", \"../data/test.csv\" \n\n # create datasets\n\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\n\n # 1. datetime features\n\t# diff between weekday and day?\n\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\n\t#day - Between 1 and the number of days in the given month of the given year.\n\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\n\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\n\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\n\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\n\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\n\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\n\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\n\n\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\n\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\n\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\n\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\n\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\n\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\n\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\n\n\t# 2. Location features\n\tdef haversine(lon1, lat1, lon2, lat2):\n\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\t dlon = lon2 - lon1\n\t dlat = lat2 - lat1\n\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n\t c = 2 * np.arcsin(np.sqrt(a))\n\t km = 6367 * c # AVG_EARTH_RADIUS=6367\n\t miles = km * 0.621371\n\t return miles\n\n\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\n\t# a = haversine_array(lat1, lng1, lat1, lng2)\n\t# b = haversine_array(lat1, lng1, lat2, lng1)\n\t# return a + b\n\n\t# def bearing_array(lat1, lng1, lat2, lng2):\n\t# AVG_EARTH_RADIUS = 6371 # in km\n\t# lng_delta_rad = np.radians(lng2 - lng1)\n\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\n\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n\t# return np.degrees(np.arctan2(y, x))\n\n\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\n\t train.dropoff_longitude, train.dropoff_latitude)\n\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\n\t test.dropoff_longitude, test.dropoff_latitude)\n\n\n\t# 3. Use outsource data\n\tweatherdata_filename = \"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\"\n\tfastestroute_data_train = \"../data/outsource_data/fastest_train.csv\"\n\tfastestroute_data_test = \"../data/outsource_data/fastest_routes_test.csv\"\n\n\n\twd = pd.read_csv(weatherdata_filename, header=0)\n\twd['date'] = pd.to_datetime(wd.date, format=\"%d-%m-%Y\")\n\twd['pickup_day'] = wd['date'].dt.day\n\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \n\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \n\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \n\n\t# Merge training data with weather data on pickup_day\n\tprint(\"Merging training data with weather data ....\")\n\twd_train = pd.merge(train, wd, on='pickup_day')\n\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\n\tgc.collect()\n\n\t# Merge wd_train with fastestroute_data\n\tfastest = pd.read_csv(fastestroute_data_train, header=0)\n\tprint(\"Merging Location data with weather and training data ....\")\n\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\n\n\tgc.collect()\n\n\n\tprint(\"===================== CHECK TRAINING DATA =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Semi-final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\t# Use the same outsource data with test set\n\t# merge outsource data with test data as well\n\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\t'total_travel_time',\n\t\t\t\t\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\n\t 'step_maneuvers','step_direction',\t'step_location_list']\n\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\n\n\tprint(\"Merging test data with Location data ....\")\n\ttest = pd.merge(test, fastest_test, on='id', how='outer')\n\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\n\t 'end_street'], axis=1)\n\tprint(\"Merging test data with weather data ....\")\n\ttest = pd.merge(test, wd, on='pickup_day')\n\n\tprint(\"===================== CHECK TEST DATA =====================\")\n\n\tprint(test.head(2))\n\tprint(\"Semi-final test data shape is: {}\".format(test.shape))\n\tprint(\"Test data columns: {}\".format(test.columns))\n\n\n\n\t# 4. Do more data munging\n\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = (wd_train_fastest.trip_duration < 60) \n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.trip_duration > 79200\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\n\twd_train_fastest = wd_train_fastest[~mask]\n\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\n\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\n\n\t# 5. Do some data maskig based on location to create jfk and lgo features\n\tjfk_lon = -73.778889\n\tjfk_lat = 40.639722\n\tlga_lon = -73.872611\n\tlga_lat = 40.77725\n\n\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\n\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\n\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\n\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\n\n\n\tprint(\"===================== CHECK TRAINING DATA AGAIN =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\treturn wd_train_fastest, test","def features_target_split():\n transactions = load_all_transactions('creditcard.csv')\n features = transactions.drop('Class', axis=1)\n target = transactions['Class']\n print('\\nCreation of feature set dataframe and target seiries successful \\n')\n return features, target","def test_set(self):\n if self._testset is None: # loads the data to memory once and when requested.\n testset_raw = self.read_dataset(self._testset_path)\n testset_spacy = self.read_spacy_pickle(self._testset_spacy_path)\n self._testset = pd.concat([testset_raw, testset_spacy], axis=1)\n\n self._testset['language'] = self._language\n self._testset['dataset_name'] = self._dataset_name\n\n return self._testset","def split_data_into_train_and_test(raw_training_data):\n train_set, test_set = train_test_split(raw_training_data, test_size=0.2, random_state=42)\n return train_set, test_set","def pickle_dataset(train_set, test_set, validation_set, path):\n\n train_set_filename = open(os.path.join(path, TRAIN_DATA_SET), 'wb')\n # Pickle classes_count\n cPickle.dump(train_set, train_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\n # Close the file\n train_set_filename.close()\n # Save hierarchy_mapping file\n test_set_filename = open(os.path.join(path, TEST_DATA_SET), 'wb')\n # Pickle hierarchy_mapping\n cPickle.dump(test_set, test_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\n # Close the file\n test_set_filename.close()\n # Save entities list\n validation_set_filename = open(os.path.join(path, VALIDATION_DATA_SET), 'wb')\n # Pickle entities\n cPickle.dump(validation_set, validation_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\n # Close the file\n validation_set_filename.close()\n\n print(\"Debug printing- the number of train samples: {0}, the number of test samples: {1}, \"\n \"the number of validation samples: {2}\".format(len(train_set), len(test_set), len(validation_set)))","def load_data_raw(source='local'):\n import os\n\n if source == 'kaggle':\n # Download the Ames Housing Dataset\n # Set the enviroment variables\n import zipfile\n os.environ['KAGGLE_USERNAME'] = \"lubomrstraka\"\n os.environ['KAGGLE_KEY'] = \"c7347462ef834e6645ce238c2f2fa561\"\n \n # Import dependencies\n os.system(\"pip install kaggle --upgrade --quiet\")\n\n # Download datasets\n os.system(\"kaggle competitions download -c house-prices-advanced-regression-techniques --quiet\")\n \n # Extract data\n with zipfile.ZipFile('house-prices-advanced-regression-techniques.zip', 'r') as archive:\n archive.extractall()\n\n # Read Train & Test Baseline Data\n train_bl = pd.read_csv('train.csv', index_col='Id')\n test_bl = pd.read_csv('test.csv', index_col='Id')\n \n # Using local data used while registering dataset\n # prevents \"Too many requests\" response from Kaggle\n elif source == 'local': \n train_bl = pd.read_csv('train.csv', index_col='Id')\n test_bl = pd.read_csv('test.csv', index_col='Id')\n \n else:\n print('Missing data!')\n train_bl = pd.DataFrame()\n test_bl = pd.DataFrame()\n\n\n return train_bl, test_bl","def wrangle_pick_data():\n # get data\n df = acquire.run()\n # prep and split data\n train, validate, test = prepare.run(df)\n # scale and define target\n # not completed yet\n return train, validate, test\n #return scale_mall(train, validate, test)","def _prepare_result_set(\n x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray\n) -> pd.DataFrame:\n # Prepare default target columns names if not provided:\n prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]\n if len(label_columns) == 0:\n # Add default label column names:\n if prediction_columns_amount == 1:\n label_columns = [\"predicted_label\"]\n else:\n label_columns = [\n f\"predicted_label_{i}\" for i in range(prediction_columns_amount)\n ]\n\n # Validate the label columns:\n if prediction_columns_amount != len(label_columns):\n # No equality between provided label column names and outputs amount:\n raise mlrun.errors.MLRunInvalidArgumentError(\n f\"The number of predicted labels: {prediction_columns_amount} \"\n f\"is not equal to the given label columns: {len(label_columns)}\"\n )\n common_labels = set(label_columns) & set(x.columns.tolist())\n if common_labels:\n # Label column exist in the original inputs:\n raise mlrun.errors.MLRunInvalidArgumentError(\n f\"The labels: {common_labels} are already existed in the given dataset.\"\n )\n\n return pd.concat(\n [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1\n )","def main(input_filepath, output_filepath):\n # return processed data and save in the output files\n in_data_y, y_output, in_data = make_data_set(input_filepath)\n in_data_y.to_csv(output_filepath)\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n return in_data_y, y_output, in_data","def split_dataset(data_set, train_size, test_size):\n # Generate random indices without replacement, to make train and test sets disjoint\n rand_indices = np.random.choice(data_set.shape[0], train_size+test_size, replace=False)\n feature_end = data_set.shape[1] - 1\n output_location = feature_end\n feature_offset = var.ALGORITHM_INFO['feature_offset']\n\n # Define the training and testing matrices\n x_train = data_set[rand_indices[0:train_size], feature_offset:feature_end]\n y_train = data_set[rand_indices[0:train_size], output_location]\n x_test = data_set[rand_indices[train_size:train_size+test_size], feature_offset:feature_end]\n y_test = data_set[rand_indices[train_size:train_size+test_size], output_location]\n favorite_test = data_set[rand_indices[train_size:train_size+test_size], 0]\n\n # Normalize features, with maximum value in training set\n # as realistically, this would be the only possibility\n\n for ii in range(x_train.shape[1]):\n maxval = np.max(np.abs(x_train[:, ii]))\n if maxval > 0:\n x_train[:, ii] = np.divide(x_train[:, ii], maxval)\n x_test[:, ii] = np.divide(x_test[:, ii], maxval)\n\n\n # Add a column of ones; done after to avoid modifying entire data_set\n x_train = np.hstack((x_train, np.ones((x_train.shape[0], 1))))\n x_test = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\n\n return (x_train, y_train), (x_test, y_test), favorite_test","def train_test_data_df(train_data_file, test_data_file):\n dtype_dict = {\n \"age\": np.int32,\n \"education-num\": np.int32,\n \"capital-gain\": np.int32,\n \"capital-loss\": np.int32,\n \"hours-per-week\": np.int32\n }\n cols = [i for i in range(15) if i != 2]\n train_data = pd.read_csv(train_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n train_data = train_data.dropna(axis=0, how=\"any\")\n test_data = pd.read_csv(test_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n test_data = test_data.dropna(axis=0, how=\"any\")\n return train_data, test_data","def train_set_official(self, stop_words=True):\n subset = self.__sub_set_from_linescsv(csv_path=self.csv_path_train)\n if not stop_words:\n with open(self.path_stop_words, 'r') as f_sw:\n stop_words = []\n for line in f_sw:\n stop_words += line.split(',')\n subset = subset.exclude_words(stop_words)\n return subset","def load_data():\n print(\"PARSING TRAIN\")\n ys_train, x_train, ids_train = load_pickle_data(\"ys_train\"), load_pickle_data(\"x_train\"), load_pickle_data(\n \"ids_train\")\n if ys_train is None or x_train is None or ids_train is None:\n ys_train, x_train, ids_train = load_csv_data(\"{}/train.csv\".format(DATA_DIR))\n dump_pickle_data(ys_train, \"ys_train\")\n dump_pickle_data(x_train, \"x_train\")\n dump_pickle_data(ids_train, \"ids_train\")\n\n print(\"PARSING TEST\")\n x_test, ids_test = load_pickle_data(\"x_test\"), load_pickle_data(\"ids_test\")\n if x_test is None or ids_test is None:\n _, x_test, ids_test = load_csv_data(\"{}/test.csv\".format(DATA_DIR))\n dump_pickle_data(x_test, \"x_test\")\n dump_pickle_data(ids_test, \"ids_test\")\n\n return ys_train, x_train, ids_train, x_test, ids_test","def fetch_bank_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n \"bank-marketing\", \"classification\", astype=\"pandas\", preprocess=preprocess\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\n column_map = {\n \"v1\": \"age\",\n \"v2\": \"job\",\n \"v3\": \"marital\",\n \"v4\": \"education\",\n \"v5\": \"default\",\n \"v6\": \"balance\",\n \"v7\": \"housing\",\n \"v8\": \"loan\",\n \"v9\": \"contact\",\n \"v10\": \"day\",\n \"v11\": \"month\",\n \"v12\": \"duration\",\n \"v13\": \"campaign\",\n \"v14\": \"pdays\",\n \"v15\": \"previous\",\n \"v16\": \"poutcome\",\n }\n if preprocess:\n\n def map_col(col):\n if col.find(\"_\") == -1:\n return column_map[col]\n prefix, suffix = col.split(\"_\")\n return column_map[prefix] + \"_\" + suffix\n\n orig_X.columns = [map_col(col) for col in orig_X.columns]\n age = pd.Series(orig_X[\"age\"] >= 25, dtype=np.float64)\n encoded_X = orig_X.assign(age=age)\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [1]},\n ],\n }\n return encoded_X, encoded_y, fairness_info\n else:\n orig_X.columns = [column_map[col] for col in orig_X.columns]\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [[25, 1000]]},\n ],\n }\n return orig_X, orig_y, fairness_info","def get_test_dataset_SetLoader(self, set_name='train'):\n set_loader = self.load_hdf5_file_SetLoader(set_name)\n\n set_data = self.dataset[set_name]\n set_fields = self.data_fields[set_name]\n\n return set_loader, set_data, set_fields","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n path = os.getcwd() # reads the current path\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\n x_train, y_train = shuffle(x_train, y_train)\n\n return x_train, y_train, x_test","def create_cleaned_dataset(PDBbind_dataset_path, general_set_PDBs_path, refined_set_PDBs_path, output_name, plot = False):\n \n # load dataset\n data = pd.read_csv(PDBbind_dataset_path)\n \n # check for NaNs in affinity data\n if data['-log(Kd/Ki)'].isnull().any() != False:\n print('There are NaNs present in affinity data!')\n \n # create list of PDB id's\n pdbid_list = list(data['PDB ID'])\n \n # remove affinity values that do not have structural data by searching PDBs\n missing = []\n for i in range(len(pdbid_list)):\n pdb = pdbid_list[i]\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==False and os.path.isdir(str(refined_set_PDBs_path) + pdb)==False:\n missing.append(pdb)\n data = data[~np.in1d(data['PDB ID'], list(missing))]\n\n # distinguish PDB id's in general and refined sets\n general_dict = {}\n refined_dict = {}\n for i in range(len(pdbid_list)):\n pdb = pdbid_list[i]\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==True:\n general_dict[pdb] = 'general'\n if os.path.isdir(str(refined_set_PDBs_path) + pdb)==True:\n refined_dict[pdb] = 'refined'\n \n # add 'set' column to data and fill with 'general' or 'refined'\n data['set'] = np.nan\n data.loc[np.in1d(data['PDB ID'], list(general_dict)), 'set'] = 'general'\n data.loc[np.in1d(data['PDB ID'], list(refined_dict)), 'set'] = 'refined'\n \n # write out csv of cleaned dataset\n data[['PDB ID', '-log(Kd/Ki)', 'set']].to_csv(output_name, index=False)\n \n # read in and view the cleaned dataset\n display(pd.read_csv(output_name))\n \n if plot == True:\n # plot affinity distributions for general and refined sets\n grid = sns.FacetGrid(data, row='set', row_order=['general', 'refined'],\n size=3, aspect=2)\n grid.map(sns.distplot, '-log(Kd/Ki)')\n else:\n return","def fetch_boston_housing_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.boston_housing_df(\n test_size=0.33\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index()\n black_median = np.median(train_X[\"B\"])\n label_median = np.median(train_y)\n\n if preprocess:\n # 1000(Bk - 0.63)^2 where Bk is the proportion of Blacks by town\n B = pd.Series(orig_X[\"B\"] > black_median, dtype=np.float64)\n encoded_X = orig_X.assign(B=B)\n fairness_info = {\n \"favorable_labels\": [[-10000.0, label_median]],\n \"protected_attributes\": [\n {\"feature\": \"B\", \"reference_group\": [0]},\n ],\n }\n return encoded_X, orig_y, fairness_info\n else:\n fairness_info = {\n \"favorable_labels\": [[-10000.0, label_median]],\n \"protected_attributes\": [\n # 1000(Bk - 0.63)^2 where Bk is the proportion of Blacks by town\n {\"feature\": \"B\", \"reference_group\": [[0.0, black_median]]},\n ],\n }\n return orig_X, orig_y, fairness_info","def load_data(y_name='Species'):\n train_path = tf.keras.utils.get_file(args.TRAIN_URL.split('/')[-1], args.TRAIN_URL)\n test_path = tf.keras.utils.get_file(args.TEST_URL.split('/')[-1], args.TEST_URL)\n\n train = pd.read_csv(train_path, names=args.CSV_COLUMN_NAMES, header=0)\n train_x, train_y = train, train.pop(y_name)\n\n test = pd.read_csv(test_path, names=args.CSV_COLUMN_NAMES, header=0)\n test_x, test_y = test, test.pop(y_name)\n\n return (train_x, train_y), (test_x, test_y)","def load_data():\n \n data = datasets.load_iris()\n df = pd.DataFrame(data.data, columns = data.feature_names)\n df2 = pd.DataFrame(data.target, columns = [\"target\"])\n\n return df.join(df2)","def GetDataSet(path):\n # Read csv files\n df_train_set = pd.read_csv(os.path.join(path, \"train.csv\"))\n df_test_set = pd.read_csv(os.path.join(path, \"test.csv\"))\n # Filter\n df_train_set.drop(\"Id\", axis=1, inplace=True)\n df_test_set.drop(\"Id\", axis=1, inplace=True)\n # Assign name\n df_train_set.name = \"train\"\n df_test_set.name = \"test\"\n # Filter还真的不能在这里做，不然返回的就是原生的DataFrame对象了\n return df_train_set, df_test_set","def from_files(cls, train_fname, test_fname, config_file):\n fguide = FeatureGuide(config_file)\n\n # Set index based on the feature guide and load only needed columns.\n index_col = cls.index_from_feature_guide(fguide)\n kwargs = dict(usecols=fguide.all_names, index_col=index_col)\n dset = cls(pd.read_csv(train_fname, **kwargs),\n pd.read_csv(test_fname, **kwargs),\n fguide)\n\n # Store pathnames as instance variables.\n dset.train_fname = os.path.abspath(train_fname)\n dset.test_fname = os.path.abspath(test_fname)\n return dset","def read_data_sets(data_path, fake_data=False, one_hot=False,\n validation_size=5000, source_url={},\n augment=False,\n percentage_train=100.,\n unbalance=False, unbalance_dict={\"percentage\": 20, \"label1\": 0, \"label2\": 8},\n ):\n\n class DataSets(object):\n pass\n\n data_sets = DataSets()\n\n if fake_data:\n data_sets.train = DataSet([], [], fake_data=True, one_hot=True)\n data_sets.validation = DataSet([], [], fake_data=True, one_hot=True)\n data_sets.test = DataSet([], [], fake_data=True, one_hot=True)\n return data_sets\n\n if not source_url: # empty string check\n if 'fashion' in data_path:\n source_url = DEFAULT_SOURCE_URL_FASHION\n else:\n source_url = DEFAULT_SOURCE_URL_MNIST\n\n if 'fashion' in data_path or 'mnist' in data_path: # mnist or fashion\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\n load_mnist(data_path, validation_size, source_url, one_hot)\n reshape = True\n else:\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\n load_medical_data(data_path)\n reshape = False\n\n # add random permutation to train & validation\n np.random.seed(42)\n\n n_train = train_images.shape[0]\n perm = np.random.permutation(n_train)\n train_images = train_images[perm]\n train_labels = train_labels[perm]\n\n n_val = val_images.shape[0]\n perm = np.random.permutation(n_val)\n val_images = val_images[perm]\n val_labels = val_labels[perm]\n\n # For experiments with data-augmentation\n if augment:\n if 'fashion' in data_path: # rotations +-10 and horizontal flips\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=True)\n elif 'mnist' in data_path: # rotations +-10\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=False)\n train_images = np.concatenate([train_images, np.expand_dims(augmented_images, 3)])\n train_labels = np.concatenate([train_labels, augmented_labels])\n # for the medical datasets, you can use the \"augment\" argument while doing patch extraction\n\n # For experiments with limited amount of data\n if percentage_train != 100.:\n train_size = int(0.01*percentage_train*train_images.shape[0])\n Xtrain_images, Xval_images, ytrain, yval = train_test_split(train_images, train_labels, train_size=train_size)\n train_images = Xtrain_images\n train_labels = ytrain\n\n # For experiments with class-imbalance distribution\n if unbalance:\n n_classes = len(np.unique(np.argmax(train_labels, 1)))\n reduceto = 0.01*unbalance_dict['percentage']\n label1 = unbalance_dict['label1']\n label2 = unbalance_dict['label2']\n\n pick_ids = []\n newsize = 0\n all_classes = np.arange(0, n_classes)\n all_classes = np.delete(all_classes, np.where(all_classes == label1)[0])\n all_classes = np.delete(all_classes, np.where(all_classes == label2)[0])\n\n for lab in [label1, label2]:\n allids = np.where(np.argmax(train_labels, 1) == lab)[0]\n selectedids = np.random.choice(allids, int(reduceto * allids.shape[0]), replace=False)\n pick_ids.append(selectedids)\n newsize += len(selectedids)\n\n new_ids = convert_list_to_array(pick_ids, newsize)\n\n other_ids = []\n othersize = 0\n for lab in all_classes.tolist():\n selectedids = np.where(np.argmax(train_labels, 1) == lab)[0]\n other_ids.append(selectedids)\n othersize += len(selectedids)\n\n keep_ids = convert_list_to_array(other_ids, othersize)\n\n # new_ids: contains the indices of the reduced (imbalance) classes\n # keep_ids: contains the indices of the rest (keep the same class distribution)\n resulting_ids = np.concatenate((new_ids, keep_ids))\n np.random.shuffle(resulting_ids)\n\n train_images = train_images[resulting_ids, ...]\n train_labels = train_labels[resulting_ids, ...]\n\n data_sets.train = DataSet(train_images, train_labels, fake_data=True, one_hot=True, reshape=reshape)\n data_sets.validation = DataSet(val_images, val_labels, fake_data=True, one_hot=True, reshape=reshape)\n data_sets.test = DataSet(test_images, test_labels, fake_data=True, one_hot=True, reshape=reshape)\n\n return data_sets","def train(self, train_set) -> None:\n super().train(train_set)\n # split into data and target\n xlist, y = zip(*train_set)\n x = sparse.vstack(xlist)\n self._classifier.fit(x, y)","def load_testset_predictions(all, weighted=False):\n if weighted:\n return load_data_from_CSV('results/testset_results_weighted_{}.csv'.format(all))\n else:\n return load_data_from_CSV('results/testset_results_{}.csv'.format(all))","def get_data(\n establishment_number=None,\n columns_to_drop=[],\n year_to_separate=2017,\n path_to_file=\"data/data_all.gz\",\n drop_na=False,\n drop_value_below_threshold=None,\n):\n date_col = \"Date\"\n visitors = \"visitors\"\n data = pd.read_csv(path_to_file, sep=\";\", decimal=\",\", index_col=\"id\")\n data[date_col] = pd.to_datetime(data[date_col])\n\n if drop_na:\n data.dropna(inplace=True)\n\n\n if drop_value_below_threshold is None:\n data.loc[data[visitors] < 0, visitors] = 0\n else:\n data = data[data[\"visitors\"] >= drop_value_below_threshold]\n\n\n if establishment_number is not None:\n data = data[data[\"library\"] == establishment_number]\n\n # Separate data in train and test DataFrame\n train = data[data.loc[:, date_col].dt.year < year_to_separate]\n test = data[data.loc[:, date_col].dt.year >= year_to_separate]\n\n # Drop columns\n to_drop = [date_col, \"Date:1\"] + columns_to_drop\n train.drop(to_drop, axis=1, inplace=True)\n test.drop(to_drop, axis=1, inplace=True)\n\n X_train = train.drop(visitors, axis=1)\n y_train = train[[visitors]]\n X_test = test.drop(visitors, axis=1)\n y_test = test[[visitors]]\n\n return X_train, y_train, X_test, y_test","def create_data_sets(reviews, labels, write_to_pickle=True, problem=\"\"):\n def sanity_check(labels):\n print str(len(labels)) + \" total labels. \" + str(sum(labels)) + \" positive labels. \" \\\n + str(len(labels) - sum(labels)) + \" negative labels. \"\n\n train_reviews = []\n train_labels = []\n dev_reviews = []\n dev_labels = []\n test_reviews = []\n test_labels = []\n\n total_train = int(len(reviews) * 0.5 / 2) # divided by 2 because of 2 classes\n total_dev = int(len(reviews) * 0.25 / 2)\n\n current_pos_training = 0\n current_neg_train = 0\n current_pos_dev = 0\n current_neg_dev = 0\n\n for (review, vote) in zip(reviews, labels):\n if vote == 1:\n if current_pos_training < total_train:\n train_reviews.append(review)\n train_labels.append(vote)\n current_pos_training += 1\n elif current_pos_dev < total_dev:\n dev_reviews.append(review)\n dev_labels.append(vote)\n current_pos_dev += 1\n else:\n test_reviews.append(review)\n test_labels.append(vote)\n\n # Negative review\n else:\n if current_neg_train < total_train:\n train_reviews.append(review)\n train_labels.append(vote)\n current_neg_train += 1\n elif current_neg_dev < total_dev:\n dev_reviews.append(review)\n dev_labels.append(vote)\n current_neg_dev += 1\n else:\n test_reviews.append(review)\n test_labels.append(vote)\n\n # Shuffle data for every dataset\n combined_lists = zip(train_reviews, train_labels)\n np.random.shuffle(combined_lists)\n train_reviews, train_labels = zip(*combined_lists)\n\n combined_lists = zip(dev_reviews, dev_labels)\n np.random.shuffle(combined_lists)\n dev_reviews, dev_labels = zip(*combined_lists)\n\n combined_lists = zip(test_reviews, test_labels)\n np.random.shuffle(combined_lists)\n test_reviews, test_labels = zip(*combined_lists)\n\n # Sanity checks\n print \"Total reviews: \" + str(len(reviews))\n print \"Original distribution: \"\n sanity_check(labels)\n print \"========================\"\n print \"Train labels\"\n sanity_check(train_labels)\n print \"========================\"\n print \"Dev labels\"\n sanity_check(dev_labels)\n print \"========================\"\n print \"Train labels\"\n sanity_check(test_labels)\n\n # Write to pickles\n N = len(reviews)\n if write_to_pickle:\n print \"Writing to pickle...\"\n pickle.dump([train_reviews, train_labels],\n open(\"TrainSet_\" + problem + '_' + str(N), \"wb\"), pickle.HIGHEST_PROTOCOL)\n\n pickle.dump([dev_reviews, dev_labels],\n open(\"DevSet_\" + problem + '_' + str(N), \"wb\"), pickle.HIGHEST_PROTOCOL)\n\n pickle.dump([test_reviews, test_labels],\n open(\"TestSet_\" + problem + '_' + str(N), \"wb\"), pickle.HIGHEST_PROTOCOL)\n print \"Done.\"\n\n return train_reviews, train_labels, dev_reviews, dev_labels, test_reviews, test_labels","def get_training_data(self):\n\n # this actually never was a set\n # src_set = self.target['src'].values\n # dst_set = self.target['dst'].values\n\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\n\n train_positive = self.target.iloc[self.train_edge_ind].values\n test_positive = self.target.iloc[self.test_edge_ind].values\n\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\n # print(f\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\")\n\n X_train = train_positive\n X_test = test_positive\n\n y_train = np.ones((self.train_edge_ind.shape[0],))\n y_test = np.ones((self.test_edge_ind.shape[0],))\n\n # X_train = np.vstack([\n # train_positive,\n # train_negative\n # ])\n\n # X_test = np.vstack([\n # test_positive,\n # test_negative\n # ])\n\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\n\n assert X_train.shape[0] == y_train.shape[0]\n assert X_test.shape[0] == y_test.shape[0]\n\n def shuffle(X, y):\n ind_shuffle = np.arange(0, X.shape[0])\n np.random.shuffle(ind_shuffle)\n return X[ind_shuffle], y[ind_shuffle]\n\n self.X_train, self.y_train = shuffle(X_train, y_train)\n self.X_test, self.y_test = shuffle(X_test, y_test)\n\n print(f\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\")\n\n # return X_train, X_test, y_train, y_test","def ConcatDF(train_set, test_set):\n return pd.concat([train_set, test_set], sort=True).reset_index(drop=True)","def train_test() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:\n X = pd.read_csv(os.path.join(conf.root_output, \"csv\", \"X.csv\"))\n y = pd.read_csv(os.path.join(conf.root_output, \"csv\", \"y.csv\")).squeeze()\n\n # Drop areas that are outside of borders of Vienna\n X.drop(columns=conf.outer_areas, errors=\"ignore\", inplace=True)\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\n return X_train, X_test, y_train, y_test","def train_test_set_split(dataset, dataset_name, test_size=0.1):\n train_indices_path = './' + dataset_name + '_train_indices(' + str(test_size) + ').txt'\n test_indices_path = './' + dataset_name + '_test_indices(' + str(test_size) + ').txt'\n try:\n train_indices = []\n test_indices = []\n file = open(train_indices_path, 'rt', encoding='utf-8')\n while True:\n line = file.readline()\n if not line:\n break\n train_indices.append(int(line[:-1]))\n file.close()\n file = open(test_indices_path, 'rt', encoding='utf-8')\n while True:\n line = file.readline()\n if not line:\n break\n test_indices.append(int(line[:-1]))\n file.close()\n train_labels = [dataset.targets[i] for i in train_indices]\n except FileNotFoundError:\n indices = np.arange(len(dataset))\n labels = np.array(dataset.targets)\n train_indices, test_indices, train_labels, _ = train_test_split(\n indices, labels, test_size=test_size, stratify=labels\n )\n file = open(train_indices_path, 'wt', encoding='utf-8')\n for i in train_indices:\n line = str(i) + '\\n'\n file.write(line)\n file.close()\n file = open(test_indices_path, 'wt', encoding='utf-8')\n for i in test_indices:\n line = str(i) + '\\n'\n file.write(line)\n file.close()\n\n train_set = torch.utils.data.Subset(dataset, indices=train_indices)\n test_set = torch.utils.data.Subset(dataset, indices=test_indices)\n return train_set, test_set, train_labels","def fetch_data(round:int) -> pd.DataFrame:\n\n \"\"\"\n You have to implement this function. This function can take \n an option (from the command line) and produces a DataFrame.\n (If you need a different kind of output, you also need to \n modify the main function. \n \"\"\"\n import numpy as np\n from sklearn.datasets import fetch_california_housing\n\n sampling_rate = 0.2*round\n np.random.seed(51)\n\n data = fetch_california_housing(download_if_missing=True)\n df = pd.DataFrame(data.data, columns=data.feature_names)\n df[target_name] = data.target\n\n ## sampling\n scores = np.random.uniform(low=0, high=1, size=df.shape[0])\n selected = scores < sampling_rate\n\n return df.iloc[selected,:].copy()","def train_dataset():\n return TabularDataset.from_path('tests/data/dummy_tabular/train.csv', sep=',')","def load_data(label_name='Species'):\n\n # Create a local copy of the training set.\n train_path = tf.keras.utils.get_file(fname=TRAIN_URL.split('/')[-1],\n origin=TRAIN_URL)\n # train_path now holds the pathname: （训练集和测试集路径） ~/.keras/datasets/iris_training.csv\n\n # Parse the local CSV file.（解析）\n train = pd.read_csv(filepath_or_buffer=train_path,\n names=CSV_COLUMN_NAMES, # list of column names\n header=0 # ignore the first row of the CSV file.\n )\n # train now holds a pandas DataFrame, which is data structure\n # analogous to a table.\n\n # 1. Assign the DataFrame's labels (the right-most column) to train_label.\n # 2. Delete (pop) the labels from the DataFrame.\n # 3. Assign the remainder of the DataFrame to train_features\n print(\"-\")\n train_features, train_label = train, train.pop(label_name)\n\n # Apply the preceding logic to the test set.\n test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)\n test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)\n test_features, test_label = test, test.pop(label_name)\n\n # Return four DataFrames.\n return (train_features, train_label), (test_features, test_label)","def load_or_generate_data(self) -> None:\n\n # Training set defined as a 5 x 5 square:\n xg1 = np.linspace(-5, 10, 5)\n xg2 = np.linspace(0, 15, 5)\n x = np.zeros((xg1.size * xg2.size, 2))\n for i, x1 in enumerate(xg1):\n for j, x2 in enumerate(xg2):\n x[i + xg1.size * j, :] = [x1, x2]\n\n y = self.branin(x)[:, None]\n self.x, self.y = x, y","def load_dataset():\n temp = gzip.open('mnist.pkl.gz')\n train, val , test = pickle.load(temp,encoding='latin1')\n temp.close()\n train_inp = [np.reshape(x, (784,1)) for x in train[0]]\n train_outp = [one_hot(y) for y in train[1]]\n training_data = zip(train_inp, train_outp)\n validation_inp = [np.reshape(x, (784, 1)) for x in val[0]]\n validation_data = zip(validation_inp, val[1])\n test_inp = [np.reshape(x, (784, 1)) for x in test[0]]\n test_data = zip(test_inp, test[1])\n return (training_data,validation_data,test_data)"],"string":"[\n \"def create_train_set(addition_to_filename = ''):\\n df = f.create_df()\\n print('original df size is ', df.shape)\\n print('original df columns ', df.columns)\\n\\n df = f.calculate_missing_prices_for_train_set(df)\\n print('df size after averaging price ', df.shape)\\n df = f.downcast_dtypes(df)\\n df = f.add_lag(all_data_df = df, df_to_add_lag= df, number_of_months=3)\\n df = f.add_days_stat(df)\\n\\n print(df.columns)\\n\\n # df = h.add_holidays(df)\\n # print('df size with holidays ', df.shape)\\n\\n timestr = time.strftime(\\\"%Y%m%d-%H%M%S\\\")\\n pickle.dump(df, open(f\\\"{timestr}_{addition_to_filename}_train.pickle.dat\\\", \\\"wb\\\"))\\n\\n # save feature names for further use\\n features_list = create_feature_names_list(df)\\n pickle.dump(features_list, open(f\\\"{timestr}_{addition_to_filename}_features.pickle.dat\\\", \\\"wb\\\"))\",\n \"def read_random_data_from_csv(\\n file_name, training_set_size, unlabeled_set_size, holdout_set_size, validation_set_size):\\n data = samp_file_to_arr(\\n file_name, training_set_size + unlabeled_set_size + holdout_set_size + validation_set_size)\\n y_raw = np.array([x[0] for x in data])\\n x_all = np.array([x[1:] for x in data])\\n # Now transform so that the lower label is -1, always. \\n uq = np.unique(y_raw) # Assumed to be only two unique labels!\\n y_all = np.zeros(len(y_raw))\\n y_all[np.where(y_raw == uq[0])[0]] = -1\\n y_all[np.where(y_raw == uq[1])[0]] = 1\\n xtrhoval, x_unl, ytrhoval, y_unl = sklearn.model_selection.train_test_split(\\n x_all, y_all, test_size=unlabeled_set_size)\\n x_trho, x_validate, y_trte, y_validate = sklearn.model_selection.train_test_split(\\n xtrhoval, ytrhoval, test_size=validation_set_size)\\n x_train, x_out, y_train, y_out = sklearn.model_selection.train_test_split(\\n x_trho, y_trte, test_size=holdout_set_size)\\n return (x_train, y_train, x_unl, y_unl, x_out, y_out, x_validate, y_validate)\",\n \"def get_train_data():\\n # train set\\n train = pd.read_csv(\\\"train.csv\\\")\\n\\n return train\",\n \"def load_data():\\n train = pd.read_csv(\\\"../input/train.csv\\\", dtype={\\\"Age\\\": np.float64}, )\\n test = pd.read_csv(\\\"../input/test.csv\\\", dtype={\\\"Age\\\": np.float64}, )\\n\\n train = train.set_index('PassengerId')\\n test = test.set_index('PassengerId')\\n\\n train = train.apply(preprocess, axis=1)\\n test = test.apply(preprocess, axis=1)\\n\\n x_train = train.drop(['Survived'], axis=1)\\n y_train = train['Survived']\\n x_test = test\\n return {'train': {'x': x_train, 'y': y_train},\\n 'test': {'x': x_test},\\n 'full_features': pd.concat([x_train, x_test])}\",\n \"def learning_set_builders(self):\\n # Transform in a dataframe:\\n original_train_x = pd.DataFrame(self.original_train_x, columns=self.original_x_header)\\n original_train_y = pd.DataFrame(self.original_train_y, columns=self.original_y_header)\\n original_test_x = pd.DataFrame(self.original_test_x, columns=self.original_x_header)\\n original_test_y = pd.DataFrame(self.original_test_y, columns=self.original_y_header)\\n original_validation_x = pd.DataFrame(self.original_validation_x, columns=self.original_x_header)\\n original_validation_y = pd.DataFrame(self.original_validation_y, columns=self.original_y_header)\\n\\n # Training set\\n x, y = self.convertor(original_train_x, original_train_y)\\n self.pairs_train_x = x.to_numpy()\\n self.pairs_train_y = y.to_numpy()\\n\\n # Headers\\n self.pairs_x_header = x.columns\\n self.pairs_y_header = y.columns\\n\\n # Testing set\\n x, y = self.convertor(original_test_x, original_test_y)\\n self.pairs_test_x = x.to_numpy()\\n self.pairs_test_y = y.to_numpy()\\n\\n # Validation set\\n x, y = self.convertor(original_validation_x, original_validation_y)\\n self.pairs_validation_x = x.to_numpy()\\n self.pairs_validation_y = y.to_numpy()\",\n \"def train_set(self):\\n if self._trainset is None: # loads the data to memory once and when requested.\\n trainset_raw = self.read_dataset(self._trainset_path)\\n trainset_spacy = self.read_spacy_pickle(self._trainset_spacy_path)\\n if trainset_raw is None and trainset_spacy is None:\\n # This is for languages we never see (French)\\n self._trainset = None\\n else:\\n self._trainset = pd.concat([trainset_raw, trainset_spacy], axis=1)\\n\\n self._trainset['language'] = self._language\\n self._trainset['dataset_name'] = self._dataset_name\\n\\n return self._trainset\",\n \"def createTrainTestSets():\\n tweets = open(noDuplicatesFilename, 'r').read().splitlines()\\n name_mapping = loadNameMapping()\\n holdoutLocations = [u'Frederiksberg, Danmark', u'T\\\\xe5rnby, Danmark', u'Kolding, Danmark', u'T\\\\xe4by, Sverige', u'Kungsbacka, Sverige', u'Kristianstad, Sverige', u'Bod\\\\xf8, Norge', u'Kvinnherad, Norge', u'Ullensaker, Norge']\\n testSetLocation = []\\n rest = []\\n for tweet in tweets:\\n if stringToTweet(tweet).getFullName() in holdoutLocations:\\n testSetLocation.append(tweet)\\n else:\\n rest.append(tweet)\\n tweets = rest\\n testIndex = int(round(len(tweets) * (1 - test_set_ratio)))\\n random.seed(1)\\n random.shuffle(tweets)\\n trainSet = tweets[:testIndex]\\n testSet = tweets[testIndex:]\\n open(trainSetFilename, 'w').write('\\\\n'.join(trainSet))\\n open(testSetNormalFilename, 'w').write('\\\\n'.join(testSet))\\n open(testSetLocationFilename, 'w').write('\\\\n'.join(testSetLocation))\\n print \\\"Wrote %d tweets to train set\\\" % len(trainSet)\\n print \\\"Wrote %d tweets to normal test set\\\" % len(testSet)\\n print \\\"Wrote %d tweets to location test set\\\" % len(testSetLocation)\",\n \"def load_testset(filename):\\n pickle_name = filename + \\\".pickle\\\"\\n try:\\n print(\\\"trying to load \\\" + filename + \\\" from pickle\\\")\\n dataset = pickle.load(open(pickle_name, \\\"rb\\\"))\\n except:\\n with open(filename, 'r') as csv_file:\\n print(\\\"no pickle exists. parsing file \\\" + filename)\\n dataset = [DataPoint(item[0:], \\\" \\\")\\n for item\\n in csv.reader(csv_file, delimiter=',')]\\n pickle.dump(dataset, open(pickle_name, \\\"wb\\\"))\\n print(\\\"loaded \\\" + filename)\\n return dataset\",\n \"def load_data(self):\\n with open('data/fordTrain.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n train = [x for i, x in enumerate(data) if i > 0] \\n # Extract features and target variable separately\\n trainx = [x[3:] for x in train]\\n trainy = [x[2] for x in train]\\n\\n with open('data/fordTest.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \\n\\n with open('data/Solution.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testy = [x[2] for i, x in enumerate(data) if i > 0] \\n\\n # Extract features and target variable, convert to numpy array\\n trainx = np.asarray(trainx, dtype=np.float32)\\n trainy = np.asarray(trainy, dtype=np.int8)\\n testx = np.asarray(testx, dtype=np.float32)\\n testy = np.asarray(testy, dtype=np.int8)\\n\\n # Return training and test sets\\n trainSet = Dataset(trainx, trainy)\\n testSet = Dataset(testx, testy)\\n return trainSet, testSet\",\n \"def load_data():\\n data = gzip.open(\\\"mnist.pkl.gz\\\", \\\"rb\\\")\\n train_set, valid_set, test_set = cPickle.load(data)\\n data.close()\\n\\n # Combine validation and train folds to recreate the master 60k set.\\n new_images = numpy.concatenate((train_set[0], valid_set[0]))\\n new_labels = numpy.concatenate((train_set[1], valid_set[1]))\\n\\n train_set = (new_images, new_labels)\\n \\n return (train_set, test_set)\",\n \"def load_train_test() -> Tuple[pd.DataFrame, pd.DataFrame]:\\n df = load_data() \\n #Sample\\n train_df = df.sample(frac=0.8, random_state=42)\\n test_df = df[~df.index.isin(train_df.index)]\\n #Rescale\\n train_mean = train_df.iloc[:, :-1].mean()\\n train_std = train_df.iloc[:, :-1].std()\\n train_df.iloc[:, :-1] = (train_df.iloc[:, :-1] - train_mean) / train_std\\n test_df.iloc[:, :-1] = (test_df.iloc[:, :-1] - train_mean) / train_std\\n return train_df, test_df\",\n \"def get_dataset(filename, target_name, training_fraction):\\n csv_reader = csv.DictReader(open(filename))\\n attributes_names = [attribute_name for attribute_name in csv_reader.fieldnames\\n if attribute_name != target_name]\\n train_dataset = Dataset(attributes_names, target_name)\\n test_dataset = Dataset(attributes_names, target_name)\\n instances = []\\n for row in csv_reader:\\n instances.append(([row[attribute_name] for attribute_name in attributes_names],\\n row[target_name]))\\n all_instances_indexes = set(range(0, len(instances)))\\n number_of_training_instances = int(len(all_instances_indexes) * training_fraction)\\n training_instances_indexes = set(sample(all_instances_indexes, number_of_training_instances))\\n test_instances_indexes = all_instances_indexes.difference(training_instances_indexes)\\n for instance_index in training_instances_indexes:\\n train_dataset.add_instance(instances[instance_index][0], instances[instance_index][1])\\n for instance_index in test_instances_indexes:\\n test_dataset.add_instance(instances[instance_index][0], instances[instance_index][1])\\n return train_dataset, test_dataset\",\n \"def create_sets(test, data, test_size=0.2, write=False):\\n y_test = test['y_old']\\n X_test = test.drop('y_old', 1)\\n y_data = data['y_old']\\n X_data = data.drop('y_old', 1)\\n X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=test_size, random_state=123)\\n if write:\\n pickle.dump((X_train, X_val, y_train, y_val), open(obj_save_path+'train_val_df.p', 'wb'))\\n #X_train, X_val, y_train, y_val = pickle.load(open(obj_save_path+'train_val_df.p', 'rb'))\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def initSets(self):\\n data_frame = pd.read_csv(self.train_file, header=None)\\n data_frame = data_frame.drop(columns=self.drop_cols)\\n features = data_frame.iloc[:, :-1].values\\n labels = data_frame.iloc[:, -1].values\\n if self.test_file is None:\\n self.train_feat, self.test_feat, self.train_labl, self.test_labl = train_test_split(features, labels, test_size=self.test_size)\\n else:\\n data_frame = pd.read_csv(self.test_file, header=None)\\n data_frame = data_frame.drop(columns=self.drop_cols)\\n self.train_feat, _, self.train_labl, _ = train_test_split(features, labels, test_size=self.test_size)\\n features = data_frame.iloc[:, :-1].values\\n labels = data_frame.iloc[:, -1].values\\n _, self.test_feat, _, self.test_labl = train_test_split(features, labels, test_size=self.test_size)\\n # kfold = KFold(n_splits=3)\\n # self.train_index, self.test_index = kfold.split(features,labels)\",\n \"def time_split_dataset(df, train_start_date, train_end_date, holdout_end_date, date_col):\\n\\n train_set = df.copy()[\\n (df[date_col] >= train_start_date) & (df[date_col] <= train_end_date)]\\n\\n test_set = df.copy()[\\n (df[date_col] > train_end_date) & (df[date_col] <= holdout_end_date)]\\n\\n return train_set, test_set\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def prepare_data(args):\\n logger.info('Loading dataframe from %s' % args.newspath)\\n df = pd.read_csv(args.newspath, encoding='gb18030')\\n logger.info('Dataframe size: %d observations %d features after loaded' % (df.shape[0], df.shape[1]))\\n\\n # exclude rows with column source == NaN\\n logger.info('Data cleansing...')\\n df = df[~pd.isna(df['source'])]\\n logger.info('Dataframe size: %d observations %d features after data cleansing' % (df.shape[0], df.shape[1]))\\n\\n # split the dataframe into training set and test set\\n logger.info('Making training set & test set...')\\n train_set, test_set = split_data(df)\\n logger.info('Traning set size: %d' % train_set.shape[0])\\n logger.info('Test set size: %d' % test_set.shape[0])\\n\\n # save the train set and test set to picke files\\n logger.info('Save dataframes to files...')\\n train_set.to_pickle(args.trainpath)\\n test_set.to_pickle(args.testpath)\",\n \"def create_train_valid_set(self):\\n\\n if not self.eq_train:\\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level, self.train_weights, self.y_train,\\n train_size=0.7, test_size=0.3\\n )\\n else:\\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, w_train_eq, w_valid_eq, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level,\\n self.train_weights, self.train_weights_eq, self.y_train,\\n train_size=0.7, test_size=0.3\\n )\\n self.train_weights_eq = w_train_eq\\n\\n #NOTE: might need to re-equalise weights in each folds as sumW_sig != sumW_bkg anymroe!\\n self.train_weights = train_w\\n self.valid_weights = valid_w #validation weights should never be equalised weights!\\n\\n print 'creating validation dataset'\\n self.X_train_high_level = X_train_high_level\\n self.X_train_low_level = self.join_objects(X_train_low_level)\\n\\n self.X_valid_high_level = X_valid_high_level\\n self.X_valid_low_level = self.join_objects(X_valid_low_level)\\n print 'finished creating validation dataset'\\n\\n self.y_train = y_train\\n self.y_valid = y_valid\",\n \"def holdout(self, X, y):\\n #X,y = self.define_dataset(self.df, self.col_list, self.target_var)\\n X_train, X_test, y_train, y_test = train_test_split(\\n X, y, test_size = 0.3, random_state = 0)\\n\\n return X_train, X_test, y_train, y_test\",\n \"def load_susy(trainsize=500, testsize=1000):\\n filename = 'datasets/susysubset.csv'\\n dataset = loadcsv(filename)\\n trainset, testset = splitdataset(dataset, trainsize, testsize)\\n return trainset, testset\",\n \"def read(train_path, test_path, label_name):\\n train_dataset = pd.read_csv(train_path)\\n test_dataset = pd.read_csv(test_path)\\n\\n train_labels = train_dataset.pop(label_name)\\n\\n imputer = DataFrameImputer().fit(train_dataset)\\n train_dataset = imputer.transform(train_dataset)\\n test_dataset = imputer.transform(test_dataset)\\n\\n train_dataset = pd.get_dummies(train_dataset)\\n test_dataset = pd.get_dummies(test_dataset)\\n\\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\\n\\n scaler = StandardScaler().fit(train_dataset)\\n train_dataset = scaler.transform(train_dataset)\\n test_dataset = scaler.transform(test_dataset)\\n\\n return train_dataset, train_labels, test_dataset\",\n \"def create_sets():\\n global train_x, train_y, val_x, val_y\\n\\n print('Creating sets')\\n\\n dataframe = pd.read_csv('LoggerBot.log', names=NAMES).sample(frac=1)\\n inputs = dataframe.values[:,:-1].astype(np.float32)\\n outputs = dataframe.values[:,-1].astype(np.int32)\\n\\n train_set_size = int(len(dataframe) * 0.7)\\n train_x, train_y = inputs[:train_set_size], outputs[:train_set_size]\\n val_x, val_y = inputs[train_set_size:], outputs[train_set_size:]\",\n \"def import_data(seed: object = 42) -> object:\\n\\n # Read input data\\n df = pd.read_csv(\\\"x_train_gr_smpl.csv\\\").astype(int)\\n\\n # label data-frame rows based on sample data\\n for x in range(10):\\n index = ~pd.read_csv(\\\"y_train_smpl_%s.csv\\\" % x, squeeze=True).astype(bool) # reversed flags (~)\\n df.loc[index, 'label'] = str(x)\\n\\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\\n output_data_ordered = df.iloc[:, 2304].to_numpy()\\n\\n # Randomise instance order (forcing the same result each time)\\n np.random.seed(seed)\\n permutation = np.random.permutation(df.shape[0])\\n\\n # Create base input and output arrays\\n input_data = input_data_ordered[permutation]\\n output_data = output_data_ordered[permutation]\\n\\n return input_data, output_data, df, input_data_ordered, output_data_ordered\",\n \"def GetDataSet(filename: str):\\n # Read csv files\\n # Assign our data set name attribute to simplify our code\\n df_train_set = pd.read_csv(filename + \\\"/train.csv\\\")\\n df_train_set.name = \\\"train\\\"\\n df_test_set = pd.read_csv(filename + \\\"/test.csv\\\")\\n df_test_set.name = \\\"test\\\"\\n # Combine train set and test set\\n df_all = ConcatDF(df_train_set, df_test_set)\\n df_all.name = \\\"all\\\"\\n # Remember to return the train set and test set for the next step\\n return df_train_set, df_test_set, df_all\",\n \"def create_test_and_train_sets(input_file):\\n \\n input_df = pd.read_csv(input_file, sep=',', header=0)\\n df_items = pd.DataFrame({'productId': input_df.productId.unique()})\\n df_sorted_items = df_items.sort_values('productId').reset_index()\\n pds_items = df_sorted_items.productId\\n \\n df_user_items = input_df.groupby(['userId', 'productId']).agg({'Expense': 'sum'})\\n \\n # create a list of (userId, productId, Expense) ratings, where userId and productId are 0-indexed\\n current_u = -1\\n ux = -1\\n pv_ratings = []\\n user_ux = []\\n \\n for timeonpg in df_user_items.itertuples():\\n user = timeonpg[0][0]\\n item = timeonpg[0][1]\\n if user != current_u:\\n user_ux.append(user)\\n ux += 1\\n current_u = user\\n ix = pds_items.searchsorted(item)[0]\\n pv_ratings.append((ux, ix, timeonpg[1]))\\n\\n # convert ratings list and user map to np array\\n pv_ratings = np.asarray(pv_ratings)\\n user_ux = np.asarray(user_ux)\\n \\n # create train and test coos matrixes\\n tr_sparse, test_sparse = _create_sparse_train_and_test(pv_ratings, ux + 1, df_items.size)\\n \\n return user_ux, pds_items.as_matrix(), tr_sparse, test_sparse\",\n \"def open_MRI_data(csv_path, train_set = 0.8, n_followups=5, normalize=True):\\n\\n data_df = pd.read_csv(csv_path)\\n\\n mri_col = data_df.columns.str.contains(\\\"SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16\\\")\\n mri_col = data_df.columns[mri_col].values\\n\\n data_df = data_df.dropna(axis=0, subset=mri_col)\\n\\n # Select only the subjects with nfollowups\\n # Code to only select 5 first appearances of each PTID\\n ptid_list = np.unique(data_df[\\\"PTID\\\"])\\n\\n idx_to_drop = []\\n for ptid in ptid_list:\\n i_list = data_df.index[data_df['PTID'] == ptid].tolist()\\n if len(i_list) < 5:\\n idx_to_drop = idx_to_drop + i_list\\n elif len(i_list) > 5:\\n idx_to_drop = idx_to_drop + i_list[5:]\\n\\n data_final = data_df.drop(idx_to_drop)\\n\\n print(data_final.shape)\\n\\n # Normalize only features\\n data_final.loc[:,mri_col] = data_final.loc[:,mri_col].apply(lambda x: (x-x.mean())/ x.std(), axis=0)\\n\\n # Divide between test and train\\n from sklearn.model_selection import GroupShuffleSplit\\n gss = GroupShuffleSplit(n_splits=1, test_size=1.0-train_set)\\n train_dataset, test_dataset = next(gss.split(X=data_final, y=data_final.DX_bl.values, groups=data_final.PTID.values))\\n\\n df_train = data_final.iloc[train_dataset]\\n df_test = data_final.iloc[test_dataset]\\n\\n df_train = df_train.reset_index(drop=True)\\n df_test = df_test.reset_index(drop=True)\\n\\n # Return the features in the correct shape (Nsamples, timesteps, nfeatures)\\n X_train = pandas_to_data_timeseries(df_train, mri_col)\\n X_test = pandas_to_data_timeseries(df_test, mri_col)\\n\\n return X_train, X_test\",\n \"def dataset()-> pd.DataFrame:\\n old= pd.read_csv(DIRECT_OLD)\\n new= pd.read_csv(DIRECT_NEW)\\n return pd.concat([old, new])\",\n \"def prep_data(df):\\n y = df.target\\n X = df.drop([\\\"target\\\"], axis=1)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\\n return X_train, X_test, y_train, y_test\",\n \"def data_prep(data, y, dropna=False):\\n\\n rand_state = 10 # Setting random state for later cv\\n df = pd.read_pickle(data) # Reading in data\\n if dropna is True:\\n df.dropna(axis=0, inplace = True)\\n else:\\n pass\\n X = df.drop(y, axis=1) # Assigning the feature space to X\\n y = df[y] # Class labels to predict\\n\\n return X, y, rand_state\",\n \"def get_train_csv(self):\\n try:\\n self.train_article = pd.read_csv(constants.DATA_DIR / 'knn_article_tags.csv')\\n except FileNotFoundError:\\n train = pd.Series([])\\n for csv_file in os.listdir(constants.CLEAN_DIR):\\n if csv_file in self.article_feat_csvs:\\n df = pd.read_csv(constants.CLEAN_DIR / csv_file)\\n feat = csv_file[8:-4]\\n g = df.dropna(axis=0).groupby(\\\"id\\\")[self.tag_ref[feat]]\\n if train.empty:\\n train = g.apply(lambda x: list(x.astype(str).str.lower()))\\n else:\\n g = g.apply(lambda x: list(x.astype(str).str.lower()))\\n train = train.combine(g, lambda x1, x2: list(set(x1+x2)), fill_value=[])\\n\\n train = pd.DataFrame({'id':train.index, 'tags':train.values})\\n train.to_csv(constants.DATA_DIR / 'knn_article_tags.csv', header=True)\\n self.train_article = train\\n\\n try:\\n self.train_image = pd.read_csv(constants.DATA_DIR / 'knn_image_tags.csv')\\n except FileNotFoundError:\\n train = pd.Series([])\\n for csv_file in os.listdir(constants.CLEAN_DIR):\\n if csv_file in self.image_feat_csvs:\\n df = pd.read_csv(constants.CLEAN_DIR / csv_file)\\n feat = csv_file[6:-4]\\n g = df.dropna(axis=0).groupby(\\\"id\\\")[self.tag_ref[feat]]\\n if train.empty:\\n train = g.apply(lambda x: list(x.astype(str).str.lower()))\\n else:\\n g = g.apply(lambda x: list(x.astype(str).str.lower()))\\n train = train.combine(g, lambda x1, x2: list(set(x1+x2)), fill_value=[])\\n\\n train = pd.DataFrame({'id':train.index, 'tags':train.values})\\n train.to_csv(constants.DATA_DIR / 'knn_image_tags.csv', header=True)\\n self.train_image = train\",\n \"def get_training_and_testing_sets(data, Y):\\r\\n data = pd.concat([data, Y], axis=1)\\r\\n x,y=data.shape\\r\\n train_X_sub1=data[0:x//6]\\r\\n dev_X_sub1 = data[x//6:x//6 + x//12]\\r\\n test_X_sub1 = data[x//6 + x//12:x//3]\\r\\n\\r\\n train_X_sub2 = data[x//3:x//3+x//6]\\r\\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\\r\\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\\r\\n\\r\\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\\r\\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\\r\\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\\r\\n\\r\\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\\r\\n train_X =train_X.append(train_X_sub3,ignore_index = True)\\r\\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\\r\\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\\r\\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\\r\\n test_X = test_X.append(test_X_sub3,ignore_index = True)\\r\\n\\r\\n\\r\\n train_X = util.shuffle(train_X)\\r\\n train_X = train_X.reset_index(drop=True)\\r\\n\\r\\n dev_X = util.shuffle(dev_X)\\r\\n dev_X = dev_X.reset_index(drop=True)\\r\\n\\r\\n test_X = util.shuffle(test_X)\\r\\n test_X = test_X.reset_index(drop=True)\\r\\n\\r\\n train_X_final=train_X\\r\\n dev_X_final = dev_X\\r\\n test_X_final = test_X\\r\\n x, y = train_X_final.shape\\r\\n train_X = train_X_final.iloc[:, 0:y - 1]\\r\\n train_Y = train_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = test_X_final.shape\\r\\n test_X = test_X_final.iloc[:, 0:y - 1]\\r\\n test_Y = test_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = dev_X_final.shape\\r\\n dev_X = dev_X_final.iloc[:, 0:y - 1]\\r\\n dev_Y = dev_X_final.iloc[:, y - 1]\\r\\n\\r\\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y\",\n \"def open_MRI_data_var(csv_path, train_set = 0.8, normalize=True):\\n data_df = pd.read_csv(csv_path)\\n\\n mri_col = data_df.columns.str.contains(\\\"SV_UCSFFSX_11_02_15_UCSFFSX51_08_01_16\\\")\\n mri_col = data_df.columns[mri_col].values\\n\\n data_df = data_df.dropna(axis=0, subset=mri_col)\\n\\n # Select only the subjects with nfollowups\\n # Code to only select 5 first appearances of each PTID\\n ptid_list = np.unique(data_df[\\\"PTID\\\"])\\n\\n idx_to_drop = []\\n data_final = data_df.drop(idx_to_drop)\\n\\n # Divide between test and train\\n from sklearn.model_selection import GroupShuffleSplit\\n gss = GroupShuffleSplit(n_splits=1, test_size=1.0-train_set)\\n train_dataset, test_dataset = next(gss.split(X=data_final, y=data_final.DX_bl.values, groups=data_final.PTID.values))\\n\\n df_train = data_final.iloc[train_dataset]\\n df_test = data_final.iloc[test_dataset]\\n\\n df_train = df_train.reset_index(drop=True)\\n df_test = df_test.reset_index(drop=True)\\n\\n # Return the features in the correct shape list of Tensors (timesteps, nfeatures)\\n X_train = pandas_to_data_timeseries_var(df_train, mri_col)\\n X_test = pandas_to_data_timeseries_var(df_test, mri_col)\\n\\n return X_train, X_test\",\n \"def load_training_set():\\n global training_set\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n train, valid, test = cPickle.load(f)\\n [training_set, training_labels] = train\\n [validation_set, validation_labels] = valid\\n [testing_set, testing_labels] = test\\n training_set = np.concatenate((training_set, validation_set))\\n f.close()\\n np.random.shuffle(training_set)\",\n \"def _prepare_ml_data(X, y, to_optimize=False):\\n size_test = 1\\n y_test = None\\n if to_optimize:\\n size_test = CONFIG.OPTIMIZE_PARAMS['size'] + 1\\n y_test = y.iloc[-size_test:]\\n X_train = X.iloc[:-size_test]\\n y_train = y.iloc[:-size_test]\\n X_test = X.iloc[-size_test:]\\n return X_train, y_train, X_test, y_test\",\n \"def import_training_data(target_col = 'label'):\\n dir = os.path.dirname(os.path.dirname(__file__)) # go up one level to get root of this experiment\\n path = os.path.join(dir, 'data','train.csv')\\n utils_logr.info('Loading data from {} as pandas df'.format(path))\\n df = pd.read_csv(path)\\n y = df[target_col]\\n df = df.drop(target_col, axis=1)\\n return df, y\",\n \"def load_occupancy_dataset(trainsize=500, testsize=1000):\\n filename = 'datasets/numericsequence.csv'\\n dataset = loadcsv(filename)\\n trainset, testset = splitdataset(dataset, trainsize, testsize)\\n return trainset, testset\",\n \"def get_trainset(set_name=\\\"car\\\"):\\n names_file = open(\\\"../../data/\\\"+set_str+\\\"/\\\"+set_name+\\\"_names.dat\\\", \\\"r\\\")\\n data_file= open(\\\"../../data/\\\"+set_str+\\\"/\\\"+set_name+\\\"_data.dat\\\", \\\"r\\\")\\n \\n names_strs = names_file.readlines()\\n v_index = 0\\n a_index = 0\\n for index,line in enumerate(names_strs):\\n if line.find(\\\"| class values\\\") != -1:\\n v_index = index\\n elif line.find(\\\"| attributes\\\") != -1:\\n a_index = index\\n break\\n class_values_strs = re.split(r\\\"\\\\W+\\\",names_strs[v_index+2].strip())\\n attribute_namea_value_list = []\\n for i in range(a_index, len(names_strs)):\\n if re.search(r\\\"^\\\\w+:\\\\s+[\\\\w,\\\\s]+\\\\.\\\",names_strs[i]) != None:\\n temp_strs = re.split(r\\\"\\\\W+\\\",names_strs[i][:-2])\\n attribute_namea_value_list.append((temp_strs[0],temp_strs[1:]))\\n# print class_values_strs\\n# print attribute_namea_value_list\\n train_set = []\\n while True:\\n temp_str = data_file.readline()[:-1]\\n if temp_str == \\\"\\\":\\n break\\n values = temp_str.split(\\\",\\\")\\n train_set.append((values[:-1], values[-1]))\\n return class_values_strs,attribute_namea_value_list,train_set\",\n \"def load_data(trainfile, testfile):\\n raw_train = pd.read_csv(trainfile, header=None)\\n raw_test = pd.read_csv(testfile, header=None)\\n train = raw_train.values\\n test = raw_test.values\\n train_features = train[0::, 1::]\\n train_label = train[::, 0]\\n test_features = test[0::, 1::]\\n test_label = test[::, 0]\\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\\n return train, train_label, \\\\\\n cv, cv_label, \\\\\\n test_features, test_label\",\n \"def construct_train_set(tweet_pos, tweet_neg):\\n tweet_pos['pred'] = 1\\n tweet_neg['pred'] = 0\\n tweet_pos.columns = ['tweet', 'pred']\\n tweet_neg.columns = ['tweet', 'pred']\\n all_tweets = tweet_neg.append(tweet_pos)\\n tweet_TR = all_tweets.reset_index().drop(['index'], axis = 1)\\n return tweet_TR\",\n \"def load(train_file, test_file):\\n print('\\\\nLoad the raw training and test set data...')\\n y_train, tx_train, ids_train = load_csv_data(train_file)\\n y_test, tx_test, ids_test = load_csv_data(test_file)\\n print('\\\\n... finished.')\\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test\",\n \"def eccv_train_and_test_set(split_csv_filepath):\\n df = pd.read_csv(split_csv_filepath, header=None)\\n filenames = [os.path.join(os.path.dirname(split_csv_filepath), 'hotornot_face', _.replace('.bmp', '.jpg')) for _ in\\n df.iloc[:, 0].tolist()]\\n scores = df.iloc[:, 1].tolist()\\n flags = df.iloc[:, 2].tolist()\\n\\n train_set = dict()\\n test_set = dict()\\n\\n for i in range(len(flags)):\\n if flags[i] == 'train':\\n train_set[filenames[i]] = scores[i]\\n elif flags[i] == 'test':\\n test_set[filenames[i]] = scores[i]\\n\\n return train_set, test_set\",\n \"def split_train_test(df_train, labels):\\n n_train = np.shape(df_train)[0]\\n X = {'train': [], 'holdout': []} # features\\n Y = {'train': [], 'holdout': []} # labels\\n p10 = int(0.1 * n_train)\\n X['holdout'] = df_train.iloc[-p10:]\\n Y['holdout'] = labels[-p10:]\\n X['train'] = df_train.iloc[:(n_train - p10)]\\n Y['train'] = labels[:(n_train - p10)]\\n return X, Y\",\n \"def validate_holdout(Xs, ys, X_holdout, y_holdout, non_bleeding_features, features_dir,\\n scorer_type, feat_indices, result_string, learning_rate_string, features):\\n # define folder to save the classifier and create it if not existing\\n parent_folder = \\\"%s/data/fnc-1/mlp_models/\\\" % (path.dirname(path.dirname(path.abspath(__file__))))\\n\\n # create the new save folder\\n save_folder = get_save_folder(parent_folder, scorer_type+\\\"_new\\\")\\n\\n # only pass a save folder if the classifier should be saved\\n best_clf = esitmator_definitions.get_estimator(scorer_type, save_folder=save_folder)\\n\\n # stack all the feature vectors of all the folds\\n X_train = np.vstack(tuple([Xs[i] for i in range(10)]))\\n y_train = np.hstack(tuple([ys[i] for i in range(10)]))\\n\\n # concat non-bleeding features\\n X_train, X_holdout, feat_indices_holdout = concat_non_bleeding_features(\\n X_train, X_holdout,\\n non_bleeding_features, features_dir, 'holdout')\\n\\n # test for oversampling: fits the current classifier, oversampled with a given\\n # method and checks the score on the holdout set\\n use_over_sampling = False\\n if use_over_sampling == True:\\n from imblearn.over_sampling import SMOTE\\n kind = ['regular', 'borderline1', 'borderline2', 'svm']\\n for m in kind:\\n sm = SMOTE(kind=m)\\n X_res, y_res = sm.fit_sample(X_train, y_train)\\n best_clf.fit(X_res, y_res)\\n y_predicted = best_clf.predict(X_holdout)\\n predicted = [LABELS[int(a)] for a in y_predicted]\\n actual = [LABELS[int(a)] for a in y_holdout]\\n fold_score, _ = score_submission(actual, predicted)\\n max_fold_score, _ = score_submission(actual, actual)\\n score = fold_score / max_fold_score\\n print(\\\"Score \\\" + m + \\\":\\\" + str(score))\\n\\n\\n #Taken from Benjamins LSTM\\n loss_monitor_file_dir = \\\"%s/data/fnc-1/model_results/loss_results/\\\" % (\\n path.dirname(path.dirname(path.abspath(__file__))))\\n loss_filename = loss_monitor_file_dir + str(datetime.datetime.now().strftime(\\\"%Y-%m-%d_%H-%M\\\")) + \\\".txt\\\"\\n # fit the classifier\\n if 'f_ext' in scorer_type:\\n append_to_loss_monitor_file(\\\"\\\\n\\\\nFOLD holdout and classifier: \\\" + scorer_type + \\\"\\\\n\\\", loss_filename)\\n append_to_loss_monitor_file(str(datetime.datetime.now()).split('.')[0], loss_filename)\\n best_clf.fit(X_train, y_train, X_holdout, np.array(y_holdout), 'holdout', loss_filename)\\n else:\\n best_clf.fit(X_train, y_train)\\n\\n # predict labels\\n y_predicted = best_clf.predict(X_holdout)\\n predicted = [LABELS[int(a)] for a in y_predicted]\\n actual = [LABELS[int(a)] for a in y_holdout]\\n\\n # calc FNC score\\n fold_score, cm = score_submission(actual, predicted)\\n max_fold_score, _ = score_submission(actual, actual)\\n score = fold_score / max_fold_score\\n\\n # calc accuracy for related/unrelated and stances\\n accuracy_stance = score_calculation.get_accuracy(y_predicted, y_holdout, stance=True)\\n accuracy_related = score_calculation.get_accuracy(y_predicted, y_holdout, stance=False)\\n f1_stance = score_calculation.get_f1score(y_predicted, y_holdout, stance=True)\\n f1_related = score_calculation.get_f1score(y_predicted, y_holdout, stance=False)\\n\\n # prepare printout for final results of holdout set\\n printout = printout_manager.get_holdout_printout(save_folder, accuracy_related, accuracy_stance, f1_related, f1_stance, score)\\n printout += printout_manager.calculate_confusion_matrix(cm)\\n print(printout) # print holdout results\\n result_string += printout + \\\"\\\\n\\\"# add results to string that is going to be saved into a file\\n\\n result_file_folder = \\\"%s\\\" % (path.dirname(path.dirname(path.abspath(__file__))))\\n printout_manager.save_file(result_string, result_file_folder + \\\"/fnc_results_holdout.txt\\\", \\\"a+\\\")\\n\\n #aligned printout for ablation:\\n summary = printout_manager.get_holdout_ablation_printout(features, score,f1_stance,save_folder)\\n printout_manager.save_file(summary, result_file_folder + \\\"/fnc_results_holdout_summary.txt\\\", \\\"a+\\\")\\n\\n # test saving and restoring model\\n #filename = scorer_type + \\\".sav\\\"\\n #save_model(best_clf, save_folder,filename)\\n #load_clf = load_model(parent_folder + scorer_type + \\\"_new_0/\\\", filename) # the 0th folder should always exist\\n #print_score_from_restored_model(load_clf, X_holdout, y_holdout)\\n\\n # add to special file that shows learning rate and loss of optimizer\\n if isinstance(best_clf, MultiThreadingFeedForwardMLP):\\n learning_rate_string += best_clf.get_learning_rates('holdout') + \\\"\\\\n\\\"\\n\\n # print feature importances\\n if scorer_type == 'randomforest':\\n result_file_folder = \\\"%s\\\" % (path.dirname(path.dirname(path.abspath(__file__))))\\n importances = best_clf.feature_importances_\\n std = np.std([tree.feature_importances_ for tree in best_clf.estimators_],\\n axis=0)\\n indices = np.argsort(importances)[::-1]\\n feat_indices.append(feat_indices_holdout)\\n\\n feat_importance_string = str(feat_indices) + \\\"\\\\n\\\"\\n for i in indices:\\n feat_importance_string += str(i) + \\\";\\\" + str(importances[i]) + \\\";\\\" + str(std[i]) + \\\"\\\\n\\\"\\n\\n # save feature importances as file\\n printout_manager.save_file(feat_importance_string, result_file_folder + \\\"/feat_importance_rf.txt\\\", \\\"a+\\\")\\n\\n return result_string, learning_rate_string\",\n \"def make_data(input_filepath, output_filepath):\\n\\n df_train = pd.read_csv(input_filepath+'train_u6lujuX_CVtuZ9i.csv', index_col=0)\\n df_test = pd.read_csv(input_filepath+'test_Y3wMUE5_7gLdaTN.csv', index_col=0)\\n print('Sizes', df_train.shape, df_test.shape)\\n print(\\\"Outcome dispersion:\\\\n\\\", df_train['Loan_Status'].value_counts())\\n\\n\\n # recode and save outcome vector\\n y = df_train['Loan_Status'].map({'N': 0, 'Y': 1})\\n\\n del df_train['Loan_Status']\\n\\n # all in one dataframe\\n df = pd.concat([df_train, df_test])\\n print(df.shape)\\n\\n from src.features.build_features import make_features\\n df = make_features(df)\\n\\n # Divide data on train and test again and save\\n data_train = df[df.index.isin(df_train.index)]\\n data_test = df[df.index.isin(df_test.index)]\\n print(data_train.shape, data_test.shape)\\n\\n data_tmp = data_train.copy()\\n data_tmp['y'] = y\\n\\n\\n data_tmp.to_csv(output_filepath + 'train_ready.csv', index=False)\\n data_test.to_csv(output_filepath + 'test_ready.csv', index=False)\\n id_test = pd.DataFrame(data=df_test.index, columns=['Loan_ID'])\\n id_test.to_csv(output_filepath + 'id_test.csv', index=False)\",\n \"def load_dataset():\\n\\n train_dataset = h5py.File('datasets/train_catvnoncat.h5', \\\"r\\\")\\n train_set_x_orig = np.array(train_dataset[\\\"train_set_x\\\"][:]) # your train set features\\n train_set_y_orig = np.array(train_dataset[\\\"train_set_y\\\"][:]) # your train set labels\\n\\n test_dataset = h5py.File('datasets/test_catvnoncat.h5', \\\"r\\\")\\n test_set_x_orig = np.array(test_dataset[\\\"test_set_x\\\"][:]) # your test set features\\n test_set_y_orig = np.array(test_dataset[\\\"test_set_y\\\"][:]) # your test set labels\\n\\n classes = np.array(test_dataset[\\\"list_classes\\\"][:]) # the list of classes\\n \\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\\n \\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\",\n \"def load_data():\\n # Load data\\n X_train = pd.read_csv(\\n \\\"data/trimmed_merged_no_track_id_or_session_id.csv\\\"\\n ) # load data from csv\\n\\n drop_columns = [\\\"mode\\\"] # drop unimportant columns\\n X_train.drop(labels=drop_columns, axis=1, inplace=True)\\n\\n y_train = X_train[\\\"skip\\\"]\\n X_train.drop(labels=\\\"skip\\\", axis=1, inplace=True) # ground truth from train_data\\n\\n # split data 60% train, 20% validation, 20% test\\n X_train, X_val, y_train, y_val = train_test_split(\\n X_train, y_train, test_size=0.2, random_state=1\\n )\\n X_train, X_test, y_train, y_test = train_test_split(\\n X_train, y_train, test_size=0.25, random_state=1\\n )\\n\\n print(\\\"X_train:\\\", len(X_train))\\n print(\\\"y_train:\\\", len(y_train))\\n print(\\\"X_val:\\\", len(X_val))\\n print(\\\"y_val:\\\", len(y_val))\\n print(\\\"X_test:\\\", len(X_test))\\n print(\\\"y_test:\\\", len(y_test))\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def make_training_set(ind_list, training_data): \\n \\n exp = training_data[ind_list[0]] \\n X_train = exp[0]\\n u_train = exp[1] \\n\\n for i in ind_list[1:]: \\n exp = training_data[i]\\n X_train = np.append(X_train, exp[0], axis=0)\\n u_train = np.append(u_train, exp[1], axis=0)\\n\\n return X_train, u_train\",\n \"def load(self, path=\\\"../data_set/final-train-dataset.csv\\\", shuffle=False,\\n onlyLabelToUse=None, useOnlyBestIndicators=False, binary=False):\\n data = []\\n labels = []\\n\\n with open(path) as csvfile:\\n reader = csv.reader(csvfile, delimiter=',')\\n skip = True\\n\\n for row in reader:\\n if skip:\\n skip = False\\n continue\\n\\n entries = []\\n\\n for i in range(SAMPLES_OF_DATA_TO_LOOK_AT):\\n # Get one time point's data, including indicators:\\n if useOnlyBestIndicators:\\n # entries.append(\\n # [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n #\\n entries.append(\\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n j in [0, 1, 2, 5, 7]])\\n else:\\n entries.append(\\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n j in range(INPUT_CHANNELS)])\\n\\n data.append(np.array(entries))\\n\\n if onlyLabelToUse is not None:\\n if binary:\\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse]) > 0.5)]\\n else:\\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse])]\\n else:\\n if binary:\\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) > 0.5) for j in range(OUTPUT_CHANNELS)]\\n else:\\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) for j in range(OUTPUT_CHANNELS)]\\n\\n labels.append(label)\\n\\n if shuffle:\\n indices = [i for i in range(len(data))]\\n np.random.shuffle(indices)\\n labels = np.array([labels[i] for i in indices])\\n data = np.array([data[i] for i in indices])\\n else:\\n data = np.array(data)\\n labels = np.array(labels)\\n\\n return data, labels\",\n \"def get_dataset(self):\\n\\n trainset = datasets.SVHN('datasets/SVHN/train/', split='train', transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.SVHN('datasets/SVHN/test/', split='test', transform=self.val_transforms,\\n target_transform=None, download=True)\\n extraset = datasets.SVHN('datasets/SVHN/extra', split='extra', transform=self.train_transforms,\\n target_transform=None, download=True)\\n\\n trainset = torch.utils.data.ConcatDataset([trainset, extraset])\\n\\n return trainset, valset\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n\\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n\\n # split the data into 10% validation-set and 90% training set\\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\\n return raw_train, raw_valid, y_train, y_valid\",\n \"def _load_training_and_test_sets(normalize):\\n class_labels = []\\n test_labels = []\\n norm = None\\n if normalize == True:\\n norm = loading.get_normalize_vector()\\n\\n for i in range(0, 10):\\n [training, test] = loading.load_number_set(i, 0.7, norm_vector=norm)\\n labels = [str(i)] * training.shape[0]\\n tlabels = [str(i)] * test.shape[0]\\n if i == 0:\\n train_points = training\\n test_points = test\\n else:\\n train_points = np.concatenate((train_points, training), axis = 0)\\n test_points = np.concatenate((test_points, test), axis = 0)\\n class_labels.extend(labels)\\n test_labels.extend(tlabels)\\n\\n return train_points, test_points, class_labels, test_labels\",\n \"def build_full_trainset(self):\\n\\n return self.construct_trainset(self.raw_ratings)\",\n \"def load_train_test_transactions(train_size=0.7):\\n X, y = features_target_split()\\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\\n print('\\\\nTraining and testing data creation successful\\\\n')\\n return X_train, X_test, y_train,y_test\",\n \"def train_test(in_path, train_out_path, test_out_path):\\n df = pd.read_csv(in_path, sep='|')\\n rng = RandomState()\\n\\n train = df.sample(frac=0.8, random_state=rng)\\n test = df.loc[~df.index.isin(train.index)]\\n\\n train.to_csv(train_out_path, sep='|', index = None, header=True)\\n test.to_csv(test_out_path, sep='|', index = None, header=True)\",\n \"def get_training_data():\\n \\n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\\n return X, y\",\n \"def fetch_training_df(df):\\n\\n gen_df = df.copy()\\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\\n return gen_df\",\n \"def prepare_dataset(dataset, manifest):\\n\\n ## importation\\n import pandas as pd\\n\\n ## craft output_filename\\n output_filename = dataset.replace(\\\".csv\\\", \\\"_labeled.csv\\\")\\n\\n ## load dataset\\n df_data = pd.read_csv(dataset)\\n df_data = df_data.set_index('ID')\\n\\n ## load manifest\\n df_cluster = pd.read_csv(manifest)\\n\\n ## merge\\n result = df_data.join(df_cluster.set_index('ID'))\\n\\n ## drop columns conatining NA\\n result = result.dropna(axis='columns')\\n\\n ## save dataset\\n result.to_csv(output_filename)\",\n \"def extract_validation_data_set_from_learning_data_set(nb_samples_per_classes: int, nb_class_names: int,\\n learning_data_set: FeatureTable) -> FeatureTable:\\n\\n # Same algorithm than generate_training_base\\n result = FeatureTable()\\n class_names_found = list()\\n nb_samples_per_class_found = list()\\n nb_samples_required = (1 + nb_class_names) * nb_samples_per_classes\\n\\n while nb_samples_required != 0:\\n individual_challenger = learning_data_set.pop_individual(\\n random.randint(0, learning_data_set.size - 1))\\n try:\\n index = class_names_found.index(individual_challenger.label)\\n except ValueError:\\n class_names_found.append(individual_challenger.label)\\n nb_samples_per_class_found.append(0)\\n index = class_names_found.index(individual_challenger.label)\\n\\n if nb_samples_per_class_found[index] != nb_samples_per_classes:\\n nb_samples_per_class_found[index] = nb_samples_per_class_found[index] + 1\\n nb_samples_required = nb_samples_required - 1\\n result.add_individual(individual_challenger)\\n else:\\n learning_data_set.add_individual(individual_challenger)\\n return result\",\n \"def get_train_and_test_sets(self,\\n all_trades_df: pd.DataFrame,\\n train_start: pd.Timestamp,\\n train_end: pd.Timestamp,\\n test_end: pd.Timestamp) -> pd.DataFrame:\\n \\n # training set\\n feature_and_target_cutoff = train_end - pd.Timedelta('120D') # last 4 months of training set\\n trades_features = all_trades_df[(all_trades_df['trade_date_dt'] >= train_start) & (all_trades_df['trade_date_dt'] < feature_and_target_cutoff)]\\n trades_target = all_trades_df[(all_trades_df['trade_date_dt'] >= feature_and_target_cutoff) & (all_trades_df['trade_date_dt'] < train_end)]\\n training_set = self.get_features_and_target(trades_features, trades_target)\\n training_set['train_or_test'] = 'train'\\n\\n # test set\\n trades_features = all_trades_df[(all_trades_df['trade_date_dt'] >= train_start) & (all_trades_df['trade_date_dt'] < train_end)]\\n trades_target = all_trades_df[(all_trades_df['trade_date_dt'] >= train_end) & (all_trades_df['trade_date_dt'] < test_end)]\\n test_set = self.get_features_and_target(trades_features, trades_target)\\n test_set['train_or_test'] = 'test'\\n \\n customer_df = pd.concat([training_set, test_set], sort=False)\\n customer_df.fillna(False, inplace=True)\\n customer_df.index.name = 'sf_account_id'\\n \\n return customer_df\",\n \"def housing_labels_(strat_train_set):\\n logging.info(\\\"copy of dataset\\\")\\n housing_labels = strat_train_set[\\\"median_house_value\\\"].copy()\\n return housing_labels\",\n \"def load_data(train=True):\\n#test set\\n start = time.clock()\\n\\n if train==False:\\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\\n df = pd.read_csv(dp)\\n X = df.values.astype(np.float32)[:,1:]\\n return X\\n#train set\\n else:\\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\\n df = pd.read_csv(dp)\\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\\n y = df.target\\n y =y.apply(lambda X: int(X[-1])).values\\n y = y.astype(np.int32)\\n X, y = shuffle(X, y)\\n #print(X.shape,y.shape)\\n end = time.clock()\\n print(end-start)\\n\\n return X,y\",\n \"def house_prices_data() -> Tuple[\\n pd.DataFrame, Datapoints, Datapoints, Datapoints, List[int]\\n]:\\n seed = 1515151555\\n train_data_full = pd.read_csv(os.path.join(\\\"data\\\", \\\"train.csv\\\")).iloc[\\n :, 1:\\n ] # remove unused id column for training\\n test_data_full = pd.read_csv(\\n os.path.join(\\\"data\\\", \\\"test.csv\\\")\\n ) # keep first id column for the later submission\\n\\n # Load transforms\\n transforms = None\\n with open(os.path.join(\\\"data\\\", \\\"transforms\\\"), \\\"r\\\") as f:\\n transforms = json.load(f)\\n transforms = [tuple(list_) for list_ in transforms]\\n\\n # Seed the train / val split\\n np.random.seed(seed)\\n train_data, eval_data = train_test_split_pandas(train_data_full)\\n print(\\n f\\\"\\\"\\\"Dataframe shapes (rows, cols):\\ntrain {len(train_data), len(train_data.columns)},\\neval {len(eval_data), len(eval_data.columns)},\\ntest {len(test_data_full), len(test_data_full.columns)}.\\\"\\\"\\\"\\n )\\n (\\n train_transformed,\\n eval_transformed,\\n test_transformed,\\n cardinalities,\\n ) = get_transformed_data(\\n train_data=train_data,\\n eval_data=eval_data,\\n test_data=test_data_full,\\n transforms=transforms,\\n )\\n print(f\\\"Baseline RMSE: {train_transformed.y.std():.3f}.\\\")\\n return (\\n test_data_full,\\n train_transformed,\\n eval_transformed,\\n test_transformed,\\n cardinalities,\\n )\",\n \"def prep_dataset(settings):\\n train_dims = settings[\\\"train_dims\\\"]\\n # Open HDF store. This is usually a soft link to our filtered dataset\\n input_df, target_df, const = load_from_store(settings[\\\"dataset_path\\\"], columns=train_dims)\\n\\n try:\\n del input_df[\\\"nions\\\"] # Delete leftover artifact from dataset split\\n except KeyError:\\n pass\\n\\n target_df = drop_outliers(target_df, settings)\\n target_df = drop_nans(target_df)\\n\\n data_df = filter_input(input_df, target_df)\\n del target_df, input_df\\n data_df = convert_dtype(data_df, settings)\\n\\n return data_df\",\n \"def loadSets(self, indir=\\\"\\\"):\\n\\n if indir==\\\"\\\":\\n print(\\\"specify folder\\\")\\n return -1\\n\\n self.train = pd.read_pickle(\\\"{}/train.pkl\\\".format(indir))\\n self.valid = pd.read_pickle(\\\"{}/valid.pkl\\\".format(indir))\\n self.test = pd.read_pickle(\\\"{}/test.pkl\\\".format(indir))\\n\\n print(\\\"sets loaded\\\")\",\n \"def merge_data():\\n\\n\\tconfig = Config()\\n\\tfilename_train, filename_test = \\\"../data/train.csv\\\", \\\"../data/test.csv\\\" \\n\\n # create datasets\\n\\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\\n\\n # 1. datetime features\\n\\t# diff between weekday and day?\\n\\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\\n\\t#day - Between 1 and the number of days in the given month of the given year.\\n\\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\\n\\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\\n\\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\\n\\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\\n\\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\\n\\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\\n\\n\\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\\n\\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\\n\\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\\n\\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\\n\\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\\n\\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\\n\\n\\t# 2. Location features\\n\\tdef haversine(lon1, lat1, lon2, lat2):\\n\\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n\\t dlon = lon2 - lon1\\n\\t dlat = lat2 - lat1\\n\\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\\n\\t c = 2 * np.arcsin(np.sqrt(a))\\n\\t km = 6367 * c # AVG_EARTH_RADIUS=6367\\n\\t miles = km * 0.621371\\n\\t return miles\\n\\n\\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\\n\\t# a = haversine_array(lat1, lng1, lat1, lng2)\\n\\t# b = haversine_array(lat1, lng1, lat2, lng1)\\n\\t# return a + b\\n\\n\\t# def bearing_array(lat1, lng1, lat2, lng2):\\n\\t# AVG_EARTH_RADIUS = 6371 # in km\\n\\t# lng_delta_rad = np.radians(lng2 - lng1)\\n\\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\\n\\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\\n\\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\\n\\t# return np.degrees(np.arctan2(y, x))\\n\\n\\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\\n\\t train.dropoff_longitude, train.dropoff_latitude)\\n\\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\\n\\t test.dropoff_longitude, test.dropoff_latitude)\\n\\n\\n\\t# 3. Use outsource data\\n\\tweatherdata_filename = \\\"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\\\"\\n\\tfastestroute_data_train = \\\"../data/outsource_data/fastest_train.csv\\\"\\n\\tfastestroute_data_test = \\\"../data/outsource_data/fastest_routes_test.csv\\\"\\n\\n\\n\\twd = pd.read_csv(weatherdata_filename, header=0)\\n\\twd['date'] = pd.to_datetime(wd.date, format=\\\"%d-%m-%Y\\\")\\n\\twd['pickup_day'] = wd['date'].dt.day\\n\\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \\n\\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \\n\\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \\n\\n\\t# Merge training data with weather data on pickup_day\\n\\tprint(\\\"Merging training data with weather data ....\\\")\\n\\twd_train = pd.merge(train, wd, on='pickup_day')\\n\\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\\n\\tgc.collect()\\n\\n\\t# Merge wd_train with fastestroute_data\\n\\tfastest = pd.read_csv(fastestroute_data_train, header=0)\\n\\tprint(\\\"Merging Location data with weather and training data ....\\\")\\n\\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\\n\\n\\tgc.collect()\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Semi-final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\t# Use the same outsource data with test set\\n\\t# merge outsource data with test data as well\\n\\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\\t'total_travel_time',\\n\\t\\t\\t\\t\\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\\n\\t 'step_maneuvers','step_direction',\\t'step_location_list']\\n\\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\\n\\n\\tprint(\\\"Merging test data with Location data ....\\\")\\n\\ttest = pd.merge(test, fastest_test, on='id', how='outer')\\n\\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\\n\\t 'end_street'], axis=1)\\n\\tprint(\\\"Merging test data with weather data ....\\\")\\n\\ttest = pd.merge(test, wd, on='pickup_day')\\n\\n\\tprint(\\\"===================== CHECK TEST DATA =====================\\\")\\n\\n\\tprint(test.head(2))\\n\\tprint(\\\"Semi-final test data shape is: {}\\\".format(test.shape))\\n\\tprint(\\\"Test data columns: {}\\\".format(test.columns))\\n\\n\\n\\n\\t# 4. Do more data munging\\n\\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = (wd_train_fastest.trip_duration < 60) \\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.trip_duration > 79200\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\\n\\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\\n\\n\\t# 5. Do some data maskig based on location to create jfk and lgo features\\n\\tjfk_lon = -73.778889\\n\\tjfk_lat = 40.639722\\n\\tlga_lon = -73.872611\\n\\tlga_lat = 40.77725\\n\\n\\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\n\\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\\n\\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\\n\\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\\n\\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA AGAIN =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\treturn wd_train_fastest, test\",\n \"def features_target_split():\\n transactions = load_all_transactions('creditcard.csv')\\n features = transactions.drop('Class', axis=1)\\n target = transactions['Class']\\n print('\\\\nCreation of feature set dataframe and target seiries successful \\\\n')\\n return features, target\",\n \"def test_set(self):\\n if self._testset is None: # loads the data to memory once and when requested.\\n testset_raw = self.read_dataset(self._testset_path)\\n testset_spacy = self.read_spacy_pickle(self._testset_spacy_path)\\n self._testset = pd.concat([testset_raw, testset_spacy], axis=1)\\n\\n self._testset['language'] = self._language\\n self._testset['dataset_name'] = self._dataset_name\\n\\n return self._testset\",\n \"def split_data_into_train_and_test(raw_training_data):\\n train_set, test_set = train_test_split(raw_training_data, test_size=0.2, random_state=42)\\n return train_set, test_set\",\n \"def pickle_dataset(train_set, test_set, validation_set, path):\\n\\n train_set_filename = open(os.path.join(path, TRAIN_DATA_SET), 'wb')\\n # Pickle classes_count\\n cPickle.dump(train_set, train_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\\n # Close the file\\n train_set_filename.close()\\n # Save hierarchy_mapping file\\n test_set_filename = open(os.path.join(path, TEST_DATA_SET), 'wb')\\n # Pickle hierarchy_mapping\\n cPickle.dump(test_set, test_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\\n # Close the file\\n test_set_filename.close()\\n # Save entities list\\n validation_set_filename = open(os.path.join(path, VALIDATION_DATA_SET), 'wb')\\n # Pickle entities\\n cPickle.dump(validation_set, validation_set_filename, protocol=cPickle.HIGHEST_PROTOCOL)\\n # Close the file\\n validation_set_filename.close()\\n\\n print(\\\"Debug printing- the number of train samples: {0}, the number of test samples: {1}, \\\"\\n \\\"the number of validation samples: {2}\\\".format(len(train_set), len(test_set), len(validation_set)))\",\n \"def load_data_raw(source='local'):\\n import os\\n\\n if source == 'kaggle':\\n # Download the Ames Housing Dataset\\n # Set the enviroment variables\\n import zipfile\\n os.environ['KAGGLE_USERNAME'] = \\\"lubomrstraka\\\"\\n os.environ['KAGGLE_KEY'] = \\\"c7347462ef834e6645ce238c2f2fa561\\\"\\n \\n # Import dependencies\\n os.system(\\\"pip install kaggle --upgrade --quiet\\\")\\n\\n # Download datasets\\n os.system(\\\"kaggle competitions download -c house-prices-advanced-regression-techniques --quiet\\\")\\n \\n # Extract data\\n with zipfile.ZipFile('house-prices-advanced-regression-techniques.zip', 'r') as archive:\\n archive.extractall()\\n\\n # Read Train & Test Baseline Data\\n train_bl = pd.read_csv('train.csv', index_col='Id')\\n test_bl = pd.read_csv('test.csv', index_col='Id')\\n \\n # Using local data used while registering dataset\\n # prevents \\\"Too many requests\\\" response from Kaggle\\n elif source == 'local': \\n train_bl = pd.read_csv('train.csv', index_col='Id')\\n test_bl = pd.read_csv('test.csv', index_col='Id')\\n \\n else:\\n print('Missing data!')\\n train_bl = pd.DataFrame()\\n test_bl = pd.DataFrame()\\n\\n\\n return train_bl, test_bl\",\n \"def wrangle_pick_data():\\n # get data\\n df = acquire.run()\\n # prep and split data\\n train, validate, test = prepare.run(df)\\n # scale and define target\\n # not completed yet\\n return train, validate, test\\n #return scale_mall(train, validate, test)\",\n \"def _prepare_result_set(\\n x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray\\n) -> pd.DataFrame:\\n # Prepare default target columns names if not provided:\\n prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]\\n if len(label_columns) == 0:\\n # Add default label column names:\\n if prediction_columns_amount == 1:\\n label_columns = [\\\"predicted_label\\\"]\\n else:\\n label_columns = [\\n f\\\"predicted_label_{i}\\\" for i in range(prediction_columns_amount)\\n ]\\n\\n # Validate the label columns:\\n if prediction_columns_amount != len(label_columns):\\n # No equality between provided label column names and outputs amount:\\n raise mlrun.errors.MLRunInvalidArgumentError(\\n f\\\"The number of predicted labels: {prediction_columns_amount} \\\"\\n f\\\"is not equal to the given label columns: {len(label_columns)}\\\"\\n )\\n common_labels = set(label_columns) & set(x.columns.tolist())\\n if common_labels:\\n # Label column exist in the original inputs:\\n raise mlrun.errors.MLRunInvalidArgumentError(\\n f\\\"The labels: {common_labels} are already existed in the given dataset.\\\"\\n )\\n\\n return pd.concat(\\n [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1\\n )\",\n \"def main(input_filepath, output_filepath):\\n # return processed data and save in the output files\\n in_data_y, y_output, in_data = make_data_set(input_filepath)\\n in_data_y.to_csv(output_filepath)\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n return in_data_y, y_output, in_data\",\n \"def split_dataset(data_set, train_size, test_size):\\n # Generate random indices without replacement, to make train and test sets disjoint\\n rand_indices = np.random.choice(data_set.shape[0], train_size+test_size, replace=False)\\n feature_end = data_set.shape[1] - 1\\n output_location = feature_end\\n feature_offset = var.ALGORITHM_INFO['feature_offset']\\n\\n # Define the training and testing matrices\\n x_train = data_set[rand_indices[0:train_size], feature_offset:feature_end]\\n y_train = data_set[rand_indices[0:train_size], output_location]\\n x_test = data_set[rand_indices[train_size:train_size+test_size], feature_offset:feature_end]\\n y_test = data_set[rand_indices[train_size:train_size+test_size], output_location]\\n favorite_test = data_set[rand_indices[train_size:train_size+test_size], 0]\\n\\n # Normalize features, with maximum value in training set\\n # as realistically, this would be the only possibility\\n\\n for ii in range(x_train.shape[1]):\\n maxval = np.max(np.abs(x_train[:, ii]))\\n if maxval > 0:\\n x_train[:, ii] = np.divide(x_train[:, ii], maxval)\\n x_test[:, ii] = np.divide(x_test[:, ii], maxval)\\n\\n\\n # Add a column of ones; done after to avoid modifying entire data_set\\n x_train = np.hstack((x_train, np.ones((x_train.shape[0], 1))))\\n x_test = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\\n\\n return (x_train, y_train), (x_test, y_test), favorite_test\",\n \"def train_test_data_df(train_data_file, test_data_file):\\n dtype_dict = {\\n \\\"age\\\": np.int32,\\n \\\"education-num\\\": np.int32,\\n \\\"capital-gain\\\": np.int32,\\n \\\"capital-loss\\\": np.int32,\\n \\\"hours-per-week\\\": np.int32\\n }\\n cols = [i for i in range(15) if i != 2]\\n train_data = pd.read_csv(train_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n train_data = train_data.dropna(axis=0, how=\\\"any\\\")\\n test_data = pd.read_csv(test_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n test_data = test_data.dropna(axis=0, how=\\\"any\\\")\\n return train_data, test_data\",\n \"def train_set_official(self, stop_words=True):\\n subset = self.__sub_set_from_linescsv(csv_path=self.csv_path_train)\\n if not stop_words:\\n with open(self.path_stop_words, 'r') as f_sw:\\n stop_words = []\\n for line in f_sw:\\n stop_words += line.split(',')\\n subset = subset.exclude_words(stop_words)\\n return subset\",\n \"def load_data():\\n print(\\\"PARSING TRAIN\\\")\\n ys_train, x_train, ids_train = load_pickle_data(\\\"ys_train\\\"), load_pickle_data(\\\"x_train\\\"), load_pickle_data(\\n \\\"ids_train\\\")\\n if ys_train is None or x_train is None or ids_train is None:\\n ys_train, x_train, ids_train = load_csv_data(\\\"{}/train.csv\\\".format(DATA_DIR))\\n dump_pickle_data(ys_train, \\\"ys_train\\\")\\n dump_pickle_data(x_train, \\\"x_train\\\")\\n dump_pickle_data(ids_train, \\\"ids_train\\\")\\n\\n print(\\\"PARSING TEST\\\")\\n x_test, ids_test = load_pickle_data(\\\"x_test\\\"), load_pickle_data(\\\"ids_test\\\")\\n if x_test is None or ids_test is None:\\n _, x_test, ids_test = load_csv_data(\\\"{}/test.csv\\\".format(DATA_DIR))\\n dump_pickle_data(x_test, \\\"x_test\\\")\\n dump_pickle_data(ids_test, \\\"ids_test\\\")\\n\\n return ys_train, x_train, ids_train, x_test, ids_test\",\n \"def fetch_bank_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\\n \\\"bank-marketing\\\", \\\"classification\\\", astype=\\\"pandas\\\", preprocess=preprocess\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\\n column_map = {\\n \\\"v1\\\": \\\"age\\\",\\n \\\"v2\\\": \\\"job\\\",\\n \\\"v3\\\": \\\"marital\\\",\\n \\\"v4\\\": \\\"education\\\",\\n \\\"v5\\\": \\\"default\\\",\\n \\\"v6\\\": \\\"balance\\\",\\n \\\"v7\\\": \\\"housing\\\",\\n \\\"v8\\\": \\\"loan\\\",\\n \\\"v9\\\": \\\"contact\\\",\\n \\\"v10\\\": \\\"day\\\",\\n \\\"v11\\\": \\\"month\\\",\\n \\\"v12\\\": \\\"duration\\\",\\n \\\"v13\\\": \\\"campaign\\\",\\n \\\"v14\\\": \\\"pdays\\\",\\n \\\"v15\\\": \\\"previous\\\",\\n \\\"v16\\\": \\\"poutcome\\\",\\n }\\n if preprocess:\\n\\n def map_col(col):\\n if col.find(\\\"_\\\") == -1:\\n return column_map[col]\\n prefix, suffix = col.split(\\\"_\\\")\\n return column_map[prefix] + \\\"_\\\" + suffix\\n\\n orig_X.columns = [map_col(col) for col in orig_X.columns]\\n age = pd.Series(orig_X[\\\"age\\\"] >= 25, dtype=np.float64)\\n encoded_X = orig_X.assign(age=age)\\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [1]},\\n ],\\n }\\n return encoded_X, encoded_y, fairness_info\\n else:\\n orig_X.columns = [column_map[col] for col in orig_X.columns]\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [[25, 1000]]},\\n ],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def get_test_dataset_SetLoader(self, set_name='train'):\\n set_loader = self.load_hdf5_file_SetLoader(set_name)\\n\\n set_data = self.dataset[set_name]\\n set_fields = self.data_fields[set_name]\\n\\n return set_loader, set_data, set_fields\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n path = os.getcwd() # reads the current path\\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\\n x_train, y_train = shuffle(x_train, y_train)\\n\\n return x_train, y_train, x_test\",\n \"def create_cleaned_dataset(PDBbind_dataset_path, general_set_PDBs_path, refined_set_PDBs_path, output_name, plot = False):\\n \\n # load dataset\\n data = pd.read_csv(PDBbind_dataset_path)\\n \\n # check for NaNs in affinity data\\n if data['-log(Kd/Ki)'].isnull().any() != False:\\n print('There are NaNs present in affinity data!')\\n \\n # create list of PDB id's\\n pdbid_list = list(data['PDB ID'])\\n \\n # remove affinity values that do not have structural data by searching PDBs\\n missing = []\\n for i in range(len(pdbid_list)):\\n pdb = pdbid_list[i]\\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==False and os.path.isdir(str(refined_set_PDBs_path) + pdb)==False:\\n missing.append(pdb)\\n data = data[~np.in1d(data['PDB ID'], list(missing))]\\n\\n # distinguish PDB id's in general and refined sets\\n general_dict = {}\\n refined_dict = {}\\n for i in range(len(pdbid_list)):\\n pdb = pdbid_list[i]\\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==True:\\n general_dict[pdb] = 'general'\\n if os.path.isdir(str(refined_set_PDBs_path) + pdb)==True:\\n refined_dict[pdb] = 'refined'\\n \\n # add 'set' column to data and fill with 'general' or 'refined'\\n data['set'] = np.nan\\n data.loc[np.in1d(data['PDB ID'], list(general_dict)), 'set'] = 'general'\\n data.loc[np.in1d(data['PDB ID'], list(refined_dict)), 'set'] = 'refined'\\n \\n # write out csv of cleaned dataset\\n data[['PDB ID', '-log(Kd/Ki)', 'set']].to_csv(output_name, index=False)\\n \\n # read in and view the cleaned dataset\\n display(pd.read_csv(output_name))\\n \\n if plot == True:\\n # plot affinity distributions for general and refined sets\\n grid = sns.FacetGrid(data, row='set', row_order=['general', 'refined'],\\n size=3, aspect=2)\\n grid.map(sns.distplot, '-log(Kd/Ki)')\\n else:\\n return\",\n \"def fetch_boston_housing_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.boston_housing_df(\\n test_size=0.33\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index()\\n black_median = np.median(train_X[\\\"B\\\"])\\n label_median = np.median(train_y)\\n\\n if preprocess:\\n # 1000(Bk - 0.63)^2 where Bk is the proportion of Blacks by town\\n B = pd.Series(orig_X[\\\"B\\\"] > black_median, dtype=np.float64)\\n encoded_X = orig_X.assign(B=B)\\n fairness_info = {\\n \\\"favorable_labels\\\": [[-10000.0, label_median]],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"B\\\", \\\"reference_group\\\": [0]},\\n ],\\n }\\n return encoded_X, orig_y, fairness_info\\n else:\\n fairness_info = {\\n \\\"favorable_labels\\\": [[-10000.0, label_median]],\\n \\\"protected_attributes\\\": [\\n # 1000(Bk - 0.63)^2 where Bk is the proportion of Blacks by town\\n {\\\"feature\\\": \\\"B\\\", \\\"reference_group\\\": [[0.0, black_median]]},\\n ],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def load_data(y_name='Species'):\\n train_path = tf.keras.utils.get_file(args.TRAIN_URL.split('/')[-1], args.TRAIN_URL)\\n test_path = tf.keras.utils.get_file(args.TEST_URL.split('/')[-1], args.TEST_URL)\\n\\n train = pd.read_csv(train_path, names=args.CSV_COLUMN_NAMES, header=0)\\n train_x, train_y = train, train.pop(y_name)\\n\\n test = pd.read_csv(test_path, names=args.CSV_COLUMN_NAMES, header=0)\\n test_x, test_y = test, test.pop(y_name)\\n\\n return (train_x, train_y), (test_x, test_y)\",\n \"def load_data():\\n \\n data = datasets.load_iris()\\n df = pd.DataFrame(data.data, columns = data.feature_names)\\n df2 = pd.DataFrame(data.target, columns = [\\\"target\\\"])\\n\\n return df.join(df2)\",\n \"def GetDataSet(path):\\n # Read csv files\\n df_train_set = pd.read_csv(os.path.join(path, \\\"train.csv\\\"))\\n df_test_set = pd.read_csv(os.path.join(path, \\\"test.csv\\\"))\\n # Filter\\n df_train_set.drop(\\\"Id\\\", axis=1, inplace=True)\\n df_test_set.drop(\\\"Id\\\", axis=1, inplace=True)\\n # Assign name\\n df_train_set.name = \\\"train\\\"\\n df_test_set.name = \\\"test\\\"\\n # Filter还真的不能在这里做，不然返回的就是原生的DataFrame对象了\\n return df_train_set, df_test_set\",\n \"def from_files(cls, train_fname, test_fname, config_file):\\n fguide = FeatureGuide(config_file)\\n\\n # Set index based on the feature guide and load only needed columns.\\n index_col = cls.index_from_feature_guide(fguide)\\n kwargs = dict(usecols=fguide.all_names, index_col=index_col)\\n dset = cls(pd.read_csv(train_fname, **kwargs),\\n pd.read_csv(test_fname, **kwargs),\\n fguide)\\n\\n # Store pathnames as instance variables.\\n dset.train_fname = os.path.abspath(train_fname)\\n dset.test_fname = os.path.abspath(test_fname)\\n return dset\",\n \"def read_data_sets(data_path, fake_data=False, one_hot=False,\\n validation_size=5000, source_url={},\\n augment=False,\\n percentage_train=100.,\\n unbalance=False, unbalance_dict={\\\"percentage\\\": 20, \\\"label1\\\": 0, \\\"label2\\\": 8},\\n ):\\n\\n class DataSets(object):\\n pass\\n\\n data_sets = DataSets()\\n\\n if fake_data:\\n data_sets.train = DataSet([], [], fake_data=True, one_hot=True)\\n data_sets.validation = DataSet([], [], fake_data=True, one_hot=True)\\n data_sets.test = DataSet([], [], fake_data=True, one_hot=True)\\n return data_sets\\n\\n if not source_url: # empty string check\\n if 'fashion' in data_path:\\n source_url = DEFAULT_SOURCE_URL_FASHION\\n else:\\n source_url = DEFAULT_SOURCE_URL_MNIST\\n\\n if 'fashion' in data_path or 'mnist' in data_path: # mnist or fashion\\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\\\\n load_mnist(data_path, validation_size, source_url, one_hot)\\n reshape = True\\n else:\\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\\\\n load_medical_data(data_path)\\n reshape = False\\n\\n # add random permutation to train & validation\\n np.random.seed(42)\\n\\n n_train = train_images.shape[0]\\n perm = np.random.permutation(n_train)\\n train_images = train_images[perm]\\n train_labels = train_labels[perm]\\n\\n n_val = val_images.shape[0]\\n perm = np.random.permutation(n_val)\\n val_images = val_images[perm]\\n val_labels = val_labels[perm]\\n\\n # For experiments with data-augmentation\\n if augment:\\n if 'fashion' in data_path: # rotations +-10 and horizontal flips\\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=True)\\n elif 'mnist' in data_path: # rotations +-10\\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=False)\\n train_images = np.concatenate([train_images, np.expand_dims(augmented_images, 3)])\\n train_labels = np.concatenate([train_labels, augmented_labels])\\n # for the medical datasets, you can use the \\\"augment\\\" argument while doing patch extraction\\n\\n # For experiments with limited amount of data\\n if percentage_train != 100.:\\n train_size = int(0.01*percentage_train*train_images.shape[0])\\n Xtrain_images, Xval_images, ytrain, yval = train_test_split(train_images, train_labels, train_size=train_size)\\n train_images = Xtrain_images\\n train_labels = ytrain\\n\\n # For experiments with class-imbalance distribution\\n if unbalance:\\n n_classes = len(np.unique(np.argmax(train_labels, 1)))\\n reduceto = 0.01*unbalance_dict['percentage']\\n label1 = unbalance_dict['label1']\\n label2 = unbalance_dict['label2']\\n\\n pick_ids = []\\n newsize = 0\\n all_classes = np.arange(0, n_classes)\\n all_classes = np.delete(all_classes, np.where(all_classes == label1)[0])\\n all_classes = np.delete(all_classes, np.where(all_classes == label2)[0])\\n\\n for lab in [label1, label2]:\\n allids = np.where(np.argmax(train_labels, 1) == lab)[0]\\n selectedids = np.random.choice(allids, int(reduceto * allids.shape[0]), replace=False)\\n pick_ids.append(selectedids)\\n newsize += len(selectedids)\\n\\n new_ids = convert_list_to_array(pick_ids, newsize)\\n\\n other_ids = []\\n othersize = 0\\n for lab in all_classes.tolist():\\n selectedids = np.where(np.argmax(train_labels, 1) == lab)[0]\\n other_ids.append(selectedids)\\n othersize += len(selectedids)\\n\\n keep_ids = convert_list_to_array(other_ids, othersize)\\n\\n # new_ids: contains the indices of the reduced (imbalance) classes\\n # keep_ids: contains the indices of the rest (keep the same class distribution)\\n resulting_ids = np.concatenate((new_ids, keep_ids))\\n np.random.shuffle(resulting_ids)\\n\\n train_images = train_images[resulting_ids, ...]\\n train_labels = train_labels[resulting_ids, ...]\\n\\n data_sets.train = DataSet(train_images, train_labels, fake_data=True, one_hot=True, reshape=reshape)\\n data_sets.validation = DataSet(val_images, val_labels, fake_data=True, one_hot=True, reshape=reshape)\\n data_sets.test = DataSet(test_images, test_labels, fake_data=True, one_hot=True, reshape=reshape)\\n\\n return data_sets\",\n \"def train(self, train_set) -> None:\\n super().train(train_set)\\n # split into data and target\\n xlist, y = zip(*train_set)\\n x = sparse.vstack(xlist)\\n self._classifier.fit(x, y)\",\n \"def load_testset_predictions(all, weighted=False):\\n if weighted:\\n return load_data_from_CSV('results/testset_results_weighted_{}.csv'.format(all))\\n else:\\n return load_data_from_CSV('results/testset_results_{}.csv'.format(all))\",\n \"def get_data(\\n establishment_number=None,\\n columns_to_drop=[],\\n year_to_separate=2017,\\n path_to_file=\\\"data/data_all.gz\\\",\\n drop_na=False,\\n drop_value_below_threshold=None,\\n):\\n date_col = \\\"Date\\\"\\n visitors = \\\"visitors\\\"\\n data = pd.read_csv(path_to_file, sep=\\\";\\\", decimal=\\\",\\\", index_col=\\\"id\\\")\\n data[date_col] = pd.to_datetime(data[date_col])\\n\\n if drop_na:\\n data.dropna(inplace=True)\\n\\n\\n if drop_value_below_threshold is None:\\n data.loc[data[visitors] < 0, visitors] = 0\\n else:\\n data = data[data[\\\"visitors\\\"] >= drop_value_below_threshold]\\n\\n\\n if establishment_number is not None:\\n data = data[data[\\\"library\\\"] == establishment_number]\\n\\n # Separate data in train and test DataFrame\\n train = data[data.loc[:, date_col].dt.year < year_to_separate]\\n test = data[data.loc[:, date_col].dt.year >= year_to_separate]\\n\\n # Drop columns\\n to_drop = [date_col, \\\"Date:1\\\"] + columns_to_drop\\n train.drop(to_drop, axis=1, inplace=True)\\n test.drop(to_drop, axis=1, inplace=True)\\n\\n X_train = train.drop(visitors, axis=1)\\n y_train = train[[visitors]]\\n X_test = test.drop(visitors, axis=1)\\n y_test = test[[visitors]]\\n\\n return X_train, y_train, X_test, y_test\",\n \"def create_data_sets(reviews, labels, write_to_pickle=True, problem=\\\"\\\"):\\n def sanity_check(labels):\\n print str(len(labels)) + \\\" total labels. \\\" + str(sum(labels)) + \\\" positive labels. \\\" \\\\\\n + str(len(labels) - sum(labels)) + \\\" negative labels. \\\"\\n\\n train_reviews = []\\n train_labels = []\\n dev_reviews = []\\n dev_labels = []\\n test_reviews = []\\n test_labels = []\\n\\n total_train = int(len(reviews) * 0.5 / 2) # divided by 2 because of 2 classes\\n total_dev = int(len(reviews) * 0.25 / 2)\\n\\n current_pos_training = 0\\n current_neg_train = 0\\n current_pos_dev = 0\\n current_neg_dev = 0\\n\\n for (review, vote) in zip(reviews, labels):\\n if vote == 1:\\n if current_pos_training < total_train:\\n train_reviews.append(review)\\n train_labels.append(vote)\\n current_pos_training += 1\\n elif current_pos_dev < total_dev:\\n dev_reviews.append(review)\\n dev_labels.append(vote)\\n current_pos_dev += 1\\n else:\\n test_reviews.append(review)\\n test_labels.append(vote)\\n\\n # Negative review\\n else:\\n if current_neg_train < total_train:\\n train_reviews.append(review)\\n train_labels.append(vote)\\n current_neg_train += 1\\n elif current_neg_dev < total_dev:\\n dev_reviews.append(review)\\n dev_labels.append(vote)\\n current_neg_dev += 1\\n else:\\n test_reviews.append(review)\\n test_labels.append(vote)\\n\\n # Shuffle data for every dataset\\n combined_lists = zip(train_reviews, train_labels)\\n np.random.shuffle(combined_lists)\\n train_reviews, train_labels = zip(*combined_lists)\\n\\n combined_lists = zip(dev_reviews, dev_labels)\\n np.random.shuffle(combined_lists)\\n dev_reviews, dev_labels = zip(*combined_lists)\\n\\n combined_lists = zip(test_reviews, test_labels)\\n np.random.shuffle(combined_lists)\\n test_reviews, test_labels = zip(*combined_lists)\\n\\n # Sanity checks\\n print \\\"Total reviews: \\\" + str(len(reviews))\\n print \\\"Original distribution: \\\"\\n sanity_check(labels)\\n print \\\"========================\\\"\\n print \\\"Train labels\\\"\\n sanity_check(train_labels)\\n print \\\"========================\\\"\\n print \\\"Dev labels\\\"\\n sanity_check(dev_labels)\\n print \\\"========================\\\"\\n print \\\"Train labels\\\"\\n sanity_check(test_labels)\\n\\n # Write to pickles\\n N = len(reviews)\\n if write_to_pickle:\\n print \\\"Writing to pickle...\\\"\\n pickle.dump([train_reviews, train_labels],\\n open(\\\"TrainSet_\\\" + problem + '_' + str(N), \\\"wb\\\"), pickle.HIGHEST_PROTOCOL)\\n\\n pickle.dump([dev_reviews, dev_labels],\\n open(\\\"DevSet_\\\" + problem + '_' + str(N), \\\"wb\\\"), pickle.HIGHEST_PROTOCOL)\\n\\n pickle.dump([test_reviews, test_labels],\\n open(\\\"TestSet_\\\" + problem + '_' + str(N), \\\"wb\\\"), pickle.HIGHEST_PROTOCOL)\\n print \\\"Done.\\\"\\n\\n return train_reviews, train_labels, dev_reviews, dev_labels, test_reviews, test_labels\",\n \"def get_training_data(self):\\n\\n # this actually never was a set\\n # src_set = self.target['src'].values\\n # dst_set = self.target['dst'].values\\n\\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\\n\\n train_positive = self.target.iloc[self.train_edge_ind].values\\n test_positive = self.target.iloc[self.test_edge_ind].values\\n\\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\\n # print(f\\\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\\\")\\n\\n X_train = train_positive\\n X_test = test_positive\\n\\n y_train = np.ones((self.train_edge_ind.shape[0],))\\n y_test = np.ones((self.test_edge_ind.shape[0],))\\n\\n # X_train = np.vstack([\\n # train_positive,\\n # train_negative\\n # ])\\n\\n # X_test = np.vstack([\\n # test_positive,\\n # test_negative\\n # ])\\n\\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\\n\\n assert X_train.shape[0] == y_train.shape[0]\\n assert X_test.shape[0] == y_test.shape[0]\\n\\n def shuffle(X, y):\\n ind_shuffle = np.arange(0, X.shape[0])\\n np.random.shuffle(ind_shuffle)\\n return X[ind_shuffle], y[ind_shuffle]\\n\\n self.X_train, self.y_train = shuffle(X_train, y_train)\\n self.X_test, self.y_test = shuffle(X_test, y_test)\\n\\n print(f\\\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\\\")\\n\\n # return X_train, X_test, y_train, y_test\",\n \"def ConcatDF(train_set, test_set):\\n return pd.concat([train_set, test_set], sort=True).reset_index(drop=True)\",\n \"def train_test() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:\\n X = pd.read_csv(os.path.join(conf.root_output, \\\"csv\\\", \\\"X.csv\\\"))\\n y = pd.read_csv(os.path.join(conf.root_output, \\\"csv\\\", \\\"y.csv\\\")).squeeze()\\n\\n # Drop areas that are outside of borders of Vienna\\n X.drop(columns=conf.outer_areas, errors=\\\"ignore\\\", inplace=True)\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\\n return X_train, X_test, y_train, y_test\",\n \"def train_test_set_split(dataset, dataset_name, test_size=0.1):\\n train_indices_path = './' + dataset_name + '_train_indices(' + str(test_size) + ').txt'\\n test_indices_path = './' + dataset_name + '_test_indices(' + str(test_size) + ').txt'\\n try:\\n train_indices = []\\n test_indices = []\\n file = open(train_indices_path, 'rt', encoding='utf-8')\\n while True:\\n line = file.readline()\\n if not line:\\n break\\n train_indices.append(int(line[:-1]))\\n file.close()\\n file = open(test_indices_path, 'rt', encoding='utf-8')\\n while True:\\n line = file.readline()\\n if not line:\\n break\\n test_indices.append(int(line[:-1]))\\n file.close()\\n train_labels = [dataset.targets[i] for i in train_indices]\\n except FileNotFoundError:\\n indices = np.arange(len(dataset))\\n labels = np.array(dataset.targets)\\n train_indices, test_indices, train_labels, _ = train_test_split(\\n indices, labels, test_size=test_size, stratify=labels\\n )\\n file = open(train_indices_path, 'wt', encoding='utf-8')\\n for i in train_indices:\\n line = str(i) + '\\\\n'\\n file.write(line)\\n file.close()\\n file = open(test_indices_path, 'wt', encoding='utf-8')\\n for i in test_indices:\\n line = str(i) + '\\\\n'\\n file.write(line)\\n file.close()\\n\\n train_set = torch.utils.data.Subset(dataset, indices=train_indices)\\n test_set = torch.utils.data.Subset(dataset, indices=test_indices)\\n return train_set, test_set, train_labels\",\n \"def fetch_data(round:int) -> pd.DataFrame:\\n\\n \\\"\\\"\\\"\\n You have to implement this function. This function can take \\n an option (from the command line) and produces a DataFrame.\\n (If you need a different kind of output, you also need to \\n modify the main function. \\n \\\"\\\"\\\"\\n import numpy as np\\n from sklearn.datasets import fetch_california_housing\\n\\n sampling_rate = 0.2*round\\n np.random.seed(51)\\n\\n data = fetch_california_housing(download_if_missing=True)\\n df = pd.DataFrame(data.data, columns=data.feature_names)\\n df[target_name] = data.target\\n\\n ## sampling\\n scores = np.random.uniform(low=0, high=1, size=df.shape[0])\\n selected = scores < sampling_rate\\n\\n return df.iloc[selected,:].copy()\",\n \"def train_dataset():\\n return TabularDataset.from_path('tests/data/dummy_tabular/train.csv', sep=',')\",\n \"def load_data(label_name='Species'):\\n\\n # Create a local copy of the training set.\\n train_path = tf.keras.utils.get_file(fname=TRAIN_URL.split('/')[-1],\\n origin=TRAIN_URL)\\n # train_path now holds the pathname: （训练集和测试集路径） ~/.keras/datasets/iris_training.csv\\n\\n # Parse the local CSV file.（解析）\\n train = pd.read_csv(filepath_or_buffer=train_path,\\n names=CSV_COLUMN_NAMES, # list of column names\\n header=0 # ignore the first row of the CSV file.\\n )\\n # train now holds a pandas DataFrame, which is data structure\\n # analogous to a table.\\n\\n # 1. Assign the DataFrame's labels (the right-most column) to train_label.\\n # 2. Delete (pop) the labels from the DataFrame.\\n # 3. Assign the remainder of the DataFrame to train_features\\n print(\\\"-\\\")\\n train_features, train_label = train, train.pop(label_name)\\n\\n # Apply the preceding logic to the test set.\\n test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)\\n test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)\\n test_features, test_label = test, test.pop(label_name)\\n\\n # Return four DataFrames.\\n return (train_features, train_label), (test_features, test_label)\",\n \"def load_or_generate_data(self) -> None:\\n\\n # Training set defined as a 5 x 5 square:\\n xg1 = np.linspace(-5, 10, 5)\\n xg2 = np.linspace(0, 15, 5)\\n x = np.zeros((xg1.size * xg2.size, 2))\\n for i, x1 in enumerate(xg1):\\n for j, x2 in enumerate(xg2):\\n x[i + xg1.size * j, :] = [x1, x2]\\n\\n y = self.branin(x)[:, None]\\n self.x, self.y = x, y\",\n \"def load_dataset():\\n temp = gzip.open('mnist.pkl.gz')\\n train, val , test = pickle.load(temp,encoding='latin1')\\n temp.close()\\n train_inp = [np.reshape(x, (784,1)) for x in train[0]]\\n train_outp = [one_hot(y) for y in train[1]]\\n training_data = zip(train_inp, train_outp)\\n validation_inp = [np.reshape(x, (784, 1)) for x in val[0]]\\n validation_data = zip(validation_inp, val[1])\\n test_inp = [np.reshape(x, (784, 1)) for x in test[0]]\\n test_data = zip(test_inp, test[1])\\n return (training_data,validation_data,test_data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6308423","0.6136544","0.60832155","0.60303485","0.59481156","0.5926183","0.58851606","0.5858881","0.5792204","0.57012284","0.56984967","0.569597","0.56708777","0.5638815","0.56349504","0.56267476","0.56263214","0.5608293","0.55986565","0.55914485","0.55911386","0.5584236","0.55594903","0.5529645","0.55275583","0.54748285","0.5469353","0.5464139","0.54296494","0.54276204","0.5425653","0.5417365","0.5397619","0.53965724","0.5396335","0.5391292","0.5386209","0.5376769","0.5374978","0.53716475","0.5364698","0.53607476","0.53564304","0.53500074","0.5347932","0.53151786","0.5314927","0.5305953","0.53016317","0.52968425","0.5294699","0.5274206","0.5270411","0.52662593","0.5260008","0.5257418","0.52549994","0.52544475","0.5253658","0.52506846","0.5250249","0.52403015","0.5239552","0.5235143","0.52329886","0.5232963","0.52318686","0.52310634","0.52221507","0.5205883","0.5184274","0.5182729","0.5181703","0.5181329","0.5178531","0.5176564","0.51758593","0.51745856","0.5171473","0.5171088","0.5167354","0.51649404","0.5155346","0.51541466","0.5148004","0.51452297","0.51404965","0.51402134","0.51309884","0.51259357","0.5125208","0.51174","0.51137286","0.5109827","0.5102247","0.5098921","0.50942546","0.50906235","0.5090301","0.50835663"],"string":"[\n \"0.6308423\",\n \"0.6136544\",\n \"0.60832155\",\n \"0.60303485\",\n \"0.59481156\",\n \"0.5926183\",\n \"0.58851606\",\n \"0.5858881\",\n \"0.5792204\",\n \"0.57012284\",\n \"0.56984967\",\n \"0.569597\",\n \"0.56708777\",\n \"0.5638815\",\n \"0.56349504\",\n \"0.56267476\",\n \"0.56263214\",\n \"0.5608293\",\n \"0.55986565\",\n \"0.55914485\",\n \"0.55911386\",\n \"0.5584236\",\n \"0.55594903\",\n \"0.5529645\",\n \"0.55275583\",\n \"0.54748285\",\n \"0.5469353\",\n \"0.5464139\",\n \"0.54296494\",\n \"0.54276204\",\n \"0.5425653\",\n \"0.5417365\",\n \"0.5397619\",\n \"0.53965724\",\n \"0.5396335\",\n \"0.5391292\",\n \"0.5386209\",\n \"0.5376769\",\n \"0.5374978\",\n \"0.53716475\",\n \"0.5364698\",\n \"0.53607476\",\n \"0.53564304\",\n \"0.53500074\",\n \"0.5347932\",\n \"0.53151786\",\n \"0.5314927\",\n \"0.5305953\",\n \"0.53016317\",\n \"0.52968425\",\n \"0.5294699\",\n \"0.5274206\",\n \"0.5270411\",\n \"0.52662593\",\n \"0.5260008\",\n \"0.5257418\",\n \"0.52549994\",\n \"0.52544475\",\n \"0.5253658\",\n \"0.52506846\",\n \"0.5250249\",\n \"0.52403015\",\n \"0.5239552\",\n \"0.5235143\",\n \"0.52329886\",\n \"0.5232963\",\n \"0.52318686\",\n \"0.52310634\",\n \"0.52221507\",\n \"0.5205883\",\n \"0.5184274\",\n \"0.5182729\",\n \"0.5181703\",\n \"0.5181329\",\n \"0.5178531\",\n \"0.5176564\",\n \"0.51758593\",\n \"0.51745856\",\n \"0.5171473\",\n \"0.5171088\",\n \"0.5167354\",\n \"0.51649404\",\n \"0.5155346\",\n \"0.51541466\",\n \"0.5148004\",\n \"0.51452297\",\n \"0.51404965\",\n \"0.51402134\",\n \"0.51309884\",\n \"0.51259357\",\n \"0.5125208\",\n \"0.51174\",\n \"0.51137286\",\n \"0.5109827\",\n \"0.5102247\",\n \"0.5098921\",\n \"0.50942546\",\n \"0.50906235\",\n \"0.5090301\",\n \"0.50835663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6827967"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18484,"cells":{"query":{"kind":"string","value":"Validate contents of blacklist. For most values, the corresponding file is saved upon completion of the experiment. See the \"Notes\" section below for details on some special cases"},"document":{"kind":"string","value":"def validate_file_blacklist(blacklist):\n valid_values = [\n # 'checkpoint',\n \"description\",\n \"heartbeat\",\n \"predictions_holdout\",\n \"predictions_in_fold\",\n \"predictions_oof\",\n \"predictions_test\",\n \"script_backup\",\n \"tested_keys\",\n \"current_heartbeat\",\n ]\n if blacklist == \"ALL\":\n G.warn('WARNING: Received `blacklist`=\"ALL\". Nothing will be saved')\n return blacklist\n\n if not blacklist:\n return []\n elif not isinstance(blacklist, list):\n raise TypeError(\"Expected blacklist to be a list, not: {}\".format(blacklist))\n elif not all([isinstance(_, str) for _ in blacklist]):\n invalid_files = [(type(_).__name__, _) for _ in blacklist if not isinstance(_, str)]\n raise TypeError(\"Expected blacklist contents to be strings, not: {}\".format(invalid_files))\n\n for a_file in blacklist:\n if a_file not in valid_values:\n raise ValueError(f\"Invalid blacklist value: {a_file}.\\nExpected one of: {valid_values}\")\n if a_file in [\"description\", \"tested_keys\"]:\n G.warn(f\"Including {a_file!r} in blacklist will severely impede library functionality\")\n\n # Blacklist experiment-specific heartbeat if general (current) heartbeat is blacklisted\n if (\"current_heartbeat\" in blacklist) and (\"heartbeat\" not in blacklist):\n blacklist.append(\"heartbeat\")\n\n return blacklist"},"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 load_blacklist(experiment):\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\n return blacklist","def load_blacklist(experiment):\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\n return blacklist","def save_blacklist(blacklist, output_dir):\n output_filepath = os.path.join(output_dir, 'blacklist.txt')\n print('[INFO] Writing blacklist to {}...'.format(output_filepath))\n with open(output_filepath, 'w') as f:\n for ent in blacklist:\n f.write('{}\\t{}\\n'.format(ent[0], ent[1]))","def read_blacklist(self, _blacklist_fname):\n try:\n blacklist_f = codecs.open(_blacklist_fname, mode='r', encoding='utf-8')\n line_idx = 0\n for fline in blacklist_f:\n line_idx = line_idx + 1\n line = fline.strip()\n if ((len(line) > 0) and (line[0] != '#')):\n # non null and not started # line ... add to the set\n if (line in self.__black_list_set):\n print u'duplication found [' + line + u'] at ' + str(line_idx) + \\\n u' ignored'\n else:\n self.__black_list_set.add(line)\n\n print u'read blacklist_file [' + _blacklist_fname + \\\n u'], number of entries: ' + str(len(self.__black_list_set))\n except IOError as e:\n print \"I/O error({0}): {1}\".format(e.errno, e.strerror)\n print \"Can not open a blacklist file {0}\".format(_blacklist_fname)\n print \"Please create blacklist file (an empty file is also fine.)\"\n sys.exit(1)\n except:\n print \"Unexpected error:\", sys.exc_info()[0]\n raise","def getBlackList(filename):\n #filename = \"filelist/blacklist_%s.txt\"%dataset.lstrip('/').replace('/','__')\n blacklist = [ ]\n if os.path.exists(filename):\n with open(filename,'r') as file:\n for line in file:\n line = line.rstrip('\\n')\n if line and '#' not in line:\n blacklist.append(line)\n return blacklist","def open_blacklist(filepath):\n with open(filepath, 'r') as f:\n blacklist = [tuple(line.strip().split('\\t')) for line in f.readlines()]\n return blacklist","def blacklist_file(self, fkey):\n self.blacklist.update([fkey])","def test_blacklist(self):\n ts = self.ts_db\n fc1 = self.field_change1\n fc2 = self.field_change2\n fc3 = self.field_change3\n fc4 = self.field_change4\n\n valid = blacklist.filter_by_benchmark_name(ts, fc1)\n self.assertTrue(valid, \"Expect this to not be filtered.\")\n valid = blacklist.filter_by_benchmark_name(ts, fc2)\n self.assertTrue(valid, \"Expect this to not be filtered.\")\n bad = blacklist.filter_by_benchmark_name(ts, fc3)\n self.assertFalse(bad, \"Expect this to be filtered by regex.\")\n bad = blacklist.filter_by_benchmark_name(ts, fc4)\n self.assertFalse(bad, \"Expect this to be filtered by blacklist.\")","def write_valids():\n har_path = os.path.join(PLT_SRC, 'data/replay/*')\n valid_path = '../data/filtered_stats/valids.txt'\n\n har_files = [f for f in glob(har_path)] # Include pc files?\n urls = \\\n [urlsafe_b64decode(f.split('/')[-1].split('.')[0]) for f in har_files]\n with open(valid_path, 'w') as f:\n for url, url_har_path in zip(urls, har_files):\n f.write('{0} {1}\\n'.format(url, url_har_path))","def _parse_blacklist(path):\n if path is None:\n return []\n with open(path, 'rt') as f:\n return [line.strip() for line in f]","def ReadBlackListFile(BlackListFile):\n blacklist = []\n if os.path.isfile(BlackListFile):\n with open(BlackListFile, 'r') as filecontent:\n for line in filecontent:\n #(chrom1, start1, chrom2, start2) = line.rstrip().split(\"\\t\")\n blacklist.append(line)\n return(blacklist)","def apply_tempest_blacklist(self, black_list):\n LOGGER.debug(\"Applying tempest blacklist...\")\n if os.path.exists(self.raw_list):\n os.remove(self.raw_list)\n os.rename(self.list, self.raw_list)\n cases_file = self.read_file(self.raw_list)\n with open(self.list, 'w', encoding='utf-8') as result_file:\n black_tests = []\n try:\n deploy_scenario = env.get('DEPLOY_SCENARIO')\n if bool(deploy_scenario):\n # if DEPLOY_SCENARIO is set we read the file\n with open(black_list, encoding='utf-8') as black_list_file:\n black_list_yaml = yaml.safe_load(black_list_file)\n black_list_file.close()\n for item in black_list_yaml:\n scenarios = item['scenarios']\n in_it = rally.RallyBase.in_iterable_re\n if in_it(deploy_scenario, scenarios):\n tests = item['tests']\n black_tests.extend(tests)\n except Exception: # pylint: disable=broad-except\n black_tests = []\n LOGGER.debug(\"Tempest blacklist file does not exist.\")\n\n for cases_line in cases_file:\n for black_tests_line in black_tests:\n if re.search(black_tests_line, cases_line):\n break\n else:\n result_file.write(str(cases_line) + '\\n')","def remove_blacklisted(blacklist, ssc, output_dir):\n print('[INFO] Writing blacklisted corpus to {}...'.format(output_dir))\n # assuming there is only 1 SSC, so take index 0\n ssc_filepaths = list(get_filepaths(ssc))[0]\n # for faster lookup\n blacklist = set(blacklist)\n for filepath in ssc_filepaths:\n with open(filepath, 'r') as f:\n # remove blacklisted entities\n lines = f.readlines()\n for i in range(1, len(lines) - 1):\n previous_tag = 'O' if lines[i-1] == '\\n' else lines[i-1].strip().split('\\t')[1]\n next_tag = 'O' if lines[i+1] == '\\n' else lines[i+1].strip().split('\\t')[1]\n single_token_entity = (previous_tag != 'I-' and next_tag != 'I-')\n blacklisted = tuple(lines[i].strip().split('\\t')) in blacklist\n if single_token_entity and blacklisted:\n lines[i] = '{}\\tO\\n'.format(lines[i].split('\\t')[0])\n # write blacklisted copy to disk\n corpus_name = os.path.basename(ssc) + '_blacklisted'\n output_directory = os.path.join(output_dir, corpus_name)\n make_dir(output_directory)\n output_filepath = os.path.join(output_directory, os.path.basename(filepath))\n with open(output_filepath, 'w') as f:\n for line in lines:\n f.write(line)","def blacklist(self) -> List[str]:\n return self.raw_config.get(\"blacklist\", [])","def blacklist_add():\n db = unitdata.kv()\n blacklist = db.get(BLACKLIST_KEY, [])\n for device in get_devices():\n if not os.path.exists(device):\n raise Error('{}: No such file or directory.'.format(device))\n if device not in blacklist:\n blacklist.append(device)\n db.set(BLACKLIST_KEY, blacklist)\n db.flush()","def load_blocked_groups(self):\n print(\" ->[*] Loading group blacklist...\")\n blacklist = set()\n if os.access(\"blocked_groups\", os.F_OK):\n with codecs.open(\"blocked_groups\", \"r\", encoding=\"utf-8\") as groups:\n blocked_groups = groups.readlines()\n for group in blocked_groups:\n blacklist.add(group)\n return blacklist","def is_blacklisted(fname):\n return is_dot(fname) or is_excluded_filetype(fname)","def is_blacklisted(self):\r\n \r\n in_blacklist = False \r\n if self.chrompos in parser.blacklist:\r\n in_blacklist = True\r\n \r\n return in_blacklist","def check_utxo_blacklist(commitment, persist=False):\n #TODO format error checking?\n fname = \"blacklist\"\n if jm_single().config.get(\"BLOCKCHAIN\", \"blockchain_source\") == 'regtest':\n fname += \"_\" + jm_single().nickname\n with jm_single().blacklist_file_lock:\n if os.path.isfile(fname):\n with open(fname, \"rb\") as f:\n blacklisted_commitments = [x.strip() for x in f.readlines()]\n else:\n blacklisted_commitments = []\n if commitment in blacklisted_commitments:\n return False\n elif persist:\n blacklisted_commitments += [commitment]\n with open(fname, \"wb\") as f:\n f.write('\\n'.join(blacklisted_commitments))\n f.flush()\n #If the commitment is new and we are *not* persisting, nothing to do\n #(we only add it to the list on sending io_auth, which represents actual\n #usage).\n return True","def Validate(self, relative_file, contents):\n pass","def handler(state, _):\n if state[0] == 'u':\n fname = player.playlist[player.playlist_pos]['filename']\n fkey = get_file_key(fname)\n col.blacklist_file(fkey)\n player.playlist_remove()\n os.remove(fname)\n print('Blacklisted: {}'.format(fname))","def test_validation_negative(self):\n self.assertFalse(self.cut._validate_iban(\"FR14 2004 1010 0505 0001 3\"))\n self.assertFalse(self.cut._validate_iban(\"XX00 1234 5678 9012 3456 7890 1234 5678 90\"))\n self.assertFalse(self.cut._validate_iban(\"YY00123456789012345678901234567890\"))\n self.assertFalse(self.cut._validate_iban(\"XX22YYY1234567890123\"))\n self.assertFalse(self.cut._validate_iban(\"foo@i.ban\"))","def validate(self):\n with open(os.path.join(settings.MEDIA_ROOT, self.file.name)) as file:\n lines = file.readlines()\n validators = ['os.', 'from os', 'io.', 'from io', 'open(', 'system(']\n for line in lines:\n for validator in validators:\n if validator in line:\n return False\n return True","def test_remove_from_blacklist(self):\n\n self.feature_test.add_to_blacklist(3)\n self.feature_test.remove_from_blacklist(3)\n self.assertFalse(3 in Feature(\"testing\").blacklist)","def CheckIfItIsBlacklisted(chr1, start1, chr2, start2, blacklist, extention):\n blacklistTag = None\n chr1 = str(chr1)\n chr2 = str(chr2)\n start1 = int(start1)\n start2 = int(start2)\n if chr1 == chr2:\n start = start1\n end = start2\n for entries in blacklist:\n (bchr1, bstart1, bchr2, bstart2) = entries.rstrip().split(\"\\t\")\n bstart1 = int(bstart1)\n bstart2 = int(bstart2)\n if(bchr1 == bchr2):\n if(chr1 == bchr1):\n if(((start <= int(bstart1 - extention)) and (start >= int(bstart1 + extention)))\n and ((end <= int(bstart2 - extention)) and (end <= int(bstart2 + extention)))):\n blacklistTag = True\n else:\n blacklistTag = False\n if(((end <= int(bstart1 - extention)) and (end >= int(bstart1 + extention)))\n and ((start <= int(bstart2 - extention)) and (start <= int(bstart2 + extention)))):\n blacklistTag = True\n else:\n blacklistTag = False\n else:\n continue\n else:\n continue\n\n else:\n for entries in blacklist:\n (bchr1, bstart1, bchr2, bstart2) = entries.rstrip().split(\"\\t\")\n bstart1 = int(bstart1)\n bstart2 = int(bstart2)\n if(((chr1 == bchr1) and (chr2 == bchr2)) or ((chr1 == bchr2) and (chr2 == bchr1))):\n if(((start1 <= int(bstart1 - extention)) and (start1 >= int(bstart1 + extention)))\n and ((start2 <= int(bstart2 - extention)) and (start2 <= int(bstart2 + extention)))):\n blacklistTag = True\n else:\n blacklistTag = False\n if(((start2 <= int(bstart1 - extention)) and (start2 >= int(bstart1 + extention)))\n and ((start1 <= int(bstart2 - extention)) and (start1 <= int(bstart2 + extention)))):\n blacklistTag = True\n else:\n blacklistTag = False\n else:\n continue\n\n return(blacklistTag)","def test_copy_required_exclude_list(self):\n exclude_list = ['*.yar', 'skip/these/file*']\n self.assertTrue(clone_rules._copy_required('base.yara', [], exclude_list))\n self.assertTrue(clone_rules._copy_required('path/to/file.yara', [], exclude_list))\n self.assertFalse(clone_rules._copy_required('file.yar', [], exclude_list))\n self.assertFalse(clone_rules._copy_required('skip/these/file.yara', [], exclude_list))","def write_data(self, blacklist=('normE', 'normEsquared', 'genRate')):\n\n start = time.time()\n self.data.write_data(blacklist=blacklist)\n end = time.time()\n self.log.info('Write time: %.2f seconds', end - start)","def saveFileListLocal(dataset,filelist,blacklist=[ ],tag=\"\"):\n if '/pnfs/' in dataset:\n tag += \"_pnfs\"\n dataset = '__'.join(dataset.split('/')[-3:])\n filename = \"filelist/filelist_%s%s.txt\"%(dataset.replace('/','__'),tag)\n with open(filename,'w+') as file:\n for line in filelist:\n if line not in blacklist:\n file.write(line+'\\n')\n return filename","def test_does_not_validate_invalid_files(self):\n bad_files = (\n 'newstest2019-defr-src-ts.de.sgm',\n 'newstest2019-defr-src-ts.de.xml',\n )\n for bad_file in bad_files:\n bad_path = join(getcwd(), 'testdata', bad_file)\n with self.assertRaises(ValueError):\n _ = valitest.ValidatableTestSet(bad_path)","def test_add_to_blacklist(self):\n\n self.feature_test.add_to_blacklist(3)\n self.assertTrue(3 in Feature(\"testing\").blacklist)","def test_allowed_list(self):\n val = DwcaValidator(yaml.load(self.yaml_allowed_list, Loader=yaml.FullLoader),\n error_handler=WhipErrorHandler)\n document = {'rightsHolder': 'INBO'}\n self.assertTrue(val.validate(document))\n document = {'rightsHolder': 'ILVO'}\n self.assertFalse(val.validate(document))\n document = {'sex': 'male'}\n self.assertTrue(val.validate(document))\n document = {'sex': 'female'}\n self.assertTrue(val.validate(document))\n document = {'sex': 'Female'}\n self.assertFalse(val.validate(document))\n document = {'age': 'adult'}\n self.assertTrue(val.validate(document))\n document = {'age': 'juvenile'}\n self.assertTrue(val.validate(document))\n document = {'age': 'adult | juvenile'}\n self.assertTrue(val.validate(document))\n document = {'age': 'adult|juvenile'}\n self.assertFalse(val.validate(document))","def load_blacklists(self, blacklist_urls: List[str]) -> Set[str]:\n items: Set[str] = set()\n sets = []\n for url in blacklist_urls:\n fname = f'/tmp/{hashlib.md5(url.encode()).hexdigest()}-blacklist'\n # Download if necessary\n if not os.path.exists(fname):\n log.debug(f'Downloading {url} -> {fname}')\n urlretrieve(url, fname)\n\n fset = set(line.strip() for line in open(fname))\n log.debug(f'Got {len(fset)} records from \\t {url}')\n sets.append(fset)\n\n log.debug(f'Total: {sum(len(s) for s in sets)} records')\n items = items.union(*sets)\n log.debug(f'Aggregated into {len(items)} records')\n return items","def _write_full_csv_blacklist(self):\n tblname = self._blacklist_new_tblname\n filename = os.path.join(self._output_dir, '{0}_blacklist.csv'.format(self._date_str))\n cursor_name = 'listgen_write_full_csv_blacklist'\n with create_db_connection(self._config.db_config) as conn, \\\n conn.cursor(name=cursor_name) as cursor, open(filename, 'w') as csvfile, CodeProfiler() as cp:\n csv_writer = csv.DictWriter(csvfile, fieldnames=['imei', 'block_date', 'reasons'], extrasaction='ignore')\n csv_writer.writeheader()\n cursor.execute(sql.SQL(\"\"\"SELECT {imei_col} AS imei,\n to_char(block_date, 'YYYYMMDD') AS block_date,\n array_to_string(reasons, '|') AS reasons\n FROM {tblname}\n WHERE {valid_filter}\n \"\"\").format(imei_col=self._output_imei_column,\n tblname=sql.Identifier(tblname),\n valid_filter=self._valid_filter_query))\n num_written_records = 0\n for row_data in cursor:\n csv_writer.writerow(row_data._asdict())\n num_written_records += 1\n num_records = self._get_total_record_count(conn, tblname)\n\n return self._gen_metadata_for_list(filename,\n num_records=num_records,\n num_written_records=num_written_records), 'blacklist', cp.duration","def test_hooks_invalid_list(self):\n with open(\".scuba.yml\", \"w\") as f:\n f.write(\n \"\"\"\n image: na\n hooks:\n user:\n - this list should be under\n - a 'script'\n \"\"\"\n )\n\n self._invalid_config()","def test_bad_bookmark_file_data(self):\n f = self.gen_filename()\n self.write_fixture(f, {'bookmarks': 'should be an object!'})\n\n man = bookmarks.BookmarkManager(f)\n self.assertIsNone(man.bookmarks)\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))","def test_read_allowlist(self):\n\n curdir = os.path.dirname(os.path.abspath(__file__))\n allowlist_file = os.path.join(curdir, \"data\", \"ima-allowlist-short.txt\")\n allowlist_sig = os.path.join(curdir, \"data\", \"ima-allowlist-short.sig\")\n allowlist_bad_sig = os.path.join(curdir, \"data\", \"ima-allowlist-bad.sig\")\n allowlist_gpg_key = os.path.join(curdir, \"data\", \"gpg-sig.pub\")\n allowlist_checksum = \"8b7c2c6a1d7af2568cc663905491bda829c04c397cdba38cc4fc4d8d8a3e69d4\"\n allowlist_bad_checksum = \"4c143670836f96535d9e617359b4d87c59e89e633e2773b4d7feae97f561b3dc\"\n\n # simple read, no fancy verification\n al_data = ima.read_allowlist(allowlist_file)\n self.assertIsNotNone(al_data, \"AllowList data is present\")\n self.assertIsNotNone(al_data[\"meta\"], \"AllowList metadata is present\")\n self.assertEqual(al_data[\"meta\"][\"version\"], 1, \"AllowList metadata version is correct\")\n self.assertEqual(al_data[\"meta\"][\"generator\"], \"keylime-legacy-format-upgrade\", \"AllowList metadata generator is correct\")\n self.assertNotIn(\"checksum\", al_data[\"meta\"], \"AllowList metadata no checksum\")\n self.assertIsNotNone(al_data[\"hashes\"], \"AllowList hashes are present\")\n self.assertEqual(len(al_data[\"hashes\"]), 21, \"AllowList hashes are correct length\")\n self.assertEqual(al_data[\"hashes\"][\"/boot/grub2/i386-pc/testload.mod\"][0], \"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\", \"AllowList sample hash is correct\")\n\n # validate checkum\n al_data = ima.read_allowlist(allowlist_file, allowlist_checksum)\n self.assertIsNotNone(al_data, \"AllowList data is present\")\n self.assertEqual(al_data[\"meta\"][\"checksum\"], allowlist_checksum, \"AllowList metadata correct checksum\")\n self.assertIsNotNone(al_data[\"hashes\"], \"AllowList hashes are present\")\n self.assertEqual(len(al_data[\"hashes\"]), 21, \"AllowList hashes are correct length\")\n self.assertEqual(al_data[\"hashes\"][\"/boot/grub2/i386-pc/testload.mod\"][0], \"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\", \"AllowList sample hash is correct\")\n\n # test with a bad checksum\n with self.assertRaises(Exception) as bad_checksum_context:\n ima.read_allowlist(allowlist_file, allowlist_bad_checksum)\n self.assertIn('Checksum of allowlist does not match', str(bad_checksum_context.exception))\n\n # validate GPG signature\n al_data = ima.read_allowlist(allowlist_file, None, allowlist_sig, allowlist_gpg_key)\n self.assertIsNotNone(al_data, \"AllowList data is present\")\n self.assertNotIn(\"checksum\", al_data[\"meta\"], \"AllowList metadata no checksum\")\n self.assertIsNotNone(al_data[\"hashes\"], \"AllowList hashes are present\")\n self.assertEqual(len(al_data[\"hashes\"]), 21, \"AllowList hashes are correct length\")\n self.assertEqual(al_data[\"hashes\"][\"/boot/grub2/i386-pc/testload.mod\"][0], \"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\", \"AllowList sample hash is correct\")\n\n # test with a bad GPG sig\n with self.assertRaises(Exception) as bad_sig_context:\n ima.read_allowlist(allowlist_file, None, allowlist_bad_sig, allowlist_gpg_key)\n self.assertIn('GPG signature verification failed', str(bad_sig_context.exception))\n\n # validate everything together\n al_data = ima.read_allowlist(allowlist_file, allowlist_checksum, allowlist_sig, allowlist_gpg_key)\n self.assertIsNotNone(al_data, \"AllowList data is present\")\n self.assertEqual(al_data[\"meta\"][\"checksum\"], allowlist_checksum, \"AllowList metadata correct checksum\")\n self.assertIsNotNone(al_data[\"hashes\"], \"AllowList hashes are present\")\n self.assertEqual(len(al_data[\"hashes\"]), 21, \"AllowList hashes are correct length\")\n self.assertEqual(al_data[\"hashes\"][\"/boot/grub2/i386-pc/testload.mod\"][0], \"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\", \"AllowList sample hash is correct\")","def is_blacklisted(self, fkey):\n return fkey in self.blacklist","def hosts_deny_format(cls):\n\n template = Helpers.File(Settings.hosts_deny_template).read()\n\n template = Helpers.Regex(\n template, r\"%%version%%\", replace_with=Settings.version\n ).replace()\n template = Helpers.Regex(\n template, r\"%%lenIP%%\", replace_with=format(len(Settings.ips), \",d\")\n ).replace()\n\n data_to_write = \"ALL: \" + \"\\nALL: \".join(Settings.ips)\n\n template = Helpers.Regex(\n template, r\"%%content%%\", replace_with=data_to_write\n ).replace()\n\n print(\"Generation of %s\" % Settings.hosts_deny_file, end=\" \")\n Helpers.File(Settings.hosts_deny_file).write(template, overwrite=True)\n print(Settings.done)","def test_check(self):\n\n self.assertTrue(PostfixExclude().check(self.file_gitignore))\n self.assertTrue(PostfixExclude().check(self.file_py))\n self.assertTrue(PostfixExclude().check(self.file_authors))\n self.assertTrue(PostfixExclude().check(self.file__init__))\n self.assertTrue(PostfixExclude().check(self.file_bin))","def test_check_exclude_perceval(self):\n\n self.assertTrue(DirExclude(['perceval']).check(self.file_gitignore))\n self.assertTrue(DirExclude(['perceval']).check(self.file_authors))\n self.assertTrue(DirExclude(['perceval']).check(self.file_tests))\n self.assertTrue(DirExclude(['perceval']).check(self.file_bin))\n\n self.assertFalse(DirExclude(['perceval']).check(self.file_perceval))","def blacklisted_file_patterns():\n with open(\"patterns.txt\", 'r') as f:\n lines = [line.strip() for line in f if line.strip()]\n return set(lines)","def cross_validate(self, contents, required=None, forbidden=None):\n if required:\n for item in required:\n self.assertTrue(\n item in contents,\n \"Required entry [{item}] not found in:\\n{contents}\".format(\n item=item, contents=contents\n )\n )\n if forbidden:\n for item in forbidden:\n self.assertTrue(\n item not in contents,\n \"Forbidden entry [{item}] found in:\\n{contents}\".format(\n item=item, contents=contents\n )\n )","def get_bad_word_list(self):\n self.bad_words = self.read_word_file(self.bad_word_file)","def is_blacklisted(self, string='') -> int:\n try:\n for word in string.split(' '):\n if word in self.blacklist:\n return(1)\n else:\n return(0)\n except Exception as error:\n print(f\"Error: self.is_blacklisted({string}) -> {error}\")","def test_unreadable_bookmark_file(self):\n f = self.gen_filename()\n self.write_fixture(f)\n os.chmod(f, 0o200) # TODO: Cross-platform solution?\n\n man = bookmarks.BookmarkManager(f)\n self.assertIsNone(man.bookmarks)\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))","def read_whitelist(whitelist_file):\n\n # create an empty dictionary to store the white lists\n whitelistdict = {}\n with open(whitelist_file, 'r') as fp:\n for line in fp:\n whitelistdict[line.strip()] = True\n\n # return the list of whitelist\n return whitelistdict.keys()","def test_is_not_firefox_file(self):\r\n bad_file = StringIO.StringIO()\r\n bad_file.write('failing tests please')\r\n bad_file.seek(0)\r\n\r\n self.assertTrue(\r\n not FBookmarkImporter.can_handle(bad_file),\r\n \"FBookmarkImporter cannot handle this file\")\r\n\r\n bad_file.close()","def checkFiles(self):\n if self.user[\"Save\"] != \"\":\n self.of_exist = os.path.exists(os.path.join(self.user[\"Save\"], \"Of\"))\n self.back_of_exist = os.path.exists(\n os.path.join(self.user[\"Save\"], \"Back_Of\")\n )\n self.img_exist = os.path.exists(os.path.join(self.user[\"Save\"], \"Images\"))\n self.depth_exist = os.path.exists(os.path.join(self.user[\"Save\"], \"Depth\"))\n\n self.object_detection_dir_exist = os.path.exists(\n os.path.join(self.user[\"Save\"], \"ObjectDetection\")\n )\n\n self.gt_exist = self.user[\"GT\"] != \"\"\n\n self.create_super_pixel_label = (\n self.super_pixel_method != \"\"\n and not os.path.exists(\n os.path.join(self.savePathJoin(\"Super_Pixel\"), self.super_pixel_method)\n )\n )\n\n self.ui.c_crash_plot_video.setEnabled(self.ui.c_crash_plot.isChecked())\n self.ui.t_low.setEnabled(not self.ui.c_optimize.isChecked())\n self.ui.t_high.setEnabled(not self.ui.c_optimize.isChecked())\n self.ui.c_optimize.setEnabled(self.gt_exist)\n self.ui.c_error_plot.setEnabled(self.gt_exist)\n self.ui.c_error_plot_video.setEnabled(self.ui.c_error_plot.isChecked())\n self.ui.c_speed_plot_video.setEnabled(self.ui.c_speed_plot.isChecked())\n self.ui.c_super_pixel_video.setEnabled(\n self.ui.combo_superpixel.currentIndex() != 0\n )\n self.ui.c_csv.setEnabled(self.ui.c_error_plot.isChecked())\n\n if self.runRequirements():\n self.ui.b_run.setEnabled(True)\n else:\n self.ui.b_run.setEnabled(False)","def validate_data_files(self):\n if self.labeled:\n image_ids = self.loader_moving_image.get_data_ids()\n label_ids = self.loader_moving_label.get_data_ids()\n check_difference_between_two_lists(list1=image_ids, list2=label_ids)","def test_check(self):\n\n self.assertTrue(DirExclude().check(self.file_gitignore))\n self.assertTrue(DirExclude().check(self.file_perceval))\n self.assertTrue(DirExclude().check(self.file_authors))\n\n self.assertFalse(DirExclude().check(self.file_tests))\n self.assertFalse(DirExclude().check(self.file_bin))","def check_blacklist(repo):\n blacklisted = [ # NOTE: keep this list up to date!\n 'builder', 'cache', 'controller', 'database', 'logger', 'logspout',\n 'publisher', 'registry', 'router', 'store-admin', 'store-daemon',\n 'store-gateway', 'store-metadata', 'store-monitor', 'swarm', 'mesos-master',\n 'mesos-marathon', 'mesos-slave', 'zookeeper',\n ]\n if any(\"deis/{}\".format(c) in repo for c in blacklisted):\n raise PermissionDenied(\"Repository name {} is not allowed\".format(repo))","def blacklist_remove():\n db = unitdata.kv()\n blacklist = db.get(BLACKLIST_KEY, [])\n for device in get_devices():\n try:\n blacklist.remove(device)\n except ValueError:\n raise Error('{}: Device not in blacklist.'.format(device))\n db.set(BLACKLIST_KEY, blacklist)\n db.flush()","async def add_blacklist(self, ctx, user: discord.Member):\r\n if user.id not in self.settings['blacklist']:\r\n try:\r\n self.settings['blacklist'].append(user.id)\r\n await ctx.send(\"User blacklisted.\")\r\n except:\r\n await ctx.send(\"An error occured.\")\r\n else:\r\n await ctx.send(\"User already blacklisted.\")","def ignore_listings(name_key):\n # for blacklist_str in models_blacklist:\n # if blacklist_str in name_key:\n # return True\n return False","def test_validate_fasta_with_invalid(self):\r\n\r\n validate_fasta(self.sample_fasta_invalid_fp, self.sample_mapping_fp,\r\n self.output_dir)\r\n\r\n expected_log_fp = join(self.output_dir,\r\n split(self.sample_fasta_invalid_fp)[1] + \"_report.log\")\r\n\r\n log_f = open(expected_log_fp, \"U\")\r\n actual_log_lines = [line.strip() for line in log_f][1:]\r\n\r\n expected_log_lines = \"\"\"Percent duplicate labels: 0.250\r\nPercent QIIME-incompatible fasta labels: 0.500\r\nPercent of labels that fail to map to SampleIDs: 0.750\r\nPercent of sequences with invalid characters: 0.500\r\nPercent of sequences with barcodes detected: 0.250\r\nPercent of sequences with barcodes detected at the beginning of the sequence: 0.000\r\nPercent of sequences with primers detected: 0.250\r\nDuplicate labels found:\r\nseq1\"\"\".split('\\n')\r\n\r\n self.assertEqual(actual_log_lines, expected_log_lines)","def update_exam_blacklist(sender, instance, **kwargs):\n exams = Exam.objects.filter(\n course_instance__instructors=instance.instructor)\n if instance.permission_allowed is False:\n exams.exclude(blacklisted=True).update(blacklisted=True)\n else:\n for exam in exams:\n if exam.has_permission():\n exam.blacklisted = False\n exam.save()","def test_remove_from_blacklist_with_string(self):\n email = 'example@example.com'\n self.feature_test.add_to_blacklist(email)\n self.feature_test.remove_from_blacklist(email)\n self.assertFalse(email in Feature(\"testing\").blacklist)","def blacklist_handler(col, player):\n def handler(state, _):\n \"\"\"\n Retains the current file in the player in the collection's blacklist\n and removes it from the airlock. The player is then advanced to the\n next file.\n \"\"\"\n if state[0] == 'u':\n fname = player.playlist[player.playlist_pos]['filename']\n fkey = get_file_key(fname)\n col.blacklist_file(fkey)\n player.playlist_remove()\n os.remove(fname)\n print('Blacklisted: {}'.format(fname))\n return handler","def test_dbrules(self):\n\n testdata = u\"\"\"\n INSERT INTO attachmentrules(scope,checktype,action,regex,description,prio) VALUES\n ('recipient@unittests.fuglu.org','contenttype','allow','application/x-executable','this user likes exe',1)\n \"\"\"\n self.session.execute(testdata)\n # copy file rules\n tmpfile = tempfile.NamedTemporaryFile(\n suffix='virus', prefix='fuglu-unittest', dir='/tmp')\n shutil.copy(TESTDATADIR + '/binaryattachment.eml', tmpfile.name)\n suspect = Suspect(\n 'sender@unittests.fuglu.org', 'recipient@unittests.fuglu.org', tmpfile.name)\n\n result = self.candidate.examine(suspect)\n resstr = actioncode_to_string(result)\n self.assertEqual(resstr, \"DUNNO\")\n\n # another recipient should still get the block\n suspect = Suspect(\n 'sender@unittests.fuglu.org', 'recipient2@unittests.fuglu.org', tmpfile.name)\n\n result = self.candidate.examine(suspect)\n if type(result) is tuple:\n result, message = result\n resstr = actioncode_to_string(result)\n self.assertEqual(resstr, \"DELETE\")\n tmpfile.close()","def blacklist_ips(self):\r\n if self.blacklist == '':\r\n return []\r\n return self.IPFilterList([addr.strip() for addr in self.blacklist.split(',')]) # pylint: disable=no-member\r","def test_check_negative():\n\n # Generate the blocks for the test file which is not present on the server\n test_file = os.path.join(os.path.dirname(__file__),\n \"../test_files/debashis-rc-biswas-3U4gGsGNsMY-unsplash.jpg\")\n # Ask the server for the hash of the last block\n response = client.get(\"/latest_block_hash\")\n last_block_hash = response.json()[\"last_block_hash\"]\n blocks = generate_blocks(test_file, last_block_hash)\n\n # Send the SHA256 checksum of the file to the server to be checked\n response = client.get(\"/check\",\n params={\"file_hash\": blocks[0].hash,\n \"index_all\": blocks[0].index_all})\n assert response.ok\n assert response.json() \\\n == {\"check\": False,\n \"hash\": \"415d4f66e1b8b9083014dcdca5ddd7d1dcca3f5a4a120603169b951b1c5fa0c9\"}","def _check_vocab_file(self, vocab_file, special_tokens):\n if not os.path.exists(vocab_file):\n raise FileNotFoundError(\"vocab file %s not found!\" % vocab_file)\n vocabs = set()\n with open(vocab_file, mode=\"rt\", encoding=\"utf8\", buffering=8192) as f:\n for vocab in f:\n vocab = vocab.strip(\"\\n\").strip()\n if not vocab:\n continue\n if vocab in special_tokens:\n continue\n vocabs.add(vocab)\n vocabs = sorted(vocabs)\n for token in reversed(special_tokens):\n vocabs.insert(0, token)\n filename = str(vocab_file).split(os.sep)[-1]\n assert filename is not None\n new_vocab_file = os.path.join(self.configs['out_dir'], filename)\n with open(new_vocab_file, mode=\"wt\", encoding=\"utf8\", buffering=8192) as f:\n for v in vocabs:\n f.write(v + \"\\n\")\n return len(vocabs), new_vocab_file","def _write_delta_csv_blacklist(self):\n cursor_name = 'listgen_write_delta_csv_blacklist'\n is_valid_query, imei_norm_with_check_digit_query = self._is_valid_and_check_digit_queries\n\n with create_db_connection(self._config.db_config) as conn, \\\n conn.cursor(name=cursor_name) as cursor, contextlib.ExitStack() as stack, CodeProfiler() as cp:\n allowed_delta_reasons = self._allowed_delta_reasons(conn, 'blacklist')\n fnames = {r: os.path.join(self._output_dir, '{0}_blacklist_delta_{1:d}_{2:d}_{3}.csv'\n .format(self._date_str,\n self._base_run_id,\n self._run_id,\n r))\n for r in allowed_delta_reasons}\n files = {r: stack.enter_context(open(fn, 'w', encoding='utf8')) for r, fn in fnames.items()}\n csv_writers = {r: csv.DictWriter(f,\n fieldnames=['imei', 'block_date', 'reasons'],\n extrasaction='ignore') for r, f in files.items()}\n for csv_writer in csv_writers.values():\n csv_writer.writeheader()\n\n cursor.execute(sql.SQL(\"\"\"SELECT {imei_col} AS imei,\n to_char(block_date, 'YYYYMMDD') AS block_date,\n array_to_string(reasons, '|') AS reasons,\n delta_reason\n FROM (SELECT *\n FROM gen_delta_blacklist(%s),\n LATERAL ({is_valid_query}) iv,\n LATERAL ({imei_norm_with_check_digit_query}) cd) changes\n WHERE {valid_filter}\n \"\"\").format(is_valid_query=is_valid_query,\n imei_norm_with_check_digit_query=imei_norm_with_check_digit_query,\n valid_filter=self._valid_filter_query,\n imei_col=self._output_imei_column),\n [self._base_run_id])\n\n metrics = {r: defaultdict(int) for r in allowed_delta_reasons}\n for row_data in cursor:\n row_data_dict = row_data._asdict()\n delta_reason = row_data_dict.pop('delta_reason')\n csv_writer = csv_writers[delta_reason]\n csv_writer.writerow(row_data_dict)\n metrics[delta_reason]['num_records'] += 1\n\n return [self._gen_metadata_for_list(fn, **metrics[r]) for r, fn in fnames.items()], \\\n 'blacklist_delta', \\\n cp.duration","def test_extensions(self):\n field = TypedFileField(required=False, ext_whitelist=self.good_extensions)\n\n for ext in self.good_extensions:\n name = 'somefooname.%s' % ext\n file = UploadedFile(name=name, size=1)\n assert field.clean(file) is file\n\n for ext in self.bad_extensions:\n name = 'somefooname.%s' % ext\n file = UploadedFile(name=name, size=1)\n with pytest.raises(forms.ValidationError):\n field.clean(file)","def whitelist(self, message):\n user = self.ts.get_user(message)\n msg_list = self.ts.get_human_readable_message(message).lower().split(' ')\n try:\n with codecs.open('whitelist.json', 'r', 'utf-8') as f:\n holder_list = json.load(f)\n except json.decoder.JSONDecodeError:\n holder_list = []\n if msg_list[1] not in holder_list:\n holder_list.append(msg_list[1])\n with codecs.open('whitelist.json', 'w', 'utf-8') as f:\n json.dump(holder_list, f, ensure_ascii=False)\n self._add_to_whisper_queue(user, '{} has been added to the whitelist'.format(msg_list[1]))\n else:\n self._add_to_whisper_queue(user, '{} is already in the whitelist!'.format(msg_list[1]))","def file_mode_checks(value):\n if not value.endswith(F_SUFFIX):\n raise argparse.ArgumentTypeError(\n \"\\nFile resource must be:\\n\"\n + \" 1) Obfuscated {hex}.cache.js GWT permutation file\"\n )\n return value","def check_bad(self, delete_bad=True):\n # XXX: work out why this is needed sometimes on network filesystems.\n result_files = glob.glob(\n os.path.join(self.location, \"results\", RSLT_NM.format(\"*\"))\n )\n\n bad_ids = []\n\n for result_file in result_files:\n # load corresponding batch file to check length.\n result_num = (\n os.path.split(result_file)[-1]\n .strip(\"xyz-result-\")\n .strip(\".jbdmp\")\n )\n batch_file = os.path.join(\n self.location, \"batches\", BTCH_NM.format(result_num)\n )\n\n batch = read_from_disk(batch_file)\n\n try:\n result = read_from_disk(result_file)\n unloadable = False\n except Exception as e:\n unloadable = True\n err = e\n\n if unloadable or (len(result) != len(batch)):\n msg = \"result {} is bad\".format(result_file)\n msg += \".\" if not delete_bad else \" - deleting it.\"\n msg += \" Error was: {}\".format(err) if unloadable else \"\"\n print(msg)\n\n if delete_bad:\n os.remove(result_file)\n\n bad_ids.append(result_num)\n\n return tuple(bad_ids)","def copy_file_check(self):\n pass","def whitelist_file(self, fkey):\n self.whitelist.update([fkey])","def filter_blacklist(df, path):\n f = open(path)\n patterns = [e.strip() for e in f.readlines()]\n f.close()\n\n def run_filter(x):\n for pat in patterns:\n if fnmatch.fnmatch(x, pat):\n return True\n return False\n\n index = df[df[PROD_NM].apply(run_filter)].index\n df.drop(columns=[PROD_NM], index=index, inplace=True)","def split_train_validation(path_to_file, valid_amount=0.2):\n path_train_file = path_to_file + \".train.labeled\"\n path_valid_file = path_to_file + \".valid.labeled\"\n # count samples\n samp_gen = dep_sample_generator(path_to_file)\n total_samples = 0\n for _ in samp_gen:\n total_samples += 1\n print(\"total samples \", total_samples)\n buffer = []\n num_validation = int(valid_amount * total_samples)\n num_training = total_samples - num_validation\n taken_for_training = 0\n t_file = open(path_train_file, 'w')\n v_file = open(path_valid_file, 'w')\n with open(path_to_file) as fp:\n sample = []\n for line in fp:\n if not line.rstrip():\n if taken_for_training < num_training:\n for l in sample:\n t_file.write(l)\n t_file.write('\\n')\n taken_for_training += 1\n else:\n for l in sample:\n v_file.write(l)\n v_file.write('\\n')\n sample = []\n else:\n sample.append(line)\n\n if taken_for_training < num_training:\n for l in sample:\n t_file.write(l)\n t_file.write('\\n')\n taken_for_training += 1\n else:\n for l in sample:\n v_file.write(l)\n v_file.write('\\n')\n t_file.close()\n v_file.close()\n print(\"num training: \", num_training, \" saved @ \", path_train_file)\n print(\"num validation: \", num_validation, \" saved @ \", path_valid_file)","def attachment_type_blacklist(self, attachment_type_blacklist: ConfigNodePropertyArray):\n\n self._attachment_type_blacklist = attachment_type_blacklist","def test_malformed_bookmark_file(self):\n f = self.gen_filename()\n with open(f, 'w') as ff:\n ff.write('bad json')\n\n man = bookmarks.BookmarkManager(f)\n self.assertIsNone(man.bookmarks)\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))","def validate_data(self):\n for pattern in self.patterns:\n if pattern == \"\":\n self.patterns.remove(\"\")\n\n if not self.patterns:\n print(\"WARNING! Missing pattern or empty string!\")\n sys.exit()","def validate(self, spec):\n d = spec.directory\n for file_name in os.listdir(d):\n if file_name.endswith(\".icon\"):\n if \" \" in file_name:\n raise ValidationException(f\"The .icon file name was '{file_name}'.\\n \"\n \".icon file may not contain spaces use a '_' instead.\")","def check_workspace(self, workspace: Workspace) -> None:\n for u_file in workspace.iter_files():\n if self.BIB_FILE.search(u_file.name):\n self._check_for_missing_bbl_file(workspace, u_file)","def test_mixed_verfication(self):\n\n lists_map = ima.process_allowlists(ALLOWLIST, '')\n lists_map_wrong = ima.process_allowlists(ALLOWLIST_WRONG, '')\n lists_map_empty = ima.process_allowlists(ALLOWLIST_EMPTY, '')\n lists_map_exclude = ima.process_allowlists(ALLOWLIST, EXCLUDELIST)\n lists_map_exclude_wrong = ima.process_allowlists(ALLOWLIST_WRONG, EXCLUDELIST)\n empty_keyring = ima_file_signatures.ImaKeyring()\n\n # every entry is covered by the allowlist and there's no keyring -> this should pass\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), str(lists_map)) is not None)\n\n curdir = os.path.dirname(os.path.abspath(__file__))\n keydir = os.path.join(curdir, \"data\", \"ima_keys\")\n keyring = ima_file_signatures.ImaKeyring()\n\n rsakeyfile = os.path.join(keydir, \"rsa2048pub.pem\")\n pubkey, keyidv2 = ima_file_signatures.get_pubkey_from_file(rsakeyfile)\n keyring.add_pubkey(pubkey, keyidv2)\n\n eckeyfile = os.path.join(keydir, \"secp256k1.pem\")\n pubkey, keyidv2 = ima_file_signatures.get_pubkey_from_file(eckeyfile)\n keyring.add_pubkey(pubkey, keyidv2)\n\n # entries are not covered by a exclude list -> this should fail\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), ima_keyring=keyring) is None)\n\n # all entries are either covered by allow list or by signature verification -> this should pass\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), str(lists_map), ima_keyring=keyring) is not None)\n\n # the signature is valid but the hash in the allowlist is wrong -> this should fail\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_wrong), ima_keyring=keyring) is None)\n\n # the signature is valid and the file is not in the allowlist -> this should pass\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_empty), ima_keyring=keyring) is not None)\n\n # the signature is invalid but the correct hash is in the allowlist -> this should fail\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map), ima_keyring=empty_keyring) is None)\n\n # the file has no signature but the hash is correct -> this should pass\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map)))\n\n # All files are in the exclude list but hashes are invalid -> this should pass\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map_exclude_wrong)) is not None)\n\n # All files are in the exclude list and their signatures are invalid -> this should pass\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_exclude), ima_keyring=empty_keyring) is not None)\n\n # All files are in the exclude list but hashes or signatures are invalid -> this should pass\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map_exclude_wrong), ima_keyring=empty_keyring) is not None)","def username_blacklist_validator(value):\r\n\treserved_usernames = [\r\n\t\t'root',\r\n\t\t'admin',\r\n\t\t'sysadmin',\r\n\t]\r\n\tif value in reserved_usernames:\r\n\t\traise ValidationError(_('%(value) is not available.'), params={'value': value},)","def handle_weblists(bot, ievent):\n ievent.reply(\"whitelist: %s blacklist: %s\" % (cfg.get('whitelist'), \\\ncfg.get('blacklist')))","def _check_for_missing_bbl_file(self, workspace: Workspace,\n u_file: UserFile) -> None:\n # Create path to bbl file - assume uses same basename as .bib.\n base_path, name = os.path.split(u_file.path)\n base, _ = os.path.splitext(name)\n bbl_file = f'{base}.bbl'\n bbl_path = os.path.join(base_path, bbl_file)\n\n if workspace.exists(bbl_path):\n # If .bbl exists we go ahead and delete .bib file and warn\n # submitter of this action.\n workspace.add_warning(u_file, self.BIB_WITH_BBL,\n self.BIB_WITH_BBL_MESSAGE,\n is_persistant=False)\n workspace.remove(u_file,\n f\"Removed the file '{u_file.name}'. Using\"\n f\" '{bbl_file}' for references.\")\n else:\n # Missing .bbl (potential missing references). Generate an\n # error and DO NOT DELETE .bib file. Note: We are using .bib as\n # flag until .bbl exists.\n workspace.add_warning(u_file, self.BIB_NO_BBL,\n self.BIB_NO_BBL_MESSAGE)\n workspace.add_error(u_file, self.BBL_MISSING,\n self.BBL_MISSING_MESSAGE.format(base=base))","def _store_blacklist_delta(self, executor):\n per_type_counts = {}\n with create_db_connection(self._config.db_config) as conn, conn.cursor() as cursor, CodeProfiler() as cp:\n tbl = sql.Identifier(self._blacklist_tblname)\n delta_tbl = sql.Identifier(self._blacklist_delta_tblname)\n # We set the end_run_id on any current row where we have a change in the delta table\n cursor.execute(sql.SQL(\"\"\"UPDATE {tbl} bl\n SET end_run_id = %s\n WHERE end_run_id IS NULL\n AND EXISTS (SELECT 1\n FROM {delta_tbl}\n WHERE imei_norm = bl.imei_norm)\n \"\"\").format(tbl=tbl, delta_tbl=delta_tbl),\n [self._run_id])\n per_type_counts['invalidated'] = cursor.rowcount\n # Now we should be able to just insert the delta list into the blacklist\n cursor.execute(sql.SQL(\"\"\"INSERT INTO {tbl}(imei_norm,\n virt_imei_shard,\n block_date,\n reasons,\n start_run_id,\n end_run_id,\n delta_reason)\n SELECT imei_norm,\n virt_imei_shard,\n block_date,\n reasons,\n start_run_id,\n end_run_id,\n delta_reason\n FROM {delta_tbl}\n \"\"\").format(tbl=tbl, delta_tbl=delta_tbl),\n [self._run_id])\n per_type_counts['new'] = cursor.rowcount\n self._analyze_helper(cursor, self._blacklist_tblname)\n\n return per_type_counts, cp.duration","def test_noFilesFromAccept(self):\n return self._acceptFailureTest(ENFILE)","def testValidDescriptions(self):\n self.assertTrue(self.app._ignore_jobs(\"\"))\n self.assertTrue(self.app._ignore_jobs(\"This is valid\"))\n self.assertTrue(self.app._ignore_jobs(\"you can telecommute\"))","def blacklistSource(self, source):\n log.info(\"blacklisting \" + source)\n if source not in GameConsole.blacklistedSources:\n GameConsole.blacklistedSources.append(source)","def validation(nameFile, fileContent):\n\n\n dayNameFile = nameFile[-5:-4]\n monthNameFile = nameFile[-8:-6]\n yearNameFile = nameFile[-13:-9]\n hourNameFile = nameFile[-19:-14]\n hourNameFile = hourNameFile.replace(\"h\", \"\")\n \n\n if nameFile[0:6] == \"drones\":\n scopeNameFile = nameFile[0:6]\n elif nameFile[0:7] == \"parcels\":\n scopeNameFile = nameFile[0:7]\n\n headerFileContent = fileContent[constants.header]\n dateFile = headerFileContent[constants.headerTime]\n dayFile = dateFile[0:1]\n monthFile = dateFile[2:4]\n yearFile = dateFile[5:9]\n hourFile = headerFileContent[1]\n hourFile = hourFile.replace(\"h\", \"\")\n scopeFile = headerFileContent[constants.scope]\n\n\n return hourNameFile == hourFile and dayNameFile == dayFile and monthNameFile == monthFile and yearNameFile == yearFile and scopeNameFile == scopeFile","def test_invalid_file_type(barred_tac_list_importer):\n expect_failure(barred_tac_list_importer, exc_message='Wrong suffix')","def validate_labels(labels, path):\n for labels_ in labels.values():\n for label in labels_:\n for ann in label['annotations']:\n assert len(ann['segmentation']) == 1\n assert len(ann['segmentation'][0]) % 2 == 0\n\n label['annotations'] = [\n ann\n for ann in label['annotations']\n if len(ann['segmentation'][0]) >= 6\n ]\n assert len(label['annotations']) > 0\n label['file_name'] = path + '/' + label['file_name']\n\n for k in labels:\n labels[k] = [\n label for label in labels[k]\n if os.path.exists(label['file_name'])\n ]\n return labels","async def check_for_blacklist(ctx):\n if ctx.guild is None:\n # raise commands.NoPrivateMessage\n return True\n return db.is_blacklisted(ctx)","def test_sanitize_warnings_skip(self):\n input = {\n \"result\": [\n {\n \"output\": \"enter input line by line; when done enter one or more control-d\\n\\n> spanning-tree portfast\\n! portfast should only be enabled on ports connected to a single host. Connecting hubs, concentrators, switches, bridges, etc. to this interface when portfast is enabled can cause temporary bridging loops. Use with CAUTION. at line 2\\nCopy completed successfully.\\n\",\n \"messages\": [\"Copy completed successfully.\"],\n },\n {\n \"output\": \"! Command: show session-configuration named capiVerify-2002-f8a137cac96e11ed89be020000000000\\n! device: tp-avd-leaf2 (vEOS-lab, EOS-4.29.1F)\\n!\\n! boot system flash:/vEOS-lab-4.29.1F.swi\\n!\\nno aaa root\\n!\\ntransceiver qsfp default-mode 4x10G\\n!\\nservice routing protocols model ribd\\n!\\nspanning-tree mode mstp\\n!\\ninterface Ethernet1\\n spanning-tree portfast\\n!\\ninterface Ethernet2\\n!\\ninterface Ethernet3\\n!\\ninterface Ethernet4\\n!\\ninterface Ethernet5\\n!\\ninterface Management1\\n!\\nno ip routing\\n!\\nend\\n\"\n },\n ],\n \"warnings\": [\n \"! portfast should only be enabled on ports connected to a single host. Connecting hubs, concentrators, switches, bridges, etc. to this interface when portfast is enabled can cause temporary bridging loops. Use with CAUTION. at line 2\"\n ],\n \"id\": \"Arista-3-4826123409839743\",\n \"warningCount\": 1,\n \"jsonrpc\": \"2.0\",\n }\n # The result should not change\n assert self.api.sanitize_warnings(input) == input","def build_blacklist(blacklists):\n\n if blacklists is None:\n xbt = pbt.BedTool('', from_string=True)\n\n else:\n xlist = [x for s in blacklists for x in s]\n n_bl = len(xlist)\n if n_bl == 0:\n xbt = pbt.BedTool()\n elif n_bl == 1:\n xbt = pbt.BedTool(xlist[0])\n else:\n xbt = pbt.BedTool(xlist[0]).\\\n cat(*[pbt.BedTool(bl) for bl in xlist[1:]])\n\n xbt = xbt.sort().merge().saveas()\n\n return xbt","def test_visible_blacklisted_with_string(self):\n email = 'example@example.com'\n self.feature_test.set_percentage(0)\n self.feature_test.add_to_blacklist(email)\n self.assertFalse(self.feature_test.is_visible(email))","def validate(self):\n print(\"Validating \")\n sha256_test = _get_file_sha256_hash(self.file_path)\n sha256_truth = self.metadata_pkg[\"hash\"]\n if sha256_test != sha256_truth:\n raise ValueError(\n f\"Hash of modelpkg file {os.path.basename(self.file_path)} ({sha256_test}) does not match truth hash ({sha256_truth}).\")","def valid_extension(value):\n if (not value.name.endswith('.png') and\n not value.name.endswith('.jpeg') and\n not value.name.endswith('.gif') and\n not value.name.endswith('.bmp') and\n not value.name.endswith('.jpg')):\n\n text = _(\"Files allowed\")\n files = \".jpg, .jpeg, .png, .gif, .bmp\"\n raise ValidationError(text + ': ' + files)","def main():\n basedir = os.path.dirname(os.path.realpath(__file__))\n file_path = os.path.join(basedir, 'input')\n\n valid_strings = 0\n\n with open(file_path, 'r') as input_file:\n for line in input_file:\n if validate(line):\n valid_strings += 1\n\n print \"Found {} valid strings.\".format(valid_strings)\n\n assert valid_strings == 55","def test_check_exclude_none(self):\n\n self.assertTrue(PostfixExclude([]).check(self.file_gitignore))\n self.assertTrue(PostfixExclude([]).check(self.file_py))\n self.assertTrue(PostfixExclude([]).check(self.file_authors))\n self.assertTrue(PostfixExclude([]).check(self.file__init__))\n self.assertTrue(PostfixExclude([]).check(self.file_bin))","def validate():","def validate(data, badchars):\n assert(all(b not in data for b in badchars))","def validate(args, data_loader, model, vocab, global_stats):\n eval_time = Timer()\n val_loss = AverageMeter()\n \n all_output_seqs = []\n all_target_seqs = []\n all_input_seqs = []\n \n for batch_idx, (input_idxs, target_idxs, input_tokens, _) in enumerate(data_loader):\n \n input_lengths = (input_idxs != 0).long().sum(dim=1)\n sorted_lengths, order = torch.sort(input_lengths, descending=True)\n \n # No grad mode\n with torch.no_grad():\n \n input_variable = Variable(input_idxs[order, :][:, :max(input_lengths)], requires_grad=False)\n target_variable = Variable(target_idxs[order, :], requires_grad=False)\n batch_size = input_variable.shape[0]\n # Sort the input token lists by length\n all_input_seqs.extend(np.array(input_tokens)[order.cpu().numpy()].tolist())\n \n output_log_probs, output_seqs = model(input_variable, list(sorted_lengths))\n all_output_seqs.extend(trim_seqs(output_seqs))\n all_target_seqs.extend([list(seq[seq > 0])] for seq in to_np(target_variable))\n\n flattened_log_probs = output_log_probs.view(batch_size * model.max_length, -1)\n batch_losses = model.citerion(flattened_log_probs, target_variable.contiguous().view(-1))\n\n val_loss.update(batch_losses[0], batch_size)\n \n bleu_score = corpus_bleu(all_target_seqs, all_output_seqs, smoothing_function=SmoothingFunction().method1)\n \n logger.info('dev valid : Epoch = %d | Loss = %.2f | Bleu = %.2f' %\n (global_stats['epoch'], val_loss.avg * 100, bleu_score * 100) +\n '| examples = %d | valid time = %.2f (s)' %\n (len(all_output_seqs), eval_time.time()))\n \n if args.display_samples:\n for sentence_input, sentence_pred, sentence_gold in zip(all_input_seqs[-5:], all_output_seqs[-5:], all_target_seqs[-5:]):\n sentence_gold = sentence_gold[0]\n \n sentence_gold = seq_to_string(np.array(sentence_gold), vocab.id2word, input_tokens=sentence_input.split(' '))\n sentence_pred = seq_to_string(np.array(sentence_pred), vocab.id2word, input_tokens=sentence_input.split(' '))\n \n print('Predicted : %s ' % (sentence_pred))\n print('-----------------------------------------------')\n print('Gold : %s ' % (sentence_gold))\n print('===============================================')\n\n \n return {'bleu_score': bleu_score * 100}","def test_block_bad_batch(self):\n pass","def test_is_valid_manifest_format_with_invalid_md5_values(caplog):\n result = is_valid_manifest_format(\n \"tests/validate_manifest_format/manifests/manifest_with_invalid_md5_values.tsv\"\n )\n\n error_log = caplog.text\n manifest_with_invalid_md5_values_helper(error_log)\n base64_encoded_md5 = '\"jd2L5LF5pSmvpfL/rkuYWA==\"'\n assert base64_encoded_md5 in error_log\n assert result == False"],"string":"[\n \"def load_blacklist(experiment):\\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\\n return blacklist\",\n \"def load_blacklist(experiment):\\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\\n return blacklist\",\n \"def save_blacklist(blacklist, output_dir):\\n output_filepath = os.path.join(output_dir, 'blacklist.txt')\\n print('[INFO] Writing blacklist to {}...'.format(output_filepath))\\n with open(output_filepath, 'w') as f:\\n for ent in blacklist:\\n f.write('{}\\\\t{}\\\\n'.format(ent[0], ent[1]))\",\n \"def read_blacklist(self, _blacklist_fname):\\n try:\\n blacklist_f = codecs.open(_blacklist_fname, mode='r', encoding='utf-8')\\n line_idx = 0\\n for fline in blacklist_f:\\n line_idx = line_idx + 1\\n line = fline.strip()\\n if ((len(line) > 0) and (line[0] != '#')):\\n # non null and not started # line ... add to the set\\n if (line in self.__black_list_set):\\n print u'duplication found [' + line + u'] at ' + str(line_idx) + \\\\\\n u' ignored'\\n else:\\n self.__black_list_set.add(line)\\n\\n print u'read blacklist_file [' + _blacklist_fname + \\\\\\n u'], number of entries: ' + str(len(self.__black_list_set))\\n except IOError as e:\\n print \\\"I/O error({0}): {1}\\\".format(e.errno, e.strerror)\\n print \\\"Can not open a blacklist file {0}\\\".format(_blacklist_fname)\\n print \\\"Please create blacklist file (an empty file is also fine.)\\\"\\n sys.exit(1)\\n except:\\n print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n raise\",\n \"def getBlackList(filename):\\n #filename = \\\"filelist/blacklist_%s.txt\\\"%dataset.lstrip('/').replace('/','__')\\n blacklist = [ ]\\n if os.path.exists(filename):\\n with open(filename,'r') as file:\\n for line in file:\\n line = line.rstrip('\\\\n')\\n if line and '#' not in line:\\n blacklist.append(line)\\n return blacklist\",\n \"def open_blacklist(filepath):\\n with open(filepath, 'r') as f:\\n blacklist = [tuple(line.strip().split('\\\\t')) for line in f.readlines()]\\n return blacklist\",\n \"def blacklist_file(self, fkey):\\n self.blacklist.update([fkey])\",\n \"def test_blacklist(self):\\n ts = self.ts_db\\n fc1 = self.field_change1\\n fc2 = self.field_change2\\n fc3 = self.field_change3\\n fc4 = self.field_change4\\n\\n valid = blacklist.filter_by_benchmark_name(ts, fc1)\\n self.assertTrue(valid, \\\"Expect this to not be filtered.\\\")\\n valid = blacklist.filter_by_benchmark_name(ts, fc2)\\n self.assertTrue(valid, \\\"Expect this to not be filtered.\\\")\\n bad = blacklist.filter_by_benchmark_name(ts, fc3)\\n self.assertFalse(bad, \\\"Expect this to be filtered by regex.\\\")\\n bad = blacklist.filter_by_benchmark_name(ts, fc4)\\n self.assertFalse(bad, \\\"Expect this to be filtered by blacklist.\\\")\",\n \"def write_valids():\\n har_path = os.path.join(PLT_SRC, 'data/replay/*')\\n valid_path = '../data/filtered_stats/valids.txt'\\n\\n har_files = [f for f in glob(har_path)] # Include pc files?\\n urls = \\\\\\n [urlsafe_b64decode(f.split('/')[-1].split('.')[0]) for f in har_files]\\n with open(valid_path, 'w') as f:\\n for url, url_har_path in zip(urls, har_files):\\n f.write('{0} {1}\\\\n'.format(url, url_har_path))\",\n \"def _parse_blacklist(path):\\n if path is None:\\n return []\\n with open(path, 'rt') as f:\\n return [line.strip() for line in f]\",\n \"def ReadBlackListFile(BlackListFile):\\n blacklist = []\\n if os.path.isfile(BlackListFile):\\n with open(BlackListFile, 'r') as filecontent:\\n for line in filecontent:\\n #(chrom1, start1, chrom2, start2) = line.rstrip().split(\\\"\\\\t\\\")\\n blacklist.append(line)\\n return(blacklist)\",\n \"def apply_tempest_blacklist(self, black_list):\\n LOGGER.debug(\\\"Applying tempest blacklist...\\\")\\n if os.path.exists(self.raw_list):\\n os.remove(self.raw_list)\\n os.rename(self.list, self.raw_list)\\n cases_file = self.read_file(self.raw_list)\\n with open(self.list, 'w', encoding='utf-8') as result_file:\\n black_tests = []\\n try:\\n deploy_scenario = env.get('DEPLOY_SCENARIO')\\n if bool(deploy_scenario):\\n # if DEPLOY_SCENARIO is set we read the file\\n with open(black_list, encoding='utf-8') as black_list_file:\\n black_list_yaml = yaml.safe_load(black_list_file)\\n black_list_file.close()\\n for item in black_list_yaml:\\n scenarios = item['scenarios']\\n in_it = rally.RallyBase.in_iterable_re\\n if in_it(deploy_scenario, scenarios):\\n tests = item['tests']\\n black_tests.extend(tests)\\n except Exception: # pylint: disable=broad-except\\n black_tests = []\\n LOGGER.debug(\\\"Tempest blacklist file does not exist.\\\")\\n\\n for cases_line in cases_file:\\n for black_tests_line in black_tests:\\n if re.search(black_tests_line, cases_line):\\n break\\n else:\\n result_file.write(str(cases_line) + '\\\\n')\",\n \"def remove_blacklisted(blacklist, ssc, output_dir):\\n print('[INFO] Writing blacklisted corpus to {}...'.format(output_dir))\\n # assuming there is only 1 SSC, so take index 0\\n ssc_filepaths = list(get_filepaths(ssc))[0]\\n # for faster lookup\\n blacklist = set(blacklist)\\n for filepath in ssc_filepaths:\\n with open(filepath, 'r') as f:\\n # remove blacklisted entities\\n lines = f.readlines()\\n for i in range(1, len(lines) - 1):\\n previous_tag = 'O' if lines[i-1] == '\\\\n' else lines[i-1].strip().split('\\\\t')[1]\\n next_tag = 'O' if lines[i+1] == '\\\\n' else lines[i+1].strip().split('\\\\t')[1]\\n single_token_entity = (previous_tag != 'I-' and next_tag != 'I-')\\n blacklisted = tuple(lines[i].strip().split('\\\\t')) in blacklist\\n if single_token_entity and blacklisted:\\n lines[i] = '{}\\\\tO\\\\n'.format(lines[i].split('\\\\t')[0])\\n # write blacklisted copy to disk\\n corpus_name = os.path.basename(ssc) + '_blacklisted'\\n output_directory = os.path.join(output_dir, corpus_name)\\n make_dir(output_directory)\\n output_filepath = os.path.join(output_directory, os.path.basename(filepath))\\n with open(output_filepath, 'w') as f:\\n for line in lines:\\n f.write(line)\",\n \"def blacklist(self) -> List[str]:\\n return self.raw_config.get(\\\"blacklist\\\", [])\",\n \"def blacklist_add():\\n db = unitdata.kv()\\n blacklist = db.get(BLACKLIST_KEY, [])\\n for device in get_devices():\\n if not os.path.exists(device):\\n raise Error('{}: No such file or directory.'.format(device))\\n if device not in blacklist:\\n blacklist.append(device)\\n db.set(BLACKLIST_KEY, blacklist)\\n db.flush()\",\n \"def load_blocked_groups(self):\\n print(\\\" ->[*] Loading group blacklist...\\\")\\n blacklist = set()\\n if os.access(\\\"blocked_groups\\\", os.F_OK):\\n with codecs.open(\\\"blocked_groups\\\", \\\"r\\\", encoding=\\\"utf-8\\\") as groups:\\n blocked_groups = groups.readlines()\\n for group in blocked_groups:\\n blacklist.add(group)\\n return blacklist\",\n \"def is_blacklisted(fname):\\n return is_dot(fname) or is_excluded_filetype(fname)\",\n \"def is_blacklisted(self):\\r\\n \\r\\n in_blacklist = False \\r\\n if self.chrompos in parser.blacklist:\\r\\n in_blacklist = True\\r\\n \\r\\n return in_blacklist\",\n \"def check_utxo_blacklist(commitment, persist=False):\\n #TODO format error checking?\\n fname = \\\"blacklist\\\"\\n if jm_single().config.get(\\\"BLOCKCHAIN\\\", \\\"blockchain_source\\\") == 'regtest':\\n fname += \\\"_\\\" + jm_single().nickname\\n with jm_single().blacklist_file_lock:\\n if os.path.isfile(fname):\\n with open(fname, \\\"rb\\\") as f:\\n blacklisted_commitments = [x.strip() for x in f.readlines()]\\n else:\\n blacklisted_commitments = []\\n if commitment in blacklisted_commitments:\\n return False\\n elif persist:\\n blacklisted_commitments += [commitment]\\n with open(fname, \\\"wb\\\") as f:\\n f.write('\\\\n'.join(blacklisted_commitments))\\n f.flush()\\n #If the commitment is new and we are *not* persisting, nothing to do\\n #(we only add it to the list on sending io_auth, which represents actual\\n #usage).\\n return True\",\n \"def Validate(self, relative_file, contents):\\n pass\",\n \"def handler(state, _):\\n if state[0] == 'u':\\n fname = player.playlist[player.playlist_pos]['filename']\\n fkey = get_file_key(fname)\\n col.blacklist_file(fkey)\\n player.playlist_remove()\\n os.remove(fname)\\n print('Blacklisted: {}'.format(fname))\",\n \"def test_validation_negative(self):\\n self.assertFalse(self.cut._validate_iban(\\\"FR14 2004 1010 0505 0001 3\\\"))\\n self.assertFalse(self.cut._validate_iban(\\\"XX00 1234 5678 9012 3456 7890 1234 5678 90\\\"))\\n self.assertFalse(self.cut._validate_iban(\\\"YY00123456789012345678901234567890\\\"))\\n self.assertFalse(self.cut._validate_iban(\\\"XX22YYY1234567890123\\\"))\\n self.assertFalse(self.cut._validate_iban(\\\"foo@i.ban\\\"))\",\n \"def validate(self):\\n with open(os.path.join(settings.MEDIA_ROOT, self.file.name)) as file:\\n lines = file.readlines()\\n validators = ['os.', 'from os', 'io.', 'from io', 'open(', 'system(']\\n for line in lines:\\n for validator in validators:\\n if validator in line:\\n return False\\n return True\",\n \"def test_remove_from_blacklist(self):\\n\\n self.feature_test.add_to_blacklist(3)\\n self.feature_test.remove_from_blacklist(3)\\n self.assertFalse(3 in Feature(\\\"testing\\\").blacklist)\",\n \"def CheckIfItIsBlacklisted(chr1, start1, chr2, start2, blacklist, extention):\\n blacklistTag = None\\n chr1 = str(chr1)\\n chr2 = str(chr2)\\n start1 = int(start1)\\n start2 = int(start2)\\n if chr1 == chr2:\\n start = start1\\n end = start2\\n for entries in blacklist:\\n (bchr1, bstart1, bchr2, bstart2) = entries.rstrip().split(\\\"\\\\t\\\")\\n bstart1 = int(bstart1)\\n bstart2 = int(bstart2)\\n if(bchr1 == bchr2):\\n if(chr1 == bchr1):\\n if(((start <= int(bstart1 - extention)) and (start >= int(bstart1 + extention)))\\n and ((end <= int(bstart2 - extention)) and (end <= int(bstart2 + extention)))):\\n blacklistTag = True\\n else:\\n blacklistTag = False\\n if(((end <= int(bstart1 - extention)) and (end >= int(bstart1 + extention)))\\n and ((start <= int(bstart2 - extention)) and (start <= int(bstart2 + extention)))):\\n blacklistTag = True\\n else:\\n blacklistTag = False\\n else:\\n continue\\n else:\\n continue\\n\\n else:\\n for entries in blacklist:\\n (bchr1, bstart1, bchr2, bstart2) = entries.rstrip().split(\\\"\\\\t\\\")\\n bstart1 = int(bstart1)\\n bstart2 = int(bstart2)\\n if(((chr1 == bchr1) and (chr2 == bchr2)) or ((chr1 == bchr2) and (chr2 == bchr1))):\\n if(((start1 <= int(bstart1 - extention)) and (start1 >= int(bstart1 + extention)))\\n and ((start2 <= int(bstart2 - extention)) and (start2 <= int(bstart2 + extention)))):\\n blacklistTag = True\\n else:\\n blacklistTag = False\\n if(((start2 <= int(bstart1 - extention)) and (start2 >= int(bstart1 + extention)))\\n and ((start1 <= int(bstart2 - extention)) and (start1 <= int(bstart2 + extention)))):\\n blacklistTag = True\\n else:\\n blacklistTag = False\\n else:\\n continue\\n\\n return(blacklistTag)\",\n \"def test_copy_required_exclude_list(self):\\n exclude_list = ['*.yar', 'skip/these/file*']\\n self.assertTrue(clone_rules._copy_required('base.yara', [], exclude_list))\\n self.assertTrue(clone_rules._copy_required('path/to/file.yara', [], exclude_list))\\n self.assertFalse(clone_rules._copy_required('file.yar', [], exclude_list))\\n self.assertFalse(clone_rules._copy_required('skip/these/file.yara', [], exclude_list))\",\n \"def write_data(self, blacklist=('normE', 'normEsquared', 'genRate')):\\n\\n start = time.time()\\n self.data.write_data(blacklist=blacklist)\\n end = time.time()\\n self.log.info('Write time: %.2f seconds', end - start)\",\n \"def saveFileListLocal(dataset,filelist,blacklist=[ ],tag=\\\"\\\"):\\n if '/pnfs/' in dataset:\\n tag += \\\"_pnfs\\\"\\n dataset = '__'.join(dataset.split('/')[-3:])\\n filename = \\\"filelist/filelist_%s%s.txt\\\"%(dataset.replace('/','__'),tag)\\n with open(filename,'w+') as file:\\n for line in filelist:\\n if line not in blacklist:\\n file.write(line+'\\\\n')\\n return filename\",\n \"def test_does_not_validate_invalid_files(self):\\n bad_files = (\\n 'newstest2019-defr-src-ts.de.sgm',\\n 'newstest2019-defr-src-ts.de.xml',\\n )\\n for bad_file in bad_files:\\n bad_path = join(getcwd(), 'testdata', bad_file)\\n with self.assertRaises(ValueError):\\n _ = valitest.ValidatableTestSet(bad_path)\",\n \"def test_add_to_blacklist(self):\\n\\n self.feature_test.add_to_blacklist(3)\\n self.assertTrue(3 in Feature(\\\"testing\\\").blacklist)\",\n \"def test_allowed_list(self):\\n val = DwcaValidator(yaml.load(self.yaml_allowed_list, Loader=yaml.FullLoader),\\n error_handler=WhipErrorHandler)\\n document = {'rightsHolder': 'INBO'}\\n self.assertTrue(val.validate(document))\\n document = {'rightsHolder': 'ILVO'}\\n self.assertFalse(val.validate(document))\\n document = {'sex': 'male'}\\n self.assertTrue(val.validate(document))\\n document = {'sex': 'female'}\\n self.assertTrue(val.validate(document))\\n document = {'sex': 'Female'}\\n self.assertFalse(val.validate(document))\\n document = {'age': 'adult'}\\n self.assertTrue(val.validate(document))\\n document = {'age': 'juvenile'}\\n self.assertTrue(val.validate(document))\\n document = {'age': 'adult | juvenile'}\\n self.assertTrue(val.validate(document))\\n document = {'age': 'adult|juvenile'}\\n self.assertFalse(val.validate(document))\",\n \"def load_blacklists(self, blacklist_urls: List[str]) -> Set[str]:\\n items: Set[str] = set()\\n sets = []\\n for url in blacklist_urls:\\n fname = f'/tmp/{hashlib.md5(url.encode()).hexdigest()}-blacklist'\\n # Download if necessary\\n if not os.path.exists(fname):\\n log.debug(f'Downloading {url} -> {fname}')\\n urlretrieve(url, fname)\\n\\n fset = set(line.strip() for line in open(fname))\\n log.debug(f'Got {len(fset)} records from \\\\t {url}')\\n sets.append(fset)\\n\\n log.debug(f'Total: {sum(len(s) for s in sets)} records')\\n items = items.union(*sets)\\n log.debug(f'Aggregated into {len(items)} records')\\n return items\",\n \"def _write_full_csv_blacklist(self):\\n tblname = self._blacklist_new_tblname\\n filename = os.path.join(self._output_dir, '{0}_blacklist.csv'.format(self._date_str))\\n cursor_name = 'listgen_write_full_csv_blacklist'\\n with create_db_connection(self._config.db_config) as conn, \\\\\\n conn.cursor(name=cursor_name) as cursor, open(filename, 'w') as csvfile, CodeProfiler() as cp:\\n csv_writer = csv.DictWriter(csvfile, fieldnames=['imei', 'block_date', 'reasons'], extrasaction='ignore')\\n csv_writer.writeheader()\\n cursor.execute(sql.SQL(\\\"\\\"\\\"SELECT {imei_col} AS imei,\\n to_char(block_date, 'YYYYMMDD') AS block_date,\\n array_to_string(reasons, '|') AS reasons\\n FROM {tblname}\\n WHERE {valid_filter}\\n \\\"\\\"\\\").format(imei_col=self._output_imei_column,\\n tblname=sql.Identifier(tblname),\\n valid_filter=self._valid_filter_query))\\n num_written_records = 0\\n for row_data in cursor:\\n csv_writer.writerow(row_data._asdict())\\n num_written_records += 1\\n num_records = self._get_total_record_count(conn, tblname)\\n\\n return self._gen_metadata_for_list(filename,\\n num_records=num_records,\\n num_written_records=num_written_records), 'blacklist', cp.duration\",\n \"def test_hooks_invalid_list(self):\\n with open(\\\".scuba.yml\\\", \\\"w\\\") as f:\\n f.write(\\n \\\"\\\"\\\"\\n image: na\\n hooks:\\n user:\\n - this list should be under\\n - a 'script'\\n \\\"\\\"\\\"\\n )\\n\\n self._invalid_config()\",\n \"def test_bad_bookmark_file_data(self):\\n f = self.gen_filename()\\n self.write_fixture(f, {'bookmarks': 'should be an object!'})\\n\\n man = bookmarks.BookmarkManager(f)\\n self.assertIsNone(man.bookmarks)\\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))\",\n \"def test_read_allowlist(self):\\n\\n curdir = os.path.dirname(os.path.abspath(__file__))\\n allowlist_file = os.path.join(curdir, \\\"data\\\", \\\"ima-allowlist-short.txt\\\")\\n allowlist_sig = os.path.join(curdir, \\\"data\\\", \\\"ima-allowlist-short.sig\\\")\\n allowlist_bad_sig = os.path.join(curdir, \\\"data\\\", \\\"ima-allowlist-bad.sig\\\")\\n allowlist_gpg_key = os.path.join(curdir, \\\"data\\\", \\\"gpg-sig.pub\\\")\\n allowlist_checksum = \\\"8b7c2c6a1d7af2568cc663905491bda829c04c397cdba38cc4fc4d8d8a3e69d4\\\"\\n allowlist_bad_checksum = \\\"4c143670836f96535d9e617359b4d87c59e89e633e2773b4d7feae97f561b3dc\\\"\\n\\n # simple read, no fancy verification\\n al_data = ima.read_allowlist(allowlist_file)\\n self.assertIsNotNone(al_data, \\\"AllowList data is present\\\")\\n self.assertIsNotNone(al_data[\\\"meta\\\"], \\\"AllowList metadata is present\\\")\\n self.assertEqual(al_data[\\\"meta\\\"][\\\"version\\\"], 1, \\\"AllowList metadata version is correct\\\")\\n self.assertEqual(al_data[\\\"meta\\\"][\\\"generator\\\"], \\\"keylime-legacy-format-upgrade\\\", \\\"AllowList metadata generator is correct\\\")\\n self.assertNotIn(\\\"checksum\\\", al_data[\\\"meta\\\"], \\\"AllowList metadata no checksum\\\")\\n self.assertIsNotNone(al_data[\\\"hashes\\\"], \\\"AllowList hashes are present\\\")\\n self.assertEqual(len(al_data[\\\"hashes\\\"]), 21, \\\"AllowList hashes are correct length\\\")\\n self.assertEqual(al_data[\\\"hashes\\\"][\\\"/boot/grub2/i386-pc/testload.mod\\\"][0], \\\"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\\\", \\\"AllowList sample hash is correct\\\")\\n\\n # validate checkum\\n al_data = ima.read_allowlist(allowlist_file, allowlist_checksum)\\n self.assertIsNotNone(al_data, \\\"AllowList data is present\\\")\\n self.assertEqual(al_data[\\\"meta\\\"][\\\"checksum\\\"], allowlist_checksum, \\\"AllowList metadata correct checksum\\\")\\n self.assertIsNotNone(al_data[\\\"hashes\\\"], \\\"AllowList hashes are present\\\")\\n self.assertEqual(len(al_data[\\\"hashes\\\"]), 21, \\\"AllowList hashes are correct length\\\")\\n self.assertEqual(al_data[\\\"hashes\\\"][\\\"/boot/grub2/i386-pc/testload.mod\\\"][0], \\\"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\\\", \\\"AllowList sample hash is correct\\\")\\n\\n # test with a bad checksum\\n with self.assertRaises(Exception) as bad_checksum_context:\\n ima.read_allowlist(allowlist_file, allowlist_bad_checksum)\\n self.assertIn('Checksum of allowlist does not match', str(bad_checksum_context.exception))\\n\\n # validate GPG signature\\n al_data = ima.read_allowlist(allowlist_file, None, allowlist_sig, allowlist_gpg_key)\\n self.assertIsNotNone(al_data, \\\"AllowList data is present\\\")\\n self.assertNotIn(\\\"checksum\\\", al_data[\\\"meta\\\"], \\\"AllowList metadata no checksum\\\")\\n self.assertIsNotNone(al_data[\\\"hashes\\\"], \\\"AllowList hashes are present\\\")\\n self.assertEqual(len(al_data[\\\"hashes\\\"]), 21, \\\"AllowList hashes are correct length\\\")\\n self.assertEqual(al_data[\\\"hashes\\\"][\\\"/boot/grub2/i386-pc/testload.mod\\\"][0], \\\"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\\\", \\\"AllowList sample hash is correct\\\")\\n\\n # test with a bad GPG sig\\n with self.assertRaises(Exception) as bad_sig_context:\\n ima.read_allowlist(allowlist_file, None, allowlist_bad_sig, allowlist_gpg_key)\\n self.assertIn('GPG signature verification failed', str(bad_sig_context.exception))\\n\\n # validate everything together\\n al_data = ima.read_allowlist(allowlist_file, allowlist_checksum, allowlist_sig, allowlist_gpg_key)\\n self.assertIsNotNone(al_data, \\\"AllowList data is present\\\")\\n self.assertEqual(al_data[\\\"meta\\\"][\\\"checksum\\\"], allowlist_checksum, \\\"AllowList metadata correct checksum\\\")\\n self.assertIsNotNone(al_data[\\\"hashes\\\"], \\\"AllowList hashes are present\\\")\\n self.assertEqual(len(al_data[\\\"hashes\\\"]), 21, \\\"AllowList hashes are correct length\\\")\\n self.assertEqual(al_data[\\\"hashes\\\"][\\\"/boot/grub2/i386-pc/testload.mod\\\"][0], \\\"68e1d012e3f193dcde955e6ffbbc80e22b0f8778\\\", \\\"AllowList sample hash is correct\\\")\",\n \"def is_blacklisted(self, fkey):\\n return fkey in self.blacklist\",\n \"def hosts_deny_format(cls):\\n\\n template = Helpers.File(Settings.hosts_deny_template).read()\\n\\n template = Helpers.Regex(\\n template, r\\\"%%version%%\\\", replace_with=Settings.version\\n ).replace()\\n template = Helpers.Regex(\\n template, r\\\"%%lenIP%%\\\", replace_with=format(len(Settings.ips), \\\",d\\\")\\n ).replace()\\n\\n data_to_write = \\\"ALL: \\\" + \\\"\\\\nALL: \\\".join(Settings.ips)\\n\\n template = Helpers.Regex(\\n template, r\\\"%%content%%\\\", replace_with=data_to_write\\n ).replace()\\n\\n print(\\\"Generation of %s\\\" % Settings.hosts_deny_file, end=\\\" \\\")\\n Helpers.File(Settings.hosts_deny_file).write(template, overwrite=True)\\n print(Settings.done)\",\n \"def test_check(self):\\n\\n self.assertTrue(PostfixExclude().check(self.file_gitignore))\\n self.assertTrue(PostfixExclude().check(self.file_py))\\n self.assertTrue(PostfixExclude().check(self.file_authors))\\n self.assertTrue(PostfixExclude().check(self.file__init__))\\n self.assertTrue(PostfixExclude().check(self.file_bin))\",\n \"def test_check_exclude_perceval(self):\\n\\n self.assertTrue(DirExclude(['perceval']).check(self.file_gitignore))\\n self.assertTrue(DirExclude(['perceval']).check(self.file_authors))\\n self.assertTrue(DirExclude(['perceval']).check(self.file_tests))\\n self.assertTrue(DirExclude(['perceval']).check(self.file_bin))\\n\\n self.assertFalse(DirExclude(['perceval']).check(self.file_perceval))\",\n \"def blacklisted_file_patterns():\\n with open(\\\"patterns.txt\\\", 'r') as f:\\n lines = [line.strip() for line in f if line.strip()]\\n return set(lines)\",\n \"def cross_validate(self, contents, required=None, forbidden=None):\\n if required:\\n for item in required:\\n self.assertTrue(\\n item in contents,\\n \\\"Required entry [{item}] not found in:\\\\n{contents}\\\".format(\\n item=item, contents=contents\\n )\\n )\\n if forbidden:\\n for item in forbidden:\\n self.assertTrue(\\n item not in contents,\\n \\\"Forbidden entry [{item}] found in:\\\\n{contents}\\\".format(\\n item=item, contents=contents\\n )\\n )\",\n \"def get_bad_word_list(self):\\n self.bad_words = self.read_word_file(self.bad_word_file)\",\n \"def is_blacklisted(self, string='') -> int:\\n try:\\n for word in string.split(' '):\\n if word in self.blacklist:\\n return(1)\\n else:\\n return(0)\\n except Exception as error:\\n print(f\\\"Error: self.is_blacklisted({string}) -> {error}\\\")\",\n \"def test_unreadable_bookmark_file(self):\\n f = self.gen_filename()\\n self.write_fixture(f)\\n os.chmod(f, 0o200) # TODO: Cross-platform solution?\\n\\n man = bookmarks.BookmarkManager(f)\\n self.assertIsNone(man.bookmarks)\\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))\",\n \"def read_whitelist(whitelist_file):\\n\\n # create an empty dictionary to store the white lists\\n whitelistdict = {}\\n with open(whitelist_file, 'r') as fp:\\n for line in fp:\\n whitelistdict[line.strip()] = True\\n\\n # return the list of whitelist\\n return whitelistdict.keys()\",\n \"def test_is_not_firefox_file(self):\\r\\n bad_file = StringIO.StringIO()\\r\\n bad_file.write('failing tests please')\\r\\n bad_file.seek(0)\\r\\n\\r\\n self.assertTrue(\\r\\n not FBookmarkImporter.can_handle(bad_file),\\r\\n \\\"FBookmarkImporter cannot handle this file\\\")\\r\\n\\r\\n bad_file.close()\",\n \"def checkFiles(self):\\n if self.user[\\\"Save\\\"] != \\\"\\\":\\n self.of_exist = os.path.exists(os.path.join(self.user[\\\"Save\\\"], \\\"Of\\\"))\\n self.back_of_exist = os.path.exists(\\n os.path.join(self.user[\\\"Save\\\"], \\\"Back_Of\\\")\\n )\\n self.img_exist = os.path.exists(os.path.join(self.user[\\\"Save\\\"], \\\"Images\\\"))\\n self.depth_exist = os.path.exists(os.path.join(self.user[\\\"Save\\\"], \\\"Depth\\\"))\\n\\n self.object_detection_dir_exist = os.path.exists(\\n os.path.join(self.user[\\\"Save\\\"], \\\"ObjectDetection\\\")\\n )\\n\\n self.gt_exist = self.user[\\\"GT\\\"] != \\\"\\\"\\n\\n self.create_super_pixel_label = (\\n self.super_pixel_method != \\\"\\\"\\n and not os.path.exists(\\n os.path.join(self.savePathJoin(\\\"Super_Pixel\\\"), self.super_pixel_method)\\n )\\n )\\n\\n self.ui.c_crash_plot_video.setEnabled(self.ui.c_crash_plot.isChecked())\\n self.ui.t_low.setEnabled(not self.ui.c_optimize.isChecked())\\n self.ui.t_high.setEnabled(not self.ui.c_optimize.isChecked())\\n self.ui.c_optimize.setEnabled(self.gt_exist)\\n self.ui.c_error_plot.setEnabled(self.gt_exist)\\n self.ui.c_error_plot_video.setEnabled(self.ui.c_error_plot.isChecked())\\n self.ui.c_speed_plot_video.setEnabled(self.ui.c_speed_plot.isChecked())\\n self.ui.c_super_pixel_video.setEnabled(\\n self.ui.combo_superpixel.currentIndex() != 0\\n )\\n self.ui.c_csv.setEnabled(self.ui.c_error_plot.isChecked())\\n\\n if self.runRequirements():\\n self.ui.b_run.setEnabled(True)\\n else:\\n self.ui.b_run.setEnabled(False)\",\n \"def validate_data_files(self):\\n if self.labeled:\\n image_ids = self.loader_moving_image.get_data_ids()\\n label_ids = self.loader_moving_label.get_data_ids()\\n check_difference_between_two_lists(list1=image_ids, list2=label_ids)\",\n \"def test_check(self):\\n\\n self.assertTrue(DirExclude().check(self.file_gitignore))\\n self.assertTrue(DirExclude().check(self.file_perceval))\\n self.assertTrue(DirExclude().check(self.file_authors))\\n\\n self.assertFalse(DirExclude().check(self.file_tests))\\n self.assertFalse(DirExclude().check(self.file_bin))\",\n \"def check_blacklist(repo):\\n blacklisted = [ # NOTE: keep this list up to date!\\n 'builder', 'cache', 'controller', 'database', 'logger', 'logspout',\\n 'publisher', 'registry', 'router', 'store-admin', 'store-daemon',\\n 'store-gateway', 'store-metadata', 'store-monitor', 'swarm', 'mesos-master',\\n 'mesos-marathon', 'mesos-slave', 'zookeeper',\\n ]\\n if any(\\\"deis/{}\\\".format(c) in repo for c in blacklisted):\\n raise PermissionDenied(\\\"Repository name {} is not allowed\\\".format(repo))\",\n \"def blacklist_remove():\\n db = unitdata.kv()\\n blacklist = db.get(BLACKLIST_KEY, [])\\n for device in get_devices():\\n try:\\n blacklist.remove(device)\\n except ValueError:\\n raise Error('{}: Device not in blacklist.'.format(device))\\n db.set(BLACKLIST_KEY, blacklist)\\n db.flush()\",\n \"async def add_blacklist(self, ctx, user: discord.Member):\\r\\n if user.id not in self.settings['blacklist']:\\r\\n try:\\r\\n self.settings['blacklist'].append(user.id)\\r\\n await ctx.send(\\\"User blacklisted.\\\")\\r\\n except:\\r\\n await ctx.send(\\\"An error occured.\\\")\\r\\n else:\\r\\n await ctx.send(\\\"User already blacklisted.\\\")\",\n \"def ignore_listings(name_key):\\n # for blacklist_str in models_blacklist:\\n # if blacklist_str in name_key:\\n # return True\\n return False\",\n \"def test_validate_fasta_with_invalid(self):\\r\\n\\r\\n validate_fasta(self.sample_fasta_invalid_fp, self.sample_mapping_fp,\\r\\n self.output_dir)\\r\\n\\r\\n expected_log_fp = join(self.output_dir,\\r\\n split(self.sample_fasta_invalid_fp)[1] + \\\"_report.log\\\")\\r\\n\\r\\n log_f = open(expected_log_fp, \\\"U\\\")\\r\\n actual_log_lines = [line.strip() for line in log_f][1:]\\r\\n\\r\\n expected_log_lines = \\\"\\\"\\\"Percent duplicate labels: 0.250\\r\\nPercent QIIME-incompatible fasta labels: 0.500\\r\\nPercent of labels that fail to map to SampleIDs: 0.750\\r\\nPercent of sequences with invalid characters: 0.500\\r\\nPercent of sequences with barcodes detected: 0.250\\r\\nPercent of sequences with barcodes detected at the beginning of the sequence: 0.000\\r\\nPercent of sequences with primers detected: 0.250\\r\\nDuplicate labels found:\\r\\nseq1\\\"\\\"\\\".split('\\\\n')\\r\\n\\r\\n self.assertEqual(actual_log_lines, expected_log_lines)\",\n \"def update_exam_blacklist(sender, instance, **kwargs):\\n exams = Exam.objects.filter(\\n course_instance__instructors=instance.instructor)\\n if instance.permission_allowed is False:\\n exams.exclude(blacklisted=True).update(blacklisted=True)\\n else:\\n for exam in exams:\\n if exam.has_permission():\\n exam.blacklisted = False\\n exam.save()\",\n \"def test_remove_from_blacklist_with_string(self):\\n email = 'example@example.com'\\n self.feature_test.add_to_blacklist(email)\\n self.feature_test.remove_from_blacklist(email)\\n self.assertFalse(email in Feature(\\\"testing\\\").blacklist)\",\n \"def blacklist_handler(col, player):\\n def handler(state, _):\\n \\\"\\\"\\\"\\n Retains the current file in the player in the collection's blacklist\\n and removes it from the airlock. The player is then advanced to the\\n next file.\\n \\\"\\\"\\\"\\n if state[0] == 'u':\\n fname = player.playlist[player.playlist_pos]['filename']\\n fkey = get_file_key(fname)\\n col.blacklist_file(fkey)\\n player.playlist_remove()\\n os.remove(fname)\\n print('Blacklisted: {}'.format(fname))\\n return handler\",\n \"def test_dbrules(self):\\n\\n testdata = u\\\"\\\"\\\"\\n INSERT INTO attachmentrules(scope,checktype,action,regex,description,prio) VALUES\\n ('recipient@unittests.fuglu.org','contenttype','allow','application/x-executable','this user likes exe',1)\\n \\\"\\\"\\\"\\n self.session.execute(testdata)\\n # copy file rules\\n tmpfile = tempfile.NamedTemporaryFile(\\n suffix='virus', prefix='fuglu-unittest', dir='/tmp')\\n shutil.copy(TESTDATADIR + '/binaryattachment.eml', tmpfile.name)\\n suspect = Suspect(\\n 'sender@unittests.fuglu.org', 'recipient@unittests.fuglu.org', tmpfile.name)\\n\\n result = self.candidate.examine(suspect)\\n resstr = actioncode_to_string(result)\\n self.assertEqual(resstr, \\\"DUNNO\\\")\\n\\n # another recipient should still get the block\\n suspect = Suspect(\\n 'sender@unittests.fuglu.org', 'recipient2@unittests.fuglu.org', tmpfile.name)\\n\\n result = self.candidate.examine(suspect)\\n if type(result) is tuple:\\n result, message = result\\n resstr = actioncode_to_string(result)\\n self.assertEqual(resstr, \\\"DELETE\\\")\\n tmpfile.close()\",\n \"def blacklist_ips(self):\\r\\n if self.blacklist == '':\\r\\n return []\\r\\n return self.IPFilterList([addr.strip() for addr in self.blacklist.split(',')]) # pylint: disable=no-member\\r\",\n \"def test_check_negative():\\n\\n # Generate the blocks for the test file which is not present on the server\\n test_file = os.path.join(os.path.dirname(__file__),\\n \\\"../test_files/debashis-rc-biswas-3U4gGsGNsMY-unsplash.jpg\\\")\\n # Ask the server for the hash of the last block\\n response = client.get(\\\"/latest_block_hash\\\")\\n last_block_hash = response.json()[\\\"last_block_hash\\\"]\\n blocks = generate_blocks(test_file, last_block_hash)\\n\\n # Send the SHA256 checksum of the file to the server to be checked\\n response = client.get(\\\"/check\\\",\\n params={\\\"file_hash\\\": blocks[0].hash,\\n \\\"index_all\\\": blocks[0].index_all})\\n assert response.ok\\n assert response.json() \\\\\\n == {\\\"check\\\": False,\\n \\\"hash\\\": \\\"415d4f66e1b8b9083014dcdca5ddd7d1dcca3f5a4a120603169b951b1c5fa0c9\\\"}\",\n \"def _check_vocab_file(self, vocab_file, special_tokens):\\n if not os.path.exists(vocab_file):\\n raise FileNotFoundError(\\\"vocab file %s not found!\\\" % vocab_file)\\n vocabs = set()\\n with open(vocab_file, mode=\\\"rt\\\", encoding=\\\"utf8\\\", buffering=8192) as f:\\n for vocab in f:\\n vocab = vocab.strip(\\\"\\\\n\\\").strip()\\n if not vocab:\\n continue\\n if vocab in special_tokens:\\n continue\\n vocabs.add(vocab)\\n vocabs = sorted(vocabs)\\n for token in reversed(special_tokens):\\n vocabs.insert(0, token)\\n filename = str(vocab_file).split(os.sep)[-1]\\n assert filename is not None\\n new_vocab_file = os.path.join(self.configs['out_dir'], filename)\\n with open(new_vocab_file, mode=\\\"wt\\\", encoding=\\\"utf8\\\", buffering=8192) as f:\\n for v in vocabs:\\n f.write(v + \\\"\\\\n\\\")\\n return len(vocabs), new_vocab_file\",\n \"def _write_delta_csv_blacklist(self):\\n cursor_name = 'listgen_write_delta_csv_blacklist'\\n is_valid_query, imei_norm_with_check_digit_query = self._is_valid_and_check_digit_queries\\n\\n with create_db_connection(self._config.db_config) as conn, \\\\\\n conn.cursor(name=cursor_name) as cursor, contextlib.ExitStack() as stack, CodeProfiler() as cp:\\n allowed_delta_reasons = self._allowed_delta_reasons(conn, 'blacklist')\\n fnames = {r: os.path.join(self._output_dir, '{0}_blacklist_delta_{1:d}_{2:d}_{3}.csv'\\n .format(self._date_str,\\n self._base_run_id,\\n self._run_id,\\n r))\\n for r in allowed_delta_reasons}\\n files = {r: stack.enter_context(open(fn, 'w', encoding='utf8')) for r, fn in fnames.items()}\\n csv_writers = {r: csv.DictWriter(f,\\n fieldnames=['imei', 'block_date', 'reasons'],\\n extrasaction='ignore') for r, f in files.items()}\\n for csv_writer in csv_writers.values():\\n csv_writer.writeheader()\\n\\n cursor.execute(sql.SQL(\\\"\\\"\\\"SELECT {imei_col} AS imei,\\n to_char(block_date, 'YYYYMMDD') AS block_date,\\n array_to_string(reasons, '|') AS reasons,\\n delta_reason\\n FROM (SELECT *\\n FROM gen_delta_blacklist(%s),\\n LATERAL ({is_valid_query}) iv,\\n LATERAL ({imei_norm_with_check_digit_query}) cd) changes\\n WHERE {valid_filter}\\n \\\"\\\"\\\").format(is_valid_query=is_valid_query,\\n imei_norm_with_check_digit_query=imei_norm_with_check_digit_query,\\n valid_filter=self._valid_filter_query,\\n imei_col=self._output_imei_column),\\n [self._base_run_id])\\n\\n metrics = {r: defaultdict(int) for r in allowed_delta_reasons}\\n for row_data in cursor:\\n row_data_dict = row_data._asdict()\\n delta_reason = row_data_dict.pop('delta_reason')\\n csv_writer = csv_writers[delta_reason]\\n csv_writer.writerow(row_data_dict)\\n metrics[delta_reason]['num_records'] += 1\\n\\n return [self._gen_metadata_for_list(fn, **metrics[r]) for r, fn in fnames.items()], \\\\\\n 'blacklist_delta', \\\\\\n cp.duration\",\n \"def test_extensions(self):\\n field = TypedFileField(required=False, ext_whitelist=self.good_extensions)\\n\\n for ext in self.good_extensions:\\n name = 'somefooname.%s' % ext\\n file = UploadedFile(name=name, size=1)\\n assert field.clean(file) is file\\n\\n for ext in self.bad_extensions:\\n name = 'somefooname.%s' % ext\\n file = UploadedFile(name=name, size=1)\\n with pytest.raises(forms.ValidationError):\\n field.clean(file)\",\n \"def whitelist(self, message):\\n user = self.ts.get_user(message)\\n msg_list = self.ts.get_human_readable_message(message).lower().split(' ')\\n try:\\n with codecs.open('whitelist.json', 'r', 'utf-8') as f:\\n holder_list = json.load(f)\\n except json.decoder.JSONDecodeError:\\n holder_list = []\\n if msg_list[1] not in holder_list:\\n holder_list.append(msg_list[1])\\n with codecs.open('whitelist.json', 'w', 'utf-8') as f:\\n json.dump(holder_list, f, ensure_ascii=False)\\n self._add_to_whisper_queue(user, '{} has been added to the whitelist'.format(msg_list[1]))\\n else:\\n self._add_to_whisper_queue(user, '{} is already in the whitelist!'.format(msg_list[1]))\",\n \"def file_mode_checks(value):\\n if not value.endswith(F_SUFFIX):\\n raise argparse.ArgumentTypeError(\\n \\\"\\\\nFile resource must be:\\\\n\\\"\\n + \\\" 1) Obfuscated {hex}.cache.js GWT permutation file\\\"\\n )\\n return value\",\n \"def check_bad(self, delete_bad=True):\\n # XXX: work out why this is needed sometimes on network filesystems.\\n result_files = glob.glob(\\n os.path.join(self.location, \\\"results\\\", RSLT_NM.format(\\\"*\\\"))\\n )\\n\\n bad_ids = []\\n\\n for result_file in result_files:\\n # load corresponding batch file to check length.\\n result_num = (\\n os.path.split(result_file)[-1]\\n .strip(\\\"xyz-result-\\\")\\n .strip(\\\".jbdmp\\\")\\n )\\n batch_file = os.path.join(\\n self.location, \\\"batches\\\", BTCH_NM.format(result_num)\\n )\\n\\n batch = read_from_disk(batch_file)\\n\\n try:\\n result = read_from_disk(result_file)\\n unloadable = False\\n except Exception as e:\\n unloadable = True\\n err = e\\n\\n if unloadable or (len(result) != len(batch)):\\n msg = \\\"result {} is bad\\\".format(result_file)\\n msg += \\\".\\\" if not delete_bad else \\\" - deleting it.\\\"\\n msg += \\\" Error was: {}\\\".format(err) if unloadable else \\\"\\\"\\n print(msg)\\n\\n if delete_bad:\\n os.remove(result_file)\\n\\n bad_ids.append(result_num)\\n\\n return tuple(bad_ids)\",\n \"def copy_file_check(self):\\n pass\",\n \"def whitelist_file(self, fkey):\\n self.whitelist.update([fkey])\",\n \"def filter_blacklist(df, path):\\n f = open(path)\\n patterns = [e.strip() for e in f.readlines()]\\n f.close()\\n\\n def run_filter(x):\\n for pat in patterns:\\n if fnmatch.fnmatch(x, pat):\\n return True\\n return False\\n\\n index = df[df[PROD_NM].apply(run_filter)].index\\n df.drop(columns=[PROD_NM], index=index, inplace=True)\",\n \"def split_train_validation(path_to_file, valid_amount=0.2):\\n path_train_file = path_to_file + \\\".train.labeled\\\"\\n path_valid_file = path_to_file + \\\".valid.labeled\\\"\\n # count samples\\n samp_gen = dep_sample_generator(path_to_file)\\n total_samples = 0\\n for _ in samp_gen:\\n total_samples += 1\\n print(\\\"total samples \\\", total_samples)\\n buffer = []\\n num_validation = int(valid_amount * total_samples)\\n num_training = total_samples - num_validation\\n taken_for_training = 0\\n t_file = open(path_train_file, 'w')\\n v_file = open(path_valid_file, 'w')\\n with open(path_to_file) as fp:\\n sample = []\\n for line in fp:\\n if not line.rstrip():\\n if taken_for_training < num_training:\\n for l in sample:\\n t_file.write(l)\\n t_file.write('\\\\n')\\n taken_for_training += 1\\n else:\\n for l in sample:\\n v_file.write(l)\\n v_file.write('\\\\n')\\n sample = []\\n else:\\n sample.append(line)\\n\\n if taken_for_training < num_training:\\n for l in sample:\\n t_file.write(l)\\n t_file.write('\\\\n')\\n taken_for_training += 1\\n else:\\n for l in sample:\\n v_file.write(l)\\n v_file.write('\\\\n')\\n t_file.close()\\n v_file.close()\\n print(\\\"num training: \\\", num_training, \\\" saved @ \\\", path_train_file)\\n print(\\\"num validation: \\\", num_validation, \\\" saved @ \\\", path_valid_file)\",\n \"def attachment_type_blacklist(self, attachment_type_blacklist: ConfigNodePropertyArray):\\n\\n self._attachment_type_blacklist = attachment_type_blacklist\",\n \"def test_malformed_bookmark_file(self):\\n f = self.gen_filename()\\n with open(f, 'w') as ff:\\n ff.write('bad json')\\n\\n man = bookmarks.BookmarkManager(f)\\n self.assertIsNone(man.bookmarks)\\n self.assertFalse(man.add_bookmark('nope', 'nope/nope/nope'))\",\n \"def validate_data(self):\\n for pattern in self.patterns:\\n if pattern == \\\"\\\":\\n self.patterns.remove(\\\"\\\")\\n\\n if not self.patterns:\\n print(\\\"WARNING! Missing pattern or empty string!\\\")\\n sys.exit()\",\n \"def validate(self, spec):\\n d = spec.directory\\n for file_name in os.listdir(d):\\n if file_name.endswith(\\\".icon\\\"):\\n if \\\" \\\" in file_name:\\n raise ValidationException(f\\\"The .icon file name was '{file_name}'.\\\\n \\\"\\n \\\".icon file may not contain spaces use a '_' instead.\\\")\",\n \"def check_workspace(self, workspace: Workspace) -> None:\\n for u_file in workspace.iter_files():\\n if self.BIB_FILE.search(u_file.name):\\n self._check_for_missing_bbl_file(workspace, u_file)\",\n \"def test_mixed_verfication(self):\\n\\n lists_map = ima.process_allowlists(ALLOWLIST, '')\\n lists_map_wrong = ima.process_allowlists(ALLOWLIST_WRONG, '')\\n lists_map_empty = ima.process_allowlists(ALLOWLIST_EMPTY, '')\\n lists_map_exclude = ima.process_allowlists(ALLOWLIST, EXCLUDELIST)\\n lists_map_exclude_wrong = ima.process_allowlists(ALLOWLIST_WRONG, EXCLUDELIST)\\n empty_keyring = ima_file_signatures.ImaKeyring()\\n\\n # every entry is covered by the allowlist and there's no keyring -> this should pass\\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), str(lists_map)) is not None)\\n\\n curdir = os.path.dirname(os.path.abspath(__file__))\\n keydir = os.path.join(curdir, \\\"data\\\", \\\"ima_keys\\\")\\n keyring = ima_file_signatures.ImaKeyring()\\n\\n rsakeyfile = os.path.join(keydir, \\\"rsa2048pub.pem\\\")\\n pubkey, keyidv2 = ima_file_signatures.get_pubkey_from_file(rsakeyfile)\\n keyring.add_pubkey(pubkey, keyidv2)\\n\\n eckeyfile = os.path.join(keydir, \\\"secp256k1.pem\\\")\\n pubkey, keyidv2 = ima_file_signatures.get_pubkey_from_file(eckeyfile)\\n keyring.add_pubkey(pubkey, keyidv2)\\n\\n # entries are not covered by a exclude list -> this should fail\\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), ima_keyring=keyring) is None)\\n\\n # all entries are either covered by allow list or by signature verification -> this should pass\\n self.assertTrue(ima.process_measurement_list(COMBINED.splitlines(), str(lists_map), ima_keyring=keyring) is not None)\\n\\n # the signature is valid but the hash in the allowlist is wrong -> this should fail\\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_wrong), ima_keyring=keyring) is None)\\n\\n # the signature is valid and the file is not in the allowlist -> this should pass\\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_empty), ima_keyring=keyring) is not None)\\n\\n # the signature is invalid but the correct hash is in the allowlist -> this should fail\\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map), ima_keyring=empty_keyring) is None)\\n\\n # the file has no signature but the hash is correct -> this should pass\\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map)))\\n\\n # All files are in the exclude list but hashes are invalid -> this should pass\\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map_exclude_wrong)) is not None)\\n\\n # All files are in the exclude list and their signatures are invalid -> this should pass\\n self.assertTrue(ima.process_measurement_list(SIGNATURES.splitlines(), str(lists_map_exclude), ima_keyring=empty_keyring) is not None)\\n\\n # All files are in the exclude list but hashes or signatures are invalid -> this should pass\\n self.assertTrue(ima.process_measurement_list(MEASUREMENTS.splitlines(), str(lists_map_exclude_wrong), ima_keyring=empty_keyring) is not None)\",\n \"def username_blacklist_validator(value):\\r\\n\\treserved_usernames = [\\r\\n\\t\\t'root',\\r\\n\\t\\t'admin',\\r\\n\\t\\t'sysadmin',\\r\\n\\t]\\r\\n\\tif value in reserved_usernames:\\r\\n\\t\\traise ValidationError(_('%(value) is not available.'), params={'value': value},)\",\n \"def handle_weblists(bot, ievent):\\n ievent.reply(\\\"whitelist: %s blacklist: %s\\\" % (cfg.get('whitelist'), \\\\\\ncfg.get('blacklist')))\",\n \"def _check_for_missing_bbl_file(self, workspace: Workspace,\\n u_file: UserFile) -> None:\\n # Create path to bbl file - assume uses same basename as .bib.\\n base_path, name = os.path.split(u_file.path)\\n base, _ = os.path.splitext(name)\\n bbl_file = f'{base}.bbl'\\n bbl_path = os.path.join(base_path, bbl_file)\\n\\n if workspace.exists(bbl_path):\\n # If .bbl exists we go ahead and delete .bib file and warn\\n # submitter of this action.\\n workspace.add_warning(u_file, self.BIB_WITH_BBL,\\n self.BIB_WITH_BBL_MESSAGE,\\n is_persistant=False)\\n workspace.remove(u_file,\\n f\\\"Removed the file '{u_file.name}'. Using\\\"\\n f\\\" '{bbl_file}' for references.\\\")\\n else:\\n # Missing .bbl (potential missing references). Generate an\\n # error and DO NOT DELETE .bib file. Note: We are using .bib as\\n # flag until .bbl exists.\\n workspace.add_warning(u_file, self.BIB_NO_BBL,\\n self.BIB_NO_BBL_MESSAGE)\\n workspace.add_error(u_file, self.BBL_MISSING,\\n self.BBL_MISSING_MESSAGE.format(base=base))\",\n \"def _store_blacklist_delta(self, executor):\\n per_type_counts = {}\\n with create_db_connection(self._config.db_config) as conn, conn.cursor() as cursor, CodeProfiler() as cp:\\n tbl = sql.Identifier(self._blacklist_tblname)\\n delta_tbl = sql.Identifier(self._blacklist_delta_tblname)\\n # We set the end_run_id on any current row where we have a change in the delta table\\n cursor.execute(sql.SQL(\\\"\\\"\\\"UPDATE {tbl} bl\\n SET end_run_id = %s\\n WHERE end_run_id IS NULL\\n AND EXISTS (SELECT 1\\n FROM {delta_tbl}\\n WHERE imei_norm = bl.imei_norm)\\n \\\"\\\"\\\").format(tbl=tbl, delta_tbl=delta_tbl),\\n [self._run_id])\\n per_type_counts['invalidated'] = cursor.rowcount\\n # Now we should be able to just insert the delta list into the blacklist\\n cursor.execute(sql.SQL(\\\"\\\"\\\"INSERT INTO {tbl}(imei_norm,\\n virt_imei_shard,\\n block_date,\\n reasons,\\n start_run_id,\\n end_run_id,\\n delta_reason)\\n SELECT imei_norm,\\n virt_imei_shard,\\n block_date,\\n reasons,\\n start_run_id,\\n end_run_id,\\n delta_reason\\n FROM {delta_tbl}\\n \\\"\\\"\\\").format(tbl=tbl, delta_tbl=delta_tbl),\\n [self._run_id])\\n per_type_counts['new'] = cursor.rowcount\\n self._analyze_helper(cursor, self._blacklist_tblname)\\n\\n return per_type_counts, cp.duration\",\n \"def test_noFilesFromAccept(self):\\n return self._acceptFailureTest(ENFILE)\",\n \"def testValidDescriptions(self):\\n self.assertTrue(self.app._ignore_jobs(\\\"\\\"))\\n self.assertTrue(self.app._ignore_jobs(\\\"This is valid\\\"))\\n self.assertTrue(self.app._ignore_jobs(\\\"you can telecommute\\\"))\",\n \"def blacklistSource(self, source):\\n log.info(\\\"blacklisting \\\" + source)\\n if source not in GameConsole.blacklistedSources:\\n GameConsole.blacklistedSources.append(source)\",\n \"def validation(nameFile, fileContent):\\n\\n\\n dayNameFile = nameFile[-5:-4]\\n monthNameFile = nameFile[-8:-6]\\n yearNameFile = nameFile[-13:-9]\\n hourNameFile = nameFile[-19:-14]\\n hourNameFile = hourNameFile.replace(\\\"h\\\", \\\"\\\")\\n \\n\\n if nameFile[0:6] == \\\"drones\\\":\\n scopeNameFile = nameFile[0:6]\\n elif nameFile[0:7] == \\\"parcels\\\":\\n scopeNameFile = nameFile[0:7]\\n\\n headerFileContent = fileContent[constants.header]\\n dateFile = headerFileContent[constants.headerTime]\\n dayFile = dateFile[0:1]\\n monthFile = dateFile[2:4]\\n yearFile = dateFile[5:9]\\n hourFile = headerFileContent[1]\\n hourFile = hourFile.replace(\\\"h\\\", \\\"\\\")\\n scopeFile = headerFileContent[constants.scope]\\n\\n\\n return hourNameFile == hourFile and dayNameFile == dayFile and monthNameFile == monthFile and yearNameFile == yearFile and scopeNameFile == scopeFile\",\n \"def test_invalid_file_type(barred_tac_list_importer):\\n expect_failure(barred_tac_list_importer, exc_message='Wrong suffix')\",\n \"def validate_labels(labels, path):\\n for labels_ in labels.values():\\n for label in labels_:\\n for ann in label['annotations']:\\n assert len(ann['segmentation']) == 1\\n assert len(ann['segmentation'][0]) % 2 == 0\\n\\n label['annotations'] = [\\n ann\\n for ann in label['annotations']\\n if len(ann['segmentation'][0]) >= 6\\n ]\\n assert len(label['annotations']) > 0\\n label['file_name'] = path + '/' + label['file_name']\\n\\n for k in labels:\\n labels[k] = [\\n label for label in labels[k]\\n if os.path.exists(label['file_name'])\\n ]\\n return labels\",\n \"async def check_for_blacklist(ctx):\\n if ctx.guild is None:\\n # raise commands.NoPrivateMessage\\n return True\\n return db.is_blacklisted(ctx)\",\n \"def test_sanitize_warnings_skip(self):\\n input = {\\n \\\"result\\\": [\\n {\\n \\\"output\\\": \\\"enter input line by line; when done enter one or more control-d\\\\n\\\\n> spanning-tree portfast\\\\n! portfast should only be enabled on ports connected to a single host. Connecting hubs, concentrators, switches, bridges, etc. to this interface when portfast is enabled can cause temporary bridging loops. Use with CAUTION. at line 2\\\\nCopy completed successfully.\\\\n\\\",\\n \\\"messages\\\": [\\\"Copy completed successfully.\\\"],\\n },\\n {\\n \\\"output\\\": \\\"! Command: show session-configuration named capiVerify-2002-f8a137cac96e11ed89be020000000000\\\\n! device: tp-avd-leaf2 (vEOS-lab, EOS-4.29.1F)\\\\n!\\\\n! boot system flash:/vEOS-lab-4.29.1F.swi\\\\n!\\\\nno aaa root\\\\n!\\\\ntransceiver qsfp default-mode 4x10G\\\\n!\\\\nservice routing protocols model ribd\\\\n!\\\\nspanning-tree mode mstp\\\\n!\\\\ninterface Ethernet1\\\\n spanning-tree portfast\\\\n!\\\\ninterface Ethernet2\\\\n!\\\\ninterface Ethernet3\\\\n!\\\\ninterface Ethernet4\\\\n!\\\\ninterface Ethernet5\\\\n!\\\\ninterface Management1\\\\n!\\\\nno ip routing\\\\n!\\\\nend\\\\n\\\"\\n },\\n ],\\n \\\"warnings\\\": [\\n \\\"! portfast should only be enabled on ports connected to a single host. Connecting hubs, concentrators, switches, bridges, etc. to this interface when portfast is enabled can cause temporary bridging loops. Use with CAUTION. at line 2\\\"\\n ],\\n \\\"id\\\": \\\"Arista-3-4826123409839743\\\",\\n \\\"warningCount\\\": 1,\\n \\\"jsonrpc\\\": \\\"2.0\\\",\\n }\\n # The result should not change\\n assert self.api.sanitize_warnings(input) == input\",\n \"def build_blacklist(blacklists):\\n\\n if blacklists is None:\\n xbt = pbt.BedTool('', from_string=True)\\n\\n else:\\n xlist = [x for s in blacklists for x in s]\\n n_bl = len(xlist)\\n if n_bl == 0:\\n xbt = pbt.BedTool()\\n elif n_bl == 1:\\n xbt = pbt.BedTool(xlist[0])\\n else:\\n xbt = pbt.BedTool(xlist[0]).\\\\\\n cat(*[pbt.BedTool(bl) for bl in xlist[1:]])\\n\\n xbt = xbt.sort().merge().saveas()\\n\\n return xbt\",\n \"def test_visible_blacklisted_with_string(self):\\n email = 'example@example.com'\\n self.feature_test.set_percentage(0)\\n self.feature_test.add_to_blacklist(email)\\n self.assertFalse(self.feature_test.is_visible(email))\",\n \"def validate(self):\\n print(\\\"Validating \\\")\\n sha256_test = _get_file_sha256_hash(self.file_path)\\n sha256_truth = self.metadata_pkg[\\\"hash\\\"]\\n if sha256_test != sha256_truth:\\n raise ValueError(\\n f\\\"Hash of modelpkg file {os.path.basename(self.file_path)} ({sha256_test}) does not match truth hash ({sha256_truth}).\\\")\",\n \"def valid_extension(value):\\n if (not value.name.endswith('.png') and\\n not value.name.endswith('.jpeg') and\\n not value.name.endswith('.gif') and\\n not value.name.endswith('.bmp') and\\n not value.name.endswith('.jpg')):\\n\\n text = _(\\\"Files allowed\\\")\\n files = \\\".jpg, .jpeg, .png, .gif, .bmp\\\"\\n raise ValidationError(text + ': ' + files)\",\n \"def main():\\n basedir = os.path.dirname(os.path.realpath(__file__))\\n file_path = os.path.join(basedir, 'input')\\n\\n valid_strings = 0\\n\\n with open(file_path, 'r') as input_file:\\n for line in input_file:\\n if validate(line):\\n valid_strings += 1\\n\\n print \\\"Found {} valid strings.\\\".format(valid_strings)\\n\\n assert valid_strings == 55\",\n \"def test_check_exclude_none(self):\\n\\n self.assertTrue(PostfixExclude([]).check(self.file_gitignore))\\n self.assertTrue(PostfixExclude([]).check(self.file_py))\\n self.assertTrue(PostfixExclude([]).check(self.file_authors))\\n self.assertTrue(PostfixExclude([]).check(self.file__init__))\\n self.assertTrue(PostfixExclude([]).check(self.file_bin))\",\n \"def validate():\",\n \"def validate(data, badchars):\\n assert(all(b not in data for b in badchars))\",\n \"def validate(args, data_loader, model, vocab, global_stats):\\n eval_time = Timer()\\n val_loss = AverageMeter()\\n \\n all_output_seqs = []\\n all_target_seqs = []\\n all_input_seqs = []\\n \\n for batch_idx, (input_idxs, target_idxs, input_tokens, _) in enumerate(data_loader):\\n \\n input_lengths = (input_idxs != 0).long().sum(dim=1)\\n sorted_lengths, order = torch.sort(input_lengths, descending=True)\\n \\n # No grad mode\\n with torch.no_grad():\\n \\n input_variable = Variable(input_idxs[order, :][:, :max(input_lengths)], requires_grad=False)\\n target_variable = Variable(target_idxs[order, :], requires_grad=False)\\n batch_size = input_variable.shape[0]\\n # Sort the input token lists by length\\n all_input_seqs.extend(np.array(input_tokens)[order.cpu().numpy()].tolist())\\n \\n output_log_probs, output_seqs = model(input_variable, list(sorted_lengths))\\n all_output_seqs.extend(trim_seqs(output_seqs))\\n all_target_seqs.extend([list(seq[seq > 0])] for seq in to_np(target_variable))\\n\\n flattened_log_probs = output_log_probs.view(batch_size * model.max_length, -1)\\n batch_losses = model.citerion(flattened_log_probs, target_variable.contiguous().view(-1))\\n\\n val_loss.update(batch_losses[0], batch_size)\\n \\n bleu_score = corpus_bleu(all_target_seqs, all_output_seqs, smoothing_function=SmoothingFunction().method1)\\n \\n logger.info('dev valid : Epoch = %d | Loss = %.2f | Bleu = %.2f' %\\n (global_stats['epoch'], val_loss.avg * 100, bleu_score * 100) +\\n '| examples = %d | valid time = %.2f (s)' %\\n (len(all_output_seqs), eval_time.time()))\\n \\n if args.display_samples:\\n for sentence_input, sentence_pred, sentence_gold in zip(all_input_seqs[-5:], all_output_seqs[-5:], all_target_seqs[-5:]):\\n sentence_gold = sentence_gold[0]\\n \\n sentence_gold = seq_to_string(np.array(sentence_gold), vocab.id2word, input_tokens=sentence_input.split(' '))\\n sentence_pred = seq_to_string(np.array(sentence_pred), vocab.id2word, input_tokens=sentence_input.split(' '))\\n \\n print('Predicted : %s ' % (sentence_pred))\\n print('-----------------------------------------------')\\n print('Gold : %s ' % (sentence_gold))\\n print('===============================================')\\n\\n \\n return {'bleu_score': bleu_score * 100}\",\n \"def test_block_bad_batch(self):\\n pass\",\n \"def test_is_valid_manifest_format_with_invalid_md5_values(caplog):\\n result = is_valid_manifest_format(\\n \\\"tests/validate_manifest_format/manifests/manifest_with_invalid_md5_values.tsv\\\"\\n )\\n\\n error_log = caplog.text\\n manifest_with_invalid_md5_values_helper(error_log)\\n base64_encoded_md5 = '\\\"jd2L5LF5pSmvpfL/rkuYWA==\\\"'\\n assert base64_encoded_md5 in error_log\\n assert result == False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6533181","0.6533181","0.6494651","0.64306563","0.6377956","0.61511934","0.6121788","0.5941797","0.5909957","0.5882721","0.5851798","0.5822298","0.56683594","0.5648593","0.5628515","0.55158484","0.54681826","0.54313976","0.5406495","0.5349667","0.5342094","0.53107697","0.5302546","0.527642","0.52742714","0.5259476","0.5247808","0.52374256","0.5211466","0.52091473","0.5207323","0.51700234","0.51649654","0.51540744","0.51489806","0.51460975","0.5107267","0.50805336","0.50324607","0.50229424","0.50108457","0.5000665","0.49914515","0.49878782","0.4985169","0.49821576","0.4976779","0.49728435","0.49704805","0.49615583","0.49512547","0.49499002","0.4949459","0.49481934","0.49273458","0.49179345","0.49175525","0.49154714","0.49119374","0.49115157","0.49045917","0.49024716","0.48982438","0.48981142","0.4894066","0.48847237","0.48690516","0.4861989","0.4859439","0.48588613","0.4857189","0.4855051","0.48494396","0.48445687","0.48314363","0.48295292","0.4827987","0.48250562","0.48183802","0.4804857","0.48025197","0.47994477","0.47955","0.47929356","0.47913495","0.47892034","0.47857228","0.4779228","0.47755107","0.47689375","0.47615156","0.4755073","0.47536516","0.47532833","0.4749696","0.47482646","0.47400865","0.473706","0.4735669","0.47343996"],"string":"[\n \"0.6533181\",\n \"0.6533181\",\n \"0.6494651\",\n \"0.64306563\",\n \"0.6377956\",\n \"0.61511934\",\n \"0.6121788\",\n \"0.5941797\",\n \"0.5909957\",\n \"0.5882721\",\n \"0.5851798\",\n \"0.5822298\",\n \"0.56683594\",\n \"0.5648593\",\n \"0.5628515\",\n \"0.55158484\",\n \"0.54681826\",\n \"0.54313976\",\n \"0.5406495\",\n \"0.5349667\",\n \"0.5342094\",\n \"0.53107697\",\n \"0.5302546\",\n \"0.527642\",\n \"0.52742714\",\n \"0.5259476\",\n \"0.5247808\",\n \"0.52374256\",\n \"0.5211466\",\n \"0.52091473\",\n \"0.5207323\",\n \"0.51700234\",\n \"0.51649654\",\n \"0.51540744\",\n \"0.51489806\",\n \"0.51460975\",\n \"0.5107267\",\n \"0.50805336\",\n \"0.50324607\",\n \"0.50229424\",\n \"0.50108457\",\n \"0.5000665\",\n \"0.49914515\",\n \"0.49878782\",\n \"0.4985169\",\n \"0.49821576\",\n \"0.4976779\",\n \"0.49728435\",\n \"0.49704805\",\n \"0.49615583\",\n \"0.49512547\",\n \"0.49499002\",\n \"0.4949459\",\n \"0.49481934\",\n \"0.49273458\",\n \"0.49179345\",\n \"0.49175525\",\n \"0.49154714\",\n \"0.49119374\",\n \"0.49115157\",\n \"0.49045917\",\n \"0.49024716\",\n \"0.48982438\",\n \"0.48981142\",\n \"0.4894066\",\n \"0.48847237\",\n \"0.48690516\",\n \"0.4861989\",\n \"0.4859439\",\n \"0.48588613\",\n \"0.4857189\",\n \"0.4855051\",\n \"0.48494396\",\n \"0.48445687\",\n \"0.48314363\",\n \"0.48295292\",\n \"0.4827987\",\n \"0.48250562\",\n \"0.48183802\",\n \"0.4804857\",\n \"0.48025197\",\n \"0.47994477\",\n \"0.47955\",\n \"0.47929356\",\n \"0.47913495\",\n \"0.47892034\",\n \"0.47857228\",\n \"0.4779228\",\n \"0.47755107\",\n \"0.47689375\",\n \"0.47615156\",\n \"0.4755073\",\n \"0.47536516\",\n \"0.47532833\",\n \"0.4749696\",\n \"0.47482646\",\n \"0.47400865\",\n \"0.473706\",\n \"0.4735669\",\n \"0.47343996\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.804985"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18485,"cells":{"query":{"kind":"string","value":"Get the scsi data, and vtd mapping data and output in the fomat viosname,lunid,disk,vtdname,partitionid"},"document":{"kind":"string","value":"def getVIOSmappings(vios,csvFile=''):\n\n \"\"\" We need the cec this vios is running to be able to map the lpar names \"\"\"\n cec = nim_attribute(vios,\"mgmt_profile\").split()[2]\n\n \"\"\" ssh comms all ok?? \"\"\"\n isOK = ssh_output(vios,\"echo OK\")\n\n if isOK == None or isOK == False:\n error_message(\"Unable to communicate with %s, host not responding.\" % vios )\n return None\n\n \"\"\" scsi lun ids mapped to the disks \"\"\"\n scsidata = {}\n vhostMapping = {} \n vtdMapping = {}\n\n vhostMapping = getvhostmapping(vios)\n vtdMapping = getvhostmapping_with_vtd(vios)\n scsidata = get_EMC_CLARRiiON_inq(vios)\n\n logdata( lparMapping )\n\n for disk in vhostMapping:\n\n lunid=scsidata.get(disk)\n lparName=lparMapping.get( cec + \"-\" + str(vtdMapping.get( vhostMapping[disk] ) ) )\n\n if csvFile:\n csvFile.write( \"\\\"%s\\\",\\\"%s\\\",\\\"%s\\\",\\\"%s\\\",\\\"%s\\\",\\\"%s\\\"\\n\" % (\n vios,\n lunid,\n disk,\n vtdMapping.get(disk),\n vhostMapping[disk],\n lparName,\n ) )\n else:\n print( \"%-16s %34s %-9s %16s %-9s %-4s\" % (\n vios,\n lunid,\n disk,\n vtdMapping.get(disk),\n vhostMapping[disk],\n lparName,\n ) )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_vscsi_attributes(self,objects):\n print(\"\\n\")\n print((\"LocalPartitionID\".ljust(35),\":\",objects.LocalPartitionID.value()))\n print((\"VirtualSlotNumber\".ljust(35),\":\",objects.VirtualSlotNumber.value()))\n print((\"RequiredAdapter\".ljust(35),\":\",objects.RequiredAdapter.value()))\n print((\"RemoteLogicalPartitionID\".ljust(35),\":\",objects.RemoteLogicalPartitionID.value()))\n print((\"RemoteSlotNumber\".ljust(35),\":\",objects.RemoteSlotNumber.value()))","def get_device_file_dict():\n cmd = 'lshw -class disk'\n desc = \"description\"\n log_name = \"logical name\"\n serial = \"serial\"\n\n dev = []\n dev_list = []\n\n ret, output, err = run_gluster_command(cmd)\n output = output.decode('ASCII')\n dev_info = output.split('\\n')\n for line in dev_info:\n if re.search(desc, line):\n if dev:\n dev_list.append(dev)\n\n dev = []\n if re.search(log_name, line) or re.search(serial, line):\n temp = line.split(':')\n temp[1] = temp[1].strip(' ')\n dev.append(temp[1])\n dev_list.append(dev)\n for line in dev_list:\n print(line)","def get_devices_lsscsi(self):\n\n try:\n message = \"Find SCSI Devices\"\n if self._include_enclosures:\n command = \"lsscsi --generic --transport | egrep 'disk|0x14|enclo'\"\n else:\n command = \"lsscsi --generic --transport | fgrep 'disk|0x14'\"\n pdata = self._run_command(command=command, message=message, logger=self._logger, shell=True)\n #\n # Format:\n # $ lsscsi --generic --transport\n # [0] [1] [2] [3] [4]\n # [0:0:0:0] disk sas:0x5000cca25103b471 /dev/sda /dev/sg0 \n # [0:0:1:0] disk sas:0x5000cca251029301 /dev/sdb /dev/sg1 \n # ...\n # [0:0:14:0] enclosu sas:0x5001636001caa0bd - /dev/sg14\n # [7:0:0:0] cd/dvd usb: 1-1.3:1.2 /dev/sr0 /dev/sg15\n #\n # Special Case:\n # Handle lines without a transport (spaces only). (screen scrapping danger)\n # [0:0:10:0] enclosu sas:0x50030480091d71fd - /dev/sg10\n # [1:0:0:0] disk <spaces> /dev/sdk /dev/sg11 <- INTEL disk!\n #\n # Another SNAFU! (and why I hate screen scrapping!!!)\n # [15:0:53597:0]disk sas:0x5000cca23b359649 /dev/sdg /dev/sg6 \n # [15:0:53598:0]disk sas:0x5000cca23b0c0a99 /dev/sdh /dev/sg7 \n # [15:0:53599:0]disk sas:0x5000cca23b0b7531 /dev/sdi /dev/sg8 \n # ...\n # [15:0:53686:0]enclosu sas:0x5000ccab040001bc - /dev/sg165\n # [15:0:53766:0]enclosu sas:0x5000ccab040001fc - /dev/sg144\n #\n # Evidently, the author of lsscsi did not think of consistent output! ;(\n #\n for line in pdata['stdout'].splitlines():\n dinfo = line.split()\n device = dict()\n if len(dinfo) < 5:\n m = re.search('(?P<device>disk|\\(0x14\\)|enclosu)', dinfo[0])\n if m:\n device['Device Type'] = m.group('device')\n sas_index = 1\n dev_index = 2\n sg_index = 3\n else:\n continue\n else:\n device['Device Type'] = dinfo[1]\n sas_index = 2\n dev_index = 3\n sg_index = 4\n\n # lsscsi does not understand 'Host Managed' device type.\n if '0x14' in device['Device Type']:\n device['Device Type'] = 'disk'\n\n # Parse remaining information.\n if 'sas:' in dinfo[sas_index]:\n device['SAS Address'] = dinfo[sas_index][4:]\n self._sas_addresses += 1\n else:\n device['SAS Address'] = \"\"\n\n # Note: Enclosure has no driver, so reports '-' for name.\n if '/dev/' in dinfo[dev_index]:\n if self._drives and not dinfo[dev_index] in self._drives:\n continue\n if self._exclude and dinfo[dev_index] in self._exclude:\n continue\n device['Linux Device Name'] = dinfo[dev_index]\n else:\n device['Linux Device Name'] = \"\"\n if '/dev/sg' in dinfo[sg_index]:\n device['SCSI Device Name'] = dinfo[sg_index]\n else:\n device['SCSI Device Name'] = \"\"\n\n self._devices.append(device)\n\n except RuntimeError as exc:\n self._logger.error(\"Failed to acquire SCSI devices: {0}\".format(exc))\n raise exc","def get_devices_spt(self):\n\n #import pdb; pdb.set_trace()\n if self._drives or self.firmware_version or self.product_name or self.vendor_name or \\\n self.serial_number or self.target_port:\n user_options = True\n else:\n user_options = False\n try:\n # Note: Extra logic to optimize spt device directory scanning.\n if not user_options:\n if self._include_enclosures:\n message = \"Find SCSI Devices\"\n command = \"{tool} show devices dtype=direct,hostmanaged,enclosure\".format(tool=self.tool)\n else:\n message = \"Find SCSI Disk Drives\"\n command = \"{tool} show devices dtype=direct,hostmanaged\".format(tool=self.tool)\n # Use common execute below.\n else:\n # Request enclosures separately.\n if self._include_enclosures:\n message = \"Find SCSI Enclosures\"\n command = \"{tool} show devices dtype=enclosure ofmt=json\".format(tool=self.tool)\n pdata = self._run_command(command=command, message=message,\n logger=self._logger, shell=False, expected_failure=True)\n if pdata['exit_code'] == self.EXIT_STATUS_SUCCESS and pdata['stdout']:\n devices = json.loads(pdata['stdout'])\n self.parse_devices_spt(devices)\n\n message = \"Find SCSI Disk Drives\"\n # Selective drives or all direct access (disk drives).\n if self._drives:\n command = \"{tool} show edt dtype=direct,hostmanaged devices={drives}\"\\\n .format(tool=self.tool, drives=\",\".join(self._drives))\n else:\n command = \"{tool} show devices dtype=direct,hostmanaged\".format(tool=self.tool)\n # Apply optional parameters.\n if self.product_name:\n command += \" pid={product}\".format(product=self.product_name)\n if self.vendor_name:\n command += \" vid={vendor}\".format(vendor=self.vendor_name)\n if self.serial_number:\n command += \" serial={serial}\".format(serial=self.serial_number)\n if self.target_port:\n command += \" tport={target}\".format(target=self.target_port)\n if self.firmware_version:\n command += \" fw_version={firmware}\".format(firmware=self.firmware_version)\n\n # Add common spt options, we want JSON output!\n if self._exclude:\n command += \" exclude={drives}\".format(drives=\",\".join(self._exclude))\n command += \" ofmt=json\"\n # Finally, execute spt and parse its' JSON output (if any).\n pdata = self._run_command(command=command, message=message,\n logger=self._logger, shell=False, expected_failure=True)\n # spt emits warning status (1) and no JSON output if no devices found.\n if pdata['exit_code'] == self.EXIT_STATUS_SUCCESS and pdata['stdout']:\n devices = json.loads(pdata['stdout'])\n self.parse_devices_spt(devices)\n\n except RuntimeError as exc:\n self._logger.error(\"Failed to acquire SCSI devices: {0}\".format(exc))\n raise exc\n\n except ValueError as exc:\n self._logger.error(\"Failed to parse spts' JSON output: {0}\".format(exc))\n raise exc","def get_partition_details(disk, partition):\n details = {}\n script = [\n 'select disk {}'.format(disk['Number']),\n 'select partition {}'.format(partition['Number']),\n 'detail partition']\n\n # Diskpart details\n try:\n # Run script\n result = run_diskpart(script)\n except subprocess.CalledProcessError:\n pass\n else:\n # Get volume letter or RAW status\n output = result.stdout.decode().strip()\n tmp = re.search(r'Volume\\s+\\d+\\s+(\\w|RAW)\\s+', output)\n if tmp:\n if tmp.group(1).upper() == 'RAW':\n details['FileSystem'] = RAW\n else:\n details['Letter'] = tmp.group(1)\n # Remove empty lines from output\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\n # Split each line on ':' skipping those without ':'\n tmp = [s.split(':') for s in tmp if ':' in s]\n # Add key/value pairs to the details variable and return dict\n details.update({key.strip(): value.strip() for (key, value) in tmp})\n\n # Get MBR type / GPT GUID for extra details on \"Unknown\" partitions\n guid = PARTITION_UIDS.get(details.get('Type').upper(), {})\n if guid:\n details.update({\n 'Description': guid.get('Description', '')[:29],\n 'OS': guid.get('OS', 'Unknown')[:27]})\n\n if 'Letter' in details:\n # Disk usage\n try:\n tmp = psutil.disk_usage('{}:\\\\'.format(details['Letter']))\n except OSError as err:\n details['FileSystem'] = 'Unknown'\n details['Error'] = err.strerror\n else:\n details['Used Space'] = human_readable_size(tmp.used)\n\n # fsutil details\n cmd = [\n 'fsutil',\n 'fsinfo',\n 'volumeinfo',\n '{}:'.format(details['Letter'])\n ]\n try:\n result = run_program(cmd)\n except subprocess.CalledProcessError:\n pass\n else:\n output = result.stdout.decode().strip()\n # Remove empty lines from output\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\n # Add \"Feature\" lines\n details['File System Features'] = [s.strip() for s in tmp\n if ':' not in s]\n # Split each line on ':' skipping those without ':'\n tmp = [s.split(':') for s in tmp if ':' in s]\n # Add key/value pairs to the details variable and return dict\n details.update({key.strip(): value.strip() for (key, value) in tmp})\n\n # Set Volume Name\n details['Name'] = details.get('Volume Name', '')\n\n # Set FileSystem Type\n if details.get('FileSystem', '') not in ['RAW', 'Unknown']:\n details['FileSystem'] = details.get('File System Name', 'Unknown')\n\n return details","def get_vdisk_map_by_uid(self, vdisk_uid):\n\n if not vdisk_uid:\n msg = _(\"Invalid parameter: vdisk_uid is None \")\n LOG.exception(msg)\n raise SVCInvalidVdiskUID\n\n map_info = {}\n\n cmd = 'svcinfo lshostvdiskmap -delim :'\n\n output, err_output = self._svc_command(cmd)\n\n if err_output:\n msg = (_(\"get_vdisk_map_scsi_id() failure: svc cmd: %(cmd)s\"\n \" error: %(error)s\") % {'cmd': cmd, 'error': err_output})\n LOG.exception(msg)\n ex_args = {'cmd': cmd, 'e': err_output}\n raise SVCCommandException(**ex_args)\n\n if len(output) < 2:\n msg = _(\"_add_svc_hostmap() failed to create \"\n \"vdisk host mapping on storage\")\n LOG.warn(msg)\n return map_info\n\n header = output.pop(0).split(':')\n scsi_id_idx = header.index(SVC_KEY_MAP_SCSI_ID)\n vdisk_uid_idx = header.index(SVC_KEY_VDISK_UID)\n hostname_idx = header.index(SVC_KEY_HOST_NAME)\n\n for line in output:\n info = line.split(':')\n host = info[hostname_idx]\n vdisk_UID = info[vdisk_uid_idx]\n scsi_id = info[scsi_id_idx]\n\n # we may have one LUN mapped to multiple hosts during LPM\n if vdisk_UID == vdisk_uid:\n map_info[host] = scsi_id\n\n if len(map_info) == 0:\n map_info = None\n\n LOG.debug(\"vdisk hostmap: %(map_info)s\"\n % {'map_info': str(map_info)})\n return map_info","def show_device_information_only(self):\n\n # TODO: Optimize formatting later!\n if self._sas_addresses:\n if self._report_header:\n print(\"\\n\")\n print(\" Linux SCSI Firmware Drive Block Curr\")\n print(\" Device Device Vendor Product Revision Capacity Length Temp Serial Number SAS Address\")\n print(\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------- ------------------\")\n\n for device in self._devices:\n if not device['Device Type'].startswith(\"disk\"):\n continue\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14} {sas:<18}'\n .format(dsf=device['Linux Device Name'],\n sdsf=device['SCSI Device Name'],\n vid=device['Vendor Identification'],\n pid=device['Product Identification'],\n fw=device['Firmware Version'],\n capacity=device['Drive Capacity'],\n blocklen=device['Block Length'],\n temp=device['Current Temperature'],\n serial=device['Serial Number'],\n sas=device['SAS Address']))\n else:\n if self._report_header:\n print(\"\\n\")\n print(\" Linux SCSI Firmware Drive Block Curr\")\n print(\" Device Device Vendor Product Revision Capacity Length Temp Serial Number\")\n print(\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------\")\n\n for device in self._devices:\n if not device['Device Type'].startswith(\"disk\"):\n continue\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14}'\n .format(dsf=device['Linux Device Name'],\n sdsf=device['SCSI Device Name'],\n vid=device['Vendor Identification'],\n pid=device['Product Identification'],\n fw=device['Firmware Version'],\n capacity=device['Drive Capacity'],\n blocklen=device['Block Length'],\n temp=device['Current Temperature'],\n serial=device['Serial Number']))\n\n if self._report_header:\n print(\"\\n\")","def mpt():\n lbl_drives = ['device','mountpoint','fstype']\n disks = [d[0:3] for d in psutil.disk_partitions()]\n drives = [dict(zip(lbl_drives,ds)) for ds in disks]\n return [d['mountpoint']for d in drives]","def get_volume_info(self, uid):\n LOG.debug(\"Entering\")\n cmd = \"svcinfo lsvdisk -bytes -filtervalue vdisk_UID=%s -delim :\" % uid\n output = self._svc_command(cmd)[0]\n\n if len(output) != 2:\n raise SVCVolumeNotFound(\n _(\"Couldn't find volume information for UID %s\") % uid)\n\n header = output[0].split(':')\n values = output[1].split(':')\n index = header.index(SVC_KEY_VDISK_ID)\n diskId = values[index]\n index = header.index(SVC_KEY_VDISK_NAME)\n name = values[index]\n index = header.index(SVC_KEY_VOLUME_GROUP)\n volumeGroup = values[index]\n index = header.index(SVC_KEY_VDISK_CAPACITY)\n capacity = values[index]\n\n info = {SVC_KEY_VDISK_ID: diskId,\n SVC_KEY_VDISK_NAME: name,\n SVC_KEY_VOLUME_GROUP: volumeGroup,\n SVC_KEY_VDISK_CAPACITY: capacity}\n\n LOG.debug(\"Exiting\")\n return info","def vdisk_in_flashcopy(self, diskname):\n LOG.debug(\"Entering\")\n cmd = ''.join([\"svcinfo lsfcmap -filtervalue \",\n \"target_vdisk_name=%s -delim :\" % diskname])\n output = self._svc_command(cmd)[0]\n\n if len(output) != 2:\n return(100, None)\n\n header = output[0].split(':')\n values = output[1].split(':')\n index = header.index('progress')\n progress = values[index]\n index = header.index('id')\n map_id = values[index]\n\n LOG.debug(\"Exiting (progress = %s, map_id = %s)\" % (progress, map_id))\n return progress, map_id","def get_block_device_list(vars = {}, log = sys.stderr):\n\n # make sure we can access to the files/directories in /proc\n if not os.access(PROC_PARTITIONS_PATH, os.F_OK):\n return None\n\n # table with valid scsi/sata/ide/raid block device names\n valid_blk_names = {}\n # add in valid sd and hd block device names\n for blk_prefix in ('sd','hd'):\n for blk_num in map (\\\n lambda x: chr(x), range(ord('a'),ord('z')+1)):\n devicename=\"%s%c\" % (blk_prefix, blk_num)\n valid_blk_names[devicename]=None\n\n # add in valid scsi raid block device names\n for M in range(0,1+1):\n for N in range(0,7+1):\n devicename = \"cciss/c%dd%d\" % (M,N)\n valid_blk_names[devicename]=None\n\n for devicename in valid_blk_names.keys():\n # devfs under 2.4 (old boot cds) used to list partitions\n # in a format such as scsi/host0/bus0/target0/lun0/disc\n # and /dev/sda, etc. were just symlinks\n try:\n devfsname= os.readlink( \"/dev/%s\" % devicename )\n valid_blk_names[devfsname]=None\n except OSError:\n pass\n\n # only do this once every system boot\n if not os.access(DEVICES_SCANNED_FLAG, os.R_OK):\n\n # this is ugly. under devfs, device\n # entries in /dev/scsi/.. and /dev/ide/...\n # don't show up until you attempt to read\n # from the associated device at /dev (/dev/sda).\n # so, lets run sfdisk -l (list partitions) against\n # most possible block devices, that way they show\n # up when it comes time to do the install.\n devicenames = valid_blk_names.keys()\n devicenames.sort()\n for devicename in devicenames:\n os.system( \"sfdisk -l /dev/%s > /dev/null 2>&1\" % devicename )\n\n # touch file\n fb = open(DEVICES_SCANNED_FLAG,\"w\")\n fb.close()\n\n devicelist= {}\n\n partitions_file= file(PROC_PARTITIONS_PATH,\"r\")\n line_count= 0\n for line in partitions_file:\n line_count= line_count + 1\n\n # skip the first two lines always\n if line_count < 2:\n continue\n\n parts= string.split(line)\n\n if len(parts) < 4:\n continue\n\n device= parts[3]\n\n # skip and ignore any partitions\n if not valid_blk_names.has_key(device):\n continue\n\n try:\n major= int(parts[0])\n minor= int(parts[1])\n blocks= int(parts[2])\n except ValueError, err:\n continue\n\n gb_size= blocks/BLOCKS_PER_GB\n\n # check to see if the blk device is readonly\n try:\n # can we write to it?\n dev_name= \"/dev/%s\" % device\n fb = open(dev_name,\"w\")\n fb.close()\n readonly=False\n except IOError, e:\n # check if EROFS errno\n if errno.errorcode.get(e.errno,None) == 'EROFS':\n readonly=True\n else:\n # got some other errno, pretend device is readonly\n readonly=True\n\n devicelist[dev_name]= {'major': major,'minor': minor,'blocks': blocks, 'size': gb_size, 'readonly': readonly}\n return devicelist","def get_device_map():\n ret = []\n vlist = subprocess.check_output(['ceph-volume', 'lvm', 'list',\n '--format=json'])\n for osd_id, data in json.loads(vlist.decode('utf8')).items():\n osd_id = normalize_osd_id(osd_id)\n for elem in data:\n for device in elem['devices']:\n ret.append({'id': osd_id, 'path': device})\n return ret","def get_lun_storage_info(lun_id):\n host = ll_hosts.get_spm_host(config.HOSTS)\n host_ip = ll_hosts.get_host_ip(host)\n executor = rhevm_helpers.get_host_executor(\n host_ip, config.VDC_ROOT_PASSWORD\n )\n # Execute 'pvscan' to display the latest volume info\n storage_resources.pvscan(host)\n logger.info(\"Executing command 'pvs | grep %s'\", lun_id)\n status, output, err = executor.run_cmd(\n shlex.split(PVS_SHOW_LUN_INFO % lun_id)\n )\n if status:\n logger.info(\n \"Status was False executing 'pvs | grep %s'. Err: %s\",\n lun_id, err\n )\n return 0, 0\n\n # Format the output into the 6 expected display parameters (PV, VG,\n # Format, LV Attributes, Physical size and Physical free size)\n formatted_output = shlex.split(output)\n logger.info(\n \"The output received when running pvs on LUN id %s is: %s\"\n % (lun_id, formatted_output)\n )\n # The 2nd last displayed data output is needed - Physical size\n lun_size = formatted_output[-2]\n lun_size = lun_size.replace(\"g\", \"\")\n lun_free_space = formatted_output[-1]\n lun_free_space = lun_free_space.replace(\"g\", \"\")\n lun_size_bytes = float(lun_size) * config.GB\n logger.info(\"The LUN size in bytes is '%s'\", str(lun_size_bytes))\n lun_free_bytes = float(lun_free_space) * config.GB\n logger.info(\"The LUN free space in bytes is '%s'\", str(lun_free_bytes))\n\n return int(lun_size_bytes), int(lun_free_bytes)","def get_generic_scsi_dev(self, nIndex):\n\t\treturn handle_to_object(call_sdk_function('PrlVmCfg_GetGenericScsiDev', self.handle, nIndex))","def get_disk_info(virt_type, instance, block_device_info=None,\n image_meta=None, rescue=False):\n\n disk_bus = get_disk_bus_for_device_type(virt_type, image_meta, \"disk\")\n cdrom_bus = get_disk_bus_for_device_type(virt_type, image_meta, \"cdrom\")\n mapping = get_disk_mapping(virt_type, instance,\n disk_bus, cdrom_bus,\n block_device_info,\n image_meta, rescue)\n\n return {'disk_bus': disk_bus,\n 'cdrom_bus': cdrom_bus,\n 'mapping': mapping}","def get_generic_scsi_device(self, nIndex):\n\t\treturn handle_to_object(call_sdk_function('PrlSrvCfg_GetGenericScsiDevice', self.handle, nIndex))","def usableparts(self):\n # First get the partition type-id for all hard disk partitions\n partid = {}\n for pline in self.fdiskl():\n partid[pline[0]] = pline[4]\n ups = {}\n for s in self.xlist(\"get-blkinfo\")[1]:\n mo = re.match(r'(/dev/[^:]*):(?: LABEL=\"([^\"]*)\")?(?:'\n ' UUID=\"([^\"]*)\")?(?: TYPE=\"([^\"]*)\")?', s)\n if mo:\n dev, label, uuid, fstype = mo.groups()\n if fstype in (None, \"linux_raid_member\", \"LVM2_member\"):\n continue\n if dev.startswith(\"/dev/loop\"):\n continue\n rem = None\n if dev.startswith(\"/dev/sd\"):\n if partid.get(dev) == \"fd\":\n # This test seems to be necessary because blkid\n # sometimes returns an fs-type, rather than\n # linux_raid_member\", for the the first device\n # in a formatted raid array\n continue\n rem = self.xlist(\"removable\", dev)[1][0].strip() == \"1\"\n ups[dev] = (fstype, label, uuid, rem)\n return ups","def show_device_information_enclosures(self):\n\n if self._report_header:\n print(\"\\n\")\n print(\" Linux SCSI Firmware Drive Block Curr Enc SCSI Enc Enc Slot\")\n print(\" Device Device Vendor Product Revision Capacity Length Temp Serial Number SAS Address Device Slot Description\")\n print(\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------- ------------------ ---------- ---- -----------------\")\n\n for device in self._devices:\n if not device['Device Type'].startswith(\"disk\"):\n continue\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14} {sas:<18} {edsf:<10} {slot:<3} {text:<16}'\n .format(dsf=device['Linux Device Name'],\n sdsf=device['SCSI Device Name'],\n vid=device['Vendor Identification'],\n pid=device['Product Identification'],\n fw=device['Firmware Version'],\n capacity=device['Drive Capacity'],\n blocklen=device['Block Length'],\n temp=device['Current Temperature'],\n serial=device['Serial Number'],\n sas=device['SAS Address'],\n edsf=device['Enclosure Device'],\n slot=device['Enclosure Slot'],\n text=device['Slot Description']))\n\n if self._report_header:\n print(\"\\n\")","def connect_instance_disk_to_mgmt(self, instance):\n for stg_elem, vios in self._get_bootdisk_iter(instance):\n msg_args = {'disk_name': stg_elem.name, 'vios_name': vios.name}\n\n # Create a new mapping. NOTE: If there's an existing mapping on\n # the other VIOS but not this one, we'll create a second mapping\n # here. It would take an extreme sequence of events to get to that\n # point, and the second mapping would be harmless anyway. The\n # alternative would be always checking all VIOSes for existing\n # mappings, which increases the response time of the common case by\n # an entire GET of VIOS+VIO_SMAP.\n LOG.debug(\"Mapping boot disk %(disk_name)s to the management \"\n \"partition from Virtual I/O Server %(vios_name)s.\",\n msg_args, instance=instance)\n try:\n tsk_map.add_vscsi_mapping(self.host_uuid, vios, self.mp_uuid,\n stg_elem)\n # If that worked, we're done. add_vscsi_mapping logged.\n return stg_elem, vios\n except Exception:\n LOG.exception(\"Failed to map boot disk %(disk_name)s to the \"\n \"management partition from Virtual I/O Server \"\n \"%(vios_name)s.\", msg_args, instance=instance)\n # Try the next hit, if available.\n # We either didn't find the boot dev, or failed all attempts to map it.\n raise npvmex.InstanceDiskMappingFailed(instance_name=instance.name)","def __init__(self,guid='',name=''):\n extent.Extent.__init__(self,guid,name,'Physical LUN/Disk')\n self.exttype='physical'\n self.alias=''\n self.alwaysadd=True\n self.direct=True\n self.serial='' # SCSI Serial ID\n self.model='' # SCSI Model\n self.vendor='' # SCSI Vendor\n self.revision='' # SCSI revision\n self.raid=RaidLevel.none # underline raid '0' '1' '5' '6' '10'\n self.sectorsize=512\n self.paths=VSACollection(san.SanPath,self,'paths',desc='Paths To Disk',icon='link_g.png') # list of paths leading to this physical disk (local or remote), key = dev/hbtl\n self.partitions=VSACollection(SanPartition,self,'partitions',icon='vm_g.png') # list of paths leading to this physical disk (local or remote), key = dev/hbtl\n self.usedin=RefDict(extent.Extent,self,'usedin',desc='Owned by Storage Extents',icon='hard_disk.png',call=lambda self:[v for v in self.usedby if v.exttype<>'partition'])\n self.assigned=[] # list of chunks assigned from this disk\n self.primordial=1 # primary extent, based on physical HW (e.g. not a logical volume)\n self.cachepvds=[] # list of provider names which have a matching cache logical volume (found in discovery, process extents)\n self.cachedrdev=None # points to cache DRBD device\n self._flush()","def __get_info(self, disk):\n # Checking the disk exists\n\tFNULL = open(os.devnull, 'w') # Defining /dev/null\n\tcheck = subprocess.call(['df', disk], stdout=FNULL,\\\n stderr=subprocess.STDOUT)\n # Disk not found\n if check != 0:\n sys.stdout.write(\"^fg(%s)!E! DiskNotFound^fg()\" % Colors.CL_BASE08)\n # Disk found\n else:\n # Executing command, parsing output and removing empty elements\n cmd = subprocess.Popen(['df', '-H', disk], stdout=subprocess.PIPE)\n cmd_out, cmd_err = cmd.communicate()\n cmd_outparsed = cmd_out.split(' ')\n cmd_outparsed = filter(None, cmd_outparsed)\n\n # Getting information\n disk_name = disk\n disk_size = cmd_outparsed[7]\n disk_used = cmd_outparsed[8]\n disk_available = cmd_outparsed[9]\n disk_percentage = cmd_outparsed[10].translate(None, \"%\")\n disk_percentage = int(disk_percentage)\n\n # Disk Name: ~\n sys.stdout.write(\"^fg(%s)[^fg()\" % Colors.CL_BASE02)\n if disk_name == DISK_DATA:\n sys.stdout.write(\"^fg(%s)~^fg()\" % Colors.CL_BASE0D)\n sys.stdout.write(\"^fg(%s): ^fg()\" % Colors.CL_BASE03)\n # Disk Name: /\n elif disk_name == DISK_ROOT:\n sys.stdout.write(\"^fg(%s)/^fg()\" % Colors.CL_BASE0D)\n sys.stdout.write(\"^fg(%s): ^fg()\" % Colors.CL_BASE03)\n \n # Disk Percentage: Good\n if 0 <= disk_percentage <= 60:\n sys.stdout.write(\"^fg(%s)%s%%^fg()\" % (Colors.CL_BASE0B, disk_percentage))\n sys.stdout.write(\"^fg(%s) %s^fg()\" % (Colors.CL_BASE0B, disk_used))\n sys.stdout.write(\"^fg(%s)/^fg()\" % Colors.CL_BASE03)\n # Disk Percentage: Fair\n elif 61 <= disk_percentage <= 90:\n sys.stdout.write(\"^fg(%s)%s%%^fg()\" % (Colors.CL_BASE09, disk_percentage))\n sys.stdout.write(\"^fg(%s) %s^fg()\" % (Colors.CL_BASE09, disk_used))\n sys.stdout.write(\"^fg(%s)/^fg()\" % Colors.CL_BASE03)\n # Disk Percentage: Weak\n elif 91 <= disk_percentage <= 100:\n sys.stdout.write(\"^fg(%s)%s%%^fg()\" % (Colors.CL_BASE08, disk_percentage))\n sys.stdout.write(\"^fg(%s) %s^fg()\" % (Colors.CL_BASE08, disk_used))\n sys.stdout.write(\"^fg(%s)/^fg()\" % Colors.CL_BASE03)\n sys.stdout.write(\"^fg(%s)%s^fg()\" % (Colors.CL_BASE0D, disk_size))\n sys.stdout.write(\"^fg(%s)]^fg()\" % Colors.CL_BASE02)","def show_mappings(project):\n # with BMI(_username, _password, project) as bmi:\n # ret = bmi.show_mounted()\n # if ret[constants.STATUS_CODE_KEY] == 200:\n # table = PrettyTable(field_names=['Target', 'Block Device'])\n # mappings = ret[constants.RETURN_VALUE_KEY]\n # for k, v in mappings.iteritems():\n # table.add_row([k, v])\n # click.echo(table.get_string())\n # else:\n # click.echo(ret[constants.MESSAGE_KEY])\n click.echo(\"Need to Re-Implement\")","def _get_dmi(cls, data):\n\t\tdata['pdi'] = cls._get_pdi(data, 14)\n\t\tdata['mdi'] = cls._get_mdi(data, 14)\n\t\tdata['dx'] = cls._get_dx(data, 14)\n\t\tdata['adx'] = data['dx_6_ema']\n\t\tdata['adxr'] = data['adx_6_ema']\n\t\treturn data","def step2(self):\n self.mr_vds = []\n # Checking UGood availablity here, because after reboot cycle,\n # init will be called.\n for mr in self.mrs:\n self.log.info(\"Check for UGood PD's availablity on controller:%d\"\n % (mr.ctrl_id))\n pds = mr.cli.list_all_drives(pd_type=\"SAS\",\n state=\"UGood\", media_type=\"HDD\",\n sector_size='512B')\n if len(pds) < 10:\n raise SALError(\"%d UGood HDD PD's are not available for RAID \"\n \"Creation on Controller:%d\"\n % (10 - len(pds), mr.ctrl_id))\n else:\n self.log.info(\"Number of UGood HDD PD's available is %d on \"\n \"controller:%d\" % (len(pds), mr.ctrl_id))\n for mr in self.mrs:\n self.vds = []\n self.log.info(\"Create R0, R1, R5 with diff properties on \"\n \"Controller: %d\" % (mr.ctrl_id))\n self.vds.append(mr.cli.add_vd(raid=0, vd_size=\"3000MB\",\n WB=\"WB\", RA='ra', cache=\"direct\"))\n self.vds.append(mr.cli.add_vd(raid=1, vd_size=\"3000MB\",\n WB=\"WT\", RA='nora', cache=\"cached\"))\n self.vds.append(mr.cli.add_vd(raid=5, vd_size=\"3000MB\",\n WB=\"WB\", RA='ra', cache=\"direct\"))\n for vd in self.vds:\n while True:\n time.sleep(5)\n if (mr.cli.init_progress(vd) == -1):\n self.log.info(\"FGI completed for VD:%s\"\n % (vd))\n break\n for vd in self.vds:\n if mr.cli.vd_get_info(vd)['state'] != 'OPTL':\n raise SALError(\"RAID-%s VD%s is not optimal\"\n % (mr.cli.vd_get_info(vd)['raid'], vd))\n else:\n self.log.info(\"RAID-%s VD%s is optimal\"\n % (mr.cli.vd_get_info(vd)['raid'], vd))\n\n self.mr_vds.extend(self.vds)\n self.log.info(\"Created R0, R1, R5 successfully on controller: %d\"\n % (mr.ctrl_id))","def get_all_disk():\n\t\tdisks = []\n\t\tdisks_lines = linux.exe_shell(\"lsblk -o NAME,VENDOR|grep -P '^sd.*[A-Z]'\")\n\t\tfor line in disks_lines.splitlines():\n\t\t\tdisk_t = line.split()\n\t\t\tif len(disk_t) > 1 and \"LSI\" not in disk_t[1]:\n\t\t\t\tdisks.append(disk_t[0])\n\t\tds = []\n\t\tfor i in disks:\n\t\t\td_t = DiskFromLsiSas3(\"\", i)\n\t\t\td_t.fill_attrs()\n\t\t\tds.append(d_t)\n\t\treturn ds","def get_partition_info(disk, host_disk):\n\n volume = \"/dev/\"+disk\n DISKINFO[volume] = {}\n DISKINFO[volume][\"Name\"] = volume\n DISKINFO[volume][\"Type\"] = \"Partition\"\n DISKINFO[volume][\"HostDevice\"] = host_disk\n DISKINFO[volume][\"Partitions\"] = []\n DISKINFO[host_disk][\"Partitions\"].append(volume)\n DISKINFO[volume][\"Vendor\"] = get_vendor(disk)\n DISKINFO[volume][\"Product\"] = \"Host Device: \"+DISKINFO[host_disk][\"Product\"]\n DISKINFO[volume][\"RawCapacity\"], DISKINFO[volume][\"Capacity\"] = get_capacity()\n DISKINFO[volume][\"Description\"] = get_description(disk)\n DISKINFO[volume][\"Flags\"] = get_capabilities(disk)\n DISKINFO[volume][\"FileSystem\"] = get_file_system(disk)\n DISKINFO[volume][\"Partitioning\"] = \"N/A\"\n DISKINFO[volume][\"UUID\"] = get_uuid(disk)\n DISKINFO[volume][\"ID\"] = get_id(disk)\n DISKINFO[volume][\"BootRecord\"], DISKINFO[volume][\"BootRecordStrings\"] = get_boot_record(disk)\n\n return volume","def device_info(node):\n\n if \"cpu\" in node and \"total_mbufs\" in node[\"cpu\"]:\n total_mbufs = node[\"cpu\"][\"total_mbufs\"]\n if total_mbufs != 0:\n print(\"Total Number of Buffers: {}\".format(total_mbufs))\n\n vpp = VppPCIUtil(node)\n vpp.get_all_devices()\n linkup_devs = vpp.get_link_up_devices()\n if len(linkup_devs):\n print(\"\\nDevices with link up (can not be used with VPP):\")\n vpp.show_vpp_devices(linkup_devs, show_header=False)\n # for dev in linkup_devs:\n # print (\" \" + dev)\n kernel_devs = vpp.get_kernel_devices()\n if len(kernel_devs):\n print(\"\\nDevices bound to kernel drivers:\")\n vpp.show_vpp_devices(kernel_devs, show_header=False)\n else:\n print(\"\\nNo devices bound to kernel drivers\")\n\n dpdk_devs = vpp.get_dpdk_devices()\n if len(dpdk_devs):\n print(\"\\nDevices bound to DPDK drivers:\")\n vpp.show_vpp_devices(dpdk_devs, show_interfaces=True, show_header=False)\n else:\n print(\"\\nNo devices bound to DPDK drivers\")\n\n other_devs = vpp.get_other_devices()\n if len(other_devs):\n print(\"\\nDevices not bound to Kernel or DPDK drivers:\")\n vpp.show_vpp_devices(other_devs, show_interfaces=True, show_header=False)\n else:\n print(\"\\nNo devices not bound to Kernel or DPDK drivers\")\n\n vpputl = VPPUtil()\n interfaces = vpputl.get_hardware(node)\n if interfaces == {}:\n return\n\n print(\"\\nDevices in use by VPP:\")\n\n if len(interfaces.items()) < 2:\n print(\"None\")\n return\n\n print(\n \"{:30} {:4} {:4} {:7} {:4} {:7}\".format(\n \"Name\", \"Numa\", \"RXQs\", \"RXDescs\", \"TXQs\", \"TXDescs\"\n )\n )\n for intf in sorted(interfaces.items()):\n name = intf[0]\n value = intf[1]\n if name == \"local0\":\n continue\n numa = rx_qs = rx_ds = tx_qs = tx_ds = \"\"\n if \"numa\" in value:\n numa = int(value[\"numa\"])\n if \"rx queues\" in value:\n rx_qs = int(value[\"rx queues\"])\n if \"rx descs\" in value:\n rx_ds = int(value[\"rx descs\"])\n if \"tx queues\" in value:\n tx_qs = int(value[\"tx queues\"])\n if \"tx descs\" in value:\n tx_ds = int(value[\"tx descs\"])\n\n print(\n \"{:30} {:>4} {:>4} {:>7} {:>4} {:>7}\".format(\n name, numa, rx_qs, rx_ds, tx_qs, tx_ds\n )\n )","def get_disk_details(disk):\n details = {}\n script = [\n 'select disk {}'.format(disk['Number']),\n 'detail disk']\n\n # Run\n try:\n result = run_diskpart(script)\n except subprocess.CalledProcessError:\n pass\n else:\n output = result.stdout.decode().strip()\n # Remove empty lines\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\n # Set disk name\n details['Name'] = tmp[4]\n # Split each line on ':' skipping those without ':'\n tmp = [s.split(':') for s in tmp if ':' in s]\n # Add key/value pairs to the details variable and return dict\n details.update({key.strip(): value.strip() for (key, value) in tmp})\n\n return details","def step6(self):\n if len(self.mrs) == 3:\n count = 3\n elif len(self.mrs) == 2:\n count = 2\n else:\n count = 1\n for mr in self.mrs[0:count]:\n self.log.info(\"Display boot drive on controller:%d\"\n % (mr.ctrl_id))\n vd_id = mr.cli.bootdrive_vd_get()\n if (int(vd_id) == -1): # -1 : No boot VD.\n self.log.info(\"No boot VD found on controller: %d\"\n % (mr.ctrl_id))\n else:\n self.log.info(\"VD ID of the boot VD: %d\"\n % int((vd_id)))","def show_device_information_long(self):\n\n for device in self._devices:\n print(\"\")\n if device['Device Type'].startswith(\"enclosu\"):\n if device.get('Device Type'):\n print(\"{0:>32}: {1}\".format(\"Device Type\", device['Device Type']))\n if device['Device Type Description']:\n print(\"{0:>32}: {1}\".format(\"Device Description\", device['Device Type Description']))\n if device.get('SCSI Device Name'):\n print(\"{0:>32}: {1}\".format(\"SCSI Device Name\", device['SCSI Device Name']))\n if device.get('Product Identification'):\n print(\"{0:>32}: {1}\".format(\"Product Identification\", device['Product Identification']))\n if device.get('Vendor Identification'):\n print(\"{0:>32}: {1}\".format(\"Vendor Identification\", device['Vendor Identification']))\n if device.get('Firmware Version'):\n print(\"{0:>32}: {1}\".format(\"Firmware Version\", device['Firmware Version']))\n if device.get('Serial Number'):\n print(\"{0:>32}: {1}\".format(\"Serial Number\", device['Serial Number']))\n if device.get('SAS Address'):\n print(\"{0:>32}: {1}\".format(\"SAS Address\", device['SAS Address']))\n else:\n if device.get('Device Type'):\n print(\"{0:>32}: {1}\".format(\"Device Type\", device['Device Type']))\n if device['Device Type Description']:\n print(\"{0:>32}: {1}\".format(\"Device Description\", device['Device Type Description']))\n if device.get('Linux Device Name'):\n print(\"{0:>32}: {1}\".format(\"Linux Device Name\", device['Linux Device Name']))\n if device.get('SCSI Device Name'):\n print(\"{0:>32}: {1}\".format(\"SCSI Device Name\", device['SCSI Device Name']))\n if device.get('Product Identification'):\n print(\"{0:>32}: {1}\".format(\"Product Identification\", device['Product Identification']))\n if device.get('Vendor Identification'):\n print(\"{0:>32}: {1}\".format(\"Vendor Identification\", device['Vendor Identification']))\n if device.get('Firmware Version'):\n print(\"{0:>32}: {1}\".format(\"Firmware Version\", device['Firmware Version']))\n if device.get('Serial Number'):\n print(\"{0:>32}: {1}\".format(\"Serial Number\", device['Serial Number']))\n if device.get('Drive Capacity'):\n print(\"{0:>32}: {1}\".format(\"Drive Capacity\", device['Drive Capacity']))\n if device.get('Block Length'):\n print(\"{0:>32}: {1}\".format(\"Block Length\", device['Block Length']))\n if device.get('Power On Hours'):\n print(\"{0:>32}: {1}\".format(\"Power On Hours\", device['Power On Hours']))\n if device.get('Current Temperature'):\n print(\"{0:>32}: {1}\".format(\"Current Temperature\", device['Current Temperature']))\n if device.get('SAS Address'):\n print(\"{0:>32}: {1}\".format(\"SAS Address\", device['SAS Address']))\n if device.get('Enclosure Device'):\n print(\"{0:>32}: {1}\".format(\"Enclosure Device\", device['Enclosure Device']))\n if device.get('Enclosure Slot'):\n print(\"{0:>32}: {1}\".format(\"Enclosure Slot\", device['Enclosure Slot']))\n if device.get('Slot Description'):\n print(\"{0:>32}: {1}\".format(\"Slot Desciption\", device['Slot Description']))\n\n if len(self._devices):\n print(\"\")","def get_info():\n\n global DISKINFO\n DISKINFO = {}\n\n #Run diskutil list to get disk names.\n runcmd = subprocess.Popen(\"diskutil list -plist\", stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)\n\n #Get the output.\n stdout = runcmd.communicate()[0]\n\n #Parse the plist (Property List).\n global PLIST\n\n PLIST = plistlib.loads(stdout)\n\n #Find the disks.\n for disk in PLIST[\"AllDisks\"]:\n #Run diskutil info to get disk info.\n runcmd = subprocess.Popen(\"diskutil info -plist \"+disk, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)\n stdout = runcmd.communicate()[0]\n\n #Parse the plist (Property List).\n PLIST = plistlib.loads(stdout)\n\n #Check if the disk is a partition.\n disk_is_partition = is_partition(disk)\n\n if not disk_is_partition:\n #These are devices.\n get_device_info(disk)\n\n else:\n #These are Partitions. Fix for disks w/ more than 9 partitions.\n host_disk = \"/dev/\"+disk.split(\"s\")[0]+\"s\"+disk.split(\"s\")[1]\n get_partition_info(disk, host_disk)\n\n #Check we found some disks.\n if not DISKINFO:\n raise RuntimeError(\"No Disks found!\")","def get_uid_by_vdisk_map(self, wwpns, scsi_id):\n if not wwpns or not scsi_id:\n return None\n\n host_name = None\n for wwpn in wwpns:\n host_name = self.get_host_name(wwpn)\n if host_name:\n break\n if not host_name:\n return\n\n cmd = \"svcinfo lshostvdiskmap -delim : %s\" % host_name\n output, err_output = self._svc_command(cmd)\n\n if err_output:\n msg = (_(\"get_uid_by_vdisk_map() failure: svc cmd: %(cmd)s \"\n \"error: %(error)s\") %\n {'cmd': cmd,\n 'error': err_output})\n LOG.exception(msg)\n ex_args = {'cmd': cmd, 'e': err_output}\n raise SVCCommandException(**ex_args)\n\n if len(output) < 2:\n return None\n\n header = output.pop(0).split(':')\n scsi_id_idx = header.index(SVC_KEY_MAP_SCSI_ID)\n vdisk_uid_idx = header.index(SVC_KEY_VDISK_UID)\n\n for line in output:\n info = line.split(':')\n if info[scsi_id_idx] == scsi_id:\n return info[vdisk_uid_idx]\n\n return None","def _try_to_get_drive_info_from_dbus(device):\n\n # ensure the device is under /dev, and extract the block device name\n path_components = os.path.split(device)\n if path_components[0] != \"/dev\":\n return {}\n block_device_name = path_components[-1]\n\n # try importing dbus, and exit gracefully if it fails\n try:\n import dbus\n except ImportError:\n return {}\n\n try:\n\n bus = dbus.SystemBus()\n\n # get the block object based on the block device name\n block_obj = bus.get_object(\n \"org.freedesktop.UDisks2\",\n \"/org/freedesktop/UDisks2/block_devices/\" + block_device_name,\n )\n block_iface = dbus.Interface(block_obj, \"org.freedesktop.DBus.Properties\")\n block = block_iface.GetAll(\"org.freedesktop.UDisks2.Block\")\n\n # get the drive object, based on the drive identifier from the block object\n drive_path = block.get(\"Drive\")\n drive_obj = bus.get_object(\"org.freedesktop.UDisks2\", drive_path)\n drive_iface = dbus.Interface(drive_obj, \"org.freedesktop.DBus.Properties\")\n drive = drive_iface.GetAll(\"org.freedesktop.UDisks2.Drive\")\n\n # extract the name and guid from the block and drive properties, calculate drivetype, and return\n return {\n \"name\": str(\n block.get(\"IdLabel\")\n or \" \".join([drive.get(\"Vendor\"), drive.get(\"Model\")]).strip()\n ),\n \"guid\": str(block.get(\"IdUUID\") or drive.get(\"Serial\") or drive.get(\"Id\")),\n \"drivetype\": _get_drivetype_from_dbus_drive_properties(drive),\n }\n\n except ValueError:\n return {}\n except dbus.exceptions.DBusException:\n return {}","def get_volume_info(host, disk_object, dc_obj):\n host_resource = get_host_resource_by_name(host)\n\n vol_id = disk_object.get_image_id()\n sd_id = disk_object.get_storage_domains().get_storage_domain()[0].get_id()\n image_id = disk_object.get_id()\n sp_id = dc_obj.get_id()\n\n args = {\n \"storagepoolID\": sp_id,\n \"storagedomainID\": sd_id,\n \"imageID\": image_id,\n \"volumeID\": vol_id,\n }\n\n return host_resource.vds_client(cmd=\"Volume.getInfo\", args=args)","def generate_cifs_table(cls) -> ty.List[ty.Tuple[str, str]]:\n exit_code, output = sp.getstatusoutput(\"mount\")\n return cls.parse_mount_table(exit_code, output)","def get_device_info(disk):\n\n host_disk = \"/dev/\"+disk\n DISKINFO[host_disk] = {}\n DISKINFO[host_disk][\"Name\"] = host_disk\n DISKINFO[host_disk][\"Type\"] = \"Device\"\n DISKINFO[host_disk][\"HostDevice\"] = \"N/A\"\n DISKINFO[host_disk][\"Partitions\"] = []\n DISKINFO[host_disk][\"Vendor\"] = get_vendor(disk)\n DISKINFO[host_disk][\"Product\"] = get_product(disk)\n DISKINFO[host_disk][\"RawCapacity\"], DISKINFO[host_disk][\"Capacity\"] = get_capacity()\n DISKINFO[host_disk][\"Description\"] = get_description(disk)\n DISKINFO[host_disk][\"Flags\"] = get_capabilities(disk)\n DISKINFO[host_disk][\"Partitioning\"] = get_partitioning(disk)\n DISKINFO[host_disk][\"FileSystem\"] = \"N/A\"\n DISKINFO[host_disk][\"UUID\"] = \"N/A\"\n DISKINFO[host_disk][\"ID\"] = get_id(disk)\n DISKINFO[host_disk][\"BootRecord\"], DISKINFO[host_disk][\"BootRecordStrings\"] = get_boot_record(disk)\n\n return host_disk","def scan_disks():\n disks = get_disks()\n\n # Get disk details\n for disk in disks:\n # Get partition style\n disk['Table'] = get_table_type(disk)\n\n # Get disk name/model and physical details\n disk.update(get_disk_details(disk))\n\n # Get partition info for disk\n disk['Partitions'] = get_partitions(disk)\n\n for partition in disk['Partitions']:\n # Get partition details\n partition.update(get_partition_details(disk, partition))\n\n # Done\n return disks","def _fetch_disk_info(resource_group_name, disk_name):\n show_disk_command = 'az disk show -g {g} -n {name} --query [sku.name,location,osType,hyperVGeneration] -o json'.format(g=resource_group_name, name=disk_name)\n disk_info = loads(_call_az_command(show_disk_command))\n # Note that disk_info will always have 4 elements if the command succeeded, if it fails it will cause an exception\n sku, location, os_type, hyper_v_version = disk_info[0], disk_info[1], disk_info[2], disk_info[3]\n return (sku, location, os_type, hyper_v_version)","def show_vdcs_detail(self):\n for v in self.vdcs:\n print self.vdcs[v]","def parse_devices_spt(self, devices=None):\n\n if not devices:\n self._logger.warning(\"The devices list is empty, so no devices parsed!\")\n return\n try:\n for entry in devices['SCSI Devices']['Device List']:\n device_type = entry['Peripheral Device Type Description']\n if self._include_enclosures:\n if not device_type.startswith('Direct') and \\\n not device_type.startswith('Host Managed') and \\\n not device_type.startswith('Enclosure'):\n continue\n else:\n if not device_type.startswith('Direct') and \\\n not device_type.startswith('Host Managed'):\n continue\n\n # Parse remaining information.\n if device_type.startswith('Direct') or device_type.startswith('Host Managed'):\n device_type = 'disk'\n if self.product_name and not self.product_name in entry['Product Identification'].strip():\n continue;\n if self.vendor_name and not self.vendor_name in entry['Vendor Identification'].strip():\n continue;\n if self.serial_number and not self.serial_number in entry['Product Serial Number'].strip():\n continue;\n if self.target_port and not self.target_port in entry['Device Target Port']:\n continue;\n elif device_type.startswith('Enclosure'):\n device_type = 'enclosure'\n\n device = dict()\n device['Device Type'] = device_type\n\n device['Device Type Description'] = entry['Peripheral Device Type Description']\n device['Product Identification'] = entry['Product Identification'].strip()\n device['Vendor Identification'] = entry['Vendor Identification'].strip()\n device['Revision Level'] = entry['Firmware Revision Level'].strip()\n\n if entry.get('Full Firmware Version') is not None:\n fwver = entry['Full Firmware Version']\n if not fwver.startswith('<not available>'):\n device['Firmware Version'] = fwver\n\n serial = entry['Product Serial Number']\n device['Serial Number'] = serial.strip()\n\n # Note: Not currently displayed. (WWN == LUN Device Identification)\n wwn = entry['Device World Wide Name']\n if wwn.startswith('<not available>'):\n wwn = \"\"\n device['Device World Wide Name'] = wwn\n\n sas_address = entry['Device Target Port']\n if not sas_address.startswith('<not available>'):\n device['SAS Address'] = sas_address\n self._sas_addresses += 1\n else:\n device['SAS Address'] = \"\"\n\n # Note: There's probably a better Pythonic way to do this?\n device['Linux Device Name'] = \"\"\n device['SCSI Device Name'] = \"\"\n device['DMMP Device Name'] = \"\"\n\n # Parse the device paths.\n for path_type in entry['Path Types']:\n if path_type.get('Linux Device'):\n # Handle multiple Linux device paths. (these are \"sd\" devices)\n if device.get('Linux Device Name') and path_type.get('SCSI Nexus'):\n new_device = copy.deepcopy(device)\n self._devices.append(new_device)\n # Fall through to update this device entry.\n # Initialize information for this (or next) device.\n device['Linux Device Name'] = path_type['Linux Device']\n device['Linux SCSI Nexus'] = path_type['SCSI Nexus']\n if path_type.get('SCSI Device'):\n device['SCSI Device Name'] = path_type['SCSI Device']\n if path_type.get('Device Target Port'):\n device['SAS Address'] = path_type['Device Target Port']\n\n elif path_type.get('SCSI Device'):\n # Handle multiple SCSI device paths. (now, \"sg\" devices only)\n if device.get('SCSI Device Name') and path_type.get('SCSI Nexus'):\n new_device = copy.deepcopy(device)\n self._devices.append(new_device)\n # Fall through to update this device entry.\n # Initialize information for this (or next) device.\n device['SCSI Device Name'] = path_type['SCSI Device']\n device['SCSI Nexus'] = path_type['SCSI Nexus']\n if path_type.get('Device Target Port'):\n device['SAS Address'] = path_type['Device Target Port']\n\n elif path_type.get('DMMP Device') is not None:\n # Initialize information for this device. (limited)\n device['DMMP Device Name'] = path_type['DMMP Device']\n\n # Hack: We don't find a SCSI device if there's no serial number or device ID (WWN).\n # This is observed on Linux VM's, so not common, but we still wish to handle this!\n if not len(device['SCSI Device Name']):\n # Funky DM-MP names are skipped! (we deal with sd and/or sg devices only)\n # /dev/mapper/centos_cos--lab--vm01-root\n if not len(device['Linux Device Name']):\n continue\n\n self._devices.append(device)\n\n except RuntimeError as exc:\n self._logger.error(\"Failed to acquire SCSI devices: {0}\".format(exc))\n raise exc","def get_disk_mapping(virt_type, instance,\n disk_bus, cdrom_bus,\n block_device_info=None,\n image_meta=None, rescue=False):\n\n inst_type = instance_types.extract_instance_type(instance)\n\n mapping = {}\n\n if virt_type == \"lxc\":\n # NOTE(zul): This information is not used by the libvirt driver\n # however we need to populate mapping so the image can be\n # created when the instance is started. This can\n # be removed when we convert LXC to use block devices.\n root_disk_bus = disk_bus\n root_device_type = 'disk'\n\n root_info = get_next_disk_info(mapping,\n root_disk_bus,\n root_device_type)\n mapping['root'] = root_info\n mapping['disk'] = root_info\n\n return mapping\n\n if rescue:\n rescue_info = get_next_disk_info(mapping,\n disk_bus)\n mapping['disk.rescue'] = rescue_info\n mapping['root'] = rescue_info\n\n os_info = get_next_disk_info(mapping,\n disk_bus)\n mapping['disk'] = os_info\n\n return mapping\n\n if image_meta and image_meta.get('disk_format') == 'iso':\n root_disk_bus = cdrom_bus\n root_device_type = 'cdrom'\n else:\n root_disk_bus = disk_bus\n root_device_type = 'disk'\n\n root_device_name = driver.block_device_info_get_root(block_device_info)\n if root_device_name is not None:\n root_device = block_device.strip_dev(root_device_name)\n root_info = {'bus': get_disk_bus_for_disk_dev(virt_type,\n root_device),\n 'dev': root_device,\n 'type': root_device_type}\n else:\n root_info = get_next_disk_info(mapping,\n root_disk_bus,\n root_device_type)\n mapping['root'] = root_info\n if not block_device.volume_in_mapping(root_info['dev'],\n block_device_info):\n mapping['disk'] = root_info\n\n eph_info = get_next_disk_info(mapping,\n disk_bus)\n ephemeral_device = False\n if not (block_device.volume_in_mapping(eph_info['dev'],\n block_device_info) or\n 0 in [eph['num'] for eph in\n driver.block_device_info_get_ephemerals(\n block_device_info)]):\n if instance['ephemeral_gb'] > 0:\n ephemeral_device = True\n\n if ephemeral_device:\n mapping['disk.local'] = eph_info\n\n for eph in driver.block_device_info_get_ephemerals(\n block_device_info):\n disk_dev = block_device.strip_dev(eph['device_name'])\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\n\n mapping[get_eph_disk(eph)] = {'bus': disk_bus,\n 'dev': disk_dev,\n 'type': 'disk'}\n\n swap = driver.block_device_info_get_swap(block_device_info)\n if driver.swap_is_usable(swap):\n disk_dev = block_device.strip_dev(swap['device_name'])\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\n\n mapping['disk.swap'] = {'bus': disk_bus,\n 'dev': disk_dev,\n 'type': 'disk'}\n elif inst_type['swap'] > 0:\n swap_info = get_next_disk_info(mapping,\n disk_bus)\n if not block_device.volume_in_mapping(swap_info['dev'],\n block_device_info):\n mapping['disk.swap'] = swap_info\n\n block_device_mapping = driver.block_device_info_get_mapping(\n block_device_info)\n\n for vol in block_device_mapping:\n disk_dev = vol['mount_device'].rpartition(\"/\")[2]\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\n\n mapping[vol['mount_device']] = {'bus': disk_bus,\n 'dev': disk_dev,\n 'type': 'disk'}\n\n if configdrive.enabled_for(instance):\n config_info = get_next_disk_info(mapping,\n disk_bus,\n last_device=True)\n mapping['disk.config'] = config_info\n\n return mapping","def find_dev_mount_point(self, usb_table):\n mounts = open(\"/proc/mounts\")\n mount_lines = mounts.readlines()\n table = usb_table\n i = 0\n for device in table:\n for line in mount_lines:\n arguments = line.split(\" \")\n if arguments[0] == device[0]:\n usb_table[i].append(arguments[1])\n usb_table[i] = self.get_drive_stat(usb_table[i])\n break\n i += 1\n return usb_table","def get_devices_summary():\n\n # This function was created to replace get_devices_information\n # because it wasn't detecting virtual systems in Palo Alto Virtual Systems\n global nipper_xml\n devices = {}\n headings = []\n\n # Add the table headings to a list\n for h in nipper_xml.findall(\"./summary/table/[@ref='SCOPE.AUDITDEVICELIST.TABLE']/headings/heading\"):\n if h not in headings:\n headings.append(h.text)\n\n for device in nipper_xml.findall(\"./summary/table/[@ref='SCOPE.AUDITDEVICELIST.TABLE']/tablebody/tablerow\"):\n values = []\n for i in device.findall('./tablecell/item'):\n if i not in values:\n values.append(i.text)\n if DEBUG:\n print \"\\t\" + note + \"%s: %s\" % (headings[headings.index('Name')], values[headings.index('Name')])\n print \"\\t\" + note + \"%s: %s\" % (headings[headings.index('Device')], values[headings.index('Device')])\n print \"\\t\" + note + \"%s: %s\" % (headings[headings.index('OS')], values[headings.index('OS')].split(\" \")[0])\n print \"\\t\" + note + \"%s: %s\" % (headings[headings.index('OS')], values[headings.index('OS')].split(\" \")[1])\n devices[values[headings.index('Name')]] = {'name': values[headings.index('Name')],\n 'type': values[headings.index('Device')],\n 'os': values[headings.index('OS')].split(' ')[0],\n 'osversion': values[headings.index('OS')].split(' ')[1]\n }\n\n if DEBUG:\n print info + \"Device Object:\"\n print devices\n raw_input(warn + \"Press enter to continue\")\n return devices","def get_gadget_info(gfname):\n partmass = pyg.readheader(gfname, 'massTable')[1]\n boxsize = pyg.readheader(gfname, 'boxsize')\n omegam = pyg.readheader(gfname, 'O0')\n omegal = pyg.readheader(gfname, 'Ol')\n h = pyg.readheader(gfname, 'h')\n npart = pyg.readheader(gfname, 'npartTotal')[1]\n return omegam, omegal, h, boxsize, partmass, npart","def collect_device_lun_SM(self):\n\n devlun = self._devlun\n\n r = re.compile( r'\\s*(/dev/\\S+).*Logical Drive\\s+(\\S+),' )\n P = subprocess.Popen('SMdevices', shell=True,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n close_fds=True)\n co,ci,ce = P.stdout, P.stdin, P.stderr\n for L in co:\n m = r.match(L)\n if m:\n devlun[m.group(1)] = m.group(2)","def _try_to_get_drive_info_from_diskutil(device):\n # we only use diskutil on OSX\n if sys.platform != \"darwin\":\n return {}\n\n # skip non-device mounts\n if not device.startswith(\"/dev/\"):\n return {}\n\n # run the command and read the results\n diskutilp = subprocess.Popen(\n \"diskutil info {}\".format(device), shell=True, stdout=subprocess.PIPE\n )\n diskutil_output, err = diskutilp.communicate()\n\n rows = [\n line.split(\":\", 1)\n for line in diskutil_output.decode().split(\"\\n\")\n if \":\" in line\n ]\n metadata = dict([(key.strip(), val.strip()) for key, val in rows])\n\n # determine what type of drive it is (not sure what an optical drive shows up as, but OSX + optical is now uncommon)\n if metadata.get(\"Protocol\") == \"USB\":\n drivetype = drivetypes.USB_DEVICE\n elif metadata.get(\"Internal\") == \"Yes\":\n drivetype = drivetypes.INTERNAL_DRIVE\n else:\n drivetype = drivetypes.UNKNOWN\n\n # extract the name and guid from the diskutil drive metadata, and return\n return {\n \"drivetype\": drivetype,\n \"name\": metadata.get(\"Volume Name\")\n or metadata.get(\"Device / Media Name\")\n or \"\",\n \"guid\": metadata.get(\"Volume UUID\") or \"\",\n }","def sd_partition_table():\n table = {}\n for partition in sd_partitions():\n table[partition] = sd_part_size(partition)\n return table","def inspect_boot_loader(g, device) -> inspect_pb2.InspectionResults:\n\n bios_bootable = False\n uefi_bootable = False\n root_fs = \"\"\n\n try:\n stream = os.popen('gdisk -l {}'.format(device))\n output = stream.read()\n print(output)\n if _inspect_for_hybrid_mbr(output):\n bios_bootable = True\n\n part_list = g.part_list('/dev/sda')\n for part in part_list:\n try:\n guid = g.part_get_gpt_type('/dev/sda', part['part_num'])\n # It covers both GPT \"EFI System\" and BIOS \"EFI (FAT-12/16/32)\".\n if guid == 'C12A7328-F81F-11D2-BA4B-00A0C93EC93B':\n uefi_bootable = True\n # TODO: detect root_fs (b/169245755)\n # It covers \"BIOS boot\", which make a protective-MBR bios-bootable.\n if guid == '21686148-6449-6E6F-744E-656564454649':\n bios_bootable = True\n except Exception:\n continue\n\n except Exception as e:\n print(\"Failed to inspect disk partition: \", e)\n\n return inspect_pb2.InspectionResults(\n bios_bootable=bios_bootable,\n uefi_bootable=uefi_bootable,\n root_fs=root_fs,\n )","def _findSdPartitionDevice (self):\n for partitionDiskName in self.getPartitionDiskNames():\n partitionOsDevice = self.getPartitionOsDeviceName(partitionDiskName)\n partitionIndex = self.getPartitionIndex(partitionDiskName)\n\n if partitionOsDevice is None:\n # not given - need to \"calculate\" it from the sd device and index\n partitionOsDevice = self.getSdDevice() + str(int(partitionIndex) + 1)\n\n self._log(\"find-sd-partition-device\").notice(\"found SD partition %s as os device %s\", partitionDiskName, partitionOsDevice)\n self._partitionOsDevice[partitionDiskName] = partitionOsDevice","def read_vmdas(self,):\n fd = self.f\n # The raw files produced by VMDAS contain a binary navigation data\n # block.\n self.cfg['sourceprog'] = 'VMDAS'\n ens = self.ensemble\n k = ens.k\n if self._source != 1 and self._debug_level >= 1:\n print(' \\n***** Apparently a VMDAS file \\n\\n')\n self._source = 1\n self.vars_read += ['time_gps',\n 'latitude_gps',\n 'longitude_gps',\n 'etime_gps',\n 'elatitude_gps',\n 'elongitude_gps',\n 'flags',\n 'ntime', ]\n utim = fd.read_ui8(4)\n date = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])\n # This byte is in hundredths of seconds (10s of milliseconds):\n time = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) / 10)))\n fd.seek(4, 1) # \"PC clock offset from UTC\" - clock drift in ms?\n ens.time_gps[k] = tmlib.date2epoch(date + time)[0]\n ens.latitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.longitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.etime_gps[k] = tmlib.date2epoch(date + tmlib.timedelta(\n milliseconds=int(fd.read_ui32(1) * 10)))[0]\n ens.elatitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.elongitude_gps[k] = fd.read_i32(1) * self._cfac\n fd.seek(12, 1)\n ens.flags[k] = fd.read_ui16(1)\n fd.seek(6, 1)\n utim = fd.read_ui8(4)\n date = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])\n ens.ntime[k] = tmlib.date2epoch(date + tmlib.timedelta(\n milliseconds=int(fd.read_ui32(1) / 10)))[0]\n fd.seek(16, 1)\n self._nbyte = 2 + 76","def init_data(partitions_file):\n mapping = []\n\n drive_size = None\n for line in partitions_file:\n if drive_size is None:\n drive_size = parse_drive_size(line.rstrip())\n else:\n partitions_list = parse_partitions(line.rstrip())\n mapping.append((drive_size, partitions_list))\n drive_size = None\n\n return mapping","def process_multipath_devices(self):\n\n devlun = self._devlun\n lunMatch = self._lunMatch\n logger = self._logger\n\n rwnn = re.compile( r'^(?P<wwn>[0-9a-f]+)\\s+(?P<dev>dm-\\S+)\\s' )\n rscsi = re.compile( r'^\\|.*\\s+(?P<scsi>sd\\S+)\\s+' )\n\n dmwnn = {}\n dmlun = {}\n lundev = None\n wnn = None\n\n P = subprocess.Popen('multipath -ll', shell=True,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n close_fds=True)\n co,ci,ce = P.stdout, P.stdin, P.stderr\n for L in co:\n M = rwnn.match(L)\n if M:\n # We have a line giving wwn and device name.\n lundev = M.group('dev')\n wnn = M.group('wwn')\n continue\n M = rscsi.match(L)\n if M and lundev is not None:\n s = M.group('scsi')\n try:\n lun = devlun[os.path.join('/dev', s)]\n except KeyError:\n pass\n else:\n dmwnn[lundev] = wnn\n dmlun[lundev] = lun\n lundev = wnn = None\n\n # Now go through the devices we have located. Match LUN names,\n blkops = self._blkops\n for dm,lun in dmlun.items():\n for r,transfer,readhead,sched,schedopts in lunMatch:\n if r.match(lun):\n logger.info('Set attributes on %s (%s)', lun, dm)\n blkops.set_io_scheduler(dm, sched)\n blkops.set_io_transfer_size(dm, transfer)\n blkops.set_io_readahead_size(dm, readhead)\n if sched == 'deadline':\n fifobatch,read_expire,write_expire,writes_starved,front_merges = schedopts\n if fifobatch is not None:\n blkops.set_io_deadline_fifo_batch(dm, fifobatch)\n if read_expire is not None:\n blkops.set_io_deadline_read_expire(dm, read_expire)\n if write_expire is not None:\n blkops.set_io_deadline_write_expire(dm, write_expire)\n if writes_starved is not None:\n blkops.set_io_deadline_writes_starved(dm, writes_starved)\n if front_merges is not None:\n blkops.set_io_deadline_front_merges(dm, front_merges)","def get_generic_scsi_devs_count(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetGenericScsiDevsCount', self.handle)","def get_status(self):\n dstat_lines = cat(self.PROC_DISKSTATS)\n pl = dict()\n for line in dstat_lines:\n if line:\n p = partition()\n (p.M, p.m, p.dev,\n p.r, p.r_merged, p.r_sectors, p.r_ms,\n p.w, p.w_merged, p.w_sectors, p.w_ms,\n p.io_progress, p.io_ms, p.ms_weighted) = line.strip().split()\n\n if p.dev in self.partitions:\n pl[p.dev] = p\n\n return pl","def _get_vm_device_status(self, device='FLOPPY'):\n valid_devices = {'FLOPPY': 'floppy',\n 'CDROM': 'cd'}\n\n # Check if the input is valid\n if device not in valid_devices:\n raise exception.IloInvalidInputError(\n \"Invalid device. Valid devices: FLOPPY or CDROM.\")\n\n manager, uri = self._get_ilo_details()\n try:\n vmedia_uri = manager['links']['VirtualMedia']['href']\n except KeyError:\n msg = ('\"VirtualMedia\" section in Manager/links does not exist')\n raise exception.IloCommandNotSupportedError(msg)\n\n for status, hds, vmed, memberuri in self._get_collection(vmedia_uri):\n status, headers, response = self._rest_get(memberuri)\n if status != 200:\n msg = self._get_extended_error(response)\n raise exception.IloError(msg)\n\n if (valid_devices[device] in\n [item.lower() for item in response['MediaTypes']]):\n vm_device_uri = response['links']['self']['href']\n return response, vm_device_uri\n\n # Requested device not found\n msg = ('Virtualmedia device \"' + device + '\" is not'\n ' found on this system.')\n raise exception.IloError(msg)","def device_mounted(uuid):\n out, err = run_cmd(['lsblk', '-o', 'NAME,UUID,MOUNTPOINT', '--json'])\n\n blockdevices = json.loads(out)['blockdevices']\n\n for blkdevice in blockdevices:\n if key_exists('children', blkdevice):\n for child in blkdevice['children']:\n if key_exists('mountpoint', child) and child['uuid'] == uuid:\n return child['mountpoint']","def get_device_slot(self, sas):\n\n for device in self._devices:\n if not device['Device Type'].startswith(\"enclosu\"):\n continue\n array_devices = device['Additional Element Information']['Additional Element Status']['Array Device Slot']\n for array_device in array_devices['Descriptor List']:\n for phy_desc in array_device['Descriptor List']:\n if sas == phy_desc['SAS Address']:\n element_index = int(array_device['Element Index'])\n # Denali vs. Pikespeak Difference.\n if not array_device['Element Index Includes Overall']:\n # Adjust for 'Overall Status Descriptor' in Element page.\n element_index += 1\n return device['SCSI Device Name'], array_device['Device Slot Number'], element_index\n return \"\", \"\", -1","def collect():\n\n command = \"cat /proc/meminfo |grep MemTotal|awk -F' ' '{print $2}'\"\n memTotal_f = round(float(os.popen(command).read())/1024/1000,0)\n memTotal = int(memTotal_f)\n cmd = 'df -h |grep \"/dev/s\"'\n metric_disk = os.popen(cmd).readlines()\n hardNum=[]\n for i in metric_disk:\n hard_space = float((i.strip().split()[1])[:-1])\n hardNum.append(hard_space)\n\n disk_info = sum(hardNum)\n disk_use = {}\n metric_disks=os.popen('df -x tmpfs -x devtmpfs | grep -Eo \" /\\S*$\" ').readlines()\n for disk in metric_disks:\n cmd = 'df|grep -E \"%s$\"' % disk.strip()\n disks = os.popen(cmd).readlines()[0]\n disk_list = disks.split()\n disk_use[disk_list[5]]=disk_list[4]\n hard = {\n \"disk_used\" : disk_use,\n \"disk_total\":disk_info,\n \"mem_total\":memTotal\n }\n\n return hard","def print_show_vsf(values1, values2, values3):\n print('VSF Domain ID : %13d' % (values1['domain_id']))\n print('MAC Address : %10s' % (values1['mac_address']['octets']))\n print('VSF Topology : %13s' % (values1['topology']))\n print('VSF Status : %13s' % (values1['status']))\n print('Uptime : %d d %2d h %2d m' % (values1['uptime']['days'],\n values1['uptime']['hours'],\n values1['uptime']['minutes']))\n if values3['is_oobm_mad_enabled']:\n print('VSF MAD : OOBM MAD enabled')\n elif values3['is_lldp_mad_enabled']:\n print('VSF MAD : LLDP MAD enabled')\n else:\n print('VSF MAD : MAD not enabled')\n print('VSF Port Speed : %13s' % (values1['port_speed']))\n print('Software Version : %13s' % (values1['software_version']))\n print('\\nMember ')\n print('ID\\tMAC Address\\t\\tModel\\t\\t\\t\\tPriority\\tStatus')\n print('----------------------------------------------------------------')\n for member in range(len(values2['vsf_member_element'])):\n print('%d %3s %5s %4d %5s' % (values2['vsf_member_element'][member]['member_id'],\n values2['vsf_member_element'][member]['mac_address']['octets'],\n values2['vsf_member_element'][member]['model'],\n values2['vsf_member_element'][member]['priority'],\n values2['vsf_member_element'][member]['status']), end='\\n\\n')","def get_mbed_devices(self):\n upper_ven = [ven.upper() for ven in self.usb_vendor_list]\n mounts_key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\MountedDevices')\n for point, label, _ in self.iter_vals(mounts_key):\n printable_label = label.decode('utf-16le', 'ignore')\n if ('DosDevices' in point and\n any(v in printable_label.upper() for v in upper_ven)):\n logger.debug(\"Found Mount point %s with usb ID %s\",point,\n printable_label)\n yield (point, printable_label)\n else:\n logger.debug(\"Skipping Mount point %r label %r\", point, label)","def modif_fstab(self):\n print \"preparation du fichier fstab\"\n self.exec_cmd(\"cp %s/etc/fstab %s/etc/fstab.pre.p2v\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\n self.exec_cmd(\"cp %s/etc/fstab_without_uuid %s/etc/fstab\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\n line = open(\"/vhosts/\"+ name_vm_dest +\"/etc/fstab_without_uuid\",\"r\").read()\n for i in self.tri(partitions[type_p2v]):\n line = line.replace(partitions[type_p2v][i][0],\"/dev/%s\" % i,1)\n fichier = open(\"/vhosts/\"+ name_vm_dest +\"/etc/fstab\",\"w\")\n fichier.write(line)\n fichier.close()","def __init__(self, devtype, device):\n #\n self.Type = devtype # SERIAL, FILE, other? \n self.Device = device # ELM327, other? used to determine which reader commands to send (separate from OBD2 cmds)\n #\n self.debug = 0 # debug level, 0 = off, higher is more...\n #\n self.State = 0 # 1 is connected, 0 is disconnected/failed\n self.recwaiting = 0 # 0 = no record waiting (can send new cmd), 1 = record waiting to be retrieved\n #\n self.Style = 'old' # 'old', 'can' used to determine how to interpret the results, gets updated by connect()\n self.Headers = 0 # ECU headers, 1 is on, 0 is off\n #\n self.attr = {} # the list of device attributes and their values\n self.attr_cmds = {} # the list of supported attribute at commands, and the associated attribute\n #\n self.RecordTrace = 0 # 0 = no, 1 = yes record a trace of the serial session\n self.tf_out = None # file to record trace to\n #\n if self.Type == \"SERIAL\":\n self.Port = None # connect later\n elif self.Type == \"FILE\":\n self.tf = None # open later\n self.eof = 0\n else:\n pass\n #\n if self.Device == \"ELM327\":\n self.attr_cmds = {\n 'AT@1' : \"Brand\",\n 'AT@2' : \"Brand2\",\n 'ATI' : \"Firmware\",\n 'ATDP' : \"Proto\",\n 'ATDPN' : \"ProtoNum\",\n 'ATRV' : \"Voltage\",\n }\n else:\n pass\n self.clear_attr()","def _usb_get_report(self):\n arraydata=self.usbdev.ctrl_transfer(bmRequestType=0xa1, bRequest=0x01, wValue=0x03b0, wIndex=0, data_or_wLength=9)\n data=arraydata.tolist()\n assert (data[0:4]==[0xb0,0x00,0x00,0x01]) or \\\n (data[0:4]==[0xb0,0xaa,0x00,0x01]) or \\\n (data[0:4]==[0xb0,0xaa,0x00,0x00]), \\\n 'unexpected device status info start: %s'%(data[0:4])\n assert data[8]==1,'unexpected last byte value: %s'%data[8]\n position=data[4:6]\n v_pos=data[6]\n self.vertical_position=v2k(v_pos,V_POS)\n self.intensity=data[7]\n self.position=v2k(position,POSITION)\n return(data)","def fillQuickList():\n global quickList\n cmd = \"/sbin/blkid\"\n proc = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n for line in proc.stdout:\n line = line.replace(':', '').strip()\n propList = line.split()\n devName = label = uuid = fsType = ''\n devName = propList[0]\n for property in propList:\n if property.startswith('UUID'):\n uuid = property.replace('UUID=', '').replace('\"', '')\n quickList[devName] = uuid","def writeRawFCD():\n global vehId, vehIdDict\n vehIdDict = {}\n vehId = 0\n day = 0\n\n def getVehId(orgId):\n \"\"\"creates new vehicle id's which consists only numerics\"\"\"\n global vehId, vehIdDict\n value = vehIdDict.get(orgId, vehId)\n if value is vehId:\n vehIdDict[orgId] = vehId\n vehId = (vehId + 1) % 65500\n return value\n\n outputFile = open(path.FQrawFCD, 'w')\n\n for period, quota, vtypeDictR, taxiSum in generatePeriodQuotaSets():\n day += 86400\n # reset dict so that every taxi (even if the vehicle is chosen several\n # times) gets its own id\n vehIdDict = {}\n # dataset=0\n sortedKeys = vtypeDictR.keys()\n sortedKeys.sort()\n for timestep in sortedKeys:\n taxiList = vtypeDictR[timestep]\n for tup in taxiList: # all elements in this timestep\n # calc timestep ->for every period /quota set a new day\n time = timestep + day\n time = calcTime.getDateFromDepart(time)\n # dataset+=1\n # print ouptut\n # veh_id date (time to simDate+time) x (remove and\n # set comma new)\n outputFile.write(str(getVehId(tup[0])) + '\\t' + time + '\\t' + tup[3][0:2] + '.' + tup[3][2:7] + tup[3][8:] +\n # y (remove and set comma new)\n # status speed form m/s in km/h\n '\\t' + tup[4][0:2] + '.' + tup[4][2:7] + tup[4][8:] + '\\t' + \"90\" + '\\t' + str(int(round(tup[2] * 3.6))) + '\\n')\n # print dataset, time\n print(vehId)\n outputFile.close()","def getVCDRCloudFS(**kwargs):\n strVCDRProdURL = kwargs['strVCDRProdURL']\n sessiontoken = kwargs['sessiontoken']\n if kwargs['cloud_fs_id'] is None:\n json_response = get_vcdr_cloud_fs_json(strVCDRProdURL, sessiontoken)\n if json_response == None:\n print(\"API Error\")\n sys.exit(1)\n cloud_fs = json_response[\"cloud_file_systems\"]\n table = PrettyTable(['Cloud FS Name', 'Cloud FS ID'])\n for i in cloud_fs:\n table.add_row([i['name'], i['id']])\n print(table)\n else:\n cloud_fs_id = kwargs['cloud_fs_id']\n json_response = get_vcdr_cloud_fs_details_json(strVCDRProdURL, cloud_fs_id, sessiontoken)\n if json_response == None:\n print(\"API Error\")\n sys.exit(1)\n print(\" \")\n print(f\"Cloud FS Name: {json_response['name']}\")\n print(f\"Capacity GiB: {json_response['capacity_gib']:,.2f}\")\n print(f\"Used GiB: {json_response['used_gib']:,.2f}\")\n print(f\"Recovery SDDC: {json_response['recovery_sddc_id']}\")\n print(\" \")","def scan_disk(disk_url, scan_file_dir):\n\n # make the dir if it doesn't exist\n if not os.path.exists(scan_file_dir):\n os.makedirs(scan_file_dir)\n \n # open up the image\n img = pytsk3.Img_Info(url=disk_url)\n \n # get the volume info\n VOL_INFO = pytsk3.Volume_Info(img)\n\n\n # print out some info about the disk image\n logger.debug(\"--- Volume info ---\")\n logger.debug(\"Current: %d\" % VOL_INFO.current)\n logger.debug(\"VS Type: %d\" % VOL_INFO.info.vstype)\n logger.debug(\"Offset: %d\" % VOL_INFO.info.offset)\n logger.debug(\"Block Size: %d\" % VOL_INFO.info.block_size)\n logger.debug(\"Endian: %d\" % VOL_INFO.info.endian)\n logger.debug(\"Partition List: %s\" % VOL_INFO.info.part_list)\n logger.debug(\"Parition Count: %d\" % VOL_INFO.info.part_count)\n logger.debug(\"--- Volume info ---\")\n\n\n # list of filenames we have to cat together at the end\n files = []\n\n sector_size = VOL_INFO.info.block_size\n\n part_number = 1\n\n sparse_number = 0\n \n copied_front_data = False\n\n # loop over each volume\n for vol in VOL_INFO:\n \n logger.debug(\"--- Partition ---\")\n logger.debug(\"Start: %d\" % vol.start)\n logger.debug(\"Length: %d\" % vol.len)\n logger.debug(\"Description: %s\" % vol.desc)\n logger.debug(\"Address: %d\" % vol.addr)\n logger.debug(\"Flags: %d\" % vol.flags)\n\n # ignore partition table at beginning\n if vol.addr == 0:\n continue\n\n # copy the MBR and other stuff if this is the unpartitioned space between 0\n # and the first partition\n\n if not copied_front_data:\n fname = save_front_data(disk_url, scan_file_dir, vol.start, vol.len*sector_size)\n files.append(fname)\n copied_front_data = True\n \n continue\n\n type = vol.desc.split(\" \")[0]\n\n # if partition type is NTFS, do an NTFS clone on it\n if vol.desc == 'NTFS (0x07)':\n \n print \"* Scanning %s (%d)...\"%(disk_url,part_number)\n \n # Win 7 specific hack, so that we only have 1 sparse file.\n if vol.len == 204800 and vol.start == 2048:\n fname = save_NTFS_partition(part_number, disk_url, scan_file_dir, meta_only=False)\n else:\n fname = save_NTFS_partition(part_number, disk_url, scan_file_dir)\n files.append(fname)\n part_number += 1\n \n else:\n # create empty sparse file since we don't support other filesystems\n #fname = save_sparse(sparse_number, scan_file_dir, vol.len*sector_size)\n #files.append(fname)\n #sparse_number += 1\n pass\n \n \n \n # cat everything together\n logger.info(\"Cat everything together\")\n filenames = \" \".join(files)\n output_img = os.path.join(scan_file_dir, \"disk.img\")\n #cmd = \"cat \" + filenames + \" > \" + os.path.join(scan_file_dir, \"disk.img\")\n \n print \"* Scan complete, merging scans into one file (%s)...\"%output_img\n \n cmd = \"cat \" + filenames + \" | cp --sparse=always /proc/self/fd/0 \" + output_img\n \n logger.info(\"Running: %s\" % cmd)\n \n subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout.read()\n\n logging.info(\"Changing permissions on '%s'\"%output_img)\n\n os.chmod(output_img,0555)","def get_cmdb_data(device_type):\n pass","def get_device_list(h):\n try:\n global html\n html = h\n # this is the result which we show on the page\n result = \"\"\n ip_address = \"\"\n mac_address = \"\"\n selected_device = \"swt4\"\n # take value of IPaddress from the page through html.var\n # check that value is None Then It takes the empty string\n if html.var(\"ip_address\") == None:\n ip_address = \"\"\n else:\n ip_address = html.var(\"ip_address\")\n\n # take value of MACAddress from the page through html.var\n # check that value is None Then It takes the empty string\n if html.var(\"mac_address\") == None:\n mac_address = \"\"\n else:\n mac_address = html.var(\"mac_address\")\n # take value of SelectedDevice from the page through html.var\n # check that value is None Then It takes the empty string\n if html.var(\"selected_device_type\") == None:\n selected_device = \"swt4\"\n else:\n selected_device = html.var(\"selected_device_type\")\n\n # call the function get_odu_list of odu-controller which return us the\n # list of devices in two dimensional list according to\n # IPAddress,MACaddress,SelectedDevice\n\n result = get_device_list_swt_profiling(\n ip_address, mac_address, selected_device)\n if result == 0 or result == 1 or result == 2:\n html.write(str(result))\n else:\n if result == None or result == \"\":\n html.write(str(result))\n else:\n swt4_profiling_form(result, selected_device)\n\n except Exception as e:\n html.write(str(e))","def GetVdiskInfo(self, vdiskid, headers=None, query_params=None, content_type=\"application/json\"):\n uri = self.client.base_url + \"/vdisks/\"+vdiskid\n return self.client.get(uri, None, headers, query_params, content_type)","def test_disk(self):\n self.command.package = self.input_ovf\n self.command.file_id = \"file1\"\n self.command.run()\n self.command.finished()\n self.check_diff(\"\"\"\n <ovf:References>\n- <ovf:File ovf:href=\"input.vmdk\" ovf:id=\"file1\" ovf:size=\"{vmdk_size}\" />\n <ovf:File ovf:href=\"input.iso\" ovf:id=\"file2\" ovf:size=\"{iso_size}\" />\n...\n <ovf:Info>Virtual disk information</ovf:Info>\n- <ovf:Disk ovf:capacity=\"1\" ovf:capacityAllocationUnits=\"byte * 2^30\" \\\novf:diskId=\"vmdisk1\" ovf:fileRef=\"file1\" ovf:format=\"http://www.vmware.com/\\\ninterfaces/specifications/vmdk.html#streamOptimized\" />\n </ovf:DiskSection>\n...\n <rasd:AddressOnParent>0</rasd:AddressOnParent>\n- <rasd:ElementName>Hard Drive</rasd:ElementName>\n- <rasd:HostResource>ovf:/disk/vmdisk1</rasd:HostResource>\n- <rasd:InstanceID>6</rasd:InstanceID>\n- <rasd:Parent>3</rasd:Parent>\n- <rasd:ResourceType>17</rasd:ResourceType>\n- </ovf:Item>\n- <ovf:Item>\n- <rasd:AddressOnParent>0</rasd:AddressOnParent>\n <rasd:AutomaticAllocation>true</rasd:AutomaticAllocation>\n\"\"\".format(vmdk_size=self.FILE_SIZE['input.vmdk'],\n iso_size=self.FILE_SIZE['input.iso']))\n self.assertFalse(os.path.exists(os.path.join(self.temp_dir,\n \"input.vmdk\")),\n \"deleted file should not be exported\")","def drive_finder():\n\n '''drive_types = {\n win32file.DRIVE_UNKNOWN: \"Unknown\\nDrive type can't be determined.\",\n win32file.DRIVE_REMOVABLE: \"Removable\\nDrive has removable media. This includes all floppy drives and many \"\n \"other varieties of storage devices.\",\n win32file.DRIVE_FIXED: \"Fixed\\nDrive has fixed (nonremovable) media. This includes all hard drives, including \"\n \"hard drives that are removable.\",\n win32file.DRIVE_REMOTE: \"Remote\\nNetwork drives. This includes drives shared anywhere on a network.\",\n win32file.DRIVE_CDROM: \"CDROM\\nDrive is a CD-ROM. No distinction is made between read-only and read/write\"\n \" CD-ROM drives.\",\n win32file.DRIVE_RAMDISK: \"RAMDisk\\nDrive is a block of random access memory (RAM) on the local computer that \"\n \"behaves like a disk drive.\",\n win32file.DRIVE_NO_ROOT_DIR: \"The root directory does not exist.\"\n }'''\n\n drives = win32api.GetLogicalDriveStrings().split('\\x00')[:-1]\n\n removable_count = 0\n removable_volumes = {}\n values = [] # Initialize list of removable drives because code will exit if there is no drive attached\n\n for device in drives:\n what_type = win32file.GetDriveType(device)\n # print(\"What type is: \", what_type)\n\n if what_type == 2:\n removable_count += 1\n device_name = (win32api.GetVolumeInformation(device))\n device_name = str(device_name[0])\n removable_volumes.update({device: device_name})\n values = list(removable_volumes.keys())\n\n # print(\"\\n\\n\\nAvailable USB drives are: \", removable_volumes)\n\n if not values:\n print(\"\\n\\n\\nNo USB Drives are available. Did you forget to plug one in?\")\n destroot = \"\" # No USB drive so return a blank.\n else:\n values.append('Exit with no pick') # Add exit option if user changes mind.\n title = 'Please choose Drive to write logs to ' + str(removable_volumes) + ': '\n value, index = pick(values, title, indicator='=>')\n\n if value != 'Exit with no pick':\n destroot = value + r'Temp\\LOGCAPTURE'\n return destroot\n else:\n print(\"\\nExiting with no action based on user selection.\")\n exit(1)\n\n return","def retr_segment_devices( segment ) :\n\n\t\t\t_logger.info( '...retr_segment_devices...' )\n\t\t\toutput = []\n\t\t\ttry :\n\t\t\t\tdb = mongo.db.auth_devices\n\t\t\t\tfor device in db.find( { 'segment' : segment } ) :\n\t\t\t\t\toutput.append({'moniker' : device['moniker'] ,\n\t\t\t\t\t\t\t\t 'description' : device['description'] ,\n\t\t\t\t\t\t\t\t 'active' : device['active'] ,\n\t\t\t\t\t\t\t\t 'device_id' : device['device_id'] ,\n\t\t\t\t\t\t\t\t 'enlisted' : device['enlisted'] ,\n\t\t\t\t\t\t\t\t 'last_kown_remote_ip' : device['last_known_remote_ip'] ,\n\t\t\t\t\t\t\t\t 'engaged' : device['engaged'] ,\n\t\t\t\t\t\t\t\t 'canononical_user' : device['canonical_user'] ,\n\t\t\t\t\t\t\t\t 'scope' : device['scope'] ,\n\t\t\t\t\t\t\t\t 'segment' : device['segment']\n\t\t\t\t\t})\n\t\t\texcept Exception as e :\n\t\t\t\t _logger.error( '...retr_segment_devices %s' % e.message )\n\t\t\treturn jsonify({'result' : output})","def get_drive_enclosure_information(self):\n\n for device in self._devices:\n if not device['Device Type'].startswith(\"disk\"):\n continue\n enc_device, device_slot, element_index = self.get_device_slot(device['SAS Address'])\n device['Enclosure Device'] = enc_device\n device['Enclosure Slot'] = device_slot\n device['Slot Description'] = self.get_array_desc_text(enc_device, element_index)","def discover_devices(vid=FT232H_VID, pid=FT232H_PID):\n try:\n # Create a libftdi context.\n ctx = None\n ctx = ftdi.new()\n # Enumerate FTDI devices.\n device_list = None\n count, device_list = ftdi.usb_find_all(ctx, vid, pid)\n if count < 0:\n raise RuntimeError('ftdi_usb_find_all returned error {0}: {1}'.format(count, ftdi.get_error_string(self._ctx)))\n # Walk through list of devices and assemble list of serial numbers.\n devices = []\n while device_list is not None:\n # Get USB device strings and add serial to list of devices.\n ret, manufacturer, description, serial = ftdi.usb_get_strings(ctx, device_list.dev, 256, 256, 256)\n if ret < 0:\n raise RuntimeError('ftdi_usb_get_strings returned error {0}: {1}'.format(ret, ftdi.get_error_string(self._ctx)))\n devices.append(serial)\n device_list = device_list.next\n return devices\n finally:\n # Make sure to clean up list and context when done.\n if device_list is not None:\n ftdi.list_free(device_list)\n if ctx is not None:\n ftdi.free(ctx)","def get_description(disk):\n #Gather info from diskutil to create some descriptions.\n # -- Internal or external --\n internal_or_external = \"Unknown \"\n\n if \"Internal\" in PLIST.keys():\n if PLIST[\"Internal\"]:\n internal_or_external = \"Internal \"\n\n else:\n internal_or_external = \"External \"\n\n # -- Type: Removable, SSD, or HDD --\n disk_type = \"Unknown \"\n\n if (\"Removable\" in PLIST.keys() and PLIST['Removable']) or \\\n (\"RemovableMedia\" in PLIST.keys() and PLIST['RemovableMedia']):\n disk_type = \"Removable Drive \"\n\n #Fix for old versions of OS X where the SolidState attribute is missing.\n #Means we assume things are HDDs if we can't otherwise figure them out.\n if disk_type == \"Unknown \" and \"SolidState\" in PLIST.keys() and PLIST[\"SolidState\"]:\n disk_type = \"Solid State Drive \"\n\n elif disk_type == \"Unknown \":\n disk_type = \"Hard Disk Drive \"\n\n # -- Bus protocol --\n bus_protocol = \"Unknown\"\n\n if \"BusProtocol\" in PLIST.keys():\n bus_protocol = str(PLIST[\"BusProtocol\"])\n\n # -- APFS containers, volumes, physical stores --\n apfs_string = \"\"\n\n if \"Content\" in PLIST.keys() and PLIST[\"Content\"] == \"Apple_APFS\":\n apfs_string = \" (APFS Physical Store)\"\n\n elif \"APFSContainerReference\" in PLIST.keys() and PLIST[\"APFSContainerReference\"] == disk:\n apfs_string = \" (APFS Container)\"\n\n elif \"FilesystemType\" in PLIST.keys() and PLIST[\"FilesystemType\"] == \"apfs\":\n apfs_string = \" (APFS Volume)\"\n\n #Assemble info into a string.\n if bus_protocol != \"Unknown\":\n return internal_or_external+disk_type+\"(Connected through \"+bus_protocol+\")\"+apfs_string\n\n elif disk_type != \"Unknown \":\n return internal_or_external+disk_type+apfs_string\n\n elif internal_or_external != \"Unknown \":\n return internal_or_external+\"Unknown Disk\"+apfs_string\n\n else:\n return \"N/A\"","def Init(self):\n # dac address 1136 to 1139 should all 255\n #只需要最低字节\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC1,addr,0))[0] for addr in range(1136,1140))\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC2,addr,0))[0] for addr in range(1136,1140))\n\n for data in rd_data:\n print(data)\n # print ('Print something I can understand: {}'.format(repr(chunks)))\n # print ram_data\n return rd_data","def get_lvs() -> List[Dict[str, str]]:\n p = subprocess.run(\n [\"lvs\", \"--reportformat\", \"json\"], check=True, capture_output=True\n )\n output = json.loads(p.stdout)\n result = []\n for lv in output[\"report\"][0][\"lv\"]:\n lvname = lv[\"lv_name\"]\n vgname = lv[\"vg_name\"]\n devname = f\"{vgname}-{lvname}\"\n path = f\"/dev/mapper/{devname}\"\n result.append({\"lv\": lvname, \"vg\": vgname, \"devname\": devname, \"devpath\": path})\n return result","def disk_partitions(all=False):\n phydevs = []\n f = open(\"/proc/filesystems\", \"r\")\n for line in f:\n if not line.startswith(\"nodev\"):\n phydevs.append(line.strip())\n\n retlist = []\n f = open('/etc/mtab', \"r\")\n for line in f:\n if not all and line.startswith('none'):\n continue\n fields = line.split()\n device = fields[0]\n mountpoint = fields[1]\n fstype = fields[2]\n if not all and fstype not in phydevs:\n continue\n if device == 'none':\n device = ''\n ntuple = disk_ntuple(device, mountpoint, fstype)\n retlist.append(ntuple)\n return retlist","def describe(self):\n if not self.name:\n raise ValueError(\"Sorry! id_type must be 'name'\")\n r = requests.get(f\"https://api.fda.gov/drug/ndc.json?search=brand_name:{self.drug_id}\")\n response = r.json()\n data = response['results'][0]\n self.brand_name = data['brand_name']\n self.generic_name = data['generic_name']\n self.active_ingredients = [i['name'] for i in data['active_ingredients']]\n self.pharm_class = get_pharm_class(self.drug_id, as_df=False)\n self.route = data['route']\n self.ndc = data['product_ndc']\n self.product_type = data['product_type']\n\n print(f\"Generic name: {self.generic_name}\")\n print(f\"Brand name: {self.brand_name}\")\n print(f\"Active ingredients: {self.active_ingredients}\")\n print(f\"Routes of administration: {self.route}\")\n print(f\"Pharmacologic Classes: {self.pharm_class}\")\n print(f\"NDC: {self.ndc}\")\n print(f\"Product type: {self.product_type}\")","def part_device(part_number):\n return \"/dev/mmcblk0p\" + part_number","def dumpData(self,out):\n #--Header\n out.packSub0('NAME',self.id)\n if getattr(self,'isDeleted',False):\n out.packSub('DELE','i',0)\n return\n if self.name == 'LEVC':\n flags = 1*self.calcFromAllLevels\n etype = 'CNAM'\n else:\n flags = 1*self.calcForEachItem + 2*self.calcFromAllLevels\n etype = 'INAM'\n out.packSub('DATA','i',flags)\n out.packSub('NNAM','B',self.chanceNone)\n out.packSub('INDX','i',len(self.entries))\n #--Entries\n for pcLevel, objectId in self.entries:\n out.packSub0(etype,objectId)\n out.packSub('INTV','h',pcLevel)","def print_metadata_stats(sector=6, filesystem=None):\n\n assert isinstance(filesystem, str)\n\n if filesystem in ['phtess2', 'php1']:\n fs = f\"/nfs/{filesystem}\"\n projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'\n lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'\n catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'\n elif filesystem in ['wh1', 'wh2']:\n projdir = \"/ar1/PROJ/luke/proj/cdips\"\n lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'\n catdir = '/ar1/local/cdips/catalogs/'\n\n statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')\n if not os.path.exists(statsdir): os.mkdir(statsdir)\n statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')\n\n stats = ap.read_stats_file(statsfile, fovcathasgaiaids=True)\n N_lcs = len(stats)\n\n print('CDIPS LIGHTCURVES STATS FOR SECTOR {}'.format(sector))\n print(42*'-')\n print('total N_lcs: {}'.format(N_lcs))\n\n for apn in [1,2,3]:\n N_nan = len(stats[stats['ndet_tf{}'.format(apn)]==0])\n print('for ap {}, {} ({:.1f}%) are all nan, leaving {} ok lcs'.\n format(apn, N_nan, N_nan/N_lcs*100, N_lcs-N_nan))\n\n print('\\nsanity check: {} TF1 LCs have stdev > 0'.\n format(len(stats[stats['stdev_tf1'] > 0])))\n\n print('Finished print_metadata_stats!')","def get_diskstats(self):\n dstats = self._run(options=['-kd'])\n extdstats = self._run(options=['-kdx'])\n dsd = self._get_childs_data(dstats)\n edd = self._get_childs_data(extdstats)\n ds = self.parse_diskstats(dsd)\n eds = self.parse_diskstats(edd)\n\n for dk, dv in ds.items():\n if dk in eds:\n ds[dk].update(eds[dk])\n\n return ds","def _GetDiskInfo(self, disk, item):\n dev_path = os.path.join(disk, 'device')\n\n # Search upwards and through symlinks looking for the item.\n while os.path.isdir(dev_path) and dev_path != '/sys':\n fname = os.path.join(dev_path, item)\n if os.path.exists(fname):\n with open(fname, 'r') as fd:\n return fd.readline().rstrip()\n\n # Move up a level and follow any symlink.\n new_path = os.path.join(dev_path, '..')\n if os.path.islink(new_path):\n new_path = os.path.abspath(os.readlink(os.path.dirname(dev_path)))\n dev_path = new_path\n return '[Unknown]'","def OBD2_cmd(self, cmd):\n obd2_record = []\n\n self.SEND_cmd(cmd)\n record = self.RTRV_record()\n\n # check that the ELM headers match the original cmd\n if record[0][0] != cmd:\n print \"PANIC! - cmd is different\"\n print \"cmd:\", cmd, \"record[0][0]:\", record[0][0]\n\n # Format result into a standard OBD2 record\n obd2_record = self.triage_record( record )\n\n if obd2_record == []:\n obd2_record = { 'command' : cmd ,\n 'responses' : { '7E8' : [] }, \n 'timestamp' : 0 }\n\n # set timestamp \n #scantime = time.time()\n obd2_record['timestamp'] = str(int(time.time()))\n\n return obd2_record","def vectors(self, adr = 0x10000):\n\n\t\tself.__vector(adr - 2, \"RST\")\n\t\tself.__vector(adr - 4, \"NMI\")\n\t\tself.__vector(adr - 6, \"SWI\")\n\t\tself.__vector(adr - 8, \"IRQ\")\n\t\tx = self.p.t.add(adr - 8, adr, \"tbl\")\n\t\tx.blockcmt += \"\\n-\\nMC6800 Vector Table\\n\\n\"","def parseDisk(disk):\n\n\tdict={}\n\n\td = disk.split(',')\n\tif len(d) != 3:\n rocks.commands.Abort('Invalid disk specification.'\n\t\t\t\t' Please see rocks add host vm help.')\n\n\tdict['device'] = d[1]\n\tdict['mode'] = d[2]\n\n\te = d[0].split(':')\n\tdict['vbd_type'] = ':'.join(e[0:-1])\n\n\tif dict['vbd_type'] == 'phy':\n\t\tdict['prefix'] = ''\n\t\tdict['name'] = e[-1]\t# allows for '/' in name for LVM\n\telse:\n\t\tdict['prefix'] = os.path.dirname(e[-1])\n\t\tdict['name'] = os.path.basename(e[-1])\n\n\treturn dict","def get_disks():\n disks = []\n\n try:\n # Run script\n result = run_diskpart(['list disk'])\n except subprocess.CalledProcessError:\n pass\n else:\n # Append disk numbers\n output = result.stdout.decode().strip()\n for tmp in re.findall(r'Disk (\\d+)\\s+\\w+\\s+(\\d+\\s+\\w+)', output):\n num = tmp[0]\n size = human_readable_size(tmp[1])\n disks.append({'Number': num, 'Size': size})\n\n return disks","def file_server_show_table_format(result):\n row = OrderedDict()\n row['Name'] = result['name']\n row['Resource Group'] = result['resourceGroup']\n row['Size'] = result['vmSize']\n disks = result['dataDisks']\n if disks:\n row['Disks'] = '{0} x {1} Gb'.format(disks['diskCount'], disks['diskSizeInGb'])\n mount_settings = result['mountSettings']\n if mount_settings:\n row['Public IP'] = mount_settings['fileServerPublicIp']\n row['Internal IP'] = mount_settings['fileServerInternalIp']\n row['Mount Point'] = mount_settings['mountPoint']\n return row","def parse_diskstats(self, input):\n # made edits to original code here to output string values instead of bytes\n dstats = {}\n dsi = input.rfind('Device:'.encode())\n if dsi == -1:\n raise ParseError('Unknown input format: %r' % input)\n\n ds = input[dsi:].splitlines()\n hdr = ds.pop(0).split()[1:]\n\n for d in ds:\n if d:\n d = d.split()\n dev = d.pop(0)\n if (dev.decode('utf-8') in self.disks) or not self.disks:\n dstats[dev.decode('utf-8')] = dict([(k.decode('utf-8'), float(v)) for k, v in zip(hdr, d)])\n return dstats","def get_ceph_drv_info():\n disks_info = []\n stat = psutil.disk_io_counters(perdisk=True)\n for drv in get_ceph_disk():\n info = CEPHDiskInfo(drv)\n disk = basename(drv)\n if disk in stat:\n info.rd_cnt = stat[disk].read_count\n info.wr_cnt = stat[disk].write_count\n info.rd_bytes = stat[disk].read_bytes\n info.wr_bytes = stat[disk].write_bytes\n info.rd_time = stat[disk].read_time\n info.wr_time = stat[disk].write_time\n\n disks_info.append(info)\n\n return disks_info","def get_hdd():\n return {\n 'HDD': string_chopped_to_float(psutil.disk_usage('/'), 'percent=', ')'),\n }","def read_uef_details(chunks):\n\n\tpos, chunk = find_next_chunk(chunks, 0, [0x0])\n\n\tif pos == None:\n\n\t\toriginator = 'Unknown'\n\n\telif chunk[1] == '':\n\n\t\toriginator = 'Unknown'\n\telse:\n\t\toriginator = chunk[1]\n\n\tpos, chunk = find_next_chunk(chunks, 0, [0x5])\n\n\tif pos == None:\n\n\t\tmachine, keyboard = 'Unknown', 'Unknown'\n\n\telse:\n\n\t\tmachines = ('BBC Model A', 'Electron', 'BBC Model B', 'BBC Master')\n\t\tkeyboards = ('Any layout', 'Physical layout', 'Remapped')\n\n\t\tmachine = ord(chunk[1][0]) & 0x0f\n\t\tkeyboard = (ord(chunk[1][0]) & 0xf0) >> 4\n\n\t\tif machine < len(machines):\n\t\t\tmachine = machines[machine]\n\t\telse:\n\t\t\tmachine = 'Unknown'\n\n\t\tif keyboard < len(keyboards):\n\t\t\tkeyboard = keyboards[keyboard]\n\t\telse:\n\t\t\tkeyboard = 'Unknown'\n\n\tpos, chunk = find_next_chunk(chunks, 0, [0xff00])\n\n\tif pos == None:\n\n\t\temulator = 'Unknown'\n\n\telif chunk[1] == '':\n\n\t\temulator = 'Unknown'\n\telse:\n\t\temulator = chunk[1]\n\n\n\t# Remove trailing null bytes\n\twhile originator[-1] == '\\000':\n\n\t\toriginator = originator[:-1]\n\n\twhile emulator[-1] == '\\000':\n\n\t\temulator = emulator[:-1]\n\n\tfeatures = ''\n\tif find_next_chunk(chunks, 0, [0x1])[0] != None:\n\t\tfeatures = features + '\\n' + 'Instructions'\n\tif find_next_chunk(chunks, 0, [0x2])[0] != None:\n\t\tfeatures = features + '\\n' + 'Credits'\n\tif find_next_chunk(chunks, 0, [0x3])[0] != None:\n\t\tfeatures = features + '\\n' + 'Inlay'\n\n\treturn originator, machine, keyboard, emulator, features","def _output_vadapter_list_verbose(outf, name, vadapter_list):\n\n if vadapter_list:\n FMT = \"%s\\n%-10s\\n%-20s\\n%-5s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n\"\n# HEADER = (\"id\",\"name\",\"io_module_id\",\"vfabric_id\",\"protocol\",\"init_type\",\"desc\",\"status\",\"mac\"\n# ,\"prmiscuous_mode\",\"silent_listener\",\"vlan\",\"wwnn\",\"wwpn\",\"fc_id\",\"spma\",\"fmpa\") \n\t\n for (n , vadapter) in vadapter_list:\n name = vadapter['NAME']\n id = vadapter['ID']\n protocol = vadapter['PROTOCOL']\n vfabric = vadapter['VFABRIC']\n io_module = vadapter['IO_MODULE_ID']\n desc = vadapter['DESC']\n status = vadapter['STATUS']\n init_type = vadapter['INIT_TYPE']\n running_mode = vadapter['RUNNING_MODE']\n if protocol == 1:\n mac = vadapter['MAC']\n promiscuous = vadapter['PROMISCUOUS']\n silent = vadapter['SILENT']\n vlan = vadapter['VLAN']\n elif protocol == 3:\n wwnn = vadapter['WWNN']\n wwpn = vadapter['WWPN']\n spma = vadapter['SPMA']\n fpma = vadapter['FPMA']\n fc_id = vadapter['FC_ID']\n\n\n if _LIMITED_SHOW:\n outf.write('General Attr:\\nId: %s\\n\\tName: %s\\n\\tIO_Module: %s\\n\\tVfabric: %s\\n\\tProtocol: %s\\n\\tInitization_Type: %s\\n\\tDescription: %s\\n\\tStatus: %-20s\\n\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status)) \n \n\t\telif _DETAIL_SHOW: \n if protocol == 3:\n outf.write('General Attr:\\nId: %s\\nName: %s\\nIO_Module: %s\\nVfabric: %s\\nProtocol: %s\\nInitization_Type: %s\\nDescription: %s\\nStatus: %-20s\\nRUNNING_MODE :%s\\nFC_Attr:\\n\\tWWNN: %s\\n\\tWWNP: %s\\n\\tFC_ID: %s\\n\\tSPMA: %s\\n\\tFMPA: %s\\n\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status, running_mode, wwnn, wwpn,fc_id,spma,fpma)) \n \n elif protocol == 1:\n\t\t\t outf.write('General Attr:\\nId: %s\\nName: %s\\nIO_Module: %s\\nVfabric: %s\\nProtocol: %s\\nInitization_Type: %s\\nDescription: %s\\nStatus: %-20s\\nRUNNING_MODE :%s\\nEN_Attr:\\n\\tMAC: %s\\n\\tVLAN: %s\\n\\tPromiscuous: %s\\n\\tSilent Listener: %s\\n\\n' % (id , name, io_module,vfabric, protocol, init_type, desc, status, running_mode, mac,vlan, promiscuous, silent))","def get_os_virtual_hard_disk(self):\n if self.is_vm_image():\n return None\n i = self.virtual_environment[self.T_I]\n sa = self.virtual_environment[self.T_SA]\n c = self.virtual_environment[self.T_C]\n now = datetime.datetime.now()\n blob = self.BLOB_BASE % (i[self.T_I_N],\n str(now.year),\n str(now.month),\n str(now.day),\n str(now.hour),\n str(now.minute),\n str(now.second),\n str(current_thread().ident))\n media_link = self.MEDIA_BASE % (sa[self.T_SA_SN],\n sa[self.T_SA_UB],\n c,\n blob)\n os_virtual_hard_disk = OSVirtualHardDisk(i[self.T_I_N], media_link)\n return os_virtual_hard_disk","def show_asm_volumes(self):\n sql = \"select NAME from v$asm_diskgroup_stat ORDER BY 1\"\n self.cur.execute(sql)\n res = self.cur.fetchall()\n key = ['{#ASMVOLUME}']\n lst = []\n for i in res:\n d = dict(zip(key, i))\n lst.append(d)\n print(json.dumps({'data': lst}))","def prepare_dev(\n data,\n journal,\n fstype,\n mkfs_args,\n mount_options,\n cluster_uuid,\n osd_uuid,\n journal_uuid,\n journal_dmcrypt,\n osd_dm_keypath,\n ):\n\n ptype_tobe = TOBE_UUID\n ptype_osd = OSD_UUID\n if osd_dm_keypath:\n ptype_tobe = DMCRYPT_TOBE_UUID\n ptype_osd = DMCRYPT_OSD_UUID\n\n rawdev = None\n if is_partition(data):\n LOG.debug('OSD data device %s is a partition', data)\n rawdev = data\n else:\n LOG.debug('Creating osd partition on %s', data)\n try:\n command_check_call(\n [\n 'sgdisk',\n '--largest-new=1',\n '--change-name=1:ceph data',\n '--partition-guid=1:{osd_uuid}'.format(\n osd_uuid=osd_uuid,\n ),\n '--typecode=1:%s' % ptype_tobe,\n '--',\n data,\n ],\n )\n update_partition('-a', data, 'created')\n command(\n [\n # wait for udev event queue to clear\n 'udevadm',\n 'settle',\n ],\n )\n except subprocess.CalledProcessError as e:\n raise Error(e)\n\n rawdev = get_partition_dev(data, 1)\n\n dev = None\n if osd_dm_keypath:\n dev = dmcrypt_map(rawdev, osd_dm_keypath, osd_uuid)\n else:\n dev = rawdev\n\n try:\n args = [\n 'mkfs',\n '-t',\n fstype,\n ]\n if mkfs_args is not None:\n args.extend(mkfs_args.split())\n if fstype == 'xfs':\n args.extend(['-f']) # always force\n else:\n args.extend(MKFS_ARGS.get(fstype, []))\n args.extend([\n '--',\n dev,\n ])\n try:\n LOG.debug('Creating %s fs on %s', fstype, dev)\n command_check_call(args)\n except subprocess.CalledProcessError as e:\n raise Error(e)\n\n #remove whitespaces from mount_options\n if mount_options is not None:\n mount_options = \"\".join(mount_options.split())\n\n path = mount(dev=dev, fstype=fstype, options=mount_options)\n\n try:\n prepare_dir(\n path=path,\n journal=journal,\n cluster_uuid=cluster_uuid,\n osd_uuid=osd_uuid,\n journal_uuid=journal_uuid,\n journal_dmcrypt=journal_dmcrypt,\n )\n finally:\n unmount(path)\n finally:\n if rawdev != dev:\n dmcrypt_unmap(osd_uuid)\n\n if not is_partition(data):\n try:\n command_check_call(\n [\n 'sgdisk',\n '--typecode=1:%s' % ptype_osd,\n '--',\n data,\n ],\n )\n except subprocess.CalledProcessError as e:\n raise Error(e)","def _device_tree(self, match, offset, end):\n log.debug('Inspecting potential DTB @ 0x{:08x}'.format(offset))\n\n def field(name):\n return int.from_bytes(match.group(name), 'big')\n\n # Skip past magic\n start = offset\n offset += 4\n\n totalsize = field('totalsize')\n if totalsize > (end - offset):\n msg = 'Invalid FDT @ 0x{:08x} - totalsize too large (0x{:08x})'\n log.debug(msg.format(start, totalsize))\n return None\n offset += 4\n\n off_dt_struct = field('off_dt_struct')\n if off_dt_struct > (end - offset):\n msg = 'Invalid FDT @ 0x{:08x} - off_dt_struct too large (0x{:08x})'\n log.debug(msg.format(start, off_dt_struct))\n return None\n offset += 4\n\n off_dt_strings = field('off_dt_strings')\n if off_dt_strings > (end - offset):\n msg = 'Invalid FDT @ 0x{:08x} - off_dt_strings too large (0x{:08x})'\n log.debug(msg.format(start, off_dt_strings))\n return None\n offset += 4\n\n off_mem_rsvmap = field('off_mem_rsvmap')\n if off_mem_rsvmap > (end - offset):\n msg = 'Invalid FDT @ 0x{:08x} - off_mem_rsvmap too large (0x{:08x})'\n log.debug(msg.format(start, off_mem_rsvmap))\n return None\n\n # Skip version, last_comp_version, boot_cpuid_phy\n offset += 16\n\n size_dt_strings = field('size_dt_strings')\n if (end - off_dt_strings) < size_dt_strings:\n msg = 'Invalid FDT @ 0x{:08x} - size_dt_strings too large (0x{:08x})'\n log.debug(msg.format(start, size_dt_strings))\n return None\n\n size_dt_struct = field('size_dt_struct')\n if (end - off_dt_struct) < size_dt_struct:\n msg = 'Invalid FDT @ 0x{:08x} - size_dt_struct too large (0x{:08x})'\n log.debug(msg.format(start, size_dt_struct))\n return None\n\n log.debug('Returning DTB @ 0x{:08x}, size={:d} bytes'.format(start, totalsize))\n return self._data[start:start + totalsize]","def _get_bootdisk_iter(self, instance):\n lpar_wrap = vm.get_instance_wrapper(self.adapter, instance)\n match_func = self._disk_match_func(DiskType.BOOT, instance)\n for vios_uuid in self.vios_uuids:\n vios_wrap = pvm_vios.VIOS.get(\n self.adapter, uuid=vios_uuid, xag=[pvm_const.XAG.VIO_SMAP])\n for scsi_map in tsk_map.find_maps(\n vios_wrap.scsi_mappings, client_lpar_id=lpar_wrap.id,\n match_func=match_func):\n yield scsi_map.backing_storage, vios_wrap"],"string":"[\n \"def print_vscsi_attributes(self,objects):\\n print(\\\"\\\\n\\\")\\n print((\\\"LocalPartitionID\\\".ljust(35),\\\":\\\",objects.LocalPartitionID.value()))\\n print((\\\"VirtualSlotNumber\\\".ljust(35),\\\":\\\",objects.VirtualSlotNumber.value()))\\n print((\\\"RequiredAdapter\\\".ljust(35),\\\":\\\",objects.RequiredAdapter.value()))\\n print((\\\"RemoteLogicalPartitionID\\\".ljust(35),\\\":\\\",objects.RemoteLogicalPartitionID.value()))\\n print((\\\"RemoteSlotNumber\\\".ljust(35),\\\":\\\",objects.RemoteSlotNumber.value()))\",\n \"def get_device_file_dict():\\n cmd = 'lshw -class disk'\\n desc = \\\"description\\\"\\n log_name = \\\"logical name\\\"\\n serial = \\\"serial\\\"\\n\\n dev = []\\n dev_list = []\\n\\n ret, output, err = run_gluster_command(cmd)\\n output = output.decode('ASCII')\\n dev_info = output.split('\\\\n')\\n for line in dev_info:\\n if re.search(desc, line):\\n if dev:\\n dev_list.append(dev)\\n\\n dev = []\\n if re.search(log_name, line) or re.search(serial, line):\\n temp = line.split(':')\\n temp[1] = temp[1].strip(' ')\\n dev.append(temp[1])\\n dev_list.append(dev)\\n for line in dev_list:\\n print(line)\",\n \"def get_devices_lsscsi(self):\\n\\n try:\\n message = \\\"Find SCSI Devices\\\"\\n if self._include_enclosures:\\n command = \\\"lsscsi --generic --transport | egrep 'disk|0x14|enclo'\\\"\\n else:\\n command = \\\"lsscsi --generic --transport | fgrep 'disk|0x14'\\\"\\n pdata = self._run_command(command=command, message=message, logger=self._logger, shell=True)\\n #\\n # Format:\\n # $ lsscsi --generic --transport\\n # [0] [1] [2] [3] [4]\\n # [0:0:0:0] disk sas:0x5000cca25103b471 /dev/sda /dev/sg0 \\n # [0:0:1:0] disk sas:0x5000cca251029301 /dev/sdb /dev/sg1 \\n # ...\\n # [0:0:14:0] enclosu sas:0x5001636001caa0bd - /dev/sg14\\n # [7:0:0:0] cd/dvd usb: 1-1.3:1.2 /dev/sr0 /dev/sg15\\n #\\n # Special Case:\\n # Handle lines without a transport (spaces only). (screen scrapping danger)\\n # [0:0:10:0] enclosu sas:0x50030480091d71fd - /dev/sg10\\n # [1:0:0:0] disk <spaces> /dev/sdk /dev/sg11 <- INTEL disk!\\n #\\n # Another SNAFU! (and why I hate screen scrapping!!!)\\n # [15:0:53597:0]disk sas:0x5000cca23b359649 /dev/sdg /dev/sg6 \\n # [15:0:53598:0]disk sas:0x5000cca23b0c0a99 /dev/sdh /dev/sg7 \\n # [15:0:53599:0]disk sas:0x5000cca23b0b7531 /dev/sdi /dev/sg8 \\n # ...\\n # [15:0:53686:0]enclosu sas:0x5000ccab040001bc - /dev/sg165\\n # [15:0:53766:0]enclosu sas:0x5000ccab040001fc - /dev/sg144\\n #\\n # Evidently, the author of lsscsi did not think of consistent output! ;(\\n #\\n for line in pdata['stdout'].splitlines():\\n dinfo = line.split()\\n device = dict()\\n if len(dinfo) < 5:\\n m = re.search('(?P<device>disk|\\\\(0x14\\\\)|enclosu)', dinfo[0])\\n if m:\\n device['Device Type'] = m.group('device')\\n sas_index = 1\\n dev_index = 2\\n sg_index = 3\\n else:\\n continue\\n else:\\n device['Device Type'] = dinfo[1]\\n sas_index = 2\\n dev_index = 3\\n sg_index = 4\\n\\n # lsscsi does not understand 'Host Managed' device type.\\n if '0x14' in device['Device Type']:\\n device['Device Type'] = 'disk'\\n\\n # Parse remaining information.\\n if 'sas:' in dinfo[sas_index]:\\n device['SAS Address'] = dinfo[sas_index][4:]\\n self._sas_addresses += 1\\n else:\\n device['SAS Address'] = \\\"\\\"\\n\\n # Note: Enclosure has no driver, so reports '-' for name.\\n if '/dev/' in dinfo[dev_index]:\\n if self._drives and not dinfo[dev_index] in self._drives:\\n continue\\n if self._exclude and dinfo[dev_index] in self._exclude:\\n continue\\n device['Linux Device Name'] = dinfo[dev_index]\\n else:\\n device['Linux Device Name'] = \\\"\\\"\\n if '/dev/sg' in dinfo[sg_index]:\\n device['SCSI Device Name'] = dinfo[sg_index]\\n else:\\n device['SCSI Device Name'] = \\\"\\\"\\n\\n self._devices.append(device)\\n\\n except RuntimeError as exc:\\n self._logger.error(\\\"Failed to acquire SCSI devices: {0}\\\".format(exc))\\n raise exc\",\n \"def get_devices_spt(self):\\n\\n #import pdb; pdb.set_trace()\\n if self._drives or self.firmware_version or self.product_name or self.vendor_name or \\\\\\n self.serial_number or self.target_port:\\n user_options = True\\n else:\\n user_options = False\\n try:\\n # Note: Extra logic to optimize spt device directory scanning.\\n if not user_options:\\n if self._include_enclosures:\\n message = \\\"Find SCSI Devices\\\"\\n command = \\\"{tool} show devices dtype=direct,hostmanaged,enclosure\\\".format(tool=self.tool)\\n else:\\n message = \\\"Find SCSI Disk Drives\\\"\\n command = \\\"{tool} show devices dtype=direct,hostmanaged\\\".format(tool=self.tool)\\n # Use common execute below.\\n else:\\n # Request enclosures separately.\\n if self._include_enclosures:\\n message = \\\"Find SCSI Enclosures\\\"\\n command = \\\"{tool} show devices dtype=enclosure ofmt=json\\\".format(tool=self.tool)\\n pdata = self._run_command(command=command, message=message,\\n logger=self._logger, shell=False, expected_failure=True)\\n if pdata['exit_code'] == self.EXIT_STATUS_SUCCESS and pdata['stdout']:\\n devices = json.loads(pdata['stdout'])\\n self.parse_devices_spt(devices)\\n\\n message = \\\"Find SCSI Disk Drives\\\"\\n # Selective drives or all direct access (disk drives).\\n if self._drives:\\n command = \\\"{tool} show edt dtype=direct,hostmanaged devices={drives}\\\"\\\\\\n .format(tool=self.tool, drives=\\\",\\\".join(self._drives))\\n else:\\n command = \\\"{tool} show devices dtype=direct,hostmanaged\\\".format(tool=self.tool)\\n # Apply optional parameters.\\n if self.product_name:\\n command += \\\" pid={product}\\\".format(product=self.product_name)\\n if self.vendor_name:\\n command += \\\" vid={vendor}\\\".format(vendor=self.vendor_name)\\n if self.serial_number:\\n command += \\\" serial={serial}\\\".format(serial=self.serial_number)\\n if self.target_port:\\n command += \\\" tport={target}\\\".format(target=self.target_port)\\n if self.firmware_version:\\n command += \\\" fw_version={firmware}\\\".format(firmware=self.firmware_version)\\n\\n # Add common spt options, we want JSON output!\\n if self._exclude:\\n command += \\\" exclude={drives}\\\".format(drives=\\\",\\\".join(self._exclude))\\n command += \\\" ofmt=json\\\"\\n # Finally, execute spt and parse its' JSON output (if any).\\n pdata = self._run_command(command=command, message=message,\\n logger=self._logger, shell=False, expected_failure=True)\\n # spt emits warning status (1) and no JSON output if no devices found.\\n if pdata['exit_code'] == self.EXIT_STATUS_SUCCESS and pdata['stdout']:\\n devices = json.loads(pdata['stdout'])\\n self.parse_devices_spt(devices)\\n\\n except RuntimeError as exc:\\n self._logger.error(\\\"Failed to acquire SCSI devices: {0}\\\".format(exc))\\n raise exc\\n\\n except ValueError as exc:\\n self._logger.error(\\\"Failed to parse spts' JSON output: {0}\\\".format(exc))\\n raise exc\",\n \"def get_partition_details(disk, partition):\\n details = {}\\n script = [\\n 'select disk {}'.format(disk['Number']),\\n 'select partition {}'.format(partition['Number']),\\n 'detail partition']\\n\\n # Diskpart details\\n try:\\n # Run script\\n result = run_diskpart(script)\\n except subprocess.CalledProcessError:\\n pass\\n else:\\n # Get volume letter or RAW status\\n output = result.stdout.decode().strip()\\n tmp = re.search(r'Volume\\\\s+\\\\d+\\\\s+(\\\\w|RAW)\\\\s+', output)\\n if tmp:\\n if tmp.group(1).upper() == 'RAW':\\n details['FileSystem'] = RAW\\n else:\\n details['Letter'] = tmp.group(1)\\n # Remove empty lines from output\\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\\n # Split each line on ':' skipping those without ':'\\n tmp = [s.split(':') for s in tmp if ':' in s]\\n # Add key/value pairs to the details variable and return dict\\n details.update({key.strip(): value.strip() for (key, value) in tmp})\\n\\n # Get MBR type / GPT GUID for extra details on \\\"Unknown\\\" partitions\\n guid = PARTITION_UIDS.get(details.get('Type').upper(), {})\\n if guid:\\n details.update({\\n 'Description': guid.get('Description', '')[:29],\\n 'OS': guid.get('OS', 'Unknown')[:27]})\\n\\n if 'Letter' in details:\\n # Disk usage\\n try:\\n tmp = psutil.disk_usage('{}:\\\\\\\\'.format(details['Letter']))\\n except OSError as err:\\n details['FileSystem'] = 'Unknown'\\n details['Error'] = err.strerror\\n else:\\n details['Used Space'] = human_readable_size(tmp.used)\\n\\n # fsutil details\\n cmd = [\\n 'fsutil',\\n 'fsinfo',\\n 'volumeinfo',\\n '{}:'.format(details['Letter'])\\n ]\\n try:\\n result = run_program(cmd)\\n except subprocess.CalledProcessError:\\n pass\\n else:\\n output = result.stdout.decode().strip()\\n # Remove empty lines from output\\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\\n # Add \\\"Feature\\\" lines\\n details['File System Features'] = [s.strip() for s in tmp\\n if ':' not in s]\\n # Split each line on ':' skipping those without ':'\\n tmp = [s.split(':') for s in tmp if ':' in s]\\n # Add key/value pairs to the details variable and return dict\\n details.update({key.strip(): value.strip() for (key, value) in tmp})\\n\\n # Set Volume Name\\n details['Name'] = details.get('Volume Name', '')\\n\\n # Set FileSystem Type\\n if details.get('FileSystem', '') not in ['RAW', 'Unknown']:\\n details['FileSystem'] = details.get('File System Name', 'Unknown')\\n\\n return details\",\n \"def get_vdisk_map_by_uid(self, vdisk_uid):\\n\\n if not vdisk_uid:\\n msg = _(\\\"Invalid parameter: vdisk_uid is None \\\")\\n LOG.exception(msg)\\n raise SVCInvalidVdiskUID\\n\\n map_info = {}\\n\\n cmd = 'svcinfo lshostvdiskmap -delim :'\\n\\n output, err_output = self._svc_command(cmd)\\n\\n if err_output:\\n msg = (_(\\\"get_vdisk_map_scsi_id() failure: svc cmd: %(cmd)s\\\"\\n \\\" error: %(error)s\\\") % {'cmd': cmd, 'error': err_output})\\n LOG.exception(msg)\\n ex_args = {'cmd': cmd, 'e': err_output}\\n raise SVCCommandException(**ex_args)\\n\\n if len(output) < 2:\\n msg = _(\\\"_add_svc_hostmap() failed to create \\\"\\n \\\"vdisk host mapping on storage\\\")\\n LOG.warn(msg)\\n return map_info\\n\\n header = output.pop(0).split(':')\\n scsi_id_idx = header.index(SVC_KEY_MAP_SCSI_ID)\\n vdisk_uid_idx = header.index(SVC_KEY_VDISK_UID)\\n hostname_idx = header.index(SVC_KEY_HOST_NAME)\\n\\n for line in output:\\n info = line.split(':')\\n host = info[hostname_idx]\\n vdisk_UID = info[vdisk_uid_idx]\\n scsi_id = info[scsi_id_idx]\\n\\n # we may have one LUN mapped to multiple hosts during LPM\\n if vdisk_UID == vdisk_uid:\\n map_info[host] = scsi_id\\n\\n if len(map_info) == 0:\\n map_info = None\\n\\n LOG.debug(\\\"vdisk hostmap: %(map_info)s\\\"\\n % {'map_info': str(map_info)})\\n return map_info\",\n \"def show_device_information_only(self):\\n\\n # TODO: Optimize formatting later!\\n if self._sas_addresses:\\n if self._report_header:\\n print(\\\"\\\\n\\\")\\n print(\\\" Linux SCSI Firmware Drive Block Curr\\\")\\n print(\\\" Device Device Vendor Product Revision Capacity Length Temp Serial Number SAS Address\\\")\\n print(\\\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------- ------------------\\\")\\n\\n for device in self._devices:\\n if not device['Device Type'].startswith(\\\"disk\\\"):\\n continue\\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14} {sas:<18}'\\n .format(dsf=device['Linux Device Name'],\\n sdsf=device['SCSI Device Name'],\\n vid=device['Vendor Identification'],\\n pid=device['Product Identification'],\\n fw=device['Firmware Version'],\\n capacity=device['Drive Capacity'],\\n blocklen=device['Block Length'],\\n temp=device['Current Temperature'],\\n serial=device['Serial Number'],\\n sas=device['SAS Address']))\\n else:\\n if self._report_header:\\n print(\\\"\\\\n\\\")\\n print(\\\" Linux SCSI Firmware Drive Block Curr\\\")\\n print(\\\" Device Device Vendor Product Revision Capacity Length Temp Serial Number\\\")\\n print(\\\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------\\\")\\n\\n for device in self._devices:\\n if not device['Device Type'].startswith(\\\"disk\\\"):\\n continue\\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14}'\\n .format(dsf=device['Linux Device Name'],\\n sdsf=device['SCSI Device Name'],\\n vid=device['Vendor Identification'],\\n pid=device['Product Identification'],\\n fw=device['Firmware Version'],\\n capacity=device['Drive Capacity'],\\n blocklen=device['Block Length'],\\n temp=device['Current Temperature'],\\n serial=device['Serial Number']))\\n\\n if self._report_header:\\n print(\\\"\\\\n\\\")\",\n \"def mpt():\\n lbl_drives = ['device','mountpoint','fstype']\\n disks = [d[0:3] for d in psutil.disk_partitions()]\\n drives = [dict(zip(lbl_drives,ds)) for ds in disks]\\n return [d['mountpoint']for d in drives]\",\n \"def get_volume_info(self, uid):\\n LOG.debug(\\\"Entering\\\")\\n cmd = \\\"svcinfo lsvdisk -bytes -filtervalue vdisk_UID=%s -delim :\\\" % uid\\n output = self._svc_command(cmd)[0]\\n\\n if len(output) != 2:\\n raise SVCVolumeNotFound(\\n _(\\\"Couldn't find volume information for UID %s\\\") % uid)\\n\\n header = output[0].split(':')\\n values = output[1].split(':')\\n index = header.index(SVC_KEY_VDISK_ID)\\n diskId = values[index]\\n index = header.index(SVC_KEY_VDISK_NAME)\\n name = values[index]\\n index = header.index(SVC_KEY_VOLUME_GROUP)\\n volumeGroup = values[index]\\n index = header.index(SVC_KEY_VDISK_CAPACITY)\\n capacity = values[index]\\n\\n info = {SVC_KEY_VDISK_ID: diskId,\\n SVC_KEY_VDISK_NAME: name,\\n SVC_KEY_VOLUME_GROUP: volumeGroup,\\n SVC_KEY_VDISK_CAPACITY: capacity}\\n\\n LOG.debug(\\\"Exiting\\\")\\n return info\",\n \"def vdisk_in_flashcopy(self, diskname):\\n LOG.debug(\\\"Entering\\\")\\n cmd = ''.join([\\\"svcinfo lsfcmap -filtervalue \\\",\\n \\\"target_vdisk_name=%s -delim :\\\" % diskname])\\n output = self._svc_command(cmd)[0]\\n\\n if len(output) != 2:\\n return(100, None)\\n\\n header = output[0].split(':')\\n values = output[1].split(':')\\n index = header.index('progress')\\n progress = values[index]\\n index = header.index('id')\\n map_id = values[index]\\n\\n LOG.debug(\\\"Exiting (progress = %s, map_id = %s)\\\" % (progress, map_id))\\n return progress, map_id\",\n \"def get_block_device_list(vars = {}, log = sys.stderr):\\n\\n # make sure we can access to the files/directories in /proc\\n if not os.access(PROC_PARTITIONS_PATH, os.F_OK):\\n return None\\n\\n # table with valid scsi/sata/ide/raid block device names\\n valid_blk_names = {}\\n # add in valid sd and hd block device names\\n for blk_prefix in ('sd','hd'):\\n for blk_num in map (\\\\\\n lambda x: chr(x), range(ord('a'),ord('z')+1)):\\n devicename=\\\"%s%c\\\" % (blk_prefix, blk_num)\\n valid_blk_names[devicename]=None\\n\\n # add in valid scsi raid block device names\\n for M in range(0,1+1):\\n for N in range(0,7+1):\\n devicename = \\\"cciss/c%dd%d\\\" % (M,N)\\n valid_blk_names[devicename]=None\\n\\n for devicename in valid_blk_names.keys():\\n # devfs under 2.4 (old boot cds) used to list partitions\\n # in a format such as scsi/host0/bus0/target0/lun0/disc\\n # and /dev/sda, etc. were just symlinks\\n try:\\n devfsname= os.readlink( \\\"/dev/%s\\\" % devicename )\\n valid_blk_names[devfsname]=None\\n except OSError:\\n pass\\n\\n # only do this once every system boot\\n if not os.access(DEVICES_SCANNED_FLAG, os.R_OK):\\n\\n # this is ugly. under devfs, device\\n # entries in /dev/scsi/.. and /dev/ide/...\\n # don't show up until you attempt to read\\n # from the associated device at /dev (/dev/sda).\\n # so, lets run sfdisk -l (list partitions) against\\n # most possible block devices, that way they show\\n # up when it comes time to do the install.\\n devicenames = valid_blk_names.keys()\\n devicenames.sort()\\n for devicename in devicenames:\\n os.system( \\\"sfdisk -l /dev/%s > /dev/null 2>&1\\\" % devicename )\\n\\n # touch file\\n fb = open(DEVICES_SCANNED_FLAG,\\\"w\\\")\\n fb.close()\\n\\n devicelist= {}\\n\\n partitions_file= file(PROC_PARTITIONS_PATH,\\\"r\\\")\\n line_count= 0\\n for line in partitions_file:\\n line_count= line_count + 1\\n\\n # skip the first two lines always\\n if line_count < 2:\\n continue\\n\\n parts= string.split(line)\\n\\n if len(parts) < 4:\\n continue\\n\\n device= parts[3]\\n\\n # skip and ignore any partitions\\n if not valid_blk_names.has_key(device):\\n continue\\n\\n try:\\n major= int(parts[0])\\n minor= int(parts[1])\\n blocks= int(parts[2])\\n except ValueError, err:\\n continue\\n\\n gb_size= blocks/BLOCKS_PER_GB\\n\\n # check to see if the blk device is readonly\\n try:\\n # can we write to it?\\n dev_name= \\\"/dev/%s\\\" % device\\n fb = open(dev_name,\\\"w\\\")\\n fb.close()\\n readonly=False\\n except IOError, e:\\n # check if EROFS errno\\n if errno.errorcode.get(e.errno,None) == 'EROFS':\\n readonly=True\\n else:\\n # got some other errno, pretend device is readonly\\n readonly=True\\n\\n devicelist[dev_name]= {'major': major,'minor': minor,'blocks': blocks, 'size': gb_size, 'readonly': readonly}\\n return devicelist\",\n \"def get_device_map():\\n ret = []\\n vlist = subprocess.check_output(['ceph-volume', 'lvm', 'list',\\n '--format=json'])\\n for osd_id, data in json.loads(vlist.decode('utf8')).items():\\n osd_id = normalize_osd_id(osd_id)\\n for elem in data:\\n for device in elem['devices']:\\n ret.append({'id': osd_id, 'path': device})\\n return ret\",\n \"def get_lun_storage_info(lun_id):\\n host = ll_hosts.get_spm_host(config.HOSTS)\\n host_ip = ll_hosts.get_host_ip(host)\\n executor = rhevm_helpers.get_host_executor(\\n host_ip, config.VDC_ROOT_PASSWORD\\n )\\n # Execute 'pvscan' to display the latest volume info\\n storage_resources.pvscan(host)\\n logger.info(\\\"Executing command 'pvs | grep %s'\\\", lun_id)\\n status, output, err = executor.run_cmd(\\n shlex.split(PVS_SHOW_LUN_INFO % lun_id)\\n )\\n if status:\\n logger.info(\\n \\\"Status was False executing 'pvs | grep %s'. Err: %s\\\",\\n lun_id, err\\n )\\n return 0, 0\\n\\n # Format the output into the 6 expected display parameters (PV, VG,\\n # Format, LV Attributes, Physical size and Physical free size)\\n formatted_output = shlex.split(output)\\n logger.info(\\n \\\"The output received when running pvs on LUN id %s is: %s\\\"\\n % (lun_id, formatted_output)\\n )\\n # The 2nd last displayed data output is needed - Physical size\\n lun_size = formatted_output[-2]\\n lun_size = lun_size.replace(\\\"g\\\", \\\"\\\")\\n lun_free_space = formatted_output[-1]\\n lun_free_space = lun_free_space.replace(\\\"g\\\", \\\"\\\")\\n lun_size_bytes = float(lun_size) * config.GB\\n logger.info(\\\"The LUN size in bytes is '%s'\\\", str(lun_size_bytes))\\n lun_free_bytes = float(lun_free_space) * config.GB\\n logger.info(\\\"The LUN free space in bytes is '%s'\\\", str(lun_free_bytes))\\n\\n return int(lun_size_bytes), int(lun_free_bytes)\",\n \"def get_generic_scsi_dev(self, nIndex):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlVmCfg_GetGenericScsiDev', self.handle, nIndex))\",\n \"def get_disk_info(virt_type, instance, block_device_info=None,\\n image_meta=None, rescue=False):\\n\\n disk_bus = get_disk_bus_for_device_type(virt_type, image_meta, \\\"disk\\\")\\n cdrom_bus = get_disk_bus_for_device_type(virt_type, image_meta, \\\"cdrom\\\")\\n mapping = get_disk_mapping(virt_type, instance,\\n disk_bus, cdrom_bus,\\n block_device_info,\\n image_meta, rescue)\\n\\n return {'disk_bus': disk_bus,\\n 'cdrom_bus': cdrom_bus,\\n 'mapping': mapping}\",\n \"def get_generic_scsi_device(self, nIndex):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlSrvCfg_GetGenericScsiDevice', self.handle, nIndex))\",\n \"def usableparts(self):\\n # First get the partition type-id for all hard disk partitions\\n partid = {}\\n for pline in self.fdiskl():\\n partid[pline[0]] = pline[4]\\n ups = {}\\n for s in self.xlist(\\\"get-blkinfo\\\")[1]:\\n mo = re.match(r'(/dev/[^:]*):(?: LABEL=\\\"([^\\\"]*)\\\")?(?:'\\n ' UUID=\\\"([^\\\"]*)\\\")?(?: TYPE=\\\"([^\\\"]*)\\\")?', s)\\n if mo:\\n dev, label, uuid, fstype = mo.groups()\\n if fstype in (None, \\\"linux_raid_member\\\", \\\"LVM2_member\\\"):\\n continue\\n if dev.startswith(\\\"/dev/loop\\\"):\\n continue\\n rem = None\\n if dev.startswith(\\\"/dev/sd\\\"):\\n if partid.get(dev) == \\\"fd\\\":\\n # This test seems to be necessary because blkid\\n # sometimes returns an fs-type, rather than\\n # linux_raid_member\\\", for the the first device\\n # in a formatted raid array\\n continue\\n rem = self.xlist(\\\"removable\\\", dev)[1][0].strip() == \\\"1\\\"\\n ups[dev] = (fstype, label, uuid, rem)\\n return ups\",\n \"def show_device_information_enclosures(self):\\n\\n if self._report_header:\\n print(\\\"\\\\n\\\")\\n print(\\\" Linux SCSI Firmware Drive Block Curr Enc SCSI Enc Enc Slot\\\")\\n print(\\\" Device Device Vendor Product Revision Capacity Length Temp Serial Number SAS Address Device Slot Description\\\")\\n print(\\\"---------- ---------- -------- ---------------- -------- ------------ ------ ---- -------------- ------------------ ---------- ---- -----------------\\\")\\n\\n for device in self._devices:\\n if not device['Device Type'].startswith(\\\"disk\\\"):\\n continue\\n print('{dsf:<10} {sdsf:<10} {vid:<8} {pid:<16} {fw:<8} {capacity:>12} {blocklen:>4} {temp:<4} {serial:<14} {sas:<18} {edsf:<10} {slot:<3} {text:<16}'\\n .format(dsf=device['Linux Device Name'],\\n sdsf=device['SCSI Device Name'],\\n vid=device['Vendor Identification'],\\n pid=device['Product Identification'],\\n fw=device['Firmware Version'],\\n capacity=device['Drive Capacity'],\\n blocklen=device['Block Length'],\\n temp=device['Current Temperature'],\\n serial=device['Serial Number'],\\n sas=device['SAS Address'],\\n edsf=device['Enclosure Device'],\\n slot=device['Enclosure Slot'],\\n text=device['Slot Description']))\\n\\n if self._report_header:\\n print(\\\"\\\\n\\\")\",\n \"def connect_instance_disk_to_mgmt(self, instance):\\n for stg_elem, vios in self._get_bootdisk_iter(instance):\\n msg_args = {'disk_name': stg_elem.name, 'vios_name': vios.name}\\n\\n # Create a new mapping. NOTE: If there's an existing mapping on\\n # the other VIOS but not this one, we'll create a second mapping\\n # here. It would take an extreme sequence of events to get to that\\n # point, and the second mapping would be harmless anyway. The\\n # alternative would be always checking all VIOSes for existing\\n # mappings, which increases the response time of the common case by\\n # an entire GET of VIOS+VIO_SMAP.\\n LOG.debug(\\\"Mapping boot disk %(disk_name)s to the management \\\"\\n \\\"partition from Virtual I/O Server %(vios_name)s.\\\",\\n msg_args, instance=instance)\\n try:\\n tsk_map.add_vscsi_mapping(self.host_uuid, vios, self.mp_uuid,\\n stg_elem)\\n # If that worked, we're done. add_vscsi_mapping logged.\\n return stg_elem, vios\\n except Exception:\\n LOG.exception(\\\"Failed to map boot disk %(disk_name)s to the \\\"\\n \\\"management partition from Virtual I/O Server \\\"\\n \\\"%(vios_name)s.\\\", msg_args, instance=instance)\\n # Try the next hit, if available.\\n # We either didn't find the boot dev, or failed all attempts to map it.\\n raise npvmex.InstanceDiskMappingFailed(instance_name=instance.name)\",\n \"def __init__(self,guid='',name=''):\\n extent.Extent.__init__(self,guid,name,'Physical LUN/Disk')\\n self.exttype='physical'\\n self.alias=''\\n self.alwaysadd=True\\n self.direct=True\\n self.serial='' # SCSI Serial ID\\n self.model='' # SCSI Model\\n self.vendor='' # SCSI Vendor\\n self.revision='' # SCSI revision\\n self.raid=RaidLevel.none # underline raid '0' '1' '5' '6' '10'\\n self.sectorsize=512\\n self.paths=VSACollection(san.SanPath,self,'paths',desc='Paths To Disk',icon='link_g.png') # list of paths leading to this physical disk (local or remote), key = dev/hbtl\\n self.partitions=VSACollection(SanPartition,self,'partitions',icon='vm_g.png') # list of paths leading to this physical disk (local or remote), key = dev/hbtl\\n self.usedin=RefDict(extent.Extent,self,'usedin',desc='Owned by Storage Extents',icon='hard_disk.png',call=lambda self:[v for v in self.usedby if v.exttype<>'partition'])\\n self.assigned=[] # list of chunks assigned from this disk\\n self.primordial=1 # primary extent, based on physical HW (e.g. not a logical volume)\\n self.cachepvds=[] # list of provider names which have a matching cache logical volume (found in discovery, process extents)\\n self.cachedrdev=None # points to cache DRBD device\\n self._flush()\",\n \"def __get_info(self, disk):\\n # Checking the disk exists\\n\\tFNULL = open(os.devnull, 'w') # Defining /dev/null\\n\\tcheck = subprocess.call(['df', disk], stdout=FNULL,\\\\\\n stderr=subprocess.STDOUT)\\n # Disk not found\\n if check != 0:\\n sys.stdout.write(\\\"^fg(%s)!E! DiskNotFound^fg()\\\" % Colors.CL_BASE08)\\n # Disk found\\n else:\\n # Executing command, parsing output and removing empty elements\\n cmd = subprocess.Popen(['df', '-H', disk], stdout=subprocess.PIPE)\\n cmd_out, cmd_err = cmd.communicate()\\n cmd_outparsed = cmd_out.split(' ')\\n cmd_outparsed = filter(None, cmd_outparsed)\\n\\n # Getting information\\n disk_name = disk\\n disk_size = cmd_outparsed[7]\\n disk_used = cmd_outparsed[8]\\n disk_available = cmd_outparsed[9]\\n disk_percentage = cmd_outparsed[10].translate(None, \\\"%\\\")\\n disk_percentage = int(disk_percentage)\\n\\n # Disk Name: ~\\n sys.stdout.write(\\\"^fg(%s)[^fg()\\\" % Colors.CL_BASE02)\\n if disk_name == DISK_DATA:\\n sys.stdout.write(\\\"^fg(%s)~^fg()\\\" % Colors.CL_BASE0D)\\n sys.stdout.write(\\\"^fg(%s): ^fg()\\\" % Colors.CL_BASE03)\\n # Disk Name: /\\n elif disk_name == DISK_ROOT:\\n sys.stdout.write(\\\"^fg(%s)/^fg()\\\" % Colors.CL_BASE0D)\\n sys.stdout.write(\\\"^fg(%s): ^fg()\\\" % Colors.CL_BASE03)\\n \\n # Disk Percentage: Good\\n if 0 <= disk_percentage <= 60:\\n sys.stdout.write(\\\"^fg(%s)%s%%^fg()\\\" % (Colors.CL_BASE0B, disk_percentage))\\n sys.stdout.write(\\\"^fg(%s) %s^fg()\\\" % (Colors.CL_BASE0B, disk_used))\\n sys.stdout.write(\\\"^fg(%s)/^fg()\\\" % Colors.CL_BASE03)\\n # Disk Percentage: Fair\\n elif 61 <= disk_percentage <= 90:\\n sys.stdout.write(\\\"^fg(%s)%s%%^fg()\\\" % (Colors.CL_BASE09, disk_percentage))\\n sys.stdout.write(\\\"^fg(%s) %s^fg()\\\" % (Colors.CL_BASE09, disk_used))\\n sys.stdout.write(\\\"^fg(%s)/^fg()\\\" % Colors.CL_BASE03)\\n # Disk Percentage: Weak\\n elif 91 <= disk_percentage <= 100:\\n sys.stdout.write(\\\"^fg(%s)%s%%^fg()\\\" % (Colors.CL_BASE08, disk_percentage))\\n sys.stdout.write(\\\"^fg(%s) %s^fg()\\\" % (Colors.CL_BASE08, disk_used))\\n sys.stdout.write(\\\"^fg(%s)/^fg()\\\" % Colors.CL_BASE03)\\n sys.stdout.write(\\\"^fg(%s)%s^fg()\\\" % (Colors.CL_BASE0D, disk_size))\\n sys.stdout.write(\\\"^fg(%s)]^fg()\\\" % Colors.CL_BASE02)\",\n \"def show_mappings(project):\\n # with BMI(_username, _password, project) as bmi:\\n # ret = bmi.show_mounted()\\n # if ret[constants.STATUS_CODE_KEY] == 200:\\n # table = PrettyTable(field_names=['Target', 'Block Device'])\\n # mappings = ret[constants.RETURN_VALUE_KEY]\\n # for k, v in mappings.iteritems():\\n # table.add_row([k, v])\\n # click.echo(table.get_string())\\n # else:\\n # click.echo(ret[constants.MESSAGE_KEY])\\n click.echo(\\\"Need to Re-Implement\\\")\",\n \"def _get_dmi(cls, data):\\n\\t\\tdata['pdi'] = cls._get_pdi(data, 14)\\n\\t\\tdata['mdi'] = cls._get_mdi(data, 14)\\n\\t\\tdata['dx'] = cls._get_dx(data, 14)\\n\\t\\tdata['adx'] = data['dx_6_ema']\\n\\t\\tdata['adxr'] = data['adx_6_ema']\\n\\t\\treturn data\",\n \"def step2(self):\\n self.mr_vds = []\\n # Checking UGood availablity here, because after reboot cycle,\\n # init will be called.\\n for mr in self.mrs:\\n self.log.info(\\\"Check for UGood PD's availablity on controller:%d\\\"\\n % (mr.ctrl_id))\\n pds = mr.cli.list_all_drives(pd_type=\\\"SAS\\\",\\n state=\\\"UGood\\\", media_type=\\\"HDD\\\",\\n sector_size='512B')\\n if len(pds) < 10:\\n raise SALError(\\\"%d UGood HDD PD's are not available for RAID \\\"\\n \\\"Creation on Controller:%d\\\"\\n % (10 - len(pds), mr.ctrl_id))\\n else:\\n self.log.info(\\\"Number of UGood HDD PD's available is %d on \\\"\\n \\\"controller:%d\\\" % (len(pds), mr.ctrl_id))\\n for mr in self.mrs:\\n self.vds = []\\n self.log.info(\\\"Create R0, R1, R5 with diff properties on \\\"\\n \\\"Controller: %d\\\" % (mr.ctrl_id))\\n self.vds.append(mr.cli.add_vd(raid=0, vd_size=\\\"3000MB\\\",\\n WB=\\\"WB\\\", RA='ra', cache=\\\"direct\\\"))\\n self.vds.append(mr.cli.add_vd(raid=1, vd_size=\\\"3000MB\\\",\\n WB=\\\"WT\\\", RA='nora', cache=\\\"cached\\\"))\\n self.vds.append(mr.cli.add_vd(raid=5, vd_size=\\\"3000MB\\\",\\n WB=\\\"WB\\\", RA='ra', cache=\\\"direct\\\"))\\n for vd in self.vds:\\n while True:\\n time.sleep(5)\\n if (mr.cli.init_progress(vd) == -1):\\n self.log.info(\\\"FGI completed for VD:%s\\\"\\n % (vd))\\n break\\n for vd in self.vds:\\n if mr.cli.vd_get_info(vd)['state'] != 'OPTL':\\n raise SALError(\\\"RAID-%s VD%s is not optimal\\\"\\n % (mr.cli.vd_get_info(vd)['raid'], vd))\\n else:\\n self.log.info(\\\"RAID-%s VD%s is optimal\\\"\\n % (mr.cli.vd_get_info(vd)['raid'], vd))\\n\\n self.mr_vds.extend(self.vds)\\n self.log.info(\\\"Created R0, R1, R5 successfully on controller: %d\\\"\\n % (mr.ctrl_id))\",\n \"def get_all_disk():\\n\\t\\tdisks = []\\n\\t\\tdisks_lines = linux.exe_shell(\\\"lsblk -o NAME,VENDOR|grep -P '^sd.*[A-Z]'\\\")\\n\\t\\tfor line in disks_lines.splitlines():\\n\\t\\t\\tdisk_t = line.split()\\n\\t\\t\\tif len(disk_t) > 1 and \\\"LSI\\\" not in disk_t[1]:\\n\\t\\t\\t\\tdisks.append(disk_t[0])\\n\\t\\tds = []\\n\\t\\tfor i in disks:\\n\\t\\t\\td_t = DiskFromLsiSas3(\\\"\\\", i)\\n\\t\\t\\td_t.fill_attrs()\\n\\t\\t\\tds.append(d_t)\\n\\t\\treturn ds\",\n \"def get_partition_info(disk, host_disk):\\n\\n volume = \\\"/dev/\\\"+disk\\n DISKINFO[volume] = {}\\n DISKINFO[volume][\\\"Name\\\"] = volume\\n DISKINFO[volume][\\\"Type\\\"] = \\\"Partition\\\"\\n DISKINFO[volume][\\\"HostDevice\\\"] = host_disk\\n DISKINFO[volume][\\\"Partitions\\\"] = []\\n DISKINFO[host_disk][\\\"Partitions\\\"].append(volume)\\n DISKINFO[volume][\\\"Vendor\\\"] = get_vendor(disk)\\n DISKINFO[volume][\\\"Product\\\"] = \\\"Host Device: \\\"+DISKINFO[host_disk][\\\"Product\\\"]\\n DISKINFO[volume][\\\"RawCapacity\\\"], DISKINFO[volume][\\\"Capacity\\\"] = get_capacity()\\n DISKINFO[volume][\\\"Description\\\"] = get_description(disk)\\n DISKINFO[volume][\\\"Flags\\\"] = get_capabilities(disk)\\n DISKINFO[volume][\\\"FileSystem\\\"] = get_file_system(disk)\\n DISKINFO[volume][\\\"Partitioning\\\"] = \\\"N/A\\\"\\n DISKINFO[volume][\\\"UUID\\\"] = get_uuid(disk)\\n DISKINFO[volume][\\\"ID\\\"] = get_id(disk)\\n DISKINFO[volume][\\\"BootRecord\\\"], DISKINFO[volume][\\\"BootRecordStrings\\\"] = get_boot_record(disk)\\n\\n return volume\",\n \"def device_info(node):\\n\\n if \\\"cpu\\\" in node and \\\"total_mbufs\\\" in node[\\\"cpu\\\"]:\\n total_mbufs = node[\\\"cpu\\\"][\\\"total_mbufs\\\"]\\n if total_mbufs != 0:\\n print(\\\"Total Number of Buffers: {}\\\".format(total_mbufs))\\n\\n vpp = VppPCIUtil(node)\\n vpp.get_all_devices()\\n linkup_devs = vpp.get_link_up_devices()\\n if len(linkup_devs):\\n print(\\\"\\\\nDevices with link up (can not be used with VPP):\\\")\\n vpp.show_vpp_devices(linkup_devs, show_header=False)\\n # for dev in linkup_devs:\\n # print (\\\" \\\" + dev)\\n kernel_devs = vpp.get_kernel_devices()\\n if len(kernel_devs):\\n print(\\\"\\\\nDevices bound to kernel drivers:\\\")\\n vpp.show_vpp_devices(kernel_devs, show_header=False)\\n else:\\n print(\\\"\\\\nNo devices bound to kernel drivers\\\")\\n\\n dpdk_devs = vpp.get_dpdk_devices()\\n if len(dpdk_devs):\\n print(\\\"\\\\nDevices bound to DPDK drivers:\\\")\\n vpp.show_vpp_devices(dpdk_devs, show_interfaces=True, show_header=False)\\n else:\\n print(\\\"\\\\nNo devices bound to DPDK drivers\\\")\\n\\n other_devs = vpp.get_other_devices()\\n if len(other_devs):\\n print(\\\"\\\\nDevices not bound to Kernel or DPDK drivers:\\\")\\n vpp.show_vpp_devices(other_devs, show_interfaces=True, show_header=False)\\n else:\\n print(\\\"\\\\nNo devices not bound to Kernel or DPDK drivers\\\")\\n\\n vpputl = VPPUtil()\\n interfaces = vpputl.get_hardware(node)\\n if interfaces == {}:\\n return\\n\\n print(\\\"\\\\nDevices in use by VPP:\\\")\\n\\n if len(interfaces.items()) < 2:\\n print(\\\"None\\\")\\n return\\n\\n print(\\n \\\"{:30} {:4} {:4} {:7} {:4} {:7}\\\".format(\\n \\\"Name\\\", \\\"Numa\\\", \\\"RXQs\\\", \\\"RXDescs\\\", \\\"TXQs\\\", \\\"TXDescs\\\"\\n )\\n )\\n for intf in sorted(interfaces.items()):\\n name = intf[0]\\n value = intf[1]\\n if name == \\\"local0\\\":\\n continue\\n numa = rx_qs = rx_ds = tx_qs = tx_ds = \\\"\\\"\\n if \\\"numa\\\" in value:\\n numa = int(value[\\\"numa\\\"])\\n if \\\"rx queues\\\" in value:\\n rx_qs = int(value[\\\"rx queues\\\"])\\n if \\\"rx descs\\\" in value:\\n rx_ds = int(value[\\\"rx descs\\\"])\\n if \\\"tx queues\\\" in value:\\n tx_qs = int(value[\\\"tx queues\\\"])\\n if \\\"tx descs\\\" in value:\\n tx_ds = int(value[\\\"tx descs\\\"])\\n\\n print(\\n \\\"{:30} {:>4} {:>4} {:>7} {:>4} {:>7}\\\".format(\\n name, numa, rx_qs, rx_ds, tx_qs, tx_ds\\n )\\n )\",\n \"def get_disk_details(disk):\\n details = {}\\n script = [\\n 'select disk {}'.format(disk['Number']),\\n 'detail disk']\\n\\n # Run\\n try:\\n result = run_diskpart(script)\\n except subprocess.CalledProcessError:\\n pass\\n else:\\n output = result.stdout.decode().strip()\\n # Remove empty lines\\n tmp = [s.strip() for s in output.splitlines() if s.strip() != '']\\n # Set disk name\\n details['Name'] = tmp[4]\\n # Split each line on ':' skipping those without ':'\\n tmp = [s.split(':') for s in tmp if ':' in s]\\n # Add key/value pairs to the details variable and return dict\\n details.update({key.strip(): value.strip() for (key, value) in tmp})\\n\\n return details\",\n \"def step6(self):\\n if len(self.mrs) == 3:\\n count = 3\\n elif len(self.mrs) == 2:\\n count = 2\\n else:\\n count = 1\\n for mr in self.mrs[0:count]:\\n self.log.info(\\\"Display boot drive on controller:%d\\\"\\n % (mr.ctrl_id))\\n vd_id = mr.cli.bootdrive_vd_get()\\n if (int(vd_id) == -1): # -1 : No boot VD.\\n self.log.info(\\\"No boot VD found on controller: %d\\\"\\n % (mr.ctrl_id))\\n else:\\n self.log.info(\\\"VD ID of the boot VD: %d\\\"\\n % int((vd_id)))\",\n \"def show_device_information_long(self):\\n\\n for device in self._devices:\\n print(\\\"\\\")\\n if device['Device Type'].startswith(\\\"enclosu\\\"):\\n if device.get('Device Type'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Type\\\", device['Device Type']))\\n if device['Device Type Description']:\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Description\\\", device['Device Type Description']))\\n if device.get('SCSI Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SCSI Device Name\\\", device['SCSI Device Name']))\\n if device.get('Product Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Product Identification\\\", device['Product Identification']))\\n if device.get('Vendor Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Vendor Identification\\\", device['Vendor Identification']))\\n if device.get('Firmware Version'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Firmware Version\\\", device['Firmware Version']))\\n if device.get('Serial Number'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Serial Number\\\", device['Serial Number']))\\n if device.get('SAS Address'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SAS Address\\\", device['SAS Address']))\\n else:\\n if device.get('Device Type'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Type\\\", device['Device Type']))\\n if device['Device Type Description']:\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Description\\\", device['Device Type Description']))\\n if device.get('Linux Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Linux Device Name\\\", device['Linux Device Name']))\\n if device.get('SCSI Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SCSI Device Name\\\", device['SCSI Device Name']))\\n if device.get('Product Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Product Identification\\\", device['Product Identification']))\\n if device.get('Vendor Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Vendor Identification\\\", device['Vendor Identification']))\\n if device.get('Firmware Version'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Firmware Version\\\", device['Firmware Version']))\\n if device.get('Serial Number'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Serial Number\\\", device['Serial Number']))\\n if device.get('Drive Capacity'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Drive Capacity\\\", device['Drive Capacity']))\\n if device.get('Block Length'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Block Length\\\", device['Block Length']))\\n if device.get('Power On Hours'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Power On Hours\\\", device['Power On Hours']))\\n if device.get('Current Temperature'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Current Temperature\\\", device['Current Temperature']))\\n if device.get('SAS Address'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SAS Address\\\", device['SAS Address']))\\n if device.get('Enclosure Device'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Enclosure Device\\\", device['Enclosure Device']))\\n if device.get('Enclosure Slot'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Enclosure Slot\\\", device['Enclosure Slot']))\\n if device.get('Slot Description'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Slot Desciption\\\", device['Slot Description']))\\n\\n if len(self._devices):\\n print(\\\"\\\")\",\n \"def get_info():\\n\\n global DISKINFO\\n DISKINFO = {}\\n\\n #Run diskutil list to get disk names.\\n runcmd = subprocess.Popen(\\\"diskutil list -plist\\\", stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)\\n\\n #Get the output.\\n stdout = runcmd.communicate()[0]\\n\\n #Parse the plist (Property List).\\n global PLIST\\n\\n PLIST = plistlib.loads(stdout)\\n\\n #Find the disks.\\n for disk in PLIST[\\\"AllDisks\\\"]:\\n #Run diskutil info to get disk info.\\n runcmd = subprocess.Popen(\\\"diskutil info -plist \\\"+disk, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)\\n stdout = runcmd.communicate()[0]\\n\\n #Parse the plist (Property List).\\n PLIST = plistlib.loads(stdout)\\n\\n #Check if the disk is a partition.\\n disk_is_partition = is_partition(disk)\\n\\n if not disk_is_partition:\\n #These are devices.\\n get_device_info(disk)\\n\\n else:\\n #These are Partitions. Fix for disks w/ more than 9 partitions.\\n host_disk = \\\"/dev/\\\"+disk.split(\\\"s\\\")[0]+\\\"s\\\"+disk.split(\\\"s\\\")[1]\\n get_partition_info(disk, host_disk)\\n\\n #Check we found some disks.\\n if not DISKINFO:\\n raise RuntimeError(\\\"No Disks found!\\\")\",\n \"def get_uid_by_vdisk_map(self, wwpns, scsi_id):\\n if not wwpns or not scsi_id:\\n return None\\n\\n host_name = None\\n for wwpn in wwpns:\\n host_name = self.get_host_name(wwpn)\\n if host_name:\\n break\\n if not host_name:\\n return\\n\\n cmd = \\\"svcinfo lshostvdiskmap -delim : %s\\\" % host_name\\n output, err_output = self._svc_command(cmd)\\n\\n if err_output:\\n msg = (_(\\\"get_uid_by_vdisk_map() failure: svc cmd: %(cmd)s \\\"\\n \\\"error: %(error)s\\\") %\\n {'cmd': cmd,\\n 'error': err_output})\\n LOG.exception(msg)\\n ex_args = {'cmd': cmd, 'e': err_output}\\n raise SVCCommandException(**ex_args)\\n\\n if len(output) < 2:\\n return None\\n\\n header = output.pop(0).split(':')\\n scsi_id_idx = header.index(SVC_KEY_MAP_SCSI_ID)\\n vdisk_uid_idx = header.index(SVC_KEY_VDISK_UID)\\n\\n for line in output:\\n info = line.split(':')\\n if info[scsi_id_idx] == scsi_id:\\n return info[vdisk_uid_idx]\\n\\n return None\",\n \"def _try_to_get_drive_info_from_dbus(device):\\n\\n # ensure the device is under /dev, and extract the block device name\\n path_components = os.path.split(device)\\n if path_components[0] != \\\"/dev\\\":\\n return {}\\n block_device_name = path_components[-1]\\n\\n # try importing dbus, and exit gracefully if it fails\\n try:\\n import dbus\\n except ImportError:\\n return {}\\n\\n try:\\n\\n bus = dbus.SystemBus()\\n\\n # get the block object based on the block device name\\n block_obj = bus.get_object(\\n \\\"org.freedesktop.UDisks2\\\",\\n \\\"/org/freedesktop/UDisks2/block_devices/\\\" + block_device_name,\\n )\\n block_iface = dbus.Interface(block_obj, \\\"org.freedesktop.DBus.Properties\\\")\\n block = block_iface.GetAll(\\\"org.freedesktop.UDisks2.Block\\\")\\n\\n # get the drive object, based on the drive identifier from the block object\\n drive_path = block.get(\\\"Drive\\\")\\n drive_obj = bus.get_object(\\\"org.freedesktop.UDisks2\\\", drive_path)\\n drive_iface = dbus.Interface(drive_obj, \\\"org.freedesktop.DBus.Properties\\\")\\n drive = drive_iface.GetAll(\\\"org.freedesktop.UDisks2.Drive\\\")\\n\\n # extract the name and guid from the block and drive properties, calculate drivetype, and return\\n return {\\n \\\"name\\\": str(\\n block.get(\\\"IdLabel\\\")\\n or \\\" \\\".join([drive.get(\\\"Vendor\\\"), drive.get(\\\"Model\\\")]).strip()\\n ),\\n \\\"guid\\\": str(block.get(\\\"IdUUID\\\") or drive.get(\\\"Serial\\\") or drive.get(\\\"Id\\\")),\\n \\\"drivetype\\\": _get_drivetype_from_dbus_drive_properties(drive),\\n }\\n\\n except ValueError:\\n return {}\\n except dbus.exceptions.DBusException:\\n return {}\",\n \"def get_volume_info(host, disk_object, dc_obj):\\n host_resource = get_host_resource_by_name(host)\\n\\n vol_id = disk_object.get_image_id()\\n sd_id = disk_object.get_storage_domains().get_storage_domain()[0].get_id()\\n image_id = disk_object.get_id()\\n sp_id = dc_obj.get_id()\\n\\n args = {\\n \\\"storagepoolID\\\": sp_id,\\n \\\"storagedomainID\\\": sd_id,\\n \\\"imageID\\\": image_id,\\n \\\"volumeID\\\": vol_id,\\n }\\n\\n return host_resource.vds_client(cmd=\\\"Volume.getInfo\\\", args=args)\",\n \"def generate_cifs_table(cls) -> ty.List[ty.Tuple[str, str]]:\\n exit_code, output = sp.getstatusoutput(\\\"mount\\\")\\n return cls.parse_mount_table(exit_code, output)\",\n \"def get_device_info(disk):\\n\\n host_disk = \\\"/dev/\\\"+disk\\n DISKINFO[host_disk] = {}\\n DISKINFO[host_disk][\\\"Name\\\"] = host_disk\\n DISKINFO[host_disk][\\\"Type\\\"] = \\\"Device\\\"\\n DISKINFO[host_disk][\\\"HostDevice\\\"] = \\\"N/A\\\"\\n DISKINFO[host_disk][\\\"Partitions\\\"] = []\\n DISKINFO[host_disk][\\\"Vendor\\\"] = get_vendor(disk)\\n DISKINFO[host_disk][\\\"Product\\\"] = get_product(disk)\\n DISKINFO[host_disk][\\\"RawCapacity\\\"], DISKINFO[host_disk][\\\"Capacity\\\"] = get_capacity()\\n DISKINFO[host_disk][\\\"Description\\\"] = get_description(disk)\\n DISKINFO[host_disk][\\\"Flags\\\"] = get_capabilities(disk)\\n DISKINFO[host_disk][\\\"Partitioning\\\"] = get_partitioning(disk)\\n DISKINFO[host_disk][\\\"FileSystem\\\"] = \\\"N/A\\\"\\n DISKINFO[host_disk][\\\"UUID\\\"] = \\\"N/A\\\"\\n DISKINFO[host_disk][\\\"ID\\\"] = get_id(disk)\\n DISKINFO[host_disk][\\\"BootRecord\\\"], DISKINFO[host_disk][\\\"BootRecordStrings\\\"] = get_boot_record(disk)\\n\\n return host_disk\",\n \"def scan_disks():\\n disks = get_disks()\\n\\n # Get disk details\\n for disk in disks:\\n # Get partition style\\n disk['Table'] = get_table_type(disk)\\n\\n # Get disk name/model and physical details\\n disk.update(get_disk_details(disk))\\n\\n # Get partition info for disk\\n disk['Partitions'] = get_partitions(disk)\\n\\n for partition in disk['Partitions']:\\n # Get partition details\\n partition.update(get_partition_details(disk, partition))\\n\\n # Done\\n return disks\",\n \"def _fetch_disk_info(resource_group_name, disk_name):\\n show_disk_command = 'az disk show -g {g} -n {name} --query [sku.name,location,osType,hyperVGeneration] -o json'.format(g=resource_group_name, name=disk_name)\\n disk_info = loads(_call_az_command(show_disk_command))\\n # Note that disk_info will always have 4 elements if the command succeeded, if it fails it will cause an exception\\n sku, location, os_type, hyper_v_version = disk_info[0], disk_info[1], disk_info[2], disk_info[3]\\n return (sku, location, os_type, hyper_v_version)\",\n \"def show_vdcs_detail(self):\\n for v in self.vdcs:\\n print self.vdcs[v]\",\n \"def parse_devices_spt(self, devices=None):\\n\\n if not devices:\\n self._logger.warning(\\\"The devices list is empty, so no devices parsed!\\\")\\n return\\n try:\\n for entry in devices['SCSI Devices']['Device List']:\\n device_type = entry['Peripheral Device Type Description']\\n if self._include_enclosures:\\n if not device_type.startswith('Direct') and \\\\\\n not device_type.startswith('Host Managed') and \\\\\\n not device_type.startswith('Enclosure'):\\n continue\\n else:\\n if not device_type.startswith('Direct') and \\\\\\n not device_type.startswith('Host Managed'):\\n continue\\n\\n # Parse remaining information.\\n if device_type.startswith('Direct') or device_type.startswith('Host Managed'):\\n device_type = 'disk'\\n if self.product_name and not self.product_name in entry['Product Identification'].strip():\\n continue;\\n if self.vendor_name and not self.vendor_name in entry['Vendor Identification'].strip():\\n continue;\\n if self.serial_number and not self.serial_number in entry['Product Serial Number'].strip():\\n continue;\\n if self.target_port and not self.target_port in entry['Device Target Port']:\\n continue;\\n elif device_type.startswith('Enclosure'):\\n device_type = 'enclosure'\\n\\n device = dict()\\n device['Device Type'] = device_type\\n\\n device['Device Type Description'] = entry['Peripheral Device Type Description']\\n device['Product Identification'] = entry['Product Identification'].strip()\\n device['Vendor Identification'] = entry['Vendor Identification'].strip()\\n device['Revision Level'] = entry['Firmware Revision Level'].strip()\\n\\n if entry.get('Full Firmware Version') is not None:\\n fwver = entry['Full Firmware Version']\\n if not fwver.startswith('<not available>'):\\n device['Firmware Version'] = fwver\\n\\n serial = entry['Product Serial Number']\\n device['Serial Number'] = serial.strip()\\n\\n # Note: Not currently displayed. (WWN == LUN Device Identification)\\n wwn = entry['Device World Wide Name']\\n if wwn.startswith('<not available>'):\\n wwn = \\\"\\\"\\n device['Device World Wide Name'] = wwn\\n\\n sas_address = entry['Device Target Port']\\n if not sas_address.startswith('<not available>'):\\n device['SAS Address'] = sas_address\\n self._sas_addresses += 1\\n else:\\n device['SAS Address'] = \\\"\\\"\\n\\n # Note: There's probably a better Pythonic way to do this?\\n device['Linux Device Name'] = \\\"\\\"\\n device['SCSI Device Name'] = \\\"\\\"\\n device['DMMP Device Name'] = \\\"\\\"\\n\\n # Parse the device paths.\\n for path_type in entry['Path Types']:\\n if path_type.get('Linux Device'):\\n # Handle multiple Linux device paths. (these are \\\"sd\\\" devices)\\n if device.get('Linux Device Name') and path_type.get('SCSI Nexus'):\\n new_device = copy.deepcopy(device)\\n self._devices.append(new_device)\\n # Fall through to update this device entry.\\n # Initialize information for this (or next) device.\\n device['Linux Device Name'] = path_type['Linux Device']\\n device['Linux SCSI Nexus'] = path_type['SCSI Nexus']\\n if path_type.get('SCSI Device'):\\n device['SCSI Device Name'] = path_type['SCSI Device']\\n if path_type.get('Device Target Port'):\\n device['SAS Address'] = path_type['Device Target Port']\\n\\n elif path_type.get('SCSI Device'):\\n # Handle multiple SCSI device paths. (now, \\\"sg\\\" devices only)\\n if device.get('SCSI Device Name') and path_type.get('SCSI Nexus'):\\n new_device = copy.deepcopy(device)\\n self._devices.append(new_device)\\n # Fall through to update this device entry.\\n # Initialize information for this (or next) device.\\n device['SCSI Device Name'] = path_type['SCSI Device']\\n device['SCSI Nexus'] = path_type['SCSI Nexus']\\n if path_type.get('Device Target Port'):\\n device['SAS Address'] = path_type['Device Target Port']\\n\\n elif path_type.get('DMMP Device') is not None:\\n # Initialize information for this device. (limited)\\n device['DMMP Device Name'] = path_type['DMMP Device']\\n\\n # Hack: We don't find a SCSI device if there's no serial number or device ID (WWN).\\n # This is observed on Linux VM's, so not common, but we still wish to handle this!\\n if not len(device['SCSI Device Name']):\\n # Funky DM-MP names are skipped! (we deal with sd and/or sg devices only)\\n # /dev/mapper/centos_cos--lab--vm01-root\\n if not len(device['Linux Device Name']):\\n continue\\n\\n self._devices.append(device)\\n\\n except RuntimeError as exc:\\n self._logger.error(\\\"Failed to acquire SCSI devices: {0}\\\".format(exc))\\n raise exc\",\n \"def get_disk_mapping(virt_type, instance,\\n disk_bus, cdrom_bus,\\n block_device_info=None,\\n image_meta=None, rescue=False):\\n\\n inst_type = instance_types.extract_instance_type(instance)\\n\\n mapping = {}\\n\\n if virt_type == \\\"lxc\\\":\\n # NOTE(zul): This information is not used by the libvirt driver\\n # however we need to populate mapping so the image can be\\n # created when the instance is started. This can\\n # be removed when we convert LXC to use block devices.\\n root_disk_bus = disk_bus\\n root_device_type = 'disk'\\n\\n root_info = get_next_disk_info(mapping,\\n root_disk_bus,\\n root_device_type)\\n mapping['root'] = root_info\\n mapping['disk'] = root_info\\n\\n return mapping\\n\\n if rescue:\\n rescue_info = get_next_disk_info(mapping,\\n disk_bus)\\n mapping['disk.rescue'] = rescue_info\\n mapping['root'] = rescue_info\\n\\n os_info = get_next_disk_info(mapping,\\n disk_bus)\\n mapping['disk'] = os_info\\n\\n return mapping\\n\\n if image_meta and image_meta.get('disk_format') == 'iso':\\n root_disk_bus = cdrom_bus\\n root_device_type = 'cdrom'\\n else:\\n root_disk_bus = disk_bus\\n root_device_type = 'disk'\\n\\n root_device_name = driver.block_device_info_get_root(block_device_info)\\n if root_device_name is not None:\\n root_device = block_device.strip_dev(root_device_name)\\n root_info = {'bus': get_disk_bus_for_disk_dev(virt_type,\\n root_device),\\n 'dev': root_device,\\n 'type': root_device_type}\\n else:\\n root_info = get_next_disk_info(mapping,\\n root_disk_bus,\\n root_device_type)\\n mapping['root'] = root_info\\n if not block_device.volume_in_mapping(root_info['dev'],\\n block_device_info):\\n mapping['disk'] = root_info\\n\\n eph_info = get_next_disk_info(mapping,\\n disk_bus)\\n ephemeral_device = False\\n if not (block_device.volume_in_mapping(eph_info['dev'],\\n block_device_info) or\\n 0 in [eph['num'] for eph in\\n driver.block_device_info_get_ephemerals(\\n block_device_info)]):\\n if instance['ephemeral_gb'] > 0:\\n ephemeral_device = True\\n\\n if ephemeral_device:\\n mapping['disk.local'] = eph_info\\n\\n for eph in driver.block_device_info_get_ephemerals(\\n block_device_info):\\n disk_dev = block_device.strip_dev(eph['device_name'])\\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\\n\\n mapping[get_eph_disk(eph)] = {'bus': disk_bus,\\n 'dev': disk_dev,\\n 'type': 'disk'}\\n\\n swap = driver.block_device_info_get_swap(block_device_info)\\n if driver.swap_is_usable(swap):\\n disk_dev = block_device.strip_dev(swap['device_name'])\\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\\n\\n mapping['disk.swap'] = {'bus': disk_bus,\\n 'dev': disk_dev,\\n 'type': 'disk'}\\n elif inst_type['swap'] > 0:\\n swap_info = get_next_disk_info(mapping,\\n disk_bus)\\n if not block_device.volume_in_mapping(swap_info['dev'],\\n block_device_info):\\n mapping['disk.swap'] = swap_info\\n\\n block_device_mapping = driver.block_device_info_get_mapping(\\n block_device_info)\\n\\n for vol in block_device_mapping:\\n disk_dev = vol['mount_device'].rpartition(\\\"/\\\")[2]\\n disk_bus = get_disk_bus_for_disk_dev(virt_type, disk_dev)\\n\\n mapping[vol['mount_device']] = {'bus': disk_bus,\\n 'dev': disk_dev,\\n 'type': 'disk'}\\n\\n if configdrive.enabled_for(instance):\\n config_info = get_next_disk_info(mapping,\\n disk_bus,\\n last_device=True)\\n mapping['disk.config'] = config_info\\n\\n return mapping\",\n \"def find_dev_mount_point(self, usb_table):\\n mounts = open(\\\"/proc/mounts\\\")\\n mount_lines = mounts.readlines()\\n table = usb_table\\n i = 0\\n for device in table:\\n for line in mount_lines:\\n arguments = line.split(\\\" \\\")\\n if arguments[0] == device[0]:\\n usb_table[i].append(arguments[1])\\n usb_table[i] = self.get_drive_stat(usb_table[i])\\n break\\n i += 1\\n return usb_table\",\n \"def get_devices_summary():\\n\\n # This function was created to replace get_devices_information\\n # because it wasn't detecting virtual systems in Palo Alto Virtual Systems\\n global nipper_xml\\n devices = {}\\n headings = []\\n\\n # Add the table headings to a list\\n for h in nipper_xml.findall(\\\"./summary/table/[@ref='SCOPE.AUDITDEVICELIST.TABLE']/headings/heading\\\"):\\n if h not in headings:\\n headings.append(h.text)\\n\\n for device in nipper_xml.findall(\\\"./summary/table/[@ref='SCOPE.AUDITDEVICELIST.TABLE']/tablebody/tablerow\\\"):\\n values = []\\n for i in device.findall('./tablecell/item'):\\n if i not in values:\\n values.append(i.text)\\n if DEBUG:\\n print \\\"\\\\t\\\" + note + \\\"%s: %s\\\" % (headings[headings.index('Name')], values[headings.index('Name')])\\n print \\\"\\\\t\\\" + note + \\\"%s: %s\\\" % (headings[headings.index('Device')], values[headings.index('Device')])\\n print \\\"\\\\t\\\" + note + \\\"%s: %s\\\" % (headings[headings.index('OS')], values[headings.index('OS')].split(\\\" \\\")[0])\\n print \\\"\\\\t\\\" + note + \\\"%s: %s\\\" % (headings[headings.index('OS')], values[headings.index('OS')].split(\\\" \\\")[1])\\n devices[values[headings.index('Name')]] = {'name': values[headings.index('Name')],\\n 'type': values[headings.index('Device')],\\n 'os': values[headings.index('OS')].split(' ')[0],\\n 'osversion': values[headings.index('OS')].split(' ')[1]\\n }\\n\\n if DEBUG:\\n print info + \\\"Device Object:\\\"\\n print devices\\n raw_input(warn + \\\"Press enter to continue\\\")\\n return devices\",\n \"def get_gadget_info(gfname):\\n partmass = pyg.readheader(gfname, 'massTable')[1]\\n boxsize = pyg.readheader(gfname, 'boxsize')\\n omegam = pyg.readheader(gfname, 'O0')\\n omegal = pyg.readheader(gfname, 'Ol')\\n h = pyg.readheader(gfname, 'h')\\n npart = pyg.readheader(gfname, 'npartTotal')[1]\\n return omegam, omegal, h, boxsize, partmass, npart\",\n \"def collect_device_lun_SM(self):\\n\\n devlun = self._devlun\\n\\n r = re.compile( r'\\\\s*(/dev/\\\\S+).*Logical Drive\\\\s+(\\\\S+),' )\\n P = subprocess.Popen('SMdevices', shell=True,\\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\\n close_fds=True)\\n co,ci,ce = P.stdout, P.stdin, P.stderr\\n for L in co:\\n m = r.match(L)\\n if m:\\n devlun[m.group(1)] = m.group(2)\",\n \"def _try_to_get_drive_info_from_diskutil(device):\\n # we only use diskutil on OSX\\n if sys.platform != \\\"darwin\\\":\\n return {}\\n\\n # skip non-device mounts\\n if not device.startswith(\\\"/dev/\\\"):\\n return {}\\n\\n # run the command and read the results\\n diskutilp = subprocess.Popen(\\n \\\"diskutil info {}\\\".format(device), shell=True, stdout=subprocess.PIPE\\n )\\n diskutil_output, err = diskutilp.communicate()\\n\\n rows = [\\n line.split(\\\":\\\", 1)\\n for line in diskutil_output.decode().split(\\\"\\\\n\\\")\\n if \\\":\\\" in line\\n ]\\n metadata = dict([(key.strip(), val.strip()) for key, val in rows])\\n\\n # determine what type of drive it is (not sure what an optical drive shows up as, but OSX + optical is now uncommon)\\n if metadata.get(\\\"Protocol\\\") == \\\"USB\\\":\\n drivetype = drivetypes.USB_DEVICE\\n elif metadata.get(\\\"Internal\\\") == \\\"Yes\\\":\\n drivetype = drivetypes.INTERNAL_DRIVE\\n else:\\n drivetype = drivetypes.UNKNOWN\\n\\n # extract the name and guid from the diskutil drive metadata, and return\\n return {\\n \\\"drivetype\\\": drivetype,\\n \\\"name\\\": metadata.get(\\\"Volume Name\\\")\\n or metadata.get(\\\"Device / Media Name\\\")\\n or \\\"\\\",\\n \\\"guid\\\": metadata.get(\\\"Volume UUID\\\") or \\\"\\\",\\n }\",\n \"def sd_partition_table():\\n table = {}\\n for partition in sd_partitions():\\n table[partition] = sd_part_size(partition)\\n return table\",\n \"def inspect_boot_loader(g, device) -> inspect_pb2.InspectionResults:\\n\\n bios_bootable = False\\n uefi_bootable = False\\n root_fs = \\\"\\\"\\n\\n try:\\n stream = os.popen('gdisk -l {}'.format(device))\\n output = stream.read()\\n print(output)\\n if _inspect_for_hybrid_mbr(output):\\n bios_bootable = True\\n\\n part_list = g.part_list('/dev/sda')\\n for part in part_list:\\n try:\\n guid = g.part_get_gpt_type('/dev/sda', part['part_num'])\\n # It covers both GPT \\\"EFI System\\\" and BIOS \\\"EFI (FAT-12/16/32)\\\".\\n if guid == 'C12A7328-F81F-11D2-BA4B-00A0C93EC93B':\\n uefi_bootable = True\\n # TODO: detect root_fs (b/169245755)\\n # It covers \\\"BIOS boot\\\", which make a protective-MBR bios-bootable.\\n if guid == '21686148-6449-6E6F-744E-656564454649':\\n bios_bootable = True\\n except Exception:\\n continue\\n\\n except Exception as e:\\n print(\\\"Failed to inspect disk partition: \\\", e)\\n\\n return inspect_pb2.InspectionResults(\\n bios_bootable=bios_bootable,\\n uefi_bootable=uefi_bootable,\\n root_fs=root_fs,\\n )\",\n \"def _findSdPartitionDevice (self):\\n for partitionDiskName in self.getPartitionDiskNames():\\n partitionOsDevice = self.getPartitionOsDeviceName(partitionDiskName)\\n partitionIndex = self.getPartitionIndex(partitionDiskName)\\n\\n if partitionOsDevice is None:\\n # not given - need to \\\"calculate\\\" it from the sd device and index\\n partitionOsDevice = self.getSdDevice() + str(int(partitionIndex) + 1)\\n\\n self._log(\\\"find-sd-partition-device\\\").notice(\\\"found SD partition %s as os device %s\\\", partitionDiskName, partitionOsDevice)\\n self._partitionOsDevice[partitionDiskName] = partitionOsDevice\",\n \"def read_vmdas(self,):\\n fd = self.f\\n # The raw files produced by VMDAS contain a binary navigation data\\n # block.\\n self.cfg['sourceprog'] = 'VMDAS'\\n ens = self.ensemble\\n k = ens.k\\n if self._source != 1 and self._debug_level >= 1:\\n print(' \\\\n***** Apparently a VMDAS file \\\\n\\\\n')\\n self._source = 1\\n self.vars_read += ['time_gps',\\n 'latitude_gps',\\n 'longitude_gps',\\n 'etime_gps',\\n 'elatitude_gps',\\n 'elongitude_gps',\\n 'flags',\\n 'ntime', ]\\n utim = fd.read_ui8(4)\\n date = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])\\n # This byte is in hundredths of seconds (10s of milliseconds):\\n time = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) / 10)))\\n fd.seek(4, 1) # \\\"PC clock offset from UTC\\\" - clock drift in ms?\\n ens.time_gps[k] = tmlib.date2epoch(date + time)[0]\\n ens.latitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.longitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.etime_gps[k] = tmlib.date2epoch(date + tmlib.timedelta(\\n milliseconds=int(fd.read_ui32(1) * 10)))[0]\\n ens.elatitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.elongitude_gps[k] = fd.read_i32(1) * self._cfac\\n fd.seek(12, 1)\\n ens.flags[k] = fd.read_ui16(1)\\n fd.seek(6, 1)\\n utim = fd.read_ui8(4)\\n date = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])\\n ens.ntime[k] = tmlib.date2epoch(date + tmlib.timedelta(\\n milliseconds=int(fd.read_ui32(1) / 10)))[0]\\n fd.seek(16, 1)\\n self._nbyte = 2 + 76\",\n \"def init_data(partitions_file):\\n mapping = []\\n\\n drive_size = None\\n for line in partitions_file:\\n if drive_size is None:\\n drive_size = parse_drive_size(line.rstrip())\\n else:\\n partitions_list = parse_partitions(line.rstrip())\\n mapping.append((drive_size, partitions_list))\\n drive_size = None\\n\\n return mapping\",\n \"def process_multipath_devices(self):\\n\\n devlun = self._devlun\\n lunMatch = self._lunMatch\\n logger = self._logger\\n\\n rwnn = re.compile( r'^(?P<wwn>[0-9a-f]+)\\\\s+(?P<dev>dm-\\\\S+)\\\\s' )\\n rscsi = re.compile( r'^\\\\|.*\\\\s+(?P<scsi>sd\\\\S+)\\\\s+' )\\n\\n dmwnn = {}\\n dmlun = {}\\n lundev = None\\n wnn = None\\n\\n P = subprocess.Popen('multipath -ll', shell=True,\\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\\n close_fds=True)\\n co,ci,ce = P.stdout, P.stdin, P.stderr\\n for L in co:\\n M = rwnn.match(L)\\n if M:\\n # We have a line giving wwn and device name.\\n lundev = M.group('dev')\\n wnn = M.group('wwn')\\n continue\\n M = rscsi.match(L)\\n if M and lundev is not None:\\n s = M.group('scsi')\\n try:\\n lun = devlun[os.path.join('/dev', s)]\\n except KeyError:\\n pass\\n else:\\n dmwnn[lundev] = wnn\\n dmlun[lundev] = lun\\n lundev = wnn = None\\n\\n # Now go through the devices we have located. Match LUN names,\\n blkops = self._blkops\\n for dm,lun in dmlun.items():\\n for r,transfer,readhead,sched,schedopts in lunMatch:\\n if r.match(lun):\\n logger.info('Set attributes on %s (%s)', lun, dm)\\n blkops.set_io_scheduler(dm, sched)\\n blkops.set_io_transfer_size(dm, transfer)\\n blkops.set_io_readahead_size(dm, readhead)\\n if sched == 'deadline':\\n fifobatch,read_expire,write_expire,writes_starved,front_merges = schedopts\\n if fifobatch is not None:\\n blkops.set_io_deadline_fifo_batch(dm, fifobatch)\\n if read_expire is not None:\\n blkops.set_io_deadline_read_expire(dm, read_expire)\\n if write_expire is not None:\\n blkops.set_io_deadline_write_expire(dm, write_expire)\\n if writes_starved is not None:\\n blkops.set_io_deadline_writes_starved(dm, writes_starved)\\n if front_merges is not None:\\n blkops.set_io_deadline_front_merges(dm, front_merges)\",\n \"def get_generic_scsi_devs_count(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetGenericScsiDevsCount', self.handle)\",\n \"def get_status(self):\\n dstat_lines = cat(self.PROC_DISKSTATS)\\n pl = dict()\\n for line in dstat_lines:\\n if line:\\n p = partition()\\n (p.M, p.m, p.dev,\\n p.r, p.r_merged, p.r_sectors, p.r_ms,\\n p.w, p.w_merged, p.w_sectors, p.w_ms,\\n p.io_progress, p.io_ms, p.ms_weighted) = line.strip().split()\\n\\n if p.dev in self.partitions:\\n pl[p.dev] = p\\n\\n return pl\",\n \"def _get_vm_device_status(self, device='FLOPPY'):\\n valid_devices = {'FLOPPY': 'floppy',\\n 'CDROM': 'cd'}\\n\\n # Check if the input is valid\\n if device not in valid_devices:\\n raise exception.IloInvalidInputError(\\n \\\"Invalid device. Valid devices: FLOPPY or CDROM.\\\")\\n\\n manager, uri = self._get_ilo_details()\\n try:\\n vmedia_uri = manager['links']['VirtualMedia']['href']\\n except KeyError:\\n msg = ('\\\"VirtualMedia\\\" section in Manager/links does not exist')\\n raise exception.IloCommandNotSupportedError(msg)\\n\\n for status, hds, vmed, memberuri in self._get_collection(vmedia_uri):\\n status, headers, response = self._rest_get(memberuri)\\n if status != 200:\\n msg = self._get_extended_error(response)\\n raise exception.IloError(msg)\\n\\n if (valid_devices[device] in\\n [item.lower() for item in response['MediaTypes']]):\\n vm_device_uri = response['links']['self']['href']\\n return response, vm_device_uri\\n\\n # Requested device not found\\n msg = ('Virtualmedia device \\\"' + device + '\\\" is not'\\n ' found on this system.')\\n raise exception.IloError(msg)\",\n \"def device_mounted(uuid):\\n out, err = run_cmd(['lsblk', '-o', 'NAME,UUID,MOUNTPOINT', '--json'])\\n\\n blockdevices = json.loads(out)['blockdevices']\\n\\n for blkdevice in blockdevices:\\n if key_exists('children', blkdevice):\\n for child in blkdevice['children']:\\n if key_exists('mountpoint', child) and child['uuid'] == uuid:\\n return child['mountpoint']\",\n \"def get_device_slot(self, sas):\\n\\n for device in self._devices:\\n if not device['Device Type'].startswith(\\\"enclosu\\\"):\\n continue\\n array_devices = device['Additional Element Information']['Additional Element Status']['Array Device Slot']\\n for array_device in array_devices['Descriptor List']:\\n for phy_desc in array_device['Descriptor List']:\\n if sas == phy_desc['SAS Address']:\\n element_index = int(array_device['Element Index'])\\n # Denali vs. Pikespeak Difference.\\n if not array_device['Element Index Includes Overall']:\\n # Adjust for 'Overall Status Descriptor' in Element page.\\n element_index += 1\\n return device['SCSI Device Name'], array_device['Device Slot Number'], element_index\\n return \\\"\\\", \\\"\\\", -1\",\n \"def collect():\\n\\n command = \\\"cat /proc/meminfo |grep MemTotal|awk -F' ' '{print $2}'\\\"\\n memTotal_f = round(float(os.popen(command).read())/1024/1000,0)\\n memTotal = int(memTotal_f)\\n cmd = 'df -h |grep \\\"/dev/s\\\"'\\n metric_disk = os.popen(cmd).readlines()\\n hardNum=[]\\n for i in metric_disk:\\n hard_space = float((i.strip().split()[1])[:-1])\\n hardNum.append(hard_space)\\n\\n disk_info = sum(hardNum)\\n disk_use = {}\\n metric_disks=os.popen('df -x tmpfs -x devtmpfs | grep -Eo \\\" /\\\\S*$\\\" ').readlines()\\n for disk in metric_disks:\\n cmd = 'df|grep -E \\\"%s$\\\"' % disk.strip()\\n disks = os.popen(cmd).readlines()[0]\\n disk_list = disks.split()\\n disk_use[disk_list[5]]=disk_list[4]\\n hard = {\\n \\\"disk_used\\\" : disk_use,\\n \\\"disk_total\\\":disk_info,\\n \\\"mem_total\\\":memTotal\\n }\\n\\n return hard\",\n \"def print_show_vsf(values1, values2, values3):\\n print('VSF Domain ID : %13d' % (values1['domain_id']))\\n print('MAC Address : %10s' % (values1['mac_address']['octets']))\\n print('VSF Topology : %13s' % (values1['topology']))\\n print('VSF Status : %13s' % (values1['status']))\\n print('Uptime : %d d %2d h %2d m' % (values1['uptime']['days'],\\n values1['uptime']['hours'],\\n values1['uptime']['minutes']))\\n if values3['is_oobm_mad_enabled']:\\n print('VSF MAD : OOBM MAD enabled')\\n elif values3['is_lldp_mad_enabled']:\\n print('VSF MAD : LLDP MAD enabled')\\n else:\\n print('VSF MAD : MAD not enabled')\\n print('VSF Port Speed : %13s' % (values1['port_speed']))\\n print('Software Version : %13s' % (values1['software_version']))\\n print('\\\\nMember ')\\n print('ID\\\\tMAC Address\\\\t\\\\tModel\\\\t\\\\t\\\\t\\\\tPriority\\\\tStatus')\\n print('----------------------------------------------------------------')\\n for member in range(len(values2['vsf_member_element'])):\\n print('%d %3s %5s %4d %5s' % (values2['vsf_member_element'][member]['member_id'],\\n values2['vsf_member_element'][member]['mac_address']['octets'],\\n values2['vsf_member_element'][member]['model'],\\n values2['vsf_member_element'][member]['priority'],\\n values2['vsf_member_element'][member]['status']), end='\\\\n\\\\n')\",\n \"def get_mbed_devices(self):\\n upper_ven = [ven.upper() for ven in self.usb_vendor_list]\\n mounts_key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\\\MountedDevices')\\n for point, label, _ in self.iter_vals(mounts_key):\\n printable_label = label.decode('utf-16le', 'ignore')\\n if ('DosDevices' in point and\\n any(v in printable_label.upper() for v in upper_ven)):\\n logger.debug(\\\"Found Mount point %s with usb ID %s\\\",point,\\n printable_label)\\n yield (point, printable_label)\\n else:\\n logger.debug(\\\"Skipping Mount point %r label %r\\\", point, label)\",\n \"def modif_fstab(self):\\n print \\\"preparation du fichier fstab\\\"\\n self.exec_cmd(\\\"cp %s/etc/fstab %s/etc/fstab.pre.p2v\\\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\\n self.exec_cmd(\\\"cp %s/etc/fstab_without_uuid %s/etc/fstab\\\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\\n line = open(\\\"/vhosts/\\\"+ name_vm_dest +\\\"/etc/fstab_without_uuid\\\",\\\"r\\\").read()\\n for i in self.tri(partitions[type_p2v]):\\n line = line.replace(partitions[type_p2v][i][0],\\\"/dev/%s\\\" % i,1)\\n fichier = open(\\\"/vhosts/\\\"+ name_vm_dest +\\\"/etc/fstab\\\",\\\"w\\\")\\n fichier.write(line)\\n fichier.close()\",\n \"def __init__(self, devtype, device):\\n #\\n self.Type = devtype # SERIAL, FILE, other? \\n self.Device = device # ELM327, other? used to determine which reader commands to send (separate from OBD2 cmds)\\n #\\n self.debug = 0 # debug level, 0 = off, higher is more...\\n #\\n self.State = 0 # 1 is connected, 0 is disconnected/failed\\n self.recwaiting = 0 # 0 = no record waiting (can send new cmd), 1 = record waiting to be retrieved\\n #\\n self.Style = 'old' # 'old', 'can' used to determine how to interpret the results, gets updated by connect()\\n self.Headers = 0 # ECU headers, 1 is on, 0 is off\\n #\\n self.attr = {} # the list of device attributes and their values\\n self.attr_cmds = {} # the list of supported attribute at commands, and the associated attribute\\n #\\n self.RecordTrace = 0 # 0 = no, 1 = yes record a trace of the serial session\\n self.tf_out = None # file to record trace to\\n #\\n if self.Type == \\\"SERIAL\\\":\\n self.Port = None # connect later\\n elif self.Type == \\\"FILE\\\":\\n self.tf = None # open later\\n self.eof = 0\\n else:\\n pass\\n #\\n if self.Device == \\\"ELM327\\\":\\n self.attr_cmds = {\\n 'AT@1' : \\\"Brand\\\",\\n 'AT@2' : \\\"Brand2\\\",\\n 'ATI' : \\\"Firmware\\\",\\n 'ATDP' : \\\"Proto\\\",\\n 'ATDPN' : \\\"ProtoNum\\\",\\n 'ATRV' : \\\"Voltage\\\",\\n }\\n else:\\n pass\\n self.clear_attr()\",\n \"def _usb_get_report(self):\\n arraydata=self.usbdev.ctrl_transfer(bmRequestType=0xa1, bRequest=0x01, wValue=0x03b0, wIndex=0, data_or_wLength=9)\\n data=arraydata.tolist()\\n assert (data[0:4]==[0xb0,0x00,0x00,0x01]) or \\\\\\n (data[0:4]==[0xb0,0xaa,0x00,0x01]) or \\\\\\n (data[0:4]==[0xb0,0xaa,0x00,0x00]), \\\\\\n 'unexpected device status info start: %s'%(data[0:4])\\n assert data[8]==1,'unexpected last byte value: %s'%data[8]\\n position=data[4:6]\\n v_pos=data[6]\\n self.vertical_position=v2k(v_pos,V_POS)\\n self.intensity=data[7]\\n self.position=v2k(position,POSITION)\\n return(data)\",\n \"def fillQuickList():\\n global quickList\\n cmd = \\\"/sbin/blkid\\\"\\n proc = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\\n for line in proc.stdout:\\n line = line.replace(':', '').strip()\\n propList = line.split()\\n devName = label = uuid = fsType = ''\\n devName = propList[0]\\n for property in propList:\\n if property.startswith('UUID'):\\n uuid = property.replace('UUID=', '').replace('\\\"', '')\\n quickList[devName] = uuid\",\n \"def writeRawFCD():\\n global vehId, vehIdDict\\n vehIdDict = {}\\n vehId = 0\\n day = 0\\n\\n def getVehId(orgId):\\n \\\"\\\"\\\"creates new vehicle id's which consists only numerics\\\"\\\"\\\"\\n global vehId, vehIdDict\\n value = vehIdDict.get(orgId, vehId)\\n if value is vehId:\\n vehIdDict[orgId] = vehId\\n vehId = (vehId + 1) % 65500\\n return value\\n\\n outputFile = open(path.FQrawFCD, 'w')\\n\\n for period, quota, vtypeDictR, taxiSum in generatePeriodQuotaSets():\\n day += 86400\\n # reset dict so that every taxi (even if the vehicle is chosen several\\n # times) gets its own id\\n vehIdDict = {}\\n # dataset=0\\n sortedKeys = vtypeDictR.keys()\\n sortedKeys.sort()\\n for timestep in sortedKeys:\\n taxiList = vtypeDictR[timestep]\\n for tup in taxiList: # all elements in this timestep\\n # calc timestep ->for every period /quota set a new day\\n time = timestep + day\\n time = calcTime.getDateFromDepart(time)\\n # dataset+=1\\n # print ouptut\\n # veh_id date (time to simDate+time) x (remove and\\n # set comma new)\\n outputFile.write(str(getVehId(tup[0])) + '\\\\t' + time + '\\\\t' + tup[3][0:2] + '.' + tup[3][2:7] + tup[3][8:] +\\n # y (remove and set comma new)\\n # status speed form m/s in km/h\\n '\\\\t' + tup[4][0:2] + '.' + tup[4][2:7] + tup[4][8:] + '\\\\t' + \\\"90\\\" + '\\\\t' + str(int(round(tup[2] * 3.6))) + '\\\\n')\\n # print dataset, time\\n print(vehId)\\n outputFile.close()\",\n \"def getVCDRCloudFS(**kwargs):\\n strVCDRProdURL = kwargs['strVCDRProdURL']\\n sessiontoken = kwargs['sessiontoken']\\n if kwargs['cloud_fs_id'] is None:\\n json_response = get_vcdr_cloud_fs_json(strVCDRProdURL, sessiontoken)\\n if json_response == None:\\n print(\\\"API Error\\\")\\n sys.exit(1)\\n cloud_fs = json_response[\\\"cloud_file_systems\\\"]\\n table = PrettyTable(['Cloud FS Name', 'Cloud FS ID'])\\n for i in cloud_fs:\\n table.add_row([i['name'], i['id']])\\n print(table)\\n else:\\n cloud_fs_id = kwargs['cloud_fs_id']\\n json_response = get_vcdr_cloud_fs_details_json(strVCDRProdURL, cloud_fs_id, sessiontoken)\\n if json_response == None:\\n print(\\\"API Error\\\")\\n sys.exit(1)\\n print(\\\" \\\")\\n print(f\\\"Cloud FS Name: {json_response['name']}\\\")\\n print(f\\\"Capacity GiB: {json_response['capacity_gib']:,.2f}\\\")\\n print(f\\\"Used GiB: {json_response['used_gib']:,.2f}\\\")\\n print(f\\\"Recovery SDDC: {json_response['recovery_sddc_id']}\\\")\\n print(\\\" \\\")\",\n \"def scan_disk(disk_url, scan_file_dir):\\n\\n # make the dir if it doesn't exist\\n if not os.path.exists(scan_file_dir):\\n os.makedirs(scan_file_dir)\\n \\n # open up the image\\n img = pytsk3.Img_Info(url=disk_url)\\n \\n # get the volume info\\n VOL_INFO = pytsk3.Volume_Info(img)\\n\\n\\n # print out some info about the disk image\\n logger.debug(\\\"--- Volume info ---\\\")\\n logger.debug(\\\"Current: %d\\\" % VOL_INFO.current)\\n logger.debug(\\\"VS Type: %d\\\" % VOL_INFO.info.vstype)\\n logger.debug(\\\"Offset: %d\\\" % VOL_INFO.info.offset)\\n logger.debug(\\\"Block Size: %d\\\" % VOL_INFO.info.block_size)\\n logger.debug(\\\"Endian: %d\\\" % VOL_INFO.info.endian)\\n logger.debug(\\\"Partition List: %s\\\" % VOL_INFO.info.part_list)\\n logger.debug(\\\"Parition Count: %d\\\" % VOL_INFO.info.part_count)\\n logger.debug(\\\"--- Volume info ---\\\")\\n\\n\\n # list of filenames we have to cat together at the end\\n files = []\\n\\n sector_size = VOL_INFO.info.block_size\\n\\n part_number = 1\\n\\n sparse_number = 0\\n \\n copied_front_data = False\\n\\n # loop over each volume\\n for vol in VOL_INFO:\\n \\n logger.debug(\\\"--- Partition ---\\\")\\n logger.debug(\\\"Start: %d\\\" % vol.start)\\n logger.debug(\\\"Length: %d\\\" % vol.len)\\n logger.debug(\\\"Description: %s\\\" % vol.desc)\\n logger.debug(\\\"Address: %d\\\" % vol.addr)\\n logger.debug(\\\"Flags: %d\\\" % vol.flags)\\n\\n # ignore partition table at beginning\\n if vol.addr == 0:\\n continue\\n\\n # copy the MBR and other stuff if this is the unpartitioned space between 0\\n # and the first partition\\n\\n if not copied_front_data:\\n fname = save_front_data(disk_url, scan_file_dir, vol.start, vol.len*sector_size)\\n files.append(fname)\\n copied_front_data = True\\n \\n continue\\n\\n type = vol.desc.split(\\\" \\\")[0]\\n\\n # if partition type is NTFS, do an NTFS clone on it\\n if vol.desc == 'NTFS (0x07)':\\n \\n print \\\"* Scanning %s (%d)...\\\"%(disk_url,part_number)\\n \\n # Win 7 specific hack, so that we only have 1 sparse file.\\n if vol.len == 204800 and vol.start == 2048:\\n fname = save_NTFS_partition(part_number, disk_url, scan_file_dir, meta_only=False)\\n else:\\n fname = save_NTFS_partition(part_number, disk_url, scan_file_dir)\\n files.append(fname)\\n part_number += 1\\n \\n else:\\n # create empty sparse file since we don't support other filesystems\\n #fname = save_sparse(sparse_number, scan_file_dir, vol.len*sector_size)\\n #files.append(fname)\\n #sparse_number += 1\\n pass\\n \\n \\n \\n # cat everything together\\n logger.info(\\\"Cat everything together\\\")\\n filenames = \\\" \\\".join(files)\\n output_img = os.path.join(scan_file_dir, \\\"disk.img\\\")\\n #cmd = \\\"cat \\\" + filenames + \\\" > \\\" + os.path.join(scan_file_dir, \\\"disk.img\\\")\\n \\n print \\\"* Scan complete, merging scans into one file (%s)...\\\"%output_img\\n \\n cmd = \\\"cat \\\" + filenames + \\\" | cp --sparse=always /proc/self/fd/0 \\\" + output_img\\n \\n logger.info(\\\"Running: %s\\\" % cmd)\\n \\n subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout.read()\\n\\n logging.info(\\\"Changing permissions on '%s'\\\"%output_img)\\n\\n os.chmod(output_img,0555)\",\n \"def get_cmdb_data(device_type):\\n pass\",\n \"def get_device_list(h):\\n try:\\n global html\\n html = h\\n # this is the result which we show on the page\\n result = \\\"\\\"\\n ip_address = \\\"\\\"\\n mac_address = \\\"\\\"\\n selected_device = \\\"swt4\\\"\\n # take value of IPaddress from the page through html.var\\n # check that value is None Then It takes the empty string\\n if html.var(\\\"ip_address\\\") == None:\\n ip_address = \\\"\\\"\\n else:\\n ip_address = html.var(\\\"ip_address\\\")\\n\\n # take value of MACAddress from the page through html.var\\n # check that value is None Then It takes the empty string\\n if html.var(\\\"mac_address\\\") == None:\\n mac_address = \\\"\\\"\\n else:\\n mac_address = html.var(\\\"mac_address\\\")\\n # take value of SelectedDevice from the page through html.var\\n # check that value is None Then It takes the empty string\\n if html.var(\\\"selected_device_type\\\") == None:\\n selected_device = \\\"swt4\\\"\\n else:\\n selected_device = html.var(\\\"selected_device_type\\\")\\n\\n # call the function get_odu_list of odu-controller which return us the\\n # list of devices in two dimensional list according to\\n # IPAddress,MACaddress,SelectedDevice\\n\\n result = get_device_list_swt_profiling(\\n ip_address, mac_address, selected_device)\\n if result == 0 or result == 1 or result == 2:\\n html.write(str(result))\\n else:\\n if result == None or result == \\\"\\\":\\n html.write(str(result))\\n else:\\n swt4_profiling_form(result, selected_device)\\n\\n except Exception as e:\\n html.write(str(e))\",\n \"def GetVdiskInfo(self, vdiskid, headers=None, query_params=None, content_type=\\\"application/json\\\"):\\n uri = self.client.base_url + \\\"/vdisks/\\\"+vdiskid\\n return self.client.get(uri, None, headers, query_params, content_type)\",\n \"def test_disk(self):\\n self.command.package = self.input_ovf\\n self.command.file_id = \\\"file1\\\"\\n self.command.run()\\n self.command.finished()\\n self.check_diff(\\\"\\\"\\\"\\n <ovf:References>\\n- <ovf:File ovf:href=\\\"input.vmdk\\\" ovf:id=\\\"file1\\\" ovf:size=\\\"{vmdk_size}\\\" />\\n <ovf:File ovf:href=\\\"input.iso\\\" ovf:id=\\\"file2\\\" ovf:size=\\\"{iso_size}\\\" />\\n...\\n <ovf:Info>Virtual disk information</ovf:Info>\\n- <ovf:Disk ovf:capacity=\\\"1\\\" ovf:capacityAllocationUnits=\\\"byte * 2^30\\\" \\\\\\novf:diskId=\\\"vmdisk1\\\" ovf:fileRef=\\\"file1\\\" ovf:format=\\\"http://www.vmware.com/\\\\\\ninterfaces/specifications/vmdk.html#streamOptimized\\\" />\\n </ovf:DiskSection>\\n...\\n <rasd:AddressOnParent>0</rasd:AddressOnParent>\\n- <rasd:ElementName>Hard Drive</rasd:ElementName>\\n- <rasd:HostResource>ovf:/disk/vmdisk1</rasd:HostResource>\\n- <rasd:InstanceID>6</rasd:InstanceID>\\n- <rasd:Parent>3</rasd:Parent>\\n- <rasd:ResourceType>17</rasd:ResourceType>\\n- </ovf:Item>\\n- <ovf:Item>\\n- <rasd:AddressOnParent>0</rasd:AddressOnParent>\\n <rasd:AutomaticAllocation>true</rasd:AutomaticAllocation>\\n\\\"\\\"\\\".format(vmdk_size=self.FILE_SIZE['input.vmdk'],\\n iso_size=self.FILE_SIZE['input.iso']))\\n self.assertFalse(os.path.exists(os.path.join(self.temp_dir,\\n \\\"input.vmdk\\\")),\\n \\\"deleted file should not be exported\\\")\",\n \"def drive_finder():\\n\\n '''drive_types = {\\n win32file.DRIVE_UNKNOWN: \\\"Unknown\\\\nDrive type can't be determined.\\\",\\n win32file.DRIVE_REMOVABLE: \\\"Removable\\\\nDrive has removable media. This includes all floppy drives and many \\\"\\n \\\"other varieties of storage devices.\\\",\\n win32file.DRIVE_FIXED: \\\"Fixed\\\\nDrive has fixed (nonremovable) media. This includes all hard drives, including \\\"\\n \\\"hard drives that are removable.\\\",\\n win32file.DRIVE_REMOTE: \\\"Remote\\\\nNetwork drives. This includes drives shared anywhere on a network.\\\",\\n win32file.DRIVE_CDROM: \\\"CDROM\\\\nDrive is a CD-ROM. No distinction is made between read-only and read/write\\\"\\n \\\" CD-ROM drives.\\\",\\n win32file.DRIVE_RAMDISK: \\\"RAMDisk\\\\nDrive is a block of random access memory (RAM) on the local computer that \\\"\\n \\\"behaves like a disk drive.\\\",\\n win32file.DRIVE_NO_ROOT_DIR: \\\"The root directory does not exist.\\\"\\n }'''\\n\\n drives = win32api.GetLogicalDriveStrings().split('\\\\x00')[:-1]\\n\\n removable_count = 0\\n removable_volumes = {}\\n values = [] # Initialize list of removable drives because code will exit if there is no drive attached\\n\\n for device in drives:\\n what_type = win32file.GetDriveType(device)\\n # print(\\\"What type is: \\\", what_type)\\n\\n if what_type == 2:\\n removable_count += 1\\n device_name = (win32api.GetVolumeInformation(device))\\n device_name = str(device_name[0])\\n removable_volumes.update({device: device_name})\\n values = list(removable_volumes.keys())\\n\\n # print(\\\"\\\\n\\\\n\\\\nAvailable USB drives are: \\\", removable_volumes)\\n\\n if not values:\\n print(\\\"\\\\n\\\\n\\\\nNo USB Drives are available. Did you forget to plug one in?\\\")\\n destroot = \\\"\\\" # No USB drive so return a blank.\\n else:\\n values.append('Exit with no pick') # Add exit option if user changes mind.\\n title = 'Please choose Drive to write logs to ' + str(removable_volumes) + ': '\\n value, index = pick(values, title, indicator='=>')\\n\\n if value != 'Exit with no pick':\\n destroot = value + r'Temp\\\\LOGCAPTURE'\\n return destroot\\n else:\\n print(\\\"\\\\nExiting with no action based on user selection.\\\")\\n exit(1)\\n\\n return\",\n \"def retr_segment_devices( segment ) :\\n\\n\\t\\t\\t_logger.info( '...retr_segment_devices...' )\\n\\t\\t\\toutput = []\\n\\t\\t\\ttry :\\n\\t\\t\\t\\tdb = mongo.db.auth_devices\\n\\t\\t\\t\\tfor device in db.find( { 'segment' : segment } ) :\\n\\t\\t\\t\\t\\toutput.append({'moniker' : device['moniker'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'description' : device['description'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'active' : device['active'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'device_id' : device['device_id'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'enlisted' : device['enlisted'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'last_kown_remote_ip' : device['last_known_remote_ip'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'engaged' : device['engaged'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'canononical_user' : device['canonical_user'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'scope' : device['scope'] ,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'segment' : device['segment']\\n\\t\\t\\t\\t\\t})\\n\\t\\t\\texcept Exception as e :\\n\\t\\t\\t\\t _logger.error( '...retr_segment_devices %s' % e.message )\\n\\t\\t\\treturn jsonify({'result' : output})\",\n \"def get_drive_enclosure_information(self):\\n\\n for device in self._devices:\\n if not device['Device Type'].startswith(\\\"disk\\\"):\\n continue\\n enc_device, device_slot, element_index = self.get_device_slot(device['SAS Address'])\\n device['Enclosure Device'] = enc_device\\n device['Enclosure Slot'] = device_slot\\n device['Slot Description'] = self.get_array_desc_text(enc_device, element_index)\",\n \"def discover_devices(vid=FT232H_VID, pid=FT232H_PID):\\n try:\\n # Create a libftdi context.\\n ctx = None\\n ctx = ftdi.new()\\n # Enumerate FTDI devices.\\n device_list = None\\n count, device_list = ftdi.usb_find_all(ctx, vid, pid)\\n if count < 0:\\n raise RuntimeError('ftdi_usb_find_all returned error {0}: {1}'.format(count, ftdi.get_error_string(self._ctx)))\\n # Walk through list of devices and assemble list of serial numbers.\\n devices = []\\n while device_list is not None:\\n # Get USB device strings and add serial to list of devices.\\n ret, manufacturer, description, serial = ftdi.usb_get_strings(ctx, device_list.dev, 256, 256, 256)\\n if ret < 0:\\n raise RuntimeError('ftdi_usb_get_strings returned error {0}: {1}'.format(ret, ftdi.get_error_string(self._ctx)))\\n devices.append(serial)\\n device_list = device_list.next\\n return devices\\n finally:\\n # Make sure to clean up list and context when done.\\n if device_list is not None:\\n ftdi.list_free(device_list)\\n if ctx is not None:\\n ftdi.free(ctx)\",\n \"def get_description(disk):\\n #Gather info from diskutil to create some descriptions.\\n # -- Internal or external --\\n internal_or_external = \\\"Unknown \\\"\\n\\n if \\\"Internal\\\" in PLIST.keys():\\n if PLIST[\\\"Internal\\\"]:\\n internal_or_external = \\\"Internal \\\"\\n\\n else:\\n internal_or_external = \\\"External \\\"\\n\\n # -- Type: Removable, SSD, or HDD --\\n disk_type = \\\"Unknown \\\"\\n\\n if (\\\"Removable\\\" in PLIST.keys() and PLIST['Removable']) or \\\\\\n (\\\"RemovableMedia\\\" in PLIST.keys() and PLIST['RemovableMedia']):\\n disk_type = \\\"Removable Drive \\\"\\n\\n #Fix for old versions of OS X where the SolidState attribute is missing.\\n #Means we assume things are HDDs if we can't otherwise figure them out.\\n if disk_type == \\\"Unknown \\\" and \\\"SolidState\\\" in PLIST.keys() and PLIST[\\\"SolidState\\\"]:\\n disk_type = \\\"Solid State Drive \\\"\\n\\n elif disk_type == \\\"Unknown \\\":\\n disk_type = \\\"Hard Disk Drive \\\"\\n\\n # -- Bus protocol --\\n bus_protocol = \\\"Unknown\\\"\\n\\n if \\\"BusProtocol\\\" in PLIST.keys():\\n bus_protocol = str(PLIST[\\\"BusProtocol\\\"])\\n\\n # -- APFS containers, volumes, physical stores --\\n apfs_string = \\\"\\\"\\n\\n if \\\"Content\\\" in PLIST.keys() and PLIST[\\\"Content\\\"] == \\\"Apple_APFS\\\":\\n apfs_string = \\\" (APFS Physical Store)\\\"\\n\\n elif \\\"APFSContainerReference\\\" in PLIST.keys() and PLIST[\\\"APFSContainerReference\\\"] == disk:\\n apfs_string = \\\" (APFS Container)\\\"\\n\\n elif \\\"FilesystemType\\\" in PLIST.keys() and PLIST[\\\"FilesystemType\\\"] == \\\"apfs\\\":\\n apfs_string = \\\" (APFS Volume)\\\"\\n\\n #Assemble info into a string.\\n if bus_protocol != \\\"Unknown\\\":\\n return internal_or_external+disk_type+\\\"(Connected through \\\"+bus_protocol+\\\")\\\"+apfs_string\\n\\n elif disk_type != \\\"Unknown \\\":\\n return internal_or_external+disk_type+apfs_string\\n\\n elif internal_or_external != \\\"Unknown \\\":\\n return internal_or_external+\\\"Unknown Disk\\\"+apfs_string\\n\\n else:\\n return \\\"N/A\\\"\",\n \"def Init(self):\\n # dac address 1136 to 1139 should all 255\\n #只需要最低字节\\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC1,addr,0))[0] for addr in range(1136,1140))\\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC2,addr,0))[0] for addr in range(1136,1140))\\n\\n for data in rd_data:\\n print(data)\\n # print ('Print something I can understand: {}'.format(repr(chunks)))\\n # print ram_data\\n return rd_data\",\n \"def get_lvs() -> List[Dict[str, str]]:\\n p = subprocess.run(\\n [\\\"lvs\\\", \\\"--reportformat\\\", \\\"json\\\"], check=True, capture_output=True\\n )\\n output = json.loads(p.stdout)\\n result = []\\n for lv in output[\\\"report\\\"][0][\\\"lv\\\"]:\\n lvname = lv[\\\"lv_name\\\"]\\n vgname = lv[\\\"vg_name\\\"]\\n devname = f\\\"{vgname}-{lvname}\\\"\\n path = f\\\"/dev/mapper/{devname}\\\"\\n result.append({\\\"lv\\\": lvname, \\\"vg\\\": vgname, \\\"devname\\\": devname, \\\"devpath\\\": path})\\n return result\",\n \"def disk_partitions(all=False):\\n phydevs = []\\n f = open(\\\"/proc/filesystems\\\", \\\"r\\\")\\n for line in f:\\n if not line.startswith(\\\"nodev\\\"):\\n phydevs.append(line.strip())\\n\\n retlist = []\\n f = open('/etc/mtab', \\\"r\\\")\\n for line in f:\\n if not all and line.startswith('none'):\\n continue\\n fields = line.split()\\n device = fields[0]\\n mountpoint = fields[1]\\n fstype = fields[2]\\n if not all and fstype not in phydevs:\\n continue\\n if device == 'none':\\n device = ''\\n ntuple = disk_ntuple(device, mountpoint, fstype)\\n retlist.append(ntuple)\\n return retlist\",\n \"def describe(self):\\n if not self.name:\\n raise ValueError(\\\"Sorry! id_type must be 'name'\\\")\\n r = requests.get(f\\\"https://api.fda.gov/drug/ndc.json?search=brand_name:{self.drug_id}\\\")\\n response = r.json()\\n data = response['results'][0]\\n self.brand_name = data['brand_name']\\n self.generic_name = data['generic_name']\\n self.active_ingredients = [i['name'] for i in data['active_ingredients']]\\n self.pharm_class = get_pharm_class(self.drug_id, as_df=False)\\n self.route = data['route']\\n self.ndc = data['product_ndc']\\n self.product_type = data['product_type']\\n\\n print(f\\\"Generic name: {self.generic_name}\\\")\\n print(f\\\"Brand name: {self.brand_name}\\\")\\n print(f\\\"Active ingredients: {self.active_ingredients}\\\")\\n print(f\\\"Routes of administration: {self.route}\\\")\\n print(f\\\"Pharmacologic Classes: {self.pharm_class}\\\")\\n print(f\\\"NDC: {self.ndc}\\\")\\n print(f\\\"Product type: {self.product_type}\\\")\",\n \"def part_device(part_number):\\n return \\\"/dev/mmcblk0p\\\" + part_number\",\n \"def dumpData(self,out):\\n #--Header\\n out.packSub0('NAME',self.id)\\n if getattr(self,'isDeleted',False):\\n out.packSub('DELE','i',0)\\n return\\n if self.name == 'LEVC':\\n flags = 1*self.calcFromAllLevels\\n etype = 'CNAM'\\n else:\\n flags = 1*self.calcForEachItem + 2*self.calcFromAllLevels\\n etype = 'INAM'\\n out.packSub('DATA','i',flags)\\n out.packSub('NNAM','B',self.chanceNone)\\n out.packSub('INDX','i',len(self.entries))\\n #--Entries\\n for pcLevel, objectId in self.entries:\\n out.packSub0(etype,objectId)\\n out.packSub('INTV','h',pcLevel)\",\n \"def print_metadata_stats(sector=6, filesystem=None):\\n\\n assert isinstance(filesystem, str)\\n\\n if filesystem in ['phtess2', 'php1']:\\n fs = f\\\"/nfs/{filesystem}\\\"\\n projdir = f'{fs}/ar0/TESS/PROJ/lbouma/cdips'\\n lcdirectory = f'/nfs/phtess2/ar0/TESS/PROJ/lbouma/CDIPS_LCS/sector-{sector}/'\\n catdir = '/nfs/phtess1/ar1/TESS/PROJ/lbouma/'\\n elif filesystem in ['wh1', 'wh2']:\\n projdir = \\\"/ar1/PROJ/luke/proj/cdips\\\"\\n lcdirectory = f'/ar1/PROJ/luke/proj/CDIPS_LCS/sector-{sector}/'\\n catdir = '/ar1/local/cdips/catalogs/'\\n\\n statsdir = join(projdir, 'results', 'cdips_lc_stats', f'sector-{sector}')\\n if not os.path.exists(statsdir): os.mkdir(statsdir)\\n statsfile = os.path.join(statsdir,'cdips_lc_statistics.txt')\\n\\n stats = ap.read_stats_file(statsfile, fovcathasgaiaids=True)\\n N_lcs = len(stats)\\n\\n print('CDIPS LIGHTCURVES STATS FOR SECTOR {}'.format(sector))\\n print(42*'-')\\n print('total N_lcs: {}'.format(N_lcs))\\n\\n for apn in [1,2,3]:\\n N_nan = len(stats[stats['ndet_tf{}'.format(apn)]==0])\\n print('for ap {}, {} ({:.1f}%) are all nan, leaving {} ok lcs'.\\n format(apn, N_nan, N_nan/N_lcs*100, N_lcs-N_nan))\\n\\n print('\\\\nsanity check: {} TF1 LCs have stdev > 0'.\\n format(len(stats[stats['stdev_tf1'] > 0])))\\n\\n print('Finished print_metadata_stats!')\",\n \"def get_diskstats(self):\\n dstats = self._run(options=['-kd'])\\n extdstats = self._run(options=['-kdx'])\\n dsd = self._get_childs_data(dstats)\\n edd = self._get_childs_data(extdstats)\\n ds = self.parse_diskstats(dsd)\\n eds = self.parse_diskstats(edd)\\n\\n for dk, dv in ds.items():\\n if dk in eds:\\n ds[dk].update(eds[dk])\\n\\n return ds\",\n \"def _GetDiskInfo(self, disk, item):\\n dev_path = os.path.join(disk, 'device')\\n\\n # Search upwards and through symlinks looking for the item.\\n while os.path.isdir(dev_path) and dev_path != '/sys':\\n fname = os.path.join(dev_path, item)\\n if os.path.exists(fname):\\n with open(fname, 'r') as fd:\\n return fd.readline().rstrip()\\n\\n # Move up a level and follow any symlink.\\n new_path = os.path.join(dev_path, '..')\\n if os.path.islink(new_path):\\n new_path = os.path.abspath(os.readlink(os.path.dirname(dev_path)))\\n dev_path = new_path\\n return '[Unknown]'\",\n \"def OBD2_cmd(self, cmd):\\n obd2_record = []\\n\\n self.SEND_cmd(cmd)\\n record = self.RTRV_record()\\n\\n # check that the ELM headers match the original cmd\\n if record[0][0] != cmd:\\n print \\\"PANIC! - cmd is different\\\"\\n print \\\"cmd:\\\", cmd, \\\"record[0][0]:\\\", record[0][0]\\n\\n # Format result into a standard OBD2 record\\n obd2_record = self.triage_record( record )\\n\\n if obd2_record == []:\\n obd2_record = { 'command' : cmd ,\\n 'responses' : { '7E8' : [] }, \\n 'timestamp' : 0 }\\n\\n # set timestamp \\n #scantime = time.time()\\n obd2_record['timestamp'] = str(int(time.time()))\\n\\n return obd2_record\",\n \"def vectors(self, adr = 0x10000):\\n\\n\\t\\tself.__vector(adr - 2, \\\"RST\\\")\\n\\t\\tself.__vector(adr - 4, \\\"NMI\\\")\\n\\t\\tself.__vector(adr - 6, \\\"SWI\\\")\\n\\t\\tself.__vector(adr - 8, \\\"IRQ\\\")\\n\\t\\tx = self.p.t.add(adr - 8, adr, \\\"tbl\\\")\\n\\t\\tx.blockcmt += \\\"\\\\n-\\\\nMC6800 Vector Table\\\\n\\\\n\\\"\",\n \"def parseDisk(disk):\\n\\n\\tdict={}\\n\\n\\td = disk.split(',')\\n\\tif len(d) != 3:\\n rocks.commands.Abort('Invalid disk specification.'\\n\\t\\t\\t\\t' Please see rocks add host vm help.')\\n\\n\\tdict['device'] = d[1]\\n\\tdict['mode'] = d[2]\\n\\n\\te = d[0].split(':')\\n\\tdict['vbd_type'] = ':'.join(e[0:-1])\\n\\n\\tif dict['vbd_type'] == 'phy':\\n\\t\\tdict['prefix'] = ''\\n\\t\\tdict['name'] = e[-1]\\t# allows for '/' in name for LVM\\n\\telse:\\n\\t\\tdict['prefix'] = os.path.dirname(e[-1])\\n\\t\\tdict['name'] = os.path.basename(e[-1])\\n\\n\\treturn dict\",\n \"def get_disks():\\n disks = []\\n\\n try:\\n # Run script\\n result = run_diskpart(['list disk'])\\n except subprocess.CalledProcessError:\\n pass\\n else:\\n # Append disk numbers\\n output = result.stdout.decode().strip()\\n for tmp in re.findall(r'Disk (\\\\d+)\\\\s+\\\\w+\\\\s+(\\\\d+\\\\s+\\\\w+)', output):\\n num = tmp[0]\\n size = human_readable_size(tmp[1])\\n disks.append({'Number': num, 'Size': size})\\n\\n return disks\",\n \"def file_server_show_table_format(result):\\n row = OrderedDict()\\n row['Name'] = result['name']\\n row['Resource Group'] = result['resourceGroup']\\n row['Size'] = result['vmSize']\\n disks = result['dataDisks']\\n if disks:\\n row['Disks'] = '{0} x {1} Gb'.format(disks['diskCount'], disks['diskSizeInGb'])\\n mount_settings = result['mountSettings']\\n if mount_settings:\\n row['Public IP'] = mount_settings['fileServerPublicIp']\\n row['Internal IP'] = mount_settings['fileServerInternalIp']\\n row['Mount Point'] = mount_settings['mountPoint']\\n return row\",\n \"def parse_diskstats(self, input):\\n # made edits to original code here to output string values instead of bytes\\n dstats = {}\\n dsi = input.rfind('Device:'.encode())\\n if dsi == -1:\\n raise ParseError('Unknown input format: %r' % input)\\n\\n ds = input[dsi:].splitlines()\\n hdr = ds.pop(0).split()[1:]\\n\\n for d in ds:\\n if d:\\n d = d.split()\\n dev = d.pop(0)\\n if (dev.decode('utf-8') in self.disks) or not self.disks:\\n dstats[dev.decode('utf-8')] = dict([(k.decode('utf-8'), float(v)) for k, v in zip(hdr, d)])\\n return dstats\",\n \"def get_ceph_drv_info():\\n disks_info = []\\n stat = psutil.disk_io_counters(perdisk=True)\\n for drv in get_ceph_disk():\\n info = CEPHDiskInfo(drv)\\n disk = basename(drv)\\n if disk in stat:\\n info.rd_cnt = stat[disk].read_count\\n info.wr_cnt = stat[disk].write_count\\n info.rd_bytes = stat[disk].read_bytes\\n info.wr_bytes = stat[disk].write_bytes\\n info.rd_time = stat[disk].read_time\\n info.wr_time = stat[disk].write_time\\n\\n disks_info.append(info)\\n\\n return disks_info\",\n \"def get_hdd():\\n return {\\n 'HDD': string_chopped_to_float(psutil.disk_usage('/'), 'percent=', ')'),\\n }\",\n \"def read_uef_details(chunks):\\n\\n\\tpos, chunk = find_next_chunk(chunks, 0, [0x0])\\n\\n\\tif pos == None:\\n\\n\\t\\toriginator = 'Unknown'\\n\\n\\telif chunk[1] == '':\\n\\n\\t\\toriginator = 'Unknown'\\n\\telse:\\n\\t\\toriginator = chunk[1]\\n\\n\\tpos, chunk = find_next_chunk(chunks, 0, [0x5])\\n\\n\\tif pos == None:\\n\\n\\t\\tmachine, keyboard = 'Unknown', 'Unknown'\\n\\n\\telse:\\n\\n\\t\\tmachines = ('BBC Model A', 'Electron', 'BBC Model B', 'BBC Master')\\n\\t\\tkeyboards = ('Any layout', 'Physical layout', 'Remapped')\\n\\n\\t\\tmachine = ord(chunk[1][0]) & 0x0f\\n\\t\\tkeyboard = (ord(chunk[1][0]) & 0xf0) >> 4\\n\\n\\t\\tif machine < len(machines):\\n\\t\\t\\tmachine = machines[machine]\\n\\t\\telse:\\n\\t\\t\\tmachine = 'Unknown'\\n\\n\\t\\tif keyboard < len(keyboards):\\n\\t\\t\\tkeyboard = keyboards[keyboard]\\n\\t\\telse:\\n\\t\\t\\tkeyboard = 'Unknown'\\n\\n\\tpos, chunk = find_next_chunk(chunks, 0, [0xff00])\\n\\n\\tif pos == None:\\n\\n\\t\\temulator = 'Unknown'\\n\\n\\telif chunk[1] == '':\\n\\n\\t\\temulator = 'Unknown'\\n\\telse:\\n\\t\\temulator = chunk[1]\\n\\n\\n\\t# Remove trailing null bytes\\n\\twhile originator[-1] == '\\\\000':\\n\\n\\t\\toriginator = originator[:-1]\\n\\n\\twhile emulator[-1] == '\\\\000':\\n\\n\\t\\temulator = emulator[:-1]\\n\\n\\tfeatures = ''\\n\\tif find_next_chunk(chunks, 0, [0x1])[0] != None:\\n\\t\\tfeatures = features + '\\\\n' + 'Instructions'\\n\\tif find_next_chunk(chunks, 0, [0x2])[0] != None:\\n\\t\\tfeatures = features + '\\\\n' + 'Credits'\\n\\tif find_next_chunk(chunks, 0, [0x3])[0] != None:\\n\\t\\tfeatures = features + '\\\\n' + 'Inlay'\\n\\n\\treturn originator, machine, keyboard, emulator, features\",\n \"def _output_vadapter_list_verbose(outf, name, vadapter_list):\\n\\n if vadapter_list:\\n FMT = \\\"%s\\\\n%-10s\\\\n%-20s\\\\n%-5s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n%s\\\\n\\\"\\n# HEADER = (\\\"id\\\",\\\"name\\\",\\\"io_module_id\\\",\\\"vfabric_id\\\",\\\"protocol\\\",\\\"init_type\\\",\\\"desc\\\",\\\"status\\\",\\\"mac\\\"\\n# ,\\\"prmiscuous_mode\\\",\\\"silent_listener\\\",\\\"vlan\\\",\\\"wwnn\\\",\\\"wwpn\\\",\\\"fc_id\\\",\\\"spma\\\",\\\"fmpa\\\") \\n\\t\\n for (n , vadapter) in vadapter_list:\\n name = vadapter['NAME']\\n id = vadapter['ID']\\n protocol = vadapter['PROTOCOL']\\n vfabric = vadapter['VFABRIC']\\n io_module = vadapter['IO_MODULE_ID']\\n desc = vadapter['DESC']\\n status = vadapter['STATUS']\\n init_type = vadapter['INIT_TYPE']\\n running_mode = vadapter['RUNNING_MODE']\\n if protocol == 1:\\n mac = vadapter['MAC']\\n promiscuous = vadapter['PROMISCUOUS']\\n silent = vadapter['SILENT']\\n vlan = vadapter['VLAN']\\n elif protocol == 3:\\n wwnn = vadapter['WWNN']\\n wwpn = vadapter['WWPN']\\n spma = vadapter['SPMA']\\n fpma = vadapter['FPMA']\\n fc_id = vadapter['FC_ID']\\n\\n\\n if _LIMITED_SHOW:\\n outf.write('General Attr:\\\\nId: %s\\\\n\\\\tName: %s\\\\n\\\\tIO_Module: %s\\\\n\\\\tVfabric: %s\\\\n\\\\tProtocol: %s\\\\n\\\\tInitization_Type: %s\\\\n\\\\tDescription: %s\\\\n\\\\tStatus: %-20s\\\\n\\\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status)) \\n \\n\\t\\telif _DETAIL_SHOW: \\n if protocol == 3:\\n outf.write('General Attr:\\\\nId: %s\\\\nName: %s\\\\nIO_Module: %s\\\\nVfabric: %s\\\\nProtocol: %s\\\\nInitization_Type: %s\\\\nDescription: %s\\\\nStatus: %-20s\\\\nRUNNING_MODE :%s\\\\nFC_Attr:\\\\n\\\\tWWNN: %s\\\\n\\\\tWWNP: %s\\\\n\\\\tFC_ID: %s\\\\n\\\\tSPMA: %s\\\\n\\\\tFMPA: %s\\\\n\\\\n' % (id, name, io_module, vfabric, protocol, init_type, desc, status, running_mode, wwnn, wwpn,fc_id,spma,fpma)) \\n \\n elif protocol == 1:\\n\\t\\t\\t outf.write('General Attr:\\\\nId: %s\\\\nName: %s\\\\nIO_Module: %s\\\\nVfabric: %s\\\\nProtocol: %s\\\\nInitization_Type: %s\\\\nDescription: %s\\\\nStatus: %-20s\\\\nRUNNING_MODE :%s\\\\nEN_Attr:\\\\n\\\\tMAC: %s\\\\n\\\\tVLAN: %s\\\\n\\\\tPromiscuous: %s\\\\n\\\\tSilent Listener: %s\\\\n\\\\n' % (id , name, io_module,vfabric, protocol, init_type, desc, status, running_mode, mac,vlan, promiscuous, silent))\",\n \"def get_os_virtual_hard_disk(self):\\n if self.is_vm_image():\\n return None\\n i = self.virtual_environment[self.T_I]\\n sa = self.virtual_environment[self.T_SA]\\n c = self.virtual_environment[self.T_C]\\n now = datetime.datetime.now()\\n blob = self.BLOB_BASE % (i[self.T_I_N],\\n str(now.year),\\n str(now.month),\\n str(now.day),\\n str(now.hour),\\n str(now.minute),\\n str(now.second),\\n str(current_thread().ident))\\n media_link = self.MEDIA_BASE % (sa[self.T_SA_SN],\\n sa[self.T_SA_UB],\\n c,\\n blob)\\n os_virtual_hard_disk = OSVirtualHardDisk(i[self.T_I_N], media_link)\\n return os_virtual_hard_disk\",\n \"def show_asm_volumes(self):\\n sql = \\\"select NAME from v$asm_diskgroup_stat ORDER BY 1\\\"\\n self.cur.execute(sql)\\n res = self.cur.fetchall()\\n key = ['{#ASMVOLUME}']\\n lst = []\\n for i in res:\\n d = dict(zip(key, i))\\n lst.append(d)\\n print(json.dumps({'data': lst}))\",\n \"def prepare_dev(\\n data,\\n journal,\\n fstype,\\n mkfs_args,\\n mount_options,\\n cluster_uuid,\\n osd_uuid,\\n journal_uuid,\\n journal_dmcrypt,\\n osd_dm_keypath,\\n ):\\n\\n ptype_tobe = TOBE_UUID\\n ptype_osd = OSD_UUID\\n if osd_dm_keypath:\\n ptype_tobe = DMCRYPT_TOBE_UUID\\n ptype_osd = DMCRYPT_OSD_UUID\\n\\n rawdev = None\\n if is_partition(data):\\n LOG.debug('OSD data device %s is a partition', data)\\n rawdev = data\\n else:\\n LOG.debug('Creating osd partition on %s', data)\\n try:\\n command_check_call(\\n [\\n 'sgdisk',\\n '--largest-new=1',\\n '--change-name=1:ceph data',\\n '--partition-guid=1:{osd_uuid}'.format(\\n osd_uuid=osd_uuid,\\n ),\\n '--typecode=1:%s' % ptype_tobe,\\n '--',\\n data,\\n ],\\n )\\n update_partition('-a', data, 'created')\\n command(\\n [\\n # wait for udev event queue to clear\\n 'udevadm',\\n 'settle',\\n ],\\n )\\n except subprocess.CalledProcessError as e:\\n raise Error(e)\\n\\n rawdev = get_partition_dev(data, 1)\\n\\n dev = None\\n if osd_dm_keypath:\\n dev = dmcrypt_map(rawdev, osd_dm_keypath, osd_uuid)\\n else:\\n dev = rawdev\\n\\n try:\\n args = [\\n 'mkfs',\\n '-t',\\n fstype,\\n ]\\n if mkfs_args is not None:\\n args.extend(mkfs_args.split())\\n if fstype == 'xfs':\\n args.extend(['-f']) # always force\\n else:\\n args.extend(MKFS_ARGS.get(fstype, []))\\n args.extend([\\n '--',\\n dev,\\n ])\\n try:\\n LOG.debug('Creating %s fs on %s', fstype, dev)\\n command_check_call(args)\\n except subprocess.CalledProcessError as e:\\n raise Error(e)\\n\\n #remove whitespaces from mount_options\\n if mount_options is not None:\\n mount_options = \\\"\\\".join(mount_options.split())\\n\\n path = mount(dev=dev, fstype=fstype, options=mount_options)\\n\\n try:\\n prepare_dir(\\n path=path,\\n journal=journal,\\n cluster_uuid=cluster_uuid,\\n osd_uuid=osd_uuid,\\n journal_uuid=journal_uuid,\\n journal_dmcrypt=journal_dmcrypt,\\n )\\n finally:\\n unmount(path)\\n finally:\\n if rawdev != dev:\\n dmcrypt_unmap(osd_uuid)\\n\\n if not is_partition(data):\\n try:\\n command_check_call(\\n [\\n 'sgdisk',\\n '--typecode=1:%s' % ptype_osd,\\n '--',\\n data,\\n ],\\n )\\n except subprocess.CalledProcessError as e:\\n raise Error(e)\",\n \"def _device_tree(self, match, offset, end):\\n log.debug('Inspecting potential DTB @ 0x{:08x}'.format(offset))\\n\\n def field(name):\\n return int.from_bytes(match.group(name), 'big')\\n\\n # Skip past magic\\n start = offset\\n offset += 4\\n\\n totalsize = field('totalsize')\\n if totalsize > (end - offset):\\n msg = 'Invalid FDT @ 0x{:08x} - totalsize too large (0x{:08x})'\\n log.debug(msg.format(start, totalsize))\\n return None\\n offset += 4\\n\\n off_dt_struct = field('off_dt_struct')\\n if off_dt_struct > (end - offset):\\n msg = 'Invalid FDT @ 0x{:08x} - off_dt_struct too large (0x{:08x})'\\n log.debug(msg.format(start, off_dt_struct))\\n return None\\n offset += 4\\n\\n off_dt_strings = field('off_dt_strings')\\n if off_dt_strings > (end - offset):\\n msg = 'Invalid FDT @ 0x{:08x} - off_dt_strings too large (0x{:08x})'\\n log.debug(msg.format(start, off_dt_strings))\\n return None\\n offset += 4\\n\\n off_mem_rsvmap = field('off_mem_rsvmap')\\n if off_mem_rsvmap > (end - offset):\\n msg = 'Invalid FDT @ 0x{:08x} - off_mem_rsvmap too large (0x{:08x})'\\n log.debug(msg.format(start, off_mem_rsvmap))\\n return None\\n\\n # Skip version, last_comp_version, boot_cpuid_phy\\n offset += 16\\n\\n size_dt_strings = field('size_dt_strings')\\n if (end - off_dt_strings) < size_dt_strings:\\n msg = 'Invalid FDT @ 0x{:08x} - size_dt_strings too large (0x{:08x})'\\n log.debug(msg.format(start, size_dt_strings))\\n return None\\n\\n size_dt_struct = field('size_dt_struct')\\n if (end - off_dt_struct) < size_dt_struct:\\n msg = 'Invalid FDT @ 0x{:08x} - size_dt_struct too large (0x{:08x})'\\n log.debug(msg.format(start, size_dt_struct))\\n return None\\n\\n log.debug('Returning DTB @ 0x{:08x}, size={:d} bytes'.format(start, totalsize))\\n return self._data[start:start + totalsize]\",\n \"def _get_bootdisk_iter(self, instance):\\n lpar_wrap = vm.get_instance_wrapper(self.adapter, instance)\\n match_func = self._disk_match_func(DiskType.BOOT, instance)\\n for vios_uuid in self.vios_uuids:\\n vios_wrap = pvm_vios.VIOS.get(\\n self.adapter, uuid=vios_uuid, xag=[pvm_const.XAG.VIO_SMAP])\\n for scsi_map in tsk_map.find_maps(\\n vios_wrap.scsi_mappings, client_lpar_id=lpar_wrap.id,\\n match_func=match_func):\\n yield scsi_map.backing_storage, vios_wrap\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6226752","0.62065095","0.613328","0.5941464","0.58446807","0.5758732","0.57254314","0.5669364","0.5603092","0.5584567","0.55693907","0.5547329","0.553814","0.55317974","0.55209994","0.5405666","0.5393464","0.5392122","0.5388479","0.5382682","0.53686225","0.53394","0.53099704","0.5270263","0.5264361","0.52484417","0.5245146","0.5174927","0.5137749","0.5128073","0.5121027","0.50899696","0.5078117","0.5053774","0.50518924","0.50381124","0.5036555","0.50294757","0.5026689","0.5022715","0.50003064","0.49893594","0.49779364","0.4967098","0.49642244","0.4955648","0.49550775","0.49482638","0.49272585","0.4918501","0.491702","0.48989943","0.4893404","0.48896575","0.48823217","0.48817718","0.48817012","0.4874126","0.48711506","0.48653927","0.485899","0.4839707","0.48396716","0.4832512","0.48292768","0.48189464","0.4810282","0.48013738","0.47999096","0.4794869","0.47945654","0.47787705","0.47769544","0.47767505","0.47760454","0.4760233","0.4753348","0.47458214","0.47038382","0.47034988","0.47004554","0.4698805","0.46939343","0.46938145","0.46910793","0.46852553","0.4684408","0.46825117","0.46820316","0.4679868","0.46761185","0.46723914","0.4667812","0.4666888","0.46651608","0.4662169","0.46564206","0.4654205","0.46516985","0.4640882"],"string":"[\n \"0.6226752\",\n \"0.62065095\",\n \"0.613328\",\n \"0.5941464\",\n \"0.58446807\",\n \"0.5758732\",\n \"0.57254314\",\n \"0.5669364\",\n \"0.5603092\",\n \"0.5584567\",\n \"0.55693907\",\n \"0.5547329\",\n \"0.553814\",\n \"0.55317974\",\n \"0.55209994\",\n \"0.5405666\",\n \"0.5393464\",\n \"0.5392122\",\n \"0.5388479\",\n \"0.5382682\",\n \"0.53686225\",\n \"0.53394\",\n \"0.53099704\",\n \"0.5270263\",\n \"0.5264361\",\n \"0.52484417\",\n \"0.5245146\",\n \"0.5174927\",\n \"0.5137749\",\n \"0.5128073\",\n \"0.5121027\",\n \"0.50899696\",\n \"0.5078117\",\n \"0.5053774\",\n \"0.50518924\",\n \"0.50381124\",\n \"0.5036555\",\n \"0.50294757\",\n \"0.5026689\",\n \"0.5022715\",\n \"0.50003064\",\n \"0.49893594\",\n \"0.49779364\",\n \"0.4967098\",\n \"0.49642244\",\n \"0.4955648\",\n \"0.49550775\",\n \"0.49482638\",\n \"0.49272585\",\n \"0.4918501\",\n \"0.491702\",\n \"0.48989943\",\n \"0.4893404\",\n \"0.48896575\",\n \"0.48823217\",\n \"0.48817718\",\n \"0.48817012\",\n \"0.4874126\",\n \"0.48711506\",\n \"0.48653927\",\n \"0.485899\",\n \"0.4839707\",\n \"0.48396716\",\n \"0.4832512\",\n \"0.48292768\",\n \"0.48189464\",\n \"0.4810282\",\n \"0.48013738\",\n \"0.47999096\",\n \"0.4794869\",\n \"0.47945654\",\n \"0.47787705\",\n \"0.47769544\",\n \"0.47767505\",\n \"0.47760454\",\n \"0.4760233\",\n \"0.4753348\",\n \"0.47458214\",\n \"0.47038382\",\n \"0.47034988\",\n \"0.47004554\",\n \"0.4698805\",\n \"0.46939343\",\n \"0.46938145\",\n \"0.46910793\",\n \"0.46852553\",\n \"0.4684408\",\n \"0.46825117\",\n \"0.46820316\",\n \"0.4679868\",\n \"0.46761185\",\n \"0.46723914\",\n \"0.4667812\",\n \"0.4666888\",\n \"0.46651608\",\n \"0.4662169\",\n \"0.46564206\",\n \"0.4654205\",\n \"0.46516985\",\n \"0.4640882\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69272226"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18486,"cells":{"query":{"kind":"string","value":"Return possible map states"},"document":{"kind":"string","value":"def get_moves(self):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_state_map(self):\n if self.is_po:\n state_map = self.curr_map\n else:\n state_map = self.grid_map\n state_map[self.curr_pos] = 0.5\n state_map[self.goal_pos] = 0.7\n return state_map","def states(self):\n knownstates = set(self.keys())\n for possiblestates in self.values():\n for i in possiblestates:\n knownstates.add(i)\n return list(knownstates)","def get_states(self):\n raise NotImplementedError()","def get_state(self):\n state_dict = OrderedDict()\n for key, target_object in self._map.items():\n state = self._get_single_state(target_object)\n if state is not None:\n # pushbuttons for example are not defined in the get function\n state_dict[key] = state\n return state_dict","def get_state(self):\n return self.state_map","def get_all_states(self):\n return self._states","def get_states(self):\n states = {}\n if hasattr(self, 'random_mask_state'):\n states['random_mask_state'] = self.random_mask_state.get_state()\n if hasattr(self, 'deformrandomstate'):\n states['deformrandomstate'] = self.deformrandomstate.get_state()\n states['randomstate'] = self.randomstate.get_state()\n return states","def get_all_states(self):\n return tuple(self._transition_probs.keys())","def get_list_of_states(self):\n return self.states","def game_of_life():\n # 3x3 neighbourhood\n offsets = [[(y, x) for y in range(-1, 2)] for x in range(-1, 2)]\n\n # Create mappings\n mappings = {}\n for i in range(2 ** 9):\n\n # Determine the initial state (key)\n key = f\"{bin(i)[2:]:0>9}\" # As binary string\n key = tuple(k == \"1\" for k in key) # As tuple of bools\n key = tuple(key[i * 3:i * 3 + 3] for i in range(3)) # Reshape into 2D grid\n\n # Alive counts\n centre = key[1][1]\n others = sum(sum(row) for row in key) - centre\n\n # Skip if state does not evaluate to True\n if centre:\n if others not in (2, 3):\n continue\n\n else:\n if others != 3:\n continue\n\n mappings[key] = True\n\n return Mapping2DRuleset(mappings, offsets)","def getAbsorbingStates(m):\n\t\n a=[]\n for r in range(len(m)):\n if(sum(m[r])==0): a.append(r)\n return a","def all_states(self) -> Tuple[State, ...]:\n return self.influence_graph.all_states()","def all_states(self):\n return self._states","def get_final_states(self):\r\n return set(self.final_states)","def getAllWorldStates(self):\n arrays = []\n for i in range(1, self.num_ingredients):\n arrays.append(list(range(7))) #dont forget to change\n return list(itertools.product(*arrays))","def getAllStates(self):\n return list(itertools.product(self.getAllWorldStates(), self.getAllTheta()))","def get_initial_states(self):\n raise NotImplementedError()","def states(self):\n states = _StatesDict({})\n for k, v in self.coordinates.items():\n states.__setitem__(k, v, allow=True)\n for k, v in self.speeds.items():\n states.__setitem__(k, v, allow=True)\n return states","def get_states(self):\n return product(*[phi.automaton().states for phi in self])","def state(self):\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}","def get_reward_states(self):\n state1 = State(7, 7)\n return [state1]","def get_map_state(self):\n return self.serialize()","def _dirty_states(self) -> Iterable[InstanceState[Any]]:\n return self.identity_map._dirty_states()","def states(self) -> Type[Any]:\n return []","def get_active_states(self):\n raise NotImplementedError()","def lattice_grain_node_states():\n ns_dict = { 0 : 'empty', \n 1 : 'moving up',\n 2 : 'moving right and up',\n 3 : 'moving right and down',\n 4 : 'moving down',\n 5 : 'moving left and down',\n 6 : 'moving left and up',\n 7 : 'rest',\n 8 : 'wall'}\n return ns_dict","def find_modifiable_states(state_data):\n\n modifiable = (state_data['pcad_mode'] == 'NPNT') & (state_data['clocking'] == 1) & (\n state_data['fep_count'] == state_data['ccd_count']) & (state_data['fep_count'] < 4)\n states_ind = np.where(modifiable)[0]\n cases = list(product([0, 1], repeat=len(states_ind)))\n return states_ind, cases","def get_state_actions_mapping(self):\n return None","def getStateAbbreviations():\n state_abbrev = {\n \"01\": \"AL\",\n \"02\": \"AK\",\n \"04\": \"AZ\",\n \"05\": \"AR\",\n \"06\": \"CA\",\n \"08\": \"CO\",\n \"09\": \"CT\",\n \"10\": \"DE\",\n \"11\": \"DC\",\n \"12\": \"FL\",\n \"13\": \"GA\",\n \"15\": \"HI\",\n \"16\": \"ID\",\n \"17\": \"IL\",\n \"18\": \"IN\",\n \"19\": \"IA\",\n \"20\": \"KS\",\n \"21\": \"KY\",\n \"22\": \"LA\",\n \"23\": \"ME\",\n \"24\": \"MD\",\n \"25\": \"MA\",\n \"26\": \"MI\",\n \"27\": \"MN\",\n \"28\": \"MS\",\n \"29\": \"MO\",\n \"30\": \"MT\",\n \"31\": \"NE\",\n \"32\": \"NV\",\n \"33\": \"NH\",\n \"34\": \"NJ\",\n \"35\": \"NM\",\n \"36\": \"NY\",\n \"37\": \"NC\",\n \"38\": \"ND\",\n \"39\": \"OH\",\n \"40\": \"OK\",\n \"41\": \"OR\",\n \"42\": \"PA\",\n \"44\": \"RI\",\n \"45\": \"SC\",\n \"46\": \"SD\",\n \"47\": \"TN\",\n \"48\": \"TX\",\n \"49\": \"UT\",\n \"50\": \"VT\",\n \"51\": \"VA\",\n \"53\": \"WA\",\n \"54\": \"WV\",\n \"55\": \"WI\",\n \"56\": \"WY\",\n \"72\": \"PR\"\n }\n return state_abbrev","def get_states():\n # Getting all hidden state through time\n all_hidden_states = tf.scan(GRU, processed_input, \n initializer=initial_hidden, name='states')\n return all_hidden_states","def others_locations(state):\n others_ = others(state)\n locations = {i: e['pos'] for i, e in others_.items()}\n return locations","def States(self) -> List[Callable]:\r\n\t\treturn self.__STATES__","def states(self):\n num_states = 2**self.num_vars\n return (int2tuple(s, self.num_vars) for s in range(num_states))","def get_states(self, structure):\n dir = IO(dir=os.path.join(self.loc, structure))\n return dir.get_values(\n structure, \"geo.log\", self.geovalid, self.finalE)","def states(self) -> Set[State]:\n return set(range(self.nb_states))","def states(self) -> Optional[Sequence['outputs.FeatureStateState']]:\n return pulumi.get(self, \"states\")","def states(self):\n from geoid.core import names\n from geoid.censusnames import geo_names, stusab\n\n states = {}\n\n for state_no, stusab in stusab.items():\n states[stusab] = {\n 'name': geo_names[(state_no,0)],\n 'stusab': stusab,\n 'number' : state_no\n }\n\n states['US'] = {\n 'name': 'United States',\n 'stusab': 'US',\n 'number' : 0\n }\n\n return states","def alleles(self) -> set[str]:\n return {self.ancestral_state} | {m.derived_state for m in self.mutations}","def all_state(cap):\n\n\tfor abbrev, capital in capital_cities.items():\n\t\tif cap.lower() == capital.lower():\n\t\t\tfor state, abbreviation in states.items():\n\t\t\t\tif abbreviation.lower() == abbrev.lower():\n\t\t\t\t\treturn [capital, state]\n\treturn None","def final_states(self):\n return list(self.iter_final_states())","def final_states(self) -> Tuple[tuple, ...]:\n return self._final_states","def _getStates(self):\n feature_states = []\n # for i, sim in enumerate(self.sims):\n # state = sim.getState()\n\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\n # if self.features:\n # feature = self.features[long_id] # Get feature for\n # feature_states.append((feature, state))\n # else:\n # feature_states.append((None, state))\n for i in range(self.batch_size):\n while not self.qout[i].empty():\n self.qout[i].get()\n while not self.qtraj[i].empty():\n self.qtraj[i].get()\n\n self.qin[i].put(('state',None))\n \n for i in range(self.batch_size):\n state = self.qout[i].get()\n # print(state)\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n\n return feature_states","def _getStates(self):\n feature_states = []\n # for i, sim in enumerate(self.sims):\n # state = sim.getState()\n\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\n # if self.features:\n # feature = self.features[long_id] # Get feature for\n # feature_states.append((feature, state))\n # else:\n # feature_states.append((None, state))\n for i in range(self.batch_size):\n while not self.qout[i].empty():\n self.qout[i].get()\n while not self.qtraj[i].empty():\n self.qtraj[i].get()\n\n self.qin[i].put(('state',None))\n \n for i in range(self.batch_size):\n state = self.qout[i].get()\n # print(state)\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n\n return feature_states","def get_map(self) -> list:\n return self.map_obstacle","def initial_states(self):\n return self._initial_states","def fill_map(self):\n\n sim = Pong(max_steps=None)\n s = sim.empty_state()\n s[DEFAULT_DIMS] = DEFUALT_VALUES\n\n # Optimization issues:\n next_state = self.next_state\n next_reward = self.next_reward\n d = self.d\n\n # Make the terminal state a self-loop\n next_state[self.n] = self.n\n\n t0 = clock()\n for i in range(0, self.n, 1000000):\n for j in range(i, min(i + 1000000, self.n)):\n s[TRAIN_DIMS] = d.index_to_state(j)\n for a in c.ACTIONS:\n sim.fast_set_and_step(s, c.A_STAY, a)\n if sim.hit == \"r\":\n next_reward[j, a] = 1\n next_state[j, a] = -1\n elif sim.miss == \"r\":\n next_reward[j, a] = -1\n next_state[j, a] = -1\n else:\n next_state[j, a] = d.state_to_index(sim.s[TRAIN_DIMS])\n print(i, clock() - t0)","def get_states(request):\n if 'cursor' in request.GET:\n cursor = request.GET['cursor']\n else:\n cursor = str(uuid4())\n if cursor in CURSORS:\n it = CURSORS[cursor]\n else:\n board = GAMES[request.matchdict['game']]\n it = CURSORS[cursor] = board.lookahead_boards(\n request.GET.get('lookahead', 1))\n slen = request.GET.get('count', 20)\n states = list(islice(it, slen))\n if len(states) < 20:\n cursor = None\n return {\n 'cursor': cursor,\n 'boards': [[b.board for b in btup] for btup in states]}","def get_states():\n try:\n ''' Returns a list of states in list named result '''\n data = State.select()\n return ListStyle.list(data, request), 200\n except Exception as e:\n abort(500)","def states(self):\n return self._x_list","def get_initial_state(moves):\n tiles = defaultdict(int)\n for tile in [final_coord(steps) for steps in moves]:\n tiles[tile] = (tiles[tile] + 1) % 2\n return tiles","def S(self):\n return self._states","def test_location_to_state():\n for num_rows in [12, 10]:\n for num_cols in [15, 9]:\n env = Four_Rooms_Environment(grid_width=num_cols, grid_height=num_rows)\n observed_states = set()\n for row in range(num_rows):\n for col in range(num_cols):\n state = env.location_to_state((row, col))\n assert state not in observed_states\n observed_states.add(state)","def get_states(self):\n from geoffrey.data import datakey\n criteria = datakey(**request.query)\n response.content_type = 'application/json'\n return utils.jsonencoder.encode(\n [s.serializable()\n for s in self.server.hub.get_states(criteria)])","def initial_states(self):\n return list(self.iter_initial_states())","def state_to_locations(state: list) -> list:\n\n locations = []\n for i in range(0, 16):\n locations.append((0, 0))\n # Each tuple represents a location on the board as (row, column)\n\n \"\"\" \"locations\" keeps track of all fifteen numbers in the given state and the goal \n state. The location of the blank in the state is stored as the tuple at locations[0], \n the location of the number 1 is stored as locations[1], so on and so forth.\"\"\"\n\n \"\"\" Due to the nature of indices on a list, when a location is stored as a tuple \n (row, column), the four rows and four columns are represented as indices from 0 \n to 3, even though the numbers 1 through 15 are represented as indices from 1 to \n 15 on the list.\"\"\"\n\n for i in range(0, 4):\n for j in range(0, 4):\n \"\"\" The loop scans the given state and reads the integer at [i][j]. The number \n is stored at its corresponding index in the list \"locations\". By the time the \n loop finishes, the locations of all fifteen numbers as well as the blank in \n the given state will have been stored in the list.\"\"\"\n num = state[i][j]\n locations[num] = (i, j)\n\n return locations","def dense_state(self):\n return {name: self.group[name].state for name in self.group.keys()}","def reachable_province(self, ctx):\n return self.reachable_tiles(ctx)","def getStates(self):\n feature_states = []\n for i, sim in enumerate(self.sims):\n state = sim.getState()\n\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n return feature_states","def getStates(self):\n feature_states = []\n for i, sim in enumerate(self.sims):\n state = sim.getState()\n\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n return feature_states","def next_states(self):\n return self.get_next_states()","def find_dark_states(excited_state, ground_states):","def get_possible_states(self) -> List[State]:\n next_states = []\n for action in self._legal_moves():\n next_states.append(self.move(action))\n return next_states","def getInitialState (states):\n length1 = len (states[0])\n length2 = len (states[1])\n\n ind1 = random.randint (0, length1 - 1)\n ind2 = random.randint (0, length2 - 1)\n return (ind1, ind2)","def complete_list_of_states():\n # funny way of getting all the states that are defined in ConcertClientState.msg\n return concert_msgs.ConductorGraph.__slots__","def state_addresses(self):\n return self.switch.state_addresses()","def generate_all_states(self):\n self.clingo = ClingoBridge() # reset clingo\n\n base = ('base', '')\n self.clingo.add_file('initial-states.lp')\n self.clingo.run([base])\n output = self.clingo.output\n\n num_states = int(len(output) / 2)\n\n states = np.full(num_states, object)\n for i in range(0, num_states):\n state_atoms = []\n for atom in output[i]:\n if atom.name == 'state':\n state_atoms.append(atom)\n states[i] = self.parse_state(state_atoms)\n return states","def get_p_states(m):\n p_lst = list()\n for i in range(m):\n p_state = input(\"p[{}] = \".format(i+1)) != \"0\"\n p_lst.append(p_state)\n return p_lst","def active_states(self):\n return self.states.get_active_states()","def getStatesBounds(self):\n return (0, 0), (self._width - 1, self._height - 1)","def states_initial(self):\n return self.states(\"Initial = YES\")","def branch_mappings(self):\n return self._return_if('_branch_mappings')","def get_output_states(self):\n return self.states[-self.num_output_states:]","def locations_to_state(locations: list) -> list:\n\n state = []\n for i in range(0, 4):\n state.append([])\n # The first layer of the list consists of the four rows of a state\n for j in range(0, 4):\n state[i].append(-1)\n \"\"\" The second layer consists of the four tiles of a row (one of them could be \n the blank).\"\"\"\n\n for i in range(0, 16):\n state[locations[i][0]][locations[i][1]] = i\n \"\"\" locations[i][0] stores the row number, locations[i][0] stores the column \n number, and i is the number on the tile.\"\"\"\n\n return state","def getstate(self):\r\n return [self.tied_indices,\r\n self.fixed_indices,\r\n self.fixed_values,\r\n self.constrained_indices,\r\n self.constraints]","def sample_states(self, grid_points=21):\n states = []\n for param_2 in range(grid_points):\n pos_2 = 2 * PI * param_2 / (grid_points - 1) - PI\n for param_1 in range(grid_points):\n pos_1 = 2 * PI * param_1 / (grid_points - 1) - PI\n\n dist_x = 0.1 * np.cos(pos_1) + 0.11 * np.cos(pos_1 + pos_2) - 0. + self.reach\n dist_y = 0.1 * np.sin(pos_1) + 0.11 * np.sin(pos_1 + pos_2) - 0.\n states.append(np.array([np.cos(pos_1), np.cos(pos_2), np.sin(pos_1), np.sin(pos_2),\n -self.reach, 0., 0., 0., dist_x, dist_y, 0.]))\n return np.array(states)","def __getstate__(self):\n exclude_keys = ['_is_zero', '_is_positive', '_is_nonneg']\n state = {}\n for key,value in self.__dict__.items():\n if key in exclude_keys:\n continue\n state[key] = value\n return state","def get_states(self):\n\n try:\n response = requests.get(self.ROOT_URL + self.ALL_STATES_ENDPOINT)\n response = response.json()\n except ValueError:\n raise OpenSkyApiException(self.PARSE_ERROR)\n except RequestException:\n raise OpenSkyApiException(self.REQUEST_ERROR)\n return self.parse_response(response)","def test_get_country_states(self):\n pass","def get_sink_states(self):\n state1 = State(4, 2)\n return [state1]","def neighboring_states(self):\n index = self.state.index(0)\n\n if index == 0:\n return [self.move(movement) for movement in ['down', 'right']]\n elif index == 1:\n return [self.move(movement) for movement in ['down', 'left', 'right']]\n elif index == 2:\n return [self.move(movement) for movement in ['down', 'left']]\n elif index == 3:\n return [self.move(movement) for movement in ['up', 'down', 'right']]\n elif index == 4:\n return [self.move(movement) for movement in ['up', 'down', 'left', 'right']]\n elif index == 5:\n return [self.move(movement) for movement in ['up', 'down', 'left']]\n elif index == 6:\n return [self.move(movement) for movement in ['up', 'right']]\n elif index == 7:\n return [self.move(movement) for movement in ['up', 'left', 'right']]\n else:\n # index == 8\n return [self.move(movement) for movement in ['up', 'left']]","def states():\n states = storage.all(State).values()\n return render_template('9-states.html', states=states)","def StateMachine(self):\n if self.mode is ALL:\n self.which_state()\n\n if self.current_state == FB:\n # print(\"FORWARD/BACKWARD\")\n self.FB()\n elif self.current_state == LAT:\n # print(\"LATERAL\")\n self.LAT()\n elif self.current_state == ROT:\n # print(\"ROTATION\")\n self.ROT()\n elif self.current_state == COMBI:\n # print(\"COMBINED\")\n self.COMBI()\n\n return self.return_bezier_params()","def all_states():\n dict = storage.all(State)\n list = []\n for state in dict.values():\n list.append(state.to_dict())\n return jsonify(list)","def getstate(self):\r\n return SparseGP.getstate(self) + [self.init]","def initializeStates(n):\n states = []\n for i in range(n):\n states.append(0)\n return states","def event_m20_11_x43():\n \"\"\"State 0,1: Are you in the map?\"\"\"\n IsPlayerInTheMap(0, 1, 0)\n assert ConditionGroup(0)\n \"\"\"State 2: End state\"\"\"\n return 0","def get_state(self,board):\n s = range(board.size())\n return [ board.getCell(x,y) for y in s for x in s]","def getColorMapFlags():\n\treturn colorMap_flag","def state_list(self) -> Sequence[TState]:\n pass","def getstate(self):\n return [elem.getstate() for elem in self]","def _compute_world_params(self) -> None:\n\n self.states = []\n for row in range(self.grid_height):\n for col in range(self.grid_width):\n cell = row * self.grid_width + col\n cell_type = self.grid[cell]\n\n possible_actions = {\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\n }\n\n self.states.append(State(cell, possible_actions, cell_type))","def states(self):\n from copy import copy\n return copy(self._states_)","def next_states(self):\n return self._states[1:]","def lookups(self, request, model_admin):\n return tuple(map(lambda x: (x.value, x.name), StatePol))","async def get_hw_switch_states(self) -> Dict[str, bool]:\n raise NotImplementedError","def states_list(self, states):\n self.log('List of states: [{}]'.format(\n ' | '.join([(lambda x: x[1:])(s) for s in\n states.keys()])))\n return","def get_valid_regions(self):\n pass","def get_state(self) -> Dict[str, Any]:\n return {\"aq_potential_num\": self.aq_potential_num, \"wq_potential_num\": self.wq_potential_num}","def get_states():\n all_states = []\n states = storage.all(State).values()\n for state in states:\n all_states.append(state.to_dict())\n return jsonify(all_states)","def getTransientStates(m):\n t= list(range(len(m)))\n for r in range(len(m)):\n if(sum(m[r])==0): t.remove(r)\n return t","def __getstate__(self):\n return {k: v for k, v in self.__dict__.iteritems() if k not in ['x', 'y', '_x', '_y']}"],"string":"[\n \"def get_state_map(self):\\n if self.is_po:\\n state_map = self.curr_map\\n else:\\n state_map = self.grid_map\\n state_map[self.curr_pos] = 0.5\\n state_map[self.goal_pos] = 0.7\\n return state_map\",\n \"def states(self):\\n knownstates = set(self.keys())\\n for possiblestates in self.values():\\n for i in possiblestates:\\n knownstates.add(i)\\n return list(knownstates)\",\n \"def get_states(self):\\n raise NotImplementedError()\",\n \"def get_state(self):\\n state_dict = OrderedDict()\\n for key, target_object in self._map.items():\\n state = self._get_single_state(target_object)\\n if state is not None:\\n # pushbuttons for example are not defined in the get function\\n state_dict[key] = state\\n return state_dict\",\n \"def get_state(self):\\n return self.state_map\",\n \"def get_all_states(self):\\n return self._states\",\n \"def get_states(self):\\n states = {}\\n if hasattr(self, 'random_mask_state'):\\n states['random_mask_state'] = self.random_mask_state.get_state()\\n if hasattr(self, 'deformrandomstate'):\\n states['deformrandomstate'] = self.deformrandomstate.get_state()\\n states['randomstate'] = self.randomstate.get_state()\\n return states\",\n \"def get_all_states(self):\\n return tuple(self._transition_probs.keys())\",\n \"def get_list_of_states(self):\\n return self.states\",\n \"def game_of_life():\\n # 3x3 neighbourhood\\n offsets = [[(y, x) for y in range(-1, 2)] for x in range(-1, 2)]\\n\\n # Create mappings\\n mappings = {}\\n for i in range(2 ** 9):\\n\\n # Determine the initial state (key)\\n key = f\\\"{bin(i)[2:]:0>9}\\\" # As binary string\\n key = tuple(k == \\\"1\\\" for k in key) # As tuple of bools\\n key = tuple(key[i * 3:i * 3 + 3] for i in range(3)) # Reshape into 2D grid\\n\\n # Alive counts\\n centre = key[1][1]\\n others = sum(sum(row) for row in key) - centre\\n\\n # Skip if state does not evaluate to True\\n if centre:\\n if others not in (2, 3):\\n continue\\n\\n else:\\n if others != 3:\\n continue\\n\\n mappings[key] = True\\n\\n return Mapping2DRuleset(mappings, offsets)\",\n \"def getAbsorbingStates(m):\\n\\t\\n a=[]\\n for r in range(len(m)):\\n if(sum(m[r])==0): a.append(r)\\n return a\",\n \"def all_states(self) -> Tuple[State, ...]:\\n return self.influence_graph.all_states()\",\n \"def all_states(self):\\n return self._states\",\n \"def get_final_states(self):\\r\\n return set(self.final_states)\",\n \"def getAllWorldStates(self):\\n arrays = []\\n for i in range(1, self.num_ingredients):\\n arrays.append(list(range(7))) #dont forget to change\\n return list(itertools.product(*arrays))\",\n \"def getAllStates(self):\\n return list(itertools.product(self.getAllWorldStates(), self.getAllTheta()))\",\n \"def get_initial_states(self):\\n raise NotImplementedError()\",\n \"def states(self):\\n states = _StatesDict({})\\n for k, v in self.coordinates.items():\\n states.__setitem__(k, v, allow=True)\\n for k, v in self.speeds.items():\\n states.__setitem__(k, v, allow=True)\\n return states\",\n \"def get_states(self):\\n return product(*[phi.automaton().states for phi in self])\",\n \"def state(self):\\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}\",\n \"def get_reward_states(self):\\n state1 = State(7, 7)\\n return [state1]\",\n \"def get_map_state(self):\\n return self.serialize()\",\n \"def _dirty_states(self) -> Iterable[InstanceState[Any]]:\\n return self.identity_map._dirty_states()\",\n \"def states(self) -> Type[Any]:\\n return []\",\n \"def get_active_states(self):\\n raise NotImplementedError()\",\n \"def lattice_grain_node_states():\\n ns_dict = { 0 : 'empty', \\n 1 : 'moving up',\\n 2 : 'moving right and up',\\n 3 : 'moving right and down',\\n 4 : 'moving down',\\n 5 : 'moving left and down',\\n 6 : 'moving left and up',\\n 7 : 'rest',\\n 8 : 'wall'}\\n return ns_dict\",\n \"def find_modifiable_states(state_data):\\n\\n modifiable = (state_data['pcad_mode'] == 'NPNT') & (state_data['clocking'] == 1) & (\\n state_data['fep_count'] == state_data['ccd_count']) & (state_data['fep_count'] < 4)\\n states_ind = np.where(modifiable)[0]\\n cases = list(product([0, 1], repeat=len(states_ind)))\\n return states_ind, cases\",\n \"def get_state_actions_mapping(self):\\n return None\",\n \"def getStateAbbreviations():\\n state_abbrev = {\\n \\\"01\\\": \\\"AL\\\",\\n \\\"02\\\": \\\"AK\\\",\\n \\\"04\\\": \\\"AZ\\\",\\n \\\"05\\\": \\\"AR\\\",\\n \\\"06\\\": \\\"CA\\\",\\n \\\"08\\\": \\\"CO\\\",\\n \\\"09\\\": \\\"CT\\\",\\n \\\"10\\\": \\\"DE\\\",\\n \\\"11\\\": \\\"DC\\\",\\n \\\"12\\\": \\\"FL\\\",\\n \\\"13\\\": \\\"GA\\\",\\n \\\"15\\\": \\\"HI\\\",\\n \\\"16\\\": \\\"ID\\\",\\n \\\"17\\\": \\\"IL\\\",\\n \\\"18\\\": \\\"IN\\\",\\n \\\"19\\\": \\\"IA\\\",\\n \\\"20\\\": \\\"KS\\\",\\n \\\"21\\\": \\\"KY\\\",\\n \\\"22\\\": \\\"LA\\\",\\n \\\"23\\\": \\\"ME\\\",\\n \\\"24\\\": \\\"MD\\\",\\n \\\"25\\\": \\\"MA\\\",\\n \\\"26\\\": \\\"MI\\\",\\n \\\"27\\\": \\\"MN\\\",\\n \\\"28\\\": \\\"MS\\\",\\n \\\"29\\\": \\\"MO\\\",\\n \\\"30\\\": \\\"MT\\\",\\n \\\"31\\\": \\\"NE\\\",\\n \\\"32\\\": \\\"NV\\\",\\n \\\"33\\\": \\\"NH\\\",\\n \\\"34\\\": \\\"NJ\\\",\\n \\\"35\\\": \\\"NM\\\",\\n \\\"36\\\": \\\"NY\\\",\\n \\\"37\\\": \\\"NC\\\",\\n \\\"38\\\": \\\"ND\\\",\\n \\\"39\\\": \\\"OH\\\",\\n \\\"40\\\": \\\"OK\\\",\\n \\\"41\\\": \\\"OR\\\",\\n \\\"42\\\": \\\"PA\\\",\\n \\\"44\\\": \\\"RI\\\",\\n \\\"45\\\": \\\"SC\\\",\\n \\\"46\\\": \\\"SD\\\",\\n \\\"47\\\": \\\"TN\\\",\\n \\\"48\\\": \\\"TX\\\",\\n \\\"49\\\": \\\"UT\\\",\\n \\\"50\\\": \\\"VT\\\",\\n \\\"51\\\": \\\"VA\\\",\\n \\\"53\\\": \\\"WA\\\",\\n \\\"54\\\": \\\"WV\\\",\\n \\\"55\\\": \\\"WI\\\",\\n \\\"56\\\": \\\"WY\\\",\\n \\\"72\\\": \\\"PR\\\"\\n }\\n return state_abbrev\",\n \"def get_states():\\n # Getting all hidden state through time\\n all_hidden_states = tf.scan(GRU, processed_input, \\n initializer=initial_hidden, name='states')\\n return all_hidden_states\",\n \"def others_locations(state):\\n others_ = others(state)\\n locations = {i: e['pos'] for i, e in others_.items()}\\n return locations\",\n \"def States(self) -> List[Callable]:\\r\\n\\t\\treturn self.__STATES__\",\n \"def states(self):\\n num_states = 2**self.num_vars\\n return (int2tuple(s, self.num_vars) for s in range(num_states))\",\n \"def get_states(self, structure):\\n dir = IO(dir=os.path.join(self.loc, structure))\\n return dir.get_values(\\n structure, \\\"geo.log\\\", self.geovalid, self.finalE)\",\n \"def states(self) -> Set[State]:\\n return set(range(self.nb_states))\",\n \"def states(self) -> Optional[Sequence['outputs.FeatureStateState']]:\\n return pulumi.get(self, \\\"states\\\")\",\n \"def states(self):\\n from geoid.core import names\\n from geoid.censusnames import geo_names, stusab\\n\\n states = {}\\n\\n for state_no, stusab in stusab.items():\\n states[stusab] = {\\n 'name': geo_names[(state_no,0)],\\n 'stusab': stusab,\\n 'number' : state_no\\n }\\n\\n states['US'] = {\\n 'name': 'United States',\\n 'stusab': 'US',\\n 'number' : 0\\n }\\n\\n return states\",\n \"def alleles(self) -> set[str]:\\n return {self.ancestral_state} | {m.derived_state for m in self.mutations}\",\n \"def all_state(cap):\\n\\n\\tfor abbrev, capital in capital_cities.items():\\n\\t\\tif cap.lower() == capital.lower():\\n\\t\\t\\tfor state, abbreviation in states.items():\\n\\t\\t\\t\\tif abbreviation.lower() == abbrev.lower():\\n\\t\\t\\t\\t\\treturn [capital, state]\\n\\treturn None\",\n \"def final_states(self):\\n return list(self.iter_final_states())\",\n \"def final_states(self) -> Tuple[tuple, ...]:\\n return self._final_states\",\n \"def _getStates(self):\\n feature_states = []\\n # for i, sim in enumerate(self.sims):\\n # state = sim.getState()\\n\\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\\n # if self.features:\\n # feature = self.features[long_id] # Get feature for\\n # feature_states.append((feature, state))\\n # else:\\n # feature_states.append((None, state))\\n for i in range(self.batch_size):\\n while not self.qout[i].empty():\\n self.qout[i].get()\\n while not self.qtraj[i].empty():\\n self.qtraj[i].get()\\n\\n self.qin[i].put(('state',None))\\n \\n for i in range(self.batch_size):\\n state = self.qout[i].get()\\n # print(state)\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n\\n return feature_states\",\n \"def _getStates(self):\\n feature_states = []\\n # for i, sim in enumerate(self.sims):\\n # state = sim.getState()\\n\\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\\n # if self.features:\\n # feature = self.features[long_id] # Get feature for\\n # feature_states.append((feature, state))\\n # else:\\n # feature_states.append((None, state))\\n for i in range(self.batch_size):\\n while not self.qout[i].empty():\\n self.qout[i].get()\\n while not self.qtraj[i].empty():\\n self.qtraj[i].get()\\n\\n self.qin[i].put(('state',None))\\n \\n for i in range(self.batch_size):\\n state = self.qout[i].get()\\n # print(state)\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n\\n return feature_states\",\n \"def get_map(self) -> list:\\n return self.map_obstacle\",\n \"def initial_states(self):\\n return self._initial_states\",\n \"def fill_map(self):\\n\\n sim = Pong(max_steps=None)\\n s = sim.empty_state()\\n s[DEFAULT_DIMS] = DEFUALT_VALUES\\n\\n # Optimization issues:\\n next_state = self.next_state\\n next_reward = self.next_reward\\n d = self.d\\n\\n # Make the terminal state a self-loop\\n next_state[self.n] = self.n\\n\\n t0 = clock()\\n for i in range(0, self.n, 1000000):\\n for j in range(i, min(i + 1000000, self.n)):\\n s[TRAIN_DIMS] = d.index_to_state(j)\\n for a in c.ACTIONS:\\n sim.fast_set_and_step(s, c.A_STAY, a)\\n if sim.hit == \\\"r\\\":\\n next_reward[j, a] = 1\\n next_state[j, a] = -1\\n elif sim.miss == \\\"r\\\":\\n next_reward[j, a] = -1\\n next_state[j, a] = -1\\n else:\\n next_state[j, a] = d.state_to_index(sim.s[TRAIN_DIMS])\\n print(i, clock() - t0)\",\n \"def get_states(request):\\n if 'cursor' in request.GET:\\n cursor = request.GET['cursor']\\n else:\\n cursor = str(uuid4())\\n if cursor in CURSORS:\\n it = CURSORS[cursor]\\n else:\\n board = GAMES[request.matchdict['game']]\\n it = CURSORS[cursor] = board.lookahead_boards(\\n request.GET.get('lookahead', 1))\\n slen = request.GET.get('count', 20)\\n states = list(islice(it, slen))\\n if len(states) < 20:\\n cursor = None\\n return {\\n 'cursor': cursor,\\n 'boards': [[b.board for b in btup] for btup in states]}\",\n \"def get_states():\\n try:\\n ''' Returns a list of states in list named result '''\\n data = State.select()\\n return ListStyle.list(data, request), 200\\n except Exception as e:\\n abort(500)\",\n \"def states(self):\\n return self._x_list\",\n \"def get_initial_state(moves):\\n tiles = defaultdict(int)\\n for tile in [final_coord(steps) for steps in moves]:\\n tiles[tile] = (tiles[tile] + 1) % 2\\n return tiles\",\n \"def S(self):\\n return self._states\",\n \"def test_location_to_state():\\n for num_rows in [12, 10]:\\n for num_cols in [15, 9]:\\n env = Four_Rooms_Environment(grid_width=num_cols, grid_height=num_rows)\\n observed_states = set()\\n for row in range(num_rows):\\n for col in range(num_cols):\\n state = env.location_to_state((row, col))\\n assert state not in observed_states\\n observed_states.add(state)\",\n \"def get_states(self):\\n from geoffrey.data import datakey\\n criteria = datakey(**request.query)\\n response.content_type = 'application/json'\\n return utils.jsonencoder.encode(\\n [s.serializable()\\n for s in self.server.hub.get_states(criteria)])\",\n \"def initial_states(self):\\n return list(self.iter_initial_states())\",\n \"def state_to_locations(state: list) -> list:\\n\\n locations = []\\n for i in range(0, 16):\\n locations.append((0, 0))\\n # Each tuple represents a location on the board as (row, column)\\n\\n \\\"\\\"\\\" \\\"locations\\\" keeps track of all fifteen numbers in the given state and the goal \\n state. The location of the blank in the state is stored as the tuple at locations[0], \\n the location of the number 1 is stored as locations[1], so on and so forth.\\\"\\\"\\\"\\n\\n \\\"\\\"\\\" Due to the nature of indices on a list, when a location is stored as a tuple \\n (row, column), the four rows and four columns are represented as indices from 0 \\n to 3, even though the numbers 1 through 15 are represented as indices from 1 to \\n 15 on the list.\\\"\\\"\\\"\\n\\n for i in range(0, 4):\\n for j in range(0, 4):\\n \\\"\\\"\\\" The loop scans the given state and reads the integer at [i][j]. The number \\n is stored at its corresponding index in the list \\\"locations\\\". By the time the \\n loop finishes, the locations of all fifteen numbers as well as the blank in \\n the given state will have been stored in the list.\\\"\\\"\\\"\\n num = state[i][j]\\n locations[num] = (i, j)\\n\\n return locations\",\n \"def dense_state(self):\\n return {name: self.group[name].state for name in self.group.keys()}\",\n \"def reachable_province(self, ctx):\\n return self.reachable_tiles(ctx)\",\n \"def getStates(self):\\n feature_states = []\\n for i, sim in enumerate(self.sims):\\n state = sim.getState()\\n\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n return feature_states\",\n \"def getStates(self):\\n feature_states = []\\n for i, sim in enumerate(self.sims):\\n state = sim.getState()\\n\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n return feature_states\",\n \"def next_states(self):\\n return self.get_next_states()\",\n \"def find_dark_states(excited_state, ground_states):\",\n \"def get_possible_states(self) -> List[State]:\\n next_states = []\\n for action in self._legal_moves():\\n next_states.append(self.move(action))\\n return next_states\",\n \"def getInitialState (states):\\n length1 = len (states[0])\\n length2 = len (states[1])\\n\\n ind1 = random.randint (0, length1 - 1)\\n ind2 = random.randint (0, length2 - 1)\\n return (ind1, ind2)\",\n \"def complete_list_of_states():\\n # funny way of getting all the states that are defined in ConcertClientState.msg\\n return concert_msgs.ConductorGraph.__slots__\",\n \"def state_addresses(self):\\n return self.switch.state_addresses()\",\n \"def generate_all_states(self):\\n self.clingo = ClingoBridge() # reset clingo\\n\\n base = ('base', '')\\n self.clingo.add_file('initial-states.lp')\\n self.clingo.run([base])\\n output = self.clingo.output\\n\\n num_states = int(len(output) / 2)\\n\\n states = np.full(num_states, object)\\n for i in range(0, num_states):\\n state_atoms = []\\n for atom in output[i]:\\n if atom.name == 'state':\\n state_atoms.append(atom)\\n states[i] = self.parse_state(state_atoms)\\n return states\",\n \"def get_p_states(m):\\n p_lst = list()\\n for i in range(m):\\n p_state = input(\\\"p[{}] = \\\".format(i+1)) != \\\"0\\\"\\n p_lst.append(p_state)\\n return p_lst\",\n \"def active_states(self):\\n return self.states.get_active_states()\",\n \"def getStatesBounds(self):\\n return (0, 0), (self._width - 1, self._height - 1)\",\n \"def states_initial(self):\\n return self.states(\\\"Initial = YES\\\")\",\n \"def branch_mappings(self):\\n return self._return_if('_branch_mappings')\",\n \"def get_output_states(self):\\n return self.states[-self.num_output_states:]\",\n \"def locations_to_state(locations: list) -> list:\\n\\n state = []\\n for i in range(0, 4):\\n state.append([])\\n # The first layer of the list consists of the four rows of a state\\n for j in range(0, 4):\\n state[i].append(-1)\\n \\\"\\\"\\\" The second layer consists of the four tiles of a row (one of them could be \\n the blank).\\\"\\\"\\\"\\n\\n for i in range(0, 16):\\n state[locations[i][0]][locations[i][1]] = i\\n \\\"\\\"\\\" locations[i][0] stores the row number, locations[i][0] stores the column \\n number, and i is the number on the tile.\\\"\\\"\\\"\\n\\n return state\",\n \"def getstate(self):\\r\\n return [self.tied_indices,\\r\\n self.fixed_indices,\\r\\n self.fixed_values,\\r\\n self.constrained_indices,\\r\\n self.constraints]\",\n \"def sample_states(self, grid_points=21):\\n states = []\\n for param_2 in range(grid_points):\\n pos_2 = 2 * PI * param_2 / (grid_points - 1) - PI\\n for param_1 in range(grid_points):\\n pos_1 = 2 * PI * param_1 / (grid_points - 1) - PI\\n\\n dist_x = 0.1 * np.cos(pos_1) + 0.11 * np.cos(pos_1 + pos_2) - 0. + self.reach\\n dist_y = 0.1 * np.sin(pos_1) + 0.11 * np.sin(pos_1 + pos_2) - 0.\\n states.append(np.array([np.cos(pos_1), np.cos(pos_2), np.sin(pos_1), np.sin(pos_2),\\n -self.reach, 0., 0., 0., dist_x, dist_y, 0.]))\\n return np.array(states)\",\n \"def __getstate__(self):\\n exclude_keys = ['_is_zero', '_is_positive', '_is_nonneg']\\n state = {}\\n for key,value in self.__dict__.items():\\n if key in exclude_keys:\\n continue\\n state[key] = value\\n return state\",\n \"def get_states(self):\\n\\n try:\\n response = requests.get(self.ROOT_URL + self.ALL_STATES_ENDPOINT)\\n response = response.json()\\n except ValueError:\\n raise OpenSkyApiException(self.PARSE_ERROR)\\n except RequestException:\\n raise OpenSkyApiException(self.REQUEST_ERROR)\\n return self.parse_response(response)\",\n \"def test_get_country_states(self):\\n pass\",\n \"def get_sink_states(self):\\n state1 = State(4, 2)\\n return [state1]\",\n \"def neighboring_states(self):\\n index = self.state.index(0)\\n\\n if index == 0:\\n return [self.move(movement) for movement in ['down', 'right']]\\n elif index == 1:\\n return [self.move(movement) for movement in ['down', 'left', 'right']]\\n elif index == 2:\\n return [self.move(movement) for movement in ['down', 'left']]\\n elif index == 3:\\n return [self.move(movement) for movement in ['up', 'down', 'right']]\\n elif index == 4:\\n return [self.move(movement) for movement in ['up', 'down', 'left', 'right']]\\n elif index == 5:\\n return [self.move(movement) for movement in ['up', 'down', 'left']]\\n elif index == 6:\\n return [self.move(movement) for movement in ['up', 'right']]\\n elif index == 7:\\n return [self.move(movement) for movement in ['up', 'left', 'right']]\\n else:\\n # index == 8\\n return [self.move(movement) for movement in ['up', 'left']]\",\n \"def states():\\n states = storage.all(State).values()\\n return render_template('9-states.html', states=states)\",\n \"def StateMachine(self):\\n if self.mode is ALL:\\n self.which_state()\\n\\n if self.current_state == FB:\\n # print(\\\"FORWARD/BACKWARD\\\")\\n self.FB()\\n elif self.current_state == LAT:\\n # print(\\\"LATERAL\\\")\\n self.LAT()\\n elif self.current_state == ROT:\\n # print(\\\"ROTATION\\\")\\n self.ROT()\\n elif self.current_state == COMBI:\\n # print(\\\"COMBINED\\\")\\n self.COMBI()\\n\\n return self.return_bezier_params()\",\n \"def all_states():\\n dict = storage.all(State)\\n list = []\\n for state in dict.values():\\n list.append(state.to_dict())\\n return jsonify(list)\",\n \"def getstate(self):\\r\\n return SparseGP.getstate(self) + [self.init]\",\n \"def initializeStates(n):\\n states = []\\n for i in range(n):\\n states.append(0)\\n return states\",\n \"def event_m20_11_x43():\\n \\\"\\\"\\\"State 0,1: Are you in the map?\\\"\\\"\\\"\\n IsPlayerInTheMap(0, 1, 0)\\n assert ConditionGroup(0)\\n \\\"\\\"\\\"State 2: End state\\\"\\\"\\\"\\n return 0\",\n \"def get_state(self,board):\\n s = range(board.size())\\n return [ board.getCell(x,y) for y in s for x in s]\",\n \"def getColorMapFlags():\\n\\treturn colorMap_flag\",\n \"def state_list(self) -> Sequence[TState]:\\n pass\",\n \"def getstate(self):\\n return [elem.getstate() for elem in self]\",\n \"def _compute_world_params(self) -> None:\\n\\n self.states = []\\n for row in range(self.grid_height):\\n for col in range(self.grid_width):\\n cell = row * self.grid_width + col\\n cell_type = self.grid[cell]\\n\\n possible_actions = {\\n Action.up: self._get_action(max(row - 1, 0) * self.grid_width + col),\\n Action.down: self._get_action(min(row + 1, self.grid_height - 1) * self.grid_width + col),\\n Action.right: self._get_action(row * self.grid_width + min(col + 1, self.grid_width - 1)),\\n Action.left: self._get_action(row * self.grid_width + max(col - 1, 0))\\n }\\n\\n self.states.append(State(cell, possible_actions, cell_type))\",\n \"def states(self):\\n from copy import copy\\n return copy(self._states_)\",\n \"def next_states(self):\\n return self._states[1:]\",\n \"def lookups(self, request, model_admin):\\n return tuple(map(lambda x: (x.value, x.name), StatePol))\",\n \"async def get_hw_switch_states(self) -> Dict[str, bool]:\\n raise NotImplementedError\",\n \"def states_list(self, states):\\n self.log('List of states: [{}]'.format(\\n ' | '.join([(lambda x: x[1:])(s) for s in\\n states.keys()])))\\n return\",\n \"def get_valid_regions(self):\\n pass\",\n \"def get_state(self) -> Dict[str, Any]:\\n return {\\\"aq_potential_num\\\": self.aq_potential_num, \\\"wq_potential_num\\\": self.wq_potential_num}\",\n \"def get_states():\\n all_states = []\\n states = storage.all(State).values()\\n for state in states:\\n all_states.append(state.to_dict())\\n return jsonify(all_states)\",\n \"def getTransientStates(m):\\n t= list(range(len(m)))\\n for r in range(len(m)):\\n if(sum(m[r])==0): t.remove(r)\\n return t\",\n \"def __getstate__(self):\\n return {k: v for k, v in self.__dict__.iteritems() if k not in ['x', 'y', '_x', '_y']}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71087664","0.7074518","0.677178","0.67380196","0.655753","0.6495594","0.6494033","0.6443897","0.64307165","0.6419317","0.6412042","0.6395079","0.63540417","0.6328602","0.63154495","0.6303637","0.62966526","0.6289538","0.6258314","0.6251944","0.6178415","0.61743706","0.61713964","0.6143961","0.61305237","0.6109762","0.60649294","0.6062444","0.60616654","0.6043731","0.60280144","0.60179615","0.6012246","0.6010317","0.5998409","0.59954643","0.5990415","0.59810406","0.5948695","0.5948419","0.5947764","0.5936327","0.5936327","0.59224296","0.59200376","0.589258","0.588971","0.58709884","0.5867912","0.58622754","0.5861727","0.5843194","0.5836482","0.5818598","0.5812321","0.5810521","0.5806053","0.580336","0.580336","0.5802557","0.57996917","0.5796726","0.57940865","0.5784249","0.5780959","0.5777917","0.5770528","0.57647645","0.5757079","0.5753402","0.5721146","0.56988484","0.5691433","0.56679595","0.5667446","0.5665367","0.5664081","0.5661313","0.5652405","0.565188","0.5643782","0.56437135","0.56407535","0.5636031","0.56354135","0.56348604","0.5616573","0.5603347","0.56029767","0.5602162","0.5599859","0.5596205","0.5595429","0.5585002","0.5581029","0.55807686","0.55785716","0.55753475","0.55704486","0.5566744","0.555262"],"string":"[\n \"0.71087664\",\n \"0.7074518\",\n \"0.677178\",\n \"0.67380196\",\n \"0.655753\",\n \"0.6495594\",\n \"0.6494033\",\n \"0.6443897\",\n \"0.64307165\",\n \"0.6419317\",\n \"0.6412042\",\n \"0.6395079\",\n \"0.63540417\",\n \"0.6328602\",\n \"0.63154495\",\n \"0.6303637\",\n \"0.62966526\",\n \"0.6289538\",\n \"0.6258314\",\n \"0.6251944\",\n \"0.6178415\",\n \"0.61743706\",\n \"0.61713964\",\n \"0.6143961\",\n \"0.61305237\",\n \"0.6109762\",\n \"0.60649294\",\n \"0.6062444\",\n \"0.60616654\",\n \"0.6043731\",\n \"0.60280144\",\n \"0.60179615\",\n \"0.6012246\",\n \"0.6010317\",\n \"0.5998409\",\n \"0.59954643\",\n \"0.5990415\",\n \"0.59810406\",\n \"0.5948695\",\n \"0.5948419\",\n \"0.5947764\",\n \"0.5936327\",\n \"0.5936327\",\n \"0.59224296\",\n \"0.59200376\",\n \"0.589258\",\n \"0.588971\",\n \"0.58709884\",\n \"0.5867912\",\n \"0.58622754\",\n \"0.5861727\",\n \"0.5843194\",\n \"0.5836482\",\n \"0.5818598\",\n \"0.5812321\",\n \"0.5810521\",\n \"0.5806053\",\n \"0.580336\",\n \"0.580336\",\n \"0.5802557\",\n \"0.57996917\",\n \"0.5796726\",\n \"0.57940865\",\n \"0.5784249\",\n \"0.5780959\",\n \"0.5777917\",\n \"0.5770528\",\n \"0.57647645\",\n \"0.5757079\",\n \"0.5753402\",\n \"0.5721146\",\n \"0.56988484\",\n \"0.5691433\",\n \"0.56679595\",\n \"0.5667446\",\n \"0.5665367\",\n \"0.5664081\",\n \"0.5661313\",\n \"0.5652405\",\n \"0.565188\",\n \"0.5643782\",\n \"0.56437135\",\n \"0.56407535\",\n \"0.5636031\",\n \"0.56354135\",\n \"0.56348604\",\n \"0.5616573\",\n \"0.5603347\",\n \"0.56029767\",\n \"0.5602162\",\n \"0.5599859\",\n \"0.5596205\",\n \"0.5595429\",\n \"0.5585002\",\n \"0.5581029\",\n \"0.55807686\",\n \"0.55785716\",\n \"0.55753475\",\n \"0.55704486\",\n \"0.5566744\",\n \"0.555262\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":18487,"cells":{"query":{"kind":"string","value":"Return a hashable state"},"document":{"kind":"string","value":"def get_state_hash(self):\n return tuple([tuple([x for x in row if x not in [' ', '#']]) for row in self.map[1:-1]])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_hashable_state(self):\n raise AIError(\"Must be implemented in child class!\")","def __hash__(self):\n return hash((self.name, self.state))","def __hash__(self):\n if getattr(self, \"_immutable\", False):\n return hash((tuple(self.states()), tuple(self.transitions())))\n raise TypeError(\"Finite state machines are mutable, \" \\\n \"and thus not hashable.\")","def hash_state(self):\n return hash(self.board.tostring())","def __hash__(self) -> int:\n return hash(\n (self._final_states, self._recoil_state, self._parent_recoil_state)\n )","def current_hash(self):","def __hash__(self):\n return hash(self.hash)","def internal_hash(self): \n return hash(tuple(sorted(self.hashtriples())))","def __hash__(self):\n return self.to_hash()","def hash(self):\n raise NotImplementedError() # To be subclassed","def __hash__(self, reinit=False):\n if not self.hash_value is None and not reinit:\n return self.hash_value\n elif isinstance(self, Leaf):\n self.hash_value = Hash.leaf_hash(self)\n return self.hash_value\n else:\n self.hash_value = Hash.node_hash(self)\n return self.hash_value","def get_hash(self):\r\n return","def get_hash(self) -> str:\n if self.call_hash:\n # Derived state from a call_node.\n return hash_struct([\"Handle\", self.fullname, \"call_hash\", self.key, self.call_hash])\n else:\n # Initial state.\n return hash_struct([\"Handle\", self.fullname, \"init\", self.key, self.args, self.kwargs])","def __getstate__(self):\n state = self.__dict__.copy()\n self.__cleanState__(state)\n return state","def get_hash(self):\n return self.__hash","def HashValue(self) -> _n_0_t_3[_n_0_t_9]:","def __hash__(self):\r\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(self.label())","def __hash__(self):\n raise NotImplementedError","def __hash__(self):\n if self.immutable:\n return hash(self._make_key())\n else:\n raise MutantError(\"This %s is currently mutable, and therefore unhashable! \"%repr(self)\n +\"Run self.make_immutable() to change this.\")","def __Hash(self):\n return self._Hash()","def hash(self):\n return self.__hash__()","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self) -> int:\n # Turn the steps into a set first, so that permuting the steps doesn't change the equality.\n self.simplify()\n return hash(tuple(sorted(self.steps_set)))","def __hash__(self):\n return hash(self.value)","def __hash__(self):\r\n return hash(self.__key())","def __hash__(self):\n hash_value = 0\n \n # approximate_online_count\n hash_value ^= self.approximate_online_count\n \n # approximate_user_count\n hash_value ^= self.approximate_user_count << 12\n \n # description\n description = self.description\n if (description is not None):\n hash_value ^= hash(description)\n \n # discovery_splash\n hash_value ^= hash(self.discovery_splash)\n \n # emojis\n emojis = self.emojis\n hash_value ^= len(emojis) << 1\n for emoji in emojis.values():\n hash_value ^= hash(emoji)\n \n # features\n features = self.features\n hash_value ^= len(features) << 5\n for feature in features:\n hash_value ^= hash(feature)\n \n # icon\n hash_value ^= hash(self.icon)\n \n # id\n hash_value ^= self.id\n \n # invite_splash\n hash_value ^= hash(self.invite_splash)\n \n # stickers\n stickers = self.stickers\n hash_value ^= len(stickers) << 9\n for sticker in stickers.values():\n hash_value ^= hash(sticker)\n \n # name\n name = self.name\n if (description is None) or (description != name):\n hash_value ^= hash(name)\n \n return hash_value","def __hash__(self):\n hash_value = 0\n \n # avatar\n hash_value ^= hash(self.avatar)\n \n # boosts_since\n boosts_since = self.boosts_since\n if (boosts_since is not None):\n hash_value ^= hash(boosts_since)\n \n # flags\n hash_value ^= self.flags\n \n # joined_at\n joined_at = self.joined_at\n if (joined_at is not None):\n hash_value ^= hash(joined_at)\n \n # nick\n nick = self.nick\n if (nick is not None):\n hash_value ^= hash(nick)\n \n # pending\n hash_value ^= self.pending\n \n # role_ids\n role_ids = self.role_ids\n if (role_ids is not None):\n hash_value ^= len(role_ids) << 4\n for role_id in role_ids:\n hash_value ^= role_id\n \n # timed_out_until\n timed_out_until = self.timed_out_until\n if (timed_out_until is not None):\n hash_value ^= hash(timed_out_until)\n \n return hash_value","def __hash__(self) -> int:","def hash_key(self):","def __getstate__(self):\n\n state = {}\n for key in self.__slots__:\n state[key] = getattr(self, key)\n\n return state","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def _get_random_state(self):\n self._validate_random_state()\n return deepcopy(self.random_state)","def __hash__(self):\n x = xxhash.xxh64()\n x.update(self.puzzle)\n return x.intdigest()","def __hash__(self):\n raise TypeError(\"Transitions are mutable, and thus not hashable.\")","def __hash__(self):\n\n return int(self._hash_value_)","def state_to_hx_hash(new_state, target_label):\n goal = None\n empty = set()\n mobile = set()\n\n for pt in new_state:\n if new_state[pt].label == target_label:\n goal = pt\n elif new_state[pt].used == 0:\n empty.add(pt)\n elif new_state[pt].used < 100:\n mobile.add(pt)\n return goal, frozenset(empty), frozenset(mobile)","def __Hash(self):\n return self._Hash()","def convertStateToHash(values):\n l = list(sorted(values.items()))\n modl = [a+b for (a, b) in l]\n return ''.join(modl)","def convertStateToHash(values):\n l = list(sorted(values.items()))\n modl = [a+b for (a, b) in l]\n return ''.join(modl)","def get_hash(self):\n return freeze_dict(self.get_hash_params())","def __hash__(self):\n return self.value.__hash__()","def hash(self):\n return self.hash_by_id(self.id)","def unique_hash(self):\n raise NotImplementedError(\"unique_hash Method not implemented\")","def hash(self) -> bytes:","def __getstate__(self):\n state: Dict[str, Any] = deepcopy(self.__dict__)\n del state['__db']\n return state","def __hash__(self):\n return hash((self.SYMBOL, self._.hash_parameters))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\r\n return hash((self.label, ))","def __hash__(self):\n return super().__hash__()","def __hash__(self) -> int:\n ...","def __hash__(self) -> int:\n ...","def __hash__(self):\n return id(self)","def __hash__(self):\n return id(self)","def __hash__(self):\n return id(self)","def __getstate__(self):\n import time\n\n state = self.__dict__.copy()\n\n # Remove the unpicklable entries\n del state['_model']\n del state['_input']\n del state['_output']\n del state['_preds_op']\n del state['_loss']\n del state['_loss_grads']\n del state['_preds']\n del state['_layer_names']\n\n model_name = str(time.time()) + '.h5'\n state['model_name'] = model_name\n self.save(model_name)\n return state","def __hash__(self):\n return hash(id(self))","def __hash__(self):\n return hash(id(self))","def __hash__(self):\n if self._mutable:\n raise ValueError(\"can not hash mutable constellation\")\n return hash(tuple(self._g))","def __hash__(self):\n\t\treturn 1","def __hash__(self):\n return hash((self._nele, self._m_s))","def __hash__( self ):\n return hash( self.data )","def hash_value(board_state):\n res = \"\"\n for i in range(1,10):\n res = res + board_state[i]\n return res","def __hash__(self):\n return hash(self.joined())","def __hash__(self) -> int:\n return hash(self.value)","def __hash__(self) -> int:\n return hash(self.__key__())","def __hash__(self):\n return hash((self._start, self._end, self._name, self._value))","def __hash__(self):\n\t\treturn hash(repr(self))","def get_hash(self):\n if self.contributes:\n return hash_from_values(self.iter_values())","def __getstate__(self):\n return dict(self.items())","def __getstate__(self):\n return dict(self.items())","def __hash__(self):\n return hash(repr(self))","def __hash__(self):\n return hash(repr(self))","def __hash__(self):\n return hash(repr(self))","def __hash__(self):\n return hash(str(self.__id__))","def __hash__(self):\n return hash(self.base_location) ^ hash(self.fold_path) ^ hash(self.field)","def hashcode(o):","def getHash(self):\n if self.chash:\n return self.chash\n else:\n self.setHash()\n return self.chash","def __hash__(self):\n return hash(\n (tuple(\n self.vector), self.index, tuple(\n self.labels), self.label))","def __hash__(self):\n return hash(self.get_all_features())","def get_state(self):\n\n # TODO: Assemble a dictionary containing the tracker state\n\n return {}","def __hash__(self):\n hash_value = 0\n \n # required\n hash_value ^= self.required << 14\n \n # title\n hash_value ^= hash(self.title)\n \n # type\n hash_value ^= hash(self.type)\n \n # values\n values = self.values\n if (values is not None):\n hash_value ^= len(values)\n \n for value in values:\n hash_value ^= hash(value)\n \n return hash_value","def __hash__(self):\n return hash(self.returnBoard())","def __getstate__(self):\n return {}","def __hash__(self):\n # These entities are not cached, so we wont use their `id` if applicable.\n hash_value = 0\n \n # bot\n hash_value ^= hash(self.bot)\n \n # description\n description = self.description\n if (description is not None):\n hash_value ^= hash(description)\n \n # icon\n hash_value ^= hash(self.icon)\n \n # id\n hash_value ^= self.id\n \n # name\n name = self.name\n if (description is None) or (description != name):\n hash_value ^= hash(name)\n \n return hash_value","def get_state(self, state):\n return state","def __hash__(self):\n return hash(self.name)","def __hash__(self):\n return hash(self.name)","def __hash__(self):\r\n return hash(type(self)) ^ hash(self.broadcastable)","def _sharded_state_dict(self, *args: Any, **kwargs: Any) -> Any:\n with self.set_state_dict_type(StateDictType.SHARDED_STATE_DICT):\n return self.state_dict(self, *args, **kwargs)","def get_state_dict(self):\n raise NotImplemented()","def __hash__(self) -> int:\n return hash(tuple(self.name,))"],"string":"[\n \"def get_hashable_state(self):\\n raise AIError(\\\"Must be implemented in child class!\\\")\",\n \"def __hash__(self):\\n return hash((self.name, self.state))\",\n \"def __hash__(self):\\n if getattr(self, \\\"_immutable\\\", False):\\n return hash((tuple(self.states()), tuple(self.transitions())))\\n raise TypeError(\\\"Finite state machines are mutable, \\\" \\\\\\n \\\"and thus not hashable.\\\")\",\n \"def hash_state(self):\\n return hash(self.board.tostring())\",\n \"def __hash__(self) -> int:\\n return hash(\\n (self._final_states, self._recoil_state, self._parent_recoil_state)\\n )\",\n \"def current_hash(self):\",\n \"def __hash__(self):\\n return hash(self.hash)\",\n \"def internal_hash(self): \\n return hash(tuple(sorted(self.hashtriples())))\",\n \"def __hash__(self):\\n return self.to_hash()\",\n \"def hash(self):\\n raise NotImplementedError() # To be subclassed\",\n \"def __hash__(self, reinit=False):\\n if not self.hash_value is None and not reinit:\\n return self.hash_value\\n elif isinstance(self, Leaf):\\n self.hash_value = Hash.leaf_hash(self)\\n return self.hash_value\\n else:\\n self.hash_value = Hash.node_hash(self)\\n return self.hash_value\",\n \"def get_hash(self):\\r\\n return\",\n \"def get_hash(self) -> str:\\n if self.call_hash:\\n # Derived state from a call_node.\\n return hash_struct([\\\"Handle\\\", self.fullname, \\\"call_hash\\\", self.key, self.call_hash])\\n else:\\n # Initial state.\\n return hash_struct([\\\"Handle\\\", self.fullname, \\\"init\\\", self.key, self.args, self.kwargs])\",\n \"def __getstate__(self):\\n state = self.__dict__.copy()\\n self.__cleanState__(state)\\n return state\",\n \"def get_hash(self):\\n return self.__hash\",\n \"def HashValue(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def __hash__(self):\\r\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(self.label())\",\n \"def __hash__(self):\\n raise NotImplementedError\",\n \"def __hash__(self):\\n if self.immutable:\\n return hash(self._make_key())\\n else:\\n raise MutantError(\\\"This %s is currently mutable, and therefore unhashable! \\\"%repr(self)\\n +\\\"Run self.make_immutable() to change this.\\\")\",\n \"def __Hash(self):\\n return self._Hash()\",\n \"def hash(self):\\n return self.__hash__()\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self) -> int:\\n # Turn the steps into a set first, so that permuting the steps doesn't change the equality.\\n self.simplify()\\n return hash(tuple(sorted(self.steps_set)))\",\n \"def __hash__(self):\\n return hash(self.value)\",\n \"def __hash__(self):\\r\\n return hash(self.__key())\",\n \"def __hash__(self):\\n hash_value = 0\\n \\n # approximate_online_count\\n hash_value ^= self.approximate_online_count\\n \\n # approximate_user_count\\n hash_value ^= self.approximate_user_count << 12\\n \\n # description\\n description = self.description\\n if (description is not None):\\n hash_value ^= hash(description)\\n \\n # discovery_splash\\n hash_value ^= hash(self.discovery_splash)\\n \\n # emojis\\n emojis = self.emojis\\n hash_value ^= len(emojis) << 1\\n for emoji in emojis.values():\\n hash_value ^= hash(emoji)\\n \\n # features\\n features = self.features\\n hash_value ^= len(features) << 5\\n for feature in features:\\n hash_value ^= hash(feature)\\n \\n # icon\\n hash_value ^= hash(self.icon)\\n \\n # id\\n hash_value ^= self.id\\n \\n # invite_splash\\n hash_value ^= hash(self.invite_splash)\\n \\n # stickers\\n stickers = self.stickers\\n hash_value ^= len(stickers) << 9\\n for sticker in stickers.values():\\n hash_value ^= hash(sticker)\\n \\n # name\\n name = self.name\\n if (description is None) or (description != name):\\n hash_value ^= hash(name)\\n \\n return hash_value\",\n \"def __hash__(self):\\n hash_value = 0\\n \\n # avatar\\n hash_value ^= hash(self.avatar)\\n \\n # boosts_since\\n boosts_since = self.boosts_since\\n if (boosts_since is not None):\\n hash_value ^= hash(boosts_since)\\n \\n # flags\\n hash_value ^= self.flags\\n \\n # joined_at\\n joined_at = self.joined_at\\n if (joined_at is not None):\\n hash_value ^= hash(joined_at)\\n \\n # nick\\n nick = self.nick\\n if (nick is not None):\\n hash_value ^= hash(nick)\\n \\n # pending\\n hash_value ^= self.pending\\n \\n # role_ids\\n role_ids = self.role_ids\\n if (role_ids is not None):\\n hash_value ^= len(role_ids) << 4\\n for role_id in role_ids:\\n hash_value ^= role_id\\n \\n # timed_out_until\\n timed_out_until = self.timed_out_until\\n if (timed_out_until is not None):\\n hash_value ^= hash(timed_out_until)\\n \\n return hash_value\",\n \"def __hash__(self) -> int:\",\n \"def hash_key(self):\",\n \"def __getstate__(self):\\n\\n state = {}\\n for key in self.__slots__:\\n state[key] = getattr(self, key)\\n\\n return state\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def _get_random_state(self):\\n self._validate_random_state()\\n return deepcopy(self.random_state)\",\n \"def __hash__(self):\\n x = xxhash.xxh64()\\n x.update(self.puzzle)\\n return x.intdigest()\",\n \"def __hash__(self):\\n raise TypeError(\\\"Transitions are mutable, and thus not hashable.\\\")\",\n \"def __hash__(self):\\n\\n return int(self._hash_value_)\",\n \"def state_to_hx_hash(new_state, target_label):\\n goal = None\\n empty = set()\\n mobile = set()\\n\\n for pt in new_state:\\n if new_state[pt].label == target_label:\\n goal = pt\\n elif new_state[pt].used == 0:\\n empty.add(pt)\\n elif new_state[pt].used < 100:\\n mobile.add(pt)\\n return goal, frozenset(empty), frozenset(mobile)\",\n \"def __Hash(self):\\n return self._Hash()\",\n \"def convertStateToHash(values):\\n l = list(sorted(values.items()))\\n modl = [a+b for (a, b) in l]\\n return ''.join(modl)\",\n \"def convertStateToHash(values):\\n l = list(sorted(values.items()))\\n modl = [a+b for (a, b) in l]\\n return ''.join(modl)\",\n \"def get_hash(self):\\n return freeze_dict(self.get_hash_params())\",\n \"def __hash__(self):\\n return self.value.__hash__()\",\n \"def hash(self):\\n return self.hash_by_id(self.id)\",\n \"def unique_hash(self):\\n raise NotImplementedError(\\\"unique_hash Method not implemented\\\")\",\n \"def hash(self) -> bytes:\",\n \"def __getstate__(self):\\n state: Dict[str, Any] = deepcopy(self.__dict__)\\n del state['__db']\\n return state\",\n \"def __hash__(self):\\n return hash((self.SYMBOL, self._.hash_parameters))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\r\\n return hash((self.label, ))\",\n \"def __hash__(self):\\n return super().__hash__()\",\n \"def __hash__(self) -> int:\\n ...\",\n \"def __hash__(self) -> int:\\n ...\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __getstate__(self):\\n import time\\n\\n state = self.__dict__.copy()\\n\\n # Remove the unpicklable entries\\n del state['_model']\\n del state['_input']\\n del state['_output']\\n del state['_preds_op']\\n del state['_loss']\\n del state['_loss_grads']\\n del state['_preds']\\n del state['_layer_names']\\n\\n model_name = str(time.time()) + '.h5'\\n state['model_name'] = model_name\\n self.save(model_name)\\n return state\",\n \"def __hash__(self):\\n return hash(id(self))\",\n \"def __hash__(self):\\n return hash(id(self))\",\n \"def __hash__(self):\\n if self._mutable:\\n raise ValueError(\\\"can not hash mutable constellation\\\")\\n return hash(tuple(self._g))\",\n \"def __hash__(self):\\n\\t\\treturn 1\",\n \"def __hash__(self):\\n return hash((self._nele, self._m_s))\",\n \"def __hash__( self ):\\n return hash( self.data )\",\n \"def hash_value(board_state):\\n res = \\\"\\\"\\n for i in range(1,10):\\n res = res + board_state[i]\\n return res\",\n \"def __hash__(self):\\n return hash(self.joined())\",\n \"def __hash__(self) -> int:\\n return hash(self.value)\",\n \"def __hash__(self) -> int:\\n return hash(self.__key__())\",\n \"def __hash__(self):\\n return hash((self._start, self._end, self._name, self._value))\",\n \"def __hash__(self):\\n\\t\\treturn hash(repr(self))\",\n \"def get_hash(self):\\n if self.contributes:\\n return hash_from_values(self.iter_values())\",\n \"def __getstate__(self):\\n return dict(self.items())\",\n \"def __getstate__(self):\\n return dict(self.items())\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(str(self.__id__))\",\n \"def __hash__(self):\\n return hash(self.base_location) ^ hash(self.fold_path) ^ hash(self.field)\",\n \"def hashcode(o):\",\n \"def getHash(self):\\n if self.chash:\\n return self.chash\\n else:\\n self.setHash()\\n return self.chash\",\n \"def __hash__(self):\\n return hash(\\n (tuple(\\n self.vector), self.index, tuple(\\n self.labels), self.label))\",\n \"def __hash__(self):\\n return hash(self.get_all_features())\",\n \"def get_state(self):\\n\\n # TODO: Assemble a dictionary containing the tracker state\\n\\n return {}\",\n \"def __hash__(self):\\n hash_value = 0\\n \\n # required\\n hash_value ^= self.required << 14\\n \\n # title\\n hash_value ^= hash(self.title)\\n \\n # type\\n hash_value ^= hash(self.type)\\n \\n # values\\n values = self.values\\n if (values is not None):\\n hash_value ^= len(values)\\n \\n for value in values:\\n hash_value ^= hash(value)\\n \\n return hash_value\",\n \"def __hash__(self):\\n return hash(self.returnBoard())\",\n \"def __getstate__(self):\\n return {}\",\n \"def __hash__(self):\\n # These entities are not cached, so we wont use their `id` if applicable.\\n hash_value = 0\\n \\n # bot\\n hash_value ^= hash(self.bot)\\n \\n # description\\n description = self.description\\n if (description is not None):\\n hash_value ^= hash(description)\\n \\n # icon\\n hash_value ^= hash(self.icon)\\n \\n # id\\n hash_value ^= self.id\\n \\n # name\\n name = self.name\\n if (description is None) or (description != name):\\n hash_value ^= hash(name)\\n \\n return hash_value\",\n \"def get_state(self, state):\\n return state\",\n \"def __hash__(self):\\n return hash(self.name)\",\n \"def __hash__(self):\\n return hash(self.name)\",\n \"def __hash__(self):\\r\\n return hash(type(self)) ^ hash(self.broadcastable)\",\n \"def _sharded_state_dict(self, *args: Any, **kwargs: Any) -> Any:\\n with self.set_state_dict_type(StateDictType.SHARDED_STATE_DICT):\\n return self.state_dict(self, *args, **kwargs)\",\n \"def get_state_dict(self):\\n raise NotImplemented()\",\n \"def __hash__(self) -> int:\\n return hash(tuple(self.name,))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8085476","0.79855424","0.7687602","0.75003594","0.69587237","0.68687993","0.6838884","0.68151635","0.68011165","0.6758","0.6713541","0.6698997","0.6691076","0.668013","0.6669868","0.66635925","0.66240686","0.6620361","0.6606971","0.6604862","0.6591645","0.6591191","0.65896535","0.65896535","0.65896535","0.65896535","0.6546318","0.65448534","0.65433604","0.65256274","0.65138054","0.65130347","0.6504495","0.6470777","0.645601","0.645601","0.645601","0.645601","0.64479643","0.6437086","0.64315605","0.6430058","0.6427403","0.6426022","0.64080673","0.64080673","0.6405443","0.63996214","0.6392037","0.6386765","0.6379238","0.6378925","0.636821","0.636804","0.636804","0.636804","0.6355918","0.6342245","0.6327816","0.6327816","0.6324905","0.6324905","0.6324905","0.63181525","0.63147473","0.63147473","0.6308342","0.6306037","0.63000256","0.6291066","0.6289535","0.62857735","0.6278686","0.6271626","0.6268141","0.626763","0.62618226","0.62610596","0.62610596","0.62594247","0.62594247","0.62594247","0.62551254","0.62536955","0.6250873","0.62477934","0.62440974","0.6235008","0.6233377","0.6232448","0.6230512","0.6225875","0.6222313","0.62190485","0.6215806","0.6215806","0.62121636","0.62063843","0.6203721","0.62006044"],"string":"[\n \"0.8085476\",\n \"0.79855424\",\n \"0.7687602\",\n \"0.75003594\",\n \"0.69587237\",\n \"0.68687993\",\n \"0.6838884\",\n \"0.68151635\",\n \"0.68011165\",\n \"0.6758\",\n \"0.6713541\",\n \"0.6698997\",\n \"0.6691076\",\n \"0.668013\",\n \"0.6669868\",\n \"0.66635925\",\n \"0.66240686\",\n \"0.6620361\",\n \"0.6606971\",\n \"0.6604862\",\n \"0.6591645\",\n \"0.6591191\",\n \"0.65896535\",\n \"0.65896535\",\n \"0.65896535\",\n \"0.65896535\",\n \"0.6546318\",\n \"0.65448534\",\n \"0.65433604\",\n \"0.65256274\",\n \"0.65138054\",\n \"0.65130347\",\n \"0.6504495\",\n \"0.6470777\",\n \"0.645601\",\n \"0.645601\",\n \"0.645601\",\n \"0.645601\",\n \"0.64479643\",\n \"0.6437086\",\n \"0.64315605\",\n \"0.6430058\",\n \"0.6427403\",\n \"0.6426022\",\n \"0.64080673\",\n \"0.64080673\",\n \"0.6405443\",\n \"0.63996214\",\n \"0.6392037\",\n \"0.6386765\",\n \"0.6379238\",\n \"0.6378925\",\n \"0.636821\",\n \"0.636804\",\n \"0.636804\",\n \"0.636804\",\n \"0.6355918\",\n \"0.6342245\",\n \"0.6327816\",\n \"0.6327816\",\n \"0.6324905\",\n \"0.6324905\",\n \"0.6324905\",\n \"0.63181525\",\n \"0.63147473\",\n \"0.63147473\",\n \"0.6308342\",\n \"0.6306037\",\n \"0.63000256\",\n \"0.6291066\",\n \"0.6289535\",\n \"0.62857735\",\n \"0.6278686\",\n \"0.6271626\",\n \"0.6268141\",\n \"0.626763\",\n \"0.62618226\",\n \"0.62610596\",\n \"0.62610596\",\n \"0.62594247\",\n \"0.62594247\",\n \"0.62594247\",\n \"0.62551254\",\n \"0.62536955\",\n \"0.6250873\",\n \"0.62477934\",\n \"0.62440974\",\n \"0.6235008\",\n \"0.6233377\",\n \"0.6232448\",\n \"0.6230512\",\n \"0.6225875\",\n \"0.6222313\",\n \"0.62190485\",\n \"0.6215806\",\n \"0.6215806\",\n \"0.62121636\",\n \"0.62063843\",\n \"0.6203721\",\n \"0.62006044\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.676722"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":18488,"cells":{"query":{"kind":"string","value":"Make an instance of the api and attempts to authenticate the user. Return the authenticated api."},"document":{"kind":"string","value":"def authenticate():\n\n # We are uploading and then downloading so we want Musicmanager\n api = Musicmanager()\n\n # Attempt to authenticate and log in\n logged_in = api.login()\n\n # If login() returns false, you have not performed oauth yet, or did not\n # write your credentials to your disk. Using oauth allows authentication\n # without providing plaintext credentials to the application\n if not logged_in:\n print('No oauth credentials found, please authenticate your account')\n\n # Performs oauth and stores generated credentials to Appdirs \n # 'user_data_dir' by default. oauth only needs to be performed once per \n # machine if the credentials are stored, which is the default behavior.\n authenticated = api.perform_oauth(open_browser=True)\n else:\n print('Successfully logged in.\\n')\n\n return api"},"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 api_authentication():\r\n apikey = request.args.get('api_key', None)\r\n from flask import _request_ctx_stack\r\n if 'Authorization' in request.headers:\r\n apikey = request.headers.get('Authorization')\r\n if apikey:\r\n user = db.session.query(model.user.User).filter_by(api_key=apikey).first()\r\n ## HACK:\r\n # login_user sets a session cookie which we really don't want.\r\n # login_user(user)\r\n if user:\r\n _request_ctx_stack.top.user = user","def auth(self):\n return self.api(self.token)","def authentication(): \n pathToConfig = os.path.join(prefix, \"twitterConfig\")\n config = json.load(open(pathToConfig))\n consumer_key = config['consumer_key']\n consumer_secret = config['consumer_secret']\n access_token = config['access_token']\n access_token_secret = config['access_token_secret']\n api = twitter.Api(consumer_key=consumer_key, consumer_secret=consumer_secret,\n access_token_key=access_token, access_token_secret=access_token_secret)\n return api","def get_authenticated_service(api_name: str, api_version: str) -> Resource:\n\n if CREDS_FILENAME.exists():\n credentials = Credentials.from_authorized_user_file(str(CREDS_FILENAME))\n # TODO make request to the access token endpoint???\n\n # FIXME verifying token\n # credentials.refresh(requests.Request())\n # print(credentials.token, credentials.expiry)\n\n # idinfo = id_token.verify_oauth2_token(\n # credentials.token, requests.Request(), credentials.client_id)\n\n # if idinfo['iss'] not in ['accounts.google.com',\n # 'https://accounts.google.com']:\n # # CREDS_FILENAME.unlink()\n # raise ValueError('Wrong issuer.')\n\n else:\n flow = InstalledAppFlow.from_client_secrets_file(CLIENT_SECRETS_FILE, SCOPES)\n credentials = flow.run_local_server(\n host=\"localhost\",\n port=8080,\n authorization_prompt_message=\"Please visit this URL: {url}\",\n success_message=\"The auth flow is complete; you may close this window.\",\n open_browser=True,\n )\n\n creds_data = {\n \"token\": None,\n \"refresh_token\": credentials.refresh_token,\n \"token_uri\": credentials.token_uri,\n \"client_id\": credentials.client_id,\n \"client_secret\": credentials.client_secret,\n \"scopes\": credentials.scopes,\n }\n\n with CREDS_FILENAME.open(\"w\") as outfile:\n json.dump(creds_data, outfile)\n\n return build(api_name, api_version, credentials=credentials)","def login_to_api(self):\n\n # set the API endpoint and POST the username/password to it\n endpoint = app.config['API']['url'] + 'login'\n response = requests.post(\n endpoint,\n verify = app.config['API']['verify_ssl'],\n json = {\n 'username': self.username,\n 'password': self.password\n }\n )\n\n # if the response is good, return True\n if response.status_code == 200:\n user = response.json()\n self._id = ObjectId(user['_id'])\n self.token = user['access_token']\n return True","def authenticate(self):\n auth = tw.OAuthHandler(self.consumer_key, self.consumer_secret)\n auth.set_access_token(self.access_token, self.access_secret)\n return tw.API(auth)","def _get_api() -> Mobileclient:\n\n api = Mobileclient()\n device_id = _get_device_id(api)\n api.oauth_login(device_id)\n\n return api","def api_auth():\n form = request.get_json(force=True)\n userdata = None\n if form['register']:\n userdata = userProvider.register_user(\n form['username'].encode('utf8'),\n form['password'].encode('utf8')\n )\n else:\n userdata = userProvider.load_authenticated_user(\n form['username'].encode('utf8'),\n form['password'].encode('utf8')\n )\n if userdata:\n user = userProvider.userdata_to_user(userdata)\n flask_login.login_user(user)\n return \"true\"\n raise Exception(\"No user loaded\")","def auth(mocker, app: FastAPI, faker: Faker) -> HTTPBasicAuth:\n # mock engine if db was not init\n if app.state.settings.API_SERVER_POSTGRES is None:\n engine = mocker.MagicMock()\n engine.minsize = 1\n engine.size = 10\n engine.freesize = 3\n engine.maxsize = 10\n app.state.engine = engine\n\n # patch authentication entry in repo\n faker_user_id = faker.pyint()\n\n # NOTE: here, instead of using the database, we patch repositories interface\n mocker.patch(\n \"simcore_service_api_server.db.repositories.api_keys.ApiKeysRepository.get_user_id\",\n autospec=True,\n return_value=faker_user_id,\n )\n mocker.patch(\n \"simcore_service_api_server.db.repositories.users.UsersRepository.get_user_id\",\n autospec=True,\n return_value=faker_user_id,\n )\n mocker.patch(\n \"simcore_service_api_server.db.repositories.users.UsersRepository.get_email_from_user_id\",\n autospec=True,\n return_value=faker.email(),\n )\n\n # patches simcore_postgres_database.utils_products.get_default_product_name\n mocker.patch(\n \"simcore_service_api_server.api.dependencies.application.get_default_product_name\",\n autospec=True,\n return_value=\"osparc\",\n )\n\n return HTTPBasicAuth(faker.word(), faker.password())","def authenticate(self) -> api.API:\r\n original_api = api.API()\r\n for c in self._credentials: # each app\r\n user_auth = tweepy.OAuthHandler(c.consumer_key, c.consumer_secret)\r\n user_auth.set_access_token(c.access_token, c.access_secret)\r\n app_auth = tweepy.AppAuthHandler(c.consumer_key, c.consumer_secret)\r\n original_api.append(tweepy.API(user_auth), tweepy.API(app_auth))\r\n return original_api","def __init__(self):\n self.api = Api(consumer_key=credentials[\"consumer_key\"],\n consumer_secret=credentials[\"consumer_secret\"],\n access_token_key=credentials[\"access_token_key\"],\n access_token_secret=credentials[\"access_token_secret\"])","def __create_api(self):\n consumer_key = os.environ.get(\"CONSUMER_KEY\")\n consumer_secret = os.environ.get(\"CONSUMER_SECRET\")\n access_token = os.getenv(\"ACCESS_TOKEN\")\n access_token_secret = os.environ.get(\"ACCESS_TOKEN_SECRET\")\n\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n api = tweepy.API(\n auth,\n compression=True,\n wait_on_rate_limit=True,\n wait_on_rate_limit_notify=True,\n )\n try:\n api.verify_credentials()\n except Exception as e:\n logging.error(\"Error creating API\", exc_info=True)\n raise e\n return api","def login_to_apic(self):\n session = Session(URL, LOGIN, PASSWORD)\n resp = session.login()\n self.assertTrue(resp.ok)\n return session","async def login(\n cls: Type[ApiType], host: str, port: str, user: str, password: str,\n websession: ClientSession) -> ApiType:\n klass = cls(websession)\n klass._base_url = 'http://{0}:{1}/'.format(host, port)\n klass._auth = BasicAuth(login=user, password=password)\n await klass._request('get', '')\n return klass","def _login_vapi(self):\n session = requests.Session()\n session.verify = self.validate_certs\n if not self.validate_certs:\n # Disable warning shown at stdout\n requests.packages.urllib3.disable_warnings()\n\n print(\"logging in\")\n client = create_vsphere_client(server=self.hostname,\n username=self.username,\n password=self.password,\n session=session)\n if client is None:\n raise Exception(\"Failed to login to %s using %s\" %\n (self.hostname, self.username))\n return client","def init_api(username, password, section):\n if SAVE_COOKIES is True:\n api = API()\n api._init_cookies()\n api.login(username, password, section)\n api.save_cookies()\n print('Cookies is saved.')\n sys.exit()\n elif os.path.exists(COOKIES_FILENAME):\n try:\n api = API(persistent=True)\n except cookielib.LoadError:\n print('Cannot read cookies from %s. ' % COOKIES_FILENAME)\n print('Use -c option to save cookies and try again.')\n sys.exit(2)\n if api.has_logged_in:\n print('API has logged in from cookies.')\n else:\n print('API fails to log in from cookies.')\n print('Use -c option to save cookies and try again.')\n sys.exit(1)\n else:\n api = API()\n if api.login(username, password, section):\n print('Login success.')\n return api","def auth(self):\n return AuthManager(self)","def api_client() -> APIClient:\n return APIClient()","def authenticated_api(username, api_root=None, parser=None):\n auth = tweepy.OAuthHandler(settings.TWITTER_CONSUMER_KEY, \n settings.TWITTER_CONSUMER_SECRET)\n try:\n user = User.objects.get(username=username)\n sa = user.social_auth.all()[0]\n auth.set_access_token(sa.tokens['oauth_token'], \n sa.tokens['oauth_token_secret'])\n return tweepy.API(auth, \n api_root=api_root or settings.TWITTER_API_ROOT, \n parser=parser or JSONParser(),\n secure=settings.TWITTER_USE_SECURE) \n except:\n return None","def __init__(self):\n self.user = None\n self.error = {}\n\n if not self.API_KEY:\n raise Exceptions.NoAPIKey\n\n if not self.API_SECRET:\n raise Exceptions.NoAPISecret\n\n # Namedtuple for settings for each request and the api functions.\n self.settings = namedtuple(\"Settings\", ['library', 'urllib', 'urllib2', 'json', 'requests'])\n self.api = UserRegistrationAPI(self)\n self.user = self._get_user_tuple()\n\n # We prefer to use requests with the updated urllib3 module.\n try:\n from distutils.version import StrictVersion\n import requests\n\n if StrictVersion(requests.__version__) < StrictVersion(\"2.0\"):\n raise Exceptions.RequestsLibraryDated(requests.__version__)\n else:\n self._settings(\"requests\")\n\n # However, we can use urllib if there is no requests or it is outdated.\n except (ImportError, Exceptions.RequestsLibraryDated):\n self._settings(\"urllib2\")\n\n # Well, something went wrong here.\n except:\n raise\n\n else:\n # Namedtuples for api, and user.\n self.user = self._get_user_tuple()","def authenticate(self):\n #it's weird i have to do this here, but the code makes this not simple\n auth_json={'email':self.user, 'password':self.password}\n #send a post with no auth. prevents an infinite loop\n auth_response = self.post('/auth', data = json.dumps(auth_json), auth =\n None)\n\n _token = auth_response.json['token']\n\n self._token = _token\n self._wrapped.auth = SpringAuth(_token)","def auth(self):\n auth = OAuthHandler(self._api_key, self._api_secret)\n auth.set_access_token(self._access_token, self._access_secret)\n return auth","def api_client() -> APIClient:\n\n return APIClient()","def logged_in_apiclient(apiclient):\n user = UserFactory()\n apiclient.force_login(user)\n return apiclient, user","def __init__(self, **kwargs):\n\n session = vk_api.VkApi(**kwargs)\n try:\n session.auth(token_only=True)\n except vk_api.AuthError as error_msg:\n print(error_msg)\n raise\n self.api = session.get_api()","def client():\n return Client(**common_data.AUTH_ARGS)","def init_api(self):\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists(self.gdrive_config.TOKEN_PICK_PATH):\n with open(self.gdrive_config.TOKEN_PICK_PATH, 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n self.gdrive_config.CREDENTIAL_PATH, self.gdrive_config.SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open(self.gdrive_config.TOKEN_PICK_PATH, 'wb') as token:\n pickle.dump(creds, token)\n\n service = build('drive', 'v3', credentials=creds)\n return service","def token_auth(self):\n self.client = APIClient()\n self.user = User.objects.create_user(username='testuser', email='test@test.com', password='testpassword')\n self.token = Token.objects.create(user=self.user)\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)","def api(self):\n if self._api is None:\n self._api = Api(self)\n return self._api","def authenticate(self):\n\n def decorate(func, *args, **kws):\n \"\"\"\n A function returned as a object in load time,\n which returns inner function do_decorate().\n \"\"\"\n def do_authenticate():\n \"\"\"\n A function to perform authentication\n every time decorated function is called.\n \"\"\"\n #try:\n if 1:\n if 'referer' not in self.session:\n path = urlsplit(self.request.url)[2]\n self.session['referer'] = path\n self.session.put()\n #except:\n # pass\n aobj = self.config.auth_obj()\n self.get_controller()\n auth_res = aobj.auth(self.controller, *args, **kws)\n if auth_res:\n return func(*args, **kws)\n aobj.auth_redirect(self.controller, *args, **kws)\n # clear controller for development environment.\n\n return do_authenticate\n\n return decorate","def authenticate():\n auth = OAuthHandler(config.CONSUMER_API_KEY, config.CONSUMER_API_SECRET)\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\n\n return auth","def authenticate():\n auth = OAuthHandler(config.CONSUMER_API_KEY, config.CONSUMER_API_SECRET)\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\n\n return auth","def __init__(self, target, id_token=None, private_key_file=None, private_key_password=None,\n username=None, client_id=None, key_id=None, issuer=None, api_token=None,\n retries=DEFAULT_RETRIES, timeout=DEFAULT_TIMEOUT, ssl_cert=None, user_agent=None,\n verify_ssl=None):\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n config = Configuration()\n config.verify_ssl = resolve_ssl_validation(verify_ssl)\n config.ssl_ca_cert = ssl_cert\n config.host = self._get_base_url(target)\n\n if id_token and api_token:\n raise PureError(\"Only one authentication option is allowed. Please use either id_token or api_token and try again!\")\n elif private_key_file and private_key_password and username and \\\n key_id and client_id and issuer and api_token:\n raise PureError(\"id_token is generated based on app ID and private key info. Please use either id_token or api_token and try again!\")\n elif api_token:\n api_token_auth_endpoint = self._get_api_token_endpoint(target)\n self._token_man = APITokenManager(api_token_auth_endpoint, api_token, verify_ssl=config.verify_ssl)\n else:\n auth_endpoint = 'https://{}/oauth2/1.0/token'.format(target)\n headers = {\n 'kid': key_id\n }\n payload = {\n 'iss': issuer,\n 'aud': client_id,\n 'sub': username,\n }\n self._token_man = TokenManager(auth_endpoint, id_token, private_key_file, private_key_password,\n payload=payload, headers=headers, verify_ssl=config.verify_ssl)\n\n self._api_client = ApiClient(configuration=config)\n self._api_client.user_agent = user_agent or self.USER_AGENT\n self._set_agent_header()\n self._set_auth_header()\n\n # Read timeout and retries\n self._retries = retries\n self._timeout = timeout\n\n # Instantiate APIs\n self._connections_api = api.ConnectionsApi(self._api_client)\n self._host_groups_api = api.HostGroupsApi(self._api_client)\n self._hosts_api = api.HostsApi(self._api_client)\n self._volume_snapshots_api = api.VolumeSnapshotsApi(self._api_client)\n self._volumes_api = api.VolumesApi(self._api_client)","def _authenticate_for(self, resp):\n # Get the auth. info from the headers\n scheme, params = resp.headers['Www-Authenticate'].split(None, 1)\n assert (scheme == 'Bearer')\n info = {k: v.strip('\"') for k, v in (i.split('=')\n for i in params.split(','))}\n\n # Request a token from the auth server\n params = {k: v for k, v in info.items() if k in ('service', 'scope')}\n auth = HTTPBasicAuth(self.username, self.password)\n r2 = requests.get(info['realm'], params=params,\n auth=auth, verify=self.verify_ssl)\n\n if r2.status_code == 401:\n raise RuntimeError(\"Authentication Error\")\n r2.raise_for_status()\n\n self.auth = BearerAuth(r2.json()['token'])","def __init__(self):\n self.authurl = Config().auth\n self.baseurl = Config().api\n self.s = Session()\n self.s.headers = {'Accept': 'application/json'}\n data = {\"grant_type\": \"client_credentials\", \"scope\": \"/read-public\", \"client_id\": Config().client_id,\n \"client_secret\": Config().client_secret}\n r = self.s.request(method=\"post\", url=self.authurl, data=data)\n self.s.headers = {'Accept': 'application/json', \"Access token\": r.json()[\"access_token\"]}","def _authenticate(self):\n # Check if token expired\n if self._token_expire and self._token_expire < _time():\n self._token = ''\n\n # Get OAuth2 token\n if not self._token:\n # Get user credentials\n credentials = json_read(get_accelize_cred())\n client_id = credentials['client_id']\n client_secret = credentials['client_secret']\n\n # Endpoint override in credentials file\n self._endpoint = credentials.get('endpoint', self._ENDPOINT)\n\n # Try to get CLI cached token\n try:\n self._token, self._token_expire = get_cli_cache(client_id)\n\n # Try to get token from web service\n except TypeError:\n response = self._request(\n 'post', f'{self._endpoint}/o/token/',\n data={\"grant_type\": \"client_credentials\"},\n auth=(client_id, client_secret),\n timeout=self._TIMEOUT)\n\n if response.status_code >= 300:\n raise _AuthenticationException(\n 'Unable to authenticate client ID starting by '\n f'\"{client_id[:10]}\": '\n f'{self._get_error_message(response)}')\n\n access = response.json()\n self._token = access['access_token']\n self._token_expire = int(_time()) + access['expires_in'] - 1\n\n # Cache token value for future CLI usage\n set_cli_cache(client_id, [self._token, self._token_expire],\n self._token_expire)","def get_auth(context):\n\n headers = context['headers']\n auth_info = {\n \"type\": \"basic\",\n \"basic\": {\n \"user\": headers['api_key'],\n \"password\": \"X\"\n }\n }\n auth = Auth().get_auth(auth_info)\n\n return auth","def authenticate(self, *args, **kwargs):\n # Validate backend and arguments. Require that the Social Auth\n # response be passed in as a keyword argument, to make sure we\n # don't match the username/password calling conventions of\n # authenticate.\n if not (self.name and kwargs.get(self.name) and 'response' in kwargs):\n return None\n\n response = kwargs.get('response')\n pipeline = PIPELINE\n kwargs = kwargs.copy()\n kwargs['backend'] = self\n\n if 'pipeline_index' in kwargs:\n pipeline = pipeline[kwargs['pipeline_index']:]\n else:\n kwargs['details'] = self.get_user_details(response)\n kwargs['uid'] = self.get_user_id(kwargs['request'])\n kwargs['is_new'] = False\n \n out = self.pipeline(pipeline, *args, **kwargs)\n if not isinstance(out, dict):\n return out\n\n social_user = out.get('social_user')\n if social_user:\n # define user.social_user attribute to track current social\n # account\n user = social_user.user\n user.social_user = social_user\n user.is_new = out.get('is_new')\n return user","def api_auth(func):\n @wraps(func)\n def _decorator(request, *args, **kwargs):\n authentication = APIAuthentication(request)\n if authentication.authenticate():\n return func(request, *args, **kwargs)\n raise Http404\n return _decorator","def __init__(self, username=None, password=None, apitoken=None):\n self.__credentials = None\n self.__headers = {}\n if apitoken:\n self.authenticate_by_token(apitoken)\n if username and password:\n self.authenticate(username, password)","def authenticate_user(self):\n cloud_config = os_client_config.OpenStackConfig().get_one_cloud(\n cloud=self.options.os_cloud, argparse=self.options,\n network_api_version=self.api_version,\n verify=not self.options.insecure)\n verify, cert = cloud_config.get_requests_verify_args()\n\n # TODO(singhj): Remove dependancy on HTTPClient\n # for the case of token-endpoint authentication\n\n # When using token-endpoint authentication legacy\n # HTTPClient will be used, otherwise SessionClient\n # will be used.\n if self.options.os_token and self.options.os_url:\n auth = None\n auth_session = None\n else:\n auth = cloud_config.get_auth()\n\n auth_session = session.Session(\n auth=auth, verify=verify, cert=cert,\n timeout=self.options.http_timeout)\n\n interface = self.options.os_endpoint_type or self.endpoint_type\n if interface.endswith('URL'):\n interface = interface[:-3]\n self.client_manager = clientmanager.ClientManager(\n retries=self.options.retries,\n raise_errors=False,\n session=auth_session,\n url=self.options.os_url,\n token=self.options.os_token,\n region_name=cloud_config.get_region_name(),\n api_version=cloud_config.get_api_version('network'),\n service_type=cloud_config.get_service_type('network'),\n service_name=cloud_config.get_service_name('network'),\n endpoint_type=interface,\n auth=auth,\n insecure=not verify,\n log_credentials=True)\n return","def authenticate():\n auth = OAuthHandler(config.TW_API_KEY, config.TW_API_SECRET)\n auth.set_access_token(config.TW_ACC_TOKEN, config.TW_ACC_SECRET)\n\n return auth","def __make_api_get(self, api_path):\n try:\n self.last_response = urllib2.urlopen(\n self.api_server + api_path, cafile=self.cacert_path)\n json_data = self.last_response.read()\n\n # Check for errors\n except urllib2.HTTPError as err:\n error = \"API HTTP error [%s] - '%s'\" % (err.code, err.read())\n raise EFIgyCliError(error, self.last_response)\n\n except urllib2.URLError as err:\n error = 'Problem calling API at location %s - %s' % (\n self.api_server + api_path, err)\n raise EFIgyCliError(error, self.last_response)\n\n # Decode json response into an object\n try:\n ret = json.loads(json_data)\n except ValueError as err:\n error = \"Problem deserialising data, expecting JSON.\\nError: %s\\nData: %s\" % (\n err, json_data)\n raise EFIgyCliError(error, self.last_response)\n\n # Return JSON deserialised object\n return ret","def as_auth_name(func):\n\n def auth_client(self):\n token = Token.objects.get(user__username=username)\n self.client = APIClient()\n self.client.credentials(HTTP_AUTHORIZATION=\"Token \" + token.key)\n return func(self)\n\n return auth_client","def __init__(self,\n identifier=None,\n password=None,\n silent=False):\n self.data_source = RestApi()\n self.user_id = self.data_source.login(identifier, password, silent)","def __init__(self):\n self.auth()","def auth():\n\n if current_user.is_authenticated:\n\n # OAuth is only necesary when we don't have a user's API\n if not current_user.zoter_api:\n\n def get_auth_url():\n request_token, request_token_secret = zoteroAuth.get_request_token()\n session['request_token'] = request_token\n session['request_token_secret'] = request_token_secret\n auth_url = zoteroAuth.get_authorize_url(request_token)\n return auth_url\n\n flash('Hi! {}, please visit <a href=\"{}\" target=\"new\">here</a> for authentication.'.format(current_user.username, get_auth_url()))\n return redirect(url_for('bookshelf'))\n \n else:\n flash('You already have an API key!')\n return redirect(url_for('sync'))","def init_api():\n auth = twitter.OAuth(\n os.environ['AUTH_KEY'], os.environ['AUTH_SECRET'],\n os.environ['API_KEY'], os.environ['API_SECRET'],\n )\n return twitter.Twitter(auth=auth)","def __init__(self, api_key, api_secret, base_url=settings.TWITTER_API_URL):\n self.api_key = api_key\n self.api_secret = api_secret\n self.base_url = base_url\n self.bearer_token = None\n self.__auth = None\n self.get_bearer_token()\n self.set_requests_auth()\n self.session = requests_retry_session()","def get_api_client():\n\n global _API_CLIENT_HANDLE\n\n if not _API_CLIENT_HANDLE:\n context = get_context()\n server_config = context.get_server_config()\n\n pc_ip = server_config.get(\"pc_ip\")\n pc_port = server_config.get(\"pc_port\")\n username = server_config.get(\"pc_username\")\n password = server_config.get(\"pc_password\")\n\n update_api_client(host=pc_ip, port=pc_port, auth=(username, password))\n\n return _API_CLIENT_HANDLE","def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})","def get_api():\n # Load auth information.\n token = load_token()\n\n if token is None:\n token_string = get_twitter_auth()\n save_token(token_string)\n token = tweepy.oauth.OAuthToken.from_string(token_string)\n\n try:\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n auth.set_access_token(token.key, token.secret)\n\n api = tweepy.API(auth)\n except tweepy.TweepError:\n print (\"Error: Unable to get tweepy API, please try again.\")\n sys.exit(1)\n\n return api","def connect(api, username, password):\n\treturn api.login(username, password)","def auth(client):\n return AuthActions(client)","def create_API_access():\n logger = logging.getLogger(__name__)\n logger.info('Creating API connection')\n\n # Find .env automagically by walking up directories until it's found\n dotenv_path = find_dotenv()\n\n # Load up the entries as environment variables\n load_dotenv(dotenv_path)\n\n # Get Twitter API credentials from environment variables\n consumer_key = os.environ.get(\"CONSUMER_KEY\")\n consumer_secret = os.environ.get(\"CONSUMER_SECRET\")\n access_token = os.environ.get(\"ACCESS_TOKEN\")\n access_secret = os.environ.get(\"ACCESS_SECRET\")\n\n # Set up authorization to Twitter API\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_secret)\n\n # Create connection object\n api = tweepy.API(auth)\n return api","def authenticate(self):\n token = self.get_config('token')\n if token:\n self.root.connection.login(\n None, None, token=token, auth_type='Bearer'\n )\n else:\n config.use_sessions = True\n self.root.load_session().get()","def __init__(\n self,\n api_url: str,\n username: str,\n password: str,\n shared_disk: str,\n ip_resolver: IPResolver,\n verify_cert: bool = False,\n create_pool: Optional[str] = None):\n self.logger = logging.getLogger(__name__)\n self.api_url = api_url\n self.username = username\n self.password = password\n self.sess = BaseUrlSession(self.api_url + '/api2/json/')\n self.sess.verify = verify_cert\n if not self.sess.verify:\n requests.packages.urllib3.disable_warnings()\n\n self.shared_disk = shared_disk\n self.create_pool = create_pool\n self.ip_resolver = ip_resolver\n self._login()","def get_api(\n password: str,\n host: str = None,\n username: str = None,\n port: int = None,\n ssl: bool = False,\n) -> Netgear:\n api: Netgear = Netgear(password, host, username, port, ssl)\n\n if not api.login():\n raise CannotLoginException\n\n return api","def auth(self):\n return auth.get_auth()","def authenticate(self):\n # Get credentials from eodag or using ecmwf conf\n email = getattr(self.config, \"credentials\", {}).get(\"username\", None)\n key = getattr(self.config, \"credentials\", {}).get(\"password\", None)\n url = getattr(self.config, \"api_endpoint\", None)\n if not all([email, key, url]):\n key, url, email = get_apikey_values()\n\n # APIRequest to check credentials\n ecmwf_connection = Connection(\n url=url,\n email=email,\n key=key,\n )\n try:\n ecmwf_connection.call(\"{}/{}\".format(url, \"who-am-i\"))\n logger.debug(\"Credentials checked on ECMWF\")\n except APIException as e:\n logger.error(e)\n raise AuthenticationError(\"Please check your ECMWF credentials.\")\n\n return {\"key\": key, \"url\": url, \"email\": email}","def __init__(self, session):\n super(FlattrApi, self).__init__(session)\n self.things = ThingApi(session)\n self.users = UsersApi(session)\n self.authenticated = AuthenticatedApi(session)","def get_api(self):\n return self.api","def make_api_request(self):\n # We can create an instance of an endpoint resource class, and use it to fetch details\n access_token = self.get_token()\n self.request.session['access_token'] = access_token\n\n doctor_api = DoctorEndpoint(access_token)\n patient_api = PatientEndpoint(access_token)\n apppointment_api = AppointmentEndpoint(access_token)\n\n # Grab the first doctor from the list; normally this would be the whole\n # practice group, but your hackathon account probably only has one doctor in it.\n doctor = doctor_api.get_and_store_data()\n self.request.session['doctor'] = doctor.id\n # Get patients and appointments for the doctor and store it in the local DB\n patient_api.get_and_store_data(doctor=doctor)\n\n date = datetime.now(tz=pytz.timezone(\n doctor.timezone)).strftime('%Y-%m-%d')\n apppointment_api.get_and_store_data(doctor=doctor, date=date)\n\n return doctor","def api(self, user=None):\n if not user.is_authenticated:\n return self.none()\n return self.filter(users=user)","def _authorize(self):\n auth = tweepy.OAuthHandler(self.keys[\"consumer_key\"], self.keys[\"consumer_secret\"])\n auth.set_access_token(self.keys[\"access_token\"], self.keys[\"access_token_secret\"])\n return tweepy.API(auth)","def test_ApiWillAuthenticate_ValidCredentials_Successfully(self):\n api = Api(self.userId, self.testToken)\n self.assertTrue(api.connected())","def get_api():\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_secret)\n api = tweepy.API(auth)\n\n return api","def authenticate( self ):\n\n print(\"Getting new token\")\n self.getFrob()\n self.getAuthKey()\n self.getToken()\n self.cacheToken()","def login():\n\n # Twitter credentials\n consumer_key = 'XXXX'\n consumer_secret = 'XXXX'\n access_token = 'XXXX'\n access_token_secret = 'XXXX'\n\n # Authenticate\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n\n # API is the main tweepy class we're going to use to interact with twitter\n api = tweepy.API(auth)\n\n # Check to make sure we can authenticate before we begin.\n # Prevents ya boi from trying to do this without an internet connection\n try:\n api.verify_credentials()\n print(\"I'm in...\")\n except:\n print(\"You didn't say the magic word!\")\n\n return api","def skyserv_authenticator(self):\n \n header = {\n 'Content-Type': accept, \n 'X-Auth-Token': self.casjobtoken,\n 'Accept': accept\n }\n # this format is disgusting but required....\n authdata = {\n 'auth' :{\n 'identity': {\n 'password': {\n 'user': {\n 'name': username,\n 'password': password\n }\n }\n }\n }\n }\n payload = json.dumps(authdata).encode(encoding='utf-8')\n try:\n post = requests.post(self.loginurl, data=payload, headers=header)\n\n if post.status_code == 200:\n response = json.loads(post.text)\n token = response[self.tokenkey]\n return token\n else:\n print('Username and/or password are invalid.')\n post.raise_for_status()\n except Exception as e:\n raise(str(e))","def __authenticate(self):\n\n self.logger.info(f\"Authenticating service principal with {self.client_id}\")\n interactor = AzureInteractor(\n self.tenant_id, self.subscription_id, self.client_id, self.client_secret\n )\n\n return interactor","def getAuthObj(self):\n if self.accessToken is None:\n self.authenticate()\n\n return OAuth1(self.apiKey, client_secret = self.apiKeySecret,\n resource_owner_key = self.accessToken,\n resource_owner_secret = self.accessTokenSecret,\n signature_type = 'auth_header')","def __init__(\n self,\n email: Optional[str] = None,\n password: Optional[str] = None,\n # loop: Optional[asyncio.AbstractEventLoop] = None,\n # session: Optional[aiohttp.ClientSession] = None,\n auth_token: Optional[str] = None,\n api_timeout: int = API_TIMEOUT,\n ) -> None:\n\n # random device id\n self._device_id: str = str(uuid1())\n\n self.sac = SureAPIClient(\n email=email, password=password, auth_token=auth_token, api_timeout=api_timeout\n )\n\n # api token management\n self._auth_token: Optional[str] = None\n if auth_token and token_seems_valid(auth_token):\n self._auth_token = auth_token\n else: # if token := find_token():\n self._auth_token = find_token()\n\n # storage for received api data\n self._resource: Dict[str, Any] = {}\n # storage for etags\n self._etags: Dict[str, str] = {}\n\n logger.debug(\"initialization completed | vars(): %s\", vars())","def enable_auth(self):\n\n self._api_manager.enable_auth()","def create_api_handler(self):\n self.github = github3.login(username=GH_USER, password=GH_PASSWORD)\n if hasattr(self.github, 'set_user_agent'):\n self.github.set_user_agent('Jonathan Reeve: http://jonreeve.com')\n self.org = self.github.organization(login='Git-Lit')\n # FIXME: logging\n print(\"ratelimit: \" + str(self.org.ratelimit_remaining))","def _get_user_client(self):\n return api.OAuthClient(settings.CLIENT_ID, settings.CLIENT_SECRET, settings.USER, settings.PASSWORD)","def authenticate(self, input: dict, request_id: str=None)->AuthenticationResult:\n self.logger.warning(message='USING A DEFAULT LOCAL INSTANCE - IMPLEMENT THIS CLASS YOURSELF TO CALL YOUR OWN BACKEND', request_id=request_id)\n return AuthenticationResult(\n success=True,\n userid=1,\n permissions=['admin']\n )","def authenticate(self):\n r = requests.head(self.url + '/v2/', verify=self.verify_ssl)\n self._authenticate_for(r)","def login(self, autologin: str) -> Auth:\n self.auth = Auth(autologin)\n return self.auth","def getAPI():\n auth = tweepy.OAuthHandler(cfg.consumer_key, cfg.consumer_secret)\n auth.set_access_token(cfg.access_token, cfg.access_token_secret)\n api = tweepy.API(auth, wait_on_rate_limit=True)\n return api","def auth(self):\n ok = False\n if self.private_token:\n ok = self.token_auth()\n if not ok:\n self.credentials_auth()","def authenticated_user(self):\r\n return AuthenticatedUser(self)","def get_one_user_from_api(prod_api, pickle_auth=False, **kwargs):\n\n try:\n email_validator.validate_email(kwargs['u_login']) # raises an exception\n except AttributeError as e:\n raise AttributeError('User email must be a string!')\n\n api_url = prod_api + \"/admin/user_asset/export\"\n response = requests.post(\n api_url,\n headers = {'API-Key': API.config['API_KEY']},\n auth=(\n kwargs['admin_login'],\n kwargs['admin_password']\n ),\n data={'login': kwargs['u_login']}\n )\n\n if response.status_code != 200:\n raise requests.RequestException(response.reason)\n logger.warn(response.content)\n\n pickle_string = response.content\n try:\n user_oid = users.import_user(pickle_string)\n except _pickle.UnpicklingError as e:\n pickled = _pickle.dumps(pickle_string)\n error_path = kwargs['u_login'] + '_import_error.pickle'\n error_dump = open(error_path, 'wb')\n error_dump.write(pickle_string)\n error_dump.close()\n raise ValueError(\"Could not import user! See '%s'\" % error_path)\n\n # we can silently reset the user's password if 'force' is in the kwargs\n if kwargs.get('force', False):\n user_object = users.User(_id=user_oid)\n user_object.update_password('password')\n\n return user_oid","def authenticate(self):\n self._authentication = None\n try:\n self._authentication = self.authenticator.authenticate()\n except RequestException as ex:\n _LOGGER.error(\"Unable to connect to August service: %s\", str(ex))\n raise CannotConnect\n\n if self._authentication.state == AuthenticationState.BAD_PASSWORD:\n raise InvalidAuth\n\n if self._authentication.state == AuthenticationState.REQUIRES_VALIDATION:\n raise RequireValidation\n\n if self._authentication.state != AuthenticationState.AUTHENTICATED:\n _LOGGER.error(\n \"Unknown authentication state: %s\", self._authentication.state\n )\n raise InvalidAuth\n\n return self._authentication","def get_api(user: 'User') -> API:\n auth = tweepy.OAuthHandler(user.api_key, user.api_secret)\n auth.set_access_token(user.api_access_token, user.api_token_secret)\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, compression=True)\n return api","def get_api(cfg):\n auth = tweepy.OAuthHandler(cfg['consumer_key'], cfg['consumer_secret'])\n auth.set_access_token(cfg['access_token'], cfg['access_token_secret'])\n return tweepy.API(auth)","def authenticate(self, username, api_key, auth_token=None):\r\n auth_endpoint = '/'.join((self.endpoint, 'v1',\r\n self.account_id, 'auth'))\r\n auth = QueueAuth(auth_endpoint, username, api_key,\r\n auth_token=auth_token)\r\n auth.auth()\r\n self.auth = auth","def handler(self):\r\n credentials = self.get_credentials()\r\n return credentials.authorize(httplib2.Http())","def login_with_api(\n self,\n *,\n body,\n headers: t.Dict[str, t.Any] = None,\n **kwargs: t.Dict[str, t.Any],\n ):\n if headers is None:\n headers = {}\n headers['Content-Type'] = 'application/x-www-form-urlencoded'\n r = self._do_call(\n method='POST',\n url=self.API_LOGIN_URL,\n headers=headers,\n body=body,\n **kwargs\n )\n if r.status == 200:\n res = json.loads(r.data.decode('utf-8'))\n self.API_TOKEN = res[self.AUTH_TOKEN_KEY]\n self.LOGIN_TIMESTAMP = dt.now()\n else:\n print(f'login failed \\nstatus:{r.status} \\n \\nurl: {self.API_LOGIN_URL}'\n '\\nIs the username and password correct?')","def __init__(self, api_key, client_id=None, client_secret=None):\n self.api = API(api_key)\n self._manifest = Manifest(self.api)\n self.oauth = OAuth(client_id, client_secret)","def _authenticated(func):\n @functools.wraps(func)\n def func_wrapper(self, *args, **kwargs):\n try:\n return func(self, *args, **kwargs)\n except exception.NotAuthorized:\n # Prevent recursion loop. After the self arg is the\n # resource_type arg from _issue_api_request(). If attempt to\n # login failed, we should just give up.\n if args[0] == 'login':\n raise\n\n # Token might've expired, get a new one, try again.\n self._login()\n return func(self, *args, **kwargs)\n return func_wrapper","def __authenticate(self):\n body = {\"authenticationToken\": self.__get_authentication_token()}\n result = self.__call(\"POST\", \"https://api.voiapp.io/v1/auth/session\", json=body)\n\n if result and \"accessToken\" in result and \"authenticationToken\" in result:\n self._accessToken = result[\"accessToken\"]\n self.__set_authentication_token(result[\"authenticationToken\"])\n else:\n _LOGGER.warning(\"Authentication failed: Erroneous response (%s)\", result)","def get(self):\n\n\t\trequest = user_auth_parser.parse_args(strict=True)\n\n\t\tresult = Authenticator.authenticate(\n\t\t\trequest[\"username\"],\n\t\t\trequest[\"password\"]\n\t\t)\n\n\t\treturn result","def authenticate(self):\n try:\n self._token = self._lookup_token()\n except:\n raise HTTPError(\n \"Unable to get short-lived access token for cyberark storage\"\n )","def __init__(self, consumer_key, consumer_secret, access_token, access_token_secret):\n self.api = self.getAPI(consumer_key, consumer_secret, access_token, access_token_secret)","def create_api_client(base_path, access_token):\n api_client = ApiClient()\n api_client.host = base_path\n api_client.set_default_header(header_name=\"Authorization\",\n header_value=f\"Bearer {access_token}\")\n return api_client","def _authenticate(self):\n\n # if auth_token is available, just return it\n if self._auth_token is not None:\n return self._auth_token\n\n # aquire the authentication lock\n with self._auth_lock:\n # re-check the auth_token as another thread could set it\n if self._auth_token is not None:\n return self._auth_token\n\n LOGGER.debug('Authenticating request')\n # pylint: disable=broad-except\n try:\n # create a keystone client if it doesn't exist\n if self._keystone is None:\n cfg = Config.instance()\n self._keystone = keystoneClientV2(\n auth_url=cfg.OS_AUTH_URL,\n username=cfg.OS_USERNAME,\n password=cfg.OS_PASSWORD,\n tenant_name=cfg.OS_TENANT_NAME\n )\n # store the authentication token\n self._auth_token = self._keystone.auth_token\n\n # get the uri of service endpoint\n endpoint = self._keystone.get_service_endpoint(\n \"ceilometer\",\n Config.instance().CEILOMETER_URL_TYPE)\n\n self._url_base = \"{}/v2/meters/%s\".format(endpoint)\n LOGGER.info('Authenticating request - success')\n self._failed_auth = False\n\n except KeystoneException as exc:\n log_level = logging.DEBUG\n\n if not self._failed_auth:\n log_level = logging.ERROR\n LOGGER.error(\n 'Suspending error logs until successful auth'\n )\n\n LOGGER.log(log_level, 'Authentication error: %s',\n six.text_type(exc),\n exc_info=0)\n\n if exc.response:\n LOGGER.debug('Response: %s', exc.response)\n\n self._auth_token = None\n self._failed_auth = True\n\n return self._auth_token","def _init_auth(\n self,\n auth: t.Optional[AuthModel] = None,\n log_level: t.Union[str, int] = LOG_LEVEL_AUTH,\n ) -> AuthModel:\n if not isinstance(auth, AuthModel):\n if self.CREDENTIALS:\n auth: AuthCredentials = AuthCredentials(\n username=self.__key,\n password=self.__secret,\n http=self.http,\n log_level=log_level,\n )\n else:\n auth: AuthApiKey = AuthApiKey(\n key=self.__key,\n secret=self.__secret,\n http=self.http,\n log_level=log_level,\n )\n return self._check_binding(auth)","def _get_api_key(self):\n self.api.apikey = self.api.action.user_show(id=self.username)['apikey']","def auth_token_api():\n data = request.get_json()\n if not data:\n response = jsonify({\n 'success': False,\n 'message': 'Missing request body'\n })\n response.status_code = 422\n return response\n\n # process argument\n login_type = data.get('auth_type')\n email = data.get('email').strip().lower()\n password = data.get('password')\n\n if not login_type or login_type not in ['email']:\n response = jsonify({\n 'success': False,\n 'message': 'Invalid auth_type'\n })\n response.status_code = 422\n return response\n\n # email authentication\n elif login_type == 'email':\n if not email:\n response = jsonify({\n 'success': False,\n 'message': 'Must provide email when auth_type is \"email\"'\n })\n response.status_code = 422\n return response\n user = db.session.query(User).filter(User.email == email, User.deleted == False).one_or_none()\n if not user:\n response = jsonify({\n 'success': False,\n 'message': 'Not Authorized: invalid email'\n })\n response.status_code = 403\n return response\n # check the user's password\n password_valid = check_password_hash(user.password, password)\n if not password_valid:\n response = jsonify({\n 'success': False,\n 'message': 'Not Authorized: invalid password'\n })\n response.status_code = 403\n return response\n\n token = generate_auth_token(user_id=user.user_id)\n response = jsonify({\n 'success': True,\n 'token': token\n })\n response.status_code == '200'\n return response"],"string":"[\n \"def api_authentication():\\r\\n apikey = request.args.get('api_key', None)\\r\\n from flask import _request_ctx_stack\\r\\n if 'Authorization' in request.headers:\\r\\n apikey = request.headers.get('Authorization')\\r\\n if apikey:\\r\\n user = db.session.query(model.user.User).filter_by(api_key=apikey).first()\\r\\n ## HACK:\\r\\n # login_user sets a session cookie which we really don't want.\\r\\n # login_user(user)\\r\\n if user:\\r\\n _request_ctx_stack.top.user = user\",\n \"def auth(self):\\n return self.api(self.token)\",\n \"def authentication(): \\n pathToConfig = os.path.join(prefix, \\\"twitterConfig\\\")\\n config = json.load(open(pathToConfig))\\n consumer_key = config['consumer_key']\\n consumer_secret = config['consumer_secret']\\n access_token = config['access_token']\\n access_token_secret = config['access_token_secret']\\n api = twitter.Api(consumer_key=consumer_key, consumer_secret=consumer_secret,\\n access_token_key=access_token, access_token_secret=access_token_secret)\\n return api\",\n \"def get_authenticated_service(api_name: str, api_version: str) -> Resource:\\n\\n if CREDS_FILENAME.exists():\\n credentials = Credentials.from_authorized_user_file(str(CREDS_FILENAME))\\n # TODO make request to the access token endpoint???\\n\\n # FIXME verifying token\\n # credentials.refresh(requests.Request())\\n # print(credentials.token, credentials.expiry)\\n\\n # idinfo = id_token.verify_oauth2_token(\\n # credentials.token, requests.Request(), credentials.client_id)\\n\\n # if idinfo['iss'] not in ['accounts.google.com',\\n # 'https://accounts.google.com']:\\n # # CREDS_FILENAME.unlink()\\n # raise ValueError('Wrong issuer.')\\n\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(CLIENT_SECRETS_FILE, SCOPES)\\n credentials = flow.run_local_server(\\n host=\\\"localhost\\\",\\n port=8080,\\n authorization_prompt_message=\\\"Please visit this URL: {url}\\\",\\n success_message=\\\"The auth flow is complete; you may close this window.\\\",\\n open_browser=True,\\n )\\n\\n creds_data = {\\n \\\"token\\\": None,\\n \\\"refresh_token\\\": credentials.refresh_token,\\n \\\"token_uri\\\": credentials.token_uri,\\n \\\"client_id\\\": credentials.client_id,\\n \\\"client_secret\\\": credentials.client_secret,\\n \\\"scopes\\\": credentials.scopes,\\n }\\n\\n with CREDS_FILENAME.open(\\\"w\\\") as outfile:\\n json.dump(creds_data, outfile)\\n\\n return build(api_name, api_version, credentials=credentials)\",\n \"def login_to_api(self):\\n\\n # set the API endpoint and POST the username/password to it\\n endpoint = app.config['API']['url'] + 'login'\\n response = requests.post(\\n endpoint,\\n verify = app.config['API']['verify_ssl'],\\n json = {\\n 'username': self.username,\\n 'password': self.password\\n }\\n )\\n\\n # if the response is good, return True\\n if response.status_code == 200:\\n user = response.json()\\n self._id = ObjectId(user['_id'])\\n self.token = user['access_token']\\n return True\",\n \"def authenticate(self):\\n auth = tw.OAuthHandler(self.consumer_key, self.consumer_secret)\\n auth.set_access_token(self.access_token, self.access_secret)\\n return tw.API(auth)\",\n \"def _get_api() -> Mobileclient:\\n\\n api = Mobileclient()\\n device_id = _get_device_id(api)\\n api.oauth_login(device_id)\\n\\n return api\",\n \"def api_auth():\\n form = request.get_json(force=True)\\n userdata = None\\n if form['register']:\\n userdata = userProvider.register_user(\\n form['username'].encode('utf8'),\\n form['password'].encode('utf8')\\n )\\n else:\\n userdata = userProvider.load_authenticated_user(\\n form['username'].encode('utf8'),\\n form['password'].encode('utf8')\\n )\\n if userdata:\\n user = userProvider.userdata_to_user(userdata)\\n flask_login.login_user(user)\\n return \\\"true\\\"\\n raise Exception(\\\"No user loaded\\\")\",\n \"def auth(mocker, app: FastAPI, faker: Faker) -> HTTPBasicAuth:\\n # mock engine if db was not init\\n if app.state.settings.API_SERVER_POSTGRES is None:\\n engine = mocker.MagicMock()\\n engine.minsize = 1\\n engine.size = 10\\n engine.freesize = 3\\n engine.maxsize = 10\\n app.state.engine = engine\\n\\n # patch authentication entry in repo\\n faker_user_id = faker.pyint()\\n\\n # NOTE: here, instead of using the database, we patch repositories interface\\n mocker.patch(\\n \\\"simcore_service_api_server.db.repositories.api_keys.ApiKeysRepository.get_user_id\\\",\\n autospec=True,\\n return_value=faker_user_id,\\n )\\n mocker.patch(\\n \\\"simcore_service_api_server.db.repositories.users.UsersRepository.get_user_id\\\",\\n autospec=True,\\n return_value=faker_user_id,\\n )\\n mocker.patch(\\n \\\"simcore_service_api_server.db.repositories.users.UsersRepository.get_email_from_user_id\\\",\\n autospec=True,\\n return_value=faker.email(),\\n )\\n\\n # patches simcore_postgres_database.utils_products.get_default_product_name\\n mocker.patch(\\n \\\"simcore_service_api_server.api.dependencies.application.get_default_product_name\\\",\\n autospec=True,\\n return_value=\\\"osparc\\\",\\n )\\n\\n return HTTPBasicAuth(faker.word(), faker.password())\",\n \"def authenticate(self) -> api.API:\\r\\n original_api = api.API()\\r\\n for c in self._credentials: # each app\\r\\n user_auth = tweepy.OAuthHandler(c.consumer_key, c.consumer_secret)\\r\\n user_auth.set_access_token(c.access_token, c.access_secret)\\r\\n app_auth = tweepy.AppAuthHandler(c.consumer_key, c.consumer_secret)\\r\\n original_api.append(tweepy.API(user_auth), tweepy.API(app_auth))\\r\\n return original_api\",\n \"def __init__(self):\\n self.api = Api(consumer_key=credentials[\\\"consumer_key\\\"],\\n consumer_secret=credentials[\\\"consumer_secret\\\"],\\n access_token_key=credentials[\\\"access_token_key\\\"],\\n access_token_secret=credentials[\\\"access_token_secret\\\"])\",\n \"def __create_api(self):\\n consumer_key = os.environ.get(\\\"CONSUMER_KEY\\\")\\n consumer_secret = os.environ.get(\\\"CONSUMER_SECRET\\\")\\n access_token = os.getenv(\\\"ACCESS_TOKEN\\\")\\n access_token_secret = os.environ.get(\\\"ACCESS_TOKEN_SECRET\\\")\\n\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n api = tweepy.API(\\n auth,\\n compression=True,\\n wait_on_rate_limit=True,\\n wait_on_rate_limit_notify=True,\\n )\\n try:\\n api.verify_credentials()\\n except Exception as e:\\n logging.error(\\\"Error creating API\\\", exc_info=True)\\n raise e\\n return api\",\n \"def login_to_apic(self):\\n session = Session(URL, LOGIN, PASSWORD)\\n resp = session.login()\\n self.assertTrue(resp.ok)\\n return session\",\n \"async def login(\\n cls: Type[ApiType], host: str, port: str, user: str, password: str,\\n websession: ClientSession) -> ApiType:\\n klass = cls(websession)\\n klass._base_url = 'http://{0}:{1}/'.format(host, port)\\n klass._auth = BasicAuth(login=user, password=password)\\n await klass._request('get', '')\\n return klass\",\n \"def _login_vapi(self):\\n session = requests.Session()\\n session.verify = self.validate_certs\\n if not self.validate_certs:\\n # Disable warning shown at stdout\\n requests.packages.urllib3.disable_warnings()\\n\\n print(\\\"logging in\\\")\\n client = create_vsphere_client(server=self.hostname,\\n username=self.username,\\n password=self.password,\\n session=session)\\n if client is None:\\n raise Exception(\\\"Failed to login to %s using %s\\\" %\\n (self.hostname, self.username))\\n return client\",\n \"def init_api(username, password, section):\\n if SAVE_COOKIES is True:\\n api = API()\\n api._init_cookies()\\n api.login(username, password, section)\\n api.save_cookies()\\n print('Cookies is saved.')\\n sys.exit()\\n elif os.path.exists(COOKIES_FILENAME):\\n try:\\n api = API(persistent=True)\\n except cookielib.LoadError:\\n print('Cannot read cookies from %s. ' % COOKIES_FILENAME)\\n print('Use -c option to save cookies and try again.')\\n sys.exit(2)\\n if api.has_logged_in:\\n print('API has logged in from cookies.')\\n else:\\n print('API fails to log in from cookies.')\\n print('Use -c option to save cookies and try again.')\\n sys.exit(1)\\n else:\\n api = API()\\n if api.login(username, password, section):\\n print('Login success.')\\n return api\",\n \"def auth(self):\\n return AuthManager(self)\",\n \"def api_client() -> APIClient:\\n return APIClient()\",\n \"def authenticated_api(username, api_root=None, parser=None):\\n auth = tweepy.OAuthHandler(settings.TWITTER_CONSUMER_KEY, \\n settings.TWITTER_CONSUMER_SECRET)\\n try:\\n user = User.objects.get(username=username)\\n sa = user.social_auth.all()[0]\\n auth.set_access_token(sa.tokens['oauth_token'], \\n sa.tokens['oauth_token_secret'])\\n return tweepy.API(auth, \\n api_root=api_root or settings.TWITTER_API_ROOT, \\n parser=parser or JSONParser(),\\n secure=settings.TWITTER_USE_SECURE) \\n except:\\n return None\",\n \"def __init__(self):\\n self.user = None\\n self.error = {}\\n\\n if not self.API_KEY:\\n raise Exceptions.NoAPIKey\\n\\n if not self.API_SECRET:\\n raise Exceptions.NoAPISecret\\n\\n # Namedtuple for settings for each request and the api functions.\\n self.settings = namedtuple(\\\"Settings\\\", ['library', 'urllib', 'urllib2', 'json', 'requests'])\\n self.api = UserRegistrationAPI(self)\\n self.user = self._get_user_tuple()\\n\\n # We prefer to use requests with the updated urllib3 module.\\n try:\\n from distutils.version import StrictVersion\\n import requests\\n\\n if StrictVersion(requests.__version__) < StrictVersion(\\\"2.0\\\"):\\n raise Exceptions.RequestsLibraryDated(requests.__version__)\\n else:\\n self._settings(\\\"requests\\\")\\n\\n # However, we can use urllib if there is no requests or it is outdated.\\n except (ImportError, Exceptions.RequestsLibraryDated):\\n self._settings(\\\"urllib2\\\")\\n\\n # Well, something went wrong here.\\n except:\\n raise\\n\\n else:\\n # Namedtuples for api, and user.\\n self.user = self._get_user_tuple()\",\n \"def authenticate(self):\\n #it's weird i have to do this here, but the code makes this not simple\\n auth_json={'email':self.user, 'password':self.password}\\n #send a post with no auth. prevents an infinite loop\\n auth_response = self.post('/auth', data = json.dumps(auth_json), auth =\\n None)\\n\\n _token = auth_response.json['token']\\n\\n self._token = _token\\n self._wrapped.auth = SpringAuth(_token)\",\n \"def auth(self):\\n auth = OAuthHandler(self._api_key, self._api_secret)\\n auth.set_access_token(self._access_token, self._access_secret)\\n return auth\",\n \"def api_client() -> APIClient:\\n\\n return APIClient()\",\n \"def logged_in_apiclient(apiclient):\\n user = UserFactory()\\n apiclient.force_login(user)\\n return apiclient, user\",\n \"def __init__(self, **kwargs):\\n\\n session = vk_api.VkApi(**kwargs)\\n try:\\n session.auth(token_only=True)\\n except vk_api.AuthError as error_msg:\\n print(error_msg)\\n raise\\n self.api = session.get_api()\",\n \"def client():\\n return Client(**common_data.AUTH_ARGS)\",\n \"def init_api(self):\\n creds = None\\n # The file token.pickle stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n if os.path.exists(self.gdrive_config.TOKEN_PICK_PATH):\\n with open(self.gdrive_config.TOKEN_PICK_PATH, 'rb') as token:\\n creds = pickle.load(token)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n self.gdrive_config.CREDENTIAL_PATH, self.gdrive_config.SCOPES)\\n creds = flow.run_local_server(port=0)\\n # Save the credentials for the next run\\n with open(self.gdrive_config.TOKEN_PICK_PATH, 'wb') as token:\\n pickle.dump(creds, token)\\n\\n service = build('drive', 'v3', credentials=creds)\\n return service\",\n \"def token_auth(self):\\n self.client = APIClient()\\n self.user = User.objects.create_user(username='testuser', email='test@test.com', password='testpassword')\\n self.token = Token.objects.create(user=self.user)\\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\",\n \"def api(self):\\n if self._api is None:\\n self._api = Api(self)\\n return self._api\",\n \"def authenticate(self):\\n\\n def decorate(func, *args, **kws):\\n \\\"\\\"\\\"\\n A function returned as a object in load time,\\n which returns inner function do_decorate().\\n \\\"\\\"\\\"\\n def do_authenticate():\\n \\\"\\\"\\\"\\n A function to perform authentication\\n every time decorated function is called.\\n \\\"\\\"\\\"\\n #try:\\n if 1:\\n if 'referer' not in self.session:\\n path = urlsplit(self.request.url)[2]\\n self.session['referer'] = path\\n self.session.put()\\n #except:\\n # pass\\n aobj = self.config.auth_obj()\\n self.get_controller()\\n auth_res = aobj.auth(self.controller, *args, **kws)\\n if auth_res:\\n return func(*args, **kws)\\n aobj.auth_redirect(self.controller, *args, **kws)\\n # clear controller for development environment.\\n\\n return do_authenticate\\n\\n return decorate\",\n \"def authenticate():\\n auth = OAuthHandler(config.CONSUMER_API_KEY, config.CONSUMER_API_SECRET)\\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\\n\\n return auth\",\n \"def authenticate():\\n auth = OAuthHandler(config.CONSUMER_API_KEY, config.CONSUMER_API_SECRET)\\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\\n\\n return auth\",\n \"def __init__(self, target, id_token=None, private_key_file=None, private_key_password=None,\\n username=None, client_id=None, key_id=None, issuer=None, api_token=None,\\n retries=DEFAULT_RETRIES, timeout=DEFAULT_TIMEOUT, ssl_cert=None, user_agent=None,\\n verify_ssl=None):\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n config = Configuration()\\n config.verify_ssl = resolve_ssl_validation(verify_ssl)\\n config.ssl_ca_cert = ssl_cert\\n config.host = self._get_base_url(target)\\n\\n if id_token and api_token:\\n raise PureError(\\\"Only one authentication option is allowed. Please use either id_token or api_token and try again!\\\")\\n elif private_key_file and private_key_password and username and \\\\\\n key_id and client_id and issuer and api_token:\\n raise PureError(\\\"id_token is generated based on app ID and private key info. Please use either id_token or api_token and try again!\\\")\\n elif api_token:\\n api_token_auth_endpoint = self._get_api_token_endpoint(target)\\n self._token_man = APITokenManager(api_token_auth_endpoint, api_token, verify_ssl=config.verify_ssl)\\n else:\\n auth_endpoint = 'https://{}/oauth2/1.0/token'.format(target)\\n headers = {\\n 'kid': key_id\\n }\\n payload = {\\n 'iss': issuer,\\n 'aud': client_id,\\n 'sub': username,\\n }\\n self._token_man = TokenManager(auth_endpoint, id_token, private_key_file, private_key_password,\\n payload=payload, headers=headers, verify_ssl=config.verify_ssl)\\n\\n self._api_client = ApiClient(configuration=config)\\n self._api_client.user_agent = user_agent or self.USER_AGENT\\n self._set_agent_header()\\n self._set_auth_header()\\n\\n # Read timeout and retries\\n self._retries = retries\\n self._timeout = timeout\\n\\n # Instantiate APIs\\n self._connections_api = api.ConnectionsApi(self._api_client)\\n self._host_groups_api = api.HostGroupsApi(self._api_client)\\n self._hosts_api = api.HostsApi(self._api_client)\\n self._volume_snapshots_api = api.VolumeSnapshotsApi(self._api_client)\\n self._volumes_api = api.VolumesApi(self._api_client)\",\n \"def _authenticate_for(self, resp):\\n # Get the auth. info from the headers\\n scheme, params = resp.headers['Www-Authenticate'].split(None, 1)\\n assert (scheme == 'Bearer')\\n info = {k: v.strip('\\\"') for k, v in (i.split('=')\\n for i in params.split(','))}\\n\\n # Request a token from the auth server\\n params = {k: v for k, v in info.items() if k in ('service', 'scope')}\\n auth = HTTPBasicAuth(self.username, self.password)\\n r2 = requests.get(info['realm'], params=params,\\n auth=auth, verify=self.verify_ssl)\\n\\n if r2.status_code == 401:\\n raise RuntimeError(\\\"Authentication Error\\\")\\n r2.raise_for_status()\\n\\n self.auth = BearerAuth(r2.json()['token'])\",\n \"def __init__(self):\\n self.authurl = Config().auth\\n self.baseurl = Config().api\\n self.s = Session()\\n self.s.headers = {'Accept': 'application/json'}\\n data = {\\\"grant_type\\\": \\\"client_credentials\\\", \\\"scope\\\": \\\"/read-public\\\", \\\"client_id\\\": Config().client_id,\\n \\\"client_secret\\\": Config().client_secret}\\n r = self.s.request(method=\\\"post\\\", url=self.authurl, data=data)\\n self.s.headers = {'Accept': 'application/json', \\\"Access token\\\": r.json()[\\\"access_token\\\"]}\",\n \"def _authenticate(self):\\n # Check if token expired\\n if self._token_expire and self._token_expire < _time():\\n self._token = ''\\n\\n # Get OAuth2 token\\n if not self._token:\\n # Get user credentials\\n credentials = json_read(get_accelize_cred())\\n client_id = credentials['client_id']\\n client_secret = credentials['client_secret']\\n\\n # Endpoint override in credentials file\\n self._endpoint = credentials.get('endpoint', self._ENDPOINT)\\n\\n # Try to get CLI cached token\\n try:\\n self._token, self._token_expire = get_cli_cache(client_id)\\n\\n # Try to get token from web service\\n except TypeError:\\n response = self._request(\\n 'post', f'{self._endpoint}/o/token/',\\n data={\\\"grant_type\\\": \\\"client_credentials\\\"},\\n auth=(client_id, client_secret),\\n timeout=self._TIMEOUT)\\n\\n if response.status_code >= 300:\\n raise _AuthenticationException(\\n 'Unable to authenticate client ID starting by '\\n f'\\\"{client_id[:10]}\\\": '\\n f'{self._get_error_message(response)}')\\n\\n access = response.json()\\n self._token = access['access_token']\\n self._token_expire = int(_time()) + access['expires_in'] - 1\\n\\n # Cache token value for future CLI usage\\n set_cli_cache(client_id, [self._token, self._token_expire],\\n self._token_expire)\",\n \"def get_auth(context):\\n\\n headers = context['headers']\\n auth_info = {\\n \\\"type\\\": \\\"basic\\\",\\n \\\"basic\\\": {\\n \\\"user\\\": headers['api_key'],\\n \\\"password\\\": \\\"X\\\"\\n }\\n }\\n auth = Auth().get_auth(auth_info)\\n\\n return auth\",\n \"def authenticate(self, *args, **kwargs):\\n # Validate backend and arguments. Require that the Social Auth\\n # response be passed in as a keyword argument, to make sure we\\n # don't match the username/password calling conventions of\\n # authenticate.\\n if not (self.name and kwargs.get(self.name) and 'response' in kwargs):\\n return None\\n\\n response = kwargs.get('response')\\n pipeline = PIPELINE\\n kwargs = kwargs.copy()\\n kwargs['backend'] = self\\n\\n if 'pipeline_index' in kwargs:\\n pipeline = pipeline[kwargs['pipeline_index']:]\\n else:\\n kwargs['details'] = self.get_user_details(response)\\n kwargs['uid'] = self.get_user_id(kwargs['request'])\\n kwargs['is_new'] = False\\n \\n out = self.pipeline(pipeline, *args, **kwargs)\\n if not isinstance(out, dict):\\n return out\\n\\n social_user = out.get('social_user')\\n if social_user:\\n # define user.social_user attribute to track current social\\n # account\\n user = social_user.user\\n user.social_user = social_user\\n user.is_new = out.get('is_new')\\n return user\",\n \"def api_auth(func):\\n @wraps(func)\\n def _decorator(request, *args, **kwargs):\\n authentication = APIAuthentication(request)\\n if authentication.authenticate():\\n return func(request, *args, **kwargs)\\n raise Http404\\n return _decorator\",\n \"def __init__(self, username=None, password=None, apitoken=None):\\n self.__credentials = None\\n self.__headers = {}\\n if apitoken:\\n self.authenticate_by_token(apitoken)\\n if username and password:\\n self.authenticate(username, password)\",\n \"def authenticate_user(self):\\n cloud_config = os_client_config.OpenStackConfig().get_one_cloud(\\n cloud=self.options.os_cloud, argparse=self.options,\\n network_api_version=self.api_version,\\n verify=not self.options.insecure)\\n verify, cert = cloud_config.get_requests_verify_args()\\n\\n # TODO(singhj): Remove dependancy on HTTPClient\\n # for the case of token-endpoint authentication\\n\\n # When using token-endpoint authentication legacy\\n # HTTPClient will be used, otherwise SessionClient\\n # will be used.\\n if self.options.os_token and self.options.os_url:\\n auth = None\\n auth_session = None\\n else:\\n auth = cloud_config.get_auth()\\n\\n auth_session = session.Session(\\n auth=auth, verify=verify, cert=cert,\\n timeout=self.options.http_timeout)\\n\\n interface = self.options.os_endpoint_type or self.endpoint_type\\n if interface.endswith('URL'):\\n interface = interface[:-3]\\n self.client_manager = clientmanager.ClientManager(\\n retries=self.options.retries,\\n raise_errors=False,\\n session=auth_session,\\n url=self.options.os_url,\\n token=self.options.os_token,\\n region_name=cloud_config.get_region_name(),\\n api_version=cloud_config.get_api_version('network'),\\n service_type=cloud_config.get_service_type('network'),\\n service_name=cloud_config.get_service_name('network'),\\n endpoint_type=interface,\\n auth=auth,\\n insecure=not verify,\\n log_credentials=True)\\n return\",\n \"def authenticate():\\n auth = OAuthHandler(config.TW_API_KEY, config.TW_API_SECRET)\\n auth.set_access_token(config.TW_ACC_TOKEN, config.TW_ACC_SECRET)\\n\\n return auth\",\n \"def __make_api_get(self, api_path):\\n try:\\n self.last_response = urllib2.urlopen(\\n self.api_server + api_path, cafile=self.cacert_path)\\n json_data = self.last_response.read()\\n\\n # Check for errors\\n except urllib2.HTTPError as err:\\n error = \\\"API HTTP error [%s] - '%s'\\\" % (err.code, err.read())\\n raise EFIgyCliError(error, self.last_response)\\n\\n except urllib2.URLError as err:\\n error = 'Problem calling API at location %s - %s' % (\\n self.api_server + api_path, err)\\n raise EFIgyCliError(error, self.last_response)\\n\\n # Decode json response into an object\\n try:\\n ret = json.loads(json_data)\\n except ValueError as err:\\n error = \\\"Problem deserialising data, expecting JSON.\\\\nError: %s\\\\nData: %s\\\" % (\\n err, json_data)\\n raise EFIgyCliError(error, self.last_response)\\n\\n # Return JSON deserialised object\\n return ret\",\n \"def as_auth_name(func):\\n\\n def auth_client(self):\\n token = Token.objects.get(user__username=username)\\n self.client = APIClient()\\n self.client.credentials(HTTP_AUTHORIZATION=\\\"Token \\\" + token.key)\\n return func(self)\\n\\n return auth_client\",\n \"def __init__(self,\\n identifier=None,\\n password=None,\\n silent=False):\\n self.data_source = RestApi()\\n self.user_id = self.data_source.login(identifier, password, silent)\",\n \"def __init__(self):\\n self.auth()\",\n \"def auth():\\n\\n if current_user.is_authenticated:\\n\\n # OAuth is only necesary when we don't have a user's API\\n if not current_user.zoter_api:\\n\\n def get_auth_url():\\n request_token, request_token_secret = zoteroAuth.get_request_token()\\n session['request_token'] = request_token\\n session['request_token_secret'] = request_token_secret\\n auth_url = zoteroAuth.get_authorize_url(request_token)\\n return auth_url\\n\\n flash('Hi! {}, please visit <a href=\\\"{}\\\" target=\\\"new\\\">here</a> for authentication.'.format(current_user.username, get_auth_url()))\\n return redirect(url_for('bookshelf'))\\n \\n else:\\n flash('You already have an API key!')\\n return redirect(url_for('sync'))\",\n \"def init_api():\\n auth = twitter.OAuth(\\n os.environ['AUTH_KEY'], os.environ['AUTH_SECRET'],\\n os.environ['API_KEY'], os.environ['API_SECRET'],\\n )\\n return twitter.Twitter(auth=auth)\",\n \"def __init__(self, api_key, api_secret, base_url=settings.TWITTER_API_URL):\\n self.api_key = api_key\\n self.api_secret = api_secret\\n self.base_url = base_url\\n self.bearer_token = None\\n self.__auth = None\\n self.get_bearer_token()\\n self.set_requests_auth()\\n self.session = requests_retry_session()\",\n \"def get_api_client():\\n\\n global _API_CLIENT_HANDLE\\n\\n if not _API_CLIENT_HANDLE:\\n context = get_context()\\n server_config = context.get_server_config()\\n\\n pc_ip = server_config.get(\\\"pc_ip\\\")\\n pc_port = server_config.get(\\\"pc_port\\\")\\n username = server_config.get(\\\"pc_username\\\")\\n password = server_config.get(\\\"pc_password\\\")\\n\\n update_api_client(host=pc_ip, port=pc_port, auth=(username, password))\\n\\n return _API_CLIENT_HANDLE\",\n \"def authenticate():\\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\\\"api\\\"'})\",\n \"def get_api():\\n # Load auth information.\\n token = load_token()\\n\\n if token is None:\\n token_string = get_twitter_auth()\\n save_token(token_string)\\n token = tweepy.oauth.OAuthToken.from_string(token_string)\\n\\n try:\\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n auth.set_access_token(token.key, token.secret)\\n\\n api = tweepy.API(auth)\\n except tweepy.TweepError:\\n print (\\\"Error: Unable to get tweepy API, please try again.\\\")\\n sys.exit(1)\\n\\n return api\",\n \"def connect(api, username, password):\\n\\treturn api.login(username, password)\",\n \"def auth(client):\\n return AuthActions(client)\",\n \"def create_API_access():\\n logger = logging.getLogger(__name__)\\n logger.info('Creating API connection')\\n\\n # Find .env automagically by walking up directories until it's found\\n dotenv_path = find_dotenv()\\n\\n # Load up the entries as environment variables\\n load_dotenv(dotenv_path)\\n\\n # Get Twitter API credentials from environment variables\\n consumer_key = os.environ.get(\\\"CONSUMER_KEY\\\")\\n consumer_secret = os.environ.get(\\\"CONSUMER_SECRET\\\")\\n access_token = os.environ.get(\\\"ACCESS_TOKEN\\\")\\n access_secret = os.environ.get(\\\"ACCESS_SECRET\\\")\\n\\n # Set up authorization to Twitter API\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_secret)\\n\\n # Create connection object\\n api = tweepy.API(auth)\\n return api\",\n \"def authenticate(self):\\n token = self.get_config('token')\\n if token:\\n self.root.connection.login(\\n None, None, token=token, auth_type='Bearer'\\n )\\n else:\\n config.use_sessions = True\\n self.root.load_session().get()\",\n \"def __init__(\\n self,\\n api_url: str,\\n username: str,\\n password: str,\\n shared_disk: str,\\n ip_resolver: IPResolver,\\n verify_cert: bool = False,\\n create_pool: Optional[str] = None):\\n self.logger = logging.getLogger(__name__)\\n self.api_url = api_url\\n self.username = username\\n self.password = password\\n self.sess = BaseUrlSession(self.api_url + '/api2/json/')\\n self.sess.verify = verify_cert\\n if not self.sess.verify:\\n requests.packages.urllib3.disable_warnings()\\n\\n self.shared_disk = shared_disk\\n self.create_pool = create_pool\\n self.ip_resolver = ip_resolver\\n self._login()\",\n \"def get_api(\\n password: str,\\n host: str = None,\\n username: str = None,\\n port: int = None,\\n ssl: bool = False,\\n) -> Netgear:\\n api: Netgear = Netgear(password, host, username, port, ssl)\\n\\n if not api.login():\\n raise CannotLoginException\\n\\n return api\",\n \"def auth(self):\\n return auth.get_auth()\",\n \"def authenticate(self):\\n # Get credentials from eodag or using ecmwf conf\\n email = getattr(self.config, \\\"credentials\\\", {}).get(\\\"username\\\", None)\\n key = getattr(self.config, \\\"credentials\\\", {}).get(\\\"password\\\", None)\\n url = getattr(self.config, \\\"api_endpoint\\\", None)\\n if not all([email, key, url]):\\n key, url, email = get_apikey_values()\\n\\n # APIRequest to check credentials\\n ecmwf_connection = Connection(\\n url=url,\\n email=email,\\n key=key,\\n )\\n try:\\n ecmwf_connection.call(\\\"{}/{}\\\".format(url, \\\"who-am-i\\\"))\\n logger.debug(\\\"Credentials checked on ECMWF\\\")\\n except APIException as e:\\n logger.error(e)\\n raise AuthenticationError(\\\"Please check your ECMWF credentials.\\\")\\n\\n return {\\\"key\\\": key, \\\"url\\\": url, \\\"email\\\": email}\",\n \"def __init__(self, session):\\n super(FlattrApi, self).__init__(session)\\n self.things = ThingApi(session)\\n self.users = UsersApi(session)\\n self.authenticated = AuthenticatedApi(session)\",\n \"def get_api(self):\\n return self.api\",\n \"def make_api_request(self):\\n # We can create an instance of an endpoint resource class, and use it to fetch details\\n access_token = self.get_token()\\n self.request.session['access_token'] = access_token\\n\\n doctor_api = DoctorEndpoint(access_token)\\n patient_api = PatientEndpoint(access_token)\\n apppointment_api = AppointmentEndpoint(access_token)\\n\\n # Grab the first doctor from the list; normally this would be the whole\\n # practice group, but your hackathon account probably only has one doctor in it.\\n doctor = doctor_api.get_and_store_data()\\n self.request.session['doctor'] = doctor.id\\n # Get patients and appointments for the doctor and store it in the local DB\\n patient_api.get_and_store_data(doctor=doctor)\\n\\n date = datetime.now(tz=pytz.timezone(\\n doctor.timezone)).strftime('%Y-%m-%d')\\n apppointment_api.get_and_store_data(doctor=doctor, date=date)\\n\\n return doctor\",\n \"def api(self, user=None):\\n if not user.is_authenticated:\\n return self.none()\\n return self.filter(users=user)\",\n \"def _authorize(self):\\n auth = tweepy.OAuthHandler(self.keys[\\\"consumer_key\\\"], self.keys[\\\"consumer_secret\\\"])\\n auth.set_access_token(self.keys[\\\"access_token\\\"], self.keys[\\\"access_token_secret\\\"])\\n return tweepy.API(auth)\",\n \"def test_ApiWillAuthenticate_ValidCredentials_Successfully(self):\\n api = Api(self.userId, self.testToken)\\n self.assertTrue(api.connected())\",\n \"def get_api():\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_secret)\\n api = tweepy.API(auth)\\n\\n return api\",\n \"def authenticate( self ):\\n\\n print(\\\"Getting new token\\\")\\n self.getFrob()\\n self.getAuthKey()\\n self.getToken()\\n self.cacheToken()\",\n \"def login():\\n\\n # Twitter credentials\\n consumer_key = 'XXXX'\\n consumer_secret = 'XXXX'\\n access_token = 'XXXX'\\n access_token_secret = 'XXXX'\\n\\n # Authenticate\\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\\n auth.set_access_token(access_token, access_token_secret)\\n\\n # API is the main tweepy class we're going to use to interact with twitter\\n api = tweepy.API(auth)\\n\\n # Check to make sure we can authenticate before we begin.\\n # Prevents ya boi from trying to do this without an internet connection\\n try:\\n api.verify_credentials()\\n print(\\\"I'm in...\\\")\\n except:\\n print(\\\"You didn't say the magic word!\\\")\\n\\n return api\",\n \"def skyserv_authenticator(self):\\n \\n header = {\\n 'Content-Type': accept, \\n 'X-Auth-Token': self.casjobtoken,\\n 'Accept': accept\\n }\\n # this format is disgusting but required....\\n authdata = {\\n 'auth' :{\\n 'identity': {\\n 'password': {\\n 'user': {\\n 'name': username,\\n 'password': password\\n }\\n }\\n }\\n }\\n }\\n payload = json.dumps(authdata).encode(encoding='utf-8')\\n try:\\n post = requests.post(self.loginurl, data=payload, headers=header)\\n\\n if post.status_code == 200:\\n response = json.loads(post.text)\\n token = response[self.tokenkey]\\n return token\\n else:\\n print('Username and/or password are invalid.')\\n post.raise_for_status()\\n except Exception as e:\\n raise(str(e))\",\n \"def __authenticate(self):\\n\\n self.logger.info(f\\\"Authenticating service principal with {self.client_id}\\\")\\n interactor = AzureInteractor(\\n self.tenant_id, self.subscription_id, self.client_id, self.client_secret\\n )\\n\\n return interactor\",\n \"def getAuthObj(self):\\n if self.accessToken is None:\\n self.authenticate()\\n\\n return OAuth1(self.apiKey, client_secret = self.apiKeySecret,\\n resource_owner_key = self.accessToken,\\n resource_owner_secret = self.accessTokenSecret,\\n signature_type = 'auth_header')\",\n \"def __init__(\\n self,\\n email: Optional[str] = None,\\n password: Optional[str] = None,\\n # loop: Optional[asyncio.AbstractEventLoop] = None,\\n # session: Optional[aiohttp.ClientSession] = None,\\n auth_token: Optional[str] = None,\\n api_timeout: int = API_TIMEOUT,\\n ) -> None:\\n\\n # random device id\\n self._device_id: str = str(uuid1())\\n\\n self.sac = SureAPIClient(\\n email=email, password=password, auth_token=auth_token, api_timeout=api_timeout\\n )\\n\\n # api token management\\n self._auth_token: Optional[str] = None\\n if auth_token and token_seems_valid(auth_token):\\n self._auth_token = auth_token\\n else: # if token := find_token():\\n self._auth_token = find_token()\\n\\n # storage for received api data\\n self._resource: Dict[str, Any] = {}\\n # storage for etags\\n self._etags: Dict[str, str] = {}\\n\\n logger.debug(\\\"initialization completed | vars(): %s\\\", vars())\",\n \"def enable_auth(self):\\n\\n self._api_manager.enable_auth()\",\n \"def create_api_handler(self):\\n self.github = github3.login(username=GH_USER, password=GH_PASSWORD)\\n if hasattr(self.github, 'set_user_agent'):\\n self.github.set_user_agent('Jonathan Reeve: http://jonreeve.com')\\n self.org = self.github.organization(login='Git-Lit')\\n # FIXME: logging\\n print(\\\"ratelimit: \\\" + str(self.org.ratelimit_remaining))\",\n \"def _get_user_client(self):\\n return api.OAuthClient(settings.CLIENT_ID, settings.CLIENT_SECRET, settings.USER, settings.PASSWORD)\",\n \"def authenticate(self, input: dict, request_id: str=None)->AuthenticationResult:\\n self.logger.warning(message='USING A DEFAULT LOCAL INSTANCE - IMPLEMENT THIS CLASS YOURSELF TO CALL YOUR OWN BACKEND', request_id=request_id)\\n return AuthenticationResult(\\n success=True,\\n userid=1,\\n permissions=['admin']\\n )\",\n \"def authenticate(self):\\n r = requests.head(self.url + '/v2/', verify=self.verify_ssl)\\n self._authenticate_for(r)\",\n \"def login(self, autologin: str) -> Auth:\\n self.auth = Auth(autologin)\\n return self.auth\",\n \"def getAPI():\\n auth = tweepy.OAuthHandler(cfg.consumer_key, cfg.consumer_secret)\\n auth.set_access_token(cfg.access_token, cfg.access_token_secret)\\n api = tweepy.API(auth, wait_on_rate_limit=True)\\n return api\",\n \"def auth(self):\\n ok = False\\n if self.private_token:\\n ok = self.token_auth()\\n if not ok:\\n self.credentials_auth()\",\n \"def authenticated_user(self):\\r\\n return AuthenticatedUser(self)\",\n \"def get_one_user_from_api(prod_api, pickle_auth=False, **kwargs):\\n\\n try:\\n email_validator.validate_email(kwargs['u_login']) # raises an exception\\n except AttributeError as e:\\n raise AttributeError('User email must be a string!')\\n\\n api_url = prod_api + \\\"/admin/user_asset/export\\\"\\n response = requests.post(\\n api_url,\\n headers = {'API-Key': API.config['API_KEY']},\\n auth=(\\n kwargs['admin_login'],\\n kwargs['admin_password']\\n ),\\n data={'login': kwargs['u_login']}\\n )\\n\\n if response.status_code != 200:\\n raise requests.RequestException(response.reason)\\n logger.warn(response.content)\\n\\n pickle_string = response.content\\n try:\\n user_oid = users.import_user(pickle_string)\\n except _pickle.UnpicklingError as e:\\n pickled = _pickle.dumps(pickle_string)\\n error_path = kwargs['u_login'] + '_import_error.pickle'\\n error_dump = open(error_path, 'wb')\\n error_dump.write(pickle_string)\\n error_dump.close()\\n raise ValueError(\\\"Could not import user! See '%s'\\\" % error_path)\\n\\n # we can silently reset the user's password if 'force' is in the kwargs\\n if kwargs.get('force', False):\\n user_object = users.User(_id=user_oid)\\n user_object.update_password('password')\\n\\n return user_oid\",\n \"def authenticate(self):\\n self._authentication = None\\n try:\\n self._authentication = self.authenticator.authenticate()\\n except RequestException as ex:\\n _LOGGER.error(\\\"Unable to connect to August service: %s\\\", str(ex))\\n raise CannotConnect\\n\\n if self._authentication.state == AuthenticationState.BAD_PASSWORD:\\n raise InvalidAuth\\n\\n if self._authentication.state == AuthenticationState.REQUIRES_VALIDATION:\\n raise RequireValidation\\n\\n if self._authentication.state != AuthenticationState.AUTHENTICATED:\\n _LOGGER.error(\\n \\\"Unknown authentication state: %s\\\", self._authentication.state\\n )\\n raise InvalidAuth\\n\\n return self._authentication\",\n \"def get_api(user: 'User') -> API:\\n auth = tweepy.OAuthHandler(user.api_key, user.api_secret)\\n auth.set_access_token(user.api_access_token, user.api_token_secret)\\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, compression=True)\\n return api\",\n \"def get_api(cfg):\\n auth = tweepy.OAuthHandler(cfg['consumer_key'], cfg['consumer_secret'])\\n auth.set_access_token(cfg['access_token'], cfg['access_token_secret'])\\n return tweepy.API(auth)\",\n \"def authenticate(self, username, api_key, auth_token=None):\\r\\n auth_endpoint = '/'.join((self.endpoint, 'v1',\\r\\n self.account_id, 'auth'))\\r\\n auth = QueueAuth(auth_endpoint, username, api_key,\\r\\n auth_token=auth_token)\\r\\n auth.auth()\\r\\n self.auth = auth\",\n \"def handler(self):\\r\\n credentials = self.get_credentials()\\r\\n return credentials.authorize(httplib2.Http())\",\n \"def login_with_api(\\n self,\\n *,\\n body,\\n headers: t.Dict[str, t.Any] = None,\\n **kwargs: t.Dict[str, t.Any],\\n ):\\n if headers is None:\\n headers = {}\\n headers['Content-Type'] = 'application/x-www-form-urlencoded'\\n r = self._do_call(\\n method='POST',\\n url=self.API_LOGIN_URL,\\n headers=headers,\\n body=body,\\n **kwargs\\n )\\n if r.status == 200:\\n res = json.loads(r.data.decode('utf-8'))\\n self.API_TOKEN = res[self.AUTH_TOKEN_KEY]\\n self.LOGIN_TIMESTAMP = dt.now()\\n else:\\n print(f'login failed \\\\nstatus:{r.status} \\\\n \\\\nurl: {self.API_LOGIN_URL}'\\n '\\\\nIs the username and password correct?')\",\n \"def __init__(self, api_key, client_id=None, client_secret=None):\\n self.api = API(api_key)\\n self._manifest = Manifest(self.api)\\n self.oauth = OAuth(client_id, client_secret)\",\n \"def _authenticated(func):\\n @functools.wraps(func)\\n def func_wrapper(self, *args, **kwargs):\\n try:\\n return func(self, *args, **kwargs)\\n except exception.NotAuthorized:\\n # Prevent recursion loop. After the self arg is the\\n # resource_type arg from _issue_api_request(). If attempt to\\n # login failed, we should just give up.\\n if args[0] == 'login':\\n raise\\n\\n # Token might've expired, get a new one, try again.\\n self._login()\\n return func(self, *args, **kwargs)\\n return func_wrapper\",\n \"def __authenticate(self):\\n body = {\\\"authenticationToken\\\": self.__get_authentication_token()}\\n result = self.__call(\\\"POST\\\", \\\"https://api.voiapp.io/v1/auth/session\\\", json=body)\\n\\n if result and \\\"accessToken\\\" in result and \\\"authenticationToken\\\" in result:\\n self._accessToken = result[\\\"accessToken\\\"]\\n self.__set_authentication_token(result[\\\"authenticationToken\\\"])\\n else:\\n _LOGGER.warning(\\\"Authentication failed: Erroneous response (%s)\\\", result)\",\n \"def get(self):\\n\\n\\t\\trequest = user_auth_parser.parse_args(strict=True)\\n\\n\\t\\tresult = Authenticator.authenticate(\\n\\t\\t\\trequest[\\\"username\\\"],\\n\\t\\t\\trequest[\\\"password\\\"]\\n\\t\\t)\\n\\n\\t\\treturn result\",\n \"def authenticate(self):\\n try:\\n self._token = self._lookup_token()\\n except:\\n raise HTTPError(\\n \\\"Unable to get short-lived access token for cyberark storage\\\"\\n )\",\n \"def __init__(self, consumer_key, consumer_secret, access_token, access_token_secret):\\n self.api = self.getAPI(consumer_key, consumer_secret, access_token, access_token_secret)\",\n \"def create_api_client(base_path, access_token):\\n api_client = ApiClient()\\n api_client.host = base_path\\n api_client.set_default_header(header_name=\\\"Authorization\\\",\\n header_value=f\\\"Bearer {access_token}\\\")\\n return api_client\",\n \"def _authenticate(self):\\n\\n # if auth_token is available, just return it\\n if self._auth_token is not None:\\n return self._auth_token\\n\\n # aquire the authentication lock\\n with self._auth_lock:\\n # re-check the auth_token as another thread could set it\\n if self._auth_token is not None:\\n return self._auth_token\\n\\n LOGGER.debug('Authenticating request')\\n # pylint: disable=broad-except\\n try:\\n # create a keystone client if it doesn't exist\\n if self._keystone is None:\\n cfg = Config.instance()\\n self._keystone = keystoneClientV2(\\n auth_url=cfg.OS_AUTH_URL,\\n username=cfg.OS_USERNAME,\\n password=cfg.OS_PASSWORD,\\n tenant_name=cfg.OS_TENANT_NAME\\n )\\n # store the authentication token\\n self._auth_token = self._keystone.auth_token\\n\\n # get the uri of service endpoint\\n endpoint = self._keystone.get_service_endpoint(\\n \\\"ceilometer\\\",\\n Config.instance().CEILOMETER_URL_TYPE)\\n\\n self._url_base = \\\"{}/v2/meters/%s\\\".format(endpoint)\\n LOGGER.info('Authenticating request - success')\\n self._failed_auth = False\\n\\n except KeystoneException as exc:\\n log_level = logging.DEBUG\\n\\n if not self._failed_auth:\\n log_level = logging.ERROR\\n LOGGER.error(\\n 'Suspending error logs until successful auth'\\n )\\n\\n LOGGER.log(log_level, 'Authentication error: %s',\\n six.text_type(exc),\\n exc_info=0)\\n\\n if exc.response:\\n LOGGER.debug('Response: %s', exc.response)\\n\\n self._auth_token = None\\n self._failed_auth = True\\n\\n return self._auth_token\",\n \"def _init_auth(\\n self,\\n auth: t.Optional[AuthModel] = None,\\n log_level: t.Union[str, int] = LOG_LEVEL_AUTH,\\n ) -> AuthModel:\\n if not isinstance(auth, AuthModel):\\n if self.CREDENTIALS:\\n auth: AuthCredentials = AuthCredentials(\\n username=self.__key,\\n password=self.__secret,\\n http=self.http,\\n log_level=log_level,\\n )\\n else:\\n auth: AuthApiKey = AuthApiKey(\\n key=self.__key,\\n secret=self.__secret,\\n http=self.http,\\n log_level=log_level,\\n )\\n return self._check_binding(auth)\",\n \"def _get_api_key(self):\\n self.api.apikey = self.api.action.user_show(id=self.username)['apikey']\",\n \"def auth_token_api():\\n data = request.get_json()\\n if not data:\\n response = jsonify({\\n 'success': False,\\n 'message': 'Missing request body'\\n })\\n response.status_code = 422\\n return response\\n\\n # process argument\\n login_type = data.get('auth_type')\\n email = data.get('email').strip().lower()\\n password = data.get('password')\\n\\n if not login_type or login_type not in ['email']:\\n response = jsonify({\\n 'success': False,\\n 'message': 'Invalid auth_type'\\n })\\n response.status_code = 422\\n return response\\n\\n # email authentication\\n elif login_type == 'email':\\n if not email:\\n response = jsonify({\\n 'success': False,\\n 'message': 'Must provide email when auth_type is \\\"email\\\"'\\n })\\n response.status_code = 422\\n return response\\n user = db.session.query(User).filter(User.email == email, User.deleted == False).one_or_none()\\n if not user:\\n response = jsonify({\\n 'success': False,\\n 'message': 'Not Authorized: invalid email'\\n })\\n response.status_code = 403\\n return response\\n # check the user's password\\n password_valid = check_password_hash(user.password, password)\\n if not password_valid:\\n response = jsonify({\\n 'success': False,\\n 'message': 'Not Authorized: invalid password'\\n })\\n response.status_code = 403\\n return response\\n\\n token = generate_auth_token(user_id=user.user_id)\\n response = jsonify({\\n 'success': True,\\n 'token': token\\n })\\n response.status_code == '200'\\n return response\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72514385","0.7194521","0.66663784","0.66555786","0.6583456","0.6552307","0.6540641","0.64537","0.6420162","0.64155054","0.6405242","0.63488394","0.6289332","0.62696224","0.62566435","0.62319964","0.6192098","0.6162103","0.61557543","0.61531574","0.61462414","0.608007","0.6075694","0.6062594","0.60612434","0.60508823","0.6043422","0.6013121","0.60126215","0.601124","0.59823835","0.59823835","0.5966953","0.5953832","0.59351796","0.5933686","0.5929654","0.59270513","0.5878144","0.58739066","0.5858527","0.58549756","0.5840574","0.5840444","0.58349943","0.58244586","0.58107626","0.58050406","0.5794414","0.57841146","0.57781523","0.57770425","0.5776352","0.57744205","0.5771926","0.5768939","0.5764042","0.5763936","0.5752974","0.5742311","0.57328504","0.5727954","0.5724669","0.5715746","0.57101655","0.5703906","0.5702824","0.5702128","0.5700769","0.5696371","0.5693578","0.56756145","0.565775","0.5657364","0.56540906","0.5652702","0.5651976","0.5646874","0.56465846","0.56456316","0.56435823","0.5637389","0.563715","0.56363404","0.5632157","0.5629348","0.56251895","0.56234276","0.56175435","0.5617009","0.5611165","0.56110436","0.560918","0.56055766","0.5605567","0.5603016","0.55980504","0.5593653","0.5590153","0.5570791"],"string":"[\n \"0.72514385\",\n \"0.7194521\",\n \"0.66663784\",\n \"0.66555786\",\n \"0.6583456\",\n \"0.6552307\",\n \"0.6540641\",\n \"0.64537\",\n \"0.6420162\",\n \"0.64155054\",\n \"0.6405242\",\n \"0.63488394\",\n \"0.6289332\",\n \"0.62696224\",\n \"0.62566435\",\n \"0.62319964\",\n \"0.6192098\",\n \"0.6162103\",\n \"0.61557543\",\n \"0.61531574\",\n \"0.61462414\",\n \"0.608007\",\n \"0.6075694\",\n \"0.6062594\",\n \"0.60612434\",\n \"0.60508823\",\n \"0.6043422\",\n \"0.6013121\",\n \"0.60126215\",\n \"0.601124\",\n \"0.59823835\",\n \"0.59823835\",\n \"0.5966953\",\n \"0.5953832\",\n \"0.59351796\",\n \"0.5933686\",\n \"0.5929654\",\n \"0.59270513\",\n \"0.5878144\",\n \"0.58739066\",\n \"0.5858527\",\n \"0.58549756\",\n \"0.5840574\",\n \"0.5840444\",\n \"0.58349943\",\n \"0.58244586\",\n \"0.58107626\",\n \"0.58050406\",\n \"0.5794414\",\n \"0.57841146\",\n \"0.57781523\",\n \"0.57770425\",\n \"0.5776352\",\n \"0.57744205\",\n \"0.5771926\",\n \"0.5768939\",\n \"0.5764042\",\n \"0.5763936\",\n \"0.5752974\",\n \"0.5742311\",\n \"0.57328504\",\n \"0.5727954\",\n \"0.5724669\",\n \"0.5715746\",\n \"0.57101655\",\n \"0.5703906\",\n \"0.5702824\",\n \"0.5702128\",\n \"0.5700769\",\n \"0.5696371\",\n \"0.5693578\",\n \"0.56756145\",\n \"0.565775\",\n \"0.5657364\",\n \"0.56540906\",\n \"0.5652702\",\n \"0.5651976\",\n \"0.5646874\",\n \"0.56465846\",\n \"0.56456316\",\n \"0.56435823\",\n \"0.5637389\",\n \"0.563715\",\n \"0.56363404\",\n \"0.5632157\",\n \"0.5629348\",\n \"0.56251895\",\n \"0.56234276\",\n \"0.56175435\",\n \"0.5617009\",\n \"0.5611165\",\n \"0.56110436\",\n \"0.560918\",\n \"0.56055766\",\n \"0.5605567\",\n \"0.5603016\",\n \"0.55980504\",\n \"0.5593653\",\n \"0.5590153\",\n \"0.5570791\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65322834"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":18489,"cells":{"query":{"kind":"string","value":"Demonstrate some api features."},"document":{"kind":"string","value":"def demonstrate():\n\n api = authenticate()\n\n # Demonstrate upload feature.\n # Create a list of one or more file paths of the mp3s you would like \n # to upload\n filepaths = []\n filepaths.append('./song1.mp3')\n\n # Upload an mp3 to your library. upload() returns a tuple of information\n # about the success or failure of uploads\n print(\"Beginning upload...\\n\")\n uploaded = api.upload(filepaths) \n\n # Print all successfully uploaded songs\n if len(uploaded[0]) > 0:\n print(\"Successfully uploaded:\")\n i = 1\n for key in uploaded[0]:\n print(\"%d. %s\" % (i, key))\n i += 1\n\n # Print all unsuccessfully uploaded songs and a description of why\n # songs weren't uploaded\n if len(uploaded[2]) == 0:\n print(\"\\nAll songs successfully uploaded.\")\n else:\n print(\"Not all songs were successfully uploaded:\")\n i = 1\n for key in uploaded[2]:\n print(\"%d. %s not uploaded: %s\" % (i, key, uploaded[2][key]))\n i += 1\n\n\n # Demonstrate download feature\n # Get information about songs previously uploaded that are available\n # to be downloaded\n uploaded_songs = api.get_uploaded_songs()\n\n if len(uploaded_songs) == 0:\n print(\"There are no songs currently available for download\")\n else:\n # Print songs that are available for download and store their ids\n # so we can download them\n song_ids = []\n print(\"\\nThe following songs are available for download\")\n for i in range(len(uploaded_songs)):\n song_ids.append(uploaded_songs[i]['id'])\n print(\"%d. %s\" % (i+1, uploaded_songs[i]['title']))\n\n # Download uploaded songs from your library\n print(\"\\nBeginning download...\")\n for i in range(len(song_ids)):\n filename, audio = api.download_song(song_ids[i])\n\n # Write song to disk\n with open(filename, 'wb') as f:\n f.write(audio)\n\n print(\"%d. Written to ./%s\" % (i + 1, filename))\n print(\"\\nDownload complete.\")\n\n # It's good practice to logout when finished\n api.logout()"},"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 api():\n\treturn \"The API call\"","def api(self) -> str:","def testApi(self):","def hello_world():\n \n apis = []\n apis.append({\n 'method' : 'POST', \n 'url' : '/api/analysis',\n 'params': [{\n 'attribute' : 'text',\n 'value' : 'string'\n }]\n })\n \n info = {}\n info['name'] = 'pyEssayAnalyser'\n info['version'] = \"3.0\"\n info['api'] = apis\n\n return jsonify(info)","def test_get_api_resources(self):\n pass","def test_simple1(self):\n api = self.load_api_description('simple1.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(len(api.resources), 1)\n\n resource = api.resources[0]\n self.assertEqual(resource.name, 'AllOrders')\n self.assertEqual(resource.path, '/')\n self.assertEqual(len(resource.operations), 1)\n\n operation = resource.operations[0]\n self.assertEqual(operation.method, 'GET')\n self.assertIsNone(operation.input)\n output = operation.output\n self.assertEqual(output.status, 200)\n self.assertEqual(output.type.type.get_reference_name(), 'list(string)')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n self.assertEqual(len(api.base), 1)\n self.assertEqual(api.base[0], 'http://test.com/starbucks')","def overview():\n return render_template('api/api.html', title='API Overview')","def test_random_programming_quotes_api(self):\n response = api_client.get(\"/?query=programming\")\n self.assertEqual(response.status_code, 200)","def test_api_samples(self):\n # load api base\n r = requests.get('{server}/api/0.1/'.format(\n server=self.get_server_url())).json()\n # load samples from url specified in api base\n r = requests.get(r['samples']).json()\n self.assertIn('count', r)\n self.assertIn('next', r)\n self.assertIn('prev', r)\n self.assertIn('samples', r)","def maya_useNewAPI():\r\n\r\n pass","def test_api() -> bool:\r\n weather = False\r\n news = False\r\n covid = False\r\n if check_weather_version():\r\n logging.info(\"Weather API version is up to date (check_weather_version())\")\r\n weather = True\r\n else:\r\n logging.info(\"Weather API version is not up to date (check_weather_version()) - ACTION REQUIRED\")\r\n if check_news_version():\r\n logging.info(\"News API version is up to date (check_news_version())\")\r\n news = True\r\n else:\r\n logging.info(\"News API version is not up to date (check_news_version()) - ACTION REQUIRED\")\r\n if check_covid_version():\r\n logging.info(\"Covid-19 API version is up to date (check_covid_version())\")\r\n covid = True\r\n else:\r\n logging.info(\"Covid-19 API version is not up to date (check_covid_version()) - ACTION REQUIRED\")\r\n return bool(weather and news and covid)","def maya_useNewAPI():\n\n pass","def test_api_sample(self):\n # load api base\n r = requests.get('{server}/api/0.1/'.format(\n server=self.get_server_url())).json()\n # load samples from url specified in api base\n r = requests.get(r['samples']).json()\n # load a sample\n r = requests.get(r['samples'][0]['url']).json()\n self.assertIn('datetime', r)\n self.assertIn('value', r)\n self.assertIn('id', r)\n self.assertIn('url', r)\n self.assertIn('sensor', r)","def main():\n return execute_api(Freta(), [Endpoint], __version__)","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def maya_useNewAPI():\n pass","def demo_hello():\n info = {'Header[User-Agent]': request.headers.get('User-Agent'),\n 'args': request.args,\n 'query_string': request.query_string,\n 'name': request.args.get('name')}\n log.info('Request Info:\\n%s', right_just_dict(info))\n return jsonify(dict(rc=0, data=dict(id=2, username=request.args.get('username'), age=request.args.get('age', 23)), msg='success'))","def maya_useNewAPI():\n\tpass","def maya_useNewAPI():\n\tpass","def test_api_base(self):\n # load api base\n r = requests.get('{server}/api/0.1/'.format(\n server=self.get_server_url()))\n j = r.json()\n self.assertIn('gages', j)\n self.assertIn('sections', j)\n self.assertIn('regions', j)\n self.assertIn('rivers', j)\n self.assertIn('sensors', j)\n self.assertIn('samples', j)","def test_59_help_api(self):\r\n Fixtures.create()\r\n url = \"/help/api\"\r\n res = self.app.get(url, follow_redirects=True)\r\n err_msg = \"There should be a help api page\"\r\n assert \"API Help\" in res.data, err_msg","def test_simple2(self):\n api = self.load_api_description('simple2.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(len(api.resources), 1)\n\n resource = api.resources[0]\n self.assertEqual(resource.name, 'AllOrders')\n self.assertEqual(resource.path, '/')\n self.assertEqual(len(resource.operations), 2)\n\n operation = resource.operations[0]\n self.assertEqual(operation.method, 'GET')\n self.assertIsNone(operation.input)\n output = operation.output\n self.assertEqual(output.status, 200)\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n operation = resource.operations[1]\n self.assertEqual(operation.method, 'POST')\n input = operation.input\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\n self.assertEqual(input.contentType[0], 'json')\n output = operation.output\n self.assertEqual(output.status, 201)\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n self.assertEqual(len(api.base), 1)\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\n\n self.assertEqual(len(api.data_types), 2)\n self.assertEqual(len(api.data_types[0].fields), 5)\n self.assertEqual(len(api.data_types[1].fields), 2)\n self.assertFalse(api.data_types[1].fields[0].optional)\n self.assertTrue(api.data_types[1].fields[1].optional)","def main():\n with requests.get(API_URL) as response:\n response.raise_for_status()\n data = response.json()\n\n title = data[\"title\"]\n extract = data[\"extract\"]\n\n click.secho(title, fg=\"green\")\n click.echo(textwrap.fill(extract))","def test_api_helloworld():\n with app.test_client() as c:\n response = c.get('/')\n assert response.status_code == 200","def test_simple4(self):\n api = self.load_api_description('simple4.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(len(api.resources), 2)\n\n resource = api.resources[1]\n self.assertEqual(resource.name, 'AllOrders')\n self.assertEqual(resource.path, '/')\n self.assertEqual(len(resource.operations), 2)\n\n operation = resource.operations[0]\n self.assertEqual(operation.method, 'GET')\n self.assertIsNone(operation.input)\n output = operation.output\n self.assertEqual(output.status, 200)\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n operation = resource.operations[1]\n self.assertEqual(operation.method, 'POST')\n input = operation.input\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\n self.assertEqual(input.contentType[0], 'json')\n output = operation.output\n self.assertEqual(output.status, 201)\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n self.assertEqual(len(output.headers), 1)\n header = output.headers[0]\n self.assertEqual(header.name, 'Location')\n self.assertEqual(header.type.type.get_reference_name(), 'href')\n self.assertEqual(header.type.ref, 'Order')\n\n self.assertEqual(len(api.base), 1)\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\n\n self.assertEqual(len(api.data_types), 2)\n self.assertEqual(len(api.data_types[0].fields), 5)\n self.assertEqual(len(api.data_types[1].fields), 2)\n self.assertFalse(api.data_types[1].fields[0].optional)\n self.assertTrue(api.data_types[1].fields[1].optional)\n\n resource = api.resources[0]\n self.assertEqual(len(resource.input_bindings), 1)\n self.assertEqual(resource.input_bindings[0].id, 'orderIdBinding')\n self.assertEqual(len(resource.operations), 2)\n self.assertEqual(resource.operations[0].input.params[0].binding, 'orderIdBinding')\n self.assertEqual(resource.operations[1].input.params[0].binding, 'orderIdBinding')","def demo():\n ...","def test_orchestrator_http_simple(self):\n pass","def test__import_api_v6(self):\n response = textwrap.dedent(\n \"\"\"\\\n var pveapi = [\n {\n \"info\" : {\n }\n }\n ]\n ;\n \"\"\"\n )\n self._test__import_api(response)","def test_api():\n # person id for one long time employee\n content = get_person(10050)\n assert content['preferredName'].endswith('immel')","def test_api_predictors_get(self):\n pass","def index():\n return 'Your api is up and running!'","def test_get(self):\n pass","def test_simple3(self):\n api = self.load_api_description('simple3.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(len(api.resources), 2)\n\n resource = api.resources[1]\n self.assertEqual(resource.name, 'AllOrders')\n self.assertEqual(resource.path, '/')\n self.assertEqual(len(resource.operations), 2)\n\n operation = resource.operations[0]\n self.assertEqual(operation.method, 'GET')\n self.assertIsNone(operation.input)\n output = operation.output\n self.assertEqual(output.status, 200)\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n operation = resource.operations[1]\n self.assertEqual(operation.method, 'POST')\n input = operation.input\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\n self.assertEqual(input.contentType[0], 'json')\n output = operation.output\n self.assertEqual(output.status, 201)\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n self.assertEqual(len(api.base), 2)\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\n self.assertEqual(api.base[1], 'https://test.com/starbucks')\n\n self.assertEqual(len(api.data_types), 4)\n self.assertEqual(len(api.data_types[0].fields), 5)\n self.assertEqual(len(api.data_types[1].fields), 2)\n self.assertFalse(api.data_types[1].fields[0].optional)\n self.assertTrue(api.data_types[1].fields[1].optional)\n\n operation = api.resources[0].operations[0]\n self.assertEqual(len(operation.errors), 2)","def predict_api():\n pass","def test_get_api(self):\n # Get metadata list\n _logger.info('Get sequencerun API')\n response = self.client.get('/sequencerun/')\n self.assertEqual(response.status_code, 200, 'Ok status response is expected')\n\n _logger.info('Check if API return result')\n result_response = response.data['results']\n self.assertGreater(len(result_response), 0, 'A result is expected')\n\n _logger.info('Check if unique data has a single entry')\n response = self.client.get('/sequencerun/?msg_attr_action=statuschanged')\n results_response = response.data['results']\n self.assertEqual(len(results_response), 1, 'Single result is expected for unique data')\n\n _logger.info('Check Invalid keyword')\n response = self.client.get('/sequencerun/?foo=bar')\n results_response = response.data['results']\n self.assertEqual(len(results_response), 0, 'No results are expected for unrecognized query parameter')","def api_html():\n\n return jsonify({'version': __version__})","def hello(): #status: WIP\r\n pass","def test_open_api(self):\n response = self.client.get(self.initiatives_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","async def api():\n return {\n \"links\": {\n \"datasets\": \"/datasets\",\n \"natural_earth_feature\": \"/natural_earth_feature\",\n \"viewport\": \"/viewport\"\n }\n }","def api():\n api_routes = [\n \"/api/v1.0/beer\",\n \"/api/v1.0/breweries\",\n ]\n return render_template(\"api.html\", api_routes = api_routes)","def hello():\n\n usage_msg = \"<br/>\\n\".join([\"Welcome to WildLife: The REST APIs for \"\n \"ZooKeeper!<br/>\",\n hello.__doc__.replace(\"\\n\", \"<br/>\\n\")])\n\n return make_response(usage_msg, 200)","def test_get_info(self):\n pass","def test_1():\n\tassert api_call().status_code == 200","def test_basic_api_with_mode(self):\n # Create an API with get and put\n self.create_and_verify_stack(\"single/basic_api_with_mode\")\n\n stack_output = self.get_stack_outputs()\n api_endpoint = stack_output.get(\"ApiEndpoint\")\n\n self.verify_get_request_response(f\"{api_endpoint}/get\", 200)\n\n # Removes get from the API\n self.update_and_verify_stack(file_path=\"single/basic_api_with_mode_update\")\n\n # API Gateway by default returns 403 if a path do not exist\n self.verify_get_request_response.retry_with(\n stop=stop_after_attempt(20),\n wait=wait_exponential(multiplier=1, min=4, max=10) + wait_random(0, 1),\n retry=retry_if_exception_type(StatusCodeError),\n after=after_log(LOG, logging.WARNING),\n reraise=True,\n )(self, f\"{api_endpoint}/get\", 403)\n\n LOG.log(msg=f\"retry times {self.verify_get_request_response.retry.statistics}\", level=logging.WARNING)","def test_random_anime_quotes_api(self):\n response = api_client.get(\"/?query=anime\")\n self.assertEqual(response.status_code, 200)","def main():\n example()","def main():\n example()","def main():\n example()","def main():\n example()","def test_simple6(self):\n api = self.load_api_description('simple6.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(len(api.resources), 2)\n\n resource = api.resources[1]\n self.assertEqual(resource.name, 'AllOrders')\n self.assertEqual(resource.path, '/')\n self.assertEqual(len(resource.operations), 2)\n\n operation = resource.operations[0]\n self.assertEqual(operation.method, 'GET')\n self.assertIsNone(operation.input)\n output = operation.output\n self.assertEqual(output.status, 200)\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n operation = resource.operations[1]\n self.assertEqual(operation.method, 'POST')\n input = operation.input\n self.assertEqual(len(input.type.type.fields), 2)\n self.assertEqual(input.contentType[0], 'json')\n output = operation.output\n self.assertEqual(output.status, 201)\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\n self.assertEqual(len(output.contentType), 1)\n self.assertEqual(output.contentType[0], 'json')\n\n self.assertEqual(len(api.base), 1)\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\n\n self.assertEqual(len(api.data_types), 2)\n self.assertEqual(len(api.data_types[0].fields), 5)\n self.assertEqual(len(api.data_types[1].fields), 2)\n nested = api.data_types[1]\n field = nested.fields[1]\n self.assertEqual(len(field.type.type.fields), 1)\n\n resource = api.resources[0]\n self.assertEqual(len(resource.input_bindings), 1)\n self.assertEqual(resource.input_bindings[0].id, 'orderIdBinding')\n self.assertEqual(len(resource.operations), 2)\n binding = resource.operations[0].input.params[0].binding\n self.assertEqual(binding.mode, 'url')\n self.assertEqual(binding.name, 'orderId')\n self.assertEqual(binding.type.type.get_reference_name(), 'string')\n self.assertEqual(resource.operations[1].input.params[0].binding, 'orderIdBinding')","def test_hello(client):\n\n res = client.get('/api')\n assert res.get_json().get('msg') != None","def example_eleven():\n # TODO\n pass","def Modifier_API(self):\n\t\tpass","def main(ctx, verbose):\n return","def index():\n response = jsonify(\n {'message':'Hello, RESTful API development!'}\n )\n \n return response, 200","def test_random_quotes_api(self):\n response = api_client.get(\"/\")\n self.assertEqual(response.status_code, 200)","def program_api(port):\n server = system.create_server(\"api\")\n server.listen(port=port)","def test_index(self):\n resp = self.app.get('/')\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertIn('Inventory Demo REST API Service', resp.data)\n # resp = self.app.get('/')\n # self.assertEqual(resp.status_code, status.HTTP_200_OK)\n # data = json.loads(resp.data)\n # self.assertEqual(data['name'], 'Inventory Demo REST API Service')","def cci_api():\n\n\n\t\t\treturn render_template( 'api.html' )","def main(path, verbose, test_mode, debug_mode):\n if verbose:\n logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\n fpl_api_url = os.getenv(\"FPL_API_URL\", \"https://fantasy.premierleague.com/api/\")\n api_client = APIClientFactory.get_api(fpl_api_url)\n if debug_mode:\n if not path:\n raise UndefinedAPIPathException(\n \"Debug mode is for exploring Artifactory API endpoints.\"\n )\n print(api_client.get(path).text)\n elif test_mode:\n pass\n else:\n prometheus_exporter(api_client)\n\n return 0","def test_ice_and_fire_external(self):\n response = self.client.get('/api/external-books?name=A Game of Thrones', format='json')\n self.assertEqual(200, response.data['status_code'])","def verb_test():\n # it also shows you how to access the method used and the decoded JSON data\n return jsonify(method=request.method, data=request.get_json(), url=request.url)","def verb_test():\n # it also shows you how to access the method used and the decoded JSON data\n return jsonify(method=request.method, data=request.get_json(), url=request.url)","def index():\n # Message to the user\n message = {\n 'api_version': 'v1.0',\n 'status': '200',\n 'message': 'Welcome to the Flask API'\n }\n # Making the message looks good\n resp = jsonify(message)\n\n # Returning the object\n return resp","def main():\n\n return redirect('/apidocs')","def test_api_response_data(self):","def test_basic_api(self):\n self.create_and_verify_stack(\"single/basic_api\")\n\n first_dep_ids = self.get_stack_deployment_ids()\n self.assertEqual(len(first_dep_ids), 1)\n\n self.set_template_resource_property(\"MyApi\", \"DefinitionUri\", self.get_s3_uri(\"swagger2.json\"))\n self.update_stack()\n\n second_dep_ids = self.get_stack_deployment_ids()\n self.assertEqual(len(second_dep_ids), 1)\n\n self.assertEqual(len(set(first_dep_ids).intersection(second_dep_ids)), 0)","def test_help_route():\n response = client.get(\"/\")\n assert response.status_code == 200\n assert response.json() == {\n \"repositories\": f\"{DOMAIN_NAME}/repositories\",\n \"developers\": f\"{DOMAIN_NAME}/developers\",\n \"docs\": f\"{DOMAIN_NAME}/docs\",\n \"redoc\": f\"{DOMAIN_NAME}/redoc\",\n }","def apiDocs():\n\treturn render_template('apiDocs.html')","def cli(ctx):","def cli(ctx):","def setUp(self):\n self.api = \"http://localhost:4031/\"\n self.version = \"0.2\"\n self.app = init_api()","def test_service_api_get(service_app):\n response = service_app.get('/')\n assert response.headers['Content-Type'] == 'application/json'\n assert response.status_code == 200\n assert json.loads(response.data) == {'description': 'service is up', 'status': 200}","def test_get1(self):\n pass","def test_api_versioning(self):\n response = self.request_knox(\n self.url,\n media_type=views_api.CORE_API_MEDIA_TYPE,\n version=views_api.CORE_API_DEFAULT_VERSION,\n )\n self.assertEqual(response.status_code, 200)","def test_api_river(self):\n # load api base\n r = requests.get('{server}/api/0.1/'.format(\n server=self.get_server_url())).json()\n # load rivers from url specified in api base\n r = requests.get(r['rivers']).json()\n r = requests.get(r['rivers'][0]['url']).json()\n self.assertIn('html', r)\n self.assertIn('url', r)\n self.assertIn('sections', r)\n self.assertIn('gages', r)\n self.assertIn('regions', r)\n self.assertIn('tributaries', r)","def test_api_urls():\n # Test the status message - 404 not good , 200 good\n assert API_RH.create_req().status_code == 200, \"The tests for URLs were successful\"","def test_api(self):\n new_route = self.route.api(\"new\")\n assert new_route != self.route\n assert new_route.route[\"api\"] == \"new\"","def test_api_user_get(self):\n pass","def test_index(self):\n resp = self.app.get(\"/\")\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertIn(b\"Product Demo REST API Service\", resp.data)","def test():\n pass","def test_doc():\n pass","def Help():\n names=api_method_dict.keys()\n names.sort()\n return ''.join(['**** ' + api_method_dict[name].__name__ + '\\n' + api_method_dict[name].__doc__ + '\\n'\n for name in names])","def describe():","def apiExample():\n Client_ID = '5b42d8679569faa6e359a3f562e8cd76'\n api_endpoint = \"http://api.soundcloud.com/tracks/13158665?client_id=%s\" \\\n % Client_ID\n request = urllib2.urlopen(api_endpoint)\n data = json.load(request)\n return data","def main():\n # data = {'user': 'ALFlanagan'}\n # login = HTTPBasicAuth('16ED26C62A6711E591E4B3E58BAF2E49', '55A57143E3404')\n # resp = requests.post('https://richmond-dot-com.bloxcms-ny1.com/tncms/webservice/v1/user/get/',\n # data=data, auth=login)\n # print(resp.text)\n services = ['business', 'job', 'user', 'editorial', 'eedition', 'classifieds']\n data = {\"user\": \"Flanagan\"}\n login = HTTPBasicAuth('089F86E02A6211E5A24353BD6EAA3E6B', '55A568C86DF17')\n for service in services:\n resp = requests.post(\"{}{}/\".format(URL_ROOT, service), data=data, auth=login)\n resp_dict = json.loads(resp.text)\n api_list = resp_dict['apis']\n for api_desc in api_list:\n print(api_desc['path'])\n for oper in api_desc['operations']:\n print(\" {}: {}\".format(oper['httpMethod'], oper['summary']))\n\n # http://www.richmond.com/tncms/webservice/v1/editorial/get/?\n data = {'id': '72c2ab48-2976-11e5-a21b-a7f967e75fca'}\n login = HTTPBasicAuth(\"16ED26C62A6711E591E4B3E58BAF2E49\", \"55A57143E3404\")\n resp = requests.post(\"{}editorial/get/\".format(RICHMOND_URL), data=data, auth=login)\n asset = json.loads(resp.text)\n pprint(asset)","def example_function():","def get(self,request,format = None):\n an_apiview = [\n 'Uses HTTP methods as function (get,post,patch,put,delete)',\n 'Is similar to a traditional Django View',\n 'Gives you the most control over your appliction logic',\n 'Is mapped manually to URLs'\n ]\n return Response({'message':'Hello!','an_apiview': an_apiview})","def get(self, request, format=None):\n an_apiview = [\n 'Uses HTTP methods as functions(get, post, patch, put, delete)',\n 'Is similar to traditional Django view',\n 'Give you the most control over your app logic',\n 'Is mapped manually to URLs'\n ]\n return Response({'message':'Hello!', 'an_apiview':an_apiview})","def test_00_api_get(self):\r\n # GET as Anonymous\r\n url = '/api/'\r\n action = 'get'\r\n self.check_limit(url, action, 'app')","def test_root_public_api(self):\n\n # GIVEN public API\n # WHEN fetching available applications and models\n response = self.api.root_api(public=True)\n\n # THEN it should succeed\n self.assertTrue(response.success)\n\n # AND it should have valid data\n self.assertEqual(len(response.data), 2)\n self.assertEqual(response.data[0]['app_label'], 'auth')\n self.assertEqual(response.data[1]['app_label'], 'admin')","def get(self, request, format=None):\n an_apiview = [\n \"User HTTp methods get, put, post, delete method\",\n \"very similar to previous Django view\",\n \"gives you more control on api logic\",\n 'Is mapped to manually to urls'\n ]\n\n return Response({'message':\"hello\", \"an_apiview\": an_apiview} )","def cli(ctx):\n #TODO","def test_simple5(self):\n api = self.load_api_description('simple5.json')\n self.assertEqual(api.name, 'Starbucks')\n self.assertEqual(api.license, 'apache2')\n self.assertEqual(api.community, 'http://community.test.com')\n\n self.assertEqual(len(api.ownership), 2)\n self.assertEqual(api.ownership[0].name, 'Peter Parker')\n self.assertEqual(api.ownership[1].name, 'Bruce Wayne')\n self.assertEqual(api.ownership[1].email, 'bat@jleague.com')\n self.assertEqual(api.ownership[1].ownerType, 'tech')\n self.assertEqual(len(api.sla), 3)\n\n sla = api.sla[2]\n self.assertEqual(sla.name, 'GOLD')\n self.assertEqual(sla.availability, 99.9)\n self.assertEqual(sla.rateLimit, 1000)\n self.assertEqual(sla.timeUnit, 'second')\n cost = sla.costModel\n self.assertEqual(cost.currency, 'USD')\n self.assertEqual(cost.unitPrice, 0.1)\n self.assertEqual(cost.requestsPerUnit, 1000)\n\n sla = api.sla[0]\n self.assertEqual(sla.name, 'FREE')\n self.assertIsNone(sla.costModel)","def basic(self):\n pass","def test_api_section(self):\n # load api base\n r = requests.get('{server}/api/0.1/'.format(\n server=self.get_server_url())).json()\n # load sections from url specified in api base\n r = requests.get(r['sections']).json()\n r = requests.get(r['sections'][0]['url']).json()\n self.assertIn('id', r)\n self.assertIn('name', r)\n self.assertIn('html', r)\n self.assertIn('url', r)\n self.assertIn('regions', r)\n self.assertIn('sensors', r)\n self.assertIn('gages', r)\n self.assertIn('description', r)\n self.assertIn('access', r)\n self.assertIn('location', r)\n self.assertIn('in_latitude', r)\n self.assertIn('in_longitude', r)\n self.assertIn('out_latitude', r)\n self.assertIn('out_longitude', r)","def simple():"],"string":"[\n \"def api():\\n\\treturn \\\"The API call\\\"\",\n \"def api(self) -> str:\",\n \"def testApi(self):\",\n \"def hello_world():\\n \\n apis = []\\n apis.append({\\n 'method' : 'POST', \\n 'url' : '/api/analysis',\\n 'params': [{\\n 'attribute' : 'text',\\n 'value' : 'string'\\n }]\\n })\\n \\n info = {}\\n info['name'] = 'pyEssayAnalyser'\\n info['version'] = \\\"3.0\\\"\\n info['api'] = apis\\n\\n return jsonify(info)\",\n \"def test_get_api_resources(self):\\n pass\",\n \"def test_simple1(self):\\n api = self.load_api_description('simple1.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(len(api.resources), 1)\\n\\n resource = api.resources[0]\\n self.assertEqual(resource.name, 'AllOrders')\\n self.assertEqual(resource.path, '/')\\n self.assertEqual(len(resource.operations), 1)\\n\\n operation = resource.operations[0]\\n self.assertEqual(operation.method, 'GET')\\n self.assertIsNone(operation.input)\\n output = operation.output\\n self.assertEqual(output.status, 200)\\n self.assertEqual(output.type.type.get_reference_name(), 'list(string)')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n self.assertEqual(len(api.base), 1)\\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\",\n \"def overview():\\n return render_template('api/api.html', title='API Overview')\",\n \"def test_random_programming_quotes_api(self):\\n response = api_client.get(\\\"/?query=programming\\\")\\n self.assertEqual(response.status_code, 200)\",\n \"def test_api_samples(self):\\n # load api base\\n r = requests.get('{server}/api/0.1/'.format(\\n server=self.get_server_url())).json()\\n # load samples from url specified in api base\\n r = requests.get(r['samples']).json()\\n self.assertIn('count', r)\\n self.assertIn('next', r)\\n self.assertIn('prev', r)\\n self.assertIn('samples', r)\",\n \"def maya_useNewAPI():\\r\\n\\r\\n pass\",\n \"def test_api() -> bool:\\r\\n weather = False\\r\\n news = False\\r\\n covid = False\\r\\n if check_weather_version():\\r\\n logging.info(\\\"Weather API version is up to date (check_weather_version())\\\")\\r\\n weather = True\\r\\n else:\\r\\n logging.info(\\\"Weather API version is not up to date (check_weather_version()) - ACTION REQUIRED\\\")\\r\\n if check_news_version():\\r\\n logging.info(\\\"News API version is up to date (check_news_version())\\\")\\r\\n news = True\\r\\n else:\\r\\n logging.info(\\\"News API version is not up to date (check_news_version()) - ACTION REQUIRED\\\")\\r\\n if check_covid_version():\\r\\n logging.info(\\\"Covid-19 API version is up to date (check_covid_version())\\\")\\r\\n covid = True\\r\\n else:\\r\\n logging.info(\\\"Covid-19 API version is not up to date (check_covid_version()) - ACTION REQUIRED\\\")\\r\\n return bool(weather and news and covid)\",\n \"def maya_useNewAPI():\\n\\n pass\",\n \"def test_api_sample(self):\\n # load api base\\n r = requests.get('{server}/api/0.1/'.format(\\n server=self.get_server_url())).json()\\n # load samples from url specified in api base\\n r = requests.get(r['samples']).json()\\n # load a sample\\n r = requests.get(r['samples'][0]['url']).json()\\n self.assertIn('datetime', r)\\n self.assertIn('value', r)\\n self.assertIn('id', r)\\n self.assertIn('url', r)\\n self.assertIn('sensor', r)\",\n \"def main():\\n return execute_api(Freta(), [Endpoint], __version__)\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def maya_useNewAPI():\\n pass\",\n \"def demo_hello():\\n info = {'Header[User-Agent]': request.headers.get('User-Agent'),\\n 'args': request.args,\\n 'query_string': request.query_string,\\n 'name': request.args.get('name')}\\n log.info('Request Info:\\\\n%s', right_just_dict(info))\\n return jsonify(dict(rc=0, data=dict(id=2, username=request.args.get('username'), age=request.args.get('age', 23)), msg='success'))\",\n \"def maya_useNewAPI():\\n\\tpass\",\n \"def maya_useNewAPI():\\n\\tpass\",\n \"def test_api_base(self):\\n # load api base\\n r = requests.get('{server}/api/0.1/'.format(\\n server=self.get_server_url()))\\n j = r.json()\\n self.assertIn('gages', j)\\n self.assertIn('sections', j)\\n self.assertIn('regions', j)\\n self.assertIn('rivers', j)\\n self.assertIn('sensors', j)\\n self.assertIn('samples', j)\",\n \"def test_59_help_api(self):\\r\\n Fixtures.create()\\r\\n url = \\\"/help/api\\\"\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n err_msg = \\\"There should be a help api page\\\"\\r\\n assert \\\"API Help\\\" in res.data, err_msg\",\n \"def test_simple2(self):\\n api = self.load_api_description('simple2.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(len(api.resources), 1)\\n\\n resource = api.resources[0]\\n self.assertEqual(resource.name, 'AllOrders')\\n self.assertEqual(resource.path, '/')\\n self.assertEqual(len(resource.operations), 2)\\n\\n operation = resource.operations[0]\\n self.assertEqual(operation.method, 'GET')\\n self.assertIsNone(operation.input)\\n output = operation.output\\n self.assertEqual(output.status, 200)\\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n operation = resource.operations[1]\\n self.assertEqual(operation.method, 'POST')\\n input = operation.input\\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\\n self.assertEqual(input.contentType[0], 'json')\\n output = operation.output\\n self.assertEqual(output.status, 201)\\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n self.assertEqual(len(api.base), 1)\\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\\n\\n self.assertEqual(len(api.data_types), 2)\\n self.assertEqual(len(api.data_types[0].fields), 5)\\n self.assertEqual(len(api.data_types[1].fields), 2)\\n self.assertFalse(api.data_types[1].fields[0].optional)\\n self.assertTrue(api.data_types[1].fields[1].optional)\",\n \"def main():\\n with requests.get(API_URL) as response:\\n response.raise_for_status()\\n data = response.json()\\n\\n title = data[\\\"title\\\"]\\n extract = data[\\\"extract\\\"]\\n\\n click.secho(title, fg=\\\"green\\\")\\n click.echo(textwrap.fill(extract))\",\n \"def test_api_helloworld():\\n with app.test_client() as c:\\n response = c.get('/')\\n assert response.status_code == 200\",\n \"def test_simple4(self):\\n api = self.load_api_description('simple4.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(len(api.resources), 2)\\n\\n resource = api.resources[1]\\n self.assertEqual(resource.name, 'AllOrders')\\n self.assertEqual(resource.path, '/')\\n self.assertEqual(len(resource.operations), 2)\\n\\n operation = resource.operations[0]\\n self.assertEqual(operation.method, 'GET')\\n self.assertIsNone(operation.input)\\n output = operation.output\\n self.assertEqual(output.status, 200)\\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n operation = resource.operations[1]\\n self.assertEqual(operation.method, 'POST')\\n input = operation.input\\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\\n self.assertEqual(input.contentType[0], 'json')\\n output = operation.output\\n self.assertEqual(output.status, 201)\\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n self.assertEqual(len(output.headers), 1)\\n header = output.headers[0]\\n self.assertEqual(header.name, 'Location')\\n self.assertEqual(header.type.type.get_reference_name(), 'href')\\n self.assertEqual(header.type.ref, 'Order')\\n\\n self.assertEqual(len(api.base), 1)\\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\\n\\n self.assertEqual(len(api.data_types), 2)\\n self.assertEqual(len(api.data_types[0].fields), 5)\\n self.assertEqual(len(api.data_types[1].fields), 2)\\n self.assertFalse(api.data_types[1].fields[0].optional)\\n self.assertTrue(api.data_types[1].fields[1].optional)\\n\\n resource = api.resources[0]\\n self.assertEqual(len(resource.input_bindings), 1)\\n self.assertEqual(resource.input_bindings[0].id, 'orderIdBinding')\\n self.assertEqual(len(resource.operations), 2)\\n self.assertEqual(resource.operations[0].input.params[0].binding, 'orderIdBinding')\\n self.assertEqual(resource.operations[1].input.params[0].binding, 'orderIdBinding')\",\n \"def demo():\\n ...\",\n \"def test_orchestrator_http_simple(self):\\n pass\",\n \"def test__import_api_v6(self):\\n response = textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n var pveapi = [\\n {\\n \\\"info\\\" : {\\n }\\n }\\n ]\\n ;\\n \\\"\\\"\\\"\\n )\\n self._test__import_api(response)\",\n \"def test_api():\\n # person id for one long time employee\\n content = get_person(10050)\\n assert content['preferredName'].endswith('immel')\",\n \"def test_api_predictors_get(self):\\n pass\",\n \"def index():\\n return 'Your api is up and running!'\",\n \"def test_get(self):\\n pass\",\n \"def test_simple3(self):\\n api = self.load_api_description('simple3.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(len(api.resources), 2)\\n\\n resource = api.resources[1]\\n self.assertEqual(resource.name, 'AllOrders')\\n self.assertEqual(resource.path, '/')\\n self.assertEqual(len(resource.operations), 2)\\n\\n operation = resource.operations[0]\\n self.assertEqual(operation.method, 'GET')\\n self.assertIsNone(operation.input)\\n output = operation.output\\n self.assertEqual(output.status, 200)\\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n operation = resource.operations[1]\\n self.assertEqual(operation.method, 'POST')\\n input = operation.input\\n self.assertEqual(input.type.type.get_reference_name(), 'OrderRequest')\\n self.assertEqual(input.contentType[0], 'json')\\n output = operation.output\\n self.assertEqual(output.status, 201)\\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n self.assertEqual(len(api.base), 2)\\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\\n self.assertEqual(api.base[1], 'https://test.com/starbucks')\\n\\n self.assertEqual(len(api.data_types), 4)\\n self.assertEqual(len(api.data_types[0].fields), 5)\\n self.assertEqual(len(api.data_types[1].fields), 2)\\n self.assertFalse(api.data_types[1].fields[0].optional)\\n self.assertTrue(api.data_types[1].fields[1].optional)\\n\\n operation = api.resources[0].operations[0]\\n self.assertEqual(len(operation.errors), 2)\",\n \"def predict_api():\\n pass\",\n \"def test_get_api(self):\\n # Get metadata list\\n _logger.info('Get sequencerun API')\\n response = self.client.get('/sequencerun/')\\n self.assertEqual(response.status_code, 200, 'Ok status response is expected')\\n\\n _logger.info('Check if API return result')\\n result_response = response.data['results']\\n self.assertGreater(len(result_response), 0, 'A result is expected')\\n\\n _logger.info('Check if unique data has a single entry')\\n response = self.client.get('/sequencerun/?msg_attr_action=statuschanged')\\n results_response = response.data['results']\\n self.assertEqual(len(results_response), 1, 'Single result is expected for unique data')\\n\\n _logger.info('Check Invalid keyword')\\n response = self.client.get('/sequencerun/?foo=bar')\\n results_response = response.data['results']\\n self.assertEqual(len(results_response), 0, 'No results are expected for unrecognized query parameter')\",\n \"def api_html():\\n\\n return jsonify({'version': __version__})\",\n \"def hello(): #status: WIP\\r\\n pass\",\n \"def test_open_api(self):\\n response = self.client.get(self.initiatives_url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"async def api():\\n return {\\n \\\"links\\\": {\\n \\\"datasets\\\": \\\"/datasets\\\",\\n \\\"natural_earth_feature\\\": \\\"/natural_earth_feature\\\",\\n \\\"viewport\\\": \\\"/viewport\\\"\\n }\\n }\",\n \"def api():\\n api_routes = [\\n \\\"/api/v1.0/beer\\\",\\n \\\"/api/v1.0/breweries\\\",\\n ]\\n return render_template(\\\"api.html\\\", api_routes = api_routes)\",\n \"def hello():\\n\\n usage_msg = \\\"<br/>\\\\n\\\".join([\\\"Welcome to WildLife: The REST APIs for \\\"\\n \\\"ZooKeeper!<br/>\\\",\\n hello.__doc__.replace(\\\"\\\\n\\\", \\\"<br/>\\\\n\\\")])\\n\\n return make_response(usage_msg, 200)\",\n \"def test_get_info(self):\\n pass\",\n \"def test_1():\\n\\tassert api_call().status_code == 200\",\n \"def test_basic_api_with_mode(self):\\n # Create an API with get and put\\n self.create_and_verify_stack(\\\"single/basic_api_with_mode\\\")\\n\\n stack_output = self.get_stack_outputs()\\n api_endpoint = stack_output.get(\\\"ApiEndpoint\\\")\\n\\n self.verify_get_request_response(f\\\"{api_endpoint}/get\\\", 200)\\n\\n # Removes get from the API\\n self.update_and_verify_stack(file_path=\\\"single/basic_api_with_mode_update\\\")\\n\\n # API Gateway by default returns 403 if a path do not exist\\n self.verify_get_request_response.retry_with(\\n stop=stop_after_attempt(20),\\n wait=wait_exponential(multiplier=1, min=4, max=10) + wait_random(0, 1),\\n retry=retry_if_exception_type(StatusCodeError),\\n after=after_log(LOG, logging.WARNING),\\n reraise=True,\\n )(self, f\\\"{api_endpoint}/get\\\", 403)\\n\\n LOG.log(msg=f\\\"retry times {self.verify_get_request_response.retry.statistics}\\\", level=logging.WARNING)\",\n \"def test_random_anime_quotes_api(self):\\n response = api_client.get(\\\"/?query=anime\\\")\\n self.assertEqual(response.status_code, 200)\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def test_simple6(self):\\n api = self.load_api_description('simple6.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(len(api.resources), 2)\\n\\n resource = api.resources[1]\\n self.assertEqual(resource.name, 'AllOrders')\\n self.assertEqual(resource.path, '/')\\n self.assertEqual(len(resource.operations), 2)\\n\\n operation = resource.operations[0]\\n self.assertEqual(operation.method, 'GET')\\n self.assertIsNone(operation.input)\\n output = operation.output\\n self.assertEqual(output.status, 200)\\n self.assertEqual(output.type.type.get_reference_name(), 'list(Order)')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n operation = resource.operations[1]\\n self.assertEqual(operation.method, 'POST')\\n input = operation.input\\n self.assertEqual(len(input.type.type.fields), 2)\\n self.assertEqual(input.contentType[0], 'json')\\n output = operation.output\\n self.assertEqual(output.status, 201)\\n self.assertEqual(output.type.type.get_reference_name(), 'Order')\\n self.assertEqual(len(output.contentType), 1)\\n self.assertEqual(output.contentType[0], 'json')\\n\\n self.assertEqual(len(api.base), 1)\\n self.assertEqual(api.base[0], 'http://test.com/starbucks')\\n\\n self.assertEqual(len(api.data_types), 2)\\n self.assertEqual(len(api.data_types[0].fields), 5)\\n self.assertEqual(len(api.data_types[1].fields), 2)\\n nested = api.data_types[1]\\n field = nested.fields[1]\\n self.assertEqual(len(field.type.type.fields), 1)\\n\\n resource = api.resources[0]\\n self.assertEqual(len(resource.input_bindings), 1)\\n self.assertEqual(resource.input_bindings[0].id, 'orderIdBinding')\\n self.assertEqual(len(resource.operations), 2)\\n binding = resource.operations[0].input.params[0].binding\\n self.assertEqual(binding.mode, 'url')\\n self.assertEqual(binding.name, 'orderId')\\n self.assertEqual(binding.type.type.get_reference_name(), 'string')\\n self.assertEqual(resource.operations[1].input.params[0].binding, 'orderIdBinding')\",\n \"def test_hello(client):\\n\\n res = client.get('/api')\\n assert res.get_json().get('msg') != None\",\n \"def example_eleven():\\n # TODO\\n pass\",\n \"def Modifier_API(self):\\n\\t\\tpass\",\n \"def main(ctx, verbose):\\n return\",\n \"def index():\\n response = jsonify(\\n {'message':'Hello, RESTful API development!'}\\n )\\n \\n return response, 200\",\n \"def test_random_quotes_api(self):\\n response = api_client.get(\\\"/\\\")\\n self.assertEqual(response.status_code, 200)\",\n \"def program_api(port):\\n server = system.create_server(\\\"api\\\")\\n server.listen(port=port)\",\n \"def test_index(self):\\n resp = self.app.get('/')\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertIn('Inventory Demo REST API Service', resp.data)\\n # resp = self.app.get('/')\\n # self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n # data = json.loads(resp.data)\\n # self.assertEqual(data['name'], 'Inventory Demo REST API Service')\",\n \"def cci_api():\\n\\n\\n\\t\\t\\treturn render_template( 'api.html' )\",\n \"def main(path, verbose, test_mode, debug_mode):\\n if verbose:\\n logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\\n\\n fpl_api_url = os.getenv(\\\"FPL_API_URL\\\", \\\"https://fantasy.premierleague.com/api/\\\")\\n api_client = APIClientFactory.get_api(fpl_api_url)\\n if debug_mode:\\n if not path:\\n raise UndefinedAPIPathException(\\n \\\"Debug mode is for exploring Artifactory API endpoints.\\\"\\n )\\n print(api_client.get(path).text)\\n elif test_mode:\\n pass\\n else:\\n prometheus_exporter(api_client)\\n\\n return 0\",\n \"def test_ice_and_fire_external(self):\\n response = self.client.get('/api/external-books?name=A Game of Thrones', format='json')\\n self.assertEqual(200, response.data['status_code'])\",\n \"def verb_test():\\n # it also shows you how to access the method used and the decoded JSON data\\n return jsonify(method=request.method, data=request.get_json(), url=request.url)\",\n \"def verb_test():\\n # it also shows you how to access the method used and the decoded JSON data\\n return jsonify(method=request.method, data=request.get_json(), url=request.url)\",\n \"def index():\\n # Message to the user\\n message = {\\n 'api_version': 'v1.0',\\n 'status': '200',\\n 'message': 'Welcome to the Flask API'\\n }\\n # Making the message looks good\\n resp = jsonify(message)\\n\\n # Returning the object\\n return resp\",\n \"def main():\\n\\n return redirect('/apidocs')\",\n \"def test_api_response_data(self):\",\n \"def test_basic_api(self):\\n self.create_and_verify_stack(\\\"single/basic_api\\\")\\n\\n first_dep_ids = self.get_stack_deployment_ids()\\n self.assertEqual(len(first_dep_ids), 1)\\n\\n self.set_template_resource_property(\\\"MyApi\\\", \\\"DefinitionUri\\\", self.get_s3_uri(\\\"swagger2.json\\\"))\\n self.update_stack()\\n\\n second_dep_ids = self.get_stack_deployment_ids()\\n self.assertEqual(len(second_dep_ids), 1)\\n\\n self.assertEqual(len(set(first_dep_ids).intersection(second_dep_ids)), 0)\",\n \"def test_help_route():\\n response = client.get(\\\"/\\\")\\n assert response.status_code == 200\\n assert response.json() == {\\n \\\"repositories\\\": f\\\"{DOMAIN_NAME}/repositories\\\",\\n \\\"developers\\\": f\\\"{DOMAIN_NAME}/developers\\\",\\n \\\"docs\\\": f\\\"{DOMAIN_NAME}/docs\\\",\\n \\\"redoc\\\": f\\\"{DOMAIN_NAME}/redoc\\\",\\n }\",\n \"def apiDocs():\\n\\treturn render_template('apiDocs.html')\",\n \"def cli(ctx):\",\n \"def cli(ctx):\",\n \"def setUp(self):\\n self.api = \\\"http://localhost:4031/\\\"\\n self.version = \\\"0.2\\\"\\n self.app = init_api()\",\n \"def test_service_api_get(service_app):\\n response = service_app.get('/')\\n assert response.headers['Content-Type'] == 'application/json'\\n assert response.status_code == 200\\n assert json.loads(response.data) == {'description': 'service is up', 'status': 200}\",\n \"def test_get1(self):\\n pass\",\n \"def test_api_versioning(self):\\n response = self.request_knox(\\n self.url,\\n media_type=views_api.CORE_API_MEDIA_TYPE,\\n version=views_api.CORE_API_DEFAULT_VERSION,\\n )\\n self.assertEqual(response.status_code, 200)\",\n \"def test_api_river(self):\\n # load api base\\n r = requests.get('{server}/api/0.1/'.format(\\n server=self.get_server_url())).json()\\n # load rivers from url specified in api base\\n r = requests.get(r['rivers']).json()\\n r = requests.get(r['rivers'][0]['url']).json()\\n self.assertIn('html', r)\\n self.assertIn('url', r)\\n self.assertIn('sections', r)\\n self.assertIn('gages', r)\\n self.assertIn('regions', r)\\n self.assertIn('tributaries', r)\",\n \"def test_api_urls():\\n # Test the status message - 404 not good , 200 good\\n assert API_RH.create_req().status_code == 200, \\\"The tests for URLs were successful\\\"\",\n \"def test_api(self):\\n new_route = self.route.api(\\\"new\\\")\\n assert new_route != self.route\\n assert new_route.route[\\\"api\\\"] == \\\"new\\\"\",\n \"def test_api_user_get(self):\\n pass\",\n \"def test_index(self):\\n resp = self.app.get(\\\"/\\\")\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertIn(b\\\"Product Demo REST API Service\\\", resp.data)\",\n \"def test():\\n pass\",\n \"def test_doc():\\n pass\",\n \"def Help():\\n names=api_method_dict.keys()\\n names.sort()\\n return ''.join(['**** ' + api_method_dict[name].__name__ + '\\\\n' + api_method_dict[name].__doc__ + '\\\\n'\\n for name in names])\",\n \"def describe():\",\n \"def apiExample():\\n Client_ID = '5b42d8679569faa6e359a3f562e8cd76'\\n api_endpoint = \\\"http://api.soundcloud.com/tracks/13158665?client_id=%s\\\" \\\\\\n % Client_ID\\n request = urllib2.urlopen(api_endpoint)\\n data = json.load(request)\\n return data\",\n \"def main():\\n # data = {'user': 'ALFlanagan'}\\n # login = HTTPBasicAuth('16ED26C62A6711E591E4B3E58BAF2E49', '55A57143E3404')\\n # resp = requests.post('https://richmond-dot-com.bloxcms-ny1.com/tncms/webservice/v1/user/get/',\\n # data=data, auth=login)\\n # print(resp.text)\\n services = ['business', 'job', 'user', 'editorial', 'eedition', 'classifieds']\\n data = {\\\"user\\\": \\\"Flanagan\\\"}\\n login = HTTPBasicAuth('089F86E02A6211E5A24353BD6EAA3E6B', '55A568C86DF17')\\n for service in services:\\n resp = requests.post(\\\"{}{}/\\\".format(URL_ROOT, service), data=data, auth=login)\\n resp_dict = json.loads(resp.text)\\n api_list = resp_dict['apis']\\n for api_desc in api_list:\\n print(api_desc['path'])\\n for oper in api_desc['operations']:\\n print(\\\" {}: {}\\\".format(oper['httpMethod'], oper['summary']))\\n\\n # http://www.richmond.com/tncms/webservice/v1/editorial/get/?\\n data = {'id': '72c2ab48-2976-11e5-a21b-a7f967e75fca'}\\n login = HTTPBasicAuth(\\\"16ED26C62A6711E591E4B3E58BAF2E49\\\", \\\"55A57143E3404\\\")\\n resp = requests.post(\\\"{}editorial/get/\\\".format(RICHMOND_URL), data=data, auth=login)\\n asset = json.loads(resp.text)\\n pprint(asset)\",\n \"def example_function():\",\n \"def get(self,request,format = None):\\n an_apiview = [\\n 'Uses HTTP methods as function (get,post,patch,put,delete)',\\n 'Is similar to a traditional Django View',\\n 'Gives you the most control over your appliction logic',\\n 'Is mapped manually to URLs'\\n ]\\n return Response({'message':'Hello!','an_apiview': an_apiview})\",\n \"def get(self, request, format=None):\\n an_apiview = [\\n 'Uses HTTP methods as functions(get, post, patch, put, delete)',\\n 'Is similar to traditional Django view',\\n 'Give you the most control over your app logic',\\n 'Is mapped manually to URLs'\\n ]\\n return Response({'message':'Hello!', 'an_apiview':an_apiview})\",\n \"def test_00_api_get(self):\\r\\n # GET as Anonymous\\r\\n url = '/api/'\\r\\n action = 'get'\\r\\n self.check_limit(url, action, 'app')\",\n \"def test_root_public_api(self):\\n\\n # GIVEN public API\\n # WHEN fetching available applications and models\\n response = self.api.root_api(public=True)\\n\\n # THEN it should succeed\\n self.assertTrue(response.success)\\n\\n # AND it should have valid data\\n self.assertEqual(len(response.data), 2)\\n self.assertEqual(response.data[0]['app_label'], 'auth')\\n self.assertEqual(response.data[1]['app_label'], 'admin')\",\n \"def get(self, request, format=None):\\n an_apiview = [\\n \\\"User HTTp methods get, put, post, delete method\\\",\\n \\\"very similar to previous Django view\\\",\\n \\\"gives you more control on api logic\\\",\\n 'Is mapped to manually to urls'\\n ]\\n\\n return Response({'message':\\\"hello\\\", \\\"an_apiview\\\": an_apiview} )\",\n \"def cli(ctx):\\n #TODO\",\n \"def test_simple5(self):\\n api = self.load_api_description('simple5.json')\\n self.assertEqual(api.name, 'Starbucks')\\n self.assertEqual(api.license, 'apache2')\\n self.assertEqual(api.community, 'http://community.test.com')\\n\\n self.assertEqual(len(api.ownership), 2)\\n self.assertEqual(api.ownership[0].name, 'Peter Parker')\\n self.assertEqual(api.ownership[1].name, 'Bruce Wayne')\\n self.assertEqual(api.ownership[1].email, 'bat@jleague.com')\\n self.assertEqual(api.ownership[1].ownerType, 'tech')\\n self.assertEqual(len(api.sla), 3)\\n\\n sla = api.sla[2]\\n self.assertEqual(sla.name, 'GOLD')\\n self.assertEqual(sla.availability, 99.9)\\n self.assertEqual(sla.rateLimit, 1000)\\n self.assertEqual(sla.timeUnit, 'second')\\n cost = sla.costModel\\n self.assertEqual(cost.currency, 'USD')\\n self.assertEqual(cost.unitPrice, 0.1)\\n self.assertEqual(cost.requestsPerUnit, 1000)\\n\\n sla = api.sla[0]\\n self.assertEqual(sla.name, 'FREE')\\n self.assertIsNone(sla.costModel)\",\n \"def basic(self):\\n pass\",\n \"def test_api_section(self):\\n # load api base\\n r = requests.get('{server}/api/0.1/'.format(\\n server=self.get_server_url())).json()\\n # load sections from url specified in api base\\n r = requests.get(r['sections']).json()\\n r = requests.get(r['sections'][0]['url']).json()\\n self.assertIn('id', r)\\n self.assertIn('name', r)\\n self.assertIn('html', r)\\n self.assertIn('url', r)\\n self.assertIn('regions', r)\\n self.assertIn('sensors', r)\\n self.assertIn('gages', r)\\n self.assertIn('description', r)\\n self.assertIn('access', r)\\n self.assertIn('location', r)\\n self.assertIn('in_latitude', r)\\n self.assertIn('in_longitude', r)\\n self.assertIn('out_latitude', r)\\n self.assertIn('out_longitude', r)\",\n \"def simple():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7289153","0.7226013","0.69252145","0.66564894","0.662737","0.66099566","0.6588083","0.65751857","0.655524","0.65286803","0.64784944","0.64357257","0.6433924","0.64230484","0.6387545","0.6387545","0.6387545","0.6387545","0.6387545","0.6349674","0.62771046","0.62771046","0.62745976","0.62291455","0.6212866","0.6211684","0.62029266","0.61825293","0.61791676","0.61774147","0.61626625","0.614542","0.6132643","0.6112178","0.60972255","0.60923517","0.607724","0.60364383","0.60345435","0.6031514","0.60310423","0.6008937","0.60045743","0.5994767","0.59814113","0.5975607","0.5969047","0.59648895","0.5960424","0.5960424","0.5960424","0.5960424","0.5959756","0.594179","0.5935402","0.59263366","0.59204614","0.59134233","0.5910603","0.5907381","0.5897556","0.5896721","0.586965","0.5862471","0.5859644","0.5859644","0.58578724","0.58494073","0.58373994","0.583364","0.58306754","0.582724","0.5821597","0.5821597","0.5810889","0.58032864","0.58030385","0.57940465","0.5792927","0.57756287","0.57714194","0.5767971","0.57649606","0.5764718","0.5761186","0.5760258","0.5757268","0.57525754","0.5744793","0.5742394","0.57357424","0.57319057","0.57317203","0.5720184","0.57198846","0.57181835","0.5717099","0.5709967","0.5704562","0.5701896"],"string":"[\n \"0.7289153\",\n \"0.7226013\",\n \"0.69252145\",\n \"0.66564894\",\n \"0.662737\",\n \"0.66099566\",\n \"0.6588083\",\n \"0.65751857\",\n \"0.655524\",\n \"0.65286803\",\n \"0.64784944\",\n \"0.64357257\",\n \"0.6433924\",\n \"0.64230484\",\n \"0.6387545\",\n \"0.6387545\",\n \"0.6387545\",\n \"0.6387545\",\n \"0.6387545\",\n \"0.6349674\",\n \"0.62771046\",\n \"0.62771046\",\n \"0.62745976\",\n \"0.62291455\",\n \"0.6212866\",\n \"0.6211684\",\n \"0.62029266\",\n \"0.61825293\",\n \"0.61791676\",\n \"0.61774147\",\n \"0.61626625\",\n \"0.614542\",\n \"0.6132643\",\n \"0.6112178\",\n \"0.60972255\",\n \"0.60923517\",\n \"0.607724\",\n \"0.60364383\",\n \"0.60345435\",\n \"0.6031514\",\n \"0.60310423\",\n \"0.6008937\",\n \"0.60045743\",\n \"0.5994767\",\n \"0.59814113\",\n \"0.5975607\",\n \"0.5969047\",\n \"0.59648895\",\n \"0.5960424\",\n \"0.5960424\",\n \"0.5960424\",\n \"0.5960424\",\n \"0.5959756\",\n \"0.594179\",\n \"0.5935402\",\n \"0.59263366\",\n \"0.59204614\",\n \"0.59134233\",\n \"0.5910603\",\n \"0.5907381\",\n \"0.5897556\",\n \"0.5896721\",\n \"0.586965\",\n \"0.5862471\",\n \"0.5859644\",\n \"0.5859644\",\n \"0.58578724\",\n \"0.58494073\",\n \"0.58373994\",\n \"0.583364\",\n \"0.58306754\",\n \"0.582724\",\n \"0.5821597\",\n \"0.5821597\",\n \"0.5810889\",\n \"0.58032864\",\n \"0.58030385\",\n \"0.57940465\",\n \"0.5792927\",\n \"0.57756287\",\n \"0.57714194\",\n \"0.5767971\",\n \"0.57649606\",\n \"0.5764718\",\n \"0.5761186\",\n \"0.5760258\",\n \"0.5757268\",\n \"0.57525754\",\n \"0.5744793\",\n \"0.5742394\",\n \"0.57357424\",\n \"0.57319057\",\n \"0.57317203\",\n \"0.5720184\",\n \"0.57198846\",\n \"0.57181835\",\n \"0.5717099\",\n \"0.5709967\",\n \"0.5704562\",\n \"0.5701896\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57035404"},"document_rank":{"kind":"string","value":"99"}}},{"rowIdx":18490,"cells":{"query":{"kind":"string","value":"Ensures that a websafe key is valid and of the desired kind."},"document":{"kind":"string","value":"def _raiseIfWebsafeKeyNotValid(websafeKey, kind):\n # Check that websafeKey is not None\n if not websafeKey:\n raise endpoints.BadRequestException(\n \"Websafe key not provided for '%s'\" % kind)\n # Try to decode the websafe key into a real key\n try:\n key = ndb.Key(urlsafe=websafeKey)\n except:\n raise endpoints.BadRequestException(\n \"Websafe key provided for '%s' could not be decoded: %s\" %\n (kind, websafeKey))\n # Ensure that the key is of the desired kind\n if key.kind() != kind:\n raise endpoints.BadRequestException(\n \"Websafe key is not of the '%s' kind: %s\" % (kind, websafeKey))\n # If all is well, return the key\n return 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 isValidKey(key):\n return True","def validate_key(key):\r\n try:\r\n secret.Secret(key)\r\n except secret.Secret.InvalidSecret as e:\r\n raise KeyIsInvalid(e.message)","def _check_key(self, key):\n raise NotImplementedError","def test_valid_key(self):\n f = lws.valid_data_key\n assert f('string', int, r'string') is False\n assert f('string', str, r'test') is False\n assert f(123, int, '123') is False\n assert f(123.00, float, '123') is False\n assert f('123', str, r'[0-9]*') is True","def validate_handshake_public_key(cls, public_key: bytes) -> None:\n ...","def check_type( string_key ) : \r\n\r\n if type( string_key ) != type( '' ) : \r\n\r\n # raise self.__class__( \"'%s': EGI wants the key to be four _characters_ (not %s) !\" % (type(string_key), ) ) \r\n raise Eggog( \"'%s': EGI wants the key to be four _characters_ (not %s) !\" % (type(string_key), ) ) \r\n \r\n else :\r\n \r\n return True","def check_key(self):\n\n if self.type == \"RSA\" and self.size < 1024:\n raise HostkeyError(\"RSA keys must at least be 1024 bits.\")\n elif self.type == \"DSA\" and self.size != 1024:\n raise HostkeyError(\"DSA keys can only be 1024 bits.\")\n elif self.type == \"ECDSA\" and self.size not in [256, 384, 521]: # yes, that is *really* 521 bits, not a typo!\n raise HostkeyError(\"ECDSA key must be either 256, 384 or 521 bits (yes, 521 not 512!)\")\n elif self.type ==\"ED25519\" and self.size != 128:\n raise HostkeyError(\"ED25519 keys have a fixed size, which cannot be altered.\") # can't really happen, size is ignored for ED25519\n\n # if privkey is already there check size\n self.key_exists = False\n self.key_current_size = 0\n if os.path.exists(self.fullpath):\n self.key_exists = True\n if self.type == \"ED25519\":\n self.curve = \"EC25519\"\n self.key_current_size = 128 # somewhat erbitrary, attack complexity on ED25519 is larger that brute forcing a 128bit key\n self.key_exists = True\n elif self.type == \"RSA1\":\n self.key_exists = True\n self.key_current_size = 1024\n else:\n try:\n with open(self.fullpath, \"rb\") as key_file:\n self.privkey = crypto_serialization.load_pem_private_key(key_file.read(), password=None, backend=crypto_default_backend())\n except IOError:\n raise HostkeyError(get_exception())\n\n if self.type == \"DSA\" or self.type == \"RSA\":\n self.key_current_size = self.privkey.key_size\n elif self.type == \"ED25519\":\n self.key_current_size = 128\n elif self.type == \"ECDSA\":\n self.pubkey = self.privkey.public_key()\n if self.pubkey.curve.name == \"secp256r1\":\n self.key_current_size = 256\n elif self.pubkey.curve.name == \"secp384r1\":\n self.key_current_size = 384\n elif self.pubkey.curve.name == \"secp521r1\":\n self.key_current_size = 521\n else:\n self.curve = self.pubkey.curve.name","def self_check() -> None:\n assert len(ZBASE32_ALPHABET) == 32\n\n # Test vector from https://github.com/matusf/z-base-32/blob/0.1.2/src/lib.rs\n assert zbase32_encode(b\"asdasd\") == \"cf3seamuco\"\n assert zbase32_decode(\"cf3seamuco\") == b\"asdasd\"\n\n # Test vector from https://www.uriports.com/blog/setting-up-openpgp-web-key-directory/\n # assert zbase32_encode(hashlib.sha1(b\"yourmail\").digest()) == \"hacabazoakmnagxwmkjerb9yehuwehbm\"\n # -> this hash is wrong, and I don't know what username gives the SHA1\n # e61980e2f0c2962c19f45a928207e0472744702b\n\n # Test vector from https://metacode.biz/openpgp/web-key-directory\n assert zbase32_encode(hashlib.sha1(b\"test-wkd\").digest()) == \"4hg7tescnttreaouu4z1izeuuyibwww1\"\n\n # Test vector from https://datatracker.ietf.org/doc/draft-koch-openpgp-webkey-service/\n assert (\n get_wkd_advanced_url(\"Joe.Doe@Example.ORG\")\n == \"https://openpgpkey.example.org/.well-known/openpgpkey/example.org/hu/iy9q119eutrkn8s1mk4r39qejnbu3n5q?l=Joe.Doe\" # noqa\n )\n assert (\n get_wkd_direct_url(\"Joe.Doe@Example.ORG\")\n == \"https://example.org/.well-known/openpgpkey/hu/iy9q119eutrkn8s1mk4r39qejnbu3n5q?l=Joe.Doe\"\n )\n\n # Test vector from https://wiki.gnupg.org/WKD\n assert (\n get_wkd_direct_url(\"bernhard.reiter@intevation.de\")\n == \"https://intevation.de/.well-known/openpgpkey/hu/it5sewh54rxz33fwmr8u6dy4bbz8itz4?l=bernhard.reiter\"\n )","def verify_hack_key(self):\r\n\t\tself.percent_english = Dict_Control(self.my_code).check_key()\r\n\t\t#If more than half the words are english, the key will pass. \r\n\t\tif self.percent_english > 50:\r\n\t\t\tself.hack_plausible = True","def check_auth_publickey(self, username, key):\n return AUTH_FAILED","def _check_key(key): # type: (str) -> None\n if not key:\n raise ValueError('Key must not be empty.')\n if '.' in key:\n raise ValueError('Key must not contain dots.')","def validate_key(self, key: keyType) -> bool:\n if isinstance(key, (dict,bool)):\n raise Exception\n if key is None:\n raise Exception\n # Numerical key object has no len(),\n # so explicitly specify which types are not allowed to use empty value as keys\n if isinstance(key, (str, tuple, set, list)) and (len(key) == 0):\n raise Exception\n return True","def _validate_key(self, key):\n if isinstance(key, str):\n key = unicode(key, 'utf-8')\n elif not isinstance(key, unicode):\n raise TypeError(\n \"`key` must be `str` or `unicode`, not `{}`\".format(\n key.__class__.__name__)\n )\n return key","def test_invalid_chars_ssck(self):\r\n valid_base = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\r\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\r\n with self.assertRaises(InvalidKeyError):\r\n # this ends up calling the constructor where the legality check should occur\r\n valid_base.replace(**{key: u'funny thing'})","def _is_valid_key(self, key):\r\n\r\n # Check the length\r\n if len(key) > 250:\r\n return False\r\n\r\n # Check that there are no spaces or control characters\r\n for char in key:\r\n if ord(char) < 33 or ord(char) == 127:\r\n return False\r\n\r\n return True","def validate(self, key, val):\n return True","def validate(self, key, val):\n return True","def checkKey(self):\n # TO DO for checking API authentication\n if self.apikey is None:\n return False\n else:\n return True","def test_validate_with_invalid_key_format_type(self):\n key_format_type = \"invalid\"\n kwargs = {'key_format_type': key_format_type}\n\n self.assertRaisesRegex(\n TypeError, \"invalid key format type\", Digest, **kwargs)","def validate(self):\n if not self.key or not self.certificates:\n raise ValueError(\"Key or certificate missing in Keypair\")","def test_key_none(self):\n try:\n AlphaVantage()\n self.fail(msg='A None api key must raise an error')\n except ValueError:\n self.assertTrue(True)","def test_key_none(self):\n try:\n AlphaVantage()\n self.fail(msg='A None api key must raise an error')\n except ValueError:\n self.assertTrue(True)","def check_len( string_key ) : \r\n\r\n if len( string_key ) != 4 :\r\n\r\n raise Eggog( \"'%s': EGI wants the key to be exactly four characters!\" % (string_key, ) ) \r\n \r\n else :\r\n \r\n return True","def test_getKey_nokey(self):\n filename = os.path.join(os.getcwd(), 'sekrit')\n key = crypto.getKey(filename)\n self.failUnlessIsInstance(key, basestring,\n \"key isn't a string! type=%r\" % type(key))","def is_key(v) -> bool:\n try:\n _validate(v, prefixes=[b\"edsk\", b\"edpk\", b\"spsk\", b\"p2sk\", b\"sppk\", b\"p2pk\"])\n except (ValueError, TypeError):\n return False\n return True","def test_invalid_chars_location(self):\r\n course_key = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\r\n valid_base = course_key.make_usage_key('tomato-again%9', 'block-head:sub-4%9')\r\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\r\n with self.assertRaises(InvalidKeyError):\r\n # this ends up calling the constructor where the legality check should occur\r\n valid_base.replace(**{key: u'funny thing'})","def _validateKey(self, key, cls = None):\n\n key_class_types = [self._BaseKey__class, self._LocalKey__class,\n self._MsgKey__class, ErrorMsgManager]\n\n if cls:\n if inspect.isclass(cls) and cls in key_class_types:\n classes = [cls]\n else:\n return None\n else:\n classes = key_class_types\n return any([isinstance(key, cls) for cls in classes])","def test_key_type(self):\n self.failureResultOf(self.producer.send_messages(\"topic\", key=\"key\", msgs=[b\"msg\"]), TypeError)","def creation_validation(**_):\n\n for property_key in constants.KEYPAIR_REQUIRED_PROPERTIES:\n utils.validate_node_property(property_key, ctx.node.properties)\n\n key_file = _get_path_to_key_file()\n key_file_in_filesystem = _search_for_key_file(key_file)\n\n if ctx.node.properties['use_external_resource']:\n if not key_file_in_filesystem:\n raise NonRecoverableError(\n 'External resource, but the key file does not exist locally.')\n try:\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\n except NonRecoverableError as e:\n raise NonRecoverableError(\n 'External resource, '\n 'but the key pair does not exist in the account: '\n '{0}'.format(str(e)))\n else:\n if key_file_in_filesystem:\n raise NonRecoverableError(\n 'Not external resource, '\n 'but the key file exists locally.')\n try:\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\n except NonRecoverableError:\n pass\n else:\n raise NonRecoverableError(\n 'Not external resource, '\n 'but the key pair exists in the account.')","def valid_key(self): \n self.so.ValidKey.restype = c_bool\n result = self.so.ValidKey()\n return result","def test_validate_yubikey(self):\n from_key = self.yk_rnd.from_key(self.yk_public_id, self.yk_key)\n self.assertTrue(pyhsm.yubikey.validate_yubikey_with_aead( \\\n self.hsm, from_key, self.aead.data, self.kh_validate))","def validate(cls, tab_dict, raise_error=True):\r\n return key_checker(['type'])(tab_dict, raise_error)","def _check_transform_key(key: Hashable) -> None:\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\n # if not isinstance(key, Hashable):\n # raise TypeError((type(key), \"transformation lookup key is not hashable\"))","def test_generate_key(self): \n k = Key().generate()\n self.assertRegex(k, \"[a-zA-Z0-9+\\/]+={0,2}\")","def keyIsValid(key):\n\n isValid = 1\n \n try:\n temp = getParam(key)\n\n except ValueError:\n isValid = 0\n warning(\" WARNING: %s not set\" % (key))\n\n return isValid","def test_allowed_chars(self):\n hash_val = self.reverse_hash.get_hash('123')\n self.assertEqual(hash_val['error'], 'allowed chars {}'.format(self.reverse_hash.letters))","def test_create_key():\n\n assert symmetric.create_key() != \"\"","def validate_authkey(value):\n if not len(value) == 32:\n raise ValidationError(\n 'Value must be a string containing 32 alphanumeric characters')","def __check_key_validity(self, key):\n if not isinstance(key, tuple):\n raise TypeError(\"key must be a tuple\")\n if len(key) != 2:\n raise ValueError(\"key must be of length two\")\n if not (isinstance(key[0], int) and isinstance(key[1], int)):\n raise TypeError(\"elements of key must be integers\")\n if not ((0 <= key[0] < self.m) and (0 <= key[1] < self.n)):\n raise exc.OutOfBoundsError(\"key is out of bounds\")","def key_valid(key_data):\n\n gpg = gnupg.GPG(gnupghome=\"gnupg\")\n import_result = gpg.import_keys(key_data)\n\n if import_result.count == 1:\n gpg.delete_keys(import_result.fingerprints[0])\n return True\n else:\n return False","def validate_license(key: str) -> bool:\r\n return bool(\r\n re.match(r'^PB-[A-Z0-9]{8}(?:-[A-Z0-9]{8}){3}$', key)\r\n )","def test_kyc_put_legal(self):\n pass","def api_key_check():\n req_path = request.path\n method_type = request.method\n app.logger.info(\">>> path = {}, method = {}\".format(req_path, method_type))\n\n if not app_props.api_key_check:\n app.logger.debug('>>> api key check closed')\n return None\n\n if req_path in app_props.api_key_white_list:\n app.logger.info('>>> {} in white list, pass'.format(req_path))\n return None\n headers = request.headers\n api_key_from_req = headers.get('x-api-key')\n if not api_key_from_req:\n app.logger.debug('>>> enter api-key error')\n return resp_json(BaseResp.err('no x-api-key header'))\n\n key_obj = Key.query.filter_by(api_key=api_key_from_req).first()\n if key_obj:\n app.logger.debug('>>> consumer_id = {}, secret_key = {}'.format(key_obj.consumer_id, key_obj.secret_key))\n g.consumer_id = key_obj.consumer_id\n g.secret_key = key_obj.secret_key\n return None\n else:\n return resp_json(BaseResp.err('Err api key'))","def test_api_key (key):\n\tdb = getattr(g,'db', None)\n\n\tif isinstance(key, unicode):\n\t\tkey = key.encode('utf-8')\n\n\tqry = \"SELECT apikey FROM api_keys WHERE apikey=%s;\"\n\twith db as cur:\n\t\treturn 0 < cur.execute(qry, (key,))","def _check_validity(self):\n pass","def test_allow_extra_keys(self):\n from natcap.invest import validation\n\n args = {'a': 'a', 'b': 'b'}\n spec = {\n 'a': {\n 'type': 'freestyle_string',\n 'name': 'a',\n 'about': 'a freestyle string',\n 'required': True\n }\n }\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\n\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\n validation.validate(args, spec)\n self.assertTrue(message in cm.output)","def test_validate_api_key(app, seed_data, key, result):\n user_id, api_key = seed_data\n if key == 'use-valid-key':\n key = api_key\n with app.app_context():\n assert auth.validate_api_key(user_id, key) == result","def check_keys(self):","def rsa_is_valid_publickey(key):\r\n # must be a dict\r\n if type(key) is not dict:\r\n return False\r\n\r\n # missing the right keys\r\n if 'e' not in key or 'n' not in key:\r\n return False\r\n\r\n # has extra data in the key\r\n if len(key) != 2:\r\n return False\r\n\r\n for item in ['e', 'n']:\r\n # must have integer or long types for the key components...\r\n if type(key[item]) is not int and type(key[item]) is not long:\r\n return False\r\n\r\n if key['e'] < key['n']:\r\n # Seems valid...\r\n return True\r\n else:\r\n return False","def test_validate_aead_cmp(self):\n secret = pyhsm.aead_cmd.YHSM_YubiKeySecret(self.yk_key, self.yk_uid)\n cleartext = secret.pack()\n self.assertTrue(self.hsm.validate_aead(self.yk_public_id, self.kh_validate, self.aead, cleartext))\n wrong_cleartext = 'X' + cleartext[1:]\n self.assertFalse(self.hsm.validate_aead(self.yk_public_id, self.kh_validate, self.aead, wrong_cleartext))","def _verify_key_size(cls, nbits):\n # Check that the key size meets the minimum key size requirements\n if(nbits < params.MINIMUM_KEY_SIZE):\n \n # Throw an exception w/ an appropriate message if nbits is too small\n raise KeyLengthTooLowError(nbits, params.MINIMUM_KEY_SIZE, \n \"The given size in bits for the cryptosystem (%d bits) is too\" \\\n \" low. For security reasons, current minimum allowed key/\" \\\n \"cryptosystem bit size is %d bits. It is recommended that \" \\\n \" only keys of that length or higher are generated or used. \" \\\n \" If you must use smaller keys, you may configure \" \\\n \"PloneVoteCryptoLib's security parameters in params.py at \" \\\n \"your own risk.\" % (nbits, params.MINIMUM_KEY_SIZE))\n \n # Check that the key size is can be expressed as whole bytes (i.e. is\n # a multiple of 8)\n if(nbits % 8 != 0):\n \n raise KeyLengthNonBytableError(nbits,\n \"The given size in bits for the cryptosystem (%d bits) is \" \\\n \"not a multiple of eight. Currently, only key sizes that are \" \\\n \"multiples of eight, and thus expressible in whole bytes, \" \\\n \"are allowed by PloneVoteCryptoLib. Perhaps you could use %d \" \\\n \"bit keys?\" % (nbits, (nbits/8 + 1)*8) )\n \n return nbits","def check_api_key(x_api_key: str = Security(api_key_header_auth)):\n\n if x_api_key != API_KEY:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Invalid API Key\",\n )","def testKeyInfoTooShort(self):\n key = 5\n self.assertRaises(ValueError, dataToToken, key, data='x', keyInfo='xx')","def vscf_raw_private_key_is_valid(self, ctx):\n vscf_raw_private_key_is_valid = self._lib.vscf_raw_private_key_is_valid\n vscf_raw_private_key_is_valid.argtypes = [POINTER(vscf_raw_private_key_t)]\n vscf_raw_private_key_is_valid.restype = c_bool\n return vscf_raw_private_key_is_valid(ctx)","def _is_valid_key(self, key):\n\t\t\n\t\t# If the key is not a string\n\t\tif not isinstance(key, str):\n\t\t\treturn False\n\t\telse:\n\t\t\tkey = str.upper(key)\n\t\t\n\t\t# If the given key does not match the standard notation XY\n\t\tif len(key) != 2:\n\t\t\treturn False\n\t\t\n\t\t# If the key is out of the board\n\t\tif key[0] not in self.columns or key[1] not in self.rows:\n\t\t\treturn False\n\t\t\n\t\t# Otherwise the key is valid\n\t\treturn True","def _cross_check(self, pub_key):\n if self.curve_name != pub_key.curve.name:\n raise ValueError(\n \"The curve in private key {} and in algorithm {} don't \"\n \"match\".format(pub_key.curve.name, self.curve_name)\n )","def is_valid_key(key, crypt_method):\n logger.info(f\"key: {key}, crypt_method: {crypt_method}\")\n if crypt_method == 'C':\n while type(key) is not int or key not in range(0, 95):\n try:\n key = Check.is_integer(key)[1]\n if key not in range(0, 95):\n raise ValueError\n except (TypeError, ValueError):\n print(\"You must enter an integer between 1 and 95!\")\n key = input(\"Enter an encryption key\\n>> \")\n elif crypt_method in ('M', 'P'):\n pass\n else:\n return False\n return True, key","def test_valid_keys(client):\n response=client.post(\"/signin\",data=dict(username=TestSignin.email, password=TestSignin.password), content_type=\"multipart/form-data\")\n data=json.loads(response.data)\n assert response.status_code==400\n assert data[\"error\"] == \"Please provide email and password as keys\"","def test_create_api_key(self):\n pass","def test_is_valid_label_key_invalid_input():\n # test length violations\n assert not is_valid_label_key(key=None) # Too short\n assert not is_valid_label_key(key=\"\") # Too short\n assert not is_valid_label_key(key=f\"{'p' * 254}/n\") # prefix too long\n assert not is_valid_label_key(key=\"/n\") # prefix too short\n assert not is_valid_label_key(key=\"p/\") # name too short\n assert not is_valid_label_key(key=\"a\" * 254) # name too long\n assert not is_valid_label_key(key=f\"d/{'b'*64}\") # name too long\n # test first character violations (not alphanum)\n assert not is_valid_label_key(key=\"-a\")\n assert not is_valid_label_key(key=\".b\")\n assert not is_valid_label_key(key=\" c\")\n # test last character violations (not alphanum)\n assert not is_valid_label_key(key=\"a-\")\n assert not is_valid_label_key(key=\"b.\")\n assert not is_valid_label_key(key=\"c \")\n assert not is_valid_label_key(key=\"sw33T#\")\n # test middle characters violations\n assert not is_valid_label_key(key=\"a$$a\")\n assert not is_valid_label_key(key=\"b b\")","def spki_req_is_valid(spki_req):\n try:\n netscape_spki_from_b64(spki_req)\n return True\n except Exception:\n return False","def test_dweet_for_with_a_valid_key(self):\n dweet = dweepy.dweet_for(self.my_thing_id, test_data, key=test_key)\n check_valid_dweet_response(self, dweet)\n self.assertEqual(dweet['thing'], self.my_thing_id)","def key_type(self):\n raise exceptions.NotImplementedError()","def validate_key_throw(*args):\n validation_result = validate_key(*args)\n if not validation_result:\n raise ValueError(str(validation_result))\n return validation_result","def remote_verifyKey(self, key, protocol):\r\n if self._authenticated.called:\r\n return Failure(InvalidKey('Only one guess is possible.'))\r\n\r\n if isinstance(protocol, Failure):\r\n self._authenticated.errback(protocol)\r\n else:\r\n if self._key != key:\r\n e = Failure(InvalidKey('Wrong key supplied.'))\r\n self._authenticated.errback(e)\r\n return e\r\n\r\n self._authenticated.callback(protocol)","def test_getKey_keyexists(self):\n filename = self.mktemp()\n with open(filename, 'wb') as fh:\n fh.write(SEKRIT_KEY)\n fh.flush()\n\n key = crypto.getKey(filename)\n self.failUnlessIsInstance(key, basestring,\n \"key isn't a string! type=%r\" % type(key))\n self.assertEqual(SEKRIT_KEY, key,\n \"\"\"The example key and the one read from file differ!\n key (in hex): %s\n SEKRIT_KEY (in hex): %s\"\"\"\n % (key.encode('hex'), SEKRIT_KEY.encode('hex')))","def is_key_valid(self,key):\n if not key or any(map(lambda s: s in key,space_chars))\\\n or any(map(lambda s: s in key,bad_chars)):\n return False \n return True","def test_invalid_keys(self):\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"this has spaces\", 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"key with spaces did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"\\x10control\\x02characters\\x11\", 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"key with control characters did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"a\" * (SERVER_MAX_KEY_LENGTH + 1), 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"long key did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(u\"unicode\\u4f1a\", 1)\n\t\texcept TypeError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"unicode key did not raise ValueError\")","def test_kyc_put_legal_share_holder(self):\n pass","def test_is_valid_annotation_key_invalid_input():\n # test length violations\n assert not is_valid_annotation_key(key=None) # Too short\n assert not is_valid_annotation_key(key=\"\") # Too short\n assert not is_valid_annotation_key(key=f\"{'p' * 254}/n\") # prefix too long\n assert not is_valid_annotation_key(key=\"/n\") # prefix too short\n assert not is_valid_annotation_key(key=\"p/\") # name too short\n assert not is_valid_annotation_key(key=\"a\" * 254) # name too long\n assert not is_valid_annotation_key(key=f\"d/{'b'*64}\") # name too long\n # test first character violations (not alphanum)\n assert not is_valid_annotation_key(key=\"-a\")\n assert not is_valid_annotation_key(key=\".b\")\n assert not is_valid_annotation_key(key=\" c\")\n # test last character violations (not alphanum)\n assert not is_valid_annotation_key(key=\"a-\")\n assert not is_valid_annotation_key(key=\"b.\")\n assert not is_valid_annotation_key(key=\"c \")\n assert not is_valid_annotation_key(key=\"sw33T#\")\n # test middle characters violations\n assert not is_valid_annotation_key(key=\"a$$a\")\n assert not is_valid_annotation_key(key=\"b b\")","def _sanityCheckKeySizes(other):\n if other.minKeySize < 512:\n raise ValueError(\"minKeySize too small\")\n if other.minKeySize > 16384:\n raise ValueError(\"minKeySize too large\")\n if other.maxKeySize < 512:\n raise ValueError(\"maxKeySize too small\")\n if other.maxKeySize > 16384:\n raise ValueError(\"maxKeySize too large\")\n if other.maxKeySize < other.minKeySize:\n raise ValueError(\"maxKeySize smaller than minKeySize\")\n # check also keys of virtual hosts\n for i in other.virtual_hosts:\n i.validate()","def validate_usage(validation_context, cert, key_usage, extended_key_usage, extended_optional):\n\n if not isinstance(validation_context, ValidationContext):\n raise TypeError(pretty_message(\n '''\n validation_context must be an instance of\n certvalidator.context.ValidationContext, not %s\n ''',\n type_name(validation_context)\n ))\n\n if validation_context.is_whitelisted(cert):\n return\n\n if key_usage is None:\n key_usage = set()\n\n if extended_key_usage is None:\n extended_key_usage = set()\n\n if not isinstance(key_usage, set):\n raise TypeError(pretty_message(\n '''\n key_usage must be a set of unicode strings, not %s\n ''',\n type_name(key_usage)\n ))\n\n if not isinstance(extended_key_usage, set):\n raise TypeError(pretty_message(\n '''\n extended_key_usage must be a set of unicode strings, not %s\n ''',\n type_name(extended_key_usage)\n ))\n\n if not isinstance(extended_optional, bool):\n raise TypeError(pretty_message(\n '''\n extended_optional must be a boolean, not %s\n ''',\n type_name(extended_optional)\n ))\n\n missing_key_usage = key_usage\n if cert.key_usage_value:\n missing_key_usage = key_usage - cert.key_usage_value.native\n\n missing_extended_key_usage = set()\n if extended_optional is False and not cert.extended_key_usage_value:\n missing_extended_key_usage = extended_key_usage\n elif cert.extended_key_usage_value is not None:\n missing_extended_key_usage = extended_key_usage - set(cert.extended_key_usage_value.native)\n\n if missing_key_usage or missing_extended_key_usage:\n plural = 's' if len(missing_key_usage | missing_extended_key_usage) > 1 else ''\n friendly_purposes = []\n for purpose in sorted(missing_key_usage | missing_extended_key_usage):\n friendly_purposes.append(purpose.replace('_', ' '))\n raise InvalidCertificateError(pretty_message(\n '''\n The X.509 certificate provided is not valid for the purpose%s of %s\n ''',\n plural,\n ', '.join(friendly_purposes)\n ))","def test_kyc_get_validation_legal(self):\n pass","def is_valid_openssh_pubkey(pubkey):\n try:\n key_type, data64 = map(_encode, pubkey.split()[0:2])\n except (ValueError, AttributeError):\n return False\n try:\n data = base64.decodebytes(data64)\n except base64.binascii.Error:\n return False\n\n int_len = 4\n str_len = struct.unpack('>I', data[:int_len])[0]\n\n if data[int_len:(int_len + str_len)] != key_type:\n return False\n\n return True","def _validity_check(settings, valid):\n\n Settings._dict_validity_check(settings, valid)","def test_youtube_keyerror(self):\n backend = self.instance('http://youtube.com/watch?id=5')\n self.assertRaises(UnknownIdException, backend.get_code)","def validate(self, key: keyType, value: valueType) -> bool:\n return self.validate_key(key) and self.validate_value(value)","def _verify_key_types(self, name, keys):\r\n if isinstance(keys, (list, tuple)):\r\n for key in keys:\r\n self._verify_key_types(name, key)\r\n elif isinstance(keys, (str, unicode)):\r\n pass\r\n else:\r\n raise TypeError(\r\n \"All %s keys must be one of the following types: \"\r\n \"<str> or <unicode>, \"\r\n \"<list> of <str> or <unicode>, \"\r\n \"<tuple> of <str> or <unicode>. \"\r\n \"Given: %s\" % (name, keys)\r\n )","def test_secret():\r\n try:\r\n straxen.get_secret('somethingnonexistent')\r\n except ValueError:\r\n # Good we got some message we cannot load something that does\r\n # not exist,\r\n pass","def patched_paramiko_transport_verify_key(self, host_key, sig): # pylint: disable=W0613\n key = self._key_info[self.host_key_type](Message(host_key)) # pylint: disable=W0212\n if key is None:\n raise SSHException(\"Unknown host key type\")\n # Patched: no more checks are done here\n self.host_key = key","def check_secret_key(cls):\n logger.info(\"attempting to check the secret key...\")\n try:\n s = cls.objects.filter(is_active=True).first()\n if s and s.secret_key == \"not-a-very-good-secret\":\n chars = \"abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)\"\n s.secret_key = get_random_string(50, chars)\n s.save()\n except DBError:\n logger.warning(\"db not ready (error on {} model)\".format(cls.__name__))\n return","def _validKey(entry):\n # be forward compatible to zope3 contained objects\n raw_id = getattr(entry, '__name__', '')\n if not raw_id:\n raw_id = entry.getId()\n\n # This substitution is based on the description of cite key restrictions at\n # http://bibdesk.sourceforge.net/manual/BibDesk%20Help_2.html\n return VALIDIDPAT.sub('', raw_id)","def test_rekey_key_format(self):\n old_key = b'0' * 32\n encrypted = encrypt('message', key=old_key)\n\n with pytest.raises(EncryptionError):\n rekey(encrypted, old_key=old_key, new_key=b'1' * 31)","def test_deploy_policy_fail_key(self):\n\n self._check_deploy_failure(\n self._create_test_app(key='',\n flavor='m1.small'),\n 'missing key')","def verify_key(self, providerkey = None):\n h = Https(API_DOMAIN)\n\n data = {'apikey' : self.apikey}\n\n if providerkey is not None:\n data['providerkey'] = providerkey\n\n h.request( \"GET\",\n \"/publicapi/verify\"+ urlencode(data),\n headers=self.headers)\n\n request_status = h.getresponse().status\n\n if request_status != 200:\n raise Exception(\"Invalid API Key %s\" % self.apikey)","def test_encrypt_key_invalid(self):\n with pytest.raises(EncryptionError):\n encrypt('message', key=b'0' * 31)","def _safe_key(self, key):\n if isinstance(key, str):\n key = key.encode('UTF-8')\n return key","def _check_key_type(cls, key: Any) -> K:\n if not isinstance(key, cls.keytype):\n raise KeyError(\n f\"{cls!r} accepts only keys of type {cls.keytype!r}, \"\n f\"got {type(key)!r}\"\n )\n return cast(K, key)","def check_valid_fernet(value):\n try:\n decoded = base64.urlsafe_b64decode(value)\n if len(decoded) != 32: return False\n return True\n except binascii.Error:\n return False","def check_api(submitted_key, users_key):\r\n if users_key != submitted_key:\r\n return False\r\n else:\r\n return True","def test_bad_request_no_key_specified(self, client):\n response = client.post(self.urls['create'], secure=True)\n assert response.status_code == status.HTTP_400_BAD_REQUEST\n request = response.wsgi_request\n assert request.idempotency_key_exempt is False\n assert request.idempotency_key_manual is False","def check_empty_key(self, key):\n if key is None or key == \"\" or key == self.empty_api_key:\n print(\"ERROR, A KEY IS EMPTY - CHECK YOUR FILE\")\n return False\n return True","def test_is_valid_label_key_valid_input():\n # test valid label keys\n assert is_valid_label_key(key=\"l0l\")\n assert is_valid_label_key(key=\"l0L\")\n assert is_valid_label_key(key=\"L-l\")\n assert is_valid_label_key(key=\"L.L\")\n assert is_valid_label_key(key=\"4-you\")\n assert is_valid_label_key(key=\"you.2\")\n assert is_valid_label_key(key=\"p/n\")\n assert is_valid_label_key(key=\"prefix/you.2\")\n assert is_valid_label_key(key=\"how.sad/to-see\")\n assert is_valid_label_key(key=f\"{'d'*253}/{'n'*63}\")","def _is_valid(self):\n # TODO: Query Google to validate credentials\n return True","def validate_snx_api_key():\n api_data = {} # type: Dict[str, str]\n response = http_request(endpoint=API_QUOTA, data=api_data)\n\n if response.get('errorNo') != 0:\n return_error('API Returned, {}:{}'.format(response.get('errorNo'), response.get('errorMsg')))\n\n return 'ok'","def _check_api_key(self):\n try:\n self.maps.places_nearby(\n location=(53.909804, 27.580184),\n radius=650,\n open_now=False,\n language=config.LANGUAGE,\n type='cafe',\n # rank_by='distance', # IMPORTANT: cannot use rank_by and radius options together\n page_token=None,\n )\n except Exception as e:\n\n with self.__writelock:\n self.print(f'ERROR: bad API key \"{self.maps.key}\" (tracker={self.stats.previous_requests})\\n')\n raise e","def _verify(pubkey: SupportedKeyTypes, sig: bytes, filehash: bytes, hashfunc: hashes.HashAlgorithm) -> None:\n if isinstance(pubkey, RSAPublicKey):\n pubkey.verify(sig, filehash, padding.PKCS1v15(), Prehashed(hashfunc))\n elif isinstance(pubkey, EllipticCurvePublicKey):\n pubkey.verify(sig, filehash, ec.ECDSA(Prehashed(hashfunc)))","def _secret_not_in_order():\n pecan.abort(400, u._(\"Secret metadata expected but not received.\"))","def test_ec(self):\n key = c.KEY_EC\n usage = [\n c.KU_DIGITALSIGNATURE,\n c.KU_NONREPUDIATION,\n c.KU_KEYAGREEMENT,\n c.KU_ENCIPHERONLY,\n c.KU_DECIPHERONLY,\n ]\n self.assertTrue(utils.check_key_usage(key, usage))","def validate_pkginfo_key_types(pkginfo, filename):\n\n # Remap string type to support unicode in both Python 2 and 3\n string = basestring if sys.version_info.major == 2 else str\n\n # Pkginfo keys and their known types. Omitted keys are left unvalidated.\n # Source: https://github.com/munki/munki/wiki/Supported-Pkginfo-Keys\n # Last updated 2019-03-13.\n pkginfo_types = {\n \"additional_startosinstall_options\": list,\n \"apple_item\": bool,\n \"autoremove\": bool,\n \"blocking_applications\": list,\n \"catalogs\": list,\n \"category\": string,\n \"copy_local\": bool,\n \"description\": string,\n \"developer\": string,\n \"display_name\": string,\n \"force_install_after_date\": datetime,\n \"forced_install\": bool,\n \"forced_uninstall\": bool,\n \"icon_name\": string,\n \"installable_condition\": string,\n \"installed_size\": int,\n \"installer_item_hash\": string,\n \"installer_item_location\": string,\n \"installer_item_size\": int,\n \"installer_type\": string,\n \"installs\": list,\n \"items_to_copy\": list,\n \"installer_choices_xml\": list,\n \"installer_environment\": dict,\n \"localized_strings\": dict,\n \"minimum_munki_version\": string,\n \"minimum_os_version\": string,\n \"maximum_os_version\": string,\n \"name\": string,\n \"notes\": string,\n \"PackageCompleteURL\": string,\n \"PackageURL\": string,\n \"package_path\": string,\n \"installcheck_script\": string,\n \"uninstallcheck_script\": string,\n \"OnDemand\": bool,\n \"postinstall_script\": string,\n \"postuninstall_script\": string,\n \"precache\": bool,\n \"preinstall_alert\": dict,\n \"preuninstall_alert\": dict,\n \"preupgrade_alert\": dict,\n \"preinstall_script\": string,\n \"preuninstall_script\": string,\n \"receipts\": list,\n \"requires\": list,\n \"RestartAction\": string,\n \"supported_architectures\": list,\n \"suppress_bundle_relocation\": bool,\n \"unattended_install\": bool,\n \"unattended_uninstall\": bool,\n \"uninstall_method\": string,\n \"uninstall_script\": string,\n \"uninstaller_item_location\": string,\n \"uninstallable\": bool,\n \"update_for\": list,\n \"version\": string,\n }\n\n passed = True\n for pkginfo_key, expected_type in pkginfo_types.items():\n if pkginfo_key in pkginfo:\n if not isinstance(pkginfo[pkginfo_key], expected_type):\n print(\n \"{}: pkginfo key {} should be type {}, not type {}\".format(\n filename, pkginfo_key, expected_type, type(pkginfo[pkginfo_key])\n )\n )\n passed = False\n\n return passed"],"string":"[\n \"def isValidKey(key):\\n return True\",\n \"def validate_key(key):\\r\\n try:\\r\\n secret.Secret(key)\\r\\n except secret.Secret.InvalidSecret as e:\\r\\n raise KeyIsInvalid(e.message)\",\n \"def _check_key(self, key):\\n raise NotImplementedError\",\n \"def test_valid_key(self):\\n f = lws.valid_data_key\\n assert f('string', int, r'string') is False\\n assert f('string', str, r'test') is False\\n assert f(123, int, '123') is False\\n assert f(123.00, float, '123') is False\\n assert f('123', str, r'[0-9]*') is True\",\n \"def validate_handshake_public_key(cls, public_key: bytes) -> None:\\n ...\",\n \"def check_type( string_key ) : \\r\\n\\r\\n if type( string_key ) != type( '' ) : \\r\\n\\r\\n # raise self.__class__( \\\"'%s': EGI wants the key to be four _characters_ (not %s) !\\\" % (type(string_key), ) ) \\r\\n raise Eggog( \\\"'%s': EGI wants the key to be four _characters_ (not %s) !\\\" % (type(string_key), ) ) \\r\\n \\r\\n else :\\r\\n \\r\\n return True\",\n \"def check_key(self):\\n\\n if self.type == \\\"RSA\\\" and self.size < 1024:\\n raise HostkeyError(\\\"RSA keys must at least be 1024 bits.\\\")\\n elif self.type == \\\"DSA\\\" and self.size != 1024:\\n raise HostkeyError(\\\"DSA keys can only be 1024 bits.\\\")\\n elif self.type == \\\"ECDSA\\\" and self.size not in [256, 384, 521]: # yes, that is *really* 521 bits, not a typo!\\n raise HostkeyError(\\\"ECDSA key must be either 256, 384 or 521 bits (yes, 521 not 512!)\\\")\\n elif self.type ==\\\"ED25519\\\" and self.size != 128:\\n raise HostkeyError(\\\"ED25519 keys have a fixed size, which cannot be altered.\\\") # can't really happen, size is ignored for ED25519\\n\\n # if privkey is already there check size\\n self.key_exists = False\\n self.key_current_size = 0\\n if os.path.exists(self.fullpath):\\n self.key_exists = True\\n if self.type == \\\"ED25519\\\":\\n self.curve = \\\"EC25519\\\"\\n self.key_current_size = 128 # somewhat erbitrary, attack complexity on ED25519 is larger that brute forcing a 128bit key\\n self.key_exists = True\\n elif self.type == \\\"RSA1\\\":\\n self.key_exists = True\\n self.key_current_size = 1024\\n else:\\n try:\\n with open(self.fullpath, \\\"rb\\\") as key_file:\\n self.privkey = crypto_serialization.load_pem_private_key(key_file.read(), password=None, backend=crypto_default_backend())\\n except IOError:\\n raise HostkeyError(get_exception())\\n\\n if self.type == \\\"DSA\\\" or self.type == \\\"RSA\\\":\\n self.key_current_size = self.privkey.key_size\\n elif self.type == \\\"ED25519\\\":\\n self.key_current_size = 128\\n elif self.type == \\\"ECDSA\\\":\\n self.pubkey = self.privkey.public_key()\\n if self.pubkey.curve.name == \\\"secp256r1\\\":\\n self.key_current_size = 256\\n elif self.pubkey.curve.name == \\\"secp384r1\\\":\\n self.key_current_size = 384\\n elif self.pubkey.curve.name == \\\"secp521r1\\\":\\n self.key_current_size = 521\\n else:\\n self.curve = self.pubkey.curve.name\",\n \"def self_check() -> None:\\n assert len(ZBASE32_ALPHABET) == 32\\n\\n # Test vector from https://github.com/matusf/z-base-32/blob/0.1.2/src/lib.rs\\n assert zbase32_encode(b\\\"asdasd\\\") == \\\"cf3seamuco\\\"\\n assert zbase32_decode(\\\"cf3seamuco\\\") == b\\\"asdasd\\\"\\n\\n # Test vector from https://www.uriports.com/blog/setting-up-openpgp-web-key-directory/\\n # assert zbase32_encode(hashlib.sha1(b\\\"yourmail\\\").digest()) == \\\"hacabazoakmnagxwmkjerb9yehuwehbm\\\"\\n # -> this hash is wrong, and I don't know what username gives the SHA1\\n # e61980e2f0c2962c19f45a928207e0472744702b\\n\\n # Test vector from https://metacode.biz/openpgp/web-key-directory\\n assert zbase32_encode(hashlib.sha1(b\\\"test-wkd\\\").digest()) == \\\"4hg7tescnttreaouu4z1izeuuyibwww1\\\"\\n\\n # Test vector from https://datatracker.ietf.org/doc/draft-koch-openpgp-webkey-service/\\n assert (\\n get_wkd_advanced_url(\\\"Joe.Doe@Example.ORG\\\")\\n == \\\"https://openpgpkey.example.org/.well-known/openpgpkey/example.org/hu/iy9q119eutrkn8s1mk4r39qejnbu3n5q?l=Joe.Doe\\\" # noqa\\n )\\n assert (\\n get_wkd_direct_url(\\\"Joe.Doe@Example.ORG\\\")\\n == \\\"https://example.org/.well-known/openpgpkey/hu/iy9q119eutrkn8s1mk4r39qejnbu3n5q?l=Joe.Doe\\\"\\n )\\n\\n # Test vector from https://wiki.gnupg.org/WKD\\n assert (\\n get_wkd_direct_url(\\\"bernhard.reiter@intevation.de\\\")\\n == \\\"https://intevation.de/.well-known/openpgpkey/hu/it5sewh54rxz33fwmr8u6dy4bbz8itz4?l=bernhard.reiter\\\"\\n )\",\n \"def verify_hack_key(self):\\r\\n\\t\\tself.percent_english = Dict_Control(self.my_code).check_key()\\r\\n\\t\\t#If more than half the words are english, the key will pass. \\r\\n\\t\\tif self.percent_english > 50:\\r\\n\\t\\t\\tself.hack_plausible = True\",\n \"def check_auth_publickey(self, username, key):\\n return AUTH_FAILED\",\n \"def _check_key(key): # type: (str) -> None\\n if not key:\\n raise ValueError('Key must not be empty.')\\n if '.' in key:\\n raise ValueError('Key must not contain dots.')\",\n \"def validate_key(self, key: keyType) -> bool:\\n if isinstance(key, (dict,bool)):\\n raise Exception\\n if key is None:\\n raise Exception\\n # Numerical key object has no len(),\\n # so explicitly specify which types are not allowed to use empty value as keys\\n if isinstance(key, (str, tuple, set, list)) and (len(key) == 0):\\n raise Exception\\n return True\",\n \"def _validate_key(self, key):\\n if isinstance(key, str):\\n key = unicode(key, 'utf-8')\\n elif not isinstance(key, unicode):\\n raise TypeError(\\n \\\"`key` must be `str` or `unicode`, not `{}`\\\".format(\\n key.__class__.__name__)\\n )\\n return key\",\n \"def test_invalid_chars_ssck(self):\\r\\n valid_base = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\\r\\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\\r\\n with self.assertRaises(InvalidKeyError):\\r\\n # this ends up calling the constructor where the legality check should occur\\r\\n valid_base.replace(**{key: u'funny thing'})\",\n \"def _is_valid_key(self, key):\\r\\n\\r\\n # Check the length\\r\\n if len(key) > 250:\\r\\n return False\\r\\n\\r\\n # Check that there are no spaces or control characters\\r\\n for char in key:\\r\\n if ord(char) < 33 or ord(char) == 127:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def validate(self, key, val):\\n return True\",\n \"def validate(self, key, val):\\n return True\",\n \"def checkKey(self):\\n # TO DO for checking API authentication\\n if self.apikey is None:\\n return False\\n else:\\n return True\",\n \"def test_validate_with_invalid_key_format_type(self):\\n key_format_type = \\\"invalid\\\"\\n kwargs = {'key_format_type': key_format_type}\\n\\n self.assertRaisesRegex(\\n TypeError, \\\"invalid key format type\\\", Digest, **kwargs)\",\n \"def validate(self):\\n if not self.key or not self.certificates:\\n raise ValueError(\\\"Key or certificate missing in Keypair\\\")\",\n \"def test_key_none(self):\\n try:\\n AlphaVantage()\\n self.fail(msg='A None api key must raise an error')\\n except ValueError:\\n self.assertTrue(True)\",\n \"def test_key_none(self):\\n try:\\n AlphaVantage()\\n self.fail(msg='A None api key must raise an error')\\n except ValueError:\\n self.assertTrue(True)\",\n \"def check_len( string_key ) : \\r\\n\\r\\n if len( string_key ) != 4 :\\r\\n\\r\\n raise Eggog( \\\"'%s': EGI wants the key to be exactly four characters!\\\" % (string_key, ) ) \\r\\n \\r\\n else :\\r\\n \\r\\n return True\",\n \"def test_getKey_nokey(self):\\n filename = os.path.join(os.getcwd(), 'sekrit')\\n key = crypto.getKey(filename)\\n self.failUnlessIsInstance(key, basestring,\\n \\\"key isn't a string! type=%r\\\" % type(key))\",\n \"def is_key(v) -> bool:\\n try:\\n _validate(v, prefixes=[b\\\"edsk\\\", b\\\"edpk\\\", b\\\"spsk\\\", b\\\"p2sk\\\", b\\\"sppk\\\", b\\\"p2pk\\\"])\\n except (ValueError, TypeError):\\n return False\\n return True\",\n \"def test_invalid_chars_location(self):\\r\\n course_key = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\\r\\n valid_base = course_key.make_usage_key('tomato-again%9', 'block-head:sub-4%9')\\r\\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\\r\\n with self.assertRaises(InvalidKeyError):\\r\\n # this ends up calling the constructor where the legality check should occur\\r\\n valid_base.replace(**{key: u'funny thing'})\",\n \"def _validateKey(self, key, cls = None):\\n\\n key_class_types = [self._BaseKey__class, self._LocalKey__class,\\n self._MsgKey__class, ErrorMsgManager]\\n\\n if cls:\\n if inspect.isclass(cls) and cls in key_class_types:\\n classes = [cls]\\n else:\\n return None\\n else:\\n classes = key_class_types\\n return any([isinstance(key, cls) for cls in classes])\",\n \"def test_key_type(self):\\n self.failureResultOf(self.producer.send_messages(\\\"topic\\\", key=\\\"key\\\", msgs=[b\\\"msg\\\"]), TypeError)\",\n \"def creation_validation(**_):\\n\\n for property_key in constants.KEYPAIR_REQUIRED_PROPERTIES:\\n utils.validate_node_property(property_key, ctx.node.properties)\\n\\n key_file = _get_path_to_key_file()\\n key_file_in_filesystem = _search_for_key_file(key_file)\\n\\n if ctx.node.properties['use_external_resource']:\\n if not key_file_in_filesystem:\\n raise NonRecoverableError(\\n 'External resource, but the key file does not exist locally.')\\n try:\\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\\n except NonRecoverableError as e:\\n raise NonRecoverableError(\\n 'External resource, '\\n 'but the key pair does not exist in the account: '\\n '{0}'.format(str(e)))\\n else:\\n if key_file_in_filesystem:\\n raise NonRecoverableError(\\n 'Not external resource, '\\n 'but the key file exists locally.')\\n try:\\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\\n except NonRecoverableError:\\n pass\\n else:\\n raise NonRecoverableError(\\n 'Not external resource, '\\n 'but the key pair exists in the account.')\",\n \"def valid_key(self): \\n self.so.ValidKey.restype = c_bool\\n result = self.so.ValidKey()\\n return result\",\n \"def test_validate_yubikey(self):\\n from_key = self.yk_rnd.from_key(self.yk_public_id, self.yk_key)\\n self.assertTrue(pyhsm.yubikey.validate_yubikey_with_aead( \\\\\\n self.hsm, from_key, self.aead.data, self.kh_validate))\",\n \"def validate(cls, tab_dict, raise_error=True):\\r\\n return key_checker(['type'])(tab_dict, raise_error)\",\n \"def _check_transform_key(key: Hashable) -> None:\\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\\n # if not isinstance(key, Hashable):\\n # raise TypeError((type(key), \\\"transformation lookup key is not hashable\\\"))\",\n \"def test_generate_key(self): \\n k = Key().generate()\\n self.assertRegex(k, \\\"[a-zA-Z0-9+\\\\/]+={0,2}\\\")\",\n \"def keyIsValid(key):\\n\\n isValid = 1\\n \\n try:\\n temp = getParam(key)\\n\\n except ValueError:\\n isValid = 0\\n warning(\\\" WARNING: %s not set\\\" % (key))\\n\\n return isValid\",\n \"def test_allowed_chars(self):\\n hash_val = self.reverse_hash.get_hash('123')\\n self.assertEqual(hash_val['error'], 'allowed chars {}'.format(self.reverse_hash.letters))\",\n \"def test_create_key():\\n\\n assert symmetric.create_key() != \\\"\\\"\",\n \"def validate_authkey(value):\\n if not len(value) == 32:\\n raise ValidationError(\\n 'Value must be a string containing 32 alphanumeric characters')\",\n \"def __check_key_validity(self, key):\\n if not isinstance(key, tuple):\\n raise TypeError(\\\"key must be a tuple\\\")\\n if len(key) != 2:\\n raise ValueError(\\\"key must be of length two\\\")\\n if not (isinstance(key[0], int) and isinstance(key[1], int)):\\n raise TypeError(\\\"elements of key must be integers\\\")\\n if not ((0 <= key[0] < self.m) and (0 <= key[1] < self.n)):\\n raise exc.OutOfBoundsError(\\\"key is out of bounds\\\")\",\n \"def key_valid(key_data):\\n\\n gpg = gnupg.GPG(gnupghome=\\\"gnupg\\\")\\n import_result = gpg.import_keys(key_data)\\n\\n if import_result.count == 1:\\n gpg.delete_keys(import_result.fingerprints[0])\\n return True\\n else:\\n return False\",\n \"def validate_license(key: str) -> bool:\\r\\n return bool(\\r\\n re.match(r'^PB-[A-Z0-9]{8}(?:-[A-Z0-9]{8}){3}$', key)\\r\\n )\",\n \"def test_kyc_put_legal(self):\\n pass\",\n \"def api_key_check():\\n req_path = request.path\\n method_type = request.method\\n app.logger.info(\\\">>> path = {}, method = {}\\\".format(req_path, method_type))\\n\\n if not app_props.api_key_check:\\n app.logger.debug('>>> api key check closed')\\n return None\\n\\n if req_path in app_props.api_key_white_list:\\n app.logger.info('>>> {} in white list, pass'.format(req_path))\\n return None\\n headers = request.headers\\n api_key_from_req = headers.get('x-api-key')\\n if not api_key_from_req:\\n app.logger.debug('>>> enter api-key error')\\n return resp_json(BaseResp.err('no x-api-key header'))\\n\\n key_obj = Key.query.filter_by(api_key=api_key_from_req).first()\\n if key_obj:\\n app.logger.debug('>>> consumer_id = {}, secret_key = {}'.format(key_obj.consumer_id, key_obj.secret_key))\\n g.consumer_id = key_obj.consumer_id\\n g.secret_key = key_obj.secret_key\\n return None\\n else:\\n return resp_json(BaseResp.err('Err api key'))\",\n \"def test_api_key (key):\\n\\tdb = getattr(g,'db', None)\\n\\n\\tif isinstance(key, unicode):\\n\\t\\tkey = key.encode('utf-8')\\n\\n\\tqry = \\\"SELECT apikey FROM api_keys WHERE apikey=%s;\\\"\\n\\twith db as cur:\\n\\t\\treturn 0 < cur.execute(qry, (key,))\",\n \"def _check_validity(self):\\n pass\",\n \"def test_allow_extra_keys(self):\\n from natcap.invest import validation\\n\\n args = {'a': 'a', 'b': 'b'}\\n spec = {\\n 'a': {\\n 'type': 'freestyle_string',\\n 'name': 'a',\\n 'about': 'a freestyle string',\\n 'required': True\\n }\\n }\\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\\n\\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\\n validation.validate(args, spec)\\n self.assertTrue(message in cm.output)\",\n \"def test_validate_api_key(app, seed_data, key, result):\\n user_id, api_key = seed_data\\n if key == 'use-valid-key':\\n key = api_key\\n with app.app_context():\\n assert auth.validate_api_key(user_id, key) == result\",\n \"def check_keys(self):\",\n \"def rsa_is_valid_publickey(key):\\r\\n # must be a dict\\r\\n if type(key) is not dict:\\r\\n return False\\r\\n\\r\\n # missing the right keys\\r\\n if 'e' not in key or 'n' not in key:\\r\\n return False\\r\\n\\r\\n # has extra data in the key\\r\\n if len(key) != 2:\\r\\n return False\\r\\n\\r\\n for item in ['e', 'n']:\\r\\n # must have integer or long types for the key components...\\r\\n if type(key[item]) is not int and type(key[item]) is not long:\\r\\n return False\\r\\n\\r\\n if key['e'] < key['n']:\\r\\n # Seems valid...\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def test_validate_aead_cmp(self):\\n secret = pyhsm.aead_cmd.YHSM_YubiKeySecret(self.yk_key, self.yk_uid)\\n cleartext = secret.pack()\\n self.assertTrue(self.hsm.validate_aead(self.yk_public_id, self.kh_validate, self.aead, cleartext))\\n wrong_cleartext = 'X' + cleartext[1:]\\n self.assertFalse(self.hsm.validate_aead(self.yk_public_id, self.kh_validate, self.aead, wrong_cleartext))\",\n \"def _verify_key_size(cls, nbits):\\n # Check that the key size meets the minimum key size requirements\\n if(nbits < params.MINIMUM_KEY_SIZE):\\n \\n # Throw an exception w/ an appropriate message if nbits is too small\\n raise KeyLengthTooLowError(nbits, params.MINIMUM_KEY_SIZE, \\n \\\"The given size in bits for the cryptosystem (%d bits) is too\\\" \\\\\\n \\\" low. For security reasons, current minimum allowed key/\\\" \\\\\\n \\\"cryptosystem bit size is %d bits. It is recommended that \\\" \\\\\\n \\\" only keys of that length or higher are generated or used. \\\" \\\\\\n \\\" If you must use smaller keys, you may configure \\\" \\\\\\n \\\"PloneVoteCryptoLib's security parameters in params.py at \\\" \\\\\\n \\\"your own risk.\\\" % (nbits, params.MINIMUM_KEY_SIZE))\\n \\n # Check that the key size is can be expressed as whole bytes (i.e. is\\n # a multiple of 8)\\n if(nbits % 8 != 0):\\n \\n raise KeyLengthNonBytableError(nbits,\\n \\\"The given size in bits for the cryptosystem (%d bits) is \\\" \\\\\\n \\\"not a multiple of eight. Currently, only key sizes that are \\\" \\\\\\n \\\"multiples of eight, and thus expressible in whole bytes, \\\" \\\\\\n \\\"are allowed by PloneVoteCryptoLib. Perhaps you could use %d \\\" \\\\\\n \\\"bit keys?\\\" % (nbits, (nbits/8 + 1)*8) )\\n \\n return nbits\",\n \"def check_api_key(x_api_key: str = Security(api_key_header_auth)):\\n\\n if x_api_key != API_KEY:\\n raise HTTPException(\\n status_code=status.HTTP_401_UNAUTHORIZED,\\n detail=\\\"Invalid API Key\\\",\\n )\",\n \"def testKeyInfoTooShort(self):\\n key = 5\\n self.assertRaises(ValueError, dataToToken, key, data='x', keyInfo='xx')\",\n \"def vscf_raw_private_key_is_valid(self, ctx):\\n vscf_raw_private_key_is_valid = self._lib.vscf_raw_private_key_is_valid\\n vscf_raw_private_key_is_valid.argtypes = [POINTER(vscf_raw_private_key_t)]\\n vscf_raw_private_key_is_valid.restype = c_bool\\n return vscf_raw_private_key_is_valid(ctx)\",\n \"def _is_valid_key(self, key):\\n\\t\\t\\n\\t\\t# If the key is not a string\\n\\t\\tif not isinstance(key, str):\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\tkey = str.upper(key)\\n\\t\\t\\n\\t\\t# If the given key does not match the standard notation XY\\n\\t\\tif len(key) != 2:\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t\\t# If the key is out of the board\\n\\t\\tif key[0] not in self.columns or key[1] not in self.rows:\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t\\t# Otherwise the key is valid\\n\\t\\treturn True\",\n \"def _cross_check(self, pub_key):\\n if self.curve_name != pub_key.curve.name:\\n raise ValueError(\\n \\\"The curve in private key {} and in algorithm {} don't \\\"\\n \\\"match\\\".format(pub_key.curve.name, self.curve_name)\\n )\",\n \"def is_valid_key(key, crypt_method):\\n logger.info(f\\\"key: {key}, crypt_method: {crypt_method}\\\")\\n if crypt_method == 'C':\\n while type(key) is not int or key not in range(0, 95):\\n try:\\n key = Check.is_integer(key)[1]\\n if key not in range(0, 95):\\n raise ValueError\\n except (TypeError, ValueError):\\n print(\\\"You must enter an integer between 1 and 95!\\\")\\n key = input(\\\"Enter an encryption key\\\\n>> \\\")\\n elif crypt_method in ('M', 'P'):\\n pass\\n else:\\n return False\\n return True, key\",\n \"def test_valid_keys(client):\\n response=client.post(\\\"/signin\\\",data=dict(username=TestSignin.email, password=TestSignin.password), content_type=\\\"multipart/form-data\\\")\\n data=json.loads(response.data)\\n assert response.status_code==400\\n assert data[\\\"error\\\"] == \\\"Please provide email and password as keys\\\"\",\n \"def test_create_api_key(self):\\n pass\",\n \"def test_is_valid_label_key_invalid_input():\\n # test length violations\\n assert not is_valid_label_key(key=None) # Too short\\n assert not is_valid_label_key(key=\\\"\\\") # Too short\\n assert not is_valid_label_key(key=f\\\"{'p' * 254}/n\\\") # prefix too long\\n assert not is_valid_label_key(key=\\\"/n\\\") # prefix too short\\n assert not is_valid_label_key(key=\\\"p/\\\") # name too short\\n assert not is_valid_label_key(key=\\\"a\\\" * 254) # name too long\\n assert not is_valid_label_key(key=f\\\"d/{'b'*64}\\\") # name too long\\n # test first character violations (not alphanum)\\n assert not is_valid_label_key(key=\\\"-a\\\")\\n assert not is_valid_label_key(key=\\\".b\\\")\\n assert not is_valid_label_key(key=\\\" c\\\")\\n # test last character violations (not alphanum)\\n assert not is_valid_label_key(key=\\\"a-\\\")\\n assert not is_valid_label_key(key=\\\"b.\\\")\\n assert not is_valid_label_key(key=\\\"c \\\")\\n assert not is_valid_label_key(key=\\\"sw33T#\\\")\\n # test middle characters violations\\n assert not is_valid_label_key(key=\\\"a$$a\\\")\\n assert not is_valid_label_key(key=\\\"b b\\\")\",\n \"def spki_req_is_valid(spki_req):\\n try:\\n netscape_spki_from_b64(spki_req)\\n return True\\n except Exception:\\n return False\",\n \"def test_dweet_for_with_a_valid_key(self):\\n dweet = dweepy.dweet_for(self.my_thing_id, test_data, key=test_key)\\n check_valid_dweet_response(self, dweet)\\n self.assertEqual(dweet['thing'], self.my_thing_id)\",\n \"def key_type(self):\\n raise exceptions.NotImplementedError()\",\n \"def validate_key_throw(*args):\\n validation_result = validate_key(*args)\\n if not validation_result:\\n raise ValueError(str(validation_result))\\n return validation_result\",\n \"def remote_verifyKey(self, key, protocol):\\r\\n if self._authenticated.called:\\r\\n return Failure(InvalidKey('Only one guess is possible.'))\\r\\n\\r\\n if isinstance(protocol, Failure):\\r\\n self._authenticated.errback(protocol)\\r\\n else:\\r\\n if self._key != key:\\r\\n e = Failure(InvalidKey('Wrong key supplied.'))\\r\\n self._authenticated.errback(e)\\r\\n return e\\r\\n\\r\\n self._authenticated.callback(protocol)\",\n \"def test_getKey_keyexists(self):\\n filename = self.mktemp()\\n with open(filename, 'wb') as fh:\\n fh.write(SEKRIT_KEY)\\n fh.flush()\\n\\n key = crypto.getKey(filename)\\n self.failUnlessIsInstance(key, basestring,\\n \\\"key isn't a string! type=%r\\\" % type(key))\\n self.assertEqual(SEKRIT_KEY, key,\\n \\\"\\\"\\\"The example key and the one read from file differ!\\n key (in hex): %s\\n SEKRIT_KEY (in hex): %s\\\"\\\"\\\"\\n % (key.encode('hex'), SEKRIT_KEY.encode('hex')))\",\n \"def is_key_valid(self,key):\\n if not key or any(map(lambda s: s in key,space_chars))\\\\\\n or any(map(lambda s: s in key,bad_chars)):\\n return False \\n return True\",\n \"def test_invalid_keys(self):\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"this has spaces\\\", 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"key with spaces did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"\\\\x10control\\\\x02characters\\\\x11\\\", 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"key with control characters did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"a\\\" * (SERVER_MAX_KEY_LENGTH + 1), 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"long key did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(u\\\"unicode\\\\u4f1a\\\", 1)\\n\\t\\texcept TypeError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"unicode key did not raise ValueError\\\")\",\n \"def test_kyc_put_legal_share_holder(self):\\n pass\",\n \"def test_is_valid_annotation_key_invalid_input():\\n # test length violations\\n assert not is_valid_annotation_key(key=None) # Too short\\n assert not is_valid_annotation_key(key=\\\"\\\") # Too short\\n assert not is_valid_annotation_key(key=f\\\"{'p' * 254}/n\\\") # prefix too long\\n assert not is_valid_annotation_key(key=\\\"/n\\\") # prefix too short\\n assert not is_valid_annotation_key(key=\\\"p/\\\") # name too short\\n assert not is_valid_annotation_key(key=\\\"a\\\" * 254) # name too long\\n assert not is_valid_annotation_key(key=f\\\"d/{'b'*64}\\\") # name too long\\n # test first character violations (not alphanum)\\n assert not is_valid_annotation_key(key=\\\"-a\\\")\\n assert not is_valid_annotation_key(key=\\\".b\\\")\\n assert not is_valid_annotation_key(key=\\\" c\\\")\\n # test last character violations (not alphanum)\\n assert not is_valid_annotation_key(key=\\\"a-\\\")\\n assert not is_valid_annotation_key(key=\\\"b.\\\")\\n assert not is_valid_annotation_key(key=\\\"c \\\")\\n assert not is_valid_annotation_key(key=\\\"sw33T#\\\")\\n # test middle characters violations\\n assert not is_valid_annotation_key(key=\\\"a$$a\\\")\\n assert not is_valid_annotation_key(key=\\\"b b\\\")\",\n \"def _sanityCheckKeySizes(other):\\n if other.minKeySize < 512:\\n raise ValueError(\\\"minKeySize too small\\\")\\n if other.minKeySize > 16384:\\n raise ValueError(\\\"minKeySize too large\\\")\\n if other.maxKeySize < 512:\\n raise ValueError(\\\"maxKeySize too small\\\")\\n if other.maxKeySize > 16384:\\n raise ValueError(\\\"maxKeySize too large\\\")\\n if other.maxKeySize < other.minKeySize:\\n raise ValueError(\\\"maxKeySize smaller than minKeySize\\\")\\n # check also keys of virtual hosts\\n for i in other.virtual_hosts:\\n i.validate()\",\n \"def validate_usage(validation_context, cert, key_usage, extended_key_usage, extended_optional):\\n\\n if not isinstance(validation_context, ValidationContext):\\n raise TypeError(pretty_message(\\n '''\\n validation_context must be an instance of\\n certvalidator.context.ValidationContext, not %s\\n ''',\\n type_name(validation_context)\\n ))\\n\\n if validation_context.is_whitelisted(cert):\\n return\\n\\n if key_usage is None:\\n key_usage = set()\\n\\n if extended_key_usage is None:\\n extended_key_usage = set()\\n\\n if not isinstance(key_usage, set):\\n raise TypeError(pretty_message(\\n '''\\n key_usage must be a set of unicode strings, not %s\\n ''',\\n type_name(key_usage)\\n ))\\n\\n if not isinstance(extended_key_usage, set):\\n raise TypeError(pretty_message(\\n '''\\n extended_key_usage must be a set of unicode strings, not %s\\n ''',\\n type_name(extended_key_usage)\\n ))\\n\\n if not isinstance(extended_optional, bool):\\n raise TypeError(pretty_message(\\n '''\\n extended_optional must be a boolean, not %s\\n ''',\\n type_name(extended_optional)\\n ))\\n\\n missing_key_usage = key_usage\\n if cert.key_usage_value:\\n missing_key_usage = key_usage - cert.key_usage_value.native\\n\\n missing_extended_key_usage = set()\\n if extended_optional is False and not cert.extended_key_usage_value:\\n missing_extended_key_usage = extended_key_usage\\n elif cert.extended_key_usage_value is not None:\\n missing_extended_key_usage = extended_key_usage - set(cert.extended_key_usage_value.native)\\n\\n if missing_key_usage or missing_extended_key_usage:\\n plural = 's' if len(missing_key_usage | missing_extended_key_usage) > 1 else ''\\n friendly_purposes = []\\n for purpose in sorted(missing_key_usage | missing_extended_key_usage):\\n friendly_purposes.append(purpose.replace('_', ' '))\\n raise InvalidCertificateError(pretty_message(\\n '''\\n The X.509 certificate provided is not valid for the purpose%s of %s\\n ''',\\n plural,\\n ', '.join(friendly_purposes)\\n ))\",\n \"def test_kyc_get_validation_legal(self):\\n pass\",\n \"def is_valid_openssh_pubkey(pubkey):\\n try:\\n key_type, data64 = map(_encode, pubkey.split()[0:2])\\n except (ValueError, AttributeError):\\n return False\\n try:\\n data = base64.decodebytes(data64)\\n except base64.binascii.Error:\\n return False\\n\\n int_len = 4\\n str_len = struct.unpack('>I', data[:int_len])[0]\\n\\n if data[int_len:(int_len + str_len)] != key_type:\\n return False\\n\\n return True\",\n \"def _validity_check(settings, valid):\\n\\n Settings._dict_validity_check(settings, valid)\",\n \"def test_youtube_keyerror(self):\\n backend = self.instance('http://youtube.com/watch?id=5')\\n self.assertRaises(UnknownIdException, backend.get_code)\",\n \"def validate(self, key: keyType, value: valueType) -> bool:\\n return self.validate_key(key) and self.validate_value(value)\",\n \"def _verify_key_types(self, name, keys):\\r\\n if isinstance(keys, (list, tuple)):\\r\\n for key in keys:\\r\\n self._verify_key_types(name, key)\\r\\n elif isinstance(keys, (str, unicode)):\\r\\n pass\\r\\n else:\\r\\n raise TypeError(\\r\\n \\\"All %s keys must be one of the following types: \\\"\\r\\n \\\"<str> or <unicode>, \\\"\\r\\n \\\"<list> of <str> or <unicode>, \\\"\\r\\n \\\"<tuple> of <str> or <unicode>. \\\"\\r\\n \\\"Given: %s\\\" % (name, keys)\\r\\n )\",\n \"def test_secret():\\r\\n try:\\r\\n straxen.get_secret('somethingnonexistent')\\r\\n except ValueError:\\r\\n # Good we got some message we cannot load something that does\\r\\n # not exist,\\r\\n pass\",\n \"def patched_paramiko_transport_verify_key(self, host_key, sig): # pylint: disable=W0613\\n key = self._key_info[self.host_key_type](Message(host_key)) # pylint: disable=W0212\\n if key is None:\\n raise SSHException(\\\"Unknown host key type\\\")\\n # Patched: no more checks are done here\\n self.host_key = key\",\n \"def check_secret_key(cls):\\n logger.info(\\\"attempting to check the secret key...\\\")\\n try:\\n s = cls.objects.filter(is_active=True).first()\\n if s and s.secret_key == \\\"not-a-very-good-secret\\\":\\n chars = \\\"abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)\\\"\\n s.secret_key = get_random_string(50, chars)\\n s.save()\\n except DBError:\\n logger.warning(\\\"db not ready (error on {} model)\\\".format(cls.__name__))\\n return\",\n \"def _validKey(entry):\\n # be forward compatible to zope3 contained objects\\n raw_id = getattr(entry, '__name__', '')\\n if not raw_id:\\n raw_id = entry.getId()\\n\\n # This substitution is based on the description of cite key restrictions at\\n # http://bibdesk.sourceforge.net/manual/BibDesk%20Help_2.html\\n return VALIDIDPAT.sub('', raw_id)\",\n \"def test_rekey_key_format(self):\\n old_key = b'0' * 32\\n encrypted = encrypt('message', key=old_key)\\n\\n with pytest.raises(EncryptionError):\\n rekey(encrypted, old_key=old_key, new_key=b'1' * 31)\",\n \"def test_deploy_policy_fail_key(self):\\n\\n self._check_deploy_failure(\\n self._create_test_app(key='',\\n flavor='m1.small'),\\n 'missing key')\",\n \"def verify_key(self, providerkey = None):\\n h = Https(API_DOMAIN)\\n\\n data = {'apikey' : self.apikey}\\n\\n if providerkey is not None:\\n data['providerkey'] = providerkey\\n\\n h.request( \\\"GET\\\",\\n \\\"/publicapi/verify\\\"+ urlencode(data),\\n headers=self.headers)\\n\\n request_status = h.getresponse().status\\n\\n if request_status != 200:\\n raise Exception(\\\"Invalid API Key %s\\\" % self.apikey)\",\n \"def test_encrypt_key_invalid(self):\\n with pytest.raises(EncryptionError):\\n encrypt('message', key=b'0' * 31)\",\n \"def _safe_key(self, key):\\n if isinstance(key, str):\\n key = key.encode('UTF-8')\\n return key\",\n \"def _check_key_type(cls, key: Any) -> K:\\n if not isinstance(key, cls.keytype):\\n raise KeyError(\\n f\\\"{cls!r} accepts only keys of type {cls.keytype!r}, \\\"\\n f\\\"got {type(key)!r}\\\"\\n )\\n return cast(K, key)\",\n \"def check_valid_fernet(value):\\n try:\\n decoded = base64.urlsafe_b64decode(value)\\n if len(decoded) != 32: return False\\n return True\\n except binascii.Error:\\n return False\",\n \"def check_api(submitted_key, users_key):\\r\\n if users_key != submitted_key:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def test_bad_request_no_key_specified(self, client):\\n response = client.post(self.urls['create'], secure=True)\\n assert response.status_code == status.HTTP_400_BAD_REQUEST\\n request = response.wsgi_request\\n assert request.idempotency_key_exempt is False\\n assert request.idempotency_key_manual is False\",\n \"def check_empty_key(self, key):\\n if key is None or key == \\\"\\\" or key == self.empty_api_key:\\n print(\\\"ERROR, A KEY IS EMPTY - CHECK YOUR FILE\\\")\\n return False\\n return True\",\n \"def test_is_valid_label_key_valid_input():\\n # test valid label keys\\n assert is_valid_label_key(key=\\\"l0l\\\")\\n assert is_valid_label_key(key=\\\"l0L\\\")\\n assert is_valid_label_key(key=\\\"L-l\\\")\\n assert is_valid_label_key(key=\\\"L.L\\\")\\n assert is_valid_label_key(key=\\\"4-you\\\")\\n assert is_valid_label_key(key=\\\"you.2\\\")\\n assert is_valid_label_key(key=\\\"p/n\\\")\\n assert is_valid_label_key(key=\\\"prefix/you.2\\\")\\n assert is_valid_label_key(key=\\\"how.sad/to-see\\\")\\n assert is_valid_label_key(key=f\\\"{'d'*253}/{'n'*63}\\\")\",\n \"def _is_valid(self):\\n # TODO: Query Google to validate credentials\\n return True\",\n \"def validate_snx_api_key():\\n api_data = {} # type: Dict[str, str]\\n response = http_request(endpoint=API_QUOTA, data=api_data)\\n\\n if response.get('errorNo') != 0:\\n return_error('API Returned, {}:{}'.format(response.get('errorNo'), response.get('errorMsg')))\\n\\n return 'ok'\",\n \"def _check_api_key(self):\\n try:\\n self.maps.places_nearby(\\n location=(53.909804, 27.580184),\\n radius=650,\\n open_now=False,\\n language=config.LANGUAGE,\\n type='cafe',\\n # rank_by='distance', # IMPORTANT: cannot use rank_by and radius options together\\n page_token=None,\\n )\\n except Exception as e:\\n\\n with self.__writelock:\\n self.print(f'ERROR: bad API key \\\"{self.maps.key}\\\" (tracker={self.stats.previous_requests})\\\\n')\\n raise e\",\n \"def _verify(pubkey: SupportedKeyTypes, sig: bytes, filehash: bytes, hashfunc: hashes.HashAlgorithm) -> None:\\n if isinstance(pubkey, RSAPublicKey):\\n pubkey.verify(sig, filehash, padding.PKCS1v15(), Prehashed(hashfunc))\\n elif isinstance(pubkey, EllipticCurvePublicKey):\\n pubkey.verify(sig, filehash, ec.ECDSA(Prehashed(hashfunc)))\",\n \"def _secret_not_in_order():\\n pecan.abort(400, u._(\\\"Secret metadata expected but not received.\\\"))\",\n \"def test_ec(self):\\n key = c.KEY_EC\\n usage = [\\n c.KU_DIGITALSIGNATURE,\\n c.KU_NONREPUDIATION,\\n c.KU_KEYAGREEMENT,\\n c.KU_ENCIPHERONLY,\\n c.KU_DECIPHERONLY,\\n ]\\n self.assertTrue(utils.check_key_usage(key, usage))\",\n \"def validate_pkginfo_key_types(pkginfo, filename):\\n\\n # Remap string type to support unicode in both Python 2 and 3\\n string = basestring if sys.version_info.major == 2 else str\\n\\n # Pkginfo keys and their known types. Omitted keys are left unvalidated.\\n # Source: https://github.com/munki/munki/wiki/Supported-Pkginfo-Keys\\n # Last updated 2019-03-13.\\n pkginfo_types = {\\n \\\"additional_startosinstall_options\\\": list,\\n \\\"apple_item\\\": bool,\\n \\\"autoremove\\\": bool,\\n \\\"blocking_applications\\\": list,\\n \\\"catalogs\\\": list,\\n \\\"category\\\": string,\\n \\\"copy_local\\\": bool,\\n \\\"description\\\": string,\\n \\\"developer\\\": string,\\n \\\"display_name\\\": string,\\n \\\"force_install_after_date\\\": datetime,\\n \\\"forced_install\\\": bool,\\n \\\"forced_uninstall\\\": bool,\\n \\\"icon_name\\\": string,\\n \\\"installable_condition\\\": string,\\n \\\"installed_size\\\": int,\\n \\\"installer_item_hash\\\": string,\\n \\\"installer_item_location\\\": string,\\n \\\"installer_item_size\\\": int,\\n \\\"installer_type\\\": string,\\n \\\"installs\\\": list,\\n \\\"items_to_copy\\\": list,\\n \\\"installer_choices_xml\\\": list,\\n \\\"installer_environment\\\": dict,\\n \\\"localized_strings\\\": dict,\\n \\\"minimum_munki_version\\\": string,\\n \\\"minimum_os_version\\\": string,\\n \\\"maximum_os_version\\\": string,\\n \\\"name\\\": string,\\n \\\"notes\\\": string,\\n \\\"PackageCompleteURL\\\": string,\\n \\\"PackageURL\\\": string,\\n \\\"package_path\\\": string,\\n \\\"installcheck_script\\\": string,\\n \\\"uninstallcheck_script\\\": string,\\n \\\"OnDemand\\\": bool,\\n \\\"postinstall_script\\\": string,\\n \\\"postuninstall_script\\\": string,\\n \\\"precache\\\": bool,\\n \\\"preinstall_alert\\\": dict,\\n \\\"preuninstall_alert\\\": dict,\\n \\\"preupgrade_alert\\\": dict,\\n \\\"preinstall_script\\\": string,\\n \\\"preuninstall_script\\\": string,\\n \\\"receipts\\\": list,\\n \\\"requires\\\": list,\\n \\\"RestartAction\\\": string,\\n \\\"supported_architectures\\\": list,\\n \\\"suppress_bundle_relocation\\\": bool,\\n \\\"unattended_install\\\": bool,\\n \\\"unattended_uninstall\\\": bool,\\n \\\"uninstall_method\\\": string,\\n \\\"uninstall_script\\\": string,\\n \\\"uninstaller_item_location\\\": string,\\n \\\"uninstallable\\\": bool,\\n \\\"update_for\\\": list,\\n \\\"version\\\": string,\\n }\\n\\n passed = True\\n for pkginfo_key, expected_type in pkginfo_types.items():\\n if pkginfo_key in pkginfo:\\n if not isinstance(pkginfo[pkginfo_key], expected_type):\\n print(\\n \\\"{}: pkginfo key {} should be type {}, not type {}\\\".format(\\n filename, pkginfo_key, expected_type, type(pkginfo[pkginfo_key])\\n )\\n )\\n passed = False\\n\\n return passed\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66728324","0.6390312","0.6318158","0.62363094","0.6178258","0.61090034","0.59969664","0.59606546","0.59150714","0.5909401","0.5852684","0.5826533","0.5803395","0.5795646","0.5785924","0.5733037","0.5733037","0.57124954","0.57062685","0.56519485","0.563369","0.563369","0.5631203","0.5601875","0.55847937","0.55493295","0.5546983","0.55395055","0.55312544","0.5512068","0.5504998","0.5498652","0.54730195","0.54680663","0.546611","0.5455199","0.54430056","0.5428327","0.54177165","0.54138166","0.54022706","0.54000086","0.53994507","0.5396056","0.5380787","0.5378785","0.5355387","0.5351222","0.53496486","0.5345155","0.53418833","0.5333368","0.53305125","0.5325554","0.5324903","0.5318767","0.5312907","0.52891284","0.5272058","0.52636504","0.5262696","0.5260152","0.52464324","0.52432096","0.5240802","0.5239029","0.5238212","0.52370304","0.5232677","0.5229456","0.52163035","0.52095217","0.52037483","0.518699","0.5185621","0.518482","0.5177928","0.5169512","0.51652026","0.5160818","0.5157751","0.51559156","0.5155592","0.51505643","0.51502764","0.51466733","0.5140742","0.51400274","0.5139235","0.5133518","0.5127518","0.5126482","0.5123606","0.5116538","0.5112866","0.5106953","0.51053005","0.5103407","0.5102532","0.5102331"],"string":"[\n \"0.66728324\",\n \"0.6390312\",\n \"0.6318158\",\n \"0.62363094\",\n \"0.6178258\",\n \"0.61090034\",\n \"0.59969664\",\n \"0.59606546\",\n \"0.59150714\",\n \"0.5909401\",\n \"0.5852684\",\n \"0.5826533\",\n \"0.5803395\",\n \"0.5795646\",\n \"0.5785924\",\n \"0.5733037\",\n \"0.5733037\",\n \"0.57124954\",\n \"0.57062685\",\n \"0.56519485\",\n \"0.563369\",\n \"0.563369\",\n \"0.5631203\",\n \"0.5601875\",\n \"0.55847937\",\n \"0.55493295\",\n \"0.5546983\",\n \"0.55395055\",\n \"0.55312544\",\n \"0.5512068\",\n \"0.5504998\",\n \"0.5498652\",\n \"0.54730195\",\n \"0.54680663\",\n \"0.546611\",\n \"0.5455199\",\n \"0.54430056\",\n \"0.5428327\",\n \"0.54177165\",\n \"0.54138166\",\n \"0.54022706\",\n \"0.54000086\",\n \"0.53994507\",\n \"0.5396056\",\n \"0.5380787\",\n \"0.5378785\",\n \"0.5355387\",\n \"0.5351222\",\n \"0.53496486\",\n \"0.5345155\",\n \"0.53418833\",\n \"0.5333368\",\n \"0.53305125\",\n \"0.5325554\",\n \"0.5324903\",\n \"0.5318767\",\n \"0.5312907\",\n \"0.52891284\",\n \"0.5272058\",\n \"0.52636504\",\n \"0.5262696\",\n \"0.5260152\",\n \"0.52464324\",\n \"0.52432096\",\n \"0.5240802\",\n \"0.5239029\",\n \"0.5238212\",\n \"0.52370304\",\n \"0.5232677\",\n \"0.5229456\",\n \"0.52163035\",\n \"0.52095217\",\n \"0.52037483\",\n \"0.518699\",\n \"0.5185621\",\n \"0.518482\",\n \"0.5177928\",\n \"0.5169512\",\n \"0.51652026\",\n \"0.5160818\",\n \"0.5157751\",\n \"0.51559156\",\n \"0.5155592\",\n \"0.51505643\",\n \"0.51502764\",\n \"0.51466733\",\n \"0.5140742\",\n \"0.51400274\",\n \"0.5139235\",\n \"0.5133518\",\n \"0.5127518\",\n \"0.5126482\",\n \"0.5123606\",\n \"0.5116538\",\n \"0.5112866\",\n \"0.5106953\",\n \"0.51053005\",\n \"0.5103407\",\n \"0.5102532\",\n \"0.5102331\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8084664"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18491,"cells":{"query":{"kind":"string","value":"Attempts to retrieve entity, performing verification in the process."},"document":{"kind":"string","value":"def _getEntityByWebsafeKey(websafeKey, kind):\n # Ensure that the websafe key is valid\n key = _raiseIfWebsafeKeyNotValid(websafeKey, kind)\n # Get the entity\n entity = key.get()\n if not entity:\n raise endpoints.NotFoundException(\n \"No '%s' entity found using websafe key: %s\" %\n (kind, websafeKey))\n # If all is well, return the entity\n return entity"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def get_entity(self):\n if not self.entity and await self.get_input_entity():\n try:\n self._entity =\\\n await self._client.get_entity(self._input_entity)\n except ValueError:\n pass\n\n return self._entity","async def test_get_entity(self):\n await test_service.get_entity(self)","def _load_entity(client, entity_type, entity_id, parent_key=None):\n\n key = _load_key(client, entity_type, entity_id, parent_key)\n entity = client.get(key)\n log('retrieved entity: ' + entity_type + ' for ID: ' + str(entity_id))\n return entity","def test_failed_entity_retrieval_by_its_primary_key(self, test_domain):\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(uuid4())","def test4ValidEntity(id):\n object = None\n try:\n object = ndb.Key(urlsafe=id).get()\n except Exception, e:\n if e.__class__.__name__ == ProtocolBufferDecodeError:\n object = None\n return object","def test_entities__PersistentEntities__1():\n zope.interface.verify.verifyObject(IEntities, PersistentEntities())","def validate(self, entity_id):\n\n safe_entity = self.find_by_id(entity_id)\n if not safe_entity:\n raise EntityNotFound(\"The param_id = {} is not valid.\".format(entity_id))\n\n return safe_entity","def fetch_entity(endpoint, values):\n values['entity'] = Entity.objects.get_or_404(name=values['entity'])","def _get_helper(cls, database, id_field, entity_id):\n entity = database.get(entity_id)\n if not entity:\n return validation.get_not_found_error(id_field, entity_id)\n return entity","def GetEntity(self):\n return self.__entity","def entity_exists(entity):\n return entity.get_by_id(entity.key.id())","def test_entity_doesnt_exist(self):\n key = ndb.Key(models.InstanceTemplateRevision, 'fake-key')\n urls = snapshots.fetch(key)\n self.failIf(urls)","def test_entities__Entities__1():\n zope.interface.verify.verifyObject(IEntities, Entities())","async def test_entity_entity_reference(self):\n test_name = 'test_entity_entity_reference'\n entity_name = 'TestEntityEntityReference'\n\n corpus = TestHelper.get_local_corpus(self.tests_subpath, test_name)\n expected_output_path = TestHelper.get_expected_output_folder_path(self.tests_subpath, test_name)\n manifest = await corpus.fetch_object_async('local:/default.manifest.cdm.json')\n\n ent_test_entity_entity_reference = await corpus.fetch_object_async('local:/{}.cdm.json/{}'.format(entity_name, entity_name), manifest)\n self.assertIsNotNone(ent_test_entity_entity_reference)\n resolved_test_entity_entity_reference = await ProjectionTestUtils.get_resolved_entity(corpus, ent_test_entity_entity_reference, [])\n self.assertIsNotNone(resolved_test_entity_entity_reference)\n await AttributeContextUtil.validate_attribute_context(self, corpus, expected_output_path, entity_name, resolved_test_entity_entity_reference)","def _load(self):\n entity: dict = self.service.find_one({\"_id\": self._id})\n\n if entity is not None:\n self.created_at = entity.get(\"created_at\")\n self.updated_at = entity.get(\"updated_at\")","def read(self, entity, content_type):\n return None","def test_entities__Entity__1():\n entity = Entity(None, IEntity, 'Entity')\n zope.interface.verify.verifyObject(IEntity, entity)","def expect_datatore_lookup(self, blob_key, expected_result):\n self.mox.StubOutWithMock(datastore, 'Get')\n blob_url = datastore.Entity('__BlobServingUrl__',\n name=blob_key)\n if expected_result:\n datastore.Get(blob_url.key()).AndReturn(True)\n else:\n datastore.Get(blob_url.key()).AndRaise(\n datastore_errors.EntityNotFoundError)","def process(self, entity: Entity) -> ProcessorResult:\n if isinstance(entity, str): # If we were unable to fetch entity (404/403)\n return ProcessorResult(False, f\"Unable to resolve Entity {entity}\")\n try:\n # First try to get the value off the entity\n entity_dict = entity.get_entity()\n data = get(entity_dict, self.field)\n if data:\n return ProcessorResult(True, data)\n\n # If not found, try to get the value from the asset identifiers\n identifier = next(iter(filter(lambda x: x['type'] == self.field, entity_dict.get('identifiers', []))), None)\n if identifier:\n return ProcessorResult(True, identifier['value'])\n\n # Return a failed processor result if no field was found on the object or it's identifiers\n return ProcessorResult(False,\n f'Unable to find {self.field} in identifiers for entity {entity.get_marquee_id()}')\n\n except ValueError:\n return ProcessorResult(False, \"Could not get field on entity\")","def test_client_verification_document_retrieve(self):\n pass","def test_client_verification_retrieve(self):\n pass","def testFetch(self):\n try:\n provU = ProvenanceProvider(self.__cfgOb, self.__cachePath, useCache=False)\n pD = {self.__provKeyName: self.__provInfoL}\n ok = provU.store(pD)\n self.assertTrue(ok)\n #\n fD = provU.fetch()\n self.assertTrue(self.__provKeyName in fD)\n self.assertDictEqual(pD, fD)\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","def test_get(self):\n self.assertEqual(\n self.attempts[0],\n self.resource.get(self.attempts[0][_ATTEMPT.attempt_id]))","def get_entity(self):\n if self.override_entity and not self.override_entity.abstract_entity:\n return self.override_entity\n elif self.get_role():\n return self.get_role().entity\n return None","def provider_entity(provider):\n try:\n return NUTEntity.objects.get(id=provider.first_access().target.id)\n except:\n return None","def _object_get(self, pk):\n try:\n return self.model.objects.get(pk=pk)\n except self.model.DoesNotExist:\n raise DoesNotExist(self.model.__name__.lower(), primary_key=pk)","async def test_entity_attribute_entity_reference(self):\n test_name = 'test_entity_attribute_entity_reference'\n entity_name = 'TestEntityAttributeEntityReference'\n\n corpus = TestHelper.get_local_corpus(self.tests_subpath, test_name)\n expected_output_path = TestHelper.get_expected_output_folder_path(self.tests_subpath, test_name)\n manifest = await corpus.fetch_object_async('local:/default.manifest.cdm.json')\n\n ent_test_entity_attribute_entity_reference = await corpus.fetch_object_async('local:/{}.cdm.json/{}'.format(entity_name, entity_name), manifest)\n self.assertIsNotNone(ent_test_entity_attribute_entity_reference)\n resolved_test_entity_attribute_entity_reference = await ProjectionTestUtils.get_resolved_entity(corpus, ent_test_entity_attribute_entity_reference, [])\n self.assertIsNotNone(resolved_test_entity_attribute_entity_reference)\n await AttributeContextUtil.validate_attribute_context(self, corpus, expected_output_path, entity_name, resolved_test_entity_attribute_entity_reference)","def test_retrieve_result(self):\n dr = EventualResult(Deferred(), None)\n uid = dr.stash()\n self.assertIdentical(dr, retrieve_result(uid))","def get(cls, entity_id, external_project_id=None,\n force_show_deleted=False,\n suppress_exception=False, session=None):\n db_entity = cls.db_repo.get(\n entity_id, external_project_id=external_project_id,\n force_show_deleted=force_show_deleted,\n suppress_exception=suppress_exception, session=session)\n if db_entity:\n return cls()._from_db_object(db_entity)\n else:\n return None","def fetch(self, vault_client):\n result = self.read(vault_client)\n if result:\n if isinstance(result, dict) and 'data' in result:\n self.existing = result['data']\n else:\n self.existing = result\n else:\n self.existing = None","def __call__(self, context=None):\n if context is None:\n context = self.context\n return self.entity.resolve(context)","def get(entity, name=None, version=None, lineage=None):","def get_target_entity(self):\n return None","def test_entities__entity_by_obj__1(stubEntities, entityAdapters):\n obj = persistent.Persistent()\n with pytest.raises(ValueError):\n IEntity(obj)","def __get__(self, model_instance, model_class):\n instance = None\n\n if model_instance is None:\n return self\n\n if hasattr(model_instance, self.__id_attr_name()):\n reference_id = getattr(model_instance, self.__id_attr_name())\n else:\n reference_id = None\n\n if reference_id is not None:\n\n resolved = getattr(model_instance, self.__resolved_attr_name())\n\n if resolved is not None:\n return resolved\n\n else:\n instance = memcache.get(str(reference_id) or '')\n instance = memcache.get(instance or '')\n\n if instance:\n # Convert to model from protobuf\n instance = db.model_from_protobuf(entity_pb.EntityProto(instance))\n\n # Check in DB after checking in memcache\n if not instance:\n instance = db.get(reference_id)\n\n '''\n # removed to make sure things keep running even after we screw with the db.\n if instance is None:\n raise ReferencePropertyResolveError(\n 'ReferenceProperty failed to be resolved: %s' %\n reference_id.to_path())\n '''\n\n if not instance:\n logging.error(\"The referenced object no longer exists; \"\n \"returning None.\")\n\n setattr(model_instance, self.__resolved_attr_name(), instance)\n return instance\n else:\n return None","def verify_instance(**kwargs):\n instance = kwargs.get('instance')\n\n from amelie.claudia.models import Mapping\n\n transaction.on_commit(lambda: tasks.verify_instance.delay(Mapping.get_type(instance), instance.id))","def _during_execute(self, db, entity):\n pass","def get(self):\n entity = self._model.get_or_insert(self._key_name)\n credential = getattr(entity, self._property_name)\n if credential and hasattr(credential, 'set_store'):\n credential.set_store(self.put)\n return credential","def test_entity_retrieval_by_its_primary_key(\n self, test_domain, identifier, persisted_person\n ):\n person = test_domain.repository_for(Person)._dao.get(persisted_person.id)\n assert person is not None\n assert person.id == identifier\n assert person == persisted_person","def entity(self):\n return self._entity","def entity(self):\n return self._entity","def test_find(self):\n PromotionFactory(code=\"SAVE30\").save()\n save50 = PromotionFactory(code=\"SAVE50\")\n save50.save()\n\n promotion = Promotion.find(save50.id)\n self.assertIsNot(promotion, None)\n self.assertEqual(promotion.id, save50.id)\n self.assertEqual(promotion.code, save50.code)\n self.assertEqual(promotion.percentage, save50.percentage)","def _create_entity(self, model_name, entity):\n model_pool = self.pool.get(model_name)\n prepared_entity = self._prepare_entity(model_name, entity)\n if not prepared_entity:\n logger.debug(\"Prepared entity is empty : %s model %s\" % (prepared_entity, model_name))\n return False\n\n logger.debug(\"Creating entity %s\\n%s\" % (model_name, prepared_entity))\n new_id = model_pool.create(self.cr, self.uid, prepared_entity)\n\n logger.debug(\"Created %s, id %s\" % (model_name, new_id))\n return new_id","def get_entity(self, entity_id):\n path = '/entity/' + str(entity_id)\n try:\n response = self.__session.get(self.__api_base_url + path)\n response.raise_for_status()\n response = response.json()\n except (requests.HTTPError, requests.ConnectionError), error:\n raise Exception(error.message)\n\n return response","async def get(self, entity_type: str):\n u = Utilities()\n\n # Get a dict of paths and information to entity types\n # await u.get_api_root_response()\n\n # Parameters to pass to aiohttp (the OAuth token we got with the oauth_token function)\n headers = {\"Authorization\": \"Bearer \" + self.token}\n #\n # The url to use\n # url = await self.create_url(\"/api/{entity}\")\n url = await u.create_url(await u.entity_url(entity_type))\n\n async with ClientSession() as session:\n async with session.get(url, headers=headers) as resp:\n # Dict with key that contain dict of all entity types as values\n Get.entity_dict = {}\n\n # Nested dictionary magic\n Get.entity_dict[entity_type] = await resp.json()","async def _async_update_data(self) -> dict[int, SurepyEntity]:\n try:\n return await self.surepy.get_entities(refresh=True)\n except SurePetcareAuthenticationError as err:\n raise ConfigEntryAuthFailed(\"Invalid username/password\") from err\n except SurePetcareError as err:\n raise UpdateFailed(f\"Unable to fetch data: {err}\") from err","def optionally_retrieve(\n cls: Type[T],\n public_id: str,\n datastore: Datastore\n ) -> Optional[T]:\n query = Query.require(cls._Q_RETRIEVE, 'Standalone Agent retrieval')\n result = query.execute(datastore, {'public_id': public_id})\n if result is None:\n return None\n return cls.decode(result)","def get_object(self):\n obj = get_object_or_404(Article, slug=self.kwargs[\"slug\"])\n self.check_object_permissions(self.request, obj)\n return obj","def retrieve(cls: Type[T], agent_id: int, datastore: Datastore) -> T:\n agent = cls.optionally_retrieve(agent_id, datastore)\n if agent is None:\n raise NotFound\n return agent","def test_beneficiaries_retrieve_validate_content_that_will_pass(self):\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve', kwargs={'pk': 1})\n response = self.client.get(url)\n\n # serialize all model object data\n beneficiaries = Beneficiary.objects.get(pk=1)\n serializer = BeneficiarySerializer(beneficiaries, many=False)\n self.assertEqual(response.json(), serializer.data)\n self.assertEqual(response.status_code, 200)","def fetchProteinEntityData(self):\n logger.info(\"Running fetchProteinEntityData...\")\n ok = False\n try:\n ptsW = ProteinTargetSequenceWorkflow(self.__cfgOb, self.__cachePath)\n ok = ptsW.exportRCSBProteinEntityFasta()\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n return ok","def test_beneficiaries_retrieve_that_will_pass(self):\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve', kwargs={'pk': 1})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)","def fetch(self):\n raise NotImplementedError()","def _request_model(self, instance, success, get_embedded=True):\n coll = self.get_collection('_model')\n if get_embedded:\n callback = partial(self._get_embedded_model_names,\n instance=instance,\n success=success)\n else:\n callback = success\n\n try:\n instance['_model']\n except KeyError:\n raise tornado.web.HTTPError(400, 'Missing model key')\n coll.find_one({'_id': instance['_model']},\n callback=callback)","def test_entities__EntityOrder__get__3(entityOrder, minimalEntity):\n with pytest.raises(KeyError):\n entityOrder.get(minimalEntity)","def test_get_entity_version(self):\n self.metadata.create_or_update(data=self.create)\n\n # Find by name\n res_name = self.metadata.get_by_name(\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\n )\n res = self.metadata.get_entity_version(\n entity=Dashboard, entity_id=res_name.id.__root__, version=0.1\n )\n\n # check we get the correct version requested and the correct entity ID\n assert res.version.__root__ == 0.1\n assert res.id == res_name.id","def get(self, key, missing=None, deferred=None,\n transaction=None, eventual=False, retry=None, timeout=None, read_time=None, model_type=None):\n start = datetime.datetime.now()\n entities = self.get_multi(keys=[key],\n missing=missing,\n deferred=deferred,\n transaction=transaction,\n eventual=eventual,\n retry=retry,\n timeout=timeout,\n read_time=read_time,\n model_type=model_type)\n if entities:\n end = datetime.datetime.now()\n print('Time taken for get {}'.format(end - start))\n return entities[0]","def getEntityByID (self, ID, fail=False):\n r = self.find(Or(Equal('DPID', ID),Equal(F('ID'), ID)))\n if len(r) == 0:\n if fail:\n raise RuntimeError(\"No entity with ID \" + str(ID))\n else:\n return None\n assert len(r) == 1\n return r[0]","def GetEntityViaMemcache(entity_key):\n entity = memcache.get(entity_key)\n if entity is not None:\n return entity\n key = ndb.Key(urlsafe=entity_key)\n entity = key.get()\n if entity is not None:\n memcache.set(entity_key, entity)\n return entity","def test_entities__EntityOrder__get__2(entityOrder, unknownEntity):\n with pytest.raises(KeyError):\n entityOrder.get(unknownEntity)","def test_entities__EntityOrder__get__5(entityOrder):\n person = IEntity(IPerson)\n with zope.component.hooks.site(None):\n with pytest.raises(zope.component.ComponentLookupError):\n entityOrder.get(person)","def process_human_entity(entity: dict, language: str) -> bool:\n try:\n claims = entity[\"claims\"]\n # Get ID\n remote_id = entity[\"title\"]\n print(\"%s\\t%s\" % (\"ID\".ljust(16), remote_id))\n # Get name from label\n name = entity[\"labels\"][language][\"value\"].lower()\n print(\"%s\\t%s\" % (\"name\".ljust(16), name))\n # Get brief\n brief = entity[\"descriptions\"][language][\"value\"].lower()\n print(\"%s\\t%s\" % (\"brief\".ljust(16), brief))\n # Birthdate\n if \"P569\" in claims:\n birthdate = claims[\"P569\"][0][\"mainsnak\"][\"datavalue\"][\"value\"][\"time\"]\n print(\"%s\\t%s\" % (\"birthdate\".ljust(16), birthdate))\n # Is Dead\n print(\"%s\\t%s\" % (\"is dead\".ljust(16), \"P570\" in claims))\n # Citizenship\n if \"P27\" in claims:\n country_id = claims[\"P27\"][0][\"mainsnak\"][\"datavalue\"][\"value\"][\"id\"]\n # Get country entity\n country_entity = get_wbentity(country_id, language)\n if \"P297\" in country_entity[\"claims\"]:\n citizenship = country_entity[\"claims\"][\"P297\"][0][\"mainsnak\"][\"datavalue\"][\"value\"]\n print(\"%s\\t%s\" % (\"citizenship\".ljust(16), citizenship))\n # Birthplace\n if \"P19\" in claims:\n city_id = claims[\"P19\"][0][\"mainsnak\"][\"datavalue\"][\"value\"][\"id\"]\n # Get city entity\n city_entity = get_wbentity(city_id, language)\n if \"P1448\" in city_entity[\"claims\"]:\n birthplace = city_entity[\"claims\"][\"P1448\"][0][\"mainsnak\"][\"datavalue\"][\"value\"][\"text\"]\n else:\n birthplace = city_entity[\"labels\"][language][\"value\"]\n print(\"%s\\t%s\" % (\"birthplace\".ljust(16), birthplace))\n # Image\n if \"P18\" in claims:\n image_id = claims[\"P18\"][0][\"mainsnak\"][\"datavalue\"][\"value\"]\n image_uri = get_image_info(image_id)\n print(\"%s\\t%s\" % (\"image\".ljust(16), image_uri))\n # Occupation\n if \"P106\" in claims:\n occupation_id = claims[\"P106\"][0][\"mainsnak\"][\"datavalue\"][\"value\"][\"id\"]\n occupation_entity = get_wbentity(occupation_id, language)\n occupation = occupation_entity[\"labels\"][language][\"value\"].lower()\n print(\"%s\\t%s\" % (\"occupation\".ljust(16), occupation))\n print_end()\n except Exception as err:\n print_err(\"%s error: %s\" % (remote_id, err))\n return False\n return True","def _verify(self):\n pass","def verify(self):\r\n pass","def test_retrieve_no_item(self):\n\n\t\titem = models.item(1)\n\t\tself.assertIsNone(item)","def _get_location_from_entity(self, entity_id):\n entity = self._hass.states.get(entity_id)\n\n if entity is None:\n _LOGGER.error(\"Unable to find entity %s\", entity_id)\n return None\n\n # Check if the entity has origin attributes\n if location.has_location(entity):\n return self._get_location_from_attributes(entity)\n\n # When everything fails just return nothing\n return None","def _resolve_user(self, data: dict):\n user_email = data.get('eml')\n if not user_email:\n raise OBDControllerError('User email not found')\n\n user: User = self.db_session.query(User).filter(User.email == user_email).first()\n if not user:\n raise OBDControllerError('User does not exist')\n\n return user","def test_check_existing_finds_existing_entities(self):\n namespace = 'default-gzip'\n collection = generate_collection(\n namespace, ['small content', 'larger content', 'biggest content'])\n key = model.get_entry_key(\n collection.namespace.namespace, collection.items[0].digest)\n\n # guarantee that one digest already exists in the datastore\n model.new_content_entry(key).put()\n response = self.call_api(\n 'preupload', self.message_to_dict(collection), 200)\n\n # we should see one enqueued task and two new URLs in the response\n items = response.json['items']\n self.assertEqual(2, len(items))\n self.assertEqual([1, 2], [int(item['index']) for item in items])\n for item in items:\n self.assertIsNotNone(item.get('upload_ticket'))\n\n # remove tasks so tearDown doesn't complain\n _ = self.execute_tasks()","def test_beneficiaries_retrieve_withoutID_that_will_fail(self):\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\n try:\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve')\n response = self.client.get(url)\n self.assertTrue(response.status_code, 200)\n except Exception as e:\n print(\"reason: \", e)","def GetEntity(self):\n\t\treturn self.acad.ActiveDocument.Utility.GetEntity()","def acl_check_entity(self, entity, auth_context, op, obj):\n acl_check = (\n entity.acl_check(auth_context, op, obj)\n if entity.has_acl()\n else self.default_acl.acl_check(auth_context, op, obj))\n if not acl_check:\n raise exceptions.AclError(\n 'unauthorized change to %s' % (\n entity.name,))","def entity(self) -> _InternalEntityType[_T]:\n self.parent._check_configure()\n return self.entity","def visit_entity(self, entity):","def verify(self):\n pass","def verify(self):\n pass","def fetch(self) -> None:\n pass","def verify(self):\n raise NotImplementedError","def test_get(self):\n objects = self.populate()\n for obj in objects:\n found = models.storage.get(type(obj), obj.id)\n self.assertIs(found, obj)\n for obj in objects:\n obj.delete()\n found = models.storage.get(type(obj), obj.id)\n self.assertIsNone(found)","def get_resource_by_pk(self, pk, **kwargs):\n try:\n instance = self.manager.get_by_id(pk, **kwargs)\n except Exception as e:\n # Hack for \"Unauthorized\" exceptions, we do want to propagate those\n response = getattr(e, \"response\", None)\n status_code = getattr(response, \"status_code\", None)\n if status_code and status_code == http_client.FORBIDDEN:\n raise e\n if status_code and status_code == http_client.UNAUTHORIZED:\n raise e\n\n instance = None\n\n return instance","def test_entity(self):\n self.request.log(\"Hello World\", entities=(Entity(1337)(12, \"Demo\"),))\n self.request.end()\n entry = self.get_entry()\n assert 'entities' in entry\n assert len(entry['entities']) == 1\n assert entry['entities'][0] == dict(entity=1337, id=12, name=\"Demo\")","async def test_intent_entity(self, dm):\n request = create_request(\"domain\", \"intent\", [{\"type\": \"entity_2\"}])\n result = await dm.apply_handler(request, create_responder(request))\n assert result.dialogue_state == \"intent_entity_2\"","def get_object(self):\n queryset = self.filter_queryset(self.get_queryset())\n obj = get_object_or_404(queryset, question=self.kwargs['pk'])\n self.check_object_permissions(self.request, obj)\n return obj","def GetCachedOr404(entity_key, active_only=True):\n entity = request_cache.GetEntitiesFromKeys([entity_key])[0]\n if entity is None:\n raise http.Http404\n elif active_only and hasattr(entity, 'deleted') and entity.deleted:\n raise http.Http404\n return entity","def test_nondependent_object_get(self):\n manager = ImporterManager(importer=UserImporter())\n for row,name in enumerate(self.usernames):\n manager.update_kvs(field_name='username',value=name,row=row)\n\n manager.get_available_rows()\n for i in range(self.n_objs):\n objs: List[RecordData] = manager.get_objs_and_meta(i) #: Returns a list of objects only if manytomany\n self.assertEqual(objs[0].available, True)\n self.assertIsNotNone(objs[0].object)\n self.assertIsInstance(objs[0].object, User)\n self.assertIsNotNone(objs[0].query)\n\n del manager","def get_object(self):\n queryset = self.filter_queryset(self.get_queryset())\n obj = queryset.get(pk=self.request.user.id)\n self.check_object_permissions(self.request, obj)\n return obj","def test_get_none(self):\n models.storage.close()\n models.storage = models.engine.db_storage.DBStorage()\n models.storage.reload()\n obj = self.populate()\n\n found = models.storage.get(type(obj[0]), None)\n self.assertEqual(found, None)\n with self.assertRaises(KeyError):\n models.storage.get(None, obj[0].id)\n with self.assertRaises(KeyError):\n models.storage.get(None, None)","def test_retrieve_not_found(self):\n\n # get a valid digest\n content = \"\"\"\\xe1\\xbc\\x84\\xce\\xbd\\xce\\xb4\\xcf\\x81\\xce\\xb1\n \\xce\\xbc\\xce\\xbf\\xce\\xb9\n \\xe1\\xbc\\x94\\xce\\xbd\\xce\\xbd\\xce\\xb5\\xcf\\x80\\xce\\xb5\"\"\"\n namespace = 'default'\n collection = generate_collection(namespace, [content])\n preupload_status = self.call_api(\n 'preupload', self.message_to_dict(collection), 200)\n message = preupload_status.json.get(u'items', [{}])[0]\n\n # get the digest\n request = preupload_status_to_request(message, content)\n embedded = validate(\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\n\n # don't upload data; try to retrieve\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\n digest=embedded['d'], namespace=handlers_endpoints_v1.Namespace())\n with self.call_should_fail('404'):\n self.call_api('retrieve', self.message_to_dict(retrieve_request), 200)","async def _do_work_claim(self) -> bool:\n # 1. Ask the LTA DB for the next Bundle to be deleted\n # configure a RestClient to talk to the LTA DB\n lta_rc = ClientCredentialsAuth(address=self.lta_rest_url,\n token_url=self.lta_auth_openid_url,\n client_id=self.client_id,\n client_secret=self.client_secret,\n timeout=self.work_timeout_seconds,\n retries=self.work_retries)\n self.logger.info(\"Asking the LTA DB for a Bundle to check for TransferRequest being finished.\")\n pop_body = {\n \"claimant\": f\"{self.name}-{self.instance_uuid}\"\n }\n response = await lta_rc.request('POST', f'/Bundles/actions/pop?source={self.source_site}&dest={self.dest_site}&status={self.input_status}', pop_body)\n self.logger.info(f\"LTA DB responded with: {response}\")\n bundle = response[\"bundle\"]\n if not bundle:\n self.logger.info(\"LTA DB did not provide a Bundle to check. Going on vacation.\")\n return False\n # update the TransferRequest that spawned the Bundle, if necessary\n await self._update_transfer_request(lta_rc, bundle)\n # even if we processed a Bundle, take a break between Bundles\n return False","def test_deleting_a_persisted_entity(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"Jim\", last_name=\"Carrey\"\n )\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\n assert deleted_person is not None\n assert deleted_person.state_.is_destroyed is True\n\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(identifier)","def test_badge_should_have_entity_id(self):\n\n badge = self.get_sample_badge()\n self.assertIsInstance(badge.entity_id, str)","def blocking_entity(self) -> Optional[Entity]:\n return self.engine.game_map.get_blocking_entity_at_location(*self.dest_xy)","def get_by_urlsafe(urlsafe, model):\n try:\n key = ndb.Key(urlsafe=urlsafe)\n except TypeError:\n raise endpoints.BadRequestException('Invalid Key')\n except Exception as e:\n if e.__class__.__name__ == 'ProtocolBufferDecodeError':\n raise endpoints.BadRequestException('Invalid Key')\n else:\n raise\n\n entity = key.get()\n if not entity:\n return None\n if not isinstance(entity, model):\n raise ValueError('Incorrect Kind')\n return entity","def _get(self, id_: str) -> Union[DBModelInstance, NoReturn]:\n record = self.model.query.get(id_)\n if record:\n return record\n else:\n # raise error to correct handling wrong inputted params\n raise ServiceBadRequest()","def tryGet(self, cls, id, default):\n pass","def get(\n self,\n entity: _EntityBindKey[_O],\n ident: _PKIdentityArgument,\n *,\n options: Optional[Sequence[ORMOption]] = None,\n populate_existing: bool = False,\n with_for_update: ForUpdateParameter = None,\n identity_token: Optional[Any] = None,\n execution_options: OrmExecuteOptionsParameter = util.EMPTY_DICT,\n bind_arguments: Optional[_BindArguments] = None,\n ) -> Optional[_O]:\n return self._get_impl(\n entity,\n ident,\n loading.load_on_pk_identity,\n options=options,\n populate_existing=populate_existing,\n with_for_update=with_for_update,\n identity_token=identity_token,\n execution_options=execution_options,\n bind_arguments=bind_arguments,\n )","def test_entities__EntityOrder__1():\n zope.interface.verify.verifyObject(IEntityOrder, EntityOrder())","def test_resource_user_resource_get_user_get(self):\n pass","def load(self):\n self.driver.expire(self.key, self.checkin)\n \n self.logger.debug(\"Loading %s\", self.key)\n \n try:\n info = self.driver.load(self.key)\n self.logger.debug(\"%s found\", self.key)\n except BackendKeyNotFound:\n self.logger.debug(\"Entry %s not found, creating a new one\", self.key)\n info = self.driver.new(self.key)\n \n self.failures = info[\"failures\"]\n self.checkin = info[\"checkin\"]\n self.status = info[\"status\"]","def test_otoroshi_controllers_adminapi_tcp_service_api_controller_find_entity_by_id_action(self):\n pass","def test_verify(self):\n self.testObject.verify()"],"string":"[\n \"async def get_entity(self):\\n if not self.entity and await self.get_input_entity():\\n try:\\n self._entity =\\\\\\n await self._client.get_entity(self._input_entity)\\n except ValueError:\\n pass\\n\\n return self._entity\",\n \"async def test_get_entity(self):\\n await test_service.get_entity(self)\",\n \"def _load_entity(client, entity_type, entity_id, parent_key=None):\\n\\n key = _load_key(client, entity_type, entity_id, parent_key)\\n entity = client.get(key)\\n log('retrieved entity: ' + entity_type + ' for ID: ' + str(entity_id))\\n return entity\",\n \"def test_failed_entity_retrieval_by_its_primary_key(self, test_domain):\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(uuid4())\",\n \"def test4ValidEntity(id):\\n object = None\\n try:\\n object = ndb.Key(urlsafe=id).get()\\n except Exception, e:\\n if e.__class__.__name__ == ProtocolBufferDecodeError:\\n object = None\\n return object\",\n \"def test_entities__PersistentEntities__1():\\n zope.interface.verify.verifyObject(IEntities, PersistentEntities())\",\n \"def validate(self, entity_id):\\n\\n safe_entity = self.find_by_id(entity_id)\\n if not safe_entity:\\n raise EntityNotFound(\\\"The param_id = {} is not valid.\\\".format(entity_id))\\n\\n return safe_entity\",\n \"def fetch_entity(endpoint, values):\\n values['entity'] = Entity.objects.get_or_404(name=values['entity'])\",\n \"def _get_helper(cls, database, id_field, entity_id):\\n entity = database.get(entity_id)\\n if not entity:\\n return validation.get_not_found_error(id_field, entity_id)\\n return entity\",\n \"def GetEntity(self):\\n return self.__entity\",\n \"def entity_exists(entity):\\n return entity.get_by_id(entity.key.id())\",\n \"def test_entity_doesnt_exist(self):\\n key = ndb.Key(models.InstanceTemplateRevision, 'fake-key')\\n urls = snapshots.fetch(key)\\n self.failIf(urls)\",\n \"def test_entities__Entities__1():\\n zope.interface.verify.verifyObject(IEntities, Entities())\",\n \"async def test_entity_entity_reference(self):\\n test_name = 'test_entity_entity_reference'\\n entity_name = 'TestEntityEntityReference'\\n\\n corpus = TestHelper.get_local_corpus(self.tests_subpath, test_name)\\n expected_output_path = TestHelper.get_expected_output_folder_path(self.tests_subpath, test_name)\\n manifest = await corpus.fetch_object_async('local:/default.manifest.cdm.json')\\n\\n ent_test_entity_entity_reference = await corpus.fetch_object_async('local:/{}.cdm.json/{}'.format(entity_name, entity_name), manifest)\\n self.assertIsNotNone(ent_test_entity_entity_reference)\\n resolved_test_entity_entity_reference = await ProjectionTestUtils.get_resolved_entity(corpus, ent_test_entity_entity_reference, [])\\n self.assertIsNotNone(resolved_test_entity_entity_reference)\\n await AttributeContextUtil.validate_attribute_context(self, corpus, expected_output_path, entity_name, resolved_test_entity_entity_reference)\",\n \"def _load(self):\\n entity: dict = self.service.find_one({\\\"_id\\\": self._id})\\n\\n if entity is not None:\\n self.created_at = entity.get(\\\"created_at\\\")\\n self.updated_at = entity.get(\\\"updated_at\\\")\",\n \"def read(self, entity, content_type):\\n return None\",\n \"def test_entities__Entity__1():\\n entity = Entity(None, IEntity, 'Entity')\\n zope.interface.verify.verifyObject(IEntity, entity)\",\n \"def expect_datatore_lookup(self, blob_key, expected_result):\\n self.mox.StubOutWithMock(datastore, 'Get')\\n blob_url = datastore.Entity('__BlobServingUrl__',\\n name=blob_key)\\n if expected_result:\\n datastore.Get(blob_url.key()).AndReturn(True)\\n else:\\n datastore.Get(blob_url.key()).AndRaise(\\n datastore_errors.EntityNotFoundError)\",\n \"def process(self, entity: Entity) -> ProcessorResult:\\n if isinstance(entity, str): # If we were unable to fetch entity (404/403)\\n return ProcessorResult(False, f\\\"Unable to resolve Entity {entity}\\\")\\n try:\\n # First try to get the value off the entity\\n entity_dict = entity.get_entity()\\n data = get(entity_dict, self.field)\\n if data:\\n return ProcessorResult(True, data)\\n\\n # If not found, try to get the value from the asset identifiers\\n identifier = next(iter(filter(lambda x: x['type'] == self.field, entity_dict.get('identifiers', []))), None)\\n if identifier:\\n return ProcessorResult(True, identifier['value'])\\n\\n # Return a failed processor result if no field was found on the object or it's identifiers\\n return ProcessorResult(False,\\n f'Unable to find {self.field} in identifiers for entity {entity.get_marquee_id()}')\\n\\n except ValueError:\\n return ProcessorResult(False, \\\"Could not get field on entity\\\")\",\n \"def test_client_verification_document_retrieve(self):\\n pass\",\n \"def test_client_verification_retrieve(self):\\n pass\",\n \"def testFetch(self):\\n try:\\n provU = ProvenanceProvider(self.__cfgOb, self.__cachePath, useCache=False)\\n pD = {self.__provKeyName: self.__provInfoL}\\n ok = provU.store(pD)\\n self.assertTrue(ok)\\n #\\n fD = provU.fetch()\\n self.assertTrue(self.__provKeyName in fD)\\n self.assertDictEqual(pD, fD)\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"def test_get(self):\\n self.assertEqual(\\n self.attempts[0],\\n self.resource.get(self.attempts[0][_ATTEMPT.attempt_id]))\",\n \"def get_entity(self):\\n if self.override_entity and not self.override_entity.abstract_entity:\\n return self.override_entity\\n elif self.get_role():\\n return self.get_role().entity\\n return None\",\n \"def provider_entity(provider):\\n try:\\n return NUTEntity.objects.get(id=provider.first_access().target.id)\\n except:\\n return None\",\n \"def _object_get(self, pk):\\n try:\\n return self.model.objects.get(pk=pk)\\n except self.model.DoesNotExist:\\n raise DoesNotExist(self.model.__name__.lower(), primary_key=pk)\",\n \"async def test_entity_attribute_entity_reference(self):\\n test_name = 'test_entity_attribute_entity_reference'\\n entity_name = 'TestEntityAttributeEntityReference'\\n\\n corpus = TestHelper.get_local_corpus(self.tests_subpath, test_name)\\n expected_output_path = TestHelper.get_expected_output_folder_path(self.tests_subpath, test_name)\\n manifest = await corpus.fetch_object_async('local:/default.manifest.cdm.json')\\n\\n ent_test_entity_attribute_entity_reference = await corpus.fetch_object_async('local:/{}.cdm.json/{}'.format(entity_name, entity_name), manifest)\\n self.assertIsNotNone(ent_test_entity_attribute_entity_reference)\\n resolved_test_entity_attribute_entity_reference = await ProjectionTestUtils.get_resolved_entity(corpus, ent_test_entity_attribute_entity_reference, [])\\n self.assertIsNotNone(resolved_test_entity_attribute_entity_reference)\\n await AttributeContextUtil.validate_attribute_context(self, corpus, expected_output_path, entity_name, resolved_test_entity_attribute_entity_reference)\",\n \"def test_retrieve_result(self):\\n dr = EventualResult(Deferred(), None)\\n uid = dr.stash()\\n self.assertIdentical(dr, retrieve_result(uid))\",\n \"def get(cls, entity_id, external_project_id=None,\\n force_show_deleted=False,\\n suppress_exception=False, session=None):\\n db_entity = cls.db_repo.get(\\n entity_id, external_project_id=external_project_id,\\n force_show_deleted=force_show_deleted,\\n suppress_exception=suppress_exception, session=session)\\n if db_entity:\\n return cls()._from_db_object(db_entity)\\n else:\\n return None\",\n \"def fetch(self, vault_client):\\n result = self.read(vault_client)\\n if result:\\n if isinstance(result, dict) and 'data' in result:\\n self.existing = result['data']\\n else:\\n self.existing = result\\n else:\\n self.existing = None\",\n \"def __call__(self, context=None):\\n if context is None:\\n context = self.context\\n return self.entity.resolve(context)\",\n \"def get(entity, name=None, version=None, lineage=None):\",\n \"def get_target_entity(self):\\n return None\",\n \"def test_entities__entity_by_obj__1(stubEntities, entityAdapters):\\n obj = persistent.Persistent()\\n with pytest.raises(ValueError):\\n IEntity(obj)\",\n \"def __get__(self, model_instance, model_class):\\n instance = None\\n\\n if model_instance is None:\\n return self\\n\\n if hasattr(model_instance, self.__id_attr_name()):\\n reference_id = getattr(model_instance, self.__id_attr_name())\\n else:\\n reference_id = None\\n\\n if reference_id is not None:\\n\\n resolved = getattr(model_instance, self.__resolved_attr_name())\\n\\n if resolved is not None:\\n return resolved\\n\\n else:\\n instance = memcache.get(str(reference_id) or '')\\n instance = memcache.get(instance or '')\\n\\n if instance:\\n # Convert to model from protobuf\\n instance = db.model_from_protobuf(entity_pb.EntityProto(instance))\\n\\n # Check in DB after checking in memcache\\n if not instance:\\n instance = db.get(reference_id)\\n\\n '''\\n # removed to make sure things keep running even after we screw with the db.\\n if instance is None:\\n raise ReferencePropertyResolveError(\\n 'ReferenceProperty failed to be resolved: %s' %\\n reference_id.to_path())\\n '''\\n\\n if not instance:\\n logging.error(\\\"The referenced object no longer exists; \\\"\\n \\\"returning None.\\\")\\n\\n setattr(model_instance, self.__resolved_attr_name(), instance)\\n return instance\\n else:\\n return None\",\n \"def verify_instance(**kwargs):\\n instance = kwargs.get('instance')\\n\\n from amelie.claudia.models import Mapping\\n\\n transaction.on_commit(lambda: tasks.verify_instance.delay(Mapping.get_type(instance), instance.id))\",\n \"def _during_execute(self, db, entity):\\n pass\",\n \"def get(self):\\n entity = self._model.get_or_insert(self._key_name)\\n credential = getattr(entity, self._property_name)\\n if credential and hasattr(credential, 'set_store'):\\n credential.set_store(self.put)\\n return credential\",\n \"def test_entity_retrieval_by_its_primary_key(\\n self, test_domain, identifier, persisted_person\\n ):\\n person = test_domain.repository_for(Person)._dao.get(persisted_person.id)\\n assert person is not None\\n assert person.id == identifier\\n assert person == persisted_person\",\n \"def entity(self):\\n return self._entity\",\n \"def entity(self):\\n return self._entity\",\n \"def test_find(self):\\n PromotionFactory(code=\\\"SAVE30\\\").save()\\n save50 = PromotionFactory(code=\\\"SAVE50\\\")\\n save50.save()\\n\\n promotion = Promotion.find(save50.id)\\n self.assertIsNot(promotion, None)\\n self.assertEqual(promotion.id, save50.id)\\n self.assertEqual(promotion.code, save50.code)\\n self.assertEqual(promotion.percentage, save50.percentage)\",\n \"def _create_entity(self, model_name, entity):\\n model_pool = self.pool.get(model_name)\\n prepared_entity = self._prepare_entity(model_name, entity)\\n if not prepared_entity:\\n logger.debug(\\\"Prepared entity is empty : %s model %s\\\" % (prepared_entity, model_name))\\n return False\\n\\n logger.debug(\\\"Creating entity %s\\\\n%s\\\" % (model_name, prepared_entity))\\n new_id = model_pool.create(self.cr, self.uid, prepared_entity)\\n\\n logger.debug(\\\"Created %s, id %s\\\" % (model_name, new_id))\\n return new_id\",\n \"def get_entity(self, entity_id):\\n path = '/entity/' + str(entity_id)\\n try:\\n response = self.__session.get(self.__api_base_url + path)\\n response.raise_for_status()\\n response = response.json()\\n except (requests.HTTPError, requests.ConnectionError), error:\\n raise Exception(error.message)\\n\\n return response\",\n \"async def get(self, entity_type: str):\\n u = Utilities()\\n\\n # Get a dict of paths and information to entity types\\n # await u.get_api_root_response()\\n\\n # Parameters to pass to aiohttp (the OAuth token we got with the oauth_token function)\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + self.token}\\n #\\n # The url to use\\n # url = await self.create_url(\\\"/api/{entity}\\\")\\n url = await u.create_url(await u.entity_url(entity_type))\\n\\n async with ClientSession() as session:\\n async with session.get(url, headers=headers) as resp:\\n # Dict with key that contain dict of all entity types as values\\n Get.entity_dict = {}\\n\\n # Nested dictionary magic\\n Get.entity_dict[entity_type] = await resp.json()\",\n \"async def _async_update_data(self) -> dict[int, SurepyEntity]:\\n try:\\n return await self.surepy.get_entities(refresh=True)\\n except SurePetcareAuthenticationError as err:\\n raise ConfigEntryAuthFailed(\\\"Invalid username/password\\\") from err\\n except SurePetcareError as err:\\n raise UpdateFailed(f\\\"Unable to fetch data: {err}\\\") from err\",\n \"def optionally_retrieve(\\n cls: Type[T],\\n public_id: str,\\n datastore: Datastore\\n ) -> Optional[T]:\\n query = Query.require(cls._Q_RETRIEVE, 'Standalone Agent retrieval')\\n result = query.execute(datastore, {'public_id': public_id})\\n if result is None:\\n return None\\n return cls.decode(result)\",\n \"def get_object(self):\\n obj = get_object_or_404(Article, slug=self.kwargs[\\\"slug\\\"])\\n self.check_object_permissions(self.request, obj)\\n return obj\",\n \"def retrieve(cls: Type[T], agent_id: int, datastore: Datastore) -> T:\\n agent = cls.optionally_retrieve(agent_id, datastore)\\n if agent is None:\\n raise NotFound\\n return agent\",\n \"def test_beneficiaries_retrieve_validate_content_that_will_pass(self):\\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve', kwargs={'pk': 1})\\n response = self.client.get(url)\\n\\n # serialize all model object data\\n beneficiaries = Beneficiary.objects.get(pk=1)\\n serializer = BeneficiarySerializer(beneficiaries, many=False)\\n self.assertEqual(response.json(), serializer.data)\\n self.assertEqual(response.status_code, 200)\",\n \"def fetchProteinEntityData(self):\\n logger.info(\\\"Running fetchProteinEntityData...\\\")\\n ok = False\\n try:\\n ptsW = ProteinTargetSequenceWorkflow(self.__cfgOb, self.__cachePath)\\n ok = ptsW.exportRCSBProteinEntityFasta()\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n return ok\",\n \"def test_beneficiaries_retrieve_that_will_pass(self):\\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve', kwargs={'pk': 1})\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\",\n \"def fetch(self):\\n raise NotImplementedError()\",\n \"def _request_model(self, instance, success, get_embedded=True):\\n coll = self.get_collection('_model')\\n if get_embedded:\\n callback = partial(self._get_embedded_model_names,\\n instance=instance,\\n success=success)\\n else:\\n callback = success\\n\\n try:\\n instance['_model']\\n except KeyError:\\n raise tornado.web.HTTPError(400, 'Missing model key')\\n coll.find_one({'_id': instance['_model']},\\n callback=callback)\",\n \"def test_entities__EntityOrder__get__3(entityOrder, minimalEntity):\\n with pytest.raises(KeyError):\\n entityOrder.get(minimalEntity)\",\n \"def test_get_entity_version(self):\\n self.metadata.create_or_update(data=self.create)\\n\\n # Find by name\\n res_name = self.metadata.get_by_name(\\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\\n )\\n res = self.metadata.get_entity_version(\\n entity=Dashboard, entity_id=res_name.id.__root__, version=0.1\\n )\\n\\n # check we get the correct version requested and the correct entity ID\\n assert res.version.__root__ == 0.1\\n assert res.id == res_name.id\",\n \"def get(self, key, missing=None, deferred=None,\\n transaction=None, eventual=False, retry=None, timeout=None, read_time=None, model_type=None):\\n start = datetime.datetime.now()\\n entities = self.get_multi(keys=[key],\\n missing=missing,\\n deferred=deferred,\\n transaction=transaction,\\n eventual=eventual,\\n retry=retry,\\n timeout=timeout,\\n read_time=read_time,\\n model_type=model_type)\\n if entities:\\n end = datetime.datetime.now()\\n print('Time taken for get {}'.format(end - start))\\n return entities[0]\",\n \"def getEntityByID (self, ID, fail=False):\\n r = self.find(Or(Equal('DPID', ID),Equal(F('ID'), ID)))\\n if len(r) == 0:\\n if fail:\\n raise RuntimeError(\\\"No entity with ID \\\" + str(ID))\\n else:\\n return None\\n assert len(r) == 1\\n return r[0]\",\n \"def GetEntityViaMemcache(entity_key):\\n entity = memcache.get(entity_key)\\n if entity is not None:\\n return entity\\n key = ndb.Key(urlsafe=entity_key)\\n entity = key.get()\\n if entity is not None:\\n memcache.set(entity_key, entity)\\n return entity\",\n \"def test_entities__EntityOrder__get__2(entityOrder, unknownEntity):\\n with pytest.raises(KeyError):\\n entityOrder.get(unknownEntity)\",\n \"def test_entities__EntityOrder__get__5(entityOrder):\\n person = IEntity(IPerson)\\n with zope.component.hooks.site(None):\\n with pytest.raises(zope.component.ComponentLookupError):\\n entityOrder.get(person)\",\n \"def process_human_entity(entity: dict, language: str) -> bool:\\n try:\\n claims = entity[\\\"claims\\\"]\\n # Get ID\\n remote_id = entity[\\\"title\\\"]\\n print(\\\"%s\\\\t%s\\\" % (\\\"ID\\\".ljust(16), remote_id))\\n # Get name from label\\n name = entity[\\\"labels\\\"][language][\\\"value\\\"].lower()\\n print(\\\"%s\\\\t%s\\\" % (\\\"name\\\".ljust(16), name))\\n # Get brief\\n brief = entity[\\\"descriptions\\\"][language][\\\"value\\\"].lower()\\n print(\\\"%s\\\\t%s\\\" % (\\\"brief\\\".ljust(16), brief))\\n # Birthdate\\n if \\\"P569\\\" in claims:\\n birthdate = claims[\\\"P569\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"time\\\"]\\n print(\\\"%s\\\\t%s\\\" % (\\\"birthdate\\\".ljust(16), birthdate))\\n # Is Dead\\n print(\\\"%s\\\\t%s\\\" % (\\\"is dead\\\".ljust(16), \\\"P570\\\" in claims))\\n # Citizenship\\n if \\\"P27\\\" in claims:\\n country_id = claims[\\\"P27\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"id\\\"]\\n # Get country entity\\n country_entity = get_wbentity(country_id, language)\\n if \\\"P297\\\" in country_entity[\\\"claims\\\"]:\\n citizenship = country_entity[\\\"claims\\\"][\\\"P297\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"]\\n print(\\\"%s\\\\t%s\\\" % (\\\"citizenship\\\".ljust(16), citizenship))\\n # Birthplace\\n if \\\"P19\\\" in claims:\\n city_id = claims[\\\"P19\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"id\\\"]\\n # Get city entity\\n city_entity = get_wbentity(city_id, language)\\n if \\\"P1448\\\" in city_entity[\\\"claims\\\"]:\\n birthplace = city_entity[\\\"claims\\\"][\\\"P1448\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"text\\\"]\\n else:\\n birthplace = city_entity[\\\"labels\\\"][language][\\\"value\\\"]\\n print(\\\"%s\\\\t%s\\\" % (\\\"birthplace\\\".ljust(16), birthplace))\\n # Image\\n if \\\"P18\\\" in claims:\\n image_id = claims[\\\"P18\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"]\\n image_uri = get_image_info(image_id)\\n print(\\\"%s\\\\t%s\\\" % (\\\"image\\\".ljust(16), image_uri))\\n # Occupation\\n if \\\"P106\\\" in claims:\\n occupation_id = claims[\\\"P106\\\"][0][\\\"mainsnak\\\"][\\\"datavalue\\\"][\\\"value\\\"][\\\"id\\\"]\\n occupation_entity = get_wbentity(occupation_id, language)\\n occupation = occupation_entity[\\\"labels\\\"][language][\\\"value\\\"].lower()\\n print(\\\"%s\\\\t%s\\\" % (\\\"occupation\\\".ljust(16), occupation))\\n print_end()\\n except Exception as err:\\n print_err(\\\"%s error: %s\\\" % (remote_id, err))\\n return False\\n return True\",\n \"def _verify(self):\\n pass\",\n \"def verify(self):\\r\\n pass\",\n \"def test_retrieve_no_item(self):\\n\\n\\t\\titem = models.item(1)\\n\\t\\tself.assertIsNone(item)\",\n \"def _get_location_from_entity(self, entity_id):\\n entity = self._hass.states.get(entity_id)\\n\\n if entity is None:\\n _LOGGER.error(\\\"Unable to find entity %s\\\", entity_id)\\n return None\\n\\n # Check if the entity has origin attributes\\n if location.has_location(entity):\\n return self._get_location_from_attributes(entity)\\n\\n # When everything fails just return nothing\\n return None\",\n \"def _resolve_user(self, data: dict):\\n user_email = data.get('eml')\\n if not user_email:\\n raise OBDControllerError('User email not found')\\n\\n user: User = self.db_session.query(User).filter(User.email == user_email).first()\\n if not user:\\n raise OBDControllerError('User does not exist')\\n\\n return user\",\n \"def test_check_existing_finds_existing_entities(self):\\n namespace = 'default-gzip'\\n collection = generate_collection(\\n namespace, ['small content', 'larger content', 'biggest content'])\\n key = model.get_entry_key(\\n collection.namespace.namespace, collection.items[0].digest)\\n\\n # guarantee that one digest already exists in the datastore\\n model.new_content_entry(key).put()\\n response = self.call_api(\\n 'preupload', self.message_to_dict(collection), 200)\\n\\n # we should see one enqueued task and two new URLs in the response\\n items = response.json['items']\\n self.assertEqual(2, len(items))\\n self.assertEqual([1, 2], [int(item['index']) for item in items])\\n for item in items:\\n self.assertIsNotNone(item.get('upload_ticket'))\\n\\n # remove tasks so tearDown doesn't complain\\n _ = self.execute_tasks()\",\n \"def test_beneficiaries_retrieve_withoutID_that_will_fail(self):\\n print('the test function name: {}'.format(sys._getframe().f_code.co_name))\\n try:\\n url = reverse('beneficiary:beneficiary-entity-by-id-retrieve')\\n response = self.client.get(url)\\n self.assertTrue(response.status_code, 200)\\n except Exception as e:\\n print(\\\"reason: \\\", e)\",\n \"def GetEntity(self):\\n\\t\\treturn self.acad.ActiveDocument.Utility.GetEntity()\",\n \"def acl_check_entity(self, entity, auth_context, op, obj):\\n acl_check = (\\n entity.acl_check(auth_context, op, obj)\\n if entity.has_acl()\\n else self.default_acl.acl_check(auth_context, op, obj))\\n if not acl_check:\\n raise exceptions.AclError(\\n 'unauthorized change to %s' % (\\n entity.name,))\",\n \"def entity(self) -> _InternalEntityType[_T]:\\n self.parent._check_configure()\\n return self.entity\",\n \"def visit_entity(self, entity):\",\n \"def verify(self):\\n pass\",\n \"def verify(self):\\n pass\",\n \"def fetch(self) -> None:\\n pass\",\n \"def verify(self):\\n raise NotImplementedError\",\n \"def test_get(self):\\n objects = self.populate()\\n for obj in objects:\\n found = models.storage.get(type(obj), obj.id)\\n self.assertIs(found, obj)\\n for obj in objects:\\n obj.delete()\\n found = models.storage.get(type(obj), obj.id)\\n self.assertIsNone(found)\",\n \"def get_resource_by_pk(self, pk, **kwargs):\\n try:\\n instance = self.manager.get_by_id(pk, **kwargs)\\n except Exception as e:\\n # Hack for \\\"Unauthorized\\\" exceptions, we do want to propagate those\\n response = getattr(e, \\\"response\\\", None)\\n status_code = getattr(response, \\\"status_code\\\", None)\\n if status_code and status_code == http_client.FORBIDDEN:\\n raise e\\n if status_code and status_code == http_client.UNAUTHORIZED:\\n raise e\\n\\n instance = None\\n\\n return instance\",\n \"def test_entity(self):\\n self.request.log(\\\"Hello World\\\", entities=(Entity(1337)(12, \\\"Demo\\\"),))\\n self.request.end()\\n entry = self.get_entry()\\n assert 'entities' in entry\\n assert len(entry['entities']) == 1\\n assert entry['entities'][0] == dict(entity=1337, id=12, name=\\\"Demo\\\")\",\n \"async def test_intent_entity(self, dm):\\n request = create_request(\\\"domain\\\", \\\"intent\\\", [{\\\"type\\\": \\\"entity_2\\\"}])\\n result = await dm.apply_handler(request, create_responder(request))\\n assert result.dialogue_state == \\\"intent_entity_2\\\"\",\n \"def get_object(self):\\n queryset = self.filter_queryset(self.get_queryset())\\n obj = get_object_or_404(queryset, question=self.kwargs['pk'])\\n self.check_object_permissions(self.request, obj)\\n return obj\",\n \"def GetCachedOr404(entity_key, active_only=True):\\n entity = request_cache.GetEntitiesFromKeys([entity_key])[0]\\n if entity is None:\\n raise http.Http404\\n elif active_only and hasattr(entity, 'deleted') and entity.deleted:\\n raise http.Http404\\n return entity\",\n \"def test_nondependent_object_get(self):\\n manager = ImporterManager(importer=UserImporter())\\n for row,name in enumerate(self.usernames):\\n manager.update_kvs(field_name='username',value=name,row=row)\\n\\n manager.get_available_rows()\\n for i in range(self.n_objs):\\n objs: List[RecordData] = manager.get_objs_and_meta(i) #: Returns a list of objects only if manytomany\\n self.assertEqual(objs[0].available, True)\\n self.assertIsNotNone(objs[0].object)\\n self.assertIsInstance(objs[0].object, User)\\n self.assertIsNotNone(objs[0].query)\\n\\n del manager\",\n \"def get_object(self):\\n queryset = self.filter_queryset(self.get_queryset())\\n obj = queryset.get(pk=self.request.user.id)\\n self.check_object_permissions(self.request, obj)\\n return obj\",\n \"def test_get_none(self):\\n models.storage.close()\\n models.storage = models.engine.db_storage.DBStorage()\\n models.storage.reload()\\n obj = self.populate()\\n\\n found = models.storage.get(type(obj[0]), None)\\n self.assertEqual(found, None)\\n with self.assertRaises(KeyError):\\n models.storage.get(None, obj[0].id)\\n with self.assertRaises(KeyError):\\n models.storage.get(None, None)\",\n \"def test_retrieve_not_found(self):\\n\\n # get a valid digest\\n content = \\\"\\\"\\\"\\\\xe1\\\\xbc\\\\x84\\\\xce\\\\xbd\\\\xce\\\\xb4\\\\xcf\\\\x81\\\\xce\\\\xb1\\n \\\\xce\\\\xbc\\\\xce\\\\xbf\\\\xce\\\\xb9\\n \\\\xe1\\\\xbc\\\\x94\\\\xce\\\\xbd\\\\xce\\\\xbd\\\\xce\\\\xb5\\\\xcf\\\\x80\\\\xce\\\\xb5\\\"\\\"\\\"\\n namespace = 'default'\\n collection = generate_collection(namespace, [content])\\n preupload_status = self.call_api(\\n 'preupload', self.message_to_dict(collection), 200)\\n message = preupload_status.json.get(u'items', [{}])[0]\\n\\n # get the digest\\n request = preupload_status_to_request(message, content)\\n embedded = validate(\\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\\n\\n # don't upload data; try to retrieve\\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\\n digest=embedded['d'], namespace=handlers_endpoints_v1.Namespace())\\n with self.call_should_fail('404'):\\n self.call_api('retrieve', self.message_to_dict(retrieve_request), 200)\",\n \"async def _do_work_claim(self) -> bool:\\n # 1. Ask the LTA DB for the next Bundle to be deleted\\n # configure a RestClient to talk to the LTA DB\\n lta_rc = ClientCredentialsAuth(address=self.lta_rest_url,\\n token_url=self.lta_auth_openid_url,\\n client_id=self.client_id,\\n client_secret=self.client_secret,\\n timeout=self.work_timeout_seconds,\\n retries=self.work_retries)\\n self.logger.info(\\\"Asking the LTA DB for a Bundle to check for TransferRequest being finished.\\\")\\n pop_body = {\\n \\\"claimant\\\": f\\\"{self.name}-{self.instance_uuid}\\\"\\n }\\n response = await lta_rc.request('POST', f'/Bundles/actions/pop?source={self.source_site}&dest={self.dest_site}&status={self.input_status}', pop_body)\\n self.logger.info(f\\\"LTA DB responded with: {response}\\\")\\n bundle = response[\\\"bundle\\\"]\\n if not bundle:\\n self.logger.info(\\\"LTA DB did not provide a Bundle to check. Going on vacation.\\\")\\n return False\\n # update the TransferRequest that spawned the Bundle, if necessary\\n await self._update_transfer_request(lta_rc, bundle)\\n # even if we processed a Bundle, take a break between Bundles\\n return False\",\n \"def test_deleting_a_persisted_entity(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"Jim\\\", last_name=\\\"Carrey\\\"\\n )\\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\\n assert deleted_person is not None\\n assert deleted_person.state_.is_destroyed is True\\n\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(identifier)\",\n \"def test_badge_should_have_entity_id(self):\\n\\n badge = self.get_sample_badge()\\n self.assertIsInstance(badge.entity_id, str)\",\n \"def blocking_entity(self) -> Optional[Entity]:\\n return self.engine.game_map.get_blocking_entity_at_location(*self.dest_xy)\",\n \"def get_by_urlsafe(urlsafe, model):\\n try:\\n key = ndb.Key(urlsafe=urlsafe)\\n except TypeError:\\n raise endpoints.BadRequestException('Invalid Key')\\n except Exception as e:\\n if e.__class__.__name__ == 'ProtocolBufferDecodeError':\\n raise endpoints.BadRequestException('Invalid Key')\\n else:\\n raise\\n\\n entity = key.get()\\n if not entity:\\n return None\\n if not isinstance(entity, model):\\n raise ValueError('Incorrect Kind')\\n return entity\",\n \"def _get(self, id_: str) -> Union[DBModelInstance, NoReturn]:\\n record = self.model.query.get(id_)\\n if record:\\n return record\\n else:\\n # raise error to correct handling wrong inputted params\\n raise ServiceBadRequest()\",\n \"def tryGet(self, cls, id, default):\\n pass\",\n \"def get(\\n self,\\n entity: _EntityBindKey[_O],\\n ident: _PKIdentityArgument,\\n *,\\n options: Optional[Sequence[ORMOption]] = None,\\n populate_existing: bool = False,\\n with_for_update: ForUpdateParameter = None,\\n identity_token: Optional[Any] = None,\\n execution_options: OrmExecuteOptionsParameter = util.EMPTY_DICT,\\n bind_arguments: Optional[_BindArguments] = None,\\n ) -> Optional[_O]:\\n return self._get_impl(\\n entity,\\n ident,\\n loading.load_on_pk_identity,\\n options=options,\\n populate_existing=populate_existing,\\n with_for_update=with_for_update,\\n identity_token=identity_token,\\n execution_options=execution_options,\\n bind_arguments=bind_arguments,\\n )\",\n \"def test_entities__EntityOrder__1():\\n zope.interface.verify.verifyObject(IEntityOrder, EntityOrder())\",\n \"def test_resource_user_resource_get_user_get(self):\\n pass\",\n \"def load(self):\\n self.driver.expire(self.key, self.checkin)\\n \\n self.logger.debug(\\\"Loading %s\\\", self.key)\\n \\n try:\\n info = self.driver.load(self.key)\\n self.logger.debug(\\\"%s found\\\", self.key)\\n except BackendKeyNotFound:\\n self.logger.debug(\\\"Entry %s not found, creating a new one\\\", self.key)\\n info = self.driver.new(self.key)\\n \\n self.failures = info[\\\"failures\\\"]\\n self.checkin = info[\\\"checkin\\\"]\\n self.status = info[\\\"status\\\"]\",\n \"def test_otoroshi_controllers_adminapi_tcp_service_api_controller_find_entity_by_id_action(self):\\n pass\",\n \"def test_verify(self):\\n self.testObject.verify()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66966546","0.64643776","0.6297204","0.5874635","0.58368284","0.580103","0.5799007","0.5783325","0.57693875","0.5749898","0.5693132","0.5627393","0.5605186","0.5590932","0.5526674","0.54998803","0.5461038","0.5455379","0.54105365","0.5380263","0.5332381","0.5312724","0.53048116","0.526788","0.52659917","0.52447486","0.5210022","0.5190415","0.518882","0.5185213","0.5177997","0.51624644","0.5142572","0.5124601","0.51218504","0.5120231","0.51138306","0.51078266","0.5107205","0.51016676","0.51016676","0.5093623","0.50817263","0.5075247","0.50128704","0.50128293","0.50062454","0.49994388","0.4993329","0.49899364","0.49760047","0.49689534","0.4968863","0.49684545","0.49603555","0.4956493","0.49538496","0.4952739","0.49341086","0.49273735","0.49231392","0.49201885","0.4914205","0.49120224","0.49099436","0.49014032","0.49000964","0.48994073","0.48931956","0.4882212","0.48787788","0.4868968","0.48659042","0.48658228","0.48658228","0.4854448","0.4844956","0.4842226","0.4839916","0.48370793","0.4835905","0.48269","0.48206002","0.48186162","0.48157537","0.48085743","0.48076388","0.48063624","0.47967136","0.47752684","0.47738463","0.47725275","0.47708368","0.47672307","0.47648796","0.47641805","0.47622967","0.47603047","0.47566107","0.47558573"],"string":"[\n \"0.66966546\",\n \"0.64643776\",\n \"0.6297204\",\n \"0.5874635\",\n \"0.58368284\",\n \"0.580103\",\n \"0.5799007\",\n \"0.5783325\",\n \"0.57693875\",\n \"0.5749898\",\n \"0.5693132\",\n \"0.5627393\",\n \"0.5605186\",\n \"0.5590932\",\n \"0.5526674\",\n \"0.54998803\",\n \"0.5461038\",\n \"0.5455379\",\n \"0.54105365\",\n \"0.5380263\",\n \"0.5332381\",\n \"0.5312724\",\n \"0.53048116\",\n \"0.526788\",\n \"0.52659917\",\n \"0.52447486\",\n \"0.5210022\",\n \"0.5190415\",\n \"0.518882\",\n \"0.5185213\",\n \"0.5177997\",\n \"0.51624644\",\n \"0.5142572\",\n \"0.5124601\",\n \"0.51218504\",\n \"0.5120231\",\n \"0.51138306\",\n \"0.51078266\",\n \"0.5107205\",\n \"0.51016676\",\n \"0.51016676\",\n \"0.5093623\",\n \"0.50817263\",\n \"0.5075247\",\n \"0.50128704\",\n \"0.50128293\",\n \"0.50062454\",\n \"0.49994388\",\n \"0.4993329\",\n \"0.49899364\",\n \"0.49760047\",\n \"0.49689534\",\n \"0.4968863\",\n \"0.49684545\",\n \"0.49603555\",\n \"0.4956493\",\n \"0.49538496\",\n \"0.4952739\",\n \"0.49341086\",\n \"0.49273735\",\n \"0.49231392\",\n \"0.49201885\",\n \"0.4914205\",\n \"0.49120224\",\n \"0.49099436\",\n \"0.49014032\",\n \"0.49000964\",\n \"0.48994073\",\n \"0.48931956\",\n \"0.4882212\",\n \"0.48787788\",\n \"0.4868968\",\n \"0.48659042\",\n \"0.48658228\",\n \"0.48658228\",\n \"0.4854448\",\n \"0.4844956\",\n \"0.4842226\",\n \"0.4839916\",\n \"0.48370793\",\n \"0.4835905\",\n \"0.48269\",\n \"0.48206002\",\n \"0.48186162\",\n \"0.48157537\",\n \"0.48085743\",\n \"0.48076388\",\n \"0.48063624\",\n \"0.47967136\",\n \"0.47752684\",\n \"0.47738463\",\n \"0.47725275\",\n \"0.47708368\",\n \"0.47672307\",\n \"0.47648796\",\n \"0.47641805\",\n \"0.47622967\",\n \"0.47603047\",\n \"0.47566107\",\n \"0.47558573\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57059026"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":18492,"cells":{"query":{"kind":"string","value":"Create Announcement & assign to memcache; used by memcache cron job & putAnnouncement()."},"document":{"kind":"string","value":"def _cacheAnnouncement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5,\n Conference.seatsAvailable > 0)\n ).fetch(projection=[Conference.name])\n if confs:\n # If there are conferences close to being sold out,\n # format announcement and set it in memcache\n announcement = '%s %s' % (\n 'Last chance to attend! The following conferences '\n 'are nearly sold out:',\n ', '.join(conf.name for conf in confs))\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\n else:\n # If there are no sold out conferences,\n # delete the memcache announcements entry\n announcement = \"\"\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\n return announcement"},"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 getAnnouncement(self, request):\n announcement = memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\"\n return StringMessage(data=announcement)","def cache_announcement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5, Conference.seatsAvailable > 0\n )).fetch(projection=[Conference.name])\n\n if confs:\n # If there are almost sold out conferences,\n # format announcement and set it in memcache\n announcement = ANNOUNCEMENT_TPL % (\n ', '.join(conf.name for conf in confs))\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\n else:\n # If there are no sold out conferences,\n # delete the memcache announcements entry\n announcement = \"\"\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\n\n return announcement","def _Announce(self):\n key = self._GetServerKey(self.peer_id)\n logging.debug('Encrypting announcement.')\n value = self._Encrypt('%s:%d' % (self.host, self.port))\n logging.debug('Posting announcement.')\n self._dht.Put(key, value)","def get_announcement(self, request):\n return StringMessage(\n data=memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\")","def add_announcement(self, cid, c_name, content, date):\n query = \"INSERT INTO announcement(cid, c_name, content, date) VALUES (%s, %s, %s, %s)\"\n inputs = (cid, c_name, content, date, )\n return self.database_manager.execute_query(query, inputs)","async def announce(self, ctx, *, msg):\n if self._announce_msg is not None:\n await self.bot.say(\"Already announcing, wait until complete to\"\n \" issue a new announcement.\")\n else:\n self._announce_msg = msg","def DORadvertise_announce(key, value, ttlval, timeout=None):\r\n\r\n post_params = {'command': 'announce', 'key': key, 'value': value,\r\n 'lifetime': str(int(ttlval))}\r\n\r\n _DORadvertise_command(post_params, timeout=timeout)\r\n\r\n return None","def add_announcement():\n '''\n ann_id = db.Announcements.insert( name=request.vars.name )\n ann = db.post(ann_id)\n '''\n\n logger.info(\"api:add_announcement ===>>> category: %r\" % (request.vars.category))\n\n if request.vars.category not in ['urgent', 'event', \"shutdown\"]:\n return response.json({})\n\n else:\n\n vars = request.vars\n logger.info(\"vars.end_date: %r\" % (vars.end_date))\n ann_id = db.Announcements.insert(\n name = vars.name,\n latitude = vars.latitude,\n longitude = vars.longitude,\n description = vars.description,\n category = vars.category,\n end_date = vars.end_date\n )\n ann = db.Announcements(ann_id)\n\n logger.info(\"api:add_announcement ==> ann= %r\" % (ann))\n #logger.info(\"api:add_announcement_category ==> ann= %r\" % (ann.category))\n\n\n return response.json(ann)","def announce(self, request_announce):\n return self.client.call('POST',\n self.name + 'announce',\n payload=request_announce)","def announceGenerate(self):","def add_comment_to_announcement():\n vars = request.vars\n logger.info(\"vars.comment_text: %r\" % (vars.comment_text))\n comment_id = db.Comments.insert(\n comment_text = vars.comment_text,\n score = 1,\n ann_id= vars.ann_id,\n )\n comment = db.Announcements(comment_id)\n\n logger.info(\"api:add_comment_to_announcement ==> comment= %r\" % (comment))\n\n return response.json(comment)","async def announce(self, ctx):\n\n await ctx.message.delete()\n if len(ctx.message.content.split()) < 2:\n return await ctx.send('You must inform all parameters!')\n\n announce_channel = discord.utils.get(ctx.guild.channels, id=announcement_channel_id)\n msg = ctx.message.content.split('!announce', 1)\n await announce_channel.send(msg[1])","async def announcement(ctx, bot: typing.Union[discord.Member, discord.User]):\n plonked = await is_plonked(ctx.author.id)\n if plonked:\n return\n \n data = await make_request(\"https://www.motiondevelopment.top/api/v1.2/bots/\", bot.id)\n print(data)\n announcement = data[\"annoucements\"]\n print(bool(announcement))\n e = discord.Embed(color=0xfecdea, title=f'Announcement ID: {announcement[\"post_id\"]}')\n if announcement != False:\n e.add_field(\n name=f'{announcement[\"post_title\"]}',\n value=announcement[\"post_body\"]\n )\n e.description = f\"Post created by {data['owner_name']} ({data['owner_id']})\"\n else: \n e.description = 'This bot doesn\\'t have an announcement. :cry:'\n\n await em(ctx, embed=e)","def prepare_announce(self, request_announce):\n return self.client.call('POST',\n self.name + 'prepare-announce',\n payload=request_announce)","def add(self, missing_dates, uncached_announcements):\n raise NotImplementedError('AbstractCache is an abstract base class')","def announce(self):\n m = rtorrent9.rpc.Multicall(self)\n self.multicall_add(m, \"d.tracker_announce\")\n\n return m.call()[-1]","def persist(self) -> None:\n\n values = {\n \"time\": self.event.time,\n \"priority\": self.event.priority,\n \"argument\": self.event.argument,\n \"kwargs\": self.event.kwargs\n }\n\n cherrypy.engine.publish(\n \"cache:set\",\n self.cache_key,\n values,\n self.time_remaining\n )","def prep_advertiser(self):\n self.prep_consumer()\n self.advertiser = Advertiser.objects.get(id=113)\n self.consumer = Consumer.objects.get(id=113)\n self.consumer.consumer_create_datetime = self.three_days_ago\n self.consumer.email_subscription.add(1)\n self.consumer.save()","def add_assignment(cls, mentor_id, title, start_date, end_date, file_name, group='0'):\n new = Assignment(mentor_id, title, start_date, end_date, file_name, group)\n db.session.add(new)\n db.session.commit()","def v2centralizedadvertise_announce(key, value, ttlval):\r\n # do basic argument checking / munging\r\n key = str(key)\r\n value = str(value)\r\n\r\n if not type(ttlval) is int and not type(ttlval) is long:\r\n raise TypeError(\"Invalid type '\"+str(type(ttlval))+\"' for ttlval.\")\r\n\r\n if ttlval < 1:\r\n raise ValueError(\"The argument ttlval must be positive, not '\"+str(ttlval)+\"'\")\r\n\r\n \r\n # build the tuple to send, then convert to a string because only strings\r\n # (bytes) can be transmitted over the network...\r\n datatosend = ('PUT',key,value,ttlval)\r\n datastringtosend = serialize_serializedata(datatosend)\r\n\r\n \r\n # send the data over a timeout socket using the session library, then\r\n # get a response from the server.\r\n sockobj = timeout_openconn(v2servername,v2serverport, timeout=10)\r\n try:\r\n session_sendmessage(sockobj, datastringtosend)\r\n rawresponse = session_recvmessage(sockobj)\r\n finally:\r\n # BUG: This raises an error right now if the call times out ( #260 )\r\n # This isn't a big problem, but it is the \"wrong\" exception\r\n sockobj.close()\r\n \r\n # We should check that the response is 'OK'\r\n try:\r\n response = serialize_deserializedata(rawresponse)\r\n if response != 'OK':\r\n raise CentralAdvertiseError(\"Centralized announce failed with '\"+response+\"'\")\r\n except ValueError, e:\r\n raise CentralAdvertiseError(\"Received unknown response from server '\"+rawresponse+\"'\")","def touch_member(self, data, ttl=None, permanent=False):","def add(self, artifact, ttl):\n self.purge()\n \n target = ( isinstance(artifact, DNSArtifact)\n and str(artifact.remote_address) or artifact.name\n ).lower().rstrip('.')\n fqdns = [ name.lower().rstrip('.') for name in artifact.onames ]\n\n if target in self.index:\n self.index[target].update(fqdns)\n return\n \n association = Association(target, fqdns, ttl)\n self.index[target] = association\n (self.new_expiry or self.expiry).append(association)\n \n return","def announce(self, discovery):\n try:\n url = self.__discovery_url()\n logger.debug(\"making announce request to %s\", url)\n response = None\n response = self.client.put(url,\n data=to_json(discovery),\n headers={\"Content-Type\": \"application/json\"},\n timeout=0.8)\n\n if response.status_code is 200:\n self.last_seen = datetime.now()\n except (requests.ConnectTimeout, requests.ConnectionError):\n logger.debug(\"announce\", exc_info=True)\n finally:\n return response","def test_announcments_add_model(self):\n announcement = Announcement(title = \"Title\", description = \"Description\", date = datetime(2015, 6, 5, 8, 10, 10, 10))\n db.session.add(announcement)\n db.session.commit()\n self.assertEqual(Announcement.query.filter_by(title = \"Title\", description = \"Description\", date = datetime(2015, 6, 5, 8, 10, 10, 10)).count(), 1)","def centralizedadvertise_announce(key, value, ttlval):\r\n # do basic argument checking / munging\r\n key = str(key)\r\n value = str(value)\r\n\r\n if not type(ttlval) is int and not type(ttlval) is long:\r\n raise TypeError(\"Invalid type '\"+str(type(ttlval))+\"' for ttlval.\")\r\n\r\n if ttlval < 1:\r\n raise ValueError(\"The argument ttlval must be positive, not '\"+str(ttlval)+\"'\")\r\n\r\n \r\n # build the tuple to send, then convert to a string because only strings\r\n # (bytes) can be transmitted over the network...\r\n datatosend = ('PUT',key,value,ttlval)\r\n datastringtosend = serialize_serializedata(datatosend)\r\n\r\n \r\n # send the data over a timeout socket using the session library, then\r\n # get a response from the server.\r\n sockobj = timeout_openconn(servername,serverport, timeout=10)\r\n try:\r\n session_sendmessage(sockobj, datastringtosend)\r\n rawresponse = session_recvmessage(sockobj)\r\n finally:\r\n # BUG: This raises an error right now if the call times out ( #260 )\r\n # This isn't a big problem, but it is the \"wrong\" exception\r\n sockobj.close()\r\n \r\n # We should check that the response is 'OK'\r\n try:\r\n response = serialize_deserializedata(rawresponse)\r\n if response != 'OK':\r\n raise CentralAdvertiseError(\"Centralized announce failed with '\"+response+\"'\")\r\n except ValueError, e:\r\n raise CentralAdvertiseError(\"Received unknown response from server '\"+rawresponse+\"'\")","def arp_announce(self):\n pass","def newTask(name, description, assigner, id=None, priority=None, submitter_email=None, whose=None):\n if whose:\n user_id = jutdaapi.find_user(whose)\n if not user_id:\n raise ValueError('bad whose assignment: '+str(whose))\n #title = name + ' for: '+assigner.title()\n # that was the old scheme\n title = '('+assigner.title()+') '+name\n\n if priority != None:\n #priority = (int(priority) + 2) / 2\n priority = int(priority)\n RA_queue = 3\n #if assigner != 'no one':\n # description += '<tasktrackermeta assigner=\"'+assigner+'\"/>'\n if isinstance(id, str):\n description += '<tasktrackermeta id=\"'+id+'\"/>'\n ticket_id = jutdaapi.create_ticket(RA_queue, title, description,\n priority=priority, submitter_email=submitter_email)\n # Is there a race condition here? In this kind of database\n # I would assume not.\n time.sleep(1)\n ticket = jutdaapi.get_detailed_ticket(ticket_id)\n t = ticketToTask(ticket)\n return t","def mempool_assert_announcement(condition: ConditionWithArgs, announcements: Set[bytes32]) -> Optional[Err]:\n announcement_hash = bytes32(condition.vars[0])\n if announcement_hash not in announcements:\n return Err.ASSERT_ANNOUNCE_CONSUMED_FAILED\n\n return None","def save(self, *args, **kwargs):\n super(News, self).save(*args, **kwargs)\n pigeonpost_queue.send(sender=self, defer_for=6*60*60)","def obj_create(self, bundle, **kwargs):\n logger.info(\"Creating a new acknowledgement...\")\n #Create the object\n bundle.obj = Acknowledgement()\n #hydrate\n bundle = self.full_hydrate(bundle)\n \n #Set the customer\n try:\n logger.info(\"Setting customer...\")\n bundle.obj.customer = Customer.objects.get(pk=bundle.data[\"customer\"][\"id\"])\n bundle.obj.discount = bundle.obj.customer.discount\n except:\n logger.error(\"Customer with ID {0} could not be found.\".format(bundle.data['customer']['id']))\n raise\n \n #Set the employee\n try:\n logger.info(\"Setting employee...\")\n bundle.obj.employee = bundle.request.user\n except User.DoesNotExist:\n logger.error(\"User with ID {0} could not be found\".format(bundle.data['employee']['id']))\n raise\n except KeyError:\n logger.critical(\"Missing employee ID.\")\n raise\n \n #Set Status\n bundle.obj.status = \"ACKNOWLEDGED\"\n \n #Set the project or create a new one\n if \"project\" in bundle.data:\n try:\n project = Project.objects.get(pk=bundle.data['project']['id'])\n except KeyError, Project.DoesNotExist:\n try:\n project = Project()\n project.codename = bundle.data['project']['codename']\n project.save()\n except KeyError:\n project = None\n \n bundle.obj.project = project\n \n #Create items without saving them \n logger.info(\"Creating items...\")\n self.items = [Item.create(acknowledgement=bundle.obj,\n commit=False,\n **product) for product in bundle.data[\"items\"]]\n \n #Calculate the total price\n logger.info(\"Calculating balance of the order...\")\n bundle.obj.calculate_totals(self.items)\n bundle = self.save(bundle)\n \n #Save the items\n logger.info(\"Saving the items to the database...\")\n for item in self.items:\n item.acknowledgement = bundle.obj\n item.save()\n \n log_message = \"Ack {0} created on {1}. Schedule to be delivered on {1}\"\n log_message = log_message.format(bundle.obj.id,\n bundle.obj.time_created.strftime('%B %d, %Y'),\n bundle.obj.delivery_date.strftime('%B %d, %Y'))\n log = Log(message=log_message,\n delivery_date=bundle.obj.delivery_date,\n acknowledgement=bundle.obj)\n log.save()\n #Create and upload the pdfs to the \n #S3 system. The save the pdfs as\n #Attributes of the acknowledgement\n logger.info(\"Creating PDF documents...\")\n bundle.obj.create_and_upload_pdfs()\n \n \n #Add the url of the pdf to the outgoing data\n #only for when an acknowledgement is create\n try:\n ack = bundle.obj.acknowledgement_pdf\n production = bundle.obj.production_pdf\n bundle.data['pdf'] = {'acknowledgement': ack.generate_url(),\n 'production': production.generate_url()}\n except AttributeError: \n logger.warn('Missing acknowledgement or production pdf')\n \n #Conditionally email ack to Decoroom\n if \"decoroom\" in bundle.obj.customer.name.lower():\n try:\n logger.info(\"Emailing Decoroom Co., Ltd. the order details...\")\n bundle.obj.email_decoroom()\n except Exception as e:\n logger.error(\"Unable to mail decoroom.\")\n logger.error(e)\n \n \n \n logger.info(u\"Acknowledgement #{0} created for {1}\".format(bundle.obj.id, \n bundle.obj.customer.name)) \n return bundle","def put_memo(dt, mem):\n\trecord = {\n\t\t\"type\": \"dated_memo\", \n\t\t\"date\": dt.to('utc').naive,\n\t\t\"text\": mem\n\t}\n\tcollection.insert(record)\n\treturn","def __init__(self):\n abstracttask.Task.__init__(self)\n self._ticket_id = ''# this is an implementation detail of jutda task tracker\n self.timespent = datetime.timedelta(0) # not editable permenently, but saves data from hours\n self.starttime = datetime.datetime.now() # ticket creation time in this implementation \n self.isappointment = False # always false for these\n self.followups = [] # not likely to be used, since other implementation doesn't have it.\n self._orig = None\n self.submitter_email = None","def __publish_alembic_cache(self, item, output, work_template, primary_publish_path,\n sg_task, comment, thumbnail_path, progress_cb):\n # determine the publish info to use\n #\n progress_cb(10, \"Determining publish details\")\n\n # get the current scene path and extract fields from it\n # using the work template:\n scene_path = os.path.abspath(cmds.file(query=True, sn=True))\n fields = work_template.get_fields(scene_path)\n publish_version = fields[\"version\"]\n tank_type = output[\"tank_type\"]\n\n fields[\"cache_name\"] = item[\"name\"]\n\n # create the publish path by applying the fields\n # with the publish template:\n publish_template = output[\"publish_template\"]\n publish_path = publish_template.apply_fields(fields)\n\n # ensure the publish folder exists:\n publish_folder = os.path.dirname(publish_path)\n self.parent.ensure_folder_exists(publish_folder)\n\n # determine the publish name:\n publish_name = fields.get(\"name\")\n if not publish_name:\n publish_name = os.path.basename(publish_path)\n\n # Find additional info from the scene:\n #\n progress_cb(10, \"Analysing scene\")\n\n alembic_args = [\n \"normals=1\",\n \"uvs=1\",\n \"facesets=0\",\n \"useinitshadgrp=0\",\n \"dynamictopology=0\",\n \"transformcache=0\",\n \"globalspace=0\",\n \"ogawa=1\",\n ]\n\n # find the animated frame range to use:\n # Don't use self._find_scene_animation_range() because with effects\n # scenes we don't have a anim curve to determine the frame range from\n start_frame = int(cmds.playbackOptions(q=True, min=True))\n end_frame = int(cmds.playbackOptions(q=True, max=True))\n alembic_args.append(\"in=%d;out=%d\" % (start_frame, end_frame))\n\n # Set the output path:\n # Note: The AbcExport command expects forward slashes!\n alembic_args.append(\"filename=%s\" % publish_path.replace(\"\\\\\", \"/\"))\n\n cache_set = item[\"name\"] + ':cache_SET'\n objects = cmds.sets(cache_set, q=True)\n alembic_args.append(\"objects=\" + ','.join(objects))\n\n attributes = set(attr for obj in objects for attr in (aac.getCacheableAttrs(str(obj)) or []))\n if attributes:\n alembic_args.append(\"userattrs=\" + ','.join(attributes))\n\n job_string = \";\".join(alembic_args)\n\n progress_cb(30, \"Preparing publish task for the farm\")\n\n thumb_name = os.path.basename(thumbnail_path)\n new_thumbnail_path = os.path.join(\"C:\\\\mnt\\\\workspace\\\\tmp\\\\thumbnails\", item[\"name\"] + \"_\" + thumb_name)\n shutil.copy2(thumbnail_path, new_thumbnail_path)\n thumbnail_path = new_thumbnail_path\n\n user = tank.util.get_current_user(self.parent.tank)\n args = aaSubmit.submitApi.create_sgpublish_args(\n publish_folder,\n publish_path,\n publish_name,\n publish_version,\n comment or \"No comment\",\n user[\"type\"],\n user[\"id\"],\n thumbnail_path,\n tank_type,\n sg_task[\"id\"],\n dependencyPaths=[primary_publish_path],\n deleteThumbnail=True,\n )\n pub_task = aaSubmit.utils.create_task_with_command(str(\"Publish \" + os.path.basename(publish_path)), args)\n\n return (job_string, pub_task)","def get_announcement(self, cid):\n cid = cid.upper()\n query = \"SELECT * from announcement where cid = %s\"\n inputs = (cid, )\n result = self.database_manager.execute_query(query, inputs)\n if result:\n announcement = \"Announcement for {} ({}): {}\".format(result[0][0], result[0][3], result[0][2])\n else:\n announcement = \"No announcement for this {}\".format(cid)\n return announcement","def test_createAnnounceAEwithATwithAA(self) -> None:\n\t\tdct = \t{ 'm2m:ae' : {\n\t\t\t\t\t'rn': \taeRN, \n\t\t\t\t\t'api': \t'NMyApp1Id',\n\t\t\t\t \t'rr': \tFalse,\n\t\t\t\t \t'srv': \t[ '3' ],\n\t\t\t\t \t'lbl':\t[ 'aLabel'],\n\t\t\t\t \t'at': \t[ REMOTECSEID ],\n\t\t\t\t \t'aa': \t[ 'lbl' ]\n\t\t\t\t}}\n\t\tr, rsc = CREATE(cseURL, 'C', T.AE, dct)\n\t\tself.assertEqual(rsc, RC.created)\n\t\tself.assertIsNotNone(findXPath(r, 'm2m:ae/lbl'))\n\t\tself.assertIsInstance(findXPath(r, 'm2m:ae/lbl'), list)\n\t\tself.assertEqual(len(findXPath(r, 'm2m:ae/lbl')), 1)\n\t\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\n\t\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\n\t\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\n\t\tself.assertIsInstance(findXPath(r, 'm2m:ae/aa'), list)\n\t\tself.assertEqual(len(findXPath(r, 'm2m:ae/aa')), 1)\n\t\tself.assertIn('lbl', findXPath(r, 'm2m:ae/aa'))\n\t\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\n\t\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\n\t\tself.assertIsNotNone(self.remoteAeRI)\n\t\tTestRemote_Annc.ae = r","def open(self):\n super(MemoryCache, self).open()\n\n def _timer():\n # Use a custom timer to try to spread expirations. Within one instance it\n # won't change anything but it will be better if you run multiple instances.\n return time.time() + self.__ttl * random.uniform(-0.25, 0.25)\n\n self.__cache = cachetools.TTLCache(\n maxsize=self.__size, ttl=self.__ttl, timer=_timer\n )","def doReceive(self, mail_message):\n logging.info(\"We have received someting new\")\n # log the entry .DEBUG\n logging.debug(mail_message.subject)\n #logging.debug(self.getBody(mail_message)) # too many output\n\n # extract the links to episodes in the email received\n episodeLinks = extract.linksToEpisodes(\n self.getBody(mail_message)) # Links list\n\n # for each one, we will create an task\n for ep in episodeLinks:\n logging.debug(\"creating a task for the episode found\")\n logging.debug(ep)\n try:\n # Create a queue\n queue = taskqueue.Queue('newEpisode')\n # Conform a task\n task = taskqueue.Task(url='/tasks/newEpisode',\n params={'episodeLink': ep,\n 'submitter': mail_message.sender,})\n # Add the task to the queue.\n queue.add(task)\n except TypeError as err:\n logging.error(\"Error\")\n logging.error(err)\n logging.error(mail_message)\n\n #return 0","def init(self):\n\n # Set the appropraite acknowledgements and references\n self.acknowledgements = mm_ace.acknowledgements()\n self.references = mm_ace.references(self.name)\n\n logger.info(self.acknowledgements)\n\n return","def newpublic_timeline(TTL=60):\n\n message_json = \"\"\n hash = hashlib.sha224(message_json).hexdigest()\n key = \"public_timeline_key:\" + hash\n # print \"Created Key\\t : %s\" % key\n\n############### REDIS SESSION CODE #####################\n\n # Check if data is in cache.\n if (R_SERVER.get(key)):\n print \"** Messages returned from Redis Cache **\"\n return cPickle.loads(R_SERVER.get(key))\n else:\n print \"** Messages returned from MongoDB **\"\n messages = public_timeline_query()\n data = []\n # print messages\n for row in messages:\n data.append({'user': row['username'], 'message': row['text'],\n 'pub_date': format_datetime(row['pub_date'])})\n\n public_timeline_JSON = jsonify(\n messages=data, Status_code=status.HTTP_200_OK)\n R_SERVER.set(key, cPickle.dumps(public_timeline_JSON))\n R_SERVER.expire(key, TTL)\n return public_timeline_JSON","def testExpirationTime(self):\n\n bye = \"Good bye!\"\n memcache.add('bye', bye, 1)\n assert memcache.get('bye') == bye\n time.sleep(2)\n assert memcache.get('bye') == None","def testAssignText(self):\n ref = cache.CacheReference(self.cache, 'key')\n ref.AssignText('text!')\n self.cache._InsertText.assert_called_once_with('key', 'text!')","def create_notification(self, notifying_href, notifying_action, notified_href, owner):\n if self.id == owner.id:\n return\n new_notification = Notification()\n new_notification.eid = make_uuid()\n new_notification.notifier = self\n new_notification.notifying_href = notifying_href\n new_notification.notifying_action = notifying_action\n new_notification.notified_href = notified_href\n new_notification.owner = owner\n new_notification.save()","def udpcentralizedadvertise_announce(key, value, ttlval):\r\n # do basic argument checking / munging\r\n key = str(key)\r\n value = str(value)\r\n\r\n if not type(ttlval) is int and not type(ttlval) is long:\r\n raise TypeError(\"Invalid type '\"+str(type(ttlval))+\"' for ttlval.\")\r\n\r\n if ttlval < 1:\r\n raise ValueError(\"The argument ttlval must be positive, not '\"+str(ttlval)+\"'\")\r\n\r\n # myrequestport = getusableport()\r\n\r\n unique_request_id = uniqueid_getid()\r\n\r\n # We'll loop through and send a request, increasing the timeout upon failure\r\n for thistimeout in udpcentralizedservertimeouts:\r\n\r\n # build the tuple to send, then convert to a string because only strings\r\n # (bytes) can be transmitted over the network...\r\n datatosend = ('PUT',key,value,ttlval, unique_request_id)\r\n datastringtosend = serialize_serializedata(datatosend)\r\n \r\n rawresponse = None\r\n\r\n try:\r\n # send the request over UDP...\r\n rawresponse = _udpcentralizedadvertise_communicate(datastringtosend, thistimeout, unique_request_id)\r\n\r\n except UDPNoResponseError:\r\n # let's increase the timeout...\r\n continue\r\n \r\n if not rawresponse == None:\r\n # We should check that the response is 'OK'\r\n try:\r\n response = rawresponse\r\n except ValueError, e:\r\n raise UDPCentralAdvertiseError(\"Received unknown response from server '\"+rawresponse+\"'\")\r\n\r\n if type(response) is not tuple or len(response) != 2:\r\n raise UDPCentralAdvertiseError(\"UDP Centralized announce received invalid response type '\"+str(response)+\"'\")\r\n if type(response[0]) is not str:\r\n raise UDPCentralAdvertiseError(\"UDP Centralized announce received response with invalid first parameter '\"+str(response)+\"'\")\r\n \r\n if response[1] != unique_request_id:\r\n raise UDPCentralAdvertiseError(\"UDP Centralized announce received different request id '\"+str(response)+\"'\")\r\n\r\n if response[0] != 'OK':\r\n raise UDPCentralAdvertiseError(\"UDP Centralized announce failed with '\"+response[0]+\"'\")\r\n\r\n else:\r\n # else all is well! Let's return success\r\n return\r\n \r\n failed_querylist.append(unique_request_id)\r\n\r\n # fell through all of the timeout values...\r\n raise UDPCentralAdvertiseError(\"UDP Centralized announce timed out!\")","def test_create_amendment(session, client, jwt, desc, json_data, roles, status, has_account, reg_num):\n headers = None\n # setup\n current_app.config.update(PAYMENT_SVC_URL=MOCK_PAY_URL)\n current_app.config.update(AUTH_SVC_URL=MOCK_URL_NO_KEY)\n if has_account and BCOL_HELP in roles:\n headers = create_header_account(jwt, roles, 'test-user', BCOL_HELP)\n elif has_account and STAFF_ROLE in roles:\n headers = create_header_account(jwt, roles, 'test-user', STAFF_ROLE)\n elif has_account and GOV_ACCOUNT_ROLE in roles:\n headers = create_header_account(jwt, roles, 'test-user', '1234')\n elif has_account:\n headers = create_header_account(jwt, roles)\n else:\n headers = create_header(jwt, roles)\n\n # test\n response = client.post('/api/v1/financing-statements/' + reg_num + '/amendments',\n json=json_data,\n headers=headers,\n content_type='application/json')\n\n # check\n # print('Response data:')\n # print(response.json)\n assert response.status_code == status","def create(self, data):\n\n\t\tmessage = data\n\t\tmessage[\"id\"] = self.count = self.count + 1\n\t\tself.messages.append(message)\n\n\t\treturn(message)","def create_issue(self, data, **kwargs):\n raise NotImplementedError","def create_issue(self, data, **kwargs):\n raise NotImplementedError","def publish(self):\n\n\t\tself.patient_update_dt = timezone.now()\n\t\tself.save()","def testAssign(self):\n ref = cache.CacheReference(self.cache, 'key')\n ref.Assign('/foo')\n self.cache._Insert.assert_called_once_with('key', '/foo')","def create_affidavit(token_info: Dict, affidavit_info: Dict):\n current_app.logger.debug('<create_affidavit ')\n user = UserService.find_by_jwt_token(token=token_info)\n # If the user already have a pending affidavit, raise error\n existing_affidavit = AffidavitModel.find_pending_by_user_id(user_id=user.identifier)\n if existing_affidavit is not None:\n raise BusinessException(Error.ACTIVE_AFFIDAVIT_EXISTS, None)\n\n contact = affidavit_info.pop('contact')\n affidavit_model = AffidavitModel(\n issuer=affidavit_info.get('issuer'),\n document_id=affidavit_info.get('documentId'),\n status_code=AffidavitStatus.PENDING.value,\n user_id=user.identifier\n )\n affidavit_model.add_to_session()\n\n # Save contact for the affidavit\n if contact:\n contact = ContactModel(**camelback2snake(contact))\n contact.add_to_session()\n\n contact_link = ContactLinkModel()\n contact_link.affidavit = affidavit_model\n contact_link.contact = contact\n contact_link.add_to_session()\n\n affidavit_model.save()\n\n return Affidavit(affidavit_model)","def create():\n config = request.data\n return add_scheduling_block(config)","def article_create(\n reporter: Reporter,\n headline: str,\n pub_date: date,\n) -> Article:\n article = reporter.article_set.create(reporter=reporter, headline=headline, pub_date=pub_date)\n return article","def _memcache_set(*args, **kwargs):\n return ndb.get_context().memcache_set(*args, **kwargs)","def prep_consumer(self):\n self.consumer = Consumer.objects.get(id=101)\n if self.consumer.subscriber:\n self.fail('It is assumed that this consumer has no subscriber.')\n self.three_days_ago = datetime.today().date() - timedelta(days=3)\n self.consumer.consumer_create_datetime = self.three_days_ago\n self.consumer.email_subscription.add(1)\n self.consumer.is_emailable = True\n self.rep = get_site_rep(self.consumer.site)\n self.consumer.save()","def test_create_launch_expire_assignments(self):\n mock_data_array = self.get_mock_assignment_data_array()\n launcher = TaskLauncher(self.db, self.task_run, mock_data_array)\n launcher.create_assignments()\n\n self.assertEqual(\n len(launcher.assignments),\n len(mock_data_array),\n \"Inequal number of assignments existed than were launched\",\n )\n self.assertEqual(\n len(launcher.units),\n len(mock_data_array) * len(mock_data_array[0].unit_data),\n \"Inequal number of units created than were expected\",\n )\n\n for unit in launcher.units:\n self.assertEqual(unit.get_db_status(), AssignmentState.CREATED)\n for assignment in launcher.assignments:\n self.assertEqual(assignment.get_status(), AssignmentState.CREATED)\n\n launcher.launch_units(\"dummy-url:3000\")\n\n for unit in launcher.units:\n self.assertEqual(unit.get_db_status(), AssignmentState.LAUNCHED)\n time.sleep(WAIT_TIME_TILL_NEXT_UNIT)\n for assignment in launcher.assignments:\n self.assertEqual(assignment.get_status(), AssignmentState.LAUNCHED)\n\n launcher.expire_units()\n\n for unit in launcher.units:\n self.assertEqual(unit.get_db_status(), AssignmentState.EXPIRED)\n for assignment in launcher.assignments:\n self.assertEqual(assignment.get_status(), AssignmentState.EXPIRED)","def doWork(self):\n\n if self.sendNextMessage():\n return\n\n # Load data from the URL. If the primary key is new, add it to the\n # new_data list. We'll sort this list by time afterwards and post\n # the oldest message.\n data = loads(urlopen(self.url).read())\n for donation in data:\n timestamp = strict_rfc3339.rfc3339_to_timestamp(donation['time'] \\\n + \"-07:00\")\n donation['timestamp'] = timestamp\n if donation['pk'] not in self.seen_keys:\n self.new_data.append(donation)\n\n # Re-sort the data by timestamp\n self.new_data = sorted(self.new_data,\n key=lambda donation: donation['timestamp'])\n\n # Special startup code. If seen_keys is empty and we have new data,\n # stuff all but the last /reportlast/ variables into the \"seen_keys\"\n # set, to prevent spamming the channel.\n if len(self.seen_keys) == 0:\n trimmed_list = []\n\n # Only allow recent donations to be posted, and limit the list\n # to /reportlast/ items.\n # If an object is ignored, put its pk in the seen_keys set.\n now = time.time()\n for obj in self.new_data:\n newobj = {}\n newobj['timestamp'] = obj['timestamp']\n newobj['name'] = obj['name'].encode('ascii', 'ignore')\n newobj['amount'] = obj['amount'].encode('ascii', 'ignore')\n newobj['game'] = obj['game'].encode('ascii', 'ignore')\n newobj['total'] = obj['total'].encode('ascii', 'ignore')\n newobj['pk'] = obj['pk']\n if (now - float(obj['timestamp'])) >= self.ignoreolderthan:\n self.seen_keys.add(obj['pk'])\n elif len(trimmed_list) >= self.reportlast:\n self.seen_keys.add(obj['pk'])\n else:\n trimmed_list.append(newobj)\n\n self.new_data = trimmed_list\n print \"We're starting up. Going to announce, in order:\"\n for don in self.new_data:\n print \"Total: $%s Name: %s\" % (don['total'], don['name'])\n \n self.sendNextMessage()","def init(self):\n\n self.acknowledgements = mm_dst.acknowledgements(self.name, self.tag)\n self.references = mm_dst.references(self.name, self.tag)\n logger.info(self.acknowledgements)\n return","def publish_task(self, n_assignments = 1, priority = 0):\n\n input_col = \"object\"\n output_col = \"task\"\n\n cursor = self.cc.cursor\n db = self.cc.db\n pbclient = self.cc.pbclient\n\n init_project_flag = False\n task_col = self.data.get(output_col, [None]*len(self.data[\"id\"]))\n for k, (i, d) in enumerate(zip(self.data[\"id\"], self.data[input_col])):\n exe_str = \"SELECT * FROM '%s' WHERE id=? AND col_name=?\" %(self.table_name)\n cursor.execute(exe_str, (i, output_col, ))\n records = cursor.fetchall()\n if records != []:\n assert len(records) == 1\n task_col[k] = eval(records[0][2])\n self.project_id = task_col[k][\"project_id\"]\n continue\n\n # Only initialize a project if the database does not contain all the tasks.\n if not init_project_flag:\n self.__init_project(self.presenter)\n init_project_flag = True\n\n task = pbclient.create_task(self.project_id, self.map_func(d), n_assignments, priority)\n if type(task) is dict and \"exception_msg\" in task.keys():\n raise Exception(\"%s\" %(task[\"exception_msg\"]))\n format_task = {\"id\": task.data[\"id\"], \\\n \"task_link\": self.__task_link(self.cc.endpoint, self.project_short_name, task.data[\"id\"]), \\\n \"task_data\": task.data[\"info\"], \\\n \"n_assignments\": task.data[\"n_answers\"], \\\n \"priority\": task.data[\"priority_0\"], \\\n \"project_id\": task.data[\"project_id\"], \\\n \"create_time\": task.data[\"created\"]\n }\n\n # BUG. need to test some special chateracters, e.g., single quote.\n exe_str = \"INSERT INTO '%s' VALUES(?,?,?)\" %(self.table_name)\n cursor.execute(exe_str, (i, output_col, str(format_task), ))\n db.commit()\n task_col[k] = format_task\n\n self.data[output_col] = task_col\n if output_col not in self.cols:\n self.cols.append(output_col)\n return self","def create_task(author, title, text, **kwargs):\n mc = MathContent(text=text)\n mc.save()\n task = Task(author=author, name=title, content=mc, **kwargs)\n task.save()\n return task","def __init__(self):\n if Config.USEMEMCACHED is True:\n self.mc = MCache(server = Config.MEMCACHED_SERVER,\n username = Config.MEMCACHED_USERNAME,\n password = Config.MEMCACHED_PASSWORD)\n else:\n self.mc = None\n self.api = DozensApi()","def create_and_deliver(self, **kwargs: Any) -> Awaitable[None]:\n\n msg = self.create_message(**kwargs)\n return self.deliver(msg)","def POST(url: str, **kwargs: str) -> None:\n\n title = kwargs.get(\"title\", \"\")\n tags = kwargs.get(\"tags\", \"\")\n comments = kwargs.get(\"comments\", \"\")\n added = kwargs.get(\"added\", \"\")\n\n result = cherrypy.engine.publish(\n \"scheduler:add\",\n 2,\n \"bookmarks:add\",\n Url(url),\n title,\n comments,\n tags,\n added\n ).pop()\n\n if not result:\n raise cherrypy.HTTPError(400)\n\n cherrypy.response.status = 204","def testAddingItem(self):\n\n foo = \"bar\"\n memcache.add('foo', foo)\n assert memcache.get('foo') == foo\n\n tres_bien = u\"Très bien\".encode('utf-8')\n memcache.add('tres_bien', tres_bien)\n assert memcache.get('tres_bien') == tres_bien\n\n items = [u'foo', 'bar', tres_bien, {1: 'one'}, 42L]\n memcache.add('items', items)\n assert memcache.get('items') == items\n\n number = 10\n memcache.add('number', number)\n assert memcache.get('number') == number\n\n long_number = long(20)\n memcache.add('long', long_number)\n assert memcache.get('long') == long_number\n\n yes = True\n memcache.add('yes', yes)\n assert memcache.get('yes') == yes\n\n greeting = 'Hello'\n memcache.set('greeting', greeting, namespace='me')\n assert memcache.get('greeting') is None\n assert memcache.get('greeting', namespace='me') == greeting\n assert memcache.get('greeting', namespace='no') is None\n\n unicode_data = ['Äquator'.decode('utf-8'),]\n memcache.set('unicode', unicode_data)\n self.assertEqual(unicode_data, memcache.get('unicode'))\n assert type(memcache.get('unicode')) == list","def edit_announcement():\n # Implement me!\n\n announcement = get_announcement(request.vars.announcement_id, auth.user.email)\n\n announcement.description = request.vars.description\n announcement.name = request.vars.name\n announcement.updated_on = datetime.datetime.utcnow()\n announcement.update_record()\n return response.json(announcement)","def test_create_an_issue(self):\n url = reverse('bulletin:issue-create',\n kwargs={'pk': self.newsletter.id})\n response = self.client.get(url,\n follow=True)\n self.assertEqual(response.status_code, 200)\n\n initial_num_newsletter_issues = self.newsletter.issues.count()\n url = reverse('bulletin:issue-create',\n kwargs={'pk': self.newsletter.id})\n response = self.client.post(url,\n data={'pub_date': '2014-10-04',\n 'name': 'Excellent issue'},\n follow=True)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(self.newsletter.issues.count(),\n initial_num_newsletter_issues + 1)","async def genericAnnounce(self, ctx, message):\n server = ctx.message.server\n server_dict = self.get_server_dict(ctx)\n\n try:\n channelId = server_dict['Transaction Channel']\n channel = server.get_channel(channelId)\n await self.bot.send_message(channel, message)\n await self.bot.say(\"Done\")\n except KeyError:\n await self.bot.say(\":x: Transaction log channel not set\")","def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\n #put your code here to implement this method\n raise NotImplementedError (\"not implemented method create\")","def task_add(self, description, tags=None, **kw):\n task = {\"description\": description}\n if tags != None:\n task['tags'] = tags\n\n task.update(kw)\n\n # Only UNIX timestamps are currently supported.\n if 'due' in kw:\n task['due'] = str(task['due'])\n\n task['status'] = 'pending'\n\n # TODO -- check only valid keywords\n\n if not 'entry' in task:\n task['entry'] = str(int(time.time()))\n\n if not 'uuid' in task:\n task['uuid'] = str(uuid.uuid4())\n\n id = self._task_add(task, 'pending')\n task['id'] = id\n return task","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 init(self):\n\n # Set the appropriate acknowledgements and references\n self.acknowledgements = mm_ace.acknowledgements()\n self.references = mm_ace.references(self.name)\n\n logger.info(self.acknowledgements)\n\n return","def do_create(service,summary,description,startday,\\\n starttime,endtime,username,email):\n event = {\n 'summary': 'Code Clinic: {}'.format(summary),\n 'description': '{}.'.format(description),\n 'start': {\n 'dateTime': '{}T{}:00'.format(startday, starttime),\n 'timeZone': 'GMT+02',\n },\n 'end': {\n 'dateTime': '{}T{}:00'.format(startday,endtime),\n 'timeZone': 'GMT+02',\n },\n 'recurrence': [\n 'RRULE:FREQ=DAILY;COUNT=1'\n ],\n 'attendees': [\n {\n 'displayName': username,\n 'email': email,\n 'optional': True,\n 'comment': 'Creator',\n 'responseStatus': 'accepted',\n },\n ],\n 'anyoneCanAddSelf': True,\n\n 'reminders': {\n 'useDefault': False,\n 'overrides': [\n {'method': 'email', 'minutes': 24 * 60},\n {'method': 'popup', 'minutes': 10},\n ],\n },\n }\n\n event = service.events().insert(calendarId='primary', body=event,\\\n sendUpdates='all').execute()\n\n return event","def create_task():","def add_cron_job(nondated_url, curr_date):\n user = os.environ.get('USER', '')\n if user == '':\n usage(\"no USER env var set.\")\n api_key = os.environ.get('MTA_API_KEY', '')\n if api_key == '':\n usage(\"no MTA_API_KEY env var set. Please add `export MTA_API_KEY=<KEY>` to .bashrc/.zshrc and try again.\")\n if platform.system().startswith('Windows'):\n cron = CronTab(tab=\"\"\"50 23 * * * pyenv activate mta && python {}/cron.py {} {} {} {} {} {}\"\"\".format(os.getcwd(), api_key, curr_date, BASE_DIR, REALTIME_COLOR_TO_FEEDID[LINE], LINE, STATS_FILENAME))\n cron.write()\n else:\n cron = CronTab(user=\"{}\".format(user))\n job = cron.new(command=\"pyenv activate mta && python {}/cron.py {} {} {} {} {} {}\".format(os.getcwd(), api_key, curr_date, BASE_DIR, REALTIME_COLOR_TO_FEEDID[LINE], LINE, STATS_FILENAME), comment=\"mta_downloader-{}\".format(FILENAME_TS))\n job.setall('50 23 * * *')\n cron.write()","def __init__(self, ttl=604800):\n self.data = OrderedDict()\n self.ttl = ttl","def __init__(self, ttl=604800):\n self.data = OrderedDict()\n self.ttl = ttl","async def create_ticket(self, member : Member, guild : Guild):\n licence_id = await servers.get_licence_id(guild.id)\n category : CategoryChannel = guild.get_channel(await self.categorys.get_category_id(licence_id))\n role = guild.get_role(await self.roles.get_role_id(licence_id))\n \n\n channel : TextChannel = await category.create_text_channel(f'ticket-{member.name}')\n\n overwrite_everyone = PermissionOverwrite()\n overwrite_everyone.send_messages = False\n overwrite_everyone.read_messages = False\n\n overwrite_member = PermissionOverwrite()\n overwrite_member.send_messages = True\n overwrite_member.read_messages = True\n\n\n everyone_role = guild.default_role\n\n await channel.set_permissions(target=everyone_role,overwrite=overwrite_everyone)\n await channel.set_permissions(target=member, overwrite=overwrite_everyone)\n await channel.set_permissions(target=role, overwrite=overwrite_member)\n await channel.send(content = member.mention + \" \" + role.mention)","def newPost(self, postLink, zserverBlogEntry): #$NON-NLS-1$\r\n atomEntry = self.createNewBlogEntry()\r\n self._populateAtomEntry(atomEntry, zserverBlogEntry)\r\n # publish entry\r\n atomRespEntry = self.createAtomEntry(postLink, atomEntry)\r\n return atomRespEntry","def update_or_create_delivery(self, orderitem_data):","def test_createAnnounceAEwithNAAttributes(self) -> None:\n\t\tdct = \t{ 'm2m:ae' : {\n\t\t\t\t\t'rn': \taeRN, \n\t\t\t\t\t'api': \t'NMyApp1Id',\n\t\t\t\t \t'rr': \tFalse,\n\t\t\t\t \t'srv': \t[ '3' ],\n\t\t\t\t \t'at': \t[ REMOTECSEID ],\n\t\t\t\t \t'aa': \t[ 'rn', 'ri', 'pi', 'ct','lt','st','acpi' ]\n\t\t\t\t}}\n\t\tr, rsc = CREATE(f'{cseURL}?rcn={RCN.modifiedAttributes:d}', 'C', T.AE, dct)\n\t\tself.assertEqual(rsc, RC.created)\n\t\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\n\t\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\n\t\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\n\t\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\n\t\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\n\t\tself.assertIsNotNone(self.remoteAeRI)\n\n\t\t# aa should be in the resource, but null\n\t\tself.assertIn('aa', findXPath(r, 'm2m:ae')) \n\t\tself.assertIsNone(findXPath(r, 'm2m:ae/aa'))\t# This must be None/Null!\n\t\tTestRemote_Annc.ae = r","def announcement_date(self, announcement_date):\n\n self._announcement_date = announcement_date","def create(self, cr, uid, vals, context=None):\n vals.update({'ref': self.pool.get('ir.sequence').get(\n cr, uid, 'maintenance.job')})\n return super(maintenance_job, self).create(cr, uid, vals, context=context)","def _add_crawl_info(crawl, crawl_id, engine_redis):\n crawl_info = json.dumps(crawl)\n engine_redis.set(crawl_id, crawl_info)\n hour = 60 * 60 \n engine_redis.expire(crawl_id, hour)","def __init__(self, *args):\n this = _ida_hexrays.new_citem_cmt_t(*args)\n try: self.this.append(this)\n except: self.this = this","def insrt_apt(cal, c_id, evnt):\n\n cal.events().insert( # pylint: disable=maybe-no-member\n calendarId=c_id,\n sendNotifications=True, body=evnt\n ).execute()","def __init__(self, config):\n # Initialize key variables\n connection_string = (\n '{}:{}'\n ''.format(\n config.memcached_hostname(), config.memcached_port()))\n self.cache = memcache.Client([connection_string], debug=0)","def get(self):\n return {'status': 'success', 'count': Announcement.query.count()}, 200","def update(self, *args, **kwargs):\n\n print(\"\\nIn MOCK ALGO OBSERVER....\")\n\n if 'remaining_tasks' in kwargs:\n\n remaining_tasks = len(kwargs['remaining_tasks'])\n\n print(\"\\tThere are {} remaining tasks\".format(remaining_tasks))\n print(\"\\tIs {} less than {}? {}\".format(remaining_tasks, min_tasks, (remaining_tasks < min_tasks)))\n\n # If we don't have the minimum number of hits out...\n if remaining_tasks < min_tasks:\n print(\"\\tRefilling queue with {} new task(s)\".format(min_tasks - remaining_tasks))\n # Fill up the tasks again\n for t in range(min_tasks - remaining_tasks):\n new_task = make_rand_task()\n tasks.append(new_task)\n\n actAMT.init_tasks(tasks, hit_type_init_file)\n del tasks[:]\n\n if 'completed_task' in kwargs:\n add_to_db(kwargs['completed_task'])","def test_addAAtoAnnounceAEwithoutAA(self) -> None:\n\t\tdct = \t{ 'm2m:ae' : {\n\t\t\t\t \t'lbl':\t[ 'aLabel'],\n\t\t\t\t \t'aa': \t[ 'lbl' ]\n\t\t\t\t}}\n\t\tr, rsc = UPDATE(aeURL, ORIGINATOR, dct)\n\t\tself.assertEqual(rsc, RC.updated)\n\t\tself.assertIsNotNone(findXPath(r, 'm2m:ae/lbl'))","def test_cache_create(self):\n self.assertTrue(self.host_updater.refresh_cache())\n self.assertTrue(os.path.exists(self.host_updater.cache_file))","def post(self):\n params = json.loads(self.request.body.decode())\n gid = params.get('gid')\n\n if gid: # apply an action on a specified task\n action = params.get('action')\n if action == 'pause':\n self.write(self._rpc.aria2.pause(self._token, gid))\n elif action == 'resume':\n self.write(self._rpc.aria2.unpause(self._token, gid))\n else: # invalid action\n self.send_error(400)\n\n else: # create a task\n url = params.get('url')\n self.write(self._rpc.aria2.addUri(\n self._token, [url], {'dir': self._download_file_dir}))","def __init__(self, data, expires_in):\n self.data = data\n self.expires_in = expires_in\n self.expires_after = time.time() + expires_in","def post_assignment(data, session):\n\n try:\n assignment = ClassicalAssignment(**AssignmentSchema.load(data=data))\n except ValidationError as e:\n raise ValidationError(\n 'Error parsing followup request: ' f'\"{e.normalized_messages()}\"'\n )\n\n run_id = assignment.run_id\n data['priority'] = assignment.priority.name\n run = session.scalars(\n ObservingRun.select(session.user_or_token).where(ObservingRun.id == run_id)\n ).first()\n if run is None:\n raise ValueError('Observing run is not accessible.')\n\n predecessor = session.scalars(\n ClassicalAssignment.select(session.user_or_token).where(\n ClassicalAssignment.obj_id == assignment.obj_id,\n ClassicalAssignment.run_id == run_id,\n )\n ).first()\n\n if predecessor is not None:\n raise ValueError('Object is already assigned to this run.')\n\n assignment = ClassicalAssignment(**data)\n\n if hasattr(session.user_or_token, 'created_by'):\n user_id = session.user_or_token.created_by.id\n else:\n user_id = session.user_or_token.id\n\n assignment.requester_id = user_id\n assignment.last_modified_by_id = user_id\n session.add(assignment)\n session.commit()\n\n flow = Flow()\n flow.push(\n '*',\n \"skyportal/REFRESH_SOURCE\",\n payload={\"obj_key\": assignment.obj.internal_key},\n )\n flow.push(\n '*',\n \"skyportal/REFRESH_OBSERVING_RUN\",\n payload={\"run_id\": assignment.run_id},\n )\n return assignment.id","def report_monthly_papers(email_address, start_year, start_month, end_year, end_month, timeout):\n \n start_year = int(start_year)\n start_month = int(start_month)\n \n if end_year is None:\n end_year = start_year if 12 > start_month else start_year + 1\n else:\n end_year = int(end_year)\n \n if end_month is None:\n end_month = start_month + 1 if 12 > start_month else 1\n else:\n end_month = int(end_month)\n \n folder = os.path.join(os.path.dirname(__file__), '%s-%s_%s-%s' \\\n % (start_year, start_month, end_year, end_month, ))\n if not os.path.exists(folder):\n os.system('mkdir %s' % (folder, )) \n \n article_list = retrieve_article_urls(start_year, start_month, end_year=end_year, end_month=end_month, timeout=timeout)\n saved_articles = [download_article(article, '%s/%s.pdf' % (folder, bibcode, )) for bibcode, article in article_list]\n summarise_articles(saved_articles, '%s/summary.pdf' % (folder, ))\n\n email_article_summary(email_address, '%s/summary.pdf' % (folder, ), start_year, start_month, end_year, end_month, len(article_list))","def test_createAnnounceAEwithATwithoutAA(self) -> None:\n\t\tdct = \t{ 'm2m:ae' : {\n\t\t\t\t\t'rn': \taeRN, \n\t\t\t\t\t'api': \t'NMyApp1Id',\n\t\t\t\t \t'rr': \tFalse,\n\t\t\t\t \t'srv': \t[ '3' ],\n\t\t\t\t \t'at': \t[ REMOTECSEID ]\n\t\t\t\t}}\n\t\tr, rsc = CREATE(cseURL, 'C', T.AE, dct)\n\t\tself.assertEqual(rsc, RC.created)\n\t\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\n\t\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\n\t\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\n\t\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\n\t\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\n\t\tself.assertIsNotNone(self.remoteAeRI)\n\t\tself.assertIsNone(findXPath(r, 'm2m:ae/aa'))\n\t\tTestRemote_Annc.ae = r","def create_task(text):\n new_task = Tasks(task_text=text) \n new_task.save()","def create(self, key, retentionSecs=None, labels={}):\n params = [key]\n self.appendRetention(params, retentionSecs)\n self.appendLabels(params, labels)\n\n return self.execute_command(self.CREATE_CMD, *params)","def create(self):\n self.created_date = timezone.now()\n self.save()","def post(self):\n request, error_message = flask_request_response.message_request(\n _api_intput_pb2.AssignTask, ASSIGN_TASK_API, POST_REQUEST\n )\n if error_message is not None:\n return flask_request_response.error_response(\n [error_message[\"err_message\"]], ASSIGN_TASK_API, POST_REQUEST\n )\n try:\n app.logger.error(\"In API calling assign_task_query_response\")\n assign_task_response = assign_task_query_response(\n request.assigned_by, request.assigned_to_list,\n request.chapter_key\n )\n app.logger.info(assign_task_response)\n return flask_request_response.json_response(\n assign_task_response,\n ASSIGN_TASK_API, POST_REQUEST, 200\n )\n except Exception as err:\n return flask_request_response.error_response(\n [str(err)], ASSIGN_TASK_API, POST_REQUEST\n )","def test_create_planned_activity(self):\n contact = Contact.objects.first()\n sales_cycle = contact.sales_cycles.first()\n\n data = {\n \"owner\": self.user.id,\n \"sales_cycle_id\": sales_cycle.id,\n \"description\": \"test text\",\n \"deadline\": timezone.now(),\n }\n\n url, parsed = self.prepare_urls('v1:activity-list', subdomain=self.company.subdomain)\n \n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n self.authenticate_user()\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n content = json.loads(response.content)\n self.assertTrue(content.has_key('owner'))\n self.assertNotEqual(content['owner'], None)\n self.assertTrue(content.has_key('company_id'))\n self.assertNotEqual(content['company_id'], None)\n\n url, parsed = self.prepare_urls('v1:activity-list', subdomain=self.company.subdomain)\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n content = json.loads(response.content)\n self.assertEqual(self.activities_count+1, content['count']) # added 1 activity","def test_limit_items(self):\n AnnouncementFactory(\n title=\"Not going to be there\",\n expires_at=timezone.now() - datetime.timedelta(days=1),\n )\n for i in range(5):\n AnnouncementFactory()\n\n response = self.get(\"announcements:feed\")\n\n assert \"Not going to be there\" not in response.content.decode()"],"string":"[\n \"def getAnnouncement(self, request):\\n announcement = memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \\\"\\\"\\n return StringMessage(data=announcement)\",\n \"def cache_announcement():\\n confs = Conference.query(ndb.AND(\\n Conference.seatsAvailable <= 5, Conference.seatsAvailable > 0\\n )).fetch(projection=[Conference.name])\\n\\n if confs:\\n # If there are almost sold out conferences,\\n # format announcement and set it in memcache\\n announcement = ANNOUNCEMENT_TPL % (\\n ', '.join(conf.name for conf in confs))\\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\\n else:\\n # If there are no sold out conferences,\\n # delete the memcache announcements entry\\n announcement = \\\"\\\"\\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\\n\\n return announcement\",\n \"def _Announce(self):\\n key = self._GetServerKey(self.peer_id)\\n logging.debug('Encrypting announcement.')\\n value = self._Encrypt('%s:%d' % (self.host, self.port))\\n logging.debug('Posting announcement.')\\n self._dht.Put(key, value)\",\n \"def get_announcement(self, request):\\n return StringMessage(\\n data=memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \\\"\\\")\",\n \"def add_announcement(self, cid, c_name, content, date):\\n query = \\\"INSERT INTO announcement(cid, c_name, content, date) VALUES (%s, %s, %s, %s)\\\"\\n inputs = (cid, c_name, content, date, )\\n return self.database_manager.execute_query(query, inputs)\",\n \"async def announce(self, ctx, *, msg):\\n if self._announce_msg is not None:\\n await self.bot.say(\\\"Already announcing, wait until complete to\\\"\\n \\\" issue a new announcement.\\\")\\n else:\\n self._announce_msg = msg\",\n \"def DORadvertise_announce(key, value, ttlval, timeout=None):\\r\\n\\r\\n post_params = {'command': 'announce', 'key': key, 'value': value,\\r\\n 'lifetime': str(int(ttlval))}\\r\\n\\r\\n _DORadvertise_command(post_params, timeout=timeout)\\r\\n\\r\\n return None\",\n \"def add_announcement():\\n '''\\n ann_id = db.Announcements.insert( name=request.vars.name )\\n ann = db.post(ann_id)\\n '''\\n\\n logger.info(\\\"api:add_announcement ===>>> category: %r\\\" % (request.vars.category))\\n\\n if request.vars.category not in ['urgent', 'event', \\\"shutdown\\\"]:\\n return response.json({})\\n\\n else:\\n\\n vars = request.vars\\n logger.info(\\\"vars.end_date: %r\\\" % (vars.end_date))\\n ann_id = db.Announcements.insert(\\n name = vars.name,\\n latitude = vars.latitude,\\n longitude = vars.longitude,\\n description = vars.description,\\n category = vars.category,\\n end_date = vars.end_date\\n )\\n ann = db.Announcements(ann_id)\\n\\n logger.info(\\\"api:add_announcement ==> ann= %r\\\" % (ann))\\n #logger.info(\\\"api:add_announcement_category ==> ann= %r\\\" % (ann.category))\\n\\n\\n return response.json(ann)\",\n \"def announce(self, request_announce):\\n return self.client.call('POST',\\n self.name + 'announce',\\n payload=request_announce)\",\n \"def announceGenerate(self):\",\n \"def add_comment_to_announcement():\\n vars = request.vars\\n logger.info(\\\"vars.comment_text: %r\\\" % (vars.comment_text))\\n comment_id = db.Comments.insert(\\n comment_text = vars.comment_text,\\n score = 1,\\n ann_id= vars.ann_id,\\n )\\n comment = db.Announcements(comment_id)\\n\\n logger.info(\\\"api:add_comment_to_announcement ==> comment= %r\\\" % (comment))\\n\\n return response.json(comment)\",\n \"async def announce(self, ctx):\\n\\n await ctx.message.delete()\\n if len(ctx.message.content.split()) < 2:\\n return await ctx.send('You must inform all parameters!')\\n\\n announce_channel = discord.utils.get(ctx.guild.channels, id=announcement_channel_id)\\n msg = ctx.message.content.split('!announce', 1)\\n await announce_channel.send(msg[1])\",\n \"async def announcement(ctx, bot: typing.Union[discord.Member, discord.User]):\\n plonked = await is_plonked(ctx.author.id)\\n if plonked:\\n return\\n \\n data = await make_request(\\\"https://www.motiondevelopment.top/api/v1.2/bots/\\\", bot.id)\\n print(data)\\n announcement = data[\\\"annoucements\\\"]\\n print(bool(announcement))\\n e = discord.Embed(color=0xfecdea, title=f'Announcement ID: {announcement[\\\"post_id\\\"]}')\\n if announcement != False:\\n e.add_field(\\n name=f'{announcement[\\\"post_title\\\"]}',\\n value=announcement[\\\"post_body\\\"]\\n )\\n e.description = f\\\"Post created by {data['owner_name']} ({data['owner_id']})\\\"\\n else: \\n e.description = 'This bot doesn\\\\'t have an announcement. :cry:'\\n\\n await em(ctx, embed=e)\",\n \"def prepare_announce(self, request_announce):\\n return self.client.call('POST',\\n self.name + 'prepare-announce',\\n payload=request_announce)\",\n \"def add(self, missing_dates, uncached_announcements):\\n raise NotImplementedError('AbstractCache is an abstract base class')\",\n \"def announce(self):\\n m = rtorrent9.rpc.Multicall(self)\\n self.multicall_add(m, \\\"d.tracker_announce\\\")\\n\\n return m.call()[-1]\",\n \"def persist(self) -> None:\\n\\n values = {\\n \\\"time\\\": self.event.time,\\n \\\"priority\\\": self.event.priority,\\n \\\"argument\\\": self.event.argument,\\n \\\"kwargs\\\": self.event.kwargs\\n }\\n\\n cherrypy.engine.publish(\\n \\\"cache:set\\\",\\n self.cache_key,\\n values,\\n self.time_remaining\\n )\",\n \"def prep_advertiser(self):\\n self.prep_consumer()\\n self.advertiser = Advertiser.objects.get(id=113)\\n self.consumer = Consumer.objects.get(id=113)\\n self.consumer.consumer_create_datetime = self.three_days_ago\\n self.consumer.email_subscription.add(1)\\n self.consumer.save()\",\n \"def add_assignment(cls, mentor_id, title, start_date, end_date, file_name, group='0'):\\n new = Assignment(mentor_id, title, start_date, end_date, file_name, group)\\n db.session.add(new)\\n db.session.commit()\",\n \"def v2centralizedadvertise_announce(key, value, ttlval):\\r\\n # do basic argument checking / munging\\r\\n key = str(key)\\r\\n value = str(value)\\r\\n\\r\\n if not type(ttlval) is int and not type(ttlval) is long:\\r\\n raise TypeError(\\\"Invalid type '\\\"+str(type(ttlval))+\\\"' for ttlval.\\\")\\r\\n\\r\\n if ttlval < 1:\\r\\n raise ValueError(\\\"The argument ttlval must be positive, not '\\\"+str(ttlval)+\\\"'\\\")\\r\\n\\r\\n \\r\\n # build the tuple to send, then convert to a string because only strings\\r\\n # (bytes) can be transmitted over the network...\\r\\n datatosend = ('PUT',key,value,ttlval)\\r\\n datastringtosend = serialize_serializedata(datatosend)\\r\\n\\r\\n \\r\\n # send the data over a timeout socket using the session library, then\\r\\n # get a response from the server.\\r\\n sockobj = timeout_openconn(v2servername,v2serverport, timeout=10)\\r\\n try:\\r\\n session_sendmessage(sockobj, datastringtosend)\\r\\n rawresponse = session_recvmessage(sockobj)\\r\\n finally:\\r\\n # BUG: This raises an error right now if the call times out ( #260 )\\r\\n # This isn't a big problem, but it is the \\\"wrong\\\" exception\\r\\n sockobj.close()\\r\\n \\r\\n # We should check that the response is 'OK'\\r\\n try:\\r\\n response = serialize_deserializedata(rawresponse)\\r\\n if response != 'OK':\\r\\n raise CentralAdvertiseError(\\\"Centralized announce failed with '\\\"+response+\\\"'\\\")\\r\\n except ValueError, e:\\r\\n raise CentralAdvertiseError(\\\"Received unknown response from server '\\\"+rawresponse+\\\"'\\\")\",\n \"def touch_member(self, data, ttl=None, permanent=False):\",\n \"def add(self, artifact, ttl):\\n self.purge()\\n \\n target = ( isinstance(artifact, DNSArtifact)\\n and str(artifact.remote_address) or artifact.name\\n ).lower().rstrip('.')\\n fqdns = [ name.lower().rstrip('.') for name in artifact.onames ]\\n\\n if target in self.index:\\n self.index[target].update(fqdns)\\n return\\n \\n association = Association(target, fqdns, ttl)\\n self.index[target] = association\\n (self.new_expiry or self.expiry).append(association)\\n \\n return\",\n \"def announce(self, discovery):\\n try:\\n url = self.__discovery_url()\\n logger.debug(\\\"making announce request to %s\\\", url)\\n response = None\\n response = self.client.put(url,\\n data=to_json(discovery),\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n timeout=0.8)\\n\\n if response.status_code is 200:\\n self.last_seen = datetime.now()\\n except (requests.ConnectTimeout, requests.ConnectionError):\\n logger.debug(\\\"announce\\\", exc_info=True)\\n finally:\\n return response\",\n \"def test_announcments_add_model(self):\\n announcement = Announcement(title = \\\"Title\\\", description = \\\"Description\\\", date = datetime(2015, 6, 5, 8, 10, 10, 10))\\n db.session.add(announcement)\\n db.session.commit()\\n self.assertEqual(Announcement.query.filter_by(title = \\\"Title\\\", description = \\\"Description\\\", date = datetime(2015, 6, 5, 8, 10, 10, 10)).count(), 1)\",\n \"def centralizedadvertise_announce(key, value, ttlval):\\r\\n # do basic argument checking / munging\\r\\n key = str(key)\\r\\n value = str(value)\\r\\n\\r\\n if not type(ttlval) is int and not type(ttlval) is long:\\r\\n raise TypeError(\\\"Invalid type '\\\"+str(type(ttlval))+\\\"' for ttlval.\\\")\\r\\n\\r\\n if ttlval < 1:\\r\\n raise ValueError(\\\"The argument ttlval must be positive, not '\\\"+str(ttlval)+\\\"'\\\")\\r\\n\\r\\n \\r\\n # build the tuple to send, then convert to a string because only strings\\r\\n # (bytes) can be transmitted over the network...\\r\\n datatosend = ('PUT',key,value,ttlval)\\r\\n datastringtosend = serialize_serializedata(datatosend)\\r\\n\\r\\n \\r\\n # send the data over a timeout socket using the session library, then\\r\\n # get a response from the server.\\r\\n sockobj = timeout_openconn(servername,serverport, timeout=10)\\r\\n try:\\r\\n session_sendmessage(sockobj, datastringtosend)\\r\\n rawresponse = session_recvmessage(sockobj)\\r\\n finally:\\r\\n # BUG: This raises an error right now if the call times out ( #260 )\\r\\n # This isn't a big problem, but it is the \\\"wrong\\\" exception\\r\\n sockobj.close()\\r\\n \\r\\n # We should check that the response is 'OK'\\r\\n try:\\r\\n response = serialize_deserializedata(rawresponse)\\r\\n if response != 'OK':\\r\\n raise CentralAdvertiseError(\\\"Centralized announce failed with '\\\"+response+\\\"'\\\")\\r\\n except ValueError, e:\\r\\n raise CentralAdvertiseError(\\\"Received unknown response from server '\\\"+rawresponse+\\\"'\\\")\",\n \"def arp_announce(self):\\n pass\",\n \"def newTask(name, description, assigner, id=None, priority=None, submitter_email=None, whose=None):\\n if whose:\\n user_id = jutdaapi.find_user(whose)\\n if not user_id:\\n raise ValueError('bad whose assignment: '+str(whose))\\n #title = name + ' for: '+assigner.title()\\n # that was the old scheme\\n title = '('+assigner.title()+') '+name\\n\\n if priority != None:\\n #priority = (int(priority) + 2) / 2\\n priority = int(priority)\\n RA_queue = 3\\n #if assigner != 'no one':\\n # description += '<tasktrackermeta assigner=\\\"'+assigner+'\\\"/>'\\n if isinstance(id, str):\\n description += '<tasktrackermeta id=\\\"'+id+'\\\"/>'\\n ticket_id = jutdaapi.create_ticket(RA_queue, title, description,\\n priority=priority, submitter_email=submitter_email)\\n # Is there a race condition here? In this kind of database\\n # I would assume not.\\n time.sleep(1)\\n ticket = jutdaapi.get_detailed_ticket(ticket_id)\\n t = ticketToTask(ticket)\\n return t\",\n \"def mempool_assert_announcement(condition: ConditionWithArgs, announcements: Set[bytes32]) -> Optional[Err]:\\n announcement_hash = bytes32(condition.vars[0])\\n if announcement_hash not in announcements:\\n return Err.ASSERT_ANNOUNCE_CONSUMED_FAILED\\n\\n return None\",\n \"def save(self, *args, **kwargs):\\n super(News, self).save(*args, **kwargs)\\n pigeonpost_queue.send(sender=self, defer_for=6*60*60)\",\n \"def obj_create(self, bundle, **kwargs):\\n logger.info(\\\"Creating a new acknowledgement...\\\")\\n #Create the object\\n bundle.obj = Acknowledgement()\\n #hydrate\\n bundle = self.full_hydrate(bundle)\\n \\n #Set the customer\\n try:\\n logger.info(\\\"Setting customer...\\\")\\n bundle.obj.customer = Customer.objects.get(pk=bundle.data[\\\"customer\\\"][\\\"id\\\"])\\n bundle.obj.discount = bundle.obj.customer.discount\\n except:\\n logger.error(\\\"Customer with ID {0} could not be found.\\\".format(bundle.data['customer']['id']))\\n raise\\n \\n #Set the employee\\n try:\\n logger.info(\\\"Setting employee...\\\")\\n bundle.obj.employee = bundle.request.user\\n except User.DoesNotExist:\\n logger.error(\\\"User with ID {0} could not be found\\\".format(bundle.data['employee']['id']))\\n raise\\n except KeyError:\\n logger.critical(\\\"Missing employee ID.\\\")\\n raise\\n \\n #Set Status\\n bundle.obj.status = \\\"ACKNOWLEDGED\\\"\\n \\n #Set the project or create a new one\\n if \\\"project\\\" in bundle.data:\\n try:\\n project = Project.objects.get(pk=bundle.data['project']['id'])\\n except KeyError, Project.DoesNotExist:\\n try:\\n project = Project()\\n project.codename = bundle.data['project']['codename']\\n project.save()\\n except KeyError:\\n project = None\\n \\n bundle.obj.project = project\\n \\n #Create items without saving them \\n logger.info(\\\"Creating items...\\\")\\n self.items = [Item.create(acknowledgement=bundle.obj,\\n commit=False,\\n **product) for product in bundle.data[\\\"items\\\"]]\\n \\n #Calculate the total price\\n logger.info(\\\"Calculating balance of the order...\\\")\\n bundle.obj.calculate_totals(self.items)\\n bundle = self.save(bundle)\\n \\n #Save the items\\n logger.info(\\\"Saving the items to the database...\\\")\\n for item in self.items:\\n item.acknowledgement = bundle.obj\\n item.save()\\n \\n log_message = \\\"Ack {0} created on {1}. Schedule to be delivered on {1}\\\"\\n log_message = log_message.format(bundle.obj.id,\\n bundle.obj.time_created.strftime('%B %d, %Y'),\\n bundle.obj.delivery_date.strftime('%B %d, %Y'))\\n log = Log(message=log_message,\\n delivery_date=bundle.obj.delivery_date,\\n acknowledgement=bundle.obj)\\n log.save()\\n #Create and upload the pdfs to the \\n #S3 system. The save the pdfs as\\n #Attributes of the acknowledgement\\n logger.info(\\\"Creating PDF documents...\\\")\\n bundle.obj.create_and_upload_pdfs()\\n \\n \\n #Add the url of the pdf to the outgoing data\\n #only for when an acknowledgement is create\\n try:\\n ack = bundle.obj.acknowledgement_pdf\\n production = bundle.obj.production_pdf\\n bundle.data['pdf'] = {'acknowledgement': ack.generate_url(),\\n 'production': production.generate_url()}\\n except AttributeError: \\n logger.warn('Missing acknowledgement or production pdf')\\n \\n #Conditionally email ack to Decoroom\\n if \\\"decoroom\\\" in bundle.obj.customer.name.lower():\\n try:\\n logger.info(\\\"Emailing Decoroom Co., Ltd. the order details...\\\")\\n bundle.obj.email_decoroom()\\n except Exception as e:\\n logger.error(\\\"Unable to mail decoroom.\\\")\\n logger.error(e)\\n \\n \\n \\n logger.info(u\\\"Acknowledgement #{0} created for {1}\\\".format(bundle.obj.id, \\n bundle.obj.customer.name)) \\n return bundle\",\n \"def put_memo(dt, mem):\\n\\trecord = {\\n\\t\\t\\\"type\\\": \\\"dated_memo\\\", \\n\\t\\t\\\"date\\\": dt.to('utc').naive,\\n\\t\\t\\\"text\\\": mem\\n\\t}\\n\\tcollection.insert(record)\\n\\treturn\",\n \"def __init__(self):\\n abstracttask.Task.__init__(self)\\n self._ticket_id = ''# this is an implementation detail of jutda task tracker\\n self.timespent = datetime.timedelta(0) # not editable permenently, but saves data from hours\\n self.starttime = datetime.datetime.now() # ticket creation time in this implementation \\n self.isappointment = False # always false for these\\n self.followups = [] # not likely to be used, since other implementation doesn't have it.\\n self._orig = None\\n self.submitter_email = None\",\n \"def __publish_alembic_cache(self, item, output, work_template, primary_publish_path,\\n sg_task, comment, thumbnail_path, progress_cb):\\n # determine the publish info to use\\n #\\n progress_cb(10, \\\"Determining publish details\\\")\\n\\n # get the current scene path and extract fields from it\\n # using the work template:\\n scene_path = os.path.abspath(cmds.file(query=True, sn=True))\\n fields = work_template.get_fields(scene_path)\\n publish_version = fields[\\\"version\\\"]\\n tank_type = output[\\\"tank_type\\\"]\\n\\n fields[\\\"cache_name\\\"] = item[\\\"name\\\"]\\n\\n # create the publish path by applying the fields\\n # with the publish template:\\n publish_template = output[\\\"publish_template\\\"]\\n publish_path = publish_template.apply_fields(fields)\\n\\n # ensure the publish folder exists:\\n publish_folder = os.path.dirname(publish_path)\\n self.parent.ensure_folder_exists(publish_folder)\\n\\n # determine the publish name:\\n publish_name = fields.get(\\\"name\\\")\\n if not publish_name:\\n publish_name = os.path.basename(publish_path)\\n\\n # Find additional info from the scene:\\n #\\n progress_cb(10, \\\"Analysing scene\\\")\\n\\n alembic_args = [\\n \\\"normals=1\\\",\\n \\\"uvs=1\\\",\\n \\\"facesets=0\\\",\\n \\\"useinitshadgrp=0\\\",\\n \\\"dynamictopology=0\\\",\\n \\\"transformcache=0\\\",\\n \\\"globalspace=0\\\",\\n \\\"ogawa=1\\\",\\n ]\\n\\n # find the animated frame range to use:\\n # Don't use self._find_scene_animation_range() because with effects\\n # scenes we don't have a anim curve to determine the frame range from\\n start_frame = int(cmds.playbackOptions(q=True, min=True))\\n end_frame = int(cmds.playbackOptions(q=True, max=True))\\n alembic_args.append(\\\"in=%d;out=%d\\\" % (start_frame, end_frame))\\n\\n # Set the output path:\\n # Note: The AbcExport command expects forward slashes!\\n alembic_args.append(\\\"filename=%s\\\" % publish_path.replace(\\\"\\\\\\\\\\\", \\\"/\\\"))\\n\\n cache_set = item[\\\"name\\\"] + ':cache_SET'\\n objects = cmds.sets(cache_set, q=True)\\n alembic_args.append(\\\"objects=\\\" + ','.join(objects))\\n\\n attributes = set(attr for obj in objects for attr in (aac.getCacheableAttrs(str(obj)) or []))\\n if attributes:\\n alembic_args.append(\\\"userattrs=\\\" + ','.join(attributes))\\n\\n job_string = \\\";\\\".join(alembic_args)\\n\\n progress_cb(30, \\\"Preparing publish task for the farm\\\")\\n\\n thumb_name = os.path.basename(thumbnail_path)\\n new_thumbnail_path = os.path.join(\\\"C:\\\\\\\\mnt\\\\\\\\workspace\\\\\\\\tmp\\\\\\\\thumbnails\\\", item[\\\"name\\\"] + \\\"_\\\" + thumb_name)\\n shutil.copy2(thumbnail_path, new_thumbnail_path)\\n thumbnail_path = new_thumbnail_path\\n\\n user = tank.util.get_current_user(self.parent.tank)\\n args = aaSubmit.submitApi.create_sgpublish_args(\\n publish_folder,\\n publish_path,\\n publish_name,\\n publish_version,\\n comment or \\\"No comment\\\",\\n user[\\\"type\\\"],\\n user[\\\"id\\\"],\\n thumbnail_path,\\n tank_type,\\n sg_task[\\\"id\\\"],\\n dependencyPaths=[primary_publish_path],\\n deleteThumbnail=True,\\n )\\n pub_task = aaSubmit.utils.create_task_with_command(str(\\\"Publish \\\" + os.path.basename(publish_path)), args)\\n\\n return (job_string, pub_task)\",\n \"def get_announcement(self, cid):\\n cid = cid.upper()\\n query = \\\"SELECT * from announcement where cid = %s\\\"\\n inputs = (cid, )\\n result = self.database_manager.execute_query(query, inputs)\\n if result:\\n announcement = \\\"Announcement for {} ({}): {}\\\".format(result[0][0], result[0][3], result[0][2])\\n else:\\n announcement = \\\"No announcement for this {}\\\".format(cid)\\n return announcement\",\n \"def test_createAnnounceAEwithATwithAA(self) -> None:\\n\\t\\tdct = \\t{ 'm2m:ae' : {\\n\\t\\t\\t\\t\\t'rn': \\taeRN, \\n\\t\\t\\t\\t\\t'api': \\t'NMyApp1Id',\\n\\t\\t\\t\\t \\t'rr': \\tFalse,\\n\\t\\t\\t\\t \\t'srv': \\t[ '3' ],\\n\\t\\t\\t\\t \\t'lbl':\\t[ 'aLabel'],\\n\\t\\t\\t\\t \\t'at': \\t[ REMOTECSEID ],\\n\\t\\t\\t\\t \\t'aa': \\t[ 'lbl' ]\\n\\t\\t\\t\\t}}\\n\\t\\tr, rsc = CREATE(cseURL, 'C', T.AE, dct)\\n\\t\\tself.assertEqual(rsc, RC.created)\\n\\t\\tself.assertIsNotNone(findXPath(r, 'm2m:ae/lbl'))\\n\\t\\tself.assertIsInstance(findXPath(r, 'm2m:ae/lbl'), list)\\n\\t\\tself.assertEqual(len(findXPath(r, 'm2m:ae/lbl')), 1)\\n\\t\\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\\n\\t\\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\\n\\t\\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\\n\\t\\tself.assertIsInstance(findXPath(r, 'm2m:ae/aa'), list)\\n\\t\\tself.assertEqual(len(findXPath(r, 'm2m:ae/aa')), 1)\\n\\t\\tself.assertIn('lbl', findXPath(r, 'm2m:ae/aa'))\\n\\t\\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\\n\\t\\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\\n\\t\\tself.assertIsNotNone(self.remoteAeRI)\\n\\t\\tTestRemote_Annc.ae = r\",\n \"def open(self):\\n super(MemoryCache, self).open()\\n\\n def _timer():\\n # Use a custom timer to try to spread expirations. Within one instance it\\n # won't change anything but it will be better if you run multiple instances.\\n return time.time() + self.__ttl * random.uniform(-0.25, 0.25)\\n\\n self.__cache = cachetools.TTLCache(\\n maxsize=self.__size, ttl=self.__ttl, timer=_timer\\n )\",\n \"def doReceive(self, mail_message):\\n logging.info(\\\"We have received someting new\\\")\\n # log the entry .DEBUG\\n logging.debug(mail_message.subject)\\n #logging.debug(self.getBody(mail_message)) # too many output\\n\\n # extract the links to episodes in the email received\\n episodeLinks = extract.linksToEpisodes(\\n self.getBody(mail_message)) # Links list\\n\\n # for each one, we will create an task\\n for ep in episodeLinks:\\n logging.debug(\\\"creating a task for the episode found\\\")\\n logging.debug(ep)\\n try:\\n # Create a queue\\n queue = taskqueue.Queue('newEpisode')\\n # Conform a task\\n task = taskqueue.Task(url='/tasks/newEpisode',\\n params={'episodeLink': ep,\\n 'submitter': mail_message.sender,})\\n # Add the task to the queue.\\n queue.add(task)\\n except TypeError as err:\\n logging.error(\\\"Error\\\")\\n logging.error(err)\\n logging.error(mail_message)\\n\\n #return 0\",\n \"def init(self):\\n\\n # Set the appropraite acknowledgements and references\\n self.acknowledgements = mm_ace.acknowledgements()\\n self.references = mm_ace.references(self.name)\\n\\n logger.info(self.acknowledgements)\\n\\n return\",\n \"def newpublic_timeline(TTL=60):\\n\\n message_json = \\\"\\\"\\n hash = hashlib.sha224(message_json).hexdigest()\\n key = \\\"public_timeline_key:\\\" + hash\\n # print \\\"Created Key\\\\t : %s\\\" % key\\n\\n############### REDIS SESSION CODE #####################\\n\\n # Check if data is in cache.\\n if (R_SERVER.get(key)):\\n print \\\"** Messages returned from Redis Cache **\\\"\\n return cPickle.loads(R_SERVER.get(key))\\n else:\\n print \\\"** Messages returned from MongoDB **\\\"\\n messages = public_timeline_query()\\n data = []\\n # print messages\\n for row in messages:\\n data.append({'user': row['username'], 'message': row['text'],\\n 'pub_date': format_datetime(row['pub_date'])})\\n\\n public_timeline_JSON = jsonify(\\n messages=data, Status_code=status.HTTP_200_OK)\\n R_SERVER.set(key, cPickle.dumps(public_timeline_JSON))\\n R_SERVER.expire(key, TTL)\\n return public_timeline_JSON\",\n \"def testExpirationTime(self):\\n\\n bye = \\\"Good bye!\\\"\\n memcache.add('bye', bye, 1)\\n assert memcache.get('bye') == bye\\n time.sleep(2)\\n assert memcache.get('bye') == None\",\n \"def testAssignText(self):\\n ref = cache.CacheReference(self.cache, 'key')\\n ref.AssignText('text!')\\n self.cache._InsertText.assert_called_once_with('key', 'text!')\",\n \"def create_notification(self, notifying_href, notifying_action, notified_href, owner):\\n if self.id == owner.id:\\n return\\n new_notification = Notification()\\n new_notification.eid = make_uuid()\\n new_notification.notifier = self\\n new_notification.notifying_href = notifying_href\\n new_notification.notifying_action = notifying_action\\n new_notification.notified_href = notified_href\\n new_notification.owner = owner\\n new_notification.save()\",\n \"def udpcentralizedadvertise_announce(key, value, ttlval):\\r\\n # do basic argument checking / munging\\r\\n key = str(key)\\r\\n value = str(value)\\r\\n\\r\\n if not type(ttlval) is int and not type(ttlval) is long:\\r\\n raise TypeError(\\\"Invalid type '\\\"+str(type(ttlval))+\\\"' for ttlval.\\\")\\r\\n\\r\\n if ttlval < 1:\\r\\n raise ValueError(\\\"The argument ttlval must be positive, not '\\\"+str(ttlval)+\\\"'\\\")\\r\\n\\r\\n # myrequestport = getusableport()\\r\\n\\r\\n unique_request_id = uniqueid_getid()\\r\\n\\r\\n # We'll loop through and send a request, increasing the timeout upon failure\\r\\n for thistimeout in udpcentralizedservertimeouts:\\r\\n\\r\\n # build the tuple to send, then convert to a string because only strings\\r\\n # (bytes) can be transmitted over the network...\\r\\n datatosend = ('PUT',key,value,ttlval, unique_request_id)\\r\\n datastringtosend = serialize_serializedata(datatosend)\\r\\n \\r\\n rawresponse = None\\r\\n\\r\\n try:\\r\\n # send the request over UDP...\\r\\n rawresponse = _udpcentralizedadvertise_communicate(datastringtosend, thistimeout, unique_request_id)\\r\\n\\r\\n except UDPNoResponseError:\\r\\n # let's increase the timeout...\\r\\n continue\\r\\n \\r\\n if not rawresponse == None:\\r\\n # We should check that the response is 'OK'\\r\\n try:\\r\\n response = rawresponse\\r\\n except ValueError, e:\\r\\n raise UDPCentralAdvertiseError(\\\"Received unknown response from server '\\\"+rawresponse+\\\"'\\\")\\r\\n\\r\\n if type(response) is not tuple or len(response) != 2:\\r\\n raise UDPCentralAdvertiseError(\\\"UDP Centralized announce received invalid response type '\\\"+str(response)+\\\"'\\\")\\r\\n if type(response[0]) is not str:\\r\\n raise UDPCentralAdvertiseError(\\\"UDP Centralized announce received response with invalid first parameter '\\\"+str(response)+\\\"'\\\")\\r\\n \\r\\n if response[1] != unique_request_id:\\r\\n raise UDPCentralAdvertiseError(\\\"UDP Centralized announce received different request id '\\\"+str(response)+\\\"'\\\")\\r\\n\\r\\n if response[0] != 'OK':\\r\\n raise UDPCentralAdvertiseError(\\\"UDP Centralized announce failed with '\\\"+response[0]+\\\"'\\\")\\r\\n\\r\\n else:\\r\\n # else all is well! Let's return success\\r\\n return\\r\\n \\r\\n failed_querylist.append(unique_request_id)\\r\\n\\r\\n # fell through all of the timeout values...\\r\\n raise UDPCentralAdvertiseError(\\\"UDP Centralized announce timed out!\\\")\",\n \"def test_create_amendment(session, client, jwt, desc, json_data, roles, status, has_account, reg_num):\\n headers = None\\n # setup\\n current_app.config.update(PAYMENT_SVC_URL=MOCK_PAY_URL)\\n current_app.config.update(AUTH_SVC_URL=MOCK_URL_NO_KEY)\\n if has_account and BCOL_HELP in roles:\\n headers = create_header_account(jwt, roles, 'test-user', BCOL_HELP)\\n elif has_account and STAFF_ROLE in roles:\\n headers = create_header_account(jwt, roles, 'test-user', STAFF_ROLE)\\n elif has_account and GOV_ACCOUNT_ROLE in roles:\\n headers = create_header_account(jwt, roles, 'test-user', '1234')\\n elif has_account:\\n headers = create_header_account(jwt, roles)\\n else:\\n headers = create_header(jwt, roles)\\n\\n # test\\n response = client.post('/api/v1/financing-statements/' + reg_num + '/amendments',\\n json=json_data,\\n headers=headers,\\n content_type='application/json')\\n\\n # check\\n # print('Response data:')\\n # print(response.json)\\n assert response.status_code == status\",\n \"def create(self, data):\\n\\n\\t\\tmessage = data\\n\\t\\tmessage[\\\"id\\\"] = self.count = self.count + 1\\n\\t\\tself.messages.append(message)\\n\\n\\t\\treturn(message)\",\n \"def create_issue(self, data, **kwargs):\\n raise NotImplementedError\",\n \"def create_issue(self, data, **kwargs):\\n raise NotImplementedError\",\n \"def publish(self):\\n\\n\\t\\tself.patient_update_dt = timezone.now()\\n\\t\\tself.save()\",\n \"def testAssign(self):\\n ref = cache.CacheReference(self.cache, 'key')\\n ref.Assign('/foo')\\n self.cache._Insert.assert_called_once_with('key', '/foo')\",\n \"def create_affidavit(token_info: Dict, affidavit_info: Dict):\\n current_app.logger.debug('<create_affidavit ')\\n user = UserService.find_by_jwt_token(token=token_info)\\n # If the user already have a pending affidavit, raise error\\n existing_affidavit = AffidavitModel.find_pending_by_user_id(user_id=user.identifier)\\n if existing_affidavit is not None:\\n raise BusinessException(Error.ACTIVE_AFFIDAVIT_EXISTS, None)\\n\\n contact = affidavit_info.pop('contact')\\n affidavit_model = AffidavitModel(\\n issuer=affidavit_info.get('issuer'),\\n document_id=affidavit_info.get('documentId'),\\n status_code=AffidavitStatus.PENDING.value,\\n user_id=user.identifier\\n )\\n affidavit_model.add_to_session()\\n\\n # Save contact for the affidavit\\n if contact:\\n contact = ContactModel(**camelback2snake(contact))\\n contact.add_to_session()\\n\\n contact_link = ContactLinkModel()\\n contact_link.affidavit = affidavit_model\\n contact_link.contact = contact\\n contact_link.add_to_session()\\n\\n affidavit_model.save()\\n\\n return Affidavit(affidavit_model)\",\n \"def create():\\n config = request.data\\n return add_scheduling_block(config)\",\n \"def article_create(\\n reporter: Reporter,\\n headline: str,\\n pub_date: date,\\n) -> Article:\\n article = reporter.article_set.create(reporter=reporter, headline=headline, pub_date=pub_date)\\n return article\",\n \"def _memcache_set(*args, **kwargs):\\n return ndb.get_context().memcache_set(*args, **kwargs)\",\n \"def prep_consumer(self):\\n self.consumer = Consumer.objects.get(id=101)\\n if self.consumer.subscriber:\\n self.fail('It is assumed that this consumer has no subscriber.')\\n self.three_days_ago = datetime.today().date() - timedelta(days=3)\\n self.consumer.consumer_create_datetime = self.three_days_ago\\n self.consumer.email_subscription.add(1)\\n self.consumer.is_emailable = True\\n self.rep = get_site_rep(self.consumer.site)\\n self.consumer.save()\",\n \"def test_create_launch_expire_assignments(self):\\n mock_data_array = self.get_mock_assignment_data_array()\\n launcher = TaskLauncher(self.db, self.task_run, mock_data_array)\\n launcher.create_assignments()\\n\\n self.assertEqual(\\n len(launcher.assignments),\\n len(mock_data_array),\\n \\\"Inequal number of assignments existed than were launched\\\",\\n )\\n self.assertEqual(\\n len(launcher.units),\\n len(mock_data_array) * len(mock_data_array[0].unit_data),\\n \\\"Inequal number of units created than were expected\\\",\\n )\\n\\n for unit in launcher.units:\\n self.assertEqual(unit.get_db_status(), AssignmentState.CREATED)\\n for assignment in launcher.assignments:\\n self.assertEqual(assignment.get_status(), AssignmentState.CREATED)\\n\\n launcher.launch_units(\\\"dummy-url:3000\\\")\\n\\n for unit in launcher.units:\\n self.assertEqual(unit.get_db_status(), AssignmentState.LAUNCHED)\\n time.sleep(WAIT_TIME_TILL_NEXT_UNIT)\\n for assignment in launcher.assignments:\\n self.assertEqual(assignment.get_status(), AssignmentState.LAUNCHED)\\n\\n launcher.expire_units()\\n\\n for unit in launcher.units:\\n self.assertEqual(unit.get_db_status(), AssignmentState.EXPIRED)\\n for assignment in launcher.assignments:\\n self.assertEqual(assignment.get_status(), AssignmentState.EXPIRED)\",\n \"def doWork(self):\\n\\n if self.sendNextMessage():\\n return\\n\\n # Load data from the URL. If the primary key is new, add it to the\\n # new_data list. We'll sort this list by time afterwards and post\\n # the oldest message.\\n data = loads(urlopen(self.url).read())\\n for donation in data:\\n timestamp = strict_rfc3339.rfc3339_to_timestamp(donation['time'] \\\\\\n + \\\"-07:00\\\")\\n donation['timestamp'] = timestamp\\n if donation['pk'] not in self.seen_keys:\\n self.new_data.append(donation)\\n\\n # Re-sort the data by timestamp\\n self.new_data = sorted(self.new_data,\\n key=lambda donation: donation['timestamp'])\\n\\n # Special startup code. If seen_keys is empty and we have new data,\\n # stuff all but the last /reportlast/ variables into the \\\"seen_keys\\\"\\n # set, to prevent spamming the channel.\\n if len(self.seen_keys) == 0:\\n trimmed_list = []\\n\\n # Only allow recent donations to be posted, and limit the list\\n # to /reportlast/ items.\\n # If an object is ignored, put its pk in the seen_keys set.\\n now = time.time()\\n for obj in self.new_data:\\n newobj = {}\\n newobj['timestamp'] = obj['timestamp']\\n newobj['name'] = obj['name'].encode('ascii', 'ignore')\\n newobj['amount'] = obj['amount'].encode('ascii', 'ignore')\\n newobj['game'] = obj['game'].encode('ascii', 'ignore')\\n newobj['total'] = obj['total'].encode('ascii', 'ignore')\\n newobj['pk'] = obj['pk']\\n if (now - float(obj['timestamp'])) >= self.ignoreolderthan:\\n self.seen_keys.add(obj['pk'])\\n elif len(trimmed_list) >= self.reportlast:\\n self.seen_keys.add(obj['pk'])\\n else:\\n trimmed_list.append(newobj)\\n\\n self.new_data = trimmed_list\\n print \\\"We're starting up. Going to announce, in order:\\\"\\n for don in self.new_data:\\n print \\\"Total: $%s Name: %s\\\" % (don['total'], don['name'])\\n \\n self.sendNextMessage()\",\n \"def init(self):\\n\\n self.acknowledgements = mm_dst.acknowledgements(self.name, self.tag)\\n self.references = mm_dst.references(self.name, self.tag)\\n logger.info(self.acknowledgements)\\n return\",\n \"def publish_task(self, n_assignments = 1, priority = 0):\\n\\n input_col = \\\"object\\\"\\n output_col = \\\"task\\\"\\n\\n cursor = self.cc.cursor\\n db = self.cc.db\\n pbclient = self.cc.pbclient\\n\\n init_project_flag = False\\n task_col = self.data.get(output_col, [None]*len(self.data[\\\"id\\\"]))\\n for k, (i, d) in enumerate(zip(self.data[\\\"id\\\"], self.data[input_col])):\\n exe_str = \\\"SELECT * FROM '%s' WHERE id=? AND col_name=?\\\" %(self.table_name)\\n cursor.execute(exe_str, (i, output_col, ))\\n records = cursor.fetchall()\\n if records != []:\\n assert len(records) == 1\\n task_col[k] = eval(records[0][2])\\n self.project_id = task_col[k][\\\"project_id\\\"]\\n continue\\n\\n # Only initialize a project if the database does not contain all the tasks.\\n if not init_project_flag:\\n self.__init_project(self.presenter)\\n init_project_flag = True\\n\\n task = pbclient.create_task(self.project_id, self.map_func(d), n_assignments, priority)\\n if type(task) is dict and \\\"exception_msg\\\" in task.keys():\\n raise Exception(\\\"%s\\\" %(task[\\\"exception_msg\\\"]))\\n format_task = {\\\"id\\\": task.data[\\\"id\\\"], \\\\\\n \\\"task_link\\\": self.__task_link(self.cc.endpoint, self.project_short_name, task.data[\\\"id\\\"]), \\\\\\n \\\"task_data\\\": task.data[\\\"info\\\"], \\\\\\n \\\"n_assignments\\\": task.data[\\\"n_answers\\\"], \\\\\\n \\\"priority\\\": task.data[\\\"priority_0\\\"], \\\\\\n \\\"project_id\\\": task.data[\\\"project_id\\\"], \\\\\\n \\\"create_time\\\": task.data[\\\"created\\\"]\\n }\\n\\n # BUG. need to test some special chateracters, e.g., single quote.\\n exe_str = \\\"INSERT INTO '%s' VALUES(?,?,?)\\\" %(self.table_name)\\n cursor.execute(exe_str, (i, output_col, str(format_task), ))\\n db.commit()\\n task_col[k] = format_task\\n\\n self.data[output_col] = task_col\\n if output_col not in self.cols:\\n self.cols.append(output_col)\\n return self\",\n \"def create_task(author, title, text, **kwargs):\\n mc = MathContent(text=text)\\n mc.save()\\n task = Task(author=author, name=title, content=mc, **kwargs)\\n task.save()\\n return task\",\n \"def __init__(self):\\n if Config.USEMEMCACHED is True:\\n self.mc = MCache(server = Config.MEMCACHED_SERVER,\\n username = Config.MEMCACHED_USERNAME,\\n password = Config.MEMCACHED_PASSWORD)\\n else:\\n self.mc = None\\n self.api = DozensApi()\",\n \"def create_and_deliver(self, **kwargs: Any) -> Awaitable[None]:\\n\\n msg = self.create_message(**kwargs)\\n return self.deliver(msg)\",\n \"def POST(url: str, **kwargs: str) -> None:\\n\\n title = kwargs.get(\\\"title\\\", \\\"\\\")\\n tags = kwargs.get(\\\"tags\\\", \\\"\\\")\\n comments = kwargs.get(\\\"comments\\\", \\\"\\\")\\n added = kwargs.get(\\\"added\\\", \\\"\\\")\\n\\n result = cherrypy.engine.publish(\\n \\\"scheduler:add\\\",\\n 2,\\n \\\"bookmarks:add\\\",\\n Url(url),\\n title,\\n comments,\\n tags,\\n added\\n ).pop()\\n\\n if not result:\\n raise cherrypy.HTTPError(400)\\n\\n cherrypy.response.status = 204\",\n \"def testAddingItem(self):\\n\\n foo = \\\"bar\\\"\\n memcache.add('foo', foo)\\n assert memcache.get('foo') == foo\\n\\n tres_bien = u\\\"Très bien\\\".encode('utf-8')\\n memcache.add('tres_bien', tres_bien)\\n assert memcache.get('tres_bien') == tres_bien\\n\\n items = [u'foo', 'bar', tres_bien, {1: 'one'}, 42L]\\n memcache.add('items', items)\\n assert memcache.get('items') == items\\n\\n number = 10\\n memcache.add('number', number)\\n assert memcache.get('number') == number\\n\\n long_number = long(20)\\n memcache.add('long', long_number)\\n assert memcache.get('long') == long_number\\n\\n yes = True\\n memcache.add('yes', yes)\\n assert memcache.get('yes') == yes\\n\\n greeting = 'Hello'\\n memcache.set('greeting', greeting, namespace='me')\\n assert memcache.get('greeting') is None\\n assert memcache.get('greeting', namespace='me') == greeting\\n assert memcache.get('greeting', namespace='no') is None\\n\\n unicode_data = ['Äquator'.decode('utf-8'),]\\n memcache.set('unicode', unicode_data)\\n self.assertEqual(unicode_data, memcache.get('unicode'))\\n assert type(memcache.get('unicode')) == list\",\n \"def edit_announcement():\\n # Implement me!\\n\\n announcement = get_announcement(request.vars.announcement_id, auth.user.email)\\n\\n announcement.description = request.vars.description\\n announcement.name = request.vars.name\\n announcement.updated_on = datetime.datetime.utcnow()\\n announcement.update_record()\\n return response.json(announcement)\",\n \"def test_create_an_issue(self):\\n url = reverse('bulletin:issue-create',\\n kwargs={'pk': self.newsletter.id})\\n response = self.client.get(url,\\n follow=True)\\n self.assertEqual(response.status_code, 200)\\n\\n initial_num_newsletter_issues = self.newsletter.issues.count()\\n url = reverse('bulletin:issue-create',\\n kwargs={'pk': self.newsletter.id})\\n response = self.client.post(url,\\n data={'pub_date': '2014-10-04',\\n 'name': 'Excellent issue'},\\n follow=True)\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(self.newsletter.issues.count(),\\n initial_num_newsletter_issues + 1)\",\n \"async def genericAnnounce(self, ctx, message):\\n server = ctx.message.server\\n server_dict = self.get_server_dict(ctx)\\n\\n try:\\n channelId = server_dict['Transaction Channel']\\n channel = server.get_channel(channelId)\\n await self.bot.send_message(channel, message)\\n await self.bot.say(\\\"Done\\\")\\n except KeyError:\\n await self.bot.say(\\\":x: Transaction log channel not set\\\")\",\n \"def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\\n #put your code here to implement this method\\n raise NotImplementedError (\\\"not implemented method create\\\")\",\n \"def task_add(self, description, tags=None, **kw):\\n task = {\\\"description\\\": description}\\n if tags != None:\\n task['tags'] = tags\\n\\n task.update(kw)\\n\\n # Only UNIX timestamps are currently supported.\\n if 'due' in kw:\\n task['due'] = str(task['due'])\\n\\n task['status'] = 'pending'\\n\\n # TODO -- check only valid keywords\\n\\n if not 'entry' in task:\\n task['entry'] = str(int(time.time()))\\n\\n if not 'uuid' in task:\\n task['uuid'] = str(uuid.uuid4())\\n\\n id = self._task_add(task, 'pending')\\n task['id'] = id\\n return task\",\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 init(self):\\n\\n # Set the appropriate acknowledgements and references\\n self.acknowledgements = mm_ace.acknowledgements()\\n self.references = mm_ace.references(self.name)\\n\\n logger.info(self.acknowledgements)\\n\\n return\",\n \"def do_create(service,summary,description,startday,\\\\\\n starttime,endtime,username,email):\\n event = {\\n 'summary': 'Code Clinic: {}'.format(summary),\\n 'description': '{}.'.format(description),\\n 'start': {\\n 'dateTime': '{}T{}:00'.format(startday, starttime),\\n 'timeZone': 'GMT+02',\\n },\\n 'end': {\\n 'dateTime': '{}T{}:00'.format(startday,endtime),\\n 'timeZone': 'GMT+02',\\n },\\n 'recurrence': [\\n 'RRULE:FREQ=DAILY;COUNT=1'\\n ],\\n 'attendees': [\\n {\\n 'displayName': username,\\n 'email': email,\\n 'optional': True,\\n 'comment': 'Creator',\\n 'responseStatus': 'accepted',\\n },\\n ],\\n 'anyoneCanAddSelf': True,\\n\\n 'reminders': {\\n 'useDefault': False,\\n 'overrides': [\\n {'method': 'email', 'minutes': 24 * 60},\\n {'method': 'popup', 'minutes': 10},\\n ],\\n },\\n }\\n\\n event = service.events().insert(calendarId='primary', body=event,\\\\\\n sendUpdates='all').execute()\\n\\n return event\",\n \"def create_task():\",\n \"def add_cron_job(nondated_url, curr_date):\\n user = os.environ.get('USER', '')\\n if user == '':\\n usage(\\\"no USER env var set.\\\")\\n api_key = os.environ.get('MTA_API_KEY', '')\\n if api_key == '':\\n usage(\\\"no MTA_API_KEY env var set. Please add `export MTA_API_KEY=<KEY>` to .bashrc/.zshrc and try again.\\\")\\n if platform.system().startswith('Windows'):\\n cron = CronTab(tab=\\\"\\\"\\\"50 23 * * * pyenv activate mta && python {}/cron.py {} {} {} {} {} {}\\\"\\\"\\\".format(os.getcwd(), api_key, curr_date, BASE_DIR, REALTIME_COLOR_TO_FEEDID[LINE], LINE, STATS_FILENAME))\\n cron.write()\\n else:\\n cron = CronTab(user=\\\"{}\\\".format(user))\\n job = cron.new(command=\\\"pyenv activate mta && python {}/cron.py {} {} {} {} {} {}\\\".format(os.getcwd(), api_key, curr_date, BASE_DIR, REALTIME_COLOR_TO_FEEDID[LINE], LINE, STATS_FILENAME), comment=\\\"mta_downloader-{}\\\".format(FILENAME_TS))\\n job.setall('50 23 * * *')\\n cron.write()\",\n \"def __init__(self, ttl=604800):\\n self.data = OrderedDict()\\n self.ttl = ttl\",\n \"def __init__(self, ttl=604800):\\n self.data = OrderedDict()\\n self.ttl = ttl\",\n \"async def create_ticket(self, member : Member, guild : Guild):\\n licence_id = await servers.get_licence_id(guild.id)\\n category : CategoryChannel = guild.get_channel(await self.categorys.get_category_id(licence_id))\\n role = guild.get_role(await self.roles.get_role_id(licence_id))\\n \\n\\n channel : TextChannel = await category.create_text_channel(f'ticket-{member.name}')\\n\\n overwrite_everyone = PermissionOverwrite()\\n overwrite_everyone.send_messages = False\\n overwrite_everyone.read_messages = False\\n\\n overwrite_member = PermissionOverwrite()\\n overwrite_member.send_messages = True\\n overwrite_member.read_messages = True\\n\\n\\n everyone_role = guild.default_role\\n\\n await channel.set_permissions(target=everyone_role,overwrite=overwrite_everyone)\\n await channel.set_permissions(target=member, overwrite=overwrite_everyone)\\n await channel.set_permissions(target=role, overwrite=overwrite_member)\\n await channel.send(content = member.mention + \\\" \\\" + role.mention)\",\n \"def newPost(self, postLink, zserverBlogEntry): #$NON-NLS-1$\\r\\n atomEntry = self.createNewBlogEntry()\\r\\n self._populateAtomEntry(atomEntry, zserverBlogEntry)\\r\\n # publish entry\\r\\n atomRespEntry = self.createAtomEntry(postLink, atomEntry)\\r\\n return atomRespEntry\",\n \"def update_or_create_delivery(self, orderitem_data):\",\n \"def test_createAnnounceAEwithNAAttributes(self) -> None:\\n\\t\\tdct = \\t{ 'm2m:ae' : {\\n\\t\\t\\t\\t\\t'rn': \\taeRN, \\n\\t\\t\\t\\t\\t'api': \\t'NMyApp1Id',\\n\\t\\t\\t\\t \\t'rr': \\tFalse,\\n\\t\\t\\t\\t \\t'srv': \\t[ '3' ],\\n\\t\\t\\t\\t \\t'at': \\t[ REMOTECSEID ],\\n\\t\\t\\t\\t \\t'aa': \\t[ 'rn', 'ri', 'pi', 'ct','lt','st','acpi' ]\\n\\t\\t\\t\\t}}\\n\\t\\tr, rsc = CREATE(f'{cseURL}?rcn={RCN.modifiedAttributes:d}', 'C', T.AE, dct)\\n\\t\\tself.assertEqual(rsc, RC.created)\\n\\t\\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\\n\\t\\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\\n\\t\\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\\n\\t\\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\\n\\t\\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\\n\\t\\tself.assertIsNotNone(self.remoteAeRI)\\n\\n\\t\\t# aa should be in the resource, but null\\n\\t\\tself.assertIn('aa', findXPath(r, 'm2m:ae')) \\n\\t\\tself.assertIsNone(findXPath(r, 'm2m:ae/aa'))\\t# This must be None/Null!\\n\\t\\tTestRemote_Annc.ae = r\",\n \"def announcement_date(self, announcement_date):\\n\\n self._announcement_date = announcement_date\",\n \"def create(self, cr, uid, vals, context=None):\\n vals.update({'ref': self.pool.get('ir.sequence').get(\\n cr, uid, 'maintenance.job')})\\n return super(maintenance_job, self).create(cr, uid, vals, context=context)\",\n \"def _add_crawl_info(crawl, crawl_id, engine_redis):\\n crawl_info = json.dumps(crawl)\\n engine_redis.set(crawl_id, crawl_info)\\n hour = 60 * 60 \\n engine_redis.expire(crawl_id, hour)\",\n \"def __init__(self, *args):\\n this = _ida_hexrays.new_citem_cmt_t(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def insrt_apt(cal, c_id, evnt):\\n\\n cal.events().insert( # pylint: disable=maybe-no-member\\n calendarId=c_id,\\n sendNotifications=True, body=evnt\\n ).execute()\",\n \"def __init__(self, config):\\n # Initialize key variables\\n connection_string = (\\n '{}:{}'\\n ''.format(\\n config.memcached_hostname(), config.memcached_port()))\\n self.cache = memcache.Client([connection_string], debug=0)\",\n \"def get(self):\\n return {'status': 'success', 'count': Announcement.query.count()}, 200\",\n \"def update(self, *args, **kwargs):\\n\\n print(\\\"\\\\nIn MOCK ALGO OBSERVER....\\\")\\n\\n if 'remaining_tasks' in kwargs:\\n\\n remaining_tasks = len(kwargs['remaining_tasks'])\\n\\n print(\\\"\\\\tThere are {} remaining tasks\\\".format(remaining_tasks))\\n print(\\\"\\\\tIs {} less than {}? {}\\\".format(remaining_tasks, min_tasks, (remaining_tasks < min_tasks)))\\n\\n # If we don't have the minimum number of hits out...\\n if remaining_tasks < min_tasks:\\n print(\\\"\\\\tRefilling queue with {} new task(s)\\\".format(min_tasks - remaining_tasks))\\n # Fill up the tasks again\\n for t in range(min_tasks - remaining_tasks):\\n new_task = make_rand_task()\\n tasks.append(new_task)\\n\\n actAMT.init_tasks(tasks, hit_type_init_file)\\n del tasks[:]\\n\\n if 'completed_task' in kwargs:\\n add_to_db(kwargs['completed_task'])\",\n \"def test_addAAtoAnnounceAEwithoutAA(self) -> None:\\n\\t\\tdct = \\t{ 'm2m:ae' : {\\n\\t\\t\\t\\t \\t'lbl':\\t[ 'aLabel'],\\n\\t\\t\\t\\t \\t'aa': \\t[ 'lbl' ]\\n\\t\\t\\t\\t}}\\n\\t\\tr, rsc = UPDATE(aeURL, ORIGINATOR, dct)\\n\\t\\tself.assertEqual(rsc, RC.updated)\\n\\t\\tself.assertIsNotNone(findXPath(r, 'm2m:ae/lbl'))\",\n \"def test_cache_create(self):\\n self.assertTrue(self.host_updater.refresh_cache())\\n self.assertTrue(os.path.exists(self.host_updater.cache_file))\",\n \"def post(self):\\n params = json.loads(self.request.body.decode())\\n gid = params.get('gid')\\n\\n if gid: # apply an action on a specified task\\n action = params.get('action')\\n if action == 'pause':\\n self.write(self._rpc.aria2.pause(self._token, gid))\\n elif action == 'resume':\\n self.write(self._rpc.aria2.unpause(self._token, gid))\\n else: # invalid action\\n self.send_error(400)\\n\\n else: # create a task\\n url = params.get('url')\\n self.write(self._rpc.aria2.addUri(\\n self._token, [url], {'dir': self._download_file_dir}))\",\n \"def __init__(self, data, expires_in):\\n self.data = data\\n self.expires_in = expires_in\\n self.expires_after = time.time() + expires_in\",\n \"def post_assignment(data, session):\\n\\n try:\\n assignment = ClassicalAssignment(**AssignmentSchema.load(data=data))\\n except ValidationError as e:\\n raise ValidationError(\\n 'Error parsing followup request: ' f'\\\"{e.normalized_messages()}\\\"'\\n )\\n\\n run_id = assignment.run_id\\n data['priority'] = assignment.priority.name\\n run = session.scalars(\\n ObservingRun.select(session.user_or_token).where(ObservingRun.id == run_id)\\n ).first()\\n if run is None:\\n raise ValueError('Observing run is not accessible.')\\n\\n predecessor = session.scalars(\\n ClassicalAssignment.select(session.user_or_token).where(\\n ClassicalAssignment.obj_id == assignment.obj_id,\\n ClassicalAssignment.run_id == run_id,\\n )\\n ).first()\\n\\n if predecessor is not None:\\n raise ValueError('Object is already assigned to this run.')\\n\\n assignment = ClassicalAssignment(**data)\\n\\n if hasattr(session.user_or_token, 'created_by'):\\n user_id = session.user_or_token.created_by.id\\n else:\\n user_id = session.user_or_token.id\\n\\n assignment.requester_id = user_id\\n assignment.last_modified_by_id = user_id\\n session.add(assignment)\\n session.commit()\\n\\n flow = Flow()\\n flow.push(\\n '*',\\n \\\"skyportal/REFRESH_SOURCE\\\",\\n payload={\\\"obj_key\\\": assignment.obj.internal_key},\\n )\\n flow.push(\\n '*',\\n \\\"skyportal/REFRESH_OBSERVING_RUN\\\",\\n payload={\\\"run_id\\\": assignment.run_id},\\n )\\n return assignment.id\",\n \"def report_monthly_papers(email_address, start_year, start_month, end_year, end_month, timeout):\\n \\n start_year = int(start_year)\\n start_month = int(start_month)\\n \\n if end_year is None:\\n end_year = start_year if 12 > start_month else start_year + 1\\n else:\\n end_year = int(end_year)\\n \\n if end_month is None:\\n end_month = start_month + 1 if 12 > start_month else 1\\n else:\\n end_month = int(end_month)\\n \\n folder = os.path.join(os.path.dirname(__file__), '%s-%s_%s-%s' \\\\\\n % (start_year, start_month, end_year, end_month, ))\\n if not os.path.exists(folder):\\n os.system('mkdir %s' % (folder, )) \\n \\n article_list = retrieve_article_urls(start_year, start_month, end_year=end_year, end_month=end_month, timeout=timeout)\\n saved_articles = [download_article(article, '%s/%s.pdf' % (folder, bibcode, )) for bibcode, article in article_list]\\n summarise_articles(saved_articles, '%s/summary.pdf' % (folder, ))\\n\\n email_article_summary(email_address, '%s/summary.pdf' % (folder, ), start_year, start_month, end_year, end_month, len(article_list))\",\n \"def test_createAnnounceAEwithATwithoutAA(self) -> None:\\n\\t\\tdct = \\t{ 'm2m:ae' : {\\n\\t\\t\\t\\t\\t'rn': \\taeRN, \\n\\t\\t\\t\\t\\t'api': \\t'NMyApp1Id',\\n\\t\\t\\t\\t \\t'rr': \\tFalse,\\n\\t\\t\\t\\t \\t'srv': \\t[ '3' ],\\n\\t\\t\\t\\t \\t'at': \\t[ REMOTECSEID ]\\n\\t\\t\\t\\t}}\\n\\t\\tr, rsc = CREATE(cseURL, 'C', T.AE, dct)\\n\\t\\tself.assertEqual(rsc, RC.created)\\n\\t\\tself.assertIsNotNone(findXPath(r, 'm2m:ae/at'))\\n\\t\\tself.assertIsInstance(findXPath(r, 'm2m:ae/at'), list)\\n\\t\\tself.assertEqual(len(findXPath(r, 'm2m:ae/at')), 1)\\n\\t\\tself.assertTrue(findXPath(r, 'm2m:ae/at')[0].startswith(f'{REMOTECSEID}/'))\\n\\t\\tTestRemote_Annc.remoteAeRI = findXPath(r, 'm2m:ae/at')[0]\\n\\t\\tself.assertIsNotNone(self.remoteAeRI)\\n\\t\\tself.assertIsNone(findXPath(r, 'm2m:ae/aa'))\\n\\t\\tTestRemote_Annc.ae = r\",\n \"def create_task(text):\\n new_task = Tasks(task_text=text) \\n new_task.save()\",\n \"def create(self, key, retentionSecs=None, labels={}):\\n params = [key]\\n self.appendRetention(params, retentionSecs)\\n self.appendLabels(params, labels)\\n\\n return self.execute_command(self.CREATE_CMD, *params)\",\n \"def create(self):\\n self.created_date = timezone.now()\\n self.save()\",\n \"def post(self):\\n request, error_message = flask_request_response.message_request(\\n _api_intput_pb2.AssignTask, ASSIGN_TASK_API, POST_REQUEST\\n )\\n if error_message is not None:\\n return flask_request_response.error_response(\\n [error_message[\\\"err_message\\\"]], ASSIGN_TASK_API, POST_REQUEST\\n )\\n try:\\n app.logger.error(\\\"In API calling assign_task_query_response\\\")\\n assign_task_response = assign_task_query_response(\\n request.assigned_by, request.assigned_to_list,\\n request.chapter_key\\n )\\n app.logger.info(assign_task_response)\\n return flask_request_response.json_response(\\n assign_task_response,\\n ASSIGN_TASK_API, POST_REQUEST, 200\\n )\\n except Exception as err:\\n return flask_request_response.error_response(\\n [str(err)], ASSIGN_TASK_API, POST_REQUEST\\n )\",\n \"def test_create_planned_activity(self):\\n contact = Contact.objects.first()\\n sales_cycle = contact.sales_cycles.first()\\n\\n data = {\\n \\\"owner\\\": self.user.id,\\n \\\"sales_cycle_id\\\": sales_cycle.id,\\n \\\"description\\\": \\\"test text\\\",\\n \\\"deadline\\\": timezone.now(),\\n }\\n\\n url, parsed = self.prepare_urls('v1:activity-list', subdomain=self.company.subdomain)\\n \\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n self.authenticate_user()\\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n content = json.loads(response.content)\\n self.assertTrue(content.has_key('owner'))\\n self.assertNotEqual(content['owner'], None)\\n self.assertTrue(content.has_key('company_id'))\\n self.assertNotEqual(content['company_id'], None)\\n\\n url, parsed = self.prepare_urls('v1:activity-list', subdomain=self.company.subdomain)\\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n content = json.loads(response.content)\\n self.assertEqual(self.activities_count+1, content['count']) # added 1 activity\",\n \"def test_limit_items(self):\\n AnnouncementFactory(\\n title=\\\"Not going to be there\\\",\\n expires_at=timezone.now() - datetime.timedelta(days=1),\\n )\\n for i in range(5):\\n AnnouncementFactory()\\n\\n response = self.get(\\\"announcements:feed\\\")\\n\\n assert \\\"Not going to be there\\\" not in response.content.decode()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6307641","0.624139","0.61396885","0.5956356","0.5670804","0.5596737","0.5565057","0.5479931","0.5426172","0.5361342","0.5328143","0.5242326","0.51843905","0.5149838","0.50455034","0.5040873","0.50037575","0.4943809","0.49090248","0.4906064","0.4883745","0.48719388","0.48582053","0.4849399","0.48446873","0.4843126","0.4827799","0.4795237","0.4785876","0.4783023","0.475709","0.47546324","0.47427043","0.47297856","0.4711029","0.4692998","0.4679195","0.46694577","0.46617463","0.46518198","0.46466643","0.4603745","0.4597548","0.45912078","0.45904568","0.45894507","0.45894507","0.4586795","0.45806646","0.45539218","0.4552495","0.4527552","0.45127785","0.45119402","0.45083734","0.45082107","0.45049834","0.45010835","0.44906712","0.44804922","0.4478457","0.44728956","0.4472701","0.44617784","0.44595036","0.44531316","0.44492912","0.4445708","0.44439045","0.44424006","0.44391486","0.44374815","0.44319358","0.44272113","0.44272113","0.4418067","0.4410546","0.4405859","0.44031996","0.43838945","0.43788132","0.43760377","0.43750215","0.4373886","0.43699995","0.43674335","0.43672746","0.43672594","0.4365225","0.43554032","0.43446317","0.43425703","0.4341556","0.43367898","0.43347415","0.43342888","0.43291956","0.43214336","0.4318773","0.43095055"],"string":"[\n \"0.6307641\",\n \"0.624139\",\n \"0.61396885\",\n \"0.5956356\",\n \"0.5670804\",\n \"0.5596737\",\n \"0.5565057\",\n \"0.5479931\",\n \"0.5426172\",\n \"0.5361342\",\n \"0.5328143\",\n \"0.5242326\",\n \"0.51843905\",\n \"0.5149838\",\n \"0.50455034\",\n \"0.5040873\",\n \"0.50037575\",\n \"0.4943809\",\n \"0.49090248\",\n \"0.4906064\",\n \"0.4883745\",\n \"0.48719388\",\n \"0.48582053\",\n \"0.4849399\",\n \"0.48446873\",\n \"0.4843126\",\n \"0.4827799\",\n \"0.4795237\",\n \"0.4785876\",\n \"0.4783023\",\n \"0.475709\",\n \"0.47546324\",\n \"0.47427043\",\n \"0.47297856\",\n \"0.4711029\",\n \"0.4692998\",\n \"0.4679195\",\n \"0.46694577\",\n \"0.46617463\",\n \"0.46518198\",\n \"0.46466643\",\n \"0.4603745\",\n \"0.4597548\",\n \"0.45912078\",\n \"0.45904568\",\n \"0.45894507\",\n \"0.45894507\",\n \"0.4586795\",\n \"0.45806646\",\n \"0.45539218\",\n \"0.4552495\",\n \"0.4527552\",\n \"0.45127785\",\n \"0.45119402\",\n \"0.45083734\",\n \"0.45082107\",\n \"0.45049834\",\n \"0.45010835\",\n \"0.44906712\",\n \"0.44804922\",\n \"0.4478457\",\n \"0.44728956\",\n \"0.4472701\",\n \"0.44617784\",\n \"0.44595036\",\n \"0.44531316\",\n \"0.44492912\",\n \"0.4445708\",\n \"0.44439045\",\n \"0.44424006\",\n \"0.44391486\",\n \"0.44374815\",\n \"0.44319358\",\n \"0.44272113\",\n \"0.44272113\",\n \"0.4418067\",\n \"0.4410546\",\n \"0.4405859\",\n \"0.44031996\",\n \"0.43838945\",\n \"0.43788132\",\n \"0.43760377\",\n \"0.43750215\",\n \"0.4373886\",\n \"0.43699995\",\n \"0.43674335\",\n \"0.43672746\",\n \"0.43672594\",\n \"0.4365225\",\n \"0.43554032\",\n \"0.43446317\",\n \"0.43425703\",\n \"0.4341556\",\n \"0.43367898\",\n \"0.43347415\",\n \"0.43342888\",\n \"0.43291956\",\n \"0.43214336\",\n \"0.4318773\",\n \"0.43095055\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6339458"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18493,"cells":{"query":{"kind":"string","value":"Register or unregister user for selected conference."},"document":{"kind":"string","value":"def _conferenceRegistration(self, request, reg=True):\n retval = None\n # Get user profile\n prof = self._getProfileFromUser()\n # Check if conference given in the websafeConferenceKey exists\n wsck = request.websafeConferenceKey\n conf = _getEntityByWebsafeKey(wsck, 'Conference')\n # Register\n if reg:\n # Check if user already registered, otherwise add\n if wsck in prof.conferenceKeysToAttend:\n raise ConflictException(\n \"You have already registered for this conference.\")\n # Check if seats available\n if conf.seatsAvailable <= 0:\n raise ConflictException(\n \"There are no seats available.\")\n # Register user, deduct one seat\n prof.conferenceKeysToAttend.append(wsck)\n conf.seatsAvailable -= 1\n retval = True\n # Unregister\n else:\n # Check if user already registered\n if wsck in prof.conferenceKeysToAttend:\n # Unregister user, add back one seat\n prof.conferenceKeysToAttend.remove(wsck)\n conf.seatsAvailable += 1\n retval = True\n else:\n retval = False\n # Update the datastore and return\n prof.put()\n conf.put()\n return BooleanMessage(data=retval)"},"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 conference_registration(self, request, reg=True):\n prof = self.profile_service.get_profile_from_user() # get user Profile\n\n # check if conf exists given websafeConfKey\n # get conference; check that it exists\n wsck = request.websafeConferenceKey\n conf = ndb.Key(urlsafe=wsck).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' % wsck)\n\n # register\n if reg:\n # check if user already registered otherwise add\n if wsck in prof.conferenceKeysToAttend:\n raise ConflictException(\n \"You have already registered for this conference\")\n\n # check if seats avail\n if conf.seatsAvailable <= 0:\n raise ConflictException(\"There are no seats available.\")\n\n # register user, take away one seat\n prof.conferenceKeysToAttend.append(wsck)\n conf.seatsAvailable -= 1\n retval = True\n\n # unregister\n else:\n # check if user already registered\n if wsck in prof.conferenceKeysToAttend:\n\n # unregister user, add back one seat\n prof.conferenceKeysToAttend.remove(wsck)\n conf.seatsAvailable += 1\n retval = True\n else:\n retval = False\n\n # write things back to the datastore & return\n prof.put()\n conf.put()\n return BooleanMessage(data=retval)","def register_for_conference(self, request):\n return self.conference_registration(request)","def registerForConference(self, request):\n return self._conferenceRegistration(request)","def unregister_from_conference(self, request):\n return self.conference_registration(request, reg=False)","def unregisterFromConference(self, request):\n return self._conferenceRegistration(request, reg=False)","def register_user():\n pass","def set_user_register(self, *args, **kwargs):\n return _uhd_swig.usrp_sink_set_user_register(self, *args, **kwargs)","def register(self, voice=False):\n payload = {\"type\": \"register\", \"username\": self.username, \"voice\": voice}\n self._send_command(payload)","def set_user_register(self, *args, **kwargs):\n return _uhd_swig.usrp_sink_sptr_set_user_register(self, *args, **kwargs)","def event_register_participant(request, event_id, participant_id):\n if request.method == 'PUT':\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\n participant = get_object_or_404(Participant, pk=participant_id)\n try:\n attendance = Attendance(participant=participant, event=event, datetime_register=datetime.now())\n attendance.save()\n except Exception as e:\n print e\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_TO_EVENT}\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\n serializer = AttendanceSerializer(attendance)\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","async def register(ctx, *args):\n user = ctx.message.author\n user_mention = ctx.author.mention\n chan_mention = \"<#876850365730021386>\"\n \n if user in self.data[\"users.json\"]:\n await ctx.message.channel.send(user_mention+\", you are already registered. :blue_heart:\")\n else:\n self.data[\"users_asked_to_be_registered.json\"].append(user)\n await ctx.message.channel.send(user_mention+\", do you accept the \"+chan_mention+\n \" (Indie Library Terms of Service). Command .accept if you do. :blue_heart:\")","def register(self, form):\n new_user = self.create_inactive_user(form)\n signals.user_registered.send(\n sender=self.__class__, user=new_user, request=self.request\n )\n return new_user","def toggle_subscription(self):\n user = self.context['request'].user\n # pylint: disable=no-member\n profile = UserProfile.objects.get(\n user=user)\n club = self.context['club']\n\n if club in profile.subscriptions.all():\n club.subscribed_users.remove(profile)\n else:\n club.subscribed_users.add(profile)","def create_conference_session(self, request):\n user = endpoints.get_current_user()\n\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n\n self.session_service.create_conference_session(request, user)\n return request","def set_user_register(self, *args, **kwargs):\n return _uhd_swig.usrp_source_set_user_register(self, *args, **kwargs)","def _register_user(request_form):\n idnr = request_form['idnr']\n\n if user_exists(idnr):\n raise UserAlreadyExistsError(idnr)\n\n response = elster_client.send_unlock_code_request_with_elster(request_form, request.remote_addr)\n request_id = escape(response['elster_request_id'])\n\n create_user(idnr, request_form['dob'].strftime(\"%d.%m.%Y\"), request_id)","def register(session: Session, email: str, password: str, given_name: str, last_name: str,\n phone: str) -> RegisterResult:\n # Validate the data integrity of the parameters\n if email is None:\n return RegisterResult.BAD_USERNAME\n if password is None or not passwordService.validate(password):\n return RegisterResult.BAD_PASSWORD\n\n # Check to see if the user already exists\n existing_user = session.query(User) \\\n .filter(User.email == email) \\\n .first()\n if existing_user is not None:\n return RegisterResult.USERNAME_ALREADY_REGISTERED\n\n # Everything seems fine, so we go ahead and create the user & the linked account.\n password_hash = passwordService.hash(password)\n new_user = User(role=UserType.VOLUNTEER, password=password_hash, first_name=given_name, last_name=last_name,\n mobile_number=phone, email=email, preferred_hours={}, experience_years=0, possibleRoles=[\"Basic\"],\n qualifications=[],\n availabilities={\"Friday\": [], \"Monday\": [], \"Sunday\": [], \"Tuesday\": [], \"Saturday\": [],\n \"Thursday\": [], \"Wednesday\": []})\n session.add(new_user)\n session.flush()\n return RegisterResult.SUCCESS","async def register(websocket):\n app['websockets'].add(websocket)\n await notify_users()","def on_signup(self, data):\n self.participant_id = data[\"participant\"][\"id\"]","def add_user(params, batch_registration=False):\n # Make sure the username is unique\n db = api.db.get_conn()\n if is_blacklisted_username(params[\"username\"]) or db.users.find_one(\n {\"username\": params[\"username\"]},\n collation=Collation(locale=\"en\", strength=CollationStrength.PRIMARY),\n ):\n raise PicoException(\"There is already a user with this username.\", 409)\n if db.teams.find_one(\n {\"team_name\": params[\"username\"]},\n collation=Collation(locale=\"en\", strength=CollationStrength.PRIMARY),\n ):\n raise PicoException(\"There is already a team with this username.\", 409)\n\n # If gid is specified, force affiliation to that team's name\n email_whitelist = None\n if params.get(\"gid\", None):\n group = api.group.get_group(gid=params[\"gid\"])\n group_settings = api.group.get_group_settings(gid=group[\"gid\"])\n params[\"affiliation\"] = group[\"name\"]\n email_whitelist = group_settings[\"email_filter\"]\n\n # If rid is specified and gid and email match,\n # get teacher status from registration token.\n # Additionally, invited users are automatically validated.\n user_is_teacher = params[\"usertype\"] == \"teacher\"\n user_was_invited = False\n join_group_as_teacher = False\n if params.get(\"rid\", None):\n key = api.token.find_key_by_token(\"registration_token\", params[\"rid\"])\n if params.get(\"gid\") != key[\"gid\"]:\n raise PicoException(\n \"Registration token group and supplied gid do not match.\"\n )\n if params[\"email\"] != key[\"email\"]:\n raise PicoException(\n \"Registration token email does not match the supplied one.\"\n )\n join_group_as_teacher = key[\"teacher\"]\n user_was_invited = True\n api.token.delete_token(key, \"registration_token\")\n\n # If not invited, validate the user's email against the whitelist\n else:\n if not verify_email_in_whitelist(params[\"email\"], email_whitelist):\n raise PicoException(\n \"Your email does not belong to the whitelist. \"\n + \"Please see the registration form for details.\"\n )\n\n # If CAPTCHAs are enabled, validate the submission if not batch registration\n if (\n api.config.get_settings()[\"captcha\"][\"enable_captcha\"]\n and not batch_registration\n and not _validate_captcha(params)\n ):\n raise PicoException(\"Incorrect captcha!\")\n\n # Create a team for the new user and set its count to 1\n tid = api.team.create_team(\n {\n \"team_name\": params[\"username\"],\n \"password\": api.common.hash_password(\"-\"),\n \"affiliation\": params[\"affiliation\"],\n }\n )\n db.teams.update_one({\"tid\": tid}, {\"$set\": {\"size\": 1}})\n\n # The first registered user automatically becomes an admin\n user_is_admin = False\n if db.users.count() == 0:\n user_is_admin = True\n user_is_teacher = True\n\n # Insert the new user in the DB\n uid = api.common.token()\n settings = api.config.get_settings()\n user = {\n \"uid\": uid,\n \"firstname\": params[\"firstname\"],\n \"lastname\": params[\"lastname\"],\n \"username\": params[\"username\"],\n \"email\": params[\"email\"],\n \"password_hash\": api.common.hash_password(params[\"password\"]),\n \"tid\": tid,\n \"usertype\": params[\"usertype\"],\n \"country\": params[\"country\"],\n \"demo\": params[\"demo\"],\n \"teacher\": user_is_teacher,\n \"admin\": user_is_admin,\n \"disabled\": False,\n \"verified\": (not settings[\"email\"][\"email_verification\"] or user_was_invited),\n \"extdata\": {},\n \"completed_minigames\": [],\n \"unlocked_walkthroughs\": [],\n \"tokens\": 0,\n }\n db.users.insert_one(user)\n\n # Determine the user team's initial eligibilities\n initial_eligibilities = [\n scoreboard[\"sid\"]\n for scoreboard in api.scoreboards.get_all_scoreboards()\n if api.scoreboards.is_eligible(user, scoreboard)\n ]\n db.teams.find_one_and_update(\n {\"tid\": tid}, {\"$set\": {\"eligibilities\": initial_eligibilities}}\n )\n\n # If gid was specified, add the newly created team to the group\n if params.get(\"gid\", None):\n api.group.join_group(params[\"gid\"], tid, teacher=join_group_as_teacher)\n\n # If email verification is enabled and user wasn't invited, send\n # validation email\n if settings[\"email\"][\"email_verification\"] and not user_was_invited:\n api.email.send_user_verification_email(params[\"username\"])\n\n return uid","async def accept(ctx, *args):\n user = ctx.message.author\n user_mention = \"<@\"+str(user.id)+\">\"\n\n if user in self.data[\"users_asked_to_be_registered.json\"]:\n self.data[\"users.json\"].append(user)\n self.data[\"users_asked_to_be_registered.json\"].remove(user)\n await ctx.message.channel.send(user_mention+\", you have been successfully registered. :blue_heart:\")\n else:\n await ctx.message.channel.send(user_mention+\", have not commanded .register yet. \"\n \"Please do so first. :blue_heart:\")","def preregister_user(username, full_name):\n registration_hash = secrets.token_hex(32)\n with get_conn().cursor() as cur:\n cur.execute(\"\"\"\n INSERT INTO unregistered_users (\n username, full_name, registration_hash\n ) VALUES (\n '%s', '%s', '%s'\n )\"\"\" % (username, full_name, registration_hash,))\n return True","def on_user_signed_up(sender, request: HttpRequest, user: User, **_):\n Event.new(EventAction.SIGN_UP).from_http(request)","def update_conference(self, request):\n return self.conference_service.update_conference_object(request)","def register():\r\n form = RegisterForm(request.form)\r\n\r\n if request.method == 'POST' and form.validate():\r\n new_user = User(form.email.data, form.password.data)\r\n g.session.add(new_user)\r\n g.session.commit()\r\n\r\n new_profile = Profile(form.first_name.data, form.last_name.data, new_user.id)\r\n g.session.add(new_profile)\r\n g.session.commit()\r\n # TODO: make it async\r\n if current_app.config[\"REQUIRE_EMAIL_CONFIRMATION\"]:\r\n send_confirmation(new_user)\r\n new_user.init_folders()\r\n logout_user()\r\n return redirect(url_for(\".login\"))\r\n return render_template(\"account/register_user.pug\", form=form)","def join_conference(request, conference_name):\n resp = VoiceResponse()\n resp.dial(hangupOnStar=True).conference(conference_name)\n return HttpResponse(str(resp))","def register(user_name: str, password: str, age=20):\n auth.register(user_name, password)\n user_handler.register(user_name, age)","async def unregister(websocket):\n USERS.remove(websocket)\n GAME.new_game()\n await update_players()","def tournament_registration(request, tournament_id):\n tournament = get_object_or_404(Tournament, id=tournament_id)\n \n if tournament.is_online():\n form = OnlineTournamentRegistrationForm(request.POST, initial={\n 'tournament': tournament\n })\n else:\n form = TournamentRegistrationForm(request.POST, initial={\n 'tournament': tournament\n })\n\n if form.is_valid():\n if tournament.is_online():\n tenhou_nickname = form.cleaned_data.get('tenhou_nickname')\n if OnlineTournamentRegistration.objects.filter(tournament=tournament, tenhou_nickname=tenhou_nickname).exists():\n messages.success(request, _('You already registered to the tournament!'))\n return redirect(tournament.get_url())\n\n instance = form.save(commit=False)\n instance.tournament = tournament\n \n # it supports auto load objects only for Russian tournaments right now\n \n try:\n instance.player = Player.objects.get(first_name_ru=instance.first_name.title(), \n last_name_ru=instance.last_name.title())\n except (Player.DoesNotExist, Player.MultipleObjectsReturned):\n # TODO if multiple players are here, let's try to filter by city\n pass\n \n try:\n instance.city_object = City.objects.get(name_ru=instance.city)\n except City.DoesNotExist:\n pass\n\n if tournament.registrations_pre_moderation:\n instance.is_approved = False\n message = _('Your registration was accepted! It will be visible on the page after administrator approvement.')\n else:\n instance.is_approved = True\n message = _('Your registration was accepted!')\n\n instance.save()\n \n messages.success(request, message)\n else:\n messages.success(request, _('Please, allow to store personal data'))\n\n return redirect(tournament.get_url())","def set_user_register(self, *args, **kwargs):\n return _uhd_swig.usrp_source_sptr_set_user_register(self, *args, **kwargs)","def registerUser():\n\t\tid=users.get_current_user()\n\t\tkey=id.email().split('@')[0]\n\t\tuser = AppUser(key_name=key)\n\t\tuser.id = id\n\t\tuser.verified=False\n\t\tuser.banned=False\n\t\tuser.premium=False\t\n\t\tuser.goodEventsCount=0\n\t\tuser.badEventsCount=0\n\t\tuser.put()\n\t\treturn user","async def register(self, ctx, osu_link: str, timezone: str = \"\"): # TODO handle teams + multiples brackets\n tournament = self.get_tournament(ctx.guild.id)\n if len(tournament.brackets) != 1:\n await self.send_reply(ctx, \"not_supported_yet\")\n return\n bracket = tournament.current_bracket\n if bracket.registration_end_date:\n registration_end_date = datetime.datetime.strptime(\n bracket.registration_end_date, tosurnament.DATABASE_DATE_FORMAT\n )\n if datetime.datetime.now() > registration_end_date:\n raise tosurnament.RegistrationEnded()\n\n players_spreadsheet = await bracket.get_players_spreadsheet()\n if not players_spreadsheet:\n raise tosurnament.NoSpreadsheet(\"players\")\n if players_spreadsheet.range_timezone:\n if not timezone:\n raise commands.MissingRequiredArgument(timezone)\n if not re.match(r\"(UTC)?[-\\+]([0-9]|1[0-4])(:[0-5][0-9])?$\", timezone, re.IGNORECASE):\n raise InvalidTimezone(timezone)\n timezone = \"UTC\" + re.sub(r\"^UTC\", \"\", timezone, flags=re.IGNORECASE)\n if players_spreadsheet.range_team_name:\n await self.send_reply(ctx, \"not_supported_yet\")\n return\n team_info = TeamInfo.get_first_blank_fields(players_spreadsheet)\n osu_name = osu.get_from_string(osu_link)\n osu_user = osu.get_user(osu_name, m=tournament.game_mode)\n if not osu_user:\n raise tosurnament.UserNotFound(osu_name)\n team_info.players.append(\n TeamInfo.PlayerInfo(\n osu_user.name,\n str(ctx.author),\n ctx.author.id,\n str(osu_user.rank),\n str(osu_user.rank),\n str(osu_user.id),\n str(int(float(osu_user.pp))),\n str(osu_user.country),\n )\n )\n team_info.timezone.set(timezone)\n self.add_update_spreadsheet_background_task(players_spreadsheet)\n roles_to_give = [tosurnament.get_role(ctx.guild.roles, tournament.player_role_id, \"Player\")]\n roles_to_give.append(tosurnament.get_role(ctx.guild.roles, bracket.role_id, bracket.name))\n await ctx.author.add_roles(*filter(None, roles_to_give))\n try:\n await ctx.author.edit(nick=osu_user.name)\n except (discord.Forbidden, discord.HTTPException):\n pass\n await self.send_reply(ctx, \"success\")","def register_user(registration_hash, graduating_year, graduating_semester, major, pw_hash):\n with get_conn().cursor() as cur:\n cur.execute(\"CALL register_user('%s', '%s', '%s', '%s', '%s')\" % (registration_hash, graduating_year, graduating_semester, major, pw_hash))\n cur.execute(\"SELECT * FROM users WHERE username=(SELECT username FROM unregistered_users WHERE registration_hash='%s')\" % registration_hash)\n return cur.fetchone()","def user_register():\n \n data = user_obj.user_register(request.forms) \n return data","def conferences_choice(self):\n while self.choice != \"q\":\n self.choice = input(\"\\033[35m(c) for create \\n(m) for modify \\n\"\n \"(d) for delete \\n(s) for see \\n\"\n \"(q) for exit \\n(r) for return start page:\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n if self.choice == \"c\":\n title = input(\"\\033[33menter title:\\033[0m\")\n summary = input(\"\\033[33menter summary:\\033[0m\")\n date = input(\"\\033[33menter date:\\033[0m\")\n hour = input(\"\\033[33menter hour \\033[0m\")\n speaker = input(\"\\033[33menter speaker ID: \\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.create_conferences(title, summary, date, hour, speaker)\n\n\n if self.choice == \"m\":\n while column not in ['title', 'resume', 'date', 'hour', 'personid']:\n id = input(\"\\033[33menter confernces id :\\033[0m\")\n column = input(\n \"\\033[33mwhat do you want to modify \\n [title, resume, date, hour, personid] :\\033[0m\")\n datta = input(\"\\033[33menter new datta:\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.update_confernces(id, column, datta)\n\n\n if self.choice == \"d\":\n id = input(\"\\033[33menter conferences ID :\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.delete_conferences(id)\n\n\n if self.choice == \"s\":\n datta = self.conf.show_conferences()\n if datta:\n for elem in datta:\n print(elem)\n if self.choice == \"r\":\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n return\n if self.choice == \"q\":\n exit()","def register(bot, update, user_data):\n if Chat.query.filter(Chat.chatID == update.message.chat_id).first():\n if Chat.query.filter(and_(Chat.chatID == update.message.chat_id, Chat.DOB != None)).first():\n messageContent = \"Already registered!\"\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent)\n return ConversationHandler.END\n\n student_data = Chat.query.filter(Chat.chatID == update.message.chat_id).first()\n user_data['Student_ID'] = student_data.PID\n \n messageContent = textwrap.dedent(\"\"\"\n Now enter your Date of Birth (DOB) in the following format:\n `DD/MM/YYYY`\n \"\"\")\n update.message.reply_text(messageContent, parse_mode='markdown')\n return PARENT_LGN\n\n messageContent = textwrap.dedent(\"\"\"\n Okay, send me your MIS credentials in this format:\n `Student-ID password`\n (in a single line, separated by a space)\n\n Use /cancel to abort.\n \"\"\")\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent, parse_mode='markdown')\n return CREDENTIALS","def test_register_twice(self):\n body, code = self.post(f\"/users\", bob, {\"phone\": \"+441234567890\", **bob_creds})\n self.assertEqual(400, code)\n self.assertEqual({\"error\": \"User already exists.\"}, body)","def add_user_to_g():\n \n if CURRENT_USER in session:\n g.user = User.query.get(session[CURRENT_USER])\n\n else:\n g.user = None","def register_user(request, extra_context=None):\r\n if request.user.is_authenticated():\r\n return redirect(reverse('dashboard'))\r\n if settings.FEATURES.get('AUTH_USE_CERTIFICATES_IMMEDIATE_SIGNUP'):\r\n # Redirect to branding to process their certificate if SSL is enabled\r\n # and registration is disabled.\r\n return external_auth.views.redirect_with_get('root', request.GET)\r\n\r\n context = {\r\n 'course_id': request.GET.get('course_id'),\r\n 'email': '',\r\n 'enrollment_action': request.GET.get('enrollment_action'),\r\n 'name': '',\r\n 'running_pipeline': None,\r\n 'platform_name': microsite.get_value(\r\n 'platform_name',\r\n settings.PLATFORM_NAME\r\n ),\r\n 'selected_provider': '',\r\n 'username': '',\r\n }\r\n\r\n if extra_context is not None:\r\n context.update(extra_context)\r\n\r\n if context.get(\"extauth_domain\", '').startswith(external_auth.views.SHIBBOLETH_DOMAIN_PREFIX):\r\n return render_to_response('register-shib.html', context)\r\n\r\n # If third-party auth is enabled, prepopulate the form with data from the\r\n # selected provider.\r\n if settings.FEATURES.get('ENABLE_THIRD_PARTY_AUTH') and pipeline.running(request):\r\n running_pipeline = pipeline.get(request)\r\n current_provider = provider.Registry.get_by_backend_name(running_pipeline.get('backend'))\r\n overrides = current_provider.get_register_form_data(running_pipeline.get('kwargs'))\r\n overrides['running_pipeline'] = running_pipeline\r\n overrides['selected_provider'] = current_provider.NAME\r\n context.update(overrides)\r\n\r\n return render_to_response('register.html', context)","def add_user_to_g():\n\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def add_user_to_g():\n\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def add_user_to_g():\n\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def put(self, **kwargs):\n contract = {\n \"pushRegKey\": [\"id\",\"+\"]\n }\n try:\n self.check_params_conform(contract)\n except ValidatorException:\n return\n\n user_id = kwargs[\"id\"]\n user = models.User.get_by_id(user_id)\n if user is None:\n self.abort(422, \"Could not find user\")\n\n user.pushRegKey = self.get_param(\"pushRegKey\")\n user.put()\n\n self.set_default_success_response()\n self.send_response()","def set_subscription(request, token, subscribe): # pylint: disable=unused-argument\r\n try:\r\n username = UsernameCipher().decrypt(token.encode())\r\n user = User.objects.get(username=username)\r\n except UnicodeDecodeError:\r\n raise Http404(\"base64url\")\r\n except UsernameDecryptionException as exn:\r\n raise Http404(exn.message)\r\n except User.DoesNotExist:\r\n raise Http404(\"username\")\r\n\r\n if subscribe:\r\n UserPreference.objects.get_or_create(user=user,\r\n key=NOTIFICATION_PREF_KEY,\r\n defaults={\r\n \"value\": UsernameCipher.encrypt(user.username)\r\n })\r\n return render_to_response(\"resubscribe.html\", {'token': token})\r\n else:\r\n UserPreference.objects.filter(user=user, key=NOTIFICATION_PREF_KEY).delete()\r\n return render_to_response(\"unsubscribe.html\", {'token': token})","def signup_user(self):\n User.user_list.append(self)","def set_user_register(self, reg_index, value):\n\n self.hd.setd(f'awgs/{self.index}/userregs/{reg_index}', int(value))","def signup(self):\n # sign up\n new_username = generate_username()\n success = signup_helper(self, new_username)\n if success:\n # go to AuthenticatedTasks\n self.user.username = new_username\n self.interrupt()","def add_user_to_g():\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def register(self, form):\n new_user = form.save(commit=False)\n username_field = getattr(new_user, 'USERNAME_FIELD', 'username')\n # Save lowercased email as username.\n setattr(new_user, username_field, form.cleaned_data['email'].lower())\n new_user.first_name = form.cleaned_data['first_name']\n new_user.last_name = form.cleaned_data['last_name']\n new_user.save()\n new_user = authenticate(username=getattr(new_user, username_field), password=form.cleaned_data['password1'])\n login(self.request, new_user)\n user_registered.send(sender=self.__class__, user=new_user, request=self.request)\n profile, _ = Profile.objects.get_or_create(user=new_user)\n self.request.session['signed_up'] = True\n profile.payment_plan = int(form.cleaned_data['payment_plan'])\n profile.company_name = form.cleaned_data['company']\n profile.phone = form.cleaned_data['phone']\n profile.save(update_fields=['payment_plan', 'company_name', 'phone'])\n if profile.payment_plan != Profile.PAYMENT_PLAN_FREE:\n messages.add_message(self.request, messages.INFO,\n 'Congratulations! We won\\'t charge you for this plan for now.')\n return new_user","def updateConference(self, request):\n return self._updateConferenceObject(request)","def event_register_collaborator(request, event_id, employee_id):\n if request.method == 'PUT':\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\n collaborator = get_object_or_404(Employee, pk=employee_id)\n event.collaborators.add(collaborator)\n event.save()\n serializer = EventSimpleRegistrationSerializer(event)\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def reg_user(self, uid, uname):\n cuser = self.get_user(uid)\n time.sleep(0.1)\n if cuser != None:\n print(\"UPDATE\")\n sql = \"UPDATE users SET 'isStudent'='', 'group'='', 'name'='' WHERE id={}\".format(\n cuser[0]\n )\n else:\n print(\"INSERT\")\n sql = \"INSERT INTO users ('uid', 'uname', 'isStudent', 'group', 'name') VALUES ({}, '{}', '', '', '')\".format(\n uid, uname\n )\n try:\n self.cursor.execute(sql)\n self.conn.commit()\n except sqlite3.IntegrityError:\n print(\"UID not unique\")","def add_user_to_g():\n # access g in templates, g only lives for life of request\n if CURR_USER_KEY in session:\n g.user = User.query.get(session[CURR_USER_KEY])\n\n else:\n g.user = None","def _register(self, user=None):\n if user is None:\n user = User.objects.create_user(\n username=self.username,\n password=self.password)\n user.is_active = self.active\n # Automatically add the user to the proper group\n if self._group_name is not None:\n group = get_or_none(Group, name=self._group_name)\n if group is not None:\n user.groups.add(group)\n\n user.save()\n profile = UserProfile(user=user, member=self)\n profile.save()","def signup():\n req = request.get_json()\n user = req['user']\n is_created = views.UserManagement().create(user)\n if not is_created:\n return jsonify(msg.ALREADY_USE), 400\n\n return jsonify(msg.SUCCESS), 200","def new_register():\n\n # Get form information.\n username = request.form.get(\"username\")\n password = request.form.get(\"password\")\n\n user_info = db.execute(\"SELECT * FROM user_library WHERE username = :username\", {\"username\": username}).fetchone()\n # Make sure user not exists.\n if user_info is not None:\n return render_template(\"error.html\", message=\"The username is already used.\")\n\n #Insert the new user if he/she does not exits \n db.execute(\"INSERT INTO user_library (username, password) VALUES (:username, :password)\",\n {\"username\": username, \"password\": password})\n db.commit()\n \n # Store the id of the current session\n user = db.execute(\"SELECT * FROM user_library WHERE username = :username\",{\"username\": username}).fetchone()\n session[\"user_id\"] = user.id\n \n return redirect(url_for('search'))","def signup(self, request, user):\n pass","def on_register(self, response):\n print('You have been registered!')\n self.on_auth(response)","def register(self):\n try:\n sha = sha1(self.email).hexdigest()\n except TypeError:\n raise SleekException(\"Could not register user.\", 401)\n\n if not redis.sadd(\"sl:account:ids\", sha):\n raise SleekException(\"Could not register new user.\", 401)\n self.save(register=True)","def test_registered_with_already_registered_user(self):\n\n print(\" ------------ Test 2 - Registration an user already registed ------------------\")\n\n user_id = uuid.uuid4()\n account = Account(user_id=user_id, password=\"my-precious\", currency=Currency(\"EUR\"))\n db.session.add(account)\n db.session.commit()\n\n response = register_user(user_id, \"my-precious\", \"EUR\")\n data = response.json()['message']\n self.assertTrue(data['status'] == 'fail')\n self.assertTrue(data['message'] == 'User already exists. Please Log in')\n self.assertTrue(response.headers['Content-Type'] == 'application/json')\n self.assertEqual(response.json()['code'], 202)","def sipserver_user_add(self, user: str, password: str = None) -> None:\n self.add_endpoint_to_sipserver(endpoint=user, password=password)","def register():\n result = register_helper(User)\n return jsonify(result[0]), result[1]","def register():\r\n form = RegistrationUserForm()\r\n if form.validate_on_submit():\r\n user = User(\r\n first_name=form.first_name.data,\r\n last_name=form.last_name.data,\r\n email=form.email.data,\r\n password=form.password.data)\r\n db.session.add(user)\r\n db.session.commit()\r\n professor = Professor(\r\n user_id=user.id,\r\n first_name=form.first_name.data,\r\n last_name=form.last_name.data,\r\n university=form.university.data\r\n )\r\n db.session.add(professor)\r\n db.session.commit()\r\n token = user.generate_confirmation_token()\r\n confirm_link = url_for('account.confirm', token=token, _external=True)\r\n get_queue().enqueue(\r\n send_email,\r\n recipient=user.email,\r\n subject='Confirm Your Account',\r\n template='account/email/confirm',\r\n user=user,\r\n confirm_link=confirm_link)\r\n flash('A confirmation link has been sent to {}.'.format(user.email),\r\n 'warning')\r\n return redirect(url_for('main.index'))\r\n return render_template('professor/signup.html', form=form)","async def register_handler(self, ctx, error):\n if isinstance(error, InvalidTimezone):\n await self.send_reply(ctx, \"invalid_timezone\", error.timezone)\n elif isinstance(error, tosurnament.RegistrationEnded):\n await self.send_reply(ctx, \"registration_ended\")","def is_in_conference(self) -> bool:","def subscribe():\n if request.method == 'POST':\n surname = request.form['surname']\n name = request.form['name']\n birthdate = request.form['birthdate']\n username = request.form['username']\n password = request.form['password']\n retype_password = request.form['retype-password']\n is_new_user_valid = create_account(name, surname, birthdate, username, password, retype_password)\n if is_new_user_valid == \"OK\":\n session_id = generate_session(username)\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), user=username,\n session_id=session_id, authjs=False, user_added_correctly=True, preview_length=get_cars_preview().__len__())\n else:\n return render_template('sign_up.html', subscription_error=is_new_user_valid)","def event_unregister_participant(request, event_id, participant_id):\n if request.method == 'PUT':\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\n participant = get_object_or_404(Participant, pk=participant_id)\n attendance = get_object_or_404(Attendance, participant=participant, event=event)\n attendance.delete()\n serializer = EventSimpleSerializer(event)\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def signup():","def register(self, user) -> None:\n self._all_members[user.name] = user\n if type(user).__name__ == 'LeaderUser':\n self._leaders.append(user)","def save(self, *args, **kwargs):\n create = self.id is None\n # Strip out the user keyword argument, since the super save method\n # does not expect it.\n user = None\n if 'user' in kwargs:\n user = kwargs.pop('user')\n super(Member, self).save(*args, **kwargs)\n # Only register if the object is not being updated\n if create:\n self._register(user=user)\n else:\n # User and UserProfile already exist so save them too\n self.userprofile.save()\n self.userprofile.user.save()","def __register(self, api):\r\n # Validate email\r\n email = self.request.get(constants.VAR_NAME_EMAIL)\r\n logging.info('User registering: ' + str(email))\r\n if not User.isEmailValid(email) or User.isAlreadyRegistered(email):\r\n logging.error('Email mismatched or already registered')\r\n self.set_error(constants.STATUS_BAD_REQUEST,\r\n self.gettext('REGISTER_ERROR'),\r\n url=self.request.url)\r\n return\r\n\r\n # Validate password\r\n password = self.request.get(constants.VAR_NAME_PASSWORD)\r\n if not User.isPasswordValid(password):\r\n logging.error('Invalid password')\r\n self.set_error(constants.STATUS_BAD_REQUEST,\r\n self.gettext('REGISTER_ERROR'),\r\n url=self.request.url)\r\n return\r\n\r\n # Calculate password hash\r\n salt_and_key = CryptoUtil.get_salt_and_key(password)\r\n salt = salt_and_key[0]\r\n key = salt_and_key[1]\r\n\r\n # Create and store user object\r\n user = User(key_name=email)\r\n user.email = email.lower()\r\n user.salt = salt\r\n user.password = key\r\n user.verified = False\r\n user.put()\r\n\r\n # Send email for verification\r\n self.__send_verification(email)\r\n\r\n if api == '':\r\n # Display message\r\n template_values = {\r\n 'message': self.gettext('PLEASE_CHECK_YOUR_EMAIL')\r\n }\r\n template = self.jinja2_env.get_template('staticmessage.html')\r\n self.response.out.write(template.render(template_values))\r\n\r\n self.ok()","def remove_user(self):\n self.currentuser = None\n self.carlocked = False","def register_user(self):\n User.add_user(User(self.email.data, self.password.data))","def add_user_to_session(self,session_id,client_id,display_name):\n self.sessions[session_id][\"USERS\"][client_id] = {\n \"display_name\" :display_name,\n \"permissions\" : {\n \"add_to_queue\" : True,\n \"playback\" : True,\n \"skip\" : True,\n \"edit_queue\" : True\n }\n }","def register():\n response = jsonify(msg='unauthorized'), 400\n if request.method == 'POST' and request.get_json():\n data = request.get_json()\n valid = validator.user_registration(data)\n\n if valid['status']:\n ecomap_user.register(data['first_name'],\n data['last_name'],\n data['email'],\n data['password'])\n msg = 'added %s %s' % (data['first_name'],\n data['last_name'])\n response = jsonify({'status_message': msg}), 201\n else:\n response = Response(json.dumps(valid),\n mimetype='application/json'), 400\n return response","def register(client):\n rv = client.register('user1', 'default')\n assert 'You were successfully registered ' \\\n 'and can login now' in rv.data\n rv = client.register('user1', 'default')\n assert 'The username is already taken' in rv.data\n rv = client.register('', 'default')\n assert 'You have to enter a username' in rv.data\n rv = client.register('meh', '')\n assert 'You have to enter a password' in rv.data\n rv = client.register('meh', 'x', 'y')\n assert 'The two passwords do not match' in rv.data\n rv = client.register('meh', 'foo', email='broken')\n assert 'You have to enter a valid email address' in rv.data","def _register(self,user,project):\n url = reverse(\"comicsite.views._register\", \n kwargs={\"site_short_name\":project.short_name})\n factory = RequestFactory()\n request = factory.get(url)\n request.user = user\n self.apply_standard_middleware(request)\n \n response = _register(request,project.short_name)\n \n \n self.assertEqual(response.status_code,\n 200,\n \"After registering as user %s at '%s', page did not\"\n \" load properly\" % (user.username,url))\n \n self.assertTrue(project.is_participant(user),\n \"After registering as user %s at '%s', user does not \"\n \" appear to be registered.\" % (user.username,url))","def add_registrar(contest, user):\n _add_role(contest, user, pcm.Registrar)","def sipserver_user_remove(self, user: str) -> None:\n self.remove_endpoint_from_sipserver(endpoint=user)","def save_user_ref(sender, created, instance, **_):\n if created:\n UserExtend.objects.create(user=instance)\n UserSettings.objects.create(user=instance)","def register_user(client_socket, parts):\n\n username = parts[1]\n one_time_id = parts[2]\n signature = base64.b64decode(parts[3])\n hostname = parts[4]\n port = parts[5]\n client_cert = parts[6]\n\n valid_signature = Cryptography.verify_signature(base64.b64decode(client_cert).decode(),\n one_time_id,\n signature)\n\n if valid_signature and User.login(username, one_time_id):\n ClientsStore.save_client(username, hostname, port, client_cert)\n ClientsStore.set_online(username, hostname, port)\n Administration.__socket_map[client_socket] = username\n client_socket.send('OK'.encode())\n else:\n client_socket.send('NOTOK'.encode())","def register(self):\r\n if self.fields_not_empty(request, [\"first_name\", \"last_name\", \"age\", \"CPR\", \"email\", \"phone_number\", \"password\", \"confirm_password\"]):\r\n return jsonify({\"error\": \"Some fields are empty\"}), 400\r\n user = self.create_user_object(request)\r\n if request.form.get(\"password\") != request.form.get(\"confirm_password\"):\r\n return jsonify({\"error\": \"Passwords did not match\"}), 400\r\n db.insert_one(user)\r\n return self.start_session(user)","def register():\n data = None\n response = {\n 'status': 400,\n 'error': 'Provide: firstname, lastname, email, othername, phone_number, and password as json.'\n }\n try:\n data = request.get_json()\n except:\n return jsonify(response), 400\n\n if not data:\n return jsonify(response), 400\n\n user_data = {\n 'firstname': data.get('firstname'),\n 'lastname': data.get('lastname'),\n 'email': data.get('email'),\n 'password': data.get('password'),\n 'othername': data.get('othername'),\n 'phone_number': data.get('phone_number'),\n 'is_admin': data.get('is_admin'),\n 'is_politician': data.get('is_politician')\n }\n valdiator_result = Validator.validate_user(user_data)\n if isinstance(valdiator_result, dict):\n return jsonify(valdiator_result), valdiator_result['status']\n if isinstance(valdiator_result, bool) and valdiator_result:\n result = politico.register_user(user_data)\n\n response = {}\n if result == 'User added':\n # return a response notifying the user that they registered successfully\n response['status'] = 201\n response['data'] = []\n response['data'].append({\n 'message': 'User registered successfully'\n })\n elif result == 'Other name taken':\n # return a response notifying the user that othername is taken\n response['status'] = 409\n response['error'] = 'The othername you chose is taken'\n elif result == 'User already exists':\n # notify the user that an account with the same email is already registered\n response['status'] = 409\n response['error'] = 'User already exists'\n return make_response(jsonify(response), response['status'])","def join_player(self, data, user):\n self.remove(user)\n\n user.room = \"100\"\n user.x = \"0\"\n user.y = \"0\"\n user.frame = \"0\"\n\n self.add(user)","def delete(self, event_id: int) -> Response:\n if current_user not in self.event.participants:\n return jsonify({\n \"status\": 404,\n \"message\": \"You are not registered as a participant\"\n })\n else:\n self.event.participants.remove(current_user)\n self.event.save_to_db()\n\n return jsonify({\n \"status\": 200,\n \"message\": \"Successfully unregister from participants\"\n })","def add_or_update_google_user(self, google_subscriber_id, name,\n profile_pic):\n if google_subscriber_id in self.known_users:\n self.known_users[google_subscriber_id].update(name, profile_pic)\n else:\n self.known_users[google_subscriber_id] = \\\n User(google_subscriber_id, name, profile_pic)\n return self.known_users[google_subscriber_id]","def __init__(self):\n self._conferences = set()","def update_user():","def auto_register(request,backend=None,error_msgs=''):\r\n # Check if a username is provided\r\n username_form = forms.AutoRegisterForm()\r\n if request.method == 'POST' and request.POST.get('username'):\r\n name = setting('SOCIAL_AUTH_PARTIAL_PIPELINE_KEY', 'partial_pipeline')\r\n username_form = forms.AutoRegisterForm(request.POST)\r\n if username_form.is_valid():\r\n username = username_form.cleaned_data['username']\r\n try:\r\n interface.get_user_without_password(username)\r\n error_msgs ='That username is already in use.'\r\n except DoesNotExistError:\r\n request.session['saved_username'] = request.POST['username']\r\n backend = request.session[name]['backend']\r\n return redirect('socialauth_complete', backend=backend)\r\n name = setting('SOCIAL_AUTH_PARTIAL_PIPELINE_KEY', 'partial_pipeline')\r\n backend=request.session[name]['backend']\r\n return render_to_response('accounts/auto_register.html', {'backend' : backend, 'error_msgs' : error_msgs, 'username_form' : username_form}, RequestContext(request))","def set_user(conn, phone_number: str, options: List[str], t: str) -> None:\n with conn.cursor() as cur:\n\n # Get user info from db.\n cur.execute(\"UPSERT INTO users (phone_number, options, t) VALUES (%s, %s, %s)\", (phone_number, options, t))\n conn.commit()\n return None","def _signup(request, eamap, retfun=None):\r\n # save this for use by student.views.create_account\r\n request.session['ExternalAuthMap'] = eamap\r\n\r\n if settings.FEATURES.get('AUTH_USE_CERTIFICATES_IMMEDIATE_SIGNUP', ''):\r\n # do signin immediately, by calling create_account, instead of asking\r\n # student to fill in form. MIT students already have information filed.\r\n username = eamap.external_email.split('@', 1)[0]\r\n username = username.replace('.', '_')\r\n post_vars = dict(username=username,\r\n honor_code=u'true',\r\n terms_of_service=u'true')\r\n log.info('doing immediate signup for %s, params=%s', username, post_vars)\r\n student.views.create_account(request, post_vars)\r\n # should check return content for successful completion before\r\n if retfun is not None:\r\n return retfun()\r\n else:\r\n return redirect('/')\r\n\r\n # default conjoin name, no spaces, flattened to ascii b/c django can't handle unicode usernames, sadly\r\n # but this only affects username, not fullname\r\n username = re.sub(r'\\s', '', _flatten_to_ascii(eamap.external_name), flags=re.UNICODE)\r\n\r\n context = {'has_extauth_info': True,\r\n 'show_signup_immediately': True,\r\n 'extauth_domain': eamap.external_domain,\r\n 'extauth_id': eamap.external_id,\r\n 'extauth_email': eamap.external_email,\r\n 'extauth_username': username,\r\n 'extauth_name': eamap.external_name,\r\n 'ask_for_tos': True,\r\n }\r\n\r\n # Some openEdX instances can't have terms of service for shib users, like\r\n # according to Stanford's Office of General Counsel\r\n uses_shibboleth = (settings.FEATURES.get('AUTH_USE_SHIB') and\r\n eamap.external_domain.startswith(SHIBBOLETH_DOMAIN_PREFIX))\r\n if uses_shibboleth and settings.FEATURES.get('SHIB_DISABLE_TOS'):\r\n context['ask_for_tos'] = False\r\n\r\n # detect if full name is blank and ask for it from user\r\n context['ask_for_fullname'] = eamap.external_name.strip() == ''\r\n\r\n # validate provided mail and if it's not valid ask the user\r\n try:\r\n validate_email(eamap.external_email)\r\n context['ask_for_email'] = False\r\n except ValidationError:\r\n context['ask_for_email'] = True\r\n\r\n log.info('EXTAUTH: Doing signup for %s', eamap.external_id)\r\n\r\n return student.views.register_user(request, extra_context=context)","def register():\n form = RegistrationForm()\n\n if form.validate_on_submit():\n db.session.add(form.user)\n db.session.add(form.allergies)\n # Hacky solution time! If a student misspelled their school name then\n # had to go through registration again, there may be an empty school\n # This query returns a list of schools with no students\n for empty_school in School.query.filter(~School.students.any()):\n db.session.delete(empty_school)\n db.session.commit()\n\n flash('You have successfully registered for the ACM-HSCC', 'alert-success')\n login_user(form.user)\n return redirect(url_for('default.home'))\n else:\n flash_form_errors(form)\n return render_template('register.html', form=form)","async def put_user_byid(request):\n user_id = request.match_info[\"user_id\"]\n try:\n user_id = int(user_id)\n except (ValueError, TypeError):\n return web.Response(text=\"Incorrect value for user_id\", status=400)\n\n user = request.cirrina.db_session.query(User).filter_by(id=user_id).first()\n if not user:\n return web.Response(status=404, text=\"User not found\")\n\n if user.username == \"admin\":\n return web.Response(status=400, text=\"Cannot change admin\")\n\n is_admin = request.GET.getone(\"is_admin\", None) # str \"true\" or \"flase\"\n if not is_admin: # if None\n return web.Response(text=\"Nothing to change\", status=204)\n\n if is_admin.lower() == \"true\":\n user.is_admin = True\n data = {\"result\": \"{u} is now admin \".format(u=user.username)}\n elif is_admin.lower() == \"false\":\n user.is_admin = False\n data = {\"result\": \"{u} is no longer admin \".format(u=user.username)}\n\n try:\n request.cirrina.db_session.commit() # pylint: disable=no-member\n except sqlalchemy.exc.DataError:\n request.cirrina.db_session.rollback() # pylint: disable=no-member\n return web.Response(status=500, text=\"Database error\")\n\n # TODO : change to a multicast group\n await app.websocket_broadcast(\n {\n \"event\": Event.changed.value,\n \"subject\": Subject.user.value,\n \"changed\": {\"id\": user_id, \"is_admin\": user.is_admin},\n }\n )\n\n return web.json_response(data)","def register_user(req_data):\n logger.debug(\"entering function register_user\")\n validate_fields(req_data, [\"email\", \"mobile\", \"password\", \"first_name\", \"last_name\"])\n\n user_email = req_data[\"email\"]\n user_mobile = req_data[\"mobile\"]\n user_password = req_data[\"password\"]\n user_first_name = req_data[\"first_name\"]\n user_last_name = req_data[\"last_name\"]\n\n existing_email_query = {\"email\": user_email}\n result = run_find_one_query(config.USERS_COL, existing_email_query, error=False)\n if result is not None:\n logger.error(\"email already exists, query result = %s\", result)\n return get_failure_response(EMAIL_EXISTS_ERR_MSG)\n\n existing_mobile_query = {\"mobile\": user_mobile}\n result = run_find_one_query(config.USERS_COL, existing_mobile_query, error=False)\n if result is not None:\n logger.error(\"mobile already exists, query result = %s\", result)\n return get_failure_response(MOBILE_EXISTS_ERR_MSG)\n\n user_id = uuid4().hex\n doc = {\n \"user_id\": user_id,\n \"first_name\": user_first_name,\n \"last_name\": user_last_name,\n \"email\": user_email,\n \"mobile\": user_mobile,\n \"password\": generate_password_hash(user_password)\n }\n logger.info(\"inserting new user into database\")\n run_insert_one_query(config.USERS_COL, doc, error=True, error_msg=REGISTRATION_FAILED_ERR_MSG)\n logger.info(\"registration successful for email = %s\", doc[\"email\"])\n\n logger.debug(\"exiting function register_user\")\n return get_success_response(REGISTRATION_SUCCESS_MSG)","def account_register(request, next_url='dashboard'):\n siteconfig = SiteConfiguration.objects.get_current()\n auth_backends = get_enabled_auth_backends()\n\n if (auth_backends[0].supports_registration and\n siteconfig.get(\"auth_enable_registration\")):\n response = register(request, next_page=reverse(next_url),\n form_class=RegistrationForm)\n\n if request.user.is_authenticated():\n # This will trigger sending an e-mail notification for\n # user registration, if enabled.\n user_registered.send(sender=None, user=request.user)\n\n return response\n\n return HttpResponseRedirect(reverse(\"login\"))","def register_ctf_user(self, username):\n self.browser.get(self.get_server_url())\n player_box = self.browser.find_element_by_id(\"ctf-player\")\n player_box.send_keys(username)\n player_box.send_keys(Keys.ENTER)","def register():\n schema = UserRegistrationSchema()\n\n data = schema.loads(request.data)\n if data.errors:\n return jsonify(data.errors), 400\n\n # Confirming that a duplicate user doesn't exist\n duplicates_q = f\"g.V().hasLabel('{User.LABEL}')\" + \\\n f\".or(has('username', '{data.data['username']}'),\" + \\\n f\"has('email', '{data.data['email']}'))\"\n duplicates = client.submit(duplicates_q).all().result()\n if duplicates:\n return jsonify_response({\"error\": \"User already exists!\"}, 400)\n\n data.data[\"password\"] = generate_password_hash(\n data.data[\"password\"]).decode(\"utf-8\")\n\n # Creating the User and it's primary account + account admin edge\n user = User.create(**data.data)\n account = Account.create(title=f\"myaccount@{user.username}\")\n holds_edge = UserHoldsAccount.create(user=user.id, account=account.id,\n relationType=\"primary\")\n admin_edge = UserIsAccountAdmin.create(user=user.id, account=account.id)\n\n response = {\n \"user\": json.loads(schema.dumps(user).data),\n \"token\": create_access_token(identity=user)\n }\n\n return jsonify_response(response, 201)","def register_user(self, cls):\n return self.register_entity('user', cls)","def signup(self, request, pk=None):\n # Django uses the `Authorization` header to determine\n # which user is making the request to sign up\n player = Player.objects.get(user=request.auth.user) #gets the user from the token\n\n try:\n # Handle the case if the client specifies a movie\n # that doesn't exist\n movie_night = MovieNight.objects.get(pk=pk)\n except MovieNight.DoesNotExist:\n return Response(\n {'message': 'MovieNight does not exist.'},\n status=status.HTTP_400_BAD_REQUEST\n )\n\n # A player wants to sign up for an movie_night\n if request.method == \"POST\":\n try:\n # Using the attendees field on the movie_night makes it simple to add a player to the movie_night\n # .add(player) will insert into the join table a new row the player_id and the movie_night_id\n movie_night.attendees.add(player)\n return Response({}, status=status.HTTP_201_CREATED)\n except Exception as ex:\n return Response({'message': ex.args[0]})\n\n # User wants to leave a previously joined movie_night\n elif request.method == \"DELETE\":\n try:\n # The many to many relationship has a .remove method that removes the player from the attendees list\n # The method deletes the row in the join table that has the player_id and movie_night_id\n movie_night.attendees.remove(player)\n return Response(None, status=status.HTTP_204_NO_CONTENT)\n except Exception as ex:\n return Response({'message': ex.args[0]})","def sipserver_user_registration_status(self, user: str, ip_address: str) -> str:\n self.endpoint_registration_status_in_sipserver(\n endpoint=user, ip_address=ip_address\n )"],"string":"[\n \"def conference_registration(self, request, reg=True):\\n prof = self.profile_service.get_profile_from_user() # get user Profile\\n\\n # check if conf exists given websafeConfKey\\n # get conference; check that it exists\\n wsck = request.websafeConferenceKey\\n conf = ndb.Key(urlsafe=wsck).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' % wsck)\\n\\n # register\\n if reg:\\n # check if user already registered otherwise add\\n if wsck in prof.conferenceKeysToAttend:\\n raise ConflictException(\\n \\\"You have already registered for this conference\\\")\\n\\n # check if seats avail\\n if conf.seatsAvailable <= 0:\\n raise ConflictException(\\\"There are no seats available.\\\")\\n\\n # register user, take away one seat\\n prof.conferenceKeysToAttend.append(wsck)\\n conf.seatsAvailable -= 1\\n retval = True\\n\\n # unregister\\n else:\\n # check if user already registered\\n if wsck in prof.conferenceKeysToAttend:\\n\\n # unregister user, add back one seat\\n prof.conferenceKeysToAttend.remove(wsck)\\n conf.seatsAvailable += 1\\n retval = True\\n else:\\n retval = False\\n\\n # write things back to the datastore & return\\n prof.put()\\n conf.put()\\n return BooleanMessage(data=retval)\",\n \"def register_for_conference(self, request):\\n return self.conference_registration(request)\",\n \"def registerForConference(self, request):\\n return self._conferenceRegistration(request)\",\n \"def unregister_from_conference(self, request):\\n return self.conference_registration(request, reg=False)\",\n \"def unregisterFromConference(self, request):\\n return self._conferenceRegistration(request, reg=False)\",\n \"def register_user():\\n pass\",\n \"def set_user_register(self, *args, **kwargs):\\n return _uhd_swig.usrp_sink_set_user_register(self, *args, **kwargs)\",\n \"def register(self, voice=False):\\n payload = {\\\"type\\\": \\\"register\\\", \\\"username\\\": self.username, \\\"voice\\\": voice}\\n self._send_command(payload)\",\n \"def set_user_register(self, *args, **kwargs):\\n return _uhd_swig.usrp_sink_sptr_set_user_register(self, *args, **kwargs)\",\n \"def event_register_participant(request, event_id, participant_id):\\n if request.method == 'PUT':\\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\\n participant = get_object_or_404(Participant, pk=participant_id)\\n try:\\n attendance = Attendance(participant=participant, event=event, datetime_register=datetime.now())\\n attendance.save()\\n except Exception as e:\\n print e\\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_TO_EVENT}\\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\\n serializer = AttendanceSerializer(attendance)\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"async def register(ctx, *args):\\n user = ctx.message.author\\n user_mention = ctx.author.mention\\n chan_mention = \\\"<#876850365730021386>\\\"\\n \\n if user in self.data[\\\"users.json\\\"]:\\n await ctx.message.channel.send(user_mention+\\\", you are already registered. :blue_heart:\\\")\\n else:\\n self.data[\\\"users_asked_to_be_registered.json\\\"].append(user)\\n await ctx.message.channel.send(user_mention+\\\", do you accept the \\\"+chan_mention+\\n \\\" (Indie Library Terms of Service). Command .accept if you do. :blue_heart:\\\")\",\n \"def register(self, form):\\n new_user = self.create_inactive_user(form)\\n signals.user_registered.send(\\n sender=self.__class__, user=new_user, request=self.request\\n )\\n return new_user\",\n \"def toggle_subscription(self):\\n user = self.context['request'].user\\n # pylint: disable=no-member\\n profile = UserProfile.objects.get(\\n user=user)\\n club = self.context['club']\\n\\n if club in profile.subscriptions.all():\\n club.subscribed_users.remove(profile)\\n else:\\n club.subscribed_users.add(profile)\",\n \"def create_conference_session(self, request):\\n user = endpoints.get_current_user()\\n\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n\\n self.session_service.create_conference_session(request, user)\\n return request\",\n \"def set_user_register(self, *args, **kwargs):\\n return _uhd_swig.usrp_source_set_user_register(self, *args, **kwargs)\",\n \"def _register_user(request_form):\\n idnr = request_form['idnr']\\n\\n if user_exists(idnr):\\n raise UserAlreadyExistsError(idnr)\\n\\n response = elster_client.send_unlock_code_request_with_elster(request_form, request.remote_addr)\\n request_id = escape(response['elster_request_id'])\\n\\n create_user(idnr, request_form['dob'].strftime(\\\"%d.%m.%Y\\\"), request_id)\",\n \"def register(session: Session, email: str, password: str, given_name: str, last_name: str,\\n phone: str) -> RegisterResult:\\n # Validate the data integrity of the parameters\\n if email is None:\\n return RegisterResult.BAD_USERNAME\\n if password is None or not passwordService.validate(password):\\n return RegisterResult.BAD_PASSWORD\\n\\n # Check to see if the user already exists\\n existing_user = session.query(User) \\\\\\n .filter(User.email == email) \\\\\\n .first()\\n if existing_user is not None:\\n return RegisterResult.USERNAME_ALREADY_REGISTERED\\n\\n # Everything seems fine, so we go ahead and create the user & the linked account.\\n password_hash = passwordService.hash(password)\\n new_user = User(role=UserType.VOLUNTEER, password=password_hash, first_name=given_name, last_name=last_name,\\n mobile_number=phone, email=email, preferred_hours={}, experience_years=0, possibleRoles=[\\\"Basic\\\"],\\n qualifications=[],\\n availabilities={\\\"Friday\\\": [], \\\"Monday\\\": [], \\\"Sunday\\\": [], \\\"Tuesday\\\": [], \\\"Saturday\\\": [],\\n \\\"Thursday\\\": [], \\\"Wednesday\\\": []})\\n session.add(new_user)\\n session.flush()\\n return RegisterResult.SUCCESS\",\n \"async def register(websocket):\\n app['websockets'].add(websocket)\\n await notify_users()\",\n \"def on_signup(self, data):\\n self.participant_id = data[\\\"participant\\\"][\\\"id\\\"]\",\n \"def add_user(params, batch_registration=False):\\n # Make sure the username is unique\\n db = api.db.get_conn()\\n if is_blacklisted_username(params[\\\"username\\\"]) or db.users.find_one(\\n {\\\"username\\\": params[\\\"username\\\"]},\\n collation=Collation(locale=\\\"en\\\", strength=CollationStrength.PRIMARY),\\n ):\\n raise PicoException(\\\"There is already a user with this username.\\\", 409)\\n if db.teams.find_one(\\n {\\\"team_name\\\": params[\\\"username\\\"]},\\n collation=Collation(locale=\\\"en\\\", strength=CollationStrength.PRIMARY),\\n ):\\n raise PicoException(\\\"There is already a team with this username.\\\", 409)\\n\\n # If gid is specified, force affiliation to that team's name\\n email_whitelist = None\\n if params.get(\\\"gid\\\", None):\\n group = api.group.get_group(gid=params[\\\"gid\\\"])\\n group_settings = api.group.get_group_settings(gid=group[\\\"gid\\\"])\\n params[\\\"affiliation\\\"] = group[\\\"name\\\"]\\n email_whitelist = group_settings[\\\"email_filter\\\"]\\n\\n # If rid is specified and gid and email match,\\n # get teacher status from registration token.\\n # Additionally, invited users are automatically validated.\\n user_is_teacher = params[\\\"usertype\\\"] == \\\"teacher\\\"\\n user_was_invited = False\\n join_group_as_teacher = False\\n if params.get(\\\"rid\\\", None):\\n key = api.token.find_key_by_token(\\\"registration_token\\\", params[\\\"rid\\\"])\\n if params.get(\\\"gid\\\") != key[\\\"gid\\\"]:\\n raise PicoException(\\n \\\"Registration token group and supplied gid do not match.\\\"\\n )\\n if params[\\\"email\\\"] != key[\\\"email\\\"]:\\n raise PicoException(\\n \\\"Registration token email does not match the supplied one.\\\"\\n )\\n join_group_as_teacher = key[\\\"teacher\\\"]\\n user_was_invited = True\\n api.token.delete_token(key, \\\"registration_token\\\")\\n\\n # If not invited, validate the user's email against the whitelist\\n else:\\n if not verify_email_in_whitelist(params[\\\"email\\\"], email_whitelist):\\n raise PicoException(\\n \\\"Your email does not belong to the whitelist. \\\"\\n + \\\"Please see the registration form for details.\\\"\\n )\\n\\n # If CAPTCHAs are enabled, validate the submission if not batch registration\\n if (\\n api.config.get_settings()[\\\"captcha\\\"][\\\"enable_captcha\\\"]\\n and not batch_registration\\n and not _validate_captcha(params)\\n ):\\n raise PicoException(\\\"Incorrect captcha!\\\")\\n\\n # Create a team for the new user and set its count to 1\\n tid = api.team.create_team(\\n {\\n \\\"team_name\\\": params[\\\"username\\\"],\\n \\\"password\\\": api.common.hash_password(\\\"-\\\"),\\n \\\"affiliation\\\": params[\\\"affiliation\\\"],\\n }\\n )\\n db.teams.update_one({\\\"tid\\\": tid}, {\\\"$set\\\": {\\\"size\\\": 1}})\\n\\n # The first registered user automatically becomes an admin\\n user_is_admin = False\\n if db.users.count() == 0:\\n user_is_admin = True\\n user_is_teacher = True\\n\\n # Insert the new user in the DB\\n uid = api.common.token()\\n settings = api.config.get_settings()\\n user = {\\n \\\"uid\\\": uid,\\n \\\"firstname\\\": params[\\\"firstname\\\"],\\n \\\"lastname\\\": params[\\\"lastname\\\"],\\n \\\"username\\\": params[\\\"username\\\"],\\n \\\"email\\\": params[\\\"email\\\"],\\n \\\"password_hash\\\": api.common.hash_password(params[\\\"password\\\"]),\\n \\\"tid\\\": tid,\\n \\\"usertype\\\": params[\\\"usertype\\\"],\\n \\\"country\\\": params[\\\"country\\\"],\\n \\\"demo\\\": params[\\\"demo\\\"],\\n \\\"teacher\\\": user_is_teacher,\\n \\\"admin\\\": user_is_admin,\\n \\\"disabled\\\": False,\\n \\\"verified\\\": (not settings[\\\"email\\\"][\\\"email_verification\\\"] or user_was_invited),\\n \\\"extdata\\\": {},\\n \\\"completed_minigames\\\": [],\\n \\\"unlocked_walkthroughs\\\": [],\\n \\\"tokens\\\": 0,\\n }\\n db.users.insert_one(user)\\n\\n # Determine the user team's initial eligibilities\\n initial_eligibilities = [\\n scoreboard[\\\"sid\\\"]\\n for scoreboard in api.scoreboards.get_all_scoreboards()\\n if api.scoreboards.is_eligible(user, scoreboard)\\n ]\\n db.teams.find_one_and_update(\\n {\\\"tid\\\": tid}, {\\\"$set\\\": {\\\"eligibilities\\\": initial_eligibilities}}\\n )\\n\\n # If gid was specified, add the newly created team to the group\\n if params.get(\\\"gid\\\", None):\\n api.group.join_group(params[\\\"gid\\\"], tid, teacher=join_group_as_teacher)\\n\\n # If email verification is enabled and user wasn't invited, send\\n # validation email\\n if settings[\\\"email\\\"][\\\"email_verification\\\"] and not user_was_invited:\\n api.email.send_user_verification_email(params[\\\"username\\\"])\\n\\n return uid\",\n \"async def accept(ctx, *args):\\n user = ctx.message.author\\n user_mention = \\\"<@\\\"+str(user.id)+\\\">\\\"\\n\\n if user in self.data[\\\"users_asked_to_be_registered.json\\\"]:\\n self.data[\\\"users.json\\\"].append(user)\\n self.data[\\\"users_asked_to_be_registered.json\\\"].remove(user)\\n await ctx.message.channel.send(user_mention+\\\", you have been successfully registered. :blue_heart:\\\")\\n else:\\n await ctx.message.channel.send(user_mention+\\\", have not commanded .register yet. \\\"\\n \\\"Please do so first. :blue_heart:\\\")\",\n \"def preregister_user(username, full_name):\\n registration_hash = secrets.token_hex(32)\\n with get_conn().cursor() as cur:\\n cur.execute(\\\"\\\"\\\"\\n INSERT INTO unregistered_users (\\n username, full_name, registration_hash\\n ) VALUES (\\n '%s', '%s', '%s'\\n )\\\"\\\"\\\" % (username, full_name, registration_hash,))\\n return True\",\n \"def on_user_signed_up(sender, request: HttpRequest, user: User, **_):\\n Event.new(EventAction.SIGN_UP).from_http(request)\",\n \"def update_conference(self, request):\\n return self.conference_service.update_conference_object(request)\",\n \"def register():\\r\\n form = RegisterForm(request.form)\\r\\n\\r\\n if request.method == 'POST' and form.validate():\\r\\n new_user = User(form.email.data, form.password.data)\\r\\n g.session.add(new_user)\\r\\n g.session.commit()\\r\\n\\r\\n new_profile = Profile(form.first_name.data, form.last_name.data, new_user.id)\\r\\n g.session.add(new_profile)\\r\\n g.session.commit()\\r\\n # TODO: make it async\\r\\n if current_app.config[\\\"REQUIRE_EMAIL_CONFIRMATION\\\"]:\\r\\n send_confirmation(new_user)\\r\\n new_user.init_folders()\\r\\n logout_user()\\r\\n return redirect(url_for(\\\".login\\\"))\\r\\n return render_template(\\\"account/register_user.pug\\\", form=form)\",\n \"def join_conference(request, conference_name):\\n resp = VoiceResponse()\\n resp.dial(hangupOnStar=True).conference(conference_name)\\n return HttpResponse(str(resp))\",\n \"def register(user_name: str, password: str, age=20):\\n auth.register(user_name, password)\\n user_handler.register(user_name, age)\",\n \"async def unregister(websocket):\\n USERS.remove(websocket)\\n GAME.new_game()\\n await update_players()\",\n \"def tournament_registration(request, tournament_id):\\n tournament = get_object_or_404(Tournament, id=tournament_id)\\n \\n if tournament.is_online():\\n form = OnlineTournamentRegistrationForm(request.POST, initial={\\n 'tournament': tournament\\n })\\n else:\\n form = TournamentRegistrationForm(request.POST, initial={\\n 'tournament': tournament\\n })\\n\\n if form.is_valid():\\n if tournament.is_online():\\n tenhou_nickname = form.cleaned_data.get('tenhou_nickname')\\n if OnlineTournamentRegistration.objects.filter(tournament=tournament, tenhou_nickname=tenhou_nickname).exists():\\n messages.success(request, _('You already registered to the tournament!'))\\n return redirect(tournament.get_url())\\n\\n instance = form.save(commit=False)\\n instance.tournament = tournament\\n \\n # it supports auto load objects only for Russian tournaments right now\\n \\n try:\\n instance.player = Player.objects.get(first_name_ru=instance.first_name.title(), \\n last_name_ru=instance.last_name.title())\\n except (Player.DoesNotExist, Player.MultipleObjectsReturned):\\n # TODO if multiple players are here, let's try to filter by city\\n pass\\n \\n try:\\n instance.city_object = City.objects.get(name_ru=instance.city)\\n except City.DoesNotExist:\\n pass\\n\\n if tournament.registrations_pre_moderation:\\n instance.is_approved = False\\n message = _('Your registration was accepted! It will be visible on the page after administrator approvement.')\\n else:\\n instance.is_approved = True\\n message = _('Your registration was accepted!')\\n\\n instance.save()\\n \\n messages.success(request, message)\\n else:\\n messages.success(request, _('Please, allow to store personal data'))\\n\\n return redirect(tournament.get_url())\",\n \"def set_user_register(self, *args, **kwargs):\\n return _uhd_swig.usrp_source_sptr_set_user_register(self, *args, **kwargs)\",\n \"def registerUser():\\n\\t\\tid=users.get_current_user()\\n\\t\\tkey=id.email().split('@')[0]\\n\\t\\tuser = AppUser(key_name=key)\\n\\t\\tuser.id = id\\n\\t\\tuser.verified=False\\n\\t\\tuser.banned=False\\n\\t\\tuser.premium=False\\t\\n\\t\\tuser.goodEventsCount=0\\n\\t\\tuser.badEventsCount=0\\n\\t\\tuser.put()\\n\\t\\treturn user\",\n \"async def register(self, ctx, osu_link: str, timezone: str = \\\"\\\"): # TODO handle teams + multiples brackets\\n tournament = self.get_tournament(ctx.guild.id)\\n if len(tournament.brackets) != 1:\\n await self.send_reply(ctx, \\\"not_supported_yet\\\")\\n return\\n bracket = tournament.current_bracket\\n if bracket.registration_end_date:\\n registration_end_date = datetime.datetime.strptime(\\n bracket.registration_end_date, tosurnament.DATABASE_DATE_FORMAT\\n )\\n if datetime.datetime.now() > registration_end_date:\\n raise tosurnament.RegistrationEnded()\\n\\n players_spreadsheet = await bracket.get_players_spreadsheet()\\n if not players_spreadsheet:\\n raise tosurnament.NoSpreadsheet(\\\"players\\\")\\n if players_spreadsheet.range_timezone:\\n if not timezone:\\n raise commands.MissingRequiredArgument(timezone)\\n if not re.match(r\\\"(UTC)?[-\\\\+]([0-9]|1[0-4])(:[0-5][0-9])?$\\\", timezone, re.IGNORECASE):\\n raise InvalidTimezone(timezone)\\n timezone = \\\"UTC\\\" + re.sub(r\\\"^UTC\\\", \\\"\\\", timezone, flags=re.IGNORECASE)\\n if players_spreadsheet.range_team_name:\\n await self.send_reply(ctx, \\\"not_supported_yet\\\")\\n return\\n team_info = TeamInfo.get_first_blank_fields(players_spreadsheet)\\n osu_name = osu.get_from_string(osu_link)\\n osu_user = osu.get_user(osu_name, m=tournament.game_mode)\\n if not osu_user:\\n raise tosurnament.UserNotFound(osu_name)\\n team_info.players.append(\\n TeamInfo.PlayerInfo(\\n osu_user.name,\\n str(ctx.author),\\n ctx.author.id,\\n str(osu_user.rank),\\n str(osu_user.rank),\\n str(osu_user.id),\\n str(int(float(osu_user.pp))),\\n str(osu_user.country),\\n )\\n )\\n team_info.timezone.set(timezone)\\n self.add_update_spreadsheet_background_task(players_spreadsheet)\\n roles_to_give = [tosurnament.get_role(ctx.guild.roles, tournament.player_role_id, \\\"Player\\\")]\\n roles_to_give.append(tosurnament.get_role(ctx.guild.roles, bracket.role_id, bracket.name))\\n await ctx.author.add_roles(*filter(None, roles_to_give))\\n try:\\n await ctx.author.edit(nick=osu_user.name)\\n except (discord.Forbidden, discord.HTTPException):\\n pass\\n await self.send_reply(ctx, \\\"success\\\")\",\n \"def register_user(registration_hash, graduating_year, graduating_semester, major, pw_hash):\\n with get_conn().cursor() as cur:\\n cur.execute(\\\"CALL register_user('%s', '%s', '%s', '%s', '%s')\\\" % (registration_hash, graduating_year, graduating_semester, major, pw_hash))\\n cur.execute(\\\"SELECT * FROM users WHERE username=(SELECT username FROM unregistered_users WHERE registration_hash='%s')\\\" % registration_hash)\\n return cur.fetchone()\",\n \"def user_register():\\n \\n data = user_obj.user_register(request.forms) \\n return data\",\n \"def conferences_choice(self):\\n while self.choice != \\\"q\\\":\\n self.choice = input(\\\"\\\\033[35m(c) for create \\\\n(m) for modify \\\\n\\\"\\n \\\"(d) for delete \\\\n(s) for see \\\\n\\\"\\n \\\"(q) for exit \\\\n(r) for return start page:\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n if self.choice == \\\"c\\\":\\n title = input(\\\"\\\\033[33menter title:\\\\033[0m\\\")\\n summary = input(\\\"\\\\033[33menter summary:\\\\033[0m\\\")\\n date = input(\\\"\\\\033[33menter date:\\\\033[0m\\\")\\n hour = input(\\\"\\\\033[33menter hour \\\\033[0m\\\")\\n speaker = input(\\\"\\\\033[33menter speaker ID: \\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.create_conferences(title, summary, date, hour, speaker)\\n\\n\\n if self.choice == \\\"m\\\":\\n while column not in ['title', 'resume', 'date', 'hour', 'personid']:\\n id = input(\\\"\\\\033[33menter confernces id :\\\\033[0m\\\")\\n column = input(\\n \\\"\\\\033[33mwhat do you want to modify \\\\n [title, resume, date, hour, personid] :\\\\033[0m\\\")\\n datta = input(\\\"\\\\033[33menter new datta:\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.update_confernces(id, column, datta)\\n\\n\\n if self.choice == \\\"d\\\":\\n id = input(\\\"\\\\033[33menter conferences ID :\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.delete_conferences(id)\\n\\n\\n if self.choice == \\\"s\\\":\\n datta = self.conf.show_conferences()\\n if datta:\\n for elem in datta:\\n print(elem)\\n if self.choice == \\\"r\\\":\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n return\\n if self.choice == \\\"q\\\":\\n exit()\",\n \"def register(bot, update, user_data):\\n if Chat.query.filter(Chat.chatID == update.message.chat_id).first():\\n if Chat.query.filter(and_(Chat.chatID == update.message.chat_id, Chat.DOB != None)).first():\\n messageContent = \\\"Already registered!\\\"\\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent)\\n return ConversationHandler.END\\n\\n student_data = Chat.query.filter(Chat.chatID == update.message.chat_id).first()\\n user_data['Student_ID'] = student_data.PID\\n \\n messageContent = textwrap.dedent(\\\"\\\"\\\"\\n Now enter your Date of Birth (DOB) in the following format:\\n `DD/MM/YYYY`\\n \\\"\\\"\\\")\\n update.message.reply_text(messageContent, parse_mode='markdown')\\n return PARENT_LGN\\n\\n messageContent = textwrap.dedent(\\\"\\\"\\\"\\n Okay, send me your MIS credentials in this format:\\n `Student-ID password`\\n (in a single line, separated by a space)\\n\\n Use /cancel to abort.\\n \\\"\\\"\\\")\\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent, parse_mode='markdown')\\n return CREDENTIALS\",\n \"def test_register_twice(self):\\n body, code = self.post(f\\\"/users\\\", bob, {\\\"phone\\\": \\\"+441234567890\\\", **bob_creds})\\n self.assertEqual(400, code)\\n self.assertEqual({\\\"error\\\": \\\"User already exists.\\\"}, body)\",\n \"def add_user_to_g():\\n \\n if CURRENT_USER in session:\\n g.user = User.query.get(session[CURRENT_USER])\\n\\n else:\\n g.user = None\",\n \"def register_user(request, extra_context=None):\\r\\n if request.user.is_authenticated():\\r\\n return redirect(reverse('dashboard'))\\r\\n if settings.FEATURES.get('AUTH_USE_CERTIFICATES_IMMEDIATE_SIGNUP'):\\r\\n # Redirect to branding to process their certificate if SSL is enabled\\r\\n # and registration is disabled.\\r\\n return external_auth.views.redirect_with_get('root', request.GET)\\r\\n\\r\\n context = {\\r\\n 'course_id': request.GET.get('course_id'),\\r\\n 'email': '',\\r\\n 'enrollment_action': request.GET.get('enrollment_action'),\\r\\n 'name': '',\\r\\n 'running_pipeline': None,\\r\\n 'platform_name': microsite.get_value(\\r\\n 'platform_name',\\r\\n settings.PLATFORM_NAME\\r\\n ),\\r\\n 'selected_provider': '',\\r\\n 'username': '',\\r\\n }\\r\\n\\r\\n if extra_context is not None:\\r\\n context.update(extra_context)\\r\\n\\r\\n if context.get(\\\"extauth_domain\\\", '').startswith(external_auth.views.SHIBBOLETH_DOMAIN_PREFIX):\\r\\n return render_to_response('register-shib.html', context)\\r\\n\\r\\n # If third-party auth is enabled, prepopulate the form with data from the\\r\\n # selected provider.\\r\\n if settings.FEATURES.get('ENABLE_THIRD_PARTY_AUTH') and pipeline.running(request):\\r\\n running_pipeline = pipeline.get(request)\\r\\n current_provider = provider.Registry.get_by_backend_name(running_pipeline.get('backend'))\\r\\n overrides = current_provider.get_register_form_data(running_pipeline.get('kwargs'))\\r\\n overrides['running_pipeline'] = running_pipeline\\r\\n overrides['selected_provider'] = current_provider.NAME\\r\\n context.update(overrides)\\r\\n\\r\\n return render_to_response('register.html', context)\",\n \"def add_user_to_g():\\n\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def add_user_to_g():\\n\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def add_user_to_g():\\n\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def put(self, **kwargs):\\n contract = {\\n \\\"pushRegKey\\\": [\\\"id\\\",\\\"+\\\"]\\n }\\n try:\\n self.check_params_conform(contract)\\n except ValidatorException:\\n return\\n\\n user_id = kwargs[\\\"id\\\"]\\n user = models.User.get_by_id(user_id)\\n if user is None:\\n self.abort(422, \\\"Could not find user\\\")\\n\\n user.pushRegKey = self.get_param(\\\"pushRegKey\\\")\\n user.put()\\n\\n self.set_default_success_response()\\n self.send_response()\",\n \"def set_subscription(request, token, subscribe): # pylint: disable=unused-argument\\r\\n try:\\r\\n username = UsernameCipher().decrypt(token.encode())\\r\\n user = User.objects.get(username=username)\\r\\n except UnicodeDecodeError:\\r\\n raise Http404(\\\"base64url\\\")\\r\\n except UsernameDecryptionException as exn:\\r\\n raise Http404(exn.message)\\r\\n except User.DoesNotExist:\\r\\n raise Http404(\\\"username\\\")\\r\\n\\r\\n if subscribe:\\r\\n UserPreference.objects.get_or_create(user=user,\\r\\n key=NOTIFICATION_PREF_KEY,\\r\\n defaults={\\r\\n \\\"value\\\": UsernameCipher.encrypt(user.username)\\r\\n })\\r\\n return render_to_response(\\\"resubscribe.html\\\", {'token': token})\\r\\n else:\\r\\n UserPreference.objects.filter(user=user, key=NOTIFICATION_PREF_KEY).delete()\\r\\n return render_to_response(\\\"unsubscribe.html\\\", {'token': token})\",\n \"def signup_user(self):\\n User.user_list.append(self)\",\n \"def set_user_register(self, reg_index, value):\\n\\n self.hd.setd(f'awgs/{self.index}/userregs/{reg_index}', int(value))\",\n \"def signup(self):\\n # sign up\\n new_username = generate_username()\\n success = signup_helper(self, new_username)\\n if success:\\n # go to AuthenticatedTasks\\n self.user.username = new_username\\n self.interrupt()\",\n \"def add_user_to_g():\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def register(self, form):\\n new_user = form.save(commit=False)\\n username_field = getattr(new_user, 'USERNAME_FIELD', 'username')\\n # Save lowercased email as username.\\n setattr(new_user, username_field, form.cleaned_data['email'].lower())\\n new_user.first_name = form.cleaned_data['first_name']\\n new_user.last_name = form.cleaned_data['last_name']\\n new_user.save()\\n new_user = authenticate(username=getattr(new_user, username_field), password=form.cleaned_data['password1'])\\n login(self.request, new_user)\\n user_registered.send(sender=self.__class__, user=new_user, request=self.request)\\n profile, _ = Profile.objects.get_or_create(user=new_user)\\n self.request.session['signed_up'] = True\\n profile.payment_plan = int(form.cleaned_data['payment_plan'])\\n profile.company_name = form.cleaned_data['company']\\n profile.phone = form.cleaned_data['phone']\\n profile.save(update_fields=['payment_plan', 'company_name', 'phone'])\\n if profile.payment_plan != Profile.PAYMENT_PLAN_FREE:\\n messages.add_message(self.request, messages.INFO,\\n 'Congratulations! We won\\\\'t charge you for this plan for now.')\\n return new_user\",\n \"def updateConference(self, request):\\n return self._updateConferenceObject(request)\",\n \"def event_register_collaborator(request, event_id, employee_id):\\n if request.method == 'PUT':\\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\\n collaborator = get_object_or_404(Employee, pk=employee_id)\\n event.collaborators.add(collaborator)\\n event.save()\\n serializer = EventSimpleRegistrationSerializer(event)\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def reg_user(self, uid, uname):\\n cuser = self.get_user(uid)\\n time.sleep(0.1)\\n if cuser != None:\\n print(\\\"UPDATE\\\")\\n sql = \\\"UPDATE users SET 'isStudent'='', 'group'='', 'name'='' WHERE id={}\\\".format(\\n cuser[0]\\n )\\n else:\\n print(\\\"INSERT\\\")\\n sql = \\\"INSERT INTO users ('uid', 'uname', 'isStudent', 'group', 'name') VALUES ({}, '{}', '', '', '')\\\".format(\\n uid, uname\\n )\\n try:\\n self.cursor.execute(sql)\\n self.conn.commit()\\n except sqlite3.IntegrityError:\\n print(\\\"UID not unique\\\")\",\n \"def add_user_to_g():\\n # access g in templates, g only lives for life of request\\n if CURR_USER_KEY in session:\\n g.user = User.query.get(session[CURR_USER_KEY])\\n\\n else:\\n g.user = None\",\n \"def _register(self, user=None):\\n if user is None:\\n user = User.objects.create_user(\\n username=self.username,\\n password=self.password)\\n user.is_active = self.active\\n # Automatically add the user to the proper group\\n if self._group_name is not None:\\n group = get_or_none(Group, name=self._group_name)\\n if group is not None:\\n user.groups.add(group)\\n\\n user.save()\\n profile = UserProfile(user=user, member=self)\\n profile.save()\",\n \"def signup():\\n req = request.get_json()\\n user = req['user']\\n is_created = views.UserManagement().create(user)\\n if not is_created:\\n return jsonify(msg.ALREADY_USE), 400\\n\\n return jsonify(msg.SUCCESS), 200\",\n \"def new_register():\\n\\n # Get form information.\\n username = request.form.get(\\\"username\\\")\\n password = request.form.get(\\\"password\\\")\\n\\n user_info = db.execute(\\\"SELECT * FROM user_library WHERE username = :username\\\", {\\\"username\\\": username}).fetchone()\\n # Make sure user not exists.\\n if user_info is not None:\\n return render_template(\\\"error.html\\\", message=\\\"The username is already used.\\\")\\n\\n #Insert the new user if he/she does not exits \\n db.execute(\\\"INSERT INTO user_library (username, password) VALUES (:username, :password)\\\",\\n {\\\"username\\\": username, \\\"password\\\": password})\\n db.commit()\\n \\n # Store the id of the current session\\n user = db.execute(\\\"SELECT * FROM user_library WHERE username = :username\\\",{\\\"username\\\": username}).fetchone()\\n session[\\\"user_id\\\"] = user.id\\n \\n return redirect(url_for('search'))\",\n \"def signup(self, request, user):\\n pass\",\n \"def on_register(self, response):\\n print('You have been registered!')\\n self.on_auth(response)\",\n \"def register(self):\\n try:\\n sha = sha1(self.email).hexdigest()\\n except TypeError:\\n raise SleekException(\\\"Could not register user.\\\", 401)\\n\\n if not redis.sadd(\\\"sl:account:ids\\\", sha):\\n raise SleekException(\\\"Could not register new user.\\\", 401)\\n self.save(register=True)\",\n \"def test_registered_with_already_registered_user(self):\\n\\n print(\\\" ------------ Test 2 - Registration an user already registed ------------------\\\")\\n\\n user_id = uuid.uuid4()\\n account = Account(user_id=user_id, password=\\\"my-precious\\\", currency=Currency(\\\"EUR\\\"))\\n db.session.add(account)\\n db.session.commit()\\n\\n response = register_user(user_id, \\\"my-precious\\\", \\\"EUR\\\")\\n data = response.json()['message']\\n self.assertTrue(data['status'] == 'fail')\\n self.assertTrue(data['message'] == 'User already exists. Please Log in')\\n self.assertTrue(response.headers['Content-Type'] == 'application/json')\\n self.assertEqual(response.json()['code'], 202)\",\n \"def sipserver_user_add(self, user: str, password: str = None) -> None:\\n self.add_endpoint_to_sipserver(endpoint=user, password=password)\",\n \"def register():\\n result = register_helper(User)\\n return jsonify(result[0]), result[1]\",\n \"def register():\\r\\n form = RegistrationUserForm()\\r\\n if form.validate_on_submit():\\r\\n user = User(\\r\\n first_name=form.first_name.data,\\r\\n last_name=form.last_name.data,\\r\\n email=form.email.data,\\r\\n password=form.password.data)\\r\\n db.session.add(user)\\r\\n db.session.commit()\\r\\n professor = Professor(\\r\\n user_id=user.id,\\r\\n first_name=form.first_name.data,\\r\\n last_name=form.last_name.data,\\r\\n university=form.university.data\\r\\n )\\r\\n db.session.add(professor)\\r\\n db.session.commit()\\r\\n token = user.generate_confirmation_token()\\r\\n confirm_link = url_for('account.confirm', token=token, _external=True)\\r\\n get_queue().enqueue(\\r\\n send_email,\\r\\n recipient=user.email,\\r\\n subject='Confirm Your Account',\\r\\n template='account/email/confirm',\\r\\n user=user,\\r\\n confirm_link=confirm_link)\\r\\n flash('A confirmation link has been sent to {}.'.format(user.email),\\r\\n 'warning')\\r\\n return redirect(url_for('main.index'))\\r\\n return render_template('professor/signup.html', form=form)\",\n \"async def register_handler(self, ctx, error):\\n if isinstance(error, InvalidTimezone):\\n await self.send_reply(ctx, \\\"invalid_timezone\\\", error.timezone)\\n elif isinstance(error, tosurnament.RegistrationEnded):\\n await self.send_reply(ctx, \\\"registration_ended\\\")\",\n \"def is_in_conference(self) -> bool:\",\n \"def subscribe():\\n if request.method == 'POST':\\n surname = request.form['surname']\\n name = request.form['name']\\n birthdate = request.form['birthdate']\\n username = request.form['username']\\n password = request.form['password']\\n retype_password = request.form['retype-password']\\n is_new_user_valid = create_account(name, surname, birthdate, username, password, retype_password)\\n if is_new_user_valid == \\\"OK\\\":\\n session_id = generate_session(username)\\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), user=username,\\n session_id=session_id, authjs=False, user_added_correctly=True, preview_length=get_cars_preview().__len__())\\n else:\\n return render_template('sign_up.html', subscription_error=is_new_user_valid)\",\n \"def event_unregister_participant(request, event_id, participant_id):\\n if request.method == 'PUT':\\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\\n participant = get_object_or_404(Participant, pk=participant_id)\\n attendance = get_object_or_404(Attendance, participant=participant, event=event)\\n attendance.delete()\\n serializer = EventSimpleSerializer(event)\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def signup():\",\n \"def register(self, user) -> None:\\n self._all_members[user.name] = user\\n if type(user).__name__ == 'LeaderUser':\\n self._leaders.append(user)\",\n \"def save(self, *args, **kwargs):\\n create = self.id is None\\n # Strip out the user keyword argument, since the super save method\\n # does not expect it.\\n user = None\\n if 'user' in kwargs:\\n user = kwargs.pop('user')\\n super(Member, self).save(*args, **kwargs)\\n # Only register if the object is not being updated\\n if create:\\n self._register(user=user)\\n else:\\n # User and UserProfile already exist so save them too\\n self.userprofile.save()\\n self.userprofile.user.save()\",\n \"def __register(self, api):\\r\\n # Validate email\\r\\n email = self.request.get(constants.VAR_NAME_EMAIL)\\r\\n logging.info('User registering: ' + str(email))\\r\\n if not User.isEmailValid(email) or User.isAlreadyRegistered(email):\\r\\n logging.error('Email mismatched or already registered')\\r\\n self.set_error(constants.STATUS_BAD_REQUEST,\\r\\n self.gettext('REGISTER_ERROR'),\\r\\n url=self.request.url)\\r\\n return\\r\\n\\r\\n # Validate password\\r\\n password = self.request.get(constants.VAR_NAME_PASSWORD)\\r\\n if not User.isPasswordValid(password):\\r\\n logging.error('Invalid password')\\r\\n self.set_error(constants.STATUS_BAD_REQUEST,\\r\\n self.gettext('REGISTER_ERROR'),\\r\\n url=self.request.url)\\r\\n return\\r\\n\\r\\n # Calculate password hash\\r\\n salt_and_key = CryptoUtil.get_salt_and_key(password)\\r\\n salt = salt_and_key[0]\\r\\n key = salt_and_key[1]\\r\\n\\r\\n # Create and store user object\\r\\n user = User(key_name=email)\\r\\n user.email = email.lower()\\r\\n user.salt = salt\\r\\n user.password = key\\r\\n user.verified = False\\r\\n user.put()\\r\\n\\r\\n # Send email for verification\\r\\n self.__send_verification(email)\\r\\n\\r\\n if api == '':\\r\\n # Display message\\r\\n template_values = {\\r\\n 'message': self.gettext('PLEASE_CHECK_YOUR_EMAIL')\\r\\n }\\r\\n template = self.jinja2_env.get_template('staticmessage.html')\\r\\n self.response.out.write(template.render(template_values))\\r\\n\\r\\n self.ok()\",\n \"def remove_user(self):\\n self.currentuser = None\\n self.carlocked = False\",\n \"def register_user(self):\\n User.add_user(User(self.email.data, self.password.data))\",\n \"def add_user_to_session(self,session_id,client_id,display_name):\\n self.sessions[session_id][\\\"USERS\\\"][client_id] = {\\n \\\"display_name\\\" :display_name,\\n \\\"permissions\\\" : {\\n \\\"add_to_queue\\\" : True,\\n \\\"playback\\\" : True,\\n \\\"skip\\\" : True,\\n \\\"edit_queue\\\" : True\\n }\\n }\",\n \"def register():\\n response = jsonify(msg='unauthorized'), 400\\n if request.method == 'POST' and request.get_json():\\n data = request.get_json()\\n valid = validator.user_registration(data)\\n\\n if valid['status']:\\n ecomap_user.register(data['first_name'],\\n data['last_name'],\\n data['email'],\\n data['password'])\\n msg = 'added %s %s' % (data['first_name'],\\n data['last_name'])\\n response = jsonify({'status_message': msg}), 201\\n else:\\n response = Response(json.dumps(valid),\\n mimetype='application/json'), 400\\n return response\",\n \"def register(client):\\n rv = client.register('user1', 'default')\\n assert 'You were successfully registered ' \\\\\\n 'and can login now' in rv.data\\n rv = client.register('user1', 'default')\\n assert 'The username is already taken' in rv.data\\n rv = client.register('', 'default')\\n assert 'You have to enter a username' in rv.data\\n rv = client.register('meh', '')\\n assert 'You have to enter a password' in rv.data\\n rv = client.register('meh', 'x', 'y')\\n assert 'The two passwords do not match' in rv.data\\n rv = client.register('meh', 'foo', email='broken')\\n assert 'You have to enter a valid email address' in rv.data\",\n \"def _register(self,user,project):\\n url = reverse(\\\"comicsite.views._register\\\", \\n kwargs={\\\"site_short_name\\\":project.short_name})\\n factory = RequestFactory()\\n request = factory.get(url)\\n request.user = user\\n self.apply_standard_middleware(request)\\n \\n response = _register(request,project.short_name)\\n \\n \\n self.assertEqual(response.status_code,\\n 200,\\n \\\"After registering as user %s at '%s', page did not\\\"\\n \\\" load properly\\\" % (user.username,url))\\n \\n self.assertTrue(project.is_participant(user),\\n \\\"After registering as user %s at '%s', user does not \\\"\\n \\\" appear to be registered.\\\" % (user.username,url))\",\n \"def add_registrar(contest, user):\\n _add_role(contest, user, pcm.Registrar)\",\n \"def sipserver_user_remove(self, user: str) -> None:\\n self.remove_endpoint_from_sipserver(endpoint=user)\",\n \"def save_user_ref(sender, created, instance, **_):\\n if created:\\n UserExtend.objects.create(user=instance)\\n UserSettings.objects.create(user=instance)\",\n \"def register_user(client_socket, parts):\\n\\n username = parts[1]\\n one_time_id = parts[2]\\n signature = base64.b64decode(parts[3])\\n hostname = parts[4]\\n port = parts[5]\\n client_cert = parts[6]\\n\\n valid_signature = Cryptography.verify_signature(base64.b64decode(client_cert).decode(),\\n one_time_id,\\n signature)\\n\\n if valid_signature and User.login(username, one_time_id):\\n ClientsStore.save_client(username, hostname, port, client_cert)\\n ClientsStore.set_online(username, hostname, port)\\n Administration.__socket_map[client_socket] = username\\n client_socket.send('OK'.encode())\\n else:\\n client_socket.send('NOTOK'.encode())\",\n \"def register(self):\\r\\n if self.fields_not_empty(request, [\\\"first_name\\\", \\\"last_name\\\", \\\"age\\\", \\\"CPR\\\", \\\"email\\\", \\\"phone_number\\\", \\\"password\\\", \\\"confirm_password\\\"]):\\r\\n return jsonify({\\\"error\\\": \\\"Some fields are empty\\\"}), 400\\r\\n user = self.create_user_object(request)\\r\\n if request.form.get(\\\"password\\\") != request.form.get(\\\"confirm_password\\\"):\\r\\n return jsonify({\\\"error\\\": \\\"Passwords did not match\\\"}), 400\\r\\n db.insert_one(user)\\r\\n return self.start_session(user)\",\n \"def register():\\n data = None\\n response = {\\n 'status': 400,\\n 'error': 'Provide: firstname, lastname, email, othername, phone_number, and password as json.'\\n }\\n try:\\n data = request.get_json()\\n except:\\n return jsonify(response), 400\\n\\n if not data:\\n return jsonify(response), 400\\n\\n user_data = {\\n 'firstname': data.get('firstname'),\\n 'lastname': data.get('lastname'),\\n 'email': data.get('email'),\\n 'password': data.get('password'),\\n 'othername': data.get('othername'),\\n 'phone_number': data.get('phone_number'),\\n 'is_admin': data.get('is_admin'),\\n 'is_politician': data.get('is_politician')\\n }\\n valdiator_result = Validator.validate_user(user_data)\\n if isinstance(valdiator_result, dict):\\n return jsonify(valdiator_result), valdiator_result['status']\\n if isinstance(valdiator_result, bool) and valdiator_result:\\n result = politico.register_user(user_data)\\n\\n response = {}\\n if result == 'User added':\\n # return a response notifying the user that they registered successfully\\n response['status'] = 201\\n response['data'] = []\\n response['data'].append({\\n 'message': 'User registered successfully'\\n })\\n elif result == 'Other name taken':\\n # return a response notifying the user that othername is taken\\n response['status'] = 409\\n response['error'] = 'The othername you chose is taken'\\n elif result == 'User already exists':\\n # notify the user that an account with the same email is already registered\\n response['status'] = 409\\n response['error'] = 'User already exists'\\n return make_response(jsonify(response), response['status'])\",\n \"def join_player(self, data, user):\\n self.remove(user)\\n\\n user.room = \\\"100\\\"\\n user.x = \\\"0\\\"\\n user.y = \\\"0\\\"\\n user.frame = \\\"0\\\"\\n\\n self.add(user)\",\n \"def delete(self, event_id: int) -> Response:\\n if current_user not in self.event.participants:\\n return jsonify({\\n \\\"status\\\": 404,\\n \\\"message\\\": \\\"You are not registered as a participant\\\"\\n })\\n else:\\n self.event.participants.remove(current_user)\\n self.event.save_to_db()\\n\\n return jsonify({\\n \\\"status\\\": 200,\\n \\\"message\\\": \\\"Successfully unregister from participants\\\"\\n })\",\n \"def add_or_update_google_user(self, google_subscriber_id, name,\\n profile_pic):\\n if google_subscriber_id in self.known_users:\\n self.known_users[google_subscriber_id].update(name, profile_pic)\\n else:\\n self.known_users[google_subscriber_id] = \\\\\\n User(google_subscriber_id, name, profile_pic)\\n return self.known_users[google_subscriber_id]\",\n \"def __init__(self):\\n self._conferences = set()\",\n \"def update_user():\",\n \"def auto_register(request,backend=None,error_msgs=''):\\r\\n # Check if a username is provided\\r\\n username_form = forms.AutoRegisterForm()\\r\\n if request.method == 'POST' and request.POST.get('username'):\\r\\n name = setting('SOCIAL_AUTH_PARTIAL_PIPELINE_KEY', 'partial_pipeline')\\r\\n username_form = forms.AutoRegisterForm(request.POST)\\r\\n if username_form.is_valid():\\r\\n username = username_form.cleaned_data['username']\\r\\n try:\\r\\n interface.get_user_without_password(username)\\r\\n error_msgs ='That username is already in use.'\\r\\n except DoesNotExistError:\\r\\n request.session['saved_username'] = request.POST['username']\\r\\n backend = request.session[name]['backend']\\r\\n return redirect('socialauth_complete', backend=backend)\\r\\n name = setting('SOCIAL_AUTH_PARTIAL_PIPELINE_KEY', 'partial_pipeline')\\r\\n backend=request.session[name]['backend']\\r\\n return render_to_response('accounts/auto_register.html', {'backend' : backend, 'error_msgs' : error_msgs, 'username_form' : username_form}, RequestContext(request))\",\n \"def set_user(conn, phone_number: str, options: List[str], t: str) -> None:\\n with conn.cursor() as cur:\\n\\n # Get user info from db.\\n cur.execute(\\\"UPSERT INTO users (phone_number, options, t) VALUES (%s, %s, %s)\\\", (phone_number, options, t))\\n conn.commit()\\n return None\",\n \"def _signup(request, eamap, retfun=None):\\r\\n # save this for use by student.views.create_account\\r\\n request.session['ExternalAuthMap'] = eamap\\r\\n\\r\\n if settings.FEATURES.get('AUTH_USE_CERTIFICATES_IMMEDIATE_SIGNUP', ''):\\r\\n # do signin immediately, by calling create_account, instead of asking\\r\\n # student to fill in form. MIT students already have information filed.\\r\\n username = eamap.external_email.split('@', 1)[0]\\r\\n username = username.replace('.', '_')\\r\\n post_vars = dict(username=username,\\r\\n honor_code=u'true',\\r\\n terms_of_service=u'true')\\r\\n log.info('doing immediate signup for %s, params=%s', username, post_vars)\\r\\n student.views.create_account(request, post_vars)\\r\\n # should check return content for successful completion before\\r\\n if retfun is not None:\\r\\n return retfun()\\r\\n else:\\r\\n return redirect('/')\\r\\n\\r\\n # default conjoin name, no spaces, flattened to ascii b/c django can't handle unicode usernames, sadly\\r\\n # but this only affects username, not fullname\\r\\n username = re.sub(r'\\\\s', '', _flatten_to_ascii(eamap.external_name), flags=re.UNICODE)\\r\\n\\r\\n context = {'has_extauth_info': True,\\r\\n 'show_signup_immediately': True,\\r\\n 'extauth_domain': eamap.external_domain,\\r\\n 'extauth_id': eamap.external_id,\\r\\n 'extauth_email': eamap.external_email,\\r\\n 'extauth_username': username,\\r\\n 'extauth_name': eamap.external_name,\\r\\n 'ask_for_tos': True,\\r\\n }\\r\\n\\r\\n # Some openEdX instances can't have terms of service for shib users, like\\r\\n # according to Stanford's Office of General Counsel\\r\\n uses_shibboleth = (settings.FEATURES.get('AUTH_USE_SHIB') and\\r\\n eamap.external_domain.startswith(SHIBBOLETH_DOMAIN_PREFIX))\\r\\n if uses_shibboleth and settings.FEATURES.get('SHIB_DISABLE_TOS'):\\r\\n context['ask_for_tos'] = False\\r\\n\\r\\n # detect if full name is blank and ask for it from user\\r\\n context['ask_for_fullname'] = eamap.external_name.strip() == ''\\r\\n\\r\\n # validate provided mail and if it's not valid ask the user\\r\\n try:\\r\\n validate_email(eamap.external_email)\\r\\n context['ask_for_email'] = False\\r\\n except ValidationError:\\r\\n context['ask_for_email'] = True\\r\\n\\r\\n log.info('EXTAUTH: Doing signup for %s', eamap.external_id)\\r\\n\\r\\n return student.views.register_user(request, extra_context=context)\",\n \"def register():\\n form = RegistrationForm()\\n\\n if form.validate_on_submit():\\n db.session.add(form.user)\\n db.session.add(form.allergies)\\n # Hacky solution time! If a student misspelled their school name then\\n # had to go through registration again, there may be an empty school\\n # This query returns a list of schools with no students\\n for empty_school in School.query.filter(~School.students.any()):\\n db.session.delete(empty_school)\\n db.session.commit()\\n\\n flash('You have successfully registered for the ACM-HSCC', 'alert-success')\\n login_user(form.user)\\n return redirect(url_for('default.home'))\\n else:\\n flash_form_errors(form)\\n return render_template('register.html', form=form)\",\n \"async def put_user_byid(request):\\n user_id = request.match_info[\\\"user_id\\\"]\\n try:\\n user_id = int(user_id)\\n except (ValueError, TypeError):\\n return web.Response(text=\\\"Incorrect value for user_id\\\", status=400)\\n\\n user = request.cirrina.db_session.query(User).filter_by(id=user_id).first()\\n if not user:\\n return web.Response(status=404, text=\\\"User not found\\\")\\n\\n if user.username == \\\"admin\\\":\\n return web.Response(status=400, text=\\\"Cannot change admin\\\")\\n\\n is_admin = request.GET.getone(\\\"is_admin\\\", None) # str \\\"true\\\" or \\\"flase\\\"\\n if not is_admin: # if None\\n return web.Response(text=\\\"Nothing to change\\\", status=204)\\n\\n if is_admin.lower() == \\\"true\\\":\\n user.is_admin = True\\n data = {\\\"result\\\": \\\"{u} is now admin \\\".format(u=user.username)}\\n elif is_admin.lower() == \\\"false\\\":\\n user.is_admin = False\\n data = {\\\"result\\\": \\\"{u} is no longer admin \\\".format(u=user.username)}\\n\\n try:\\n request.cirrina.db_session.commit() # pylint: disable=no-member\\n except sqlalchemy.exc.DataError:\\n request.cirrina.db_session.rollback() # pylint: disable=no-member\\n return web.Response(status=500, text=\\\"Database error\\\")\\n\\n # TODO : change to a multicast group\\n await app.websocket_broadcast(\\n {\\n \\\"event\\\": Event.changed.value,\\n \\\"subject\\\": Subject.user.value,\\n \\\"changed\\\": {\\\"id\\\": user_id, \\\"is_admin\\\": user.is_admin},\\n }\\n )\\n\\n return web.json_response(data)\",\n \"def register_user(req_data):\\n logger.debug(\\\"entering function register_user\\\")\\n validate_fields(req_data, [\\\"email\\\", \\\"mobile\\\", \\\"password\\\", \\\"first_name\\\", \\\"last_name\\\"])\\n\\n user_email = req_data[\\\"email\\\"]\\n user_mobile = req_data[\\\"mobile\\\"]\\n user_password = req_data[\\\"password\\\"]\\n user_first_name = req_data[\\\"first_name\\\"]\\n user_last_name = req_data[\\\"last_name\\\"]\\n\\n existing_email_query = {\\\"email\\\": user_email}\\n result = run_find_one_query(config.USERS_COL, existing_email_query, error=False)\\n if result is not None:\\n logger.error(\\\"email already exists, query result = %s\\\", result)\\n return get_failure_response(EMAIL_EXISTS_ERR_MSG)\\n\\n existing_mobile_query = {\\\"mobile\\\": user_mobile}\\n result = run_find_one_query(config.USERS_COL, existing_mobile_query, error=False)\\n if result is not None:\\n logger.error(\\\"mobile already exists, query result = %s\\\", result)\\n return get_failure_response(MOBILE_EXISTS_ERR_MSG)\\n\\n user_id = uuid4().hex\\n doc = {\\n \\\"user_id\\\": user_id,\\n \\\"first_name\\\": user_first_name,\\n \\\"last_name\\\": user_last_name,\\n \\\"email\\\": user_email,\\n \\\"mobile\\\": user_mobile,\\n \\\"password\\\": generate_password_hash(user_password)\\n }\\n logger.info(\\\"inserting new user into database\\\")\\n run_insert_one_query(config.USERS_COL, doc, error=True, error_msg=REGISTRATION_FAILED_ERR_MSG)\\n logger.info(\\\"registration successful for email = %s\\\", doc[\\\"email\\\"])\\n\\n logger.debug(\\\"exiting function register_user\\\")\\n return get_success_response(REGISTRATION_SUCCESS_MSG)\",\n \"def account_register(request, next_url='dashboard'):\\n siteconfig = SiteConfiguration.objects.get_current()\\n auth_backends = get_enabled_auth_backends()\\n\\n if (auth_backends[0].supports_registration and\\n siteconfig.get(\\\"auth_enable_registration\\\")):\\n response = register(request, next_page=reverse(next_url),\\n form_class=RegistrationForm)\\n\\n if request.user.is_authenticated():\\n # This will trigger sending an e-mail notification for\\n # user registration, if enabled.\\n user_registered.send(sender=None, user=request.user)\\n\\n return response\\n\\n return HttpResponseRedirect(reverse(\\\"login\\\"))\",\n \"def register_ctf_user(self, username):\\n self.browser.get(self.get_server_url())\\n player_box = self.browser.find_element_by_id(\\\"ctf-player\\\")\\n player_box.send_keys(username)\\n player_box.send_keys(Keys.ENTER)\",\n \"def register():\\n schema = UserRegistrationSchema()\\n\\n data = schema.loads(request.data)\\n if data.errors:\\n return jsonify(data.errors), 400\\n\\n # Confirming that a duplicate user doesn't exist\\n duplicates_q = f\\\"g.V().hasLabel('{User.LABEL}')\\\" + \\\\\\n f\\\".or(has('username', '{data.data['username']}'),\\\" + \\\\\\n f\\\"has('email', '{data.data['email']}'))\\\"\\n duplicates = client.submit(duplicates_q).all().result()\\n if duplicates:\\n return jsonify_response({\\\"error\\\": \\\"User already exists!\\\"}, 400)\\n\\n data.data[\\\"password\\\"] = generate_password_hash(\\n data.data[\\\"password\\\"]).decode(\\\"utf-8\\\")\\n\\n # Creating the User and it's primary account + account admin edge\\n user = User.create(**data.data)\\n account = Account.create(title=f\\\"myaccount@{user.username}\\\")\\n holds_edge = UserHoldsAccount.create(user=user.id, account=account.id,\\n relationType=\\\"primary\\\")\\n admin_edge = UserIsAccountAdmin.create(user=user.id, account=account.id)\\n\\n response = {\\n \\\"user\\\": json.loads(schema.dumps(user).data),\\n \\\"token\\\": create_access_token(identity=user)\\n }\\n\\n return jsonify_response(response, 201)\",\n \"def register_user(self, cls):\\n return self.register_entity('user', cls)\",\n \"def signup(self, request, pk=None):\\n # Django uses the `Authorization` header to determine\\n # which user is making the request to sign up\\n player = Player.objects.get(user=request.auth.user) #gets the user from the token\\n\\n try:\\n # Handle the case if the client specifies a movie\\n # that doesn't exist\\n movie_night = MovieNight.objects.get(pk=pk)\\n except MovieNight.DoesNotExist:\\n return Response(\\n {'message': 'MovieNight does not exist.'},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n\\n # A player wants to sign up for an movie_night\\n if request.method == \\\"POST\\\":\\n try:\\n # Using the attendees field on the movie_night makes it simple to add a player to the movie_night\\n # .add(player) will insert into the join table a new row the player_id and the movie_night_id\\n movie_night.attendees.add(player)\\n return Response({}, status=status.HTTP_201_CREATED)\\n except Exception as ex:\\n return Response({'message': ex.args[0]})\\n\\n # User wants to leave a previously joined movie_night\\n elif request.method == \\\"DELETE\\\":\\n try:\\n # The many to many relationship has a .remove method that removes the player from the attendees list\\n # The method deletes the row in the join table that has the player_id and movie_night_id\\n movie_night.attendees.remove(player)\\n return Response(None, status=status.HTTP_204_NO_CONTENT)\\n except Exception as ex:\\n return Response({'message': ex.args[0]})\",\n \"def sipserver_user_registration_status(self, user: str, ip_address: str) -> str:\\n self.endpoint_registration_status_in_sipserver(\\n endpoint=user, ip_address=ip_address\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6628083","0.64813596","0.6294456","0.6073893","0.58963567","0.5738126","0.55700624","0.55217904","0.54235077","0.541907","0.5362311","0.53576905","0.5338589","0.52861226","0.5157034","0.51566017","0.5153176","0.5135656","0.5130247","0.5127976","0.5102711","0.5102175","0.5093956","0.5064531","0.506041","0.50293416","0.50259125","0.5017605","0.5016001","0.5014886","0.5014291","0.4988519","0.49784207","0.4963024","0.4960102","0.49546626","0.4934511","0.49272528","0.4911942","0.49107623","0.49107623","0.49107623","0.49040744","0.49022144","0.48913825","0.48876524","0.48845136","0.4883964","0.4875964","0.48671827","0.48670706","0.48620832","0.48557964","0.48553762","0.48470697","0.48447615","0.48440248","0.48389572","0.4823419","0.48147264","0.4778343","0.47735015","0.476801","0.47673386","0.47514698","0.47489342","0.47431773","0.47413898","0.47374728","0.4716951","0.4713602","0.4707261","0.4699999","0.4697245","0.46937573","0.46931064","0.46885392","0.46874982","0.46817967","0.467576","0.4674342","0.46711522","0.4657333","0.46499556","0.4649597","0.46472293","0.46468112","0.46443313","0.46425518","0.46340683","0.46246952","0.46010256","0.45985827","0.45964846","0.45961663","0.45958444","0.459448","0.45942903","0.4593279","0.45921937"],"string":"[\n \"0.6628083\",\n \"0.64813596\",\n \"0.6294456\",\n \"0.6073893\",\n \"0.58963567\",\n \"0.5738126\",\n \"0.55700624\",\n \"0.55217904\",\n \"0.54235077\",\n \"0.541907\",\n \"0.5362311\",\n \"0.53576905\",\n \"0.5338589\",\n \"0.52861226\",\n \"0.5157034\",\n \"0.51566017\",\n \"0.5153176\",\n \"0.5135656\",\n \"0.5130247\",\n \"0.5127976\",\n \"0.5102711\",\n \"0.5102175\",\n \"0.5093956\",\n \"0.5064531\",\n \"0.506041\",\n \"0.50293416\",\n \"0.50259125\",\n \"0.5017605\",\n \"0.5016001\",\n \"0.5014886\",\n \"0.5014291\",\n \"0.4988519\",\n \"0.49784207\",\n \"0.4963024\",\n \"0.4960102\",\n \"0.49546626\",\n \"0.4934511\",\n \"0.49272528\",\n \"0.4911942\",\n \"0.49107623\",\n \"0.49107623\",\n \"0.49107623\",\n \"0.49040744\",\n \"0.49022144\",\n \"0.48913825\",\n \"0.48876524\",\n \"0.48845136\",\n \"0.4883964\",\n \"0.4875964\",\n \"0.48671827\",\n \"0.48670706\",\n \"0.48620832\",\n \"0.48557964\",\n \"0.48553762\",\n \"0.48470697\",\n \"0.48447615\",\n \"0.48440248\",\n \"0.48389572\",\n \"0.4823419\",\n \"0.48147264\",\n \"0.4778343\",\n \"0.47735015\",\n \"0.476801\",\n \"0.47673386\",\n \"0.47514698\",\n \"0.47489342\",\n \"0.47431773\",\n \"0.47413898\",\n \"0.47374728\",\n \"0.4716951\",\n \"0.4713602\",\n \"0.4707261\",\n \"0.4699999\",\n \"0.4697245\",\n \"0.46937573\",\n \"0.46931064\",\n \"0.46885392\",\n \"0.46874982\",\n \"0.46817967\",\n \"0.467576\",\n \"0.4674342\",\n \"0.46711522\",\n \"0.4657333\",\n \"0.46499556\",\n \"0.4649597\",\n \"0.46472293\",\n \"0.46468112\",\n \"0.46443313\",\n \"0.46425518\",\n \"0.46340683\",\n \"0.46246952\",\n \"0.46010256\",\n \"0.45985827\",\n \"0.45964846\",\n \"0.45961663\",\n \"0.45958444\",\n \"0.459448\",\n \"0.45942903\",\n \"0.4593279\",\n \"0.45921937\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67306554"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18494,"cells":{"query":{"kind":"string","value":"Copy relevant fields from Conference to ConferenceForm."},"document":{"kind":"string","value":"def _copyConferenceToForm(self, conf, displayName):\n cf = ConferenceForm()\n for field in cf.all_fields():\n if hasattr(conf, field.name):\n # Convert Date to date string; just copy others\n if field.name.endswith('Date'):\n setattr(cf, field.name, str(getattr(conf, field.name)))\n else:\n setattr(cf, field.name, getattr(conf, field.name))\n elif field.name == \"websafeKey\":\n setattr(cf, field.name, conf.key.urlsafe())\n if displayName:\n setattr(cf, 'organizerDisplayName', displayName)\n cf.check_initialized()\n return cf"},"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 _copyConferenceToForm(self, conf, displayName):\n cf = ConferenceForm()\n for field in cf.all_fields():\n if hasattr(conf, field.name):\n # convert Date to date string; just copy others\n if field.name.endswith('Date'):\n setattr(cf, field.name, str(getattr(conf, field.name)))\n else:\n setattr(cf, field.name, getattr(conf, field.name))\n elif field.name == \"websafeKey\":\n setattr(cf, field.name, conf.key.urlsafe())\n if displayName:\n setattr(cf, 'organizerDisplayName', displayName)\n cf.check_initialized()\n return cf","def _copySpeakerToForm(self, speaker):\n sf = SpeakerForm()\n for field in sf.all_fields():\n if hasattr(speaker, field.name):\n setattr(sf, field.name, getattr(speaker, field.name))\n elif field.name == \"websafeKey\":\n setattr(sf, field.name, speaker.key.urlsafe())\n sf.check_initialized()\n return sf","def update_c_mandatory_fields(request, campaign_id):\n # print(request.POST)\n campaign = Campaign.objects.get(id=campaign_id)\n form = CampaignForm(request.POST, instance = campaign)\n # print(form)\n if form.is_valid():\n form.save()\n return redirect('add_campaign_spec', id=campaign_id)\n else:\n # return redirect('clonecampaign', id=campaign_id)\n print(form.errors)\n return redirect(reverse('edit_campaign', kwargs={'campaign_id':campaign_id}))","def clone(self):\n\n # recrea la instancia del form\n form_clone = FormularioDinamico(\n lang=self.lang,\n name=\"clon de %s\" % self.name,\n is_active=self.is_active,\n form_title=self.form_title,\n form_template=self.form_template,\n send_email=self.send_email,\n from_email=self.from_email,\n recipient_list=self.recipient_list,\n subject_template=self.subject_template,\n body_template=self.body_template,\n error_class=self.error_class,\n required_css_class=self.required_css_class,\n autorespond=self.autorespond,\n autorespond_subject_template=self.autorespond_subject_template,\n autorespond_body_template=self.autorespond_body_template,\n autorespond_email_field=self.autorespond_email_field\n )\n\n form_clone.save()\n\n content_type = ContentType.objects.get_for_model(form_clone)\n\n # recrea todos los fields\n for field in self.get_fields():\n field_clone = DynaFormField(\n content_type=content_type,\n object_pk=form_clone.pk,\n field_name=field.field_name,\n field_label=field.field_label,\n field_type=field.field_type,\n field_widget=field.field_widget,\n field_help=field.field_help,\n is_required=field.is_required,\n is_hidden=field.is_hidden,\n default_value=field.default_value,\n choices=field.choices,\n choices_delimiter=field.choices_delimiter,\n choices_queryset=field.choices_queryset,\n choices_queryset_filter=field.choices_queryset_filter,\n choices_queryset_empty_label=field.choices_queryset_empty_label,\n choices_queryset_label=field.choices_queryset_label,\n choices_related_field=field.choices_related_field,\n field_order=field.field_order\n )\n\n field_clone.save()","def prepare(self, form):\n \n return form","def _copySessionToForm(self, session):\n sf = SessionForm()\n for field in sf.all_fields():\n if hasattr(session, field.name):\n # Convert date field to date string\n if field.name == 'date':\n setattr(sf, field.name, str(getattr(session, field.name)))\n # Convert time field to time string\n elif field.name == 'startTime':\n setattr(sf, field.name,\n getattr(session, field.name).strftime('%H:%M'))\n # Convert typeOfSession string field to enum\n elif field.name == 'typeOfSession':\n setattr(sf, field.name,\n getattr(SessionType, getattr(session, field.name)))\n # Copy other fields verbatim\n else:\n setattr(sf, field.name, getattr(session, field.name))\n # Ensure that the SessionForm contains websafeKey\n elif field.name == \"websafeKey\":\n setattr(sf, field.name, session.key.urlsafe())\n sf.check_initialized()\n return sf","def getConference(self, request):\n # Get Conference object from request; bail if not found\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\n 'Conference')\n prof = conf.key.parent().get()\n # Return ConferenceForm\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))","def get_conference(self, request):\n # get Conference object from request; bail if not found\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' %\n request.websafeConferenceKey)\n prof = conf.key.parent().get()\n # return ConferenceForm\n return self.conference_service.copy_conference_to_form(ConferenceForm(),\n conf,\n prof.displayName)","def copyform(form, settings):\r\n for name, value in form.iteritems():\r\n setattr(settings, name, value)\r\n settings.commit()","def get_conferences_to_attend(self, request):\n prof = self.profile_service.get_profile_from_user() # get user Profile\n conf_keys = [ndb.Key(urlsafe=wsck) for wsck in\n prof.conferenceKeysToAttend]\n conferences = ndb.get_multi(conf_keys)\n\n # get organizers\n organisers = [ndb.Key(Profile, conf.organizerUserId) for conf in\n conferences]\n profiles = ndb.get_multi(organisers)\n\n # put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(items=[\n self.conference_service.copy_conference_to_form(\n ConferenceForm(), conf, names[conf.organizerUserId]\n ) for conf in conferences])","def _replace_fields(self):\n for name, value in self._cleaned_data.items():\n setattr(self, name, value)","def copyCommonFields(self):\n self.fetchDataToForm(self.selected_row, self.selected_column, fields = \"Recent\")","def copy_form(self, from_module, form, to_module, rename=False):\n copy_source = deepcopy(form.to_json())\n # only one form can be a release notes form, so set them to False explicitly when copying\n copy_source['is_release_notes_form'] = False\n copy_source['enable_release_notes'] = False\n if 'unique_id' in copy_source:\n del copy_source['unique_id']\n\n if rename:\n for lang, name in copy_source['name'].items():\n with override(lang):\n copy_source['name'][lang] = _('Copy of {name}').format(name=name)\n\n copy_form = to_module.add_insert_form(from_module, FormBase.wrap(copy_source))\n to_app = to_module.get_app()\n save_xform(to_app, copy_form, form.source.encode('utf-8'))\n\n return copy_form","def get_conferences_created(self, request):\n # make sure user is authed\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = self.auth.get_user_id(user)\n\n # create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(items=[\n self.conference_service.copy_conference_to_form(ConferenceForm(),\n conf,\n prof.displayName)\n for conf in confs])","def __init__(self, corp_app, field_objs, *args, **kwargs):\n self.corp_app = corp_app\n self.field_objs = field_objs\n super(CorpMembForm, self).__init__(*args, **kwargs)\n\n for field in field_objs:\n if field.field_type not in ['section_break', 'page_break']:\n if field.field_name:\n field_key = field.field_name\n else:\n field_key = \"field_%s\" % field.id\n\n # if field is display only, remove it from the form\n # for example, on the edit page, we\n # set corporate_membership_type\n # and payment_method as the display only fields\n if hasattr(field, 'display_only') and field.display_only:\n del self.fields[field_key]\n else:\n # get field class and set field initial\n self.fields[field_key] = field.get_field_class()\n if ((not field.field_name) \\\n or field.field_name == 'authorized_domains') \\\n and self.instance:\n initial = field.get_value(self.instance)\n if field.field_type in [\n 'MultipleChoiceField/django.forms.CheckboxSelectMultiple',\n 'MultipleChoiceField']:\n if initial:\n self.fields[field_key].initial = [\n item.strip() for item in initial.split(',')]\n else:\n self.fields[field_key].initial = initial\n\n #self.fields['captcha'] = CaptchaField(label=_('Type the code below'))","def _copyProfileToForm(self, prof):\n pf = ProfileForm()\n for field in pf.all_fields():\n if hasattr(prof, field.name):\n # Convert t-shirt string to Enum; just copy others\n if field.name == 'teeShirtSize':\n setattr(pf, field.name,\n getattr(TeeShirtSize, getattr(prof, field.name)))\n else:\n setattr(pf, field.name, getattr(prof, field.name))\n pf.check_initialized()\n return pf","def set_up_fields(self, fields):\n self.fields = {\n 'name': self.name,\n 'email': self.email\n }\n for key in fields.keys():\n # special keys first, not to be used in the template\n if key.upper() == 'CC':\n self.is_cc = fields[key]\n elif key.upper() == 'BCC':\n self.is_bcc = fields[key]\n else:\n self.fields[key] = fields[key]","def _createConferenceObject(self, request):\n # Preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n if not request.name:\n raise endpoints.BadRequestException(\n \"Conference 'name' field required\")\n # Copy ConferenceForm/ProtoRPC Message into dict\n data = {\n field.name: getattr(request, field.name) for field in\n request.all_fields()\n }\n del data['websafeKey']\n del data['organizerDisplayName']\n # Add default values for those missing (both data model and\n # outbound Message)\n for df in CONF_DEFAULTS:\n if data[df] in (None, []):\n data[df] = CONF_DEFAULTS[df]\n setattr(request, df, CONF_DEFAULTS[df])\n # Convert dates from strings to Date objects; set month based\n # on start_date\n if data['startDate']:\n data['startDate'] = datetime.strptime(\n data['startDate'][:10], \"%Y-%m-%d\").date()\n data['month'] = data['startDate'].month\n else:\n data['month'] = 0\n if data['endDate']:\n data['endDate'] = datetime.strptime(\n data['endDate'][:10], \"%Y-%m-%d\").date()\n # Set seatsAvailable to be same as maxAttendees on creation\n if data[\"maxAttendees\"] > 0:\n data[\"seatsAvailable\"] = data[\"maxAttendees\"]\n # Get the user profile key, then set the conference's parent\n # to that value.\n # NOTE: The original code made a call to allocate_ids in order to\n # generate an ID for the conference. Since the profiles utilize\n # strings (email addresses) for their IDs, resulting in no risk\n # of colliding with NDB's auto-generated numeric IDs, I decided\n # to let NDB generate the conference ID automatically.\n # https://cloud.google.com/appengine/docs/python/ndb/entities?hl=en#numeric_keys\n p_key = ndb.Key(Profile, user_id)\n data['parent'] = p_key\n data['organizerUserId'] = request.organizerUserId = user_id\n # Create Conference, send email to organizer confirming\n # creation of Conference and return (modified) ConferenceForm\n Conference(**data).put()\n taskqueue.add(params={'email': user.email(),\n 'conferenceInfo': repr(request)},\n url='/tasks/send_confirmation_email'\n )\n return request","def individual_formfields():\n # Instantiate Consent Tracker\n consent = s3db.auth_Consent(processing_types = VOL_CONSENT_OPTIONS)\n\n formfields = [utable.first_name,\n utable.last_name,\n Field(\"addr_L3\",\n label = T(\"Location\"),\n requires = IS_IN_SET(districts_and_uk),\n ),\n Field(\"addr_street\",\n label = T(\"Street Address\"),\n ),\n Field(\"addr_postcode\",\n label = T(\"Postcode\"),\n ),\n Field(\"mobile\",\n label = T(\"Contact Number (Preferred)\"),\n requires = IS_PHONE_NUMBER_MULTI(),\n comment = DIV(_class = \"tooltip\",\n _title = \"%s|%s\" % (T(\"Contact Number (Preferred)\"),\n T(\"Ideally a Mobile Number, so that we can send you Text Messages.\")),\n ),\n ),\n Field(\"home\",\n label = T(\"Contact Number (Secondary)\"),\n requires = IS_EMPTY_OR(IS_PHONE_NUMBER_MULTI()),\n ),\n utable.email,\n utable[passfield],\n # Password Verification Field\n Field(\"password_two\", \"password\",\n label = auth_messages.verify_password,\n requires = IS_EXPR(\"value==%s\" % \\\n repr(request.vars.get(passfield)),\n error_message = auth_messages.mismatched_password,\n ),\n ),\n\n # Skills\n s3db.hrm_multi_skill_id(empty = False,\n label = T(\"Volunteer Offer\"),\n ),\n Field(\"skills_details\",\n label = T(\"Please specify details\"),\n ),\n Field(\"certificates\", \"list:string\",\n label = T(\"Qualifications\"),\n requires = IS_IN_SET(certificates, multiple=True),\n widget = S3MultiSelectWidget(header=\"\",\n selectedList=3),\n ),\n Field(\"experience\",\n label = T(\"Skills and Experience\"),\n widget = lambda f, v: \\\n s3_comments_widget(f, v, _placeholder = \"e.g. Co-ordination, Event Management, PCV qualified.\")\n ),\n Field(\"resources\",\n label = T(\"Offers of Resources\"),\n widget = lambda f, v: \\\n s3_comments_widget(f, v, _placeholder = \"e.g. Minibus.\")\n ),\n Field(\"where_operate\", \"list:string\",\n label = T(\"Where would you be willing to volunteer?\"),\n requires = IS_IN_SET(districts, multiple=True),\n widget = S3MultiSelectWidget(header=\"\",\n selectedList=3),\n ),\n Field(\"travel\", \"integer\",\n label = T(\"Willing to Travel?\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"slots\", \"list:string\",\n label = T(\"Times\"),\n requires = IS_IN_SET(slots, multiple=True),\n widget = S3MultiSelectWidget(header=\"\",\n selectedList=3),\n ),\n Field(\"significant_physical\", \"integer\",\n label = T(\"That require significant physical activity (including lifting and carrying) and may involve being outdoors (e.g. clean up of affected properties)\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"some_physical\", \"integer\",\n label = T(\"That require some physical activity and may involve being outdoors (e.g. door knocking)\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"little_physical\", \"integer\",\n label = T(\"That require little physical activity and are based indoors (e.g. preparing refreshments)\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"health_details\",\n label = T(\"If you wish, you can give us some further information on any fitness, medical or mobility issues that might limit the kind of activities you are able to volunteer for; this will help us to suggest suitable opportunities for you\"),\n ),\n Field(\"faith_requirements\", \"integer\",\n label = T(\"Do you have any faith requirements that you would like help with if you are coming to Support Cumbria?\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"faith_requirements_details\",\n label = T(\"If Yes please outline\"),\n ),\n Field(\"emergency_contact_name\",\n label = T(\"Contact Name\"),\n requires = IS_NOT_EMPTY(),\n ),\n Field(\"emergency_contact_number\",\n label = T(\"Contact Number\"),\n requires = IS_PHONE_NUMBER_MULTI(),\n ),\n Field(\"emergency_contact_relationship\",\n label = T(\"Relationship\"),\n requires = IS_NOT_EMPTY(),\n ),\n Field(\"workplace\", \"integer\",\n label = T(\"Are you volunteering under your workplace volunteering scheme?\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n Field(\"workplace_details\",\n label = T(\"If yes please name your employer\"),\n ),\n Field(\"dbs\", \"integer\",\n label = T(\"Are you DBS checked?\"),\n requires = IS_IN_SET({0: T(\"No\"),\n 1: T(\"Yes\"),\n }),\n widget = lambda f, v: \\\n SQLFORM.widgets.radio.widget(f, v,\n style=\"divs\"),\n ),\n #Field(\"convictions\", \"integer\",\n # label = T(\"Do you have any unspent convictions?\"),\n # comment = T(\"Please tick 'Yes' if you have any convictions that are not yet spent under the Rehabilitation of Offenders Act 1974. The term 'convictions' is used to refer to any sentence or disposal issued by a court. If all your convictions are spent, you can tick 'No'. If you're not sure if your convictions are unspent or spent, you can use a tool available at www.disclosurecalculator.org.uk and read guidance at hub.unlock.org.uk/roa\"),\n # requires = IS_IN_SET({0: T(\"No\"),\n # 1: T(\"Yes\"),\n # }),\n # widget = lambda f, v: \\\n # SQLFORM.widgets.radio.widget(f, v,\n # style=\"divs\"),\n # ),\n # Consent (GDPR + FOC)\n Field(\"consent\",\n label = T(\"Consent\"),\n widget = consent.widget,\n ),\n ]\n\n required_fields = [\"first_name\",\n \"last_name\",\n \"addr_L3\",\n \"addr_street\",\n \"addr_postcode\",\n \"mobile\",\n \"emergency_contact\",\n \"where_operate\",\n ]\n\n return formfields, required_fields","def _createConferenceObject(self, request):\n # preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n if not request.name:\n raise endpoints.BadRequestException(\"Conference 'name' field required\")\n\n # copy ConferenceForm/ProtoRPC Message into dict\n data = {field.name: getattr(request, field.name) for field in request.all_fields()}\n del data['websafeKey']\n del data['organizerDisplayName']\n\n # add default values for those missing (both data model & outbound Message)\n for df in DEFAULTS:\n if data[df] in (None, []):\n data[df] = DEFAULTS[df]\n setattr(request, df, DEFAULTS[df])\n\n # convert dates from strings to Date objects; set month based on start_date\n if data['startDate']:\n data['startDate'] = datetime.strptime(data['startDate'][:10], \"%Y-%m-%d\").date()\n data['month'] = data['startDate'].month\n else:\n data['month'] = 0\n if data['endDate']:\n data['endDate'] = datetime.strptime(data['endDate'][:10], \"%Y-%m-%d\").date()\n\n # set seatsAvailable to be same as maxAttendees on creation\n if data[\"maxAttendees\"] > 0:\n data[\"seatsAvailable\"] = data[\"maxAttendees\"]\n # generate Profile Key based on user ID and Conference\n # ID based on Profile key get Conference key from ID\n p_key = ndb.Key(Profile, user_id)\n c_id = Conference.allocate_ids(size=1, parent=p_key)[0]\n c_key = ndb.Key(Conference, c_id, parent=p_key)\n data['key'] = c_key\n data['organizerUserId'] = request.organizerUserId = user_id\n\n # create Conference, send email to organizer confirming\n # creation of Conference & return (modified) ConferenceForm\n Conference(**data).put()\n taskqueue.add(params={'email': user.email(),\n 'conferenceInfo': repr(request)},\n url='/tasks/send_confirmation_email'\n )\n return request","def getConference(self, request):\n # get Conference object from request; bail if not found\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' % request.websafeConferenceKey)\n prof = conf.key.parent().get()\n # return ConferenceForm\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))","def get_form(self, form_class):\n form = super(InvitationCreateView, self).get_form(form_class)\n form.fields[\"receiver\"].queryset = self.get_available_invitees()\n form.fields[\"team\"].queryset = self.get_available_teams()\n form.fields[\"team\"].empty_label = None\n return form","def update_fields(self):\n if hasattr(self.day, \"body_composition\"):\n for f in self.get_fields():\n name = f.get_field().name\n value = getattr(self.day.body_composition, name, None)\n if value is not None:\n f.set_field(value)\n else:\n f.set_field(\"\")","def make_fields(self):\n for name, prop in self.edit:\n instance_value = self.model.get(name)\n post_value = self.data[name] if (self.data and self.data.has_key(name)) else instance_value\n form_field_class = self.get_field_type(prop)\n form_field = form_field_class(model=self.model, property=prop, name=name, instance_value=instance_value, post_value=post_value)\n self.add(form_field)","def test_form_has_fields(self):\r\n self.form = SubscriptionForm()\r\n expect = ['name', 'cpf', 'email', 'phone']\r\n self.assertSequenceEqual(expect, list(self.form.fields))","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n 'company_name': 'Company name',\n 'street_address1': 'Street Address 1',\n 'street_address2': 'Street Address 2',\n 'country': 'Country or State',\n 'postcode': 'Postcode',\n 'town_or_city': 'Town or City',\n 'payment': 'Paid for number of months',\n 'setting_daystart': 'Hour when your day starts',\n 'setting_dayend': 'hour when your day ends'\n }\n\n for field in self.fields:\n if field != 'country':\n if self.fields[field].required:\n placeholder = f'{placeholders[field]} *'\n else:\n placeholder = placeholders[field]\n self.fields[field].widget.attrs['placeholder'] = placeholder\n if field == 'setting_daystart' or field == 'setting_dayend' or field == 'payment':\n self.fields[field].widget.attrs['class'] = 'width-numbers'\n else:\n self.fields[field].widget.attrs['class'] = 'profile-form-input'\n self.fields[field].label = placeholder","def __init__(self, *args, **kwargs):\n\n\t\tsuper(AssonaForm, self).__init__(*args, **kwargs)\n\n\t\tfor field in self.fields:\n\t\t\tif field == 'versicherungskarte':\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control-file'\n\t\t\t\t})\n\t\t\telse:\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control'\n\t\t\t\t})","def get_form(self):\n # setup request layer\n self.request = TestRequest()\n # get add view\n form = getMultiAdapter((self.experiments, self.request),\n name=\"newProjection\")\n # update the form once to initialise all widgets\n form.update()\n # go through all widgets on the form and update the request with default values\n data = {}\n for widget in form.widgets.values():\n data[widget.name] = widget.value\n data.update({\n 'form.widgets.IDublinCore.title': u\"My CC Experiment\",\n 'form.widgets.IDublinCore.description': u'This is my experiment description',\n 'form.widgets.species_distribution_models': unicode(self.sdmexp.UID()),\n 'form.widgets.species_distribution_models.model': [unicode(self.sdmmodel.UID())],\n 'form.widgets.future_climate_datasets': [unicode(self.future.UID())]\n })\n self.request.form.update(data)\n form = getMultiAdapter((self.experiments, self.request),\n name=\"newProjection\")\n return form","def update_models_from_create_event_form(form, venue, event):\n\n # Venue Info\n event.venue.name = form.venue_name.data\n event.venue.address = form.address.data\n event.venue.city = form.city.data\n event.venue.state = CreateEventForm.convert_choice_to_value(\n form.state.data, \"STATES\"\n )\n event.venue.zip_code = form.zip_code.data\n\n # Event info\n event.title = form.title.data\n event_type = EventType.query.get(form.event_type.data)\n event_category = EventCategory.query.get(form.category.data)\n start_time = CreateEventForm.convert_choice_to_value(form.start_time.data, \"TIMES\")\n end_time = CreateEventForm.convert_choice_to_value(form.end_time.data, \"TIMES\")\n event.start_datetime = datetime.combine(form.start_date.data, start_time)\n event.end_datetime = datetime.combine(form.end_date.data, end_time)","def populate_form(self, **kwargs):\n for name, value in kwargs.items():\n self.populate_field(name, value)","def copy_fields(self, entity, all_fields=False):\n\n if all_fields:\n fields = self.get_all_fields()\n else:\n fields = self.get_non_pk_fields()\n\n for field in fields.keys():\n setattr(self, field, getattr(entity, field, None))","def __init__(self, *args, **kwargs):\n step_numeral, step_name = kwargs.pop('step', (None, None))\n corp_app = kwargs.pop('corp_app', '')\n file_path = kwargs.pop('file_path', '')\n\n super(CSVForm, self).__init__(*args, **kwargs)\n\n if step_numeral == 1:\n \"\"\"\n Basic Form: Application & File Uploader\n \"\"\"\n self.fields['corp_app'] = forms.ModelChoiceField(\n label=_('Corp Application'), queryset=CorpApp.objects.all())\n\n self.fields['update_option'] = forms.CharField(\n widget=forms.RadioSelect(\n choices=(('skip', 'Skip'),\n ('update', 'Update Blank Fields'),\n ('override', 'Override All Fields'),)),\n initial='skip',\n label=_('Select an Option for the Existing Records:')\n )\n\n self.fields['csv'] = forms.FileField(label=_(\"CSV File\"))\n\n if step_numeral == 2:\n \"\"\"\n Basic Form + Mapping Fields\n \"\"\"\n\n # file to make field-mapping form\n csv = csv_to_dict(file_path)\n\n # choices list\n choices = csv[0].keys()\n\n # make tuples; sort tuples (case-insensitive)\n choice_tuples = [(c, c) for c in csv[0].keys()]\n\n # insert blank option\n choice_tuples.insert(0, ('', ''))\n choice_tuples = sorted(choice_tuples, key=lambda c: c[0].lower())\n\n app_fields = CorpField.objects.filter(corp_app=corp_app)\n required_fields = ['name', 'corporate_membership_type']\n for field in app_fields:\n if field.field_type not in ['section_break', 'page_break']:\n if field.field_name:\n field_key = field.field_name\n else:\n field_key = \"field_%s\" % field.id\n is_required = False\n if field_key in required_fields:\n is_required = True\n self.fields[field_key] = ChoiceField(**{\n 'label': field.label,\n 'choices': choice_tuples,\n 'required': is_required,\n })\n for choice in choices:\n if (field.label).lower() == choice.lower() or \\\n field_key.lower() == choice.lower():\n self.fields[field_key].initial = choice\n\n extra_fields = (('secret_code', 'Secret Code'),\n ('join_dt', 'Join Date'),\n ('renew_dt', 'Renew Date'),\n ('expiration_dt', 'Expiration Date'),\n ('approved', 'Approved'),\n ('dues_rep', 'Dues Representative'),\n ('status', 'Status'),\n ('status_detail', 'Status Detail'))\n # corp_memb_field_names = [smart_str(field.name)\n # for field in CorporateMembership._meta.fields]\n for key, label in extra_fields:\n if key not in self.fields.keys():\n self.fields[key] = ChoiceField(**{\n 'label': label,\n 'choices': choice_tuples,\n 'required': False,\n })\n for choice in choices:\n if label.lower() == choice.lower() or \\\n key.lower() == choice.lower():\n self.fields[key].initial = choice","def __init__(self, *args, **kwargs):\n\n\t\tsuper(SchadensmeldungForm, self).__init__(*args, **kwargs)\n\n\t\tfor field in self.fields:\n\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t'class': 'form-control'\n\t\t\t\t})","def __init__(self, *args, **kwargs):\n\n\t\tsuper(BusinessbikeForm, self).__init__(*args, **kwargs)\n\n\t\tfor field in self.fields:\n\t\t\tif field == 'versicherungskarte':\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control-file'\n\t\t\t\t})\n\t\t\telse:\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control'\n\t\t\t\t})","def test_pass_through_prep_country_fields(self):\n original_flag = self.form.country_optional\n self.form.country_optional = False # True\n original_fields = self.form.fields\n self.form.fields = original_fields.copy()\n remaining_fields = original_fields.copy()\n opts, field_rows = {'fake_opts': 'fake'}, [{'name': 'assigned_field'}]\n args = ['arbitrary', 'input', 'args']\n kwargs = {'test_1': 'data_1', 'test_2': 'data_2'}\n\n expected = (opts.copy(), field_rows.copy(), remaining_fields.copy(), *args, kwargs.copy())\n actual = self.form.prep_country_fields(opts, field_rows, remaining_fields, *args, **kwargs)\n self.assertEqual(expected, actual)\n\n self.form.country_optional = original_flag\n self.form.fields = original_fields","def populate_create_event_form(form, venue, event):\n # Venue info\n venue = event.venue\n form.venue_name.data = venue.name\n form.address.data = venue.address\n form.city.data = venue.city\n form.state.data = CreateEventForm.convert_choice_to_id(venue.state, \"STATES\")\n form.zip_code.data = venue.zip_code\n\n # Event Info\n form.title.data = event.title\n form.event_type.data = event.event_type.id\n form.category.data = event.event_category.id\n form.start_date.data = event.start_date()\n form.end_date.data = event.end_date()\n form.start_time.data = CreateEventForm.convert_choice_to_id(\n event.start_time(), \"TIMES\"\n )\n form.end_time.data = CreateEventForm.convert_choice_to_id(event.end_time(), \"TIMES\")","def construct_instance(form, instance, fields=None, exclude=None):\n cleaned_data = form.cleaned_data\n file_field_list = []\n\n for name, field in instance._fields.items():\n if not name in cleaned_data:\n continue\n if fields is not None and name not in fields:\n continue\n if exclude and name in exclude:\n continue\n\n if isinstance(field, (mongoengine.URLField, mongoengine.EmailField)):\n if cleaned_data[name]:\n field.__set__(instance, cleaned_data[name])\n else:\n field.__set__(instance, None)\n continue\n field.__set__(instance, cleaned_data[name])\n\n return instance","def xtest_form_has_fields(self):\n expected = ['name', 'cpf', 'email', 'phone']\n form = SubscriptionForm()\n self.assertSequenceEqual(expected, list(form.fields))","def __init__(self, *args, **kwargs):\n\n # Les lignes suivantes permettent de modifier les label d'un champ dans la page\n super(ModelForm, self).__init__(*args, **kwargs)\n self.fields[\"nom_de_l_evenement\"].label = \"Nom de l'évènement\"\n self.fields[\"date_de_l_evenement\"].label = \"Date de l'évènement\" # utiliser plutôt l'attribut label comme pour AbonnementEvenementForm\n self.fields[\"fichier\"].label = \"Photo(s)\"","def __init__(self, *args, **kwargs):\n user = kwargs.pop('user')\n super(ChooseAppointmentForm, self).__init__(*args, **kwargs)\n if(user.first_name=='patient'):\n self.appointments = user.patient_appointment.all()\n appointment_partner = 'doctor' # patient is partnered with a doctor and vice versa\n else:\n self.appointments = user.doctor_appointment.all()\n appointment_partner = 'patient'\n choices = []\n\n for i, appointment in enumerate(self.appointments):\n partner_first_name = appointment.associated_patient.patient_user_profile.first_name if (appointment_partner=='patient') else appointment.associated_doctor.doctor_user_profile.first_name\n partner_last_name = appointment.associated_patient.patient_user_profile.last_name if (appointment_partner=='patient') else appointment.associated_doctor.doctor_user_profile.last_name\n choices.append((appointment, 'Appointment: {}, on {}, at {} with {} {}'\n .format(appointment.title, appointment.date, appointment.time, partner_first_name, partner_last_name)))\n\n self.fields['appointments'] = forms.ChoiceField(label=\"\", choices=choices, widget=forms.RadioSelect)","def _save_extra_fields(self, event):\n\n term = self.cleaned_data[\"term_name\"]\n week = self.cleaned_data[\"term_week\"]\n day = self.cleaned_data[\"day_of_week\"]\n\n year = int(settings.DEFAULT_ACADEMIC_YEAR)\n date = datetimes.termweek_to_date(year, term, week, day)\n \n start_hour = self.cleaned_data[\"start_hour\"]\n start_minute = self.cleaned_data[\"start_minute\"]\n end_hour = self.cleaned_data[\"end_hour\"]\n end_minute = self.cleaned_data[\"end_minute\"]\n\n tz = timezone.get_current_timezone()\n\n start_naive = datetime.datetime(date.year, date.month, date.day,\n start_hour, start_minute)\n event.start = tz.localize(start_naive)\n\n end_naive = datetime.datetime(date.year, date.month, date.day,\n end_hour, end_minute)\n event.end = tz.localize(end_naive)\n\n event.metadata[\"people\"] = self.cleaned_data[\"people\"]\n event.metadata[\"type\"] = self.cleaned_data[\"event_type\"]\n\n if self.cleaned_data[\"cancel\"] is True:\n event.status = models.Event.STATUS_CANCELLED\n else:\n event.status = models.Event.STATUS_LIVE","def get_form_kwargs(self):\n\n press_release_obj = PressRelease.objects.get(\n id=self.kwargs['press_release_pk'])\n\n kwargs = super(PressReleaseUpdateView, self).get_form_kwargs()\n kwargs.update(\n {'field': self.kwargs['field'],\n 'press_release_obj': press_release_obj}\n )\n\n return kwargs","def form_data(self, clear=[], **kwargs):\n form_data = {\n 'payer_name': 'William Williams',\n 'billing_address': '1 Main Street',\n 'billing_city': 'Anytown',\n 'country': 'USA',\n 'billing_state': 'MD',\n 'billing_zip': '20852',\n 'payment_type': 'CreditCard',\n 'project_code': '15-4FF',\n 'payment_amount': '3000',\n 'information_consent': True,\n }\n for key in clear:\n del form_data[key]\n for key, value in kwargs.items():\n form_data[key] = value\n return form_data","def __init__(self, *args, **kwargs):\n super(ProfileForm, self).__init__(*args, **kwargs) \n for field in self.fields:\n self.fields[field].widget.attrs.update(\n {\n 'class': 'form-control',\n }\n )","def __init__(self, *args, **kwargs):\n\n\t\tsuper(BikeleasingForm, self).__init__(*args, **kwargs)\n\n\t\tfor field in self.fields:\n\t\t\tif field == 'versicherungskarte':\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control-file'\n\t\t\t\t})\n\t\t\telse:\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control'\n\t\t\t\t})","def __init__(self, *args, **kwargs):\n\n\t\tsuper(EuroradForm, self).__init__(*args, **kwargs)\n\n\t\tfor field in self.fields:\n\t\t\tif field == 'versicherungskarte':\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control-file'\n\t\t\t\t})\n\t\t\telse:\n\t\t\t\tself.fields[field].widget.attrs.update({\n\t\t\t\t\t'class': 'form-control'\n\t\t\t\t})","def form_data(self):\n from couchforms import XMLSyntaxError\n from .utils import convert_xform_to_json, adjust_datetimes\n from corehq.form_processor.utils.metadata import scrub_form_meta\n xml = self.get_xml()\n try:\n form_json = convert_xform_to_json(xml)\n except XMLSyntaxError:\n return {}\n # we can assume all sql domains are new timezone domains\n with force_phone_timezones_should_be_processed():\n adjust_datetimes(form_json)\n\n scrub_form_meta(self.form_id, form_json)\n return form_json","def copy_fields(self, model):\n fields = super(HistoricalRecords, self).copy_fields(model)\n for name, field in self.additional_fields.items():\n assert name not in fields\n assert hasattr(self, 'get_%s_value' % name)\n fields[name] = field\n return fields","def complete_form_data():\n\n missing_fields = {\n 'link' : 'http://bvsalud.org',\n 'originator' : 'BIREME',\n 'source_type': 1,\n 'source_language': 1,\n 'originator_location' : 1,\n\n 'main-descriptor-content_type-object_id-TOTAL_FORMS' : '1',\n\n 'main-descriptor-content_type-object_id-0-id' : '',\n 'main-descriptor-content_type-object_id-0-text' : 'malaria',\n 'main-descriptor-content_type-object_id-0-code' : '^d8462',\n 'main-descriptor-content_type-object_id-0-status' : '0',\n\n 'main-resourcethematic-content_type-object_id-TOTAL_FORMS' : '1',\n 'main-resourcethematic-content_type-object_id-0-thematic_area' : '1',\n 'main-resourcethematic-content_type-object_id-0-status' : '0',\n }\n\n complete_form_data = minimal_form_data()\n complete_form_data.update(missing_fields)\n\n return complete_form_data","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields[\"from\"] = forms.DateTimeField(required=True)","def make_form(self):","def getConferencesToAttend(self, request):\n prof = self._getProfileFromUser() # get user Profile\n conf_keys = [\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\n ]\n conferences = ndb.get_multi(conf_keys)\n # Get organizers\n organisers = [\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\n ]\n profiles = ndb.get_multi(organisers)\n # Put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n # Return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\n for conf in conferences\n ]\n )","def build_conference(self, data) -> dict:\n builder = ConferenceBuilder()\n builder.add_title(title=data.get(\"name\"), subtitle=data.get(\"subtitle\"))\n builder.set_short_description(value=data.get(\"description\", \"\"))\n builder.set_opening_date(get_value(data, \"dates[0]\"))\n builder.set_closing_date(get_value(data, \"dates[1]\"))\n builder.add_inspire_categories(data.get(\"field_of_interest\", []))\n builder.add_public_note(value=data.get(\"additional_info\", \"\"))\n builder.add_series(\n name=data.get(\"series_name\"), number=data.get(\"series_number\")\n )\n for address in data.get(\"addresses\"):\n builder.add_address(\n cities=[address.get(\"city\")],\n state=address.get(\"state\"),\n place_name=address.get(\"venue\"),\n country_code=country_name_to_code(address.get(\"country\")),\n )\n for contact in data.get(\"contacts\", []):\n builder.add_contact(**contact)\n for acr in data.get(\"acronyms\", []):\n builder.add_acronym(acr)\n for website in data.get(\"websites\", []):\n builder.add_url(website)\n for keyword in data.get(\"keywords\", []):\n builder.add_keyword(value=keyword)\n\n builder.record[\"$schema\"] = url_for(\n \"invenio_jsonschemas.get_schema\",\n schema_path=\"records/conferences.json\",\n _external=True,\n )\n\n return builder.record","def formfields():\n\n T = current.T\n request = current.request\n\n auth = current.auth\n auth_settings = auth.settings\n auth_messages = auth.messages\n\n utable = auth_settings.table_user\n passfield = auth_settings.password_field\n\n # Last name is required\n utable.last_name.requires = IS_NOT_EMPTY(error_message=T(\"input required\"))\n\n # Don't check for duplicate email (will be done in onvalidation)\n # => user might choose to use the current email address of the account\n # => if registration key or code are invalid, we don't want to give away\n # any existing email addresses\n utable.email.requires = [IS_EMAIL(error_message = auth_messages.invalid_email),\n IS_LOWER(),\n ]\n\n # Instantiate Consent Tracker\n consent = ConsentTracking(processing_types=[\"STORE\", \"RULES_ISS\"])\n\n # Form fields\n formfields = [utable.first_name,\n utable.last_name,\n utable.email,\n utable[passfield],\n Field(\"password_two\", \"password\",\n label = auth_messages.verify_password,\n requires = IS_EXPR(\"value==%s\" % \\\n repr(request.vars.get(passfield)),\n error_message = auth_messages.mismatched_password,\n ),\n comment = DIV(_class = \"tooltip\",\n _title = \"%s|%s\" % (auth_messages.verify_password,\n T(\"Enter the same password again\"),\n ),\n ),\n ),\n Field(\"code\",\n label = T(\"Registration Code\"),\n requires = IS_NOT_EMPTY(),\n ),\n Field(\"consent\",\n label = T(\"Consent\"),\n widget = consent.widget,\n ),\n ]\n\n\n # Required fields\n required_fields = [\"first_name\",\n \"last_name\",\n ]\n\n return formfields, required_fields","def __init__(self, *args, **kwargs):\n super(RegisterForm, self).__init__(*args, **kwargs)\n\n self.fields['nick'].widget.attrs.update({\n 'label': 'Přezdívka',\n 'placeholder': 'Mirek'\n })\n\n self.fields['name'].widget.attrs.update({\n 'label': 'Jméno',\n 'placeholder': 'Mirek'\n })\n\n self.fields['surname'].widget.attrs.update({\n 'label': 'Příjmení',\n 'placeholder': 'Dušín'\n })\n\n self.fields['email'].widget.attrs.update({\n 'label': 'E-mail',\n 'placeholder': 'mirek@rychlesipy.cz'\n })\n\n self.fields['age'].widget.attrs.update({'label': 'Věk'})\n self.fields['age'].initial = 18\n\n self.fields['race'].widget.attrs.update({'label': 'Rasa'})\n self.fields['race'].queryset = Race.objects.filter(\n active=True).only('id', 'name')\n\n self.fields['group'].widget.attrs.update({\n 'label': 'Skupina',\n 'placeholder': 'Rychlé Šípy'\n })\n\n for field in self.fields.keys():\n self.fields[field].widget.attrs.update({\n 'required': self.fields[field].required,\n 'title': '',\n 'class': 'form-control'\n })","def getConferencesCreated(self, request):\n # Make sure user is authenticated\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n # Create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # Return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(\n conf, getattr(prof, 'displayName')) for conf in confs\n ]\n )","def _form_data(self, response):\n SQFI_audit_type = response.xpath(self.filters[6]).extract_first()\n SQFI_audit_type_val = response.xpath(self.filters[7]).extract_first()\n food_sector_categories = response.xpath(self.filters[8]).extract_first()\n food_sector_categories_val = response.xpath(self.filters[9]).extract()\n audit_rating = response.xpath(self.filters[10]).extract_first()\n audit_rating_val = response.xpath(self.filters[11]).extract()\n country = response.xpath(self.filters[12]).extract_first()\n country_val = response.xpath(self.filters[13]).extract()\n form_data = {\n SQFI_audit_type: SQFI_audit_type_val,\n food_sector_categories: food_sector_categories_val,\n audit_rating: audit_rating_val,\n country: country_val,\n }\n return form_data","def __init__(self, *args, **kwargs):\n super(SignupForm, self).__init__(*args, **kwargs)\n self.fields['email'].required = True\n self.fields['first_name'].required = True\n self.fields['password'].widget = forms.PasswordInput() \n\n for field in self.fields:\n self.fields[field].widget.attrs.update(\n {\n 'class': 'form-control',\n }\n )","def formfields(cls):\n\n T = current.T\n request = current.request\n\n auth = current.auth\n auth_settings = auth.settings\n auth_messages = auth.messages\n\n utable = auth_settings.table_user\n passfield = auth_settings.password_field\n\n # Instantiate Consent Tracker\n consent = ConsentTracking(processing_types=[\"SHARE\", \"RULES_PRO\", \"TPNDO\"])\n\n # Last name is required\n utable.last_name.requires = IS_NOT_EMPTY(error_message=T(\"input required\"))\n\n #ltable = s3db.gis_location\n\n # Lookup projects with provider self-registration\n projects = cls.selectable_projects()\n\n # Lookup site services\n services = cls.selectable_services()\n\n # Lookup applicable organisation types\n org_types = applicable_org_types(None, group=TESTSTATIONS, represent=True)\n\n # Form fields\n formfields = [# -- User account ---\n utable.first_name,\n utable.last_name,\n utable.email,\n utable[passfield],\n\n # Password Verification Field\n Field(\"password_two\", \"password\",\n label = auth_messages.verify_password,\n requires = IS_EXPR(\"value==%s\" % \\\n repr(request.vars.get(passfield)),\n error_message = auth_messages.mismatched_password,\n ),\n comment = DIV(_class = \"tooltip\",\n _title = \"%s|%s\" % (auth_messages.verify_password,\n T(\"Enter the same password again\"),\n ),\n ),\n ),\n # -- Test Station ---\n Field(\"organisation\",\n label = T(\"Name\"),\n requires = [IS_NOT_EMPTY(), IS_LENGTH(60)],\n comment = DIV(_class = \"tooltip\",\n _title = \"%s|%s\" % (T(\"Test Station Name\"),\n T(\"Specify the name of the test station (max 60 characters)\"),\n ),\n ),\n ),\n Field(\"organisation_type\", \"integer\",\n label = T(\"Organization Type\"),\n requires = IS_IN_SET(org_types),\n ),\n\n # -- Address --\n Field(\"location\", \"json\",\n widget = LocationSelector(\n levels = (\"L1\", \"L2\", \"L3\", \"L4\"),\n required_levels = (\"L1\", \"L2\", \"L3\"),\n show_address = True,\n address_required = True,\n show_postcode = True,\n postcode_required = True,\n show_map = True,\n ),\n ),\n # -- Service Offer --\n Field(\"opening_times\",\n label = T(\"Opening Hours\"),\n requires = IS_NOT_EMPTY(),\n ),\n Field(\"service_mode\", \"integer\",\n label = T(\"Service Mode\"),\n requires = IS_IN_SET(cls.selectable_services_modes()),\n ),\n Field(\"services\", \"list:integer\",\n label = T(\"Services\"),\n requires = IS_IN_SET(services,\n multiple = True,\n zero = None,\n ),\n widget = WithAdvice(S3GroupedOptionsWidget(cols=1),\n # Widget intro from CMS\n text = (\"org\",\n \"facility\",\n \"SiteServiceIntro\",\n ),\n ),\n ),\n # -- Contact and Appointments --\n Field(\"facility_phone\",\n label = T(\"Telephone\"),\n requires = IS_EMPTY_OR(IS_PHONE_NUMBER_MULTI()),\n ),\n Field(\"facility_email\",\n label = T(\"Email\"),\n requires = IS_EMPTY_OR(IS_EMAIL()),\n ),\n Field(\"facility_website\",\n label = T(\"Website\"),\n ),\n Field(\"booking_mode\", \"integer\",\n label = T(\"Appointments via\"),\n requires = IS_EMPTY_OR(IS_IN_SET(\n cls.selectable_booking_modes(),\n )),\n ),\n Field(\"comments\", \"text\",\n label = T(\"Comments\"),\n widget = s3_comments_widget,\n ),\n\n # -- Administrative --\n Field(\"projects\", \"list:integer\",\n label = T(\"Programs\"),\n requires = [IS_IN_SET(projects,\n multiple = True,\n zero = None,\n ),\n IS_NOT_EMPTY(),\n ],\n widget = WithAdvice(S3GroupedOptionsWidget(cols=1),\n # Widget intro from CMS\n text = (\"org\",\n \"organisation\",\n \"ProjectParticipationIntro\",\n ),\n ),\n ),\n # -- Privacy and Consent --\n Field(\"consent\",\n label = T(\"Consent\"),\n widget = consent.widget,\n ),\n ]\n\n # Required fields\n required_fields = [\"first_name\",\n \"last_name\",\n ]\n\n # Subheadings\n subheadings = ((0, T(\"User Account\")),\n (5, T(\"Test Station\")),\n (7, T(\"Address\")),\n (8, T(\"Service Offer\")),\n (11, T(\"Contact and Appointments\")),\n (16, T(\"Administrative\")),\n (17, \"%s / %s\" % (T(\"Privacy\"), T(\"Terms of Service\"))),\n )\n\n # Geocoder\n current.response.s3.scripts.append(\"/%s/static/themes/RLP/js/geocoderPlugin.js\" % request.application)\n\n return formfields, required_fields, subheadings","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n 'business_name': 'Please enter your business name',\n 'user_type': 'Please select the type of user',\n 'phone': 'Phone Number',\n 'postcode': 'Postcode',\n 'city': 'City',\n 'street_address': 'Street Address',\n 'street_address2': 'Street Address 2',\n 'county': 'County',\n 'country': 'Country'\n }\n\n # to force cursor to start in business name field\n self.fields['business_name'].widget.attrs['autofocus'] = True\n for field in self.fields:\n placeholder = placeholders[field]\n self.fields[field].widget.attrs['placeholder'] = False\n self.fields[field].label = placeholder\n self.fields[field].widget.attrs['class'] = 'form-control'","def test_prep_country_fields(self):\n original_flag = self.form.country_optional\n self.form.country_optional = True\n original_fields = self.form.fields\n original_removed = getattr(self.form, 'removed_fields', None)\n original_computed = getattr(self.form, 'computed_fields', None)\n self.form.fields = original_fields.copy()\n if original_removed is not None:\n self.form.removed_fields = original_removed.copy()\n if original_computed is not None:\n self.form.computed_fields = original_computed.copy()\n remaining = original_fields.copy()\n opts, field_rows = {'fake_opts': 'fake', 'fields': ['nope']}, [{'name': 'assigned_field'}]\n args = ['arbitrary', 'input', 'args']\n kwargs = {'test_1': 'data_1', 'test_2': 'data_2'}\n field_names = (self.form.country_field_name, 'country_flag', )\n if not any(remaining.get(name, None) for name in field_names):\n fix_fields = {name: self.get_missing_field(name) for name in field_names if name not in remaining}\n remaining.update(fix_fields)\n expected_add = {name: remaining[name] for name in field_names if name in remaining}\n expected_field_rows = field_rows.copy()\n expected_field_rows.append(expected_add)\n expected_remaining = {name: field for name, field in remaining.items() if name not in expected_add}\n expected_opts = deepcopy(opts)\n expected_opts['fields'].append(field_names)\n\n sent = (opts, field_rows, remaining, *args)\n r_opts, r_rows, r_remaining, *r_args, r_kwargs = self.form.prep_country_fields(*sent, **kwargs)\n self.assertEqual(expected_opts, r_opts)\n self.assertEqual(expected_field_rows, r_rows)\n self.assertEqual(expected_remaining, r_remaining)\n self.assertEqual(args, r_args)\n self.assertEqual(kwargs, r_kwargs)\n\n self.form.country_optional = original_flag\n self.form.fields = original_fields\n if original_removed is not None:\n self.form.removed_fields = original_removed\n if original_computed is not None:\n self.form.computed_fields = original_computed","def getConferencesCreated(self, request):\n # make sure user is authed\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n # create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\n )","def populate_obj(self, obj):\n for name, field in iteritems(self._fields):\n if name == 'studies':\n for study_form in self.studies.entries:\n study_form.form.populate_obj(\n obj.studies[study_form.study_id.data])\n else:\n field.populate_obj(obj, name)","def form_valid(self, form):\n if self.fields is None:\n self.object.__dict__.update({\n field.name:form.cleaned_data[field.name] for field in form.visible_fields()\n })\n else:\n self.object.__dict__.update({\n field:form.cleaned_data[field] for field in self.fields\n })\n self.object.save()\n if self.request.is_ajax():\n return self.ajax_form_valid()\n else:\n return HttpResponseRedirect(self.get_success_url())","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n \"first_name\": \"First Name\",\n \"last_name\": \"Last Name\",\n \"default_phone_num\": \"Phone Number\",\n \"default_passport_num\": \"Passport Number\",\n }\n\n self.fields[\"default_phone_num\"].widget.attrs[\"autofocus\"] = True\n for field in self.fields:\n placeholder = placeholders[field]\n self.fields[field].widget.attrs[\"placeholder\"] = placeholder\n self.fields[field].widget.attrs[\n \"class\"\n ] = \"border-black rounded-0 \\\n all-form-input\"\n self.fields[field].label = False\n self.helper = FormHelper()\n self.helper.form_tag = True\n self.helper.layout = Layout(\n Div(\n Field(\n \"first_name\",\n ),\n Field(\n \"last_name\",\n ),\n Field(\n \"default_phone_num\",\n ),\n Field(\n \"default_passport_num\",\n ),\n ),\n ButtonHolder(\n Submit(\"submit\", \"Save\", css_class=\"m-0 btn btn-outline\"),\n ),\n )","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n 'shift_name': 'Shift name',\n 'min_agents': 'Minimum Number of Agents',\n 'shift_start': 'Start time',\n 'shift_end': 'End time',\n 'weekday_sunday': 'Sunday',\n 'weekday_monday': 'Monday',\n 'weekday_tuesday': 'Tuesday',\n 'weekday_wednesday': 'Wednesday',\n 'weekday_thursday': 'Thursday',\n 'weekday_friday': 'Friday',\n 'weekday_saturday': 'Saturday'\n }\n\n for field in self.fields:\n if self.fields[field].required:\n placeholder = f'{placeholders[field]} *'\n else:\n placeholder = placeholders[field]\n self.fields[field].widget.attrs['placeholder'] = placeholder\n self.fields[field].widget.attrs['class'] = 'profile-form-input'\n self.fields[field].label = False","def get_minimal_add_form(self):\r\n\r\n # Get all the required fields and make a modelform for it.\r\n exclude_fields = []\r\n\r\n for field in self.model._meta.fields:\r\n if field.blank:\r\n exclude_fields.append(field.name)\r\n\r\n instance_form = modelform_factory(self.model, form=self.add_form,\r\n exclude=exclude_fields)\r\n\r\n return instance_form","def __init__(field, form, content):","def __init__(field, form, content):","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['first_name'].required = False\n self.fields['last_name'].required = False\n self.fields['institution'].required = False\n self.fields['institution_logo'].required = False\n self.fields['allow_notifications'].required = False","def __init__(self, *args, **kwargs):\n # defaults to \"text\" and \"identity\"\n \n self.text_name = kwargs.pop('text_name', 'text')\n self.identity_name = kwargs.pop('identity_name', 'identity')\n \n super(EnvayaSMSIncomingForm, self).__init__(*args, **kwargs)\n \n #The following two fields are non-mandatory because actions other than 'incoming' won't have them.\n self.fields[self.text_name] = forms.CharField(required=False)\n self.fields[self.identity_name] = forms.CharField(required=False)\n\n self.fields['phone_number'] = forms.CharField() #which envaya phone forwarded us the msg?\n self.fields['action'] = forms.CharField() #what is the action?","def test_fields_presence(self):\n form = SWCEventRequestForm()\n fields_left = set(form.fields.keys())\n fields_right = set([\n 'name', 'email', 'affiliation', 'location', 'country',\n 'conference', 'preferred_date', 'language', 'workshop_type',\n 'approx_attendees', 'attendee_domains', 'attendee_domains_other',\n 'attendee_academic_levels', 'attendee_computing_levels',\n 'cover_travel_accomodation', 'understand_admin_fee',\n 'travel_reimbursement', 'travel_reimbursement_other',\n 'admin_fee_payment', 'comment', 'captcha', 'privacy_consent',\n ])\n self.assertEqual(fields_left, fields_right)","def __init__(self, owner=None, *args, **kwargs):\n super(CalendarForm, self).__init__(*args, **kwargs)\n if owner is not None:\n self.owner = owner\n without_owner = UserProfile.objects.all().exclude(pk=owner.pk)\n self.fields['readers'].queryset = without_owner\n self.fields['modifiers'].queryset = without_owner","def _create_and_initialise_fields(self):\n for team in self.category.breaking_teams.all():\n self.fields[self._fieldname_remark(team)] = OptionalChoiceField(choices=BreakingTeam.REMARK_CHOICES, required=False)\n try:\n self.initial[self._fieldname_remark(team)] = self._bt(team).remark\n except KeyError:\n self.initial[self._fieldname_remark(team)] = None","def __init__(self, *args, **kwargs):\n super(CreateForm, self).__init__(*args, **kwargs)\n\n # set form fields order\n self.fields.keyOrder = ['to_user', 'subject', 'message']","def test_prep_field_properties(self):\n original_data = self.form.data\n test_data = original_data.copy()\n # modify values in data\n test_data._mutable = False\n self.form.data = test_data\n original_fields = self.form.fields\n test_fields = original_fields.copy()\n # modify fields\n self.form.fields = test_fields\n test_fields_info = {name: field.__dict__.copy() for name, field in test_fields.items()}\n original_get_overrides = self.form.get_overrides\n def skip_overrides(): return {}\n self.form.get_overrides = skip_overrides\n original_alt_field_info = getattr(self.form, 'alt_field_info', None)\n self.form.alt_field_info = self.alt_field_info\n self.form.test_condition_response = True\n expected_fields_info = test_fields_info.copy()\n result_fields = self.form.prep_fields()\n result_fields_info = {name: field.__dict__.copy() for name, field in result_fields.items()}\n modified_info = self.alt_field_info['alt_test_feature']\n first_label = modified_info['first']['label']\n first_initial = modified_info['first']['initial']\n last_initial = modified_info['last']['initial']\n for name, opts in modified_info.items():\n expected_fields_info[name].update(opts)\n\n self.assertEqual(first_label, result_fields['first'].label)\n self.assertEqual(first_initial, result_fields['first'].initial)\n self.assertEqual(last_initial, result_fields['last'].initial)\n for key, val in expected_fields_info.items():\n self.assertEqual(val, result_fields_info[key])\n self.assertDictEqual(expected_fields_info, result_fields_info)\n\n self.form.test_condition_response = False\n self.form.alt_field_info = original_alt_field_info\n if original_alt_field_info is None:\n del self.form.alt_field_info\n self.form.fields = original_fields\n self.form.data = original_data\n self.form.get_overrides = original_get_overrides","def form_data(self) -> dict:\n return copy.deepcopy(self.data)","def test_fields_presence(self):\n form = DCEventRequestForm()\n fields_left = set(form.fields.keys())\n fields_right = set([\n 'name', 'email', 'affiliation', 'location', 'country',\n 'conference', 'preferred_date', 'language', 'workshop_type',\n 'approx_attendees', 'attendee_domains', 'attendee_domains_other',\n 'data_types', 'data_types_other', 'attendee_academic_levels',\n 'attendee_data_analysis_level', 'cover_travel_accomodation',\n 'understand_admin_fee', 'fee_waiver_request',\n 'travel_reimbursement', 'travel_reimbursement_other',\n 'comment', 'privacy_consent', 'captcha',\n ])\n self.assertEqual(fields_left, fields_right)","def get_current_fields(self):\n return self.form.fields.copy()","def form_valid(self, form):\n author = Profile.objects.get(user=self.request.user)\n desk = Desk.objects.get(owner__user=self.user)\n form.instance.author = author\n form.instance.desk = desk\n\n return super(BoardCreate, self).form_valid(form)","def __init__(self, *args, **kwargs):\n\n self._caffe = kwargs.pop('caffe')\n\n kwargs.setdefault('label_suffix', '')\n super(CompanyForm, self).__init__(*args, **kwargs)\n self.fields['name'].label = 'Nazwa'","def _construct_form(self, i, **kwargs):\n defaults = {'auto_id': self.auto_id, 'prefix': self.add_prefix(i)}\n if self.is_bound:\n defaults['data'] = self.data\n defaults['files'] = self.files\n if self.initial:\n try:\n defaults['initial'] = self.initial[i]\n except IndexError:\n pass\n # Allow extra forms to be empty.\n if i >= self.initial_form_count():\n defaults['empty_permitted'] = True\n defaults.update(kwargs)\n form = self.form(self.params[len(self.params) - i - 1][1], self.params[len(self.params) - i - 1][0], i, **defaults) #passando o params[i] para o form[i]\n self.add_fields(form, i)\n return form","def make_from_instance(instance):\n data = model_to_dict(instance)\n data['id'] = instance.id\n if instance.location:\n data['latitude'] = round(instance.location.y, 7)\n data['longitude'] = round(instance.location.x, 7)\n else:\n data['latitude'] = None\n data['longitude'] = None\n return GeneralInformationForm(\n initial=data\n )","def get_form_kwargs(self):\n kwargs = super(ProfileUpdate, self).get_form_kwargs()\n self.profile = self.get_object()\n extra_kwargs = {'profile': self.profile}\n kwargs.update(extra_kwargs)\n return kwargs","def clear_new_talk_fields(self):\r\n self.newTalkWidget.talkDetailsWidget.titleLineEdit.clear()\r\n self.newTalkWidget.talkDetailsWidget.presenterLineEdit.clear()\r\n self.newTalkWidget.talkDetailsWidget.descriptionTextEdit.clear()\r\n self.newTalkWidget.talkDetailsWidget.categoryLineEdit.clear()","def form_CustomisedFormLayoutFields(request):\n schema = schemaish.Structure()\n schema.add( 'firstName', schemaish.String())\n schema.add( 'surname', schemaish.String())\n schema.add( 'age', schemaish.Integer())\n schema.add( 'sex', schemaish.String())\n\n form = formish.Form(schema, 'form')\n\n return form","def initcomponentform():\n form = ComponentsForm()\n form.name.value = []\n form.id.value = []\n form.currentstock.value = []\n form.reorderlevel.value = []\n form.unitprice.value = []\n form.supplier.value = []\n form.location.value = []\n form.datasheet.value = []\n return form","def form(self, form):\n\n self._form = form","def form(self, form):\n\n self._form = form","def get_form(self):\n # setup request layer\n self.request = TestRequest()\n # get add view\n form = getMultiAdapter((self.experiments, self.request),\n name=\"newBiodiverse\")\n # update the form once to initialise all widgets\n form.update()\n # go through all widgets on the form and update the request with default values\n data = {}\n for widget in form.widgets.values():\n data[widget.name] = widget.value\n data.update({\n 'form.widgets.IDublinCore.title': u\"My BD Experiment\",\n 'form.widgets.IDublinCore.description': u'This is my experiment description',\n 'form.widgets.projection.count': '1',\n 'form.widgets.projection.experiment.0': unicode(self.sdmexp.UID()),\n 'form.widgets.projection.dataset.0.count': 1,\n 'form.widgets.projection.dataset.0.0.uuid': unicode(self.sdmproj.UID()),\n 'form.widgets.projection.dataset.0.0.threshold': '0.0',\n 'form.widgets.cluster_size': '5000',\n })\n self.request.form.update(data)\n form = getMultiAdapter((self.experiments, self.request),\n name=\"newBiodiverse\")\n return form","def get_form_kwargs(self):\n kwargs = super(FlashbackCreateView, self).get_form_kwargs()\n kwargs[\"owner\"] = self.character.roster\n return kwargs","def _changeform_view(self, request, object_id, form_url, extra_context):\n to_field = request.POST.get(TO_FIELD_VAR, request.GET.get(TO_FIELD_VAR))\n if to_field and not self.to_field_allowed(request, to_field):\n raise DisallowedModelAdminToField(\"The field %s cannot be referenced.\" % to_field)\n\n model = self.model\n opts = model._meta\n\n if request.method == 'POST' and '_saveasnew' in request.POST:\n object_id = None\n\n add = object_id is None\n\n if add:\n if not self.has_add_permission(request):\n raise PermissionDenied\n obj = None\n\n else:\n obj = self.get_object(request, unquote(object_id), to_field)\n\n if (\n (not self.has_module_permission(request))\n and (not self.has_change_permission(request, obj))):\n raise PermissionDenied\n\n if obj is None:\n return self._get_obj_does_not_exist_redirect(request, opts, object_id)\n\n ModelForm = self.get_form(request, obj)\n if request.method == 'POST':\n form = ModelForm(request.POST, request.FILES, instance=obj)\n if form.is_valid():\n form_validated = True\n new_object = self.save_form(request, form, change=not add)\n else:\n form_validated = False\n new_object = form.instance\n formsets, inline_instances = self._create_formsets(request, new_object, change=not add)\n if all_valid(formsets) and form_validated:\n response = self.do_saving(\n request=request, new_object=new_object, form=form, formsets=formsets, add=add)\n if response:\n return response\n else:\n form_validated = False\n else:\n form_validated = False\n else:\n if add:\n initial = self.get_changeform_initial_data(request)\n form = ModelForm(initial=initial)\n formsets, inline_instances = self._create_formsets(\n request, form.instance, change=False)\n self_formsets, self_inline_instances = self._create_self_formsets(\n request, form.instance, change=False)\n else:\n form = ModelForm(instance=obj)\n formsets, inline_instances = self._create_formsets(request, obj, change=True)\n self_formsets, self_inline_instances = self._create_self_formsets(\n request, obj, change=True)\n\n adminForm = helpers.AdminForm(\n form,\n list(self.get_fieldsets(request, obj)),\n self.get_prepopulated_fields(request, obj),\n self.get_readonly_fields(request, obj),\n model_admin=self)\n media = self.media + adminForm.media\n\n inline_formsets = self.get_inline_formsets(request, formsets, inline_instances, obj)\n for inline_formset in inline_formsets:\n media = media + inline_formset.media\n\n context = dict(\n self.admin_site.each_context(request),\n title=(_('Add %s') if add else _('Change %s')) % force_text(opts.verbose_name),\n adminform=adminForm,\n object_id=object_id,\n original=obj,\n is_popup=(IS_POPUP_VAR in request.POST or\n IS_POPUP_VAR in request.GET),\n to_field=to_field,\n media=media,\n inline_admin_formsets=inline_formsets,\n errors=helpers.AdminErrorList(form, formsets),\n preserved_filters=self.get_preserved_filters(request),\n )\n\n # Hide the \"Save\" and \"Save and continue\" buttons if \"Save as New\" was\n # previously chosen to prevent the interface from getting confusing.\n if (self.readonly_model or (\n request.method == 'POST' and not form_validated and \"_saveasnew\" in request.POST)):\n context['show_save'] = False\n context['show_save_and_continue'] = False\n # Use the change template instead of the add template.\n add = False\n\n context.update(extra_context or {})\n\n return self.render_change_form(\n request, context, add=add, change=not add, obj=obj, form_url=form_url)","def form_valid(self, form):\n author = Profile.objects.get(user=self.request.user)\n object = self.get_object()\n content_type = ContentType.objects.get_for_model(object)\n form.instance.author = author\n form.instance.content_type = content_type\n form.instance.object_id = object.pk\n\n return super(CommentCreate, self).form_valid(form)","def __init__(self, *args, **kwargs):\n\n self._caffe = kwargs.pop('caffe')\n self._creator = kwargs.pop('creator')\n\n kwargs.setdefault('label_suffix', '')\n super(CashReportForm, self).__init__(*args, **kwargs)\n self.fields['cash_before_shift'].label = 'Pieniądze na początku zmiany'\n self.fields['cash_after_shift'].label = 'Pieniądze na końcu zmiany'\n self.fields['card_payments'].label = 'Karty'\n self.fields['amount_due'].label = 'Łączna należność'","def get_form_kwargs(self):\n kwargs = {'initial': self.get_initial()}\n if self.request.method in ('POST', 'PUT'):\n kwargs.update({\n 'data': self.request.POST,\n 'files': self.request.FILES,\n })\n kwargs.update(self.get_additional_form_args())\n return kwargs","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n placeholders = {\n 'group_id': 'Guest Group',\n 'first_name': 'First Name',\n 'last_name': 'Last Name',\n 'address_line_1': 'Address Line 1',\n 'address_line_2': 'Address Line 2',\n 'city': 'City',\n 'county': 'County',\n 'postcode': 'Post Code',\n 'country': 'Country',\n 'email': 'Email'\n }\n\n for field in self.fields:\n if field != 'country':\n placeholder = placeholders[field]\n self.fields[field].widget.attrs['placeholder'] = placeholder\n self.fields[field].widget.attrs['class'] = 'stripe-style-input'\n self.fields[field].label = False","def fill_sequence_data_fields(self):\n sid = self._get_selected_sequence()\n if sid:\n sequence_data = Data().get_sequence_data(sid)\n if sequence_data:\n code = Data().get_country_code(sequence_data[1])\n self.ui.e_country.setText(code)\n self.ui.cb_roadtype.setCurrentIndex(sequence_data[2] + 1)","def contact_create(request):\n\n if request.method == \"POST\":\n form = ContactForm(request.POST)\n profile_form = ContactProfileForm(request.POST)\n\n if form.is_valid() and profile_form.is_valid():\n contact = form.save()\n\n # Populate the required 'contact' field before saving\n profile = profile_form.save(commit=False)\n profile.contact = contact\n profile.save()\n\n messages.success(request, _(\"The contact %(name)s was successfully created\") % \n {'name': unicode(contact)})\n return HttpResponseRedirect(reverse(\"moderation.views.contact\", args=(contact.pk,)))\n else:\n form_initial = {'phone_number': request.GET.get('phone_number', '')}\n form = ContactForm(initial=form_initial)\n\n profile_initial = {}\n if \"facility\" in request.GET:\n facility = get_object_or_404(Facility, pk=request.GET['facility'])\n profile_initial['facility'] = facility.pk\n profile_form = ContactProfileForm(initial=profile_initial)\n\n return render_to_response(\"contact_create.html\", \n { 'form': form, \n 'profile_form': profile_form,\n },\n context_instance=RequestContext(request))","def updateConference(self, request):\n return self._updateConferenceObject(request)","def form_valid(self, form):\n print self.kwargs['pk']\n self.object = form.save(commit=False)\n self.object.contact = Contact.objects.get(pk=self.kwargs['pk'])\n self.object.status = 'Abierto'\n self.object.save()\n user_full = UserFullName.objects.get(id=self.request.user.id)\n notes = 'Hola tienes un nuevo caso asignado'\n CaseTrack.objects.create(user_from=user_full, user_to=self.object.assigned, notes=notes, case=self.object)\n return super(ModelFormMixin, self).form_valid(form)"],"string":"[\n \"def _copyConferenceToForm(self, conf, displayName):\\n cf = ConferenceForm()\\n for field in cf.all_fields():\\n if hasattr(conf, field.name):\\n # convert Date to date string; just copy others\\n if field.name.endswith('Date'):\\n setattr(cf, field.name, str(getattr(conf, field.name)))\\n else:\\n setattr(cf, field.name, getattr(conf, field.name))\\n elif field.name == \\\"websafeKey\\\":\\n setattr(cf, field.name, conf.key.urlsafe())\\n if displayName:\\n setattr(cf, 'organizerDisplayName', displayName)\\n cf.check_initialized()\\n return cf\",\n \"def _copySpeakerToForm(self, speaker):\\n sf = SpeakerForm()\\n for field in sf.all_fields():\\n if hasattr(speaker, field.name):\\n setattr(sf, field.name, getattr(speaker, field.name))\\n elif field.name == \\\"websafeKey\\\":\\n setattr(sf, field.name, speaker.key.urlsafe())\\n sf.check_initialized()\\n return sf\",\n \"def update_c_mandatory_fields(request, campaign_id):\\n # print(request.POST)\\n campaign = Campaign.objects.get(id=campaign_id)\\n form = CampaignForm(request.POST, instance = campaign)\\n # print(form)\\n if form.is_valid():\\n form.save()\\n return redirect('add_campaign_spec', id=campaign_id)\\n else:\\n # return redirect('clonecampaign', id=campaign_id)\\n print(form.errors)\\n return redirect(reverse('edit_campaign', kwargs={'campaign_id':campaign_id}))\",\n \"def clone(self):\\n\\n # recrea la instancia del form\\n form_clone = FormularioDinamico(\\n lang=self.lang,\\n name=\\\"clon de %s\\\" % self.name,\\n is_active=self.is_active,\\n form_title=self.form_title,\\n form_template=self.form_template,\\n send_email=self.send_email,\\n from_email=self.from_email,\\n recipient_list=self.recipient_list,\\n subject_template=self.subject_template,\\n body_template=self.body_template,\\n error_class=self.error_class,\\n required_css_class=self.required_css_class,\\n autorespond=self.autorespond,\\n autorespond_subject_template=self.autorespond_subject_template,\\n autorespond_body_template=self.autorespond_body_template,\\n autorespond_email_field=self.autorespond_email_field\\n )\\n\\n form_clone.save()\\n\\n content_type = ContentType.objects.get_for_model(form_clone)\\n\\n # recrea todos los fields\\n for field in self.get_fields():\\n field_clone = DynaFormField(\\n content_type=content_type,\\n object_pk=form_clone.pk,\\n field_name=field.field_name,\\n field_label=field.field_label,\\n field_type=field.field_type,\\n field_widget=field.field_widget,\\n field_help=field.field_help,\\n is_required=field.is_required,\\n is_hidden=field.is_hidden,\\n default_value=field.default_value,\\n choices=field.choices,\\n choices_delimiter=field.choices_delimiter,\\n choices_queryset=field.choices_queryset,\\n choices_queryset_filter=field.choices_queryset_filter,\\n choices_queryset_empty_label=field.choices_queryset_empty_label,\\n choices_queryset_label=field.choices_queryset_label,\\n choices_related_field=field.choices_related_field,\\n field_order=field.field_order\\n )\\n\\n field_clone.save()\",\n \"def prepare(self, form):\\n \\n return form\",\n \"def _copySessionToForm(self, session):\\n sf = SessionForm()\\n for field in sf.all_fields():\\n if hasattr(session, field.name):\\n # Convert date field to date string\\n if field.name == 'date':\\n setattr(sf, field.name, str(getattr(session, field.name)))\\n # Convert time field to time string\\n elif field.name == 'startTime':\\n setattr(sf, field.name,\\n getattr(session, field.name).strftime('%H:%M'))\\n # Convert typeOfSession string field to enum\\n elif field.name == 'typeOfSession':\\n setattr(sf, field.name,\\n getattr(SessionType, getattr(session, field.name)))\\n # Copy other fields verbatim\\n else:\\n setattr(sf, field.name, getattr(session, field.name))\\n # Ensure that the SessionForm contains websafeKey\\n elif field.name == \\\"websafeKey\\\":\\n setattr(sf, field.name, session.key.urlsafe())\\n sf.check_initialized()\\n return sf\",\n \"def getConference(self, request):\\n # Get Conference object from request; bail if not found\\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\\n 'Conference')\\n prof = conf.key.parent().get()\\n # Return ConferenceForm\\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\",\n \"def get_conference(self, request):\\n # get Conference object from request; bail if not found\\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' %\\n request.websafeConferenceKey)\\n prof = conf.key.parent().get()\\n # return ConferenceForm\\n return self.conference_service.copy_conference_to_form(ConferenceForm(),\\n conf,\\n prof.displayName)\",\n \"def copyform(form, settings):\\r\\n for name, value in form.iteritems():\\r\\n setattr(settings, name, value)\\r\\n settings.commit()\",\n \"def get_conferences_to_attend(self, request):\\n prof = self.profile_service.get_profile_from_user() # get user Profile\\n conf_keys = [ndb.Key(urlsafe=wsck) for wsck in\\n prof.conferenceKeysToAttend]\\n conferences = ndb.get_multi(conf_keys)\\n\\n # get organizers\\n organisers = [ndb.Key(Profile, conf.organizerUserId) for conf in\\n conferences]\\n profiles = ndb.get_multi(organisers)\\n\\n # put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(items=[\\n self.conference_service.copy_conference_to_form(\\n ConferenceForm(), conf, names[conf.organizerUserId]\\n ) for conf in conferences])\",\n \"def _replace_fields(self):\\n for name, value in self._cleaned_data.items():\\n setattr(self, name, value)\",\n \"def copyCommonFields(self):\\n self.fetchDataToForm(self.selected_row, self.selected_column, fields = \\\"Recent\\\")\",\n \"def copy_form(self, from_module, form, to_module, rename=False):\\n copy_source = deepcopy(form.to_json())\\n # only one form can be a release notes form, so set them to False explicitly when copying\\n copy_source['is_release_notes_form'] = False\\n copy_source['enable_release_notes'] = False\\n if 'unique_id' in copy_source:\\n del copy_source['unique_id']\\n\\n if rename:\\n for lang, name in copy_source['name'].items():\\n with override(lang):\\n copy_source['name'][lang] = _('Copy of {name}').format(name=name)\\n\\n copy_form = to_module.add_insert_form(from_module, FormBase.wrap(copy_source))\\n to_app = to_module.get_app()\\n save_xform(to_app, copy_form, form.source.encode('utf-8'))\\n\\n return copy_form\",\n \"def get_conferences_created(self, request):\\n # make sure user is authed\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = self.auth.get_user_id(user)\\n\\n # create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(items=[\\n self.conference_service.copy_conference_to_form(ConferenceForm(),\\n conf,\\n prof.displayName)\\n for conf in confs])\",\n \"def __init__(self, corp_app, field_objs, *args, **kwargs):\\n self.corp_app = corp_app\\n self.field_objs = field_objs\\n super(CorpMembForm, self).__init__(*args, **kwargs)\\n\\n for field in field_objs:\\n if field.field_type not in ['section_break', 'page_break']:\\n if field.field_name:\\n field_key = field.field_name\\n else:\\n field_key = \\\"field_%s\\\" % field.id\\n\\n # if field is display only, remove it from the form\\n # for example, on the edit page, we\\n # set corporate_membership_type\\n # and payment_method as the display only fields\\n if hasattr(field, 'display_only') and field.display_only:\\n del self.fields[field_key]\\n else:\\n # get field class and set field initial\\n self.fields[field_key] = field.get_field_class()\\n if ((not field.field_name) \\\\\\n or field.field_name == 'authorized_domains') \\\\\\n and self.instance:\\n initial = field.get_value(self.instance)\\n if field.field_type in [\\n 'MultipleChoiceField/django.forms.CheckboxSelectMultiple',\\n 'MultipleChoiceField']:\\n if initial:\\n self.fields[field_key].initial = [\\n item.strip() for item in initial.split(',')]\\n else:\\n self.fields[field_key].initial = initial\\n\\n #self.fields['captcha'] = CaptchaField(label=_('Type the code below'))\",\n \"def _copyProfileToForm(self, prof):\\n pf = ProfileForm()\\n for field in pf.all_fields():\\n if hasattr(prof, field.name):\\n # Convert t-shirt string to Enum; just copy others\\n if field.name == 'teeShirtSize':\\n setattr(pf, field.name,\\n getattr(TeeShirtSize, getattr(prof, field.name)))\\n else:\\n setattr(pf, field.name, getattr(prof, field.name))\\n pf.check_initialized()\\n return pf\",\n \"def set_up_fields(self, fields):\\n self.fields = {\\n 'name': self.name,\\n 'email': self.email\\n }\\n for key in fields.keys():\\n # special keys first, not to be used in the template\\n if key.upper() == 'CC':\\n self.is_cc = fields[key]\\n elif key.upper() == 'BCC':\\n self.is_bcc = fields[key]\\n else:\\n self.fields[key] = fields[key]\",\n \"def _createConferenceObject(self, request):\\n # Preload necessary data items\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = user.email()\\n if not request.name:\\n raise endpoints.BadRequestException(\\n \\\"Conference 'name' field required\\\")\\n # Copy ConferenceForm/ProtoRPC Message into dict\\n data = {\\n field.name: getattr(request, field.name) for field in\\n request.all_fields()\\n }\\n del data['websafeKey']\\n del data['organizerDisplayName']\\n # Add default values for those missing (both data model and\\n # outbound Message)\\n for df in CONF_DEFAULTS:\\n if data[df] in (None, []):\\n data[df] = CONF_DEFAULTS[df]\\n setattr(request, df, CONF_DEFAULTS[df])\\n # Convert dates from strings to Date objects; set month based\\n # on start_date\\n if data['startDate']:\\n data['startDate'] = datetime.strptime(\\n data['startDate'][:10], \\\"%Y-%m-%d\\\").date()\\n data['month'] = data['startDate'].month\\n else:\\n data['month'] = 0\\n if data['endDate']:\\n data['endDate'] = datetime.strptime(\\n data['endDate'][:10], \\\"%Y-%m-%d\\\").date()\\n # Set seatsAvailable to be same as maxAttendees on creation\\n if data[\\\"maxAttendees\\\"] > 0:\\n data[\\\"seatsAvailable\\\"] = data[\\\"maxAttendees\\\"]\\n # Get the user profile key, then set the conference's parent\\n # to that value.\\n # NOTE: The original code made a call to allocate_ids in order to\\n # generate an ID for the conference. Since the profiles utilize\\n # strings (email addresses) for their IDs, resulting in no risk\\n # of colliding with NDB's auto-generated numeric IDs, I decided\\n # to let NDB generate the conference ID automatically.\\n # https://cloud.google.com/appengine/docs/python/ndb/entities?hl=en#numeric_keys\\n p_key = ndb.Key(Profile, user_id)\\n data['parent'] = p_key\\n data['organizerUserId'] = request.organizerUserId = user_id\\n # Create Conference, send email to organizer confirming\\n # creation of Conference and return (modified) ConferenceForm\\n Conference(**data).put()\\n taskqueue.add(params={'email': user.email(),\\n 'conferenceInfo': repr(request)},\\n url='/tasks/send_confirmation_email'\\n )\\n return request\",\n \"def individual_formfields():\\n # Instantiate Consent Tracker\\n consent = s3db.auth_Consent(processing_types = VOL_CONSENT_OPTIONS)\\n\\n formfields = [utable.first_name,\\n utable.last_name,\\n Field(\\\"addr_L3\\\",\\n label = T(\\\"Location\\\"),\\n requires = IS_IN_SET(districts_and_uk),\\n ),\\n Field(\\\"addr_street\\\",\\n label = T(\\\"Street Address\\\"),\\n ),\\n Field(\\\"addr_postcode\\\",\\n label = T(\\\"Postcode\\\"),\\n ),\\n Field(\\\"mobile\\\",\\n label = T(\\\"Contact Number (Preferred)\\\"),\\n requires = IS_PHONE_NUMBER_MULTI(),\\n comment = DIV(_class = \\\"tooltip\\\",\\n _title = \\\"%s|%s\\\" % (T(\\\"Contact Number (Preferred)\\\"),\\n T(\\\"Ideally a Mobile Number, so that we can send you Text Messages.\\\")),\\n ),\\n ),\\n Field(\\\"home\\\",\\n label = T(\\\"Contact Number (Secondary)\\\"),\\n requires = IS_EMPTY_OR(IS_PHONE_NUMBER_MULTI()),\\n ),\\n utable.email,\\n utable[passfield],\\n # Password Verification Field\\n Field(\\\"password_two\\\", \\\"password\\\",\\n label = auth_messages.verify_password,\\n requires = IS_EXPR(\\\"value==%s\\\" % \\\\\\n repr(request.vars.get(passfield)),\\n error_message = auth_messages.mismatched_password,\\n ),\\n ),\\n\\n # Skills\\n s3db.hrm_multi_skill_id(empty = False,\\n label = T(\\\"Volunteer Offer\\\"),\\n ),\\n Field(\\\"skills_details\\\",\\n label = T(\\\"Please specify details\\\"),\\n ),\\n Field(\\\"certificates\\\", \\\"list:string\\\",\\n label = T(\\\"Qualifications\\\"),\\n requires = IS_IN_SET(certificates, multiple=True),\\n widget = S3MultiSelectWidget(header=\\\"\\\",\\n selectedList=3),\\n ),\\n Field(\\\"experience\\\",\\n label = T(\\\"Skills and Experience\\\"),\\n widget = lambda f, v: \\\\\\n s3_comments_widget(f, v, _placeholder = \\\"e.g. Co-ordination, Event Management, PCV qualified.\\\")\\n ),\\n Field(\\\"resources\\\",\\n label = T(\\\"Offers of Resources\\\"),\\n widget = lambda f, v: \\\\\\n s3_comments_widget(f, v, _placeholder = \\\"e.g. Minibus.\\\")\\n ),\\n Field(\\\"where_operate\\\", \\\"list:string\\\",\\n label = T(\\\"Where would you be willing to volunteer?\\\"),\\n requires = IS_IN_SET(districts, multiple=True),\\n widget = S3MultiSelectWidget(header=\\\"\\\",\\n selectedList=3),\\n ),\\n Field(\\\"travel\\\", \\\"integer\\\",\\n label = T(\\\"Willing to Travel?\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"slots\\\", \\\"list:string\\\",\\n label = T(\\\"Times\\\"),\\n requires = IS_IN_SET(slots, multiple=True),\\n widget = S3MultiSelectWidget(header=\\\"\\\",\\n selectedList=3),\\n ),\\n Field(\\\"significant_physical\\\", \\\"integer\\\",\\n label = T(\\\"That require significant physical activity (including lifting and carrying) and may involve being outdoors (e.g. clean up of affected properties)\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"some_physical\\\", \\\"integer\\\",\\n label = T(\\\"That require some physical activity and may involve being outdoors (e.g. door knocking)\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"little_physical\\\", \\\"integer\\\",\\n label = T(\\\"That require little physical activity and are based indoors (e.g. preparing refreshments)\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"health_details\\\",\\n label = T(\\\"If you wish, you can give us some further information on any fitness, medical or mobility issues that might limit the kind of activities you are able to volunteer for; this will help us to suggest suitable opportunities for you\\\"),\\n ),\\n Field(\\\"faith_requirements\\\", \\\"integer\\\",\\n label = T(\\\"Do you have any faith requirements that you would like help with if you are coming to Support Cumbria?\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"faith_requirements_details\\\",\\n label = T(\\\"If Yes please outline\\\"),\\n ),\\n Field(\\\"emergency_contact_name\\\",\\n label = T(\\\"Contact Name\\\"),\\n requires = IS_NOT_EMPTY(),\\n ),\\n Field(\\\"emergency_contact_number\\\",\\n label = T(\\\"Contact Number\\\"),\\n requires = IS_PHONE_NUMBER_MULTI(),\\n ),\\n Field(\\\"emergency_contact_relationship\\\",\\n label = T(\\\"Relationship\\\"),\\n requires = IS_NOT_EMPTY(),\\n ),\\n Field(\\\"workplace\\\", \\\"integer\\\",\\n label = T(\\\"Are you volunteering under your workplace volunteering scheme?\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n Field(\\\"workplace_details\\\",\\n label = T(\\\"If yes please name your employer\\\"),\\n ),\\n Field(\\\"dbs\\\", \\\"integer\\\",\\n label = T(\\\"Are you DBS checked?\\\"),\\n requires = IS_IN_SET({0: T(\\\"No\\\"),\\n 1: T(\\\"Yes\\\"),\\n }),\\n widget = lambda f, v: \\\\\\n SQLFORM.widgets.radio.widget(f, v,\\n style=\\\"divs\\\"),\\n ),\\n #Field(\\\"convictions\\\", \\\"integer\\\",\\n # label = T(\\\"Do you have any unspent convictions?\\\"),\\n # comment = T(\\\"Please tick 'Yes' if you have any convictions that are not yet spent under the Rehabilitation of Offenders Act 1974. The term 'convictions' is used to refer to any sentence or disposal issued by a court. If all your convictions are spent, you can tick 'No'. If you're not sure if your convictions are unspent or spent, you can use a tool available at www.disclosurecalculator.org.uk and read guidance at hub.unlock.org.uk/roa\\\"),\\n # requires = IS_IN_SET({0: T(\\\"No\\\"),\\n # 1: T(\\\"Yes\\\"),\\n # }),\\n # widget = lambda f, v: \\\\\\n # SQLFORM.widgets.radio.widget(f, v,\\n # style=\\\"divs\\\"),\\n # ),\\n # Consent (GDPR + FOC)\\n Field(\\\"consent\\\",\\n label = T(\\\"Consent\\\"),\\n widget = consent.widget,\\n ),\\n ]\\n\\n required_fields = [\\\"first_name\\\",\\n \\\"last_name\\\",\\n \\\"addr_L3\\\",\\n \\\"addr_street\\\",\\n \\\"addr_postcode\\\",\\n \\\"mobile\\\",\\n \\\"emergency_contact\\\",\\n \\\"where_operate\\\",\\n ]\\n\\n return formfields, required_fields\",\n \"def _createConferenceObject(self, request):\\n # preload necessary data items\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = getUserId(user)\\n\\n if not request.name:\\n raise endpoints.BadRequestException(\\\"Conference 'name' field required\\\")\\n\\n # copy ConferenceForm/ProtoRPC Message into dict\\n data = {field.name: getattr(request, field.name) for field in request.all_fields()}\\n del data['websafeKey']\\n del data['organizerDisplayName']\\n\\n # add default values for those missing (both data model & outbound Message)\\n for df in DEFAULTS:\\n if data[df] in (None, []):\\n data[df] = DEFAULTS[df]\\n setattr(request, df, DEFAULTS[df])\\n\\n # convert dates from strings to Date objects; set month based on start_date\\n if data['startDate']:\\n data['startDate'] = datetime.strptime(data['startDate'][:10], \\\"%Y-%m-%d\\\").date()\\n data['month'] = data['startDate'].month\\n else:\\n data['month'] = 0\\n if data['endDate']:\\n data['endDate'] = datetime.strptime(data['endDate'][:10], \\\"%Y-%m-%d\\\").date()\\n\\n # set seatsAvailable to be same as maxAttendees on creation\\n if data[\\\"maxAttendees\\\"] > 0:\\n data[\\\"seatsAvailable\\\"] = data[\\\"maxAttendees\\\"]\\n # generate Profile Key based on user ID and Conference\\n # ID based on Profile key get Conference key from ID\\n p_key = ndb.Key(Profile, user_id)\\n c_id = Conference.allocate_ids(size=1, parent=p_key)[0]\\n c_key = ndb.Key(Conference, c_id, parent=p_key)\\n data['key'] = c_key\\n data['organizerUserId'] = request.organizerUserId = user_id\\n\\n # create Conference, send email to organizer confirming\\n # creation of Conference & return (modified) ConferenceForm\\n Conference(**data).put()\\n taskqueue.add(params={'email': user.email(),\\n 'conferenceInfo': repr(request)},\\n url='/tasks/send_confirmation_email'\\n )\\n return request\",\n \"def getConference(self, request):\\n # get Conference object from request; bail if not found\\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' % request.websafeConferenceKey)\\n prof = conf.key.parent().get()\\n # return ConferenceForm\\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\",\n \"def get_form(self, form_class):\\n form = super(InvitationCreateView, self).get_form(form_class)\\n form.fields[\\\"receiver\\\"].queryset = self.get_available_invitees()\\n form.fields[\\\"team\\\"].queryset = self.get_available_teams()\\n form.fields[\\\"team\\\"].empty_label = None\\n return form\",\n \"def update_fields(self):\\n if hasattr(self.day, \\\"body_composition\\\"):\\n for f in self.get_fields():\\n name = f.get_field().name\\n value = getattr(self.day.body_composition, name, None)\\n if value is not None:\\n f.set_field(value)\\n else:\\n f.set_field(\\\"\\\")\",\n \"def make_fields(self):\\n for name, prop in self.edit:\\n instance_value = self.model.get(name)\\n post_value = self.data[name] if (self.data and self.data.has_key(name)) else instance_value\\n form_field_class = self.get_field_type(prop)\\n form_field = form_field_class(model=self.model, property=prop, name=name, instance_value=instance_value, post_value=post_value)\\n self.add(form_field)\",\n \"def test_form_has_fields(self):\\r\\n self.form = SubscriptionForm()\\r\\n expect = ['name', 'cpf', 'email', 'phone']\\r\\n self.assertSequenceEqual(expect, list(self.form.fields))\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n placeholders = {\\n 'company_name': 'Company name',\\n 'street_address1': 'Street Address 1',\\n 'street_address2': 'Street Address 2',\\n 'country': 'Country or State',\\n 'postcode': 'Postcode',\\n 'town_or_city': 'Town or City',\\n 'payment': 'Paid for number of months',\\n 'setting_daystart': 'Hour when your day starts',\\n 'setting_dayend': 'hour when your day ends'\\n }\\n\\n for field in self.fields:\\n if field != 'country':\\n if self.fields[field].required:\\n placeholder = f'{placeholders[field]} *'\\n else:\\n placeholder = placeholders[field]\\n self.fields[field].widget.attrs['placeholder'] = placeholder\\n if field == 'setting_daystart' or field == 'setting_dayend' or field == 'payment':\\n self.fields[field].widget.attrs['class'] = 'width-numbers'\\n else:\\n self.fields[field].widget.attrs['class'] = 'profile-form-input'\\n self.fields[field].label = placeholder\",\n \"def __init__(self, *args, **kwargs):\\n\\n\\t\\tsuper(AssonaForm, self).__init__(*args, **kwargs)\\n\\n\\t\\tfor field in self.fields:\\n\\t\\t\\tif field == 'versicherungskarte':\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control-file'\\n\\t\\t\\t\\t})\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control'\\n\\t\\t\\t\\t})\",\n \"def get_form(self):\\n # setup request layer\\n self.request = TestRequest()\\n # get add view\\n form = getMultiAdapter((self.experiments, self.request),\\n name=\\\"newProjection\\\")\\n # update the form once to initialise all widgets\\n form.update()\\n # go through all widgets on the form and update the request with default values\\n data = {}\\n for widget in form.widgets.values():\\n data[widget.name] = widget.value\\n data.update({\\n 'form.widgets.IDublinCore.title': u\\\"My CC Experiment\\\",\\n 'form.widgets.IDublinCore.description': u'This is my experiment description',\\n 'form.widgets.species_distribution_models': unicode(self.sdmexp.UID()),\\n 'form.widgets.species_distribution_models.model': [unicode(self.sdmmodel.UID())],\\n 'form.widgets.future_climate_datasets': [unicode(self.future.UID())]\\n })\\n self.request.form.update(data)\\n form = getMultiAdapter((self.experiments, self.request),\\n name=\\\"newProjection\\\")\\n return form\",\n \"def update_models_from_create_event_form(form, venue, event):\\n\\n # Venue Info\\n event.venue.name = form.venue_name.data\\n event.venue.address = form.address.data\\n event.venue.city = form.city.data\\n event.venue.state = CreateEventForm.convert_choice_to_value(\\n form.state.data, \\\"STATES\\\"\\n )\\n event.venue.zip_code = form.zip_code.data\\n\\n # Event info\\n event.title = form.title.data\\n event_type = EventType.query.get(form.event_type.data)\\n event_category = EventCategory.query.get(form.category.data)\\n start_time = CreateEventForm.convert_choice_to_value(form.start_time.data, \\\"TIMES\\\")\\n end_time = CreateEventForm.convert_choice_to_value(form.end_time.data, \\\"TIMES\\\")\\n event.start_datetime = datetime.combine(form.start_date.data, start_time)\\n event.end_datetime = datetime.combine(form.end_date.data, end_time)\",\n \"def populate_form(self, **kwargs):\\n for name, value in kwargs.items():\\n self.populate_field(name, value)\",\n \"def copy_fields(self, entity, all_fields=False):\\n\\n if all_fields:\\n fields = self.get_all_fields()\\n else:\\n fields = self.get_non_pk_fields()\\n\\n for field in fields.keys():\\n setattr(self, field, getattr(entity, field, None))\",\n \"def __init__(self, *args, **kwargs):\\n step_numeral, step_name = kwargs.pop('step', (None, None))\\n corp_app = kwargs.pop('corp_app', '')\\n file_path = kwargs.pop('file_path', '')\\n\\n super(CSVForm, self).__init__(*args, **kwargs)\\n\\n if step_numeral == 1:\\n \\\"\\\"\\\"\\n Basic Form: Application & File Uploader\\n \\\"\\\"\\\"\\n self.fields['corp_app'] = forms.ModelChoiceField(\\n label=_('Corp Application'), queryset=CorpApp.objects.all())\\n\\n self.fields['update_option'] = forms.CharField(\\n widget=forms.RadioSelect(\\n choices=(('skip', 'Skip'),\\n ('update', 'Update Blank Fields'),\\n ('override', 'Override All Fields'),)),\\n initial='skip',\\n label=_('Select an Option for the Existing Records:')\\n )\\n\\n self.fields['csv'] = forms.FileField(label=_(\\\"CSV File\\\"))\\n\\n if step_numeral == 2:\\n \\\"\\\"\\\"\\n Basic Form + Mapping Fields\\n \\\"\\\"\\\"\\n\\n # file to make field-mapping form\\n csv = csv_to_dict(file_path)\\n\\n # choices list\\n choices = csv[0].keys()\\n\\n # make tuples; sort tuples (case-insensitive)\\n choice_tuples = [(c, c) for c in csv[0].keys()]\\n\\n # insert blank option\\n choice_tuples.insert(0, ('', ''))\\n choice_tuples = sorted(choice_tuples, key=lambda c: c[0].lower())\\n\\n app_fields = CorpField.objects.filter(corp_app=corp_app)\\n required_fields = ['name', 'corporate_membership_type']\\n for field in app_fields:\\n if field.field_type not in ['section_break', 'page_break']:\\n if field.field_name:\\n field_key = field.field_name\\n else:\\n field_key = \\\"field_%s\\\" % field.id\\n is_required = False\\n if field_key in required_fields:\\n is_required = True\\n self.fields[field_key] = ChoiceField(**{\\n 'label': field.label,\\n 'choices': choice_tuples,\\n 'required': is_required,\\n })\\n for choice in choices:\\n if (field.label).lower() == choice.lower() or \\\\\\n field_key.lower() == choice.lower():\\n self.fields[field_key].initial = choice\\n\\n extra_fields = (('secret_code', 'Secret Code'),\\n ('join_dt', 'Join Date'),\\n ('renew_dt', 'Renew Date'),\\n ('expiration_dt', 'Expiration Date'),\\n ('approved', 'Approved'),\\n ('dues_rep', 'Dues Representative'),\\n ('status', 'Status'),\\n ('status_detail', 'Status Detail'))\\n # corp_memb_field_names = [smart_str(field.name)\\n # for field in CorporateMembership._meta.fields]\\n for key, label in extra_fields:\\n if key not in self.fields.keys():\\n self.fields[key] = ChoiceField(**{\\n 'label': label,\\n 'choices': choice_tuples,\\n 'required': False,\\n })\\n for choice in choices:\\n if label.lower() == choice.lower() or \\\\\\n key.lower() == choice.lower():\\n self.fields[key].initial = choice\",\n \"def __init__(self, *args, **kwargs):\\n\\n\\t\\tsuper(SchadensmeldungForm, self).__init__(*args, **kwargs)\\n\\n\\t\\tfor field in self.fields:\\n\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t'class': 'form-control'\\n\\t\\t\\t\\t})\",\n \"def __init__(self, *args, **kwargs):\\n\\n\\t\\tsuper(BusinessbikeForm, self).__init__(*args, **kwargs)\\n\\n\\t\\tfor field in self.fields:\\n\\t\\t\\tif field == 'versicherungskarte':\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control-file'\\n\\t\\t\\t\\t})\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control'\\n\\t\\t\\t\\t})\",\n \"def test_pass_through_prep_country_fields(self):\\n original_flag = self.form.country_optional\\n self.form.country_optional = False # True\\n original_fields = self.form.fields\\n self.form.fields = original_fields.copy()\\n remaining_fields = original_fields.copy()\\n opts, field_rows = {'fake_opts': 'fake'}, [{'name': 'assigned_field'}]\\n args = ['arbitrary', 'input', 'args']\\n kwargs = {'test_1': 'data_1', 'test_2': 'data_2'}\\n\\n expected = (opts.copy(), field_rows.copy(), remaining_fields.copy(), *args, kwargs.copy())\\n actual = self.form.prep_country_fields(opts, field_rows, remaining_fields, *args, **kwargs)\\n self.assertEqual(expected, actual)\\n\\n self.form.country_optional = original_flag\\n self.form.fields = original_fields\",\n \"def populate_create_event_form(form, venue, event):\\n # Venue info\\n venue = event.venue\\n form.venue_name.data = venue.name\\n form.address.data = venue.address\\n form.city.data = venue.city\\n form.state.data = CreateEventForm.convert_choice_to_id(venue.state, \\\"STATES\\\")\\n form.zip_code.data = venue.zip_code\\n\\n # Event Info\\n form.title.data = event.title\\n form.event_type.data = event.event_type.id\\n form.category.data = event.event_category.id\\n form.start_date.data = event.start_date()\\n form.end_date.data = event.end_date()\\n form.start_time.data = CreateEventForm.convert_choice_to_id(\\n event.start_time(), \\\"TIMES\\\"\\n )\\n form.end_time.data = CreateEventForm.convert_choice_to_id(event.end_time(), \\\"TIMES\\\")\",\n \"def construct_instance(form, instance, fields=None, exclude=None):\\n cleaned_data = form.cleaned_data\\n file_field_list = []\\n\\n for name, field in instance._fields.items():\\n if not name in cleaned_data:\\n continue\\n if fields is not None and name not in fields:\\n continue\\n if exclude and name in exclude:\\n continue\\n\\n if isinstance(field, (mongoengine.URLField, mongoengine.EmailField)):\\n if cleaned_data[name]:\\n field.__set__(instance, cleaned_data[name])\\n else:\\n field.__set__(instance, None)\\n continue\\n field.__set__(instance, cleaned_data[name])\\n\\n return instance\",\n \"def xtest_form_has_fields(self):\\n expected = ['name', 'cpf', 'email', 'phone']\\n form = SubscriptionForm()\\n self.assertSequenceEqual(expected, list(form.fields))\",\n \"def __init__(self, *args, **kwargs):\\n\\n # Les lignes suivantes permettent de modifier les label d'un champ dans la page\\n super(ModelForm, self).__init__(*args, **kwargs)\\n self.fields[\\\"nom_de_l_evenement\\\"].label = \\\"Nom de l'évènement\\\"\\n self.fields[\\\"date_de_l_evenement\\\"].label = \\\"Date de l'évènement\\\" # utiliser plutôt l'attribut label comme pour AbonnementEvenementForm\\n self.fields[\\\"fichier\\\"].label = \\\"Photo(s)\\\"\",\n \"def __init__(self, *args, **kwargs):\\n user = kwargs.pop('user')\\n super(ChooseAppointmentForm, self).__init__(*args, **kwargs)\\n if(user.first_name=='patient'):\\n self.appointments = user.patient_appointment.all()\\n appointment_partner = 'doctor' # patient is partnered with a doctor and vice versa\\n else:\\n self.appointments = user.doctor_appointment.all()\\n appointment_partner = 'patient'\\n choices = []\\n\\n for i, appointment in enumerate(self.appointments):\\n partner_first_name = appointment.associated_patient.patient_user_profile.first_name if (appointment_partner=='patient') else appointment.associated_doctor.doctor_user_profile.first_name\\n partner_last_name = appointment.associated_patient.patient_user_profile.last_name if (appointment_partner=='patient') else appointment.associated_doctor.doctor_user_profile.last_name\\n choices.append((appointment, 'Appointment: {}, on {}, at {} with {} {}'\\n .format(appointment.title, appointment.date, appointment.time, partner_first_name, partner_last_name)))\\n\\n self.fields['appointments'] = forms.ChoiceField(label=\\\"\\\", choices=choices, widget=forms.RadioSelect)\",\n \"def _save_extra_fields(self, event):\\n\\n term = self.cleaned_data[\\\"term_name\\\"]\\n week = self.cleaned_data[\\\"term_week\\\"]\\n day = self.cleaned_data[\\\"day_of_week\\\"]\\n\\n year = int(settings.DEFAULT_ACADEMIC_YEAR)\\n date = datetimes.termweek_to_date(year, term, week, day)\\n \\n start_hour = self.cleaned_data[\\\"start_hour\\\"]\\n start_minute = self.cleaned_data[\\\"start_minute\\\"]\\n end_hour = self.cleaned_data[\\\"end_hour\\\"]\\n end_minute = self.cleaned_data[\\\"end_minute\\\"]\\n\\n tz = timezone.get_current_timezone()\\n\\n start_naive = datetime.datetime(date.year, date.month, date.day,\\n start_hour, start_minute)\\n event.start = tz.localize(start_naive)\\n\\n end_naive = datetime.datetime(date.year, date.month, date.day,\\n end_hour, end_minute)\\n event.end = tz.localize(end_naive)\\n\\n event.metadata[\\\"people\\\"] = self.cleaned_data[\\\"people\\\"]\\n event.metadata[\\\"type\\\"] = self.cleaned_data[\\\"event_type\\\"]\\n\\n if self.cleaned_data[\\\"cancel\\\"] is True:\\n event.status = models.Event.STATUS_CANCELLED\\n else:\\n event.status = models.Event.STATUS_LIVE\",\n \"def get_form_kwargs(self):\\n\\n press_release_obj = PressRelease.objects.get(\\n id=self.kwargs['press_release_pk'])\\n\\n kwargs = super(PressReleaseUpdateView, self).get_form_kwargs()\\n kwargs.update(\\n {'field': self.kwargs['field'],\\n 'press_release_obj': press_release_obj}\\n )\\n\\n return kwargs\",\n \"def form_data(self, clear=[], **kwargs):\\n form_data = {\\n 'payer_name': 'William Williams',\\n 'billing_address': '1 Main Street',\\n 'billing_city': 'Anytown',\\n 'country': 'USA',\\n 'billing_state': 'MD',\\n 'billing_zip': '20852',\\n 'payment_type': 'CreditCard',\\n 'project_code': '15-4FF',\\n 'payment_amount': '3000',\\n 'information_consent': True,\\n }\\n for key in clear:\\n del form_data[key]\\n for key, value in kwargs.items():\\n form_data[key] = value\\n return form_data\",\n \"def __init__(self, *args, **kwargs):\\n super(ProfileForm, self).__init__(*args, **kwargs) \\n for field in self.fields:\\n self.fields[field].widget.attrs.update(\\n {\\n 'class': 'form-control',\\n }\\n )\",\n \"def __init__(self, *args, **kwargs):\\n\\n\\t\\tsuper(BikeleasingForm, self).__init__(*args, **kwargs)\\n\\n\\t\\tfor field in self.fields:\\n\\t\\t\\tif field == 'versicherungskarte':\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control-file'\\n\\t\\t\\t\\t})\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control'\\n\\t\\t\\t\\t})\",\n \"def __init__(self, *args, **kwargs):\\n\\n\\t\\tsuper(EuroradForm, self).__init__(*args, **kwargs)\\n\\n\\t\\tfor field in self.fields:\\n\\t\\t\\tif field == 'versicherungskarte':\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control-file'\\n\\t\\t\\t\\t})\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.fields[field].widget.attrs.update({\\n\\t\\t\\t\\t\\t'class': 'form-control'\\n\\t\\t\\t\\t})\",\n \"def form_data(self):\\n from couchforms import XMLSyntaxError\\n from .utils import convert_xform_to_json, adjust_datetimes\\n from corehq.form_processor.utils.metadata import scrub_form_meta\\n xml = self.get_xml()\\n try:\\n form_json = convert_xform_to_json(xml)\\n except XMLSyntaxError:\\n return {}\\n # we can assume all sql domains are new timezone domains\\n with force_phone_timezones_should_be_processed():\\n adjust_datetimes(form_json)\\n\\n scrub_form_meta(self.form_id, form_json)\\n return form_json\",\n \"def copy_fields(self, model):\\n fields = super(HistoricalRecords, self).copy_fields(model)\\n for name, field in self.additional_fields.items():\\n assert name not in fields\\n assert hasattr(self, 'get_%s_value' % name)\\n fields[name] = field\\n return fields\",\n \"def complete_form_data():\\n\\n missing_fields = {\\n 'link' : 'http://bvsalud.org',\\n 'originator' : 'BIREME',\\n 'source_type': 1,\\n 'source_language': 1,\\n 'originator_location' : 1,\\n\\n 'main-descriptor-content_type-object_id-TOTAL_FORMS' : '1',\\n\\n 'main-descriptor-content_type-object_id-0-id' : '',\\n 'main-descriptor-content_type-object_id-0-text' : 'malaria',\\n 'main-descriptor-content_type-object_id-0-code' : '^d8462',\\n 'main-descriptor-content_type-object_id-0-status' : '0',\\n\\n 'main-resourcethematic-content_type-object_id-TOTAL_FORMS' : '1',\\n 'main-resourcethematic-content_type-object_id-0-thematic_area' : '1',\\n 'main-resourcethematic-content_type-object_id-0-status' : '0',\\n }\\n\\n complete_form_data = minimal_form_data()\\n complete_form_data.update(missing_fields)\\n\\n return complete_form_data\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.fields[\\\"from\\\"] = forms.DateTimeField(required=True)\",\n \"def make_form(self):\",\n \"def getConferencesToAttend(self, request):\\n prof = self._getProfileFromUser() # get user Profile\\n conf_keys = [\\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\\n ]\\n conferences = ndb.get_multi(conf_keys)\\n # Get organizers\\n organisers = [\\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\\n ]\\n profiles = ndb.get_multi(organisers)\\n # Put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n # Return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\\n for conf in conferences\\n ]\\n )\",\n \"def build_conference(self, data) -> dict:\\n builder = ConferenceBuilder()\\n builder.add_title(title=data.get(\\\"name\\\"), subtitle=data.get(\\\"subtitle\\\"))\\n builder.set_short_description(value=data.get(\\\"description\\\", \\\"\\\"))\\n builder.set_opening_date(get_value(data, \\\"dates[0]\\\"))\\n builder.set_closing_date(get_value(data, \\\"dates[1]\\\"))\\n builder.add_inspire_categories(data.get(\\\"field_of_interest\\\", []))\\n builder.add_public_note(value=data.get(\\\"additional_info\\\", \\\"\\\"))\\n builder.add_series(\\n name=data.get(\\\"series_name\\\"), number=data.get(\\\"series_number\\\")\\n )\\n for address in data.get(\\\"addresses\\\"):\\n builder.add_address(\\n cities=[address.get(\\\"city\\\")],\\n state=address.get(\\\"state\\\"),\\n place_name=address.get(\\\"venue\\\"),\\n country_code=country_name_to_code(address.get(\\\"country\\\")),\\n )\\n for contact in data.get(\\\"contacts\\\", []):\\n builder.add_contact(**contact)\\n for acr in data.get(\\\"acronyms\\\", []):\\n builder.add_acronym(acr)\\n for website in data.get(\\\"websites\\\", []):\\n builder.add_url(website)\\n for keyword in data.get(\\\"keywords\\\", []):\\n builder.add_keyword(value=keyword)\\n\\n builder.record[\\\"$schema\\\"] = url_for(\\n \\\"invenio_jsonschemas.get_schema\\\",\\n schema_path=\\\"records/conferences.json\\\",\\n _external=True,\\n )\\n\\n return builder.record\",\n \"def formfields():\\n\\n T = current.T\\n request = current.request\\n\\n auth = current.auth\\n auth_settings = auth.settings\\n auth_messages = auth.messages\\n\\n utable = auth_settings.table_user\\n passfield = auth_settings.password_field\\n\\n # Last name is required\\n utable.last_name.requires = IS_NOT_EMPTY(error_message=T(\\\"input required\\\"))\\n\\n # Don't check for duplicate email (will be done in onvalidation)\\n # => user might choose to use the current email address of the account\\n # => if registration key or code are invalid, we don't want to give away\\n # any existing email addresses\\n utable.email.requires = [IS_EMAIL(error_message = auth_messages.invalid_email),\\n IS_LOWER(),\\n ]\\n\\n # Instantiate Consent Tracker\\n consent = ConsentTracking(processing_types=[\\\"STORE\\\", \\\"RULES_ISS\\\"])\\n\\n # Form fields\\n formfields = [utable.first_name,\\n utable.last_name,\\n utable.email,\\n utable[passfield],\\n Field(\\\"password_two\\\", \\\"password\\\",\\n label = auth_messages.verify_password,\\n requires = IS_EXPR(\\\"value==%s\\\" % \\\\\\n repr(request.vars.get(passfield)),\\n error_message = auth_messages.mismatched_password,\\n ),\\n comment = DIV(_class = \\\"tooltip\\\",\\n _title = \\\"%s|%s\\\" % (auth_messages.verify_password,\\n T(\\\"Enter the same password again\\\"),\\n ),\\n ),\\n ),\\n Field(\\\"code\\\",\\n label = T(\\\"Registration Code\\\"),\\n requires = IS_NOT_EMPTY(),\\n ),\\n Field(\\\"consent\\\",\\n label = T(\\\"Consent\\\"),\\n widget = consent.widget,\\n ),\\n ]\\n\\n\\n # Required fields\\n required_fields = [\\\"first_name\\\",\\n \\\"last_name\\\",\\n ]\\n\\n return formfields, required_fields\",\n \"def __init__(self, *args, **kwargs):\\n super(RegisterForm, self).__init__(*args, **kwargs)\\n\\n self.fields['nick'].widget.attrs.update({\\n 'label': 'Přezdívka',\\n 'placeholder': 'Mirek'\\n })\\n\\n self.fields['name'].widget.attrs.update({\\n 'label': 'Jméno',\\n 'placeholder': 'Mirek'\\n })\\n\\n self.fields['surname'].widget.attrs.update({\\n 'label': 'Příjmení',\\n 'placeholder': 'Dušín'\\n })\\n\\n self.fields['email'].widget.attrs.update({\\n 'label': 'E-mail',\\n 'placeholder': 'mirek@rychlesipy.cz'\\n })\\n\\n self.fields['age'].widget.attrs.update({'label': 'Věk'})\\n self.fields['age'].initial = 18\\n\\n self.fields['race'].widget.attrs.update({'label': 'Rasa'})\\n self.fields['race'].queryset = Race.objects.filter(\\n active=True).only('id', 'name')\\n\\n self.fields['group'].widget.attrs.update({\\n 'label': 'Skupina',\\n 'placeholder': 'Rychlé Šípy'\\n })\\n\\n for field in self.fields.keys():\\n self.fields[field].widget.attrs.update({\\n 'required': self.fields[field].required,\\n 'title': '',\\n 'class': 'form-control'\\n })\",\n \"def getConferencesCreated(self, request):\\n # Make sure user is authenticated\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = user.email()\\n # Create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # Return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(\\n conf, getattr(prof, 'displayName')) for conf in confs\\n ]\\n )\",\n \"def _form_data(self, response):\\n SQFI_audit_type = response.xpath(self.filters[6]).extract_first()\\n SQFI_audit_type_val = response.xpath(self.filters[7]).extract_first()\\n food_sector_categories = response.xpath(self.filters[8]).extract_first()\\n food_sector_categories_val = response.xpath(self.filters[9]).extract()\\n audit_rating = response.xpath(self.filters[10]).extract_first()\\n audit_rating_val = response.xpath(self.filters[11]).extract()\\n country = response.xpath(self.filters[12]).extract_first()\\n country_val = response.xpath(self.filters[13]).extract()\\n form_data = {\\n SQFI_audit_type: SQFI_audit_type_val,\\n food_sector_categories: food_sector_categories_val,\\n audit_rating: audit_rating_val,\\n country: country_val,\\n }\\n return form_data\",\n \"def __init__(self, *args, **kwargs):\\n super(SignupForm, self).__init__(*args, **kwargs)\\n self.fields['email'].required = True\\n self.fields['first_name'].required = True\\n self.fields['password'].widget = forms.PasswordInput() \\n\\n for field in self.fields:\\n self.fields[field].widget.attrs.update(\\n {\\n 'class': 'form-control',\\n }\\n )\",\n \"def formfields(cls):\\n\\n T = current.T\\n request = current.request\\n\\n auth = current.auth\\n auth_settings = auth.settings\\n auth_messages = auth.messages\\n\\n utable = auth_settings.table_user\\n passfield = auth_settings.password_field\\n\\n # Instantiate Consent Tracker\\n consent = ConsentTracking(processing_types=[\\\"SHARE\\\", \\\"RULES_PRO\\\", \\\"TPNDO\\\"])\\n\\n # Last name is required\\n utable.last_name.requires = IS_NOT_EMPTY(error_message=T(\\\"input required\\\"))\\n\\n #ltable = s3db.gis_location\\n\\n # Lookup projects with provider self-registration\\n projects = cls.selectable_projects()\\n\\n # Lookup site services\\n services = cls.selectable_services()\\n\\n # Lookup applicable organisation types\\n org_types = applicable_org_types(None, group=TESTSTATIONS, represent=True)\\n\\n # Form fields\\n formfields = [# -- User account ---\\n utable.first_name,\\n utable.last_name,\\n utable.email,\\n utable[passfield],\\n\\n # Password Verification Field\\n Field(\\\"password_two\\\", \\\"password\\\",\\n label = auth_messages.verify_password,\\n requires = IS_EXPR(\\\"value==%s\\\" % \\\\\\n repr(request.vars.get(passfield)),\\n error_message = auth_messages.mismatched_password,\\n ),\\n comment = DIV(_class = \\\"tooltip\\\",\\n _title = \\\"%s|%s\\\" % (auth_messages.verify_password,\\n T(\\\"Enter the same password again\\\"),\\n ),\\n ),\\n ),\\n # -- Test Station ---\\n Field(\\\"organisation\\\",\\n label = T(\\\"Name\\\"),\\n requires = [IS_NOT_EMPTY(), IS_LENGTH(60)],\\n comment = DIV(_class = \\\"tooltip\\\",\\n _title = \\\"%s|%s\\\" % (T(\\\"Test Station Name\\\"),\\n T(\\\"Specify the name of the test station (max 60 characters)\\\"),\\n ),\\n ),\\n ),\\n Field(\\\"organisation_type\\\", \\\"integer\\\",\\n label = T(\\\"Organization Type\\\"),\\n requires = IS_IN_SET(org_types),\\n ),\\n\\n # -- Address --\\n Field(\\\"location\\\", \\\"json\\\",\\n widget = LocationSelector(\\n levels = (\\\"L1\\\", \\\"L2\\\", \\\"L3\\\", \\\"L4\\\"),\\n required_levels = (\\\"L1\\\", \\\"L2\\\", \\\"L3\\\"),\\n show_address = True,\\n address_required = True,\\n show_postcode = True,\\n postcode_required = True,\\n show_map = True,\\n ),\\n ),\\n # -- Service Offer --\\n Field(\\\"opening_times\\\",\\n label = T(\\\"Opening Hours\\\"),\\n requires = IS_NOT_EMPTY(),\\n ),\\n Field(\\\"service_mode\\\", \\\"integer\\\",\\n label = T(\\\"Service Mode\\\"),\\n requires = IS_IN_SET(cls.selectable_services_modes()),\\n ),\\n Field(\\\"services\\\", \\\"list:integer\\\",\\n label = T(\\\"Services\\\"),\\n requires = IS_IN_SET(services,\\n multiple = True,\\n zero = None,\\n ),\\n widget = WithAdvice(S3GroupedOptionsWidget(cols=1),\\n # Widget intro from CMS\\n text = (\\\"org\\\",\\n \\\"facility\\\",\\n \\\"SiteServiceIntro\\\",\\n ),\\n ),\\n ),\\n # -- Contact and Appointments --\\n Field(\\\"facility_phone\\\",\\n label = T(\\\"Telephone\\\"),\\n requires = IS_EMPTY_OR(IS_PHONE_NUMBER_MULTI()),\\n ),\\n Field(\\\"facility_email\\\",\\n label = T(\\\"Email\\\"),\\n requires = IS_EMPTY_OR(IS_EMAIL()),\\n ),\\n Field(\\\"facility_website\\\",\\n label = T(\\\"Website\\\"),\\n ),\\n Field(\\\"booking_mode\\\", \\\"integer\\\",\\n label = T(\\\"Appointments via\\\"),\\n requires = IS_EMPTY_OR(IS_IN_SET(\\n cls.selectable_booking_modes(),\\n )),\\n ),\\n Field(\\\"comments\\\", \\\"text\\\",\\n label = T(\\\"Comments\\\"),\\n widget = s3_comments_widget,\\n ),\\n\\n # -- Administrative --\\n Field(\\\"projects\\\", \\\"list:integer\\\",\\n label = T(\\\"Programs\\\"),\\n requires = [IS_IN_SET(projects,\\n multiple = True,\\n zero = None,\\n ),\\n IS_NOT_EMPTY(),\\n ],\\n widget = WithAdvice(S3GroupedOptionsWidget(cols=1),\\n # Widget intro from CMS\\n text = (\\\"org\\\",\\n \\\"organisation\\\",\\n \\\"ProjectParticipationIntro\\\",\\n ),\\n ),\\n ),\\n # -- Privacy and Consent --\\n Field(\\\"consent\\\",\\n label = T(\\\"Consent\\\"),\\n widget = consent.widget,\\n ),\\n ]\\n\\n # Required fields\\n required_fields = [\\\"first_name\\\",\\n \\\"last_name\\\",\\n ]\\n\\n # Subheadings\\n subheadings = ((0, T(\\\"User Account\\\")),\\n (5, T(\\\"Test Station\\\")),\\n (7, T(\\\"Address\\\")),\\n (8, T(\\\"Service Offer\\\")),\\n (11, T(\\\"Contact and Appointments\\\")),\\n (16, T(\\\"Administrative\\\")),\\n (17, \\\"%s / %s\\\" % (T(\\\"Privacy\\\"), T(\\\"Terms of Service\\\"))),\\n )\\n\\n # Geocoder\\n current.response.s3.scripts.append(\\\"/%s/static/themes/RLP/js/geocoderPlugin.js\\\" % request.application)\\n\\n return formfields, required_fields, subheadings\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n placeholders = {\\n 'business_name': 'Please enter your business name',\\n 'user_type': 'Please select the type of user',\\n 'phone': 'Phone Number',\\n 'postcode': 'Postcode',\\n 'city': 'City',\\n 'street_address': 'Street Address',\\n 'street_address2': 'Street Address 2',\\n 'county': 'County',\\n 'country': 'Country'\\n }\\n\\n # to force cursor to start in business name field\\n self.fields['business_name'].widget.attrs['autofocus'] = True\\n for field in self.fields:\\n placeholder = placeholders[field]\\n self.fields[field].widget.attrs['placeholder'] = False\\n self.fields[field].label = placeholder\\n self.fields[field].widget.attrs['class'] = 'form-control'\",\n \"def test_prep_country_fields(self):\\n original_flag = self.form.country_optional\\n self.form.country_optional = True\\n original_fields = self.form.fields\\n original_removed = getattr(self.form, 'removed_fields', None)\\n original_computed = getattr(self.form, 'computed_fields', None)\\n self.form.fields = original_fields.copy()\\n if original_removed is not None:\\n self.form.removed_fields = original_removed.copy()\\n if original_computed is not None:\\n self.form.computed_fields = original_computed.copy()\\n remaining = original_fields.copy()\\n opts, field_rows = {'fake_opts': 'fake', 'fields': ['nope']}, [{'name': 'assigned_field'}]\\n args = ['arbitrary', 'input', 'args']\\n kwargs = {'test_1': 'data_1', 'test_2': 'data_2'}\\n field_names = (self.form.country_field_name, 'country_flag', )\\n if not any(remaining.get(name, None) for name in field_names):\\n fix_fields = {name: self.get_missing_field(name) for name in field_names if name not in remaining}\\n remaining.update(fix_fields)\\n expected_add = {name: remaining[name] for name in field_names if name in remaining}\\n expected_field_rows = field_rows.copy()\\n expected_field_rows.append(expected_add)\\n expected_remaining = {name: field for name, field in remaining.items() if name not in expected_add}\\n expected_opts = deepcopy(opts)\\n expected_opts['fields'].append(field_names)\\n\\n sent = (opts, field_rows, remaining, *args)\\n r_opts, r_rows, r_remaining, *r_args, r_kwargs = self.form.prep_country_fields(*sent, **kwargs)\\n self.assertEqual(expected_opts, r_opts)\\n self.assertEqual(expected_field_rows, r_rows)\\n self.assertEqual(expected_remaining, r_remaining)\\n self.assertEqual(args, r_args)\\n self.assertEqual(kwargs, r_kwargs)\\n\\n self.form.country_optional = original_flag\\n self.form.fields = original_fields\\n if original_removed is not None:\\n self.form.removed_fields = original_removed\\n if original_computed is not None:\\n self.form.computed_fields = original_computed\",\n \"def getConferencesCreated(self, request):\\n # make sure user is authed\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = getUserId(user)\\n\\n # create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\\n )\",\n \"def populate_obj(self, obj):\\n for name, field in iteritems(self._fields):\\n if name == 'studies':\\n for study_form in self.studies.entries:\\n study_form.form.populate_obj(\\n obj.studies[study_form.study_id.data])\\n else:\\n field.populate_obj(obj, name)\",\n \"def form_valid(self, form):\\n if self.fields is None:\\n self.object.__dict__.update({\\n field.name:form.cleaned_data[field.name] for field in form.visible_fields()\\n })\\n else:\\n self.object.__dict__.update({\\n field:form.cleaned_data[field] for field in self.fields\\n })\\n self.object.save()\\n if self.request.is_ajax():\\n return self.ajax_form_valid()\\n else:\\n return HttpResponseRedirect(self.get_success_url())\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n placeholders = {\\n \\\"first_name\\\": \\\"First Name\\\",\\n \\\"last_name\\\": \\\"Last Name\\\",\\n \\\"default_phone_num\\\": \\\"Phone Number\\\",\\n \\\"default_passport_num\\\": \\\"Passport Number\\\",\\n }\\n\\n self.fields[\\\"default_phone_num\\\"].widget.attrs[\\\"autofocus\\\"] = True\\n for field in self.fields:\\n placeholder = placeholders[field]\\n self.fields[field].widget.attrs[\\\"placeholder\\\"] = placeholder\\n self.fields[field].widget.attrs[\\n \\\"class\\\"\\n ] = \\\"border-black rounded-0 \\\\\\n all-form-input\\\"\\n self.fields[field].label = False\\n self.helper = FormHelper()\\n self.helper.form_tag = True\\n self.helper.layout = Layout(\\n Div(\\n Field(\\n \\\"first_name\\\",\\n ),\\n Field(\\n \\\"last_name\\\",\\n ),\\n Field(\\n \\\"default_phone_num\\\",\\n ),\\n Field(\\n \\\"default_passport_num\\\",\\n ),\\n ),\\n ButtonHolder(\\n Submit(\\\"submit\\\", \\\"Save\\\", css_class=\\\"m-0 btn btn-outline\\\"),\\n ),\\n )\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n placeholders = {\\n 'shift_name': 'Shift name',\\n 'min_agents': 'Minimum Number of Agents',\\n 'shift_start': 'Start time',\\n 'shift_end': 'End time',\\n 'weekday_sunday': 'Sunday',\\n 'weekday_monday': 'Monday',\\n 'weekday_tuesday': 'Tuesday',\\n 'weekday_wednesday': 'Wednesday',\\n 'weekday_thursday': 'Thursday',\\n 'weekday_friday': 'Friday',\\n 'weekday_saturday': 'Saturday'\\n }\\n\\n for field in self.fields:\\n if self.fields[field].required:\\n placeholder = f'{placeholders[field]} *'\\n else:\\n placeholder = placeholders[field]\\n self.fields[field].widget.attrs['placeholder'] = placeholder\\n self.fields[field].widget.attrs['class'] = 'profile-form-input'\\n self.fields[field].label = False\",\n \"def get_minimal_add_form(self):\\r\\n\\r\\n # Get all the required fields and make a modelform for it.\\r\\n exclude_fields = []\\r\\n\\r\\n for field in self.model._meta.fields:\\r\\n if field.blank:\\r\\n exclude_fields.append(field.name)\\r\\n\\r\\n instance_form = modelform_factory(self.model, form=self.add_form,\\r\\n exclude=exclude_fields)\\r\\n\\r\\n return instance_form\",\n \"def __init__(field, form, content):\",\n \"def __init__(field, form, content):\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.fields['first_name'].required = False\\n self.fields['last_name'].required = False\\n self.fields['institution'].required = False\\n self.fields['institution_logo'].required = False\\n self.fields['allow_notifications'].required = False\",\n \"def __init__(self, *args, **kwargs):\\n # defaults to \\\"text\\\" and \\\"identity\\\"\\n \\n self.text_name = kwargs.pop('text_name', 'text')\\n self.identity_name = kwargs.pop('identity_name', 'identity')\\n \\n super(EnvayaSMSIncomingForm, self).__init__(*args, **kwargs)\\n \\n #The following two fields are non-mandatory because actions other than 'incoming' won't have them.\\n self.fields[self.text_name] = forms.CharField(required=False)\\n self.fields[self.identity_name] = forms.CharField(required=False)\\n\\n self.fields['phone_number'] = forms.CharField() #which envaya phone forwarded us the msg?\\n self.fields['action'] = forms.CharField() #what is the action?\",\n \"def test_fields_presence(self):\\n form = SWCEventRequestForm()\\n fields_left = set(form.fields.keys())\\n fields_right = set([\\n 'name', 'email', 'affiliation', 'location', 'country',\\n 'conference', 'preferred_date', 'language', 'workshop_type',\\n 'approx_attendees', 'attendee_domains', 'attendee_domains_other',\\n 'attendee_academic_levels', 'attendee_computing_levels',\\n 'cover_travel_accomodation', 'understand_admin_fee',\\n 'travel_reimbursement', 'travel_reimbursement_other',\\n 'admin_fee_payment', 'comment', 'captcha', 'privacy_consent',\\n ])\\n self.assertEqual(fields_left, fields_right)\",\n \"def __init__(self, owner=None, *args, **kwargs):\\n super(CalendarForm, self).__init__(*args, **kwargs)\\n if owner is not None:\\n self.owner = owner\\n without_owner = UserProfile.objects.all().exclude(pk=owner.pk)\\n self.fields['readers'].queryset = without_owner\\n self.fields['modifiers'].queryset = without_owner\",\n \"def _create_and_initialise_fields(self):\\n for team in self.category.breaking_teams.all():\\n self.fields[self._fieldname_remark(team)] = OptionalChoiceField(choices=BreakingTeam.REMARK_CHOICES, required=False)\\n try:\\n self.initial[self._fieldname_remark(team)] = self._bt(team).remark\\n except KeyError:\\n self.initial[self._fieldname_remark(team)] = None\",\n \"def __init__(self, *args, **kwargs):\\n super(CreateForm, self).__init__(*args, **kwargs)\\n\\n # set form fields order\\n self.fields.keyOrder = ['to_user', 'subject', 'message']\",\n \"def test_prep_field_properties(self):\\n original_data = self.form.data\\n test_data = original_data.copy()\\n # modify values in data\\n test_data._mutable = False\\n self.form.data = test_data\\n original_fields = self.form.fields\\n test_fields = original_fields.copy()\\n # modify fields\\n self.form.fields = test_fields\\n test_fields_info = {name: field.__dict__.copy() for name, field in test_fields.items()}\\n original_get_overrides = self.form.get_overrides\\n def skip_overrides(): return {}\\n self.form.get_overrides = skip_overrides\\n original_alt_field_info = getattr(self.form, 'alt_field_info', None)\\n self.form.alt_field_info = self.alt_field_info\\n self.form.test_condition_response = True\\n expected_fields_info = test_fields_info.copy()\\n result_fields = self.form.prep_fields()\\n result_fields_info = {name: field.__dict__.copy() for name, field in result_fields.items()}\\n modified_info = self.alt_field_info['alt_test_feature']\\n first_label = modified_info['first']['label']\\n first_initial = modified_info['first']['initial']\\n last_initial = modified_info['last']['initial']\\n for name, opts in modified_info.items():\\n expected_fields_info[name].update(opts)\\n\\n self.assertEqual(first_label, result_fields['first'].label)\\n self.assertEqual(first_initial, result_fields['first'].initial)\\n self.assertEqual(last_initial, result_fields['last'].initial)\\n for key, val in expected_fields_info.items():\\n self.assertEqual(val, result_fields_info[key])\\n self.assertDictEqual(expected_fields_info, result_fields_info)\\n\\n self.form.test_condition_response = False\\n self.form.alt_field_info = original_alt_field_info\\n if original_alt_field_info is None:\\n del self.form.alt_field_info\\n self.form.fields = original_fields\\n self.form.data = original_data\\n self.form.get_overrides = original_get_overrides\",\n \"def form_data(self) -> dict:\\n return copy.deepcopy(self.data)\",\n \"def test_fields_presence(self):\\n form = DCEventRequestForm()\\n fields_left = set(form.fields.keys())\\n fields_right = set([\\n 'name', 'email', 'affiliation', 'location', 'country',\\n 'conference', 'preferred_date', 'language', 'workshop_type',\\n 'approx_attendees', 'attendee_domains', 'attendee_domains_other',\\n 'data_types', 'data_types_other', 'attendee_academic_levels',\\n 'attendee_data_analysis_level', 'cover_travel_accomodation',\\n 'understand_admin_fee', 'fee_waiver_request',\\n 'travel_reimbursement', 'travel_reimbursement_other',\\n 'comment', 'privacy_consent', 'captcha',\\n ])\\n self.assertEqual(fields_left, fields_right)\",\n \"def get_current_fields(self):\\n return self.form.fields.copy()\",\n \"def form_valid(self, form):\\n author = Profile.objects.get(user=self.request.user)\\n desk = Desk.objects.get(owner__user=self.user)\\n form.instance.author = author\\n form.instance.desk = desk\\n\\n return super(BoardCreate, self).form_valid(form)\",\n \"def __init__(self, *args, **kwargs):\\n\\n self._caffe = kwargs.pop('caffe')\\n\\n kwargs.setdefault('label_suffix', '')\\n super(CompanyForm, self).__init__(*args, **kwargs)\\n self.fields['name'].label = 'Nazwa'\",\n \"def _construct_form(self, i, **kwargs):\\n defaults = {'auto_id': self.auto_id, 'prefix': self.add_prefix(i)}\\n if self.is_bound:\\n defaults['data'] = self.data\\n defaults['files'] = self.files\\n if self.initial:\\n try:\\n defaults['initial'] = self.initial[i]\\n except IndexError:\\n pass\\n # Allow extra forms to be empty.\\n if i >= self.initial_form_count():\\n defaults['empty_permitted'] = True\\n defaults.update(kwargs)\\n form = self.form(self.params[len(self.params) - i - 1][1], self.params[len(self.params) - i - 1][0], i, **defaults) #passando o params[i] para o form[i]\\n self.add_fields(form, i)\\n return form\",\n \"def make_from_instance(instance):\\n data = model_to_dict(instance)\\n data['id'] = instance.id\\n if instance.location:\\n data['latitude'] = round(instance.location.y, 7)\\n data['longitude'] = round(instance.location.x, 7)\\n else:\\n data['latitude'] = None\\n data['longitude'] = None\\n return GeneralInformationForm(\\n initial=data\\n )\",\n \"def get_form_kwargs(self):\\n kwargs = super(ProfileUpdate, self).get_form_kwargs()\\n self.profile = self.get_object()\\n extra_kwargs = {'profile': self.profile}\\n kwargs.update(extra_kwargs)\\n return kwargs\",\n \"def clear_new_talk_fields(self):\\r\\n self.newTalkWidget.talkDetailsWidget.titleLineEdit.clear()\\r\\n self.newTalkWidget.talkDetailsWidget.presenterLineEdit.clear()\\r\\n self.newTalkWidget.talkDetailsWidget.descriptionTextEdit.clear()\\r\\n self.newTalkWidget.talkDetailsWidget.categoryLineEdit.clear()\",\n \"def form_CustomisedFormLayoutFields(request):\\n schema = schemaish.Structure()\\n schema.add( 'firstName', schemaish.String())\\n schema.add( 'surname', schemaish.String())\\n schema.add( 'age', schemaish.Integer())\\n schema.add( 'sex', schemaish.String())\\n\\n form = formish.Form(schema, 'form')\\n\\n return form\",\n \"def initcomponentform():\\n form = ComponentsForm()\\n form.name.value = []\\n form.id.value = []\\n form.currentstock.value = []\\n form.reorderlevel.value = []\\n form.unitprice.value = []\\n form.supplier.value = []\\n form.location.value = []\\n form.datasheet.value = []\\n return form\",\n \"def form(self, form):\\n\\n self._form = form\",\n \"def form(self, form):\\n\\n self._form = form\",\n \"def get_form(self):\\n # setup request layer\\n self.request = TestRequest()\\n # get add view\\n form = getMultiAdapter((self.experiments, self.request),\\n name=\\\"newBiodiverse\\\")\\n # update the form once to initialise all widgets\\n form.update()\\n # go through all widgets on the form and update the request with default values\\n data = {}\\n for widget in form.widgets.values():\\n data[widget.name] = widget.value\\n data.update({\\n 'form.widgets.IDublinCore.title': u\\\"My BD Experiment\\\",\\n 'form.widgets.IDublinCore.description': u'This is my experiment description',\\n 'form.widgets.projection.count': '1',\\n 'form.widgets.projection.experiment.0': unicode(self.sdmexp.UID()),\\n 'form.widgets.projection.dataset.0.count': 1,\\n 'form.widgets.projection.dataset.0.0.uuid': unicode(self.sdmproj.UID()),\\n 'form.widgets.projection.dataset.0.0.threshold': '0.0',\\n 'form.widgets.cluster_size': '5000',\\n })\\n self.request.form.update(data)\\n form = getMultiAdapter((self.experiments, self.request),\\n name=\\\"newBiodiverse\\\")\\n return form\",\n \"def get_form_kwargs(self):\\n kwargs = super(FlashbackCreateView, self).get_form_kwargs()\\n kwargs[\\\"owner\\\"] = self.character.roster\\n return kwargs\",\n \"def _changeform_view(self, request, object_id, form_url, extra_context):\\n to_field = request.POST.get(TO_FIELD_VAR, request.GET.get(TO_FIELD_VAR))\\n if to_field and not self.to_field_allowed(request, to_field):\\n raise DisallowedModelAdminToField(\\\"The field %s cannot be referenced.\\\" % to_field)\\n\\n model = self.model\\n opts = model._meta\\n\\n if request.method == 'POST' and '_saveasnew' in request.POST:\\n object_id = None\\n\\n add = object_id is None\\n\\n if add:\\n if not self.has_add_permission(request):\\n raise PermissionDenied\\n obj = None\\n\\n else:\\n obj = self.get_object(request, unquote(object_id), to_field)\\n\\n if (\\n (not self.has_module_permission(request))\\n and (not self.has_change_permission(request, obj))):\\n raise PermissionDenied\\n\\n if obj is None:\\n return self._get_obj_does_not_exist_redirect(request, opts, object_id)\\n\\n ModelForm = self.get_form(request, obj)\\n if request.method == 'POST':\\n form = ModelForm(request.POST, request.FILES, instance=obj)\\n if form.is_valid():\\n form_validated = True\\n new_object = self.save_form(request, form, change=not add)\\n else:\\n form_validated = False\\n new_object = form.instance\\n formsets, inline_instances = self._create_formsets(request, new_object, change=not add)\\n if all_valid(formsets) and form_validated:\\n response = self.do_saving(\\n request=request, new_object=new_object, form=form, formsets=formsets, add=add)\\n if response:\\n return response\\n else:\\n form_validated = False\\n else:\\n form_validated = False\\n else:\\n if add:\\n initial = self.get_changeform_initial_data(request)\\n form = ModelForm(initial=initial)\\n formsets, inline_instances = self._create_formsets(\\n request, form.instance, change=False)\\n self_formsets, self_inline_instances = self._create_self_formsets(\\n request, form.instance, change=False)\\n else:\\n form = ModelForm(instance=obj)\\n formsets, inline_instances = self._create_formsets(request, obj, change=True)\\n self_formsets, self_inline_instances = self._create_self_formsets(\\n request, obj, change=True)\\n\\n adminForm = helpers.AdminForm(\\n form,\\n list(self.get_fieldsets(request, obj)),\\n self.get_prepopulated_fields(request, obj),\\n self.get_readonly_fields(request, obj),\\n model_admin=self)\\n media = self.media + adminForm.media\\n\\n inline_formsets = self.get_inline_formsets(request, formsets, inline_instances, obj)\\n for inline_formset in inline_formsets:\\n media = media + inline_formset.media\\n\\n context = dict(\\n self.admin_site.each_context(request),\\n title=(_('Add %s') if add else _('Change %s')) % force_text(opts.verbose_name),\\n adminform=adminForm,\\n object_id=object_id,\\n original=obj,\\n is_popup=(IS_POPUP_VAR in request.POST or\\n IS_POPUP_VAR in request.GET),\\n to_field=to_field,\\n media=media,\\n inline_admin_formsets=inline_formsets,\\n errors=helpers.AdminErrorList(form, formsets),\\n preserved_filters=self.get_preserved_filters(request),\\n )\\n\\n # Hide the \\\"Save\\\" and \\\"Save and continue\\\" buttons if \\\"Save as New\\\" was\\n # previously chosen to prevent the interface from getting confusing.\\n if (self.readonly_model or (\\n request.method == 'POST' and not form_validated and \\\"_saveasnew\\\" in request.POST)):\\n context['show_save'] = False\\n context['show_save_and_continue'] = False\\n # Use the change template instead of the add template.\\n add = False\\n\\n context.update(extra_context or {})\\n\\n return self.render_change_form(\\n request, context, add=add, change=not add, obj=obj, form_url=form_url)\",\n \"def form_valid(self, form):\\n author = Profile.objects.get(user=self.request.user)\\n object = self.get_object()\\n content_type = ContentType.objects.get_for_model(object)\\n form.instance.author = author\\n form.instance.content_type = content_type\\n form.instance.object_id = object.pk\\n\\n return super(CommentCreate, self).form_valid(form)\",\n \"def __init__(self, *args, **kwargs):\\n\\n self._caffe = kwargs.pop('caffe')\\n self._creator = kwargs.pop('creator')\\n\\n kwargs.setdefault('label_suffix', '')\\n super(CashReportForm, self).__init__(*args, **kwargs)\\n self.fields['cash_before_shift'].label = 'Pieniądze na początku zmiany'\\n self.fields['cash_after_shift'].label = 'Pieniądze na końcu zmiany'\\n self.fields['card_payments'].label = 'Karty'\\n self.fields['amount_due'].label = 'Łączna należność'\",\n \"def get_form_kwargs(self):\\n kwargs = {'initial': self.get_initial()}\\n if self.request.method in ('POST', 'PUT'):\\n kwargs.update({\\n 'data': self.request.POST,\\n 'files': self.request.FILES,\\n })\\n kwargs.update(self.get_additional_form_args())\\n return kwargs\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n placeholders = {\\n 'group_id': 'Guest Group',\\n 'first_name': 'First Name',\\n 'last_name': 'Last Name',\\n 'address_line_1': 'Address Line 1',\\n 'address_line_2': 'Address Line 2',\\n 'city': 'City',\\n 'county': 'County',\\n 'postcode': 'Post Code',\\n 'country': 'Country',\\n 'email': 'Email'\\n }\\n\\n for field in self.fields:\\n if field != 'country':\\n placeholder = placeholders[field]\\n self.fields[field].widget.attrs['placeholder'] = placeholder\\n self.fields[field].widget.attrs['class'] = 'stripe-style-input'\\n self.fields[field].label = False\",\n \"def fill_sequence_data_fields(self):\\n sid = self._get_selected_sequence()\\n if sid:\\n sequence_data = Data().get_sequence_data(sid)\\n if sequence_data:\\n code = Data().get_country_code(sequence_data[1])\\n self.ui.e_country.setText(code)\\n self.ui.cb_roadtype.setCurrentIndex(sequence_data[2] + 1)\",\n \"def contact_create(request):\\n\\n if request.method == \\\"POST\\\":\\n form = ContactForm(request.POST)\\n profile_form = ContactProfileForm(request.POST)\\n\\n if form.is_valid() and profile_form.is_valid():\\n contact = form.save()\\n\\n # Populate the required 'contact' field before saving\\n profile = profile_form.save(commit=False)\\n profile.contact = contact\\n profile.save()\\n\\n messages.success(request, _(\\\"The contact %(name)s was successfully created\\\") % \\n {'name': unicode(contact)})\\n return HttpResponseRedirect(reverse(\\\"moderation.views.contact\\\", args=(contact.pk,)))\\n else:\\n form_initial = {'phone_number': request.GET.get('phone_number', '')}\\n form = ContactForm(initial=form_initial)\\n\\n profile_initial = {}\\n if \\\"facility\\\" in request.GET:\\n facility = get_object_or_404(Facility, pk=request.GET['facility'])\\n profile_initial['facility'] = facility.pk\\n profile_form = ContactProfileForm(initial=profile_initial)\\n\\n return render_to_response(\\\"contact_create.html\\\", \\n { 'form': form, \\n 'profile_form': profile_form,\\n },\\n context_instance=RequestContext(request))\",\n \"def updateConference(self, request):\\n return self._updateConferenceObject(request)\",\n \"def form_valid(self, form):\\n print self.kwargs['pk']\\n self.object = form.save(commit=False)\\n self.object.contact = Contact.objects.get(pk=self.kwargs['pk'])\\n self.object.status = 'Abierto'\\n self.object.save()\\n user_full = UserFullName.objects.get(id=self.request.user.id)\\n notes = 'Hola tienes un nuevo caso asignado'\\n CaseTrack.objects.create(user_from=user_full, user_to=self.object.assigned, notes=notes, case=self.object)\\n return super(ModelFormMixin, self).form_valid(form)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7727503","0.6019656","0.5861441","0.57970494","0.57781494","0.57305723","0.5715702","0.56819946","0.5602228","0.5507136","0.5495653","0.5426627","0.5412202","0.53416955","0.5293119","0.5292693","0.5292534","0.5275663","0.52391994","0.52208537","0.52118427","0.52099943","0.5197609","0.51803076","0.5153143","0.50826246","0.5079311","0.5069185","0.5055291","0.5053549","0.5042321","0.5041396","0.50343317","0.502466","0.5024372","0.5000455","0.49943054","0.4993331","0.49657345","0.49630326","0.4952357","0.4948841","0.4945919","0.49397394","0.4910671","0.49102375","0.49032047","0.48951006","0.4878333","0.48731452","0.48580626","0.48574275","0.48572057","0.48468554","0.48354843","0.48215106","0.4818267","0.48147997","0.48143578","0.4805253","0.48050392","0.47841755","0.47699407","0.4764032","0.4753419","0.47387823","0.47383267","0.4738114","0.4738114","0.47356415","0.47230324","0.47060347","0.4704448","0.47026968","0.47013196","0.46993282","0.46955743","0.46888065","0.46847194","0.46786404","0.46720004","0.46704015","0.46689916","0.46671426","0.466404","0.46524248","0.46514365","0.46410212","0.46410212","0.46406564","0.46318135","0.4625765","0.4625324","0.4622096","0.46182266","0.46137735","0.4611123","0.46092615","0.46075758","0.4606719"],"string":"[\n \"0.7727503\",\n \"0.6019656\",\n \"0.5861441\",\n \"0.57970494\",\n \"0.57781494\",\n \"0.57305723\",\n \"0.5715702\",\n \"0.56819946\",\n \"0.5602228\",\n \"0.5507136\",\n \"0.5495653\",\n \"0.5426627\",\n \"0.5412202\",\n \"0.53416955\",\n \"0.5293119\",\n \"0.5292693\",\n \"0.5292534\",\n \"0.5275663\",\n \"0.52391994\",\n \"0.52208537\",\n \"0.52118427\",\n \"0.52099943\",\n \"0.5197609\",\n \"0.51803076\",\n \"0.5153143\",\n \"0.50826246\",\n \"0.5079311\",\n \"0.5069185\",\n \"0.5055291\",\n \"0.5053549\",\n \"0.5042321\",\n \"0.5041396\",\n \"0.50343317\",\n \"0.502466\",\n \"0.5024372\",\n \"0.5000455\",\n \"0.49943054\",\n \"0.4993331\",\n \"0.49657345\",\n \"0.49630326\",\n \"0.4952357\",\n \"0.4948841\",\n \"0.4945919\",\n \"0.49397394\",\n \"0.4910671\",\n \"0.49102375\",\n \"0.49032047\",\n \"0.48951006\",\n \"0.4878333\",\n \"0.48731452\",\n \"0.48580626\",\n \"0.48574275\",\n \"0.48572057\",\n \"0.48468554\",\n \"0.48354843\",\n \"0.48215106\",\n \"0.4818267\",\n \"0.48147997\",\n \"0.48143578\",\n \"0.4805253\",\n \"0.48050392\",\n \"0.47841755\",\n \"0.47699407\",\n \"0.4764032\",\n \"0.4753419\",\n \"0.47387823\",\n \"0.47383267\",\n \"0.4738114\",\n \"0.4738114\",\n \"0.47356415\",\n \"0.47230324\",\n \"0.47060347\",\n \"0.4704448\",\n \"0.47026968\",\n \"0.47013196\",\n \"0.46993282\",\n \"0.46955743\",\n \"0.46888065\",\n \"0.46847194\",\n \"0.46786404\",\n \"0.46720004\",\n \"0.46704015\",\n \"0.46689916\",\n \"0.46671426\",\n \"0.466404\",\n \"0.46524248\",\n \"0.46514365\",\n \"0.46410212\",\n \"0.46410212\",\n \"0.46406564\",\n \"0.46318135\",\n \"0.4625765\",\n \"0.4625324\",\n \"0.4622096\",\n \"0.46182266\",\n \"0.46137735\",\n \"0.4611123\",\n \"0.46092615\",\n \"0.46075758\",\n \"0.4606719\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77495456"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18495,"cells":{"query":{"kind":"string","value":"Create or update a conference, returning ConferenceForm/request."},"document":{"kind":"string","value":"def _createConferenceObject(self, request):\n # Preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n if not request.name:\n raise endpoints.BadRequestException(\n \"Conference 'name' field required\")\n # Copy ConferenceForm/ProtoRPC Message into dict\n data = {\n field.name: getattr(request, field.name) for field in\n request.all_fields()\n }\n del data['websafeKey']\n del data['organizerDisplayName']\n # Add default values for those missing (both data model and\n # outbound Message)\n for df in CONF_DEFAULTS:\n if data[df] in (None, []):\n data[df] = CONF_DEFAULTS[df]\n setattr(request, df, CONF_DEFAULTS[df])\n # Convert dates from strings to Date objects; set month based\n # on start_date\n if data['startDate']:\n data['startDate'] = datetime.strptime(\n data['startDate'][:10], \"%Y-%m-%d\").date()\n data['month'] = data['startDate'].month\n else:\n data['month'] = 0\n if data['endDate']:\n data['endDate'] = datetime.strptime(\n data['endDate'][:10], \"%Y-%m-%d\").date()\n # Set seatsAvailable to be same as maxAttendees on creation\n if data[\"maxAttendees\"] > 0:\n data[\"seatsAvailable\"] = data[\"maxAttendees\"]\n # Get the user profile key, then set the conference's parent\n # to that value.\n # NOTE: The original code made a call to allocate_ids in order to\n # generate an ID for the conference. Since the profiles utilize\n # strings (email addresses) for their IDs, resulting in no risk\n # of colliding with NDB's auto-generated numeric IDs, I decided\n # to let NDB generate the conference ID automatically.\n # https://cloud.google.com/appengine/docs/python/ndb/entities?hl=en#numeric_keys\n p_key = ndb.Key(Profile, user_id)\n data['parent'] = p_key\n data['organizerUserId'] = request.organizerUserId = user_id\n # Create Conference, send email to organizer confirming\n # creation of Conference and return (modified) ConferenceForm\n Conference(**data).put()\n taskqueue.add(params={'email': user.email(),\n 'conferenceInfo': repr(request)},\n url='/tasks/send_confirmation_email'\n )\n return request"},"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 _createConferenceObject(self, request):\n # preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n if not request.name:\n raise endpoints.BadRequestException(\"Conference 'name' field required\")\n\n # copy ConferenceForm/ProtoRPC Message into dict\n data = {field.name: getattr(request, field.name) for field in request.all_fields()}\n del data['websafeKey']\n del data['organizerDisplayName']\n\n # add default values for those missing (both data model & outbound Message)\n for df in DEFAULTS:\n if data[df] in (None, []):\n data[df] = DEFAULTS[df]\n setattr(request, df, DEFAULTS[df])\n\n # convert dates from strings to Date objects; set month based on start_date\n if data['startDate']:\n data['startDate'] = datetime.strptime(data['startDate'][:10], \"%Y-%m-%d\").date()\n data['month'] = data['startDate'].month\n else:\n data['month'] = 0\n if data['endDate']:\n data['endDate'] = datetime.strptime(data['endDate'][:10], \"%Y-%m-%d\").date()\n\n # set seatsAvailable to be same as maxAttendees on creation\n if data[\"maxAttendees\"] > 0:\n data[\"seatsAvailable\"] = data[\"maxAttendees\"]\n # generate Profile Key based on user ID and Conference\n # ID based on Profile key get Conference key from ID\n p_key = ndb.Key(Profile, user_id)\n c_id = Conference.allocate_ids(size=1, parent=p_key)[0]\n c_key = ndb.Key(Conference, c_id, parent=p_key)\n data['key'] = c_key\n data['organizerUserId'] = request.organizerUserId = user_id\n\n # create Conference, send email to organizer confirming\n # creation of Conference & return (modified) ConferenceForm\n Conference(**data).put()\n taskqueue.add(params={'email': user.email(),\n 'conferenceInfo': repr(request)},\n url='/tasks/send_confirmation_email'\n )\n return request","def updateConference(self, request):\n return self._updateConferenceObject(request)","def update_conference(self, request):\n return self.conference_service.update_conference_object(request)","def updateConference(self, request):\n return self._updateConferenceObject(request)","def get_conference(self, request):\n # get Conference object from request; bail if not found\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' %\n request.websafeConferenceKey)\n prof = conf.key.parent().get()\n # return ConferenceForm\n return self.conference_service.copy_conference_to_form(ConferenceForm(),\n conf,\n prof.displayName)","def getConference(self, request):\n # get Conference object from request; bail if not found\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' % request.websafeConferenceKey)\n prof = conf.key.parent().get()\n # return ConferenceForm\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))","def getConference(self, request):\n # Get Conference object from request; bail if not found\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\n 'Conference')\n prof = conf.key.parent().get()\n # Return ConferenceForm\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))","def _copyConferenceToForm(self, conf, displayName):\n cf = ConferenceForm()\n for field in cf.all_fields():\n if hasattr(conf, field.name):\n # convert Date to date string; just copy others\n if field.name.endswith('Date'):\n setattr(cf, field.name, str(getattr(conf, field.name)))\n else:\n setattr(cf, field.name, getattr(conf, field.name))\n elif field.name == \"websafeKey\":\n setattr(cf, field.name, conf.key.urlsafe())\n if displayName:\n setattr(cf, 'organizerDisplayName', displayName)\n cf.check_initialized()\n return cf","def _copyConferenceToForm(self, conf, displayName):\n cf = ConferenceForm()\n for field in cf.all_fields():\n if hasattr(conf, field.name):\n # Convert Date to date string; just copy others\n if field.name.endswith('Date'):\n setattr(cf, field.name, str(getattr(conf, field.name)))\n else:\n setattr(cf, field.name, getattr(conf, field.name))\n elif field.name == \"websafeKey\":\n setattr(cf, field.name, conf.key.urlsafe())\n if displayName:\n setattr(cf, 'organizerDisplayName', displayName)\n cf.check_initialized()\n return cf","def registerForConference(self, request):\n return self._conferenceRegistration(request)","def build_conference(self, data) -> dict:\n builder = ConferenceBuilder()\n builder.add_title(title=data.get(\"name\"), subtitle=data.get(\"subtitle\"))\n builder.set_short_description(value=data.get(\"description\", \"\"))\n builder.set_opening_date(get_value(data, \"dates[0]\"))\n builder.set_closing_date(get_value(data, \"dates[1]\"))\n builder.add_inspire_categories(data.get(\"field_of_interest\", []))\n builder.add_public_note(value=data.get(\"additional_info\", \"\"))\n builder.add_series(\n name=data.get(\"series_name\"), number=data.get(\"series_number\")\n )\n for address in data.get(\"addresses\"):\n builder.add_address(\n cities=[address.get(\"city\")],\n state=address.get(\"state\"),\n place_name=address.get(\"venue\"),\n country_code=country_name_to_code(address.get(\"country\")),\n )\n for contact in data.get(\"contacts\", []):\n builder.add_contact(**contact)\n for acr in data.get(\"acronyms\", []):\n builder.add_acronym(acr)\n for website in data.get(\"websites\", []):\n builder.add_url(website)\n for keyword in data.get(\"keywords\", []):\n builder.add_keyword(value=keyword)\n\n builder.record[\"$schema\"] = url_for(\n \"invenio_jsonschemas.get_schema\",\n schema_path=\"records/conferences.json\",\n _external=True,\n )\n\n return builder.record","def register_for_conference(self, request):\n return self.conference_registration(request)","def conference(self, sid):\r\n return conferences.Conference(self, sid)","def get_conferences_created(self, request):\n # make sure user is authed\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = self.auth.get_user_id(user)\n\n # create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(items=[\n self.conference_service.copy_conference_to_form(ConferenceForm(),\n conf,\n prof.displayName)\n for conf in confs])","def create_conference_session(self, request):\n user = endpoints.get_current_user()\n\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n\n self.session_service.create_conference_session(request, user)\n return request","def get_conferences_to_attend(self, request):\n prof = self.profile_service.get_profile_from_user() # get user Profile\n conf_keys = [ndb.Key(urlsafe=wsck) for wsck in\n prof.conferenceKeysToAttend]\n conferences = ndb.get_multi(conf_keys)\n\n # get organizers\n organisers = [ndb.Key(Profile, conf.organizerUserId) for conf in\n conferences]\n profiles = ndb.get_multi(organisers)\n\n # put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(items=[\n self.conference_service.copy_conference_to_form(\n ConferenceForm(), conf, names[conf.organizerUserId]\n ) for conf in conferences])","def getConferencesCreated(self, request):\n # make sure user is authed\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n # create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\n )","def post(self):\n json_data = request.get_json()\n json_data[\"sender_id\"] = current_user.id\n try:\n new_campaign = self.schema.load(json_data)\n except ValidationError as err:\n return {\"message\": err.messages}, HTTPStatus.BAD_REQUEST\n if Campaign.query.filter_by(mailchimp_id=new_campaign.mailchimp_id).first() is not None:\n return {\"message\": \"Campaign already exists.\"}, HTTPStatus.CONFLICT\n db.session.add(new_campaign)\n db.session.commit()\n return self.schema.dump(new_campaign), HTTPStatus.CREATED","def ConferenceRecord(self, default={}):\n return HEP.JSONReferenceObject(self.data.get('conference_record', default))","def test_create_form_update_existing(self):\n survey = self._create_test_survey()\n assert survey is not None\n\n survey = SurveyForm.create(self.test_survey_name, self.test_form_update, update_if_exists=True)\n assert survey is not None\n\n survey = SurveyForm.get(self.test_survey_name)\n assert survey is not None\n assert survey.form == self.test_form_update","def getConferencesCreated(self, request):\n # Make sure user is authenticated\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n # Create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # Return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(\n conf, getattr(prof, 'displayName')) for conf in confs\n ]\n )","def create_appointment():\n\n form = AppointmentForm()\n\n if form.validate_on_submit():\n\n appointment = Appointment(\n title = form.title.data,\n description = form.description.data,\n location = form.location.data,\n start = form.start.data,\n client = form.client.data,\n user = current_user\n )\n\n try:\n db.session.add(appointment)\n db.session.commit()\n\n flash('Successfully created the appointment.')\n\n return redirect(url_for('appointment.read_appointments'))\n except:\n flash('Error creating the appointment')\n\n return render_template('appointments/form.html.j2', form=form, title='Create appointment')","def join_conference(request, conference_name):\n resp = VoiceResponse()\n resp.dial(hangupOnStar=True).conference(conference_name)\n return HttpResponse(str(resp))","def _createSpeakerObject(self, request):\n # Preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n if not request.name:\n raise endpoints.BadRequestException(\n \"Speaker 'name' field required\")\n # Copy SpeakerForm/ProtoRPC Message into dict\n data = {\n field.name: getattr(request, field.name) for field in\n request.all_fields()\n }\n del data['websafeKey']\n # Add default values for those missing (both data model and\n # outbound Message)\n for df in SPEAKER_DEFAULTS:\n if data[df] in (None, []):\n data[df] = SPEAKER_DEFAULTS[df]\n # Create Speaker and return SpeakerForm\n speaker = Speaker(**data)\n speaker.put()\n return self._copySpeakerToForm(speaker)","def _createSessionObject(self, request):\n # Preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = user.email()\n # Get the conference entity\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\n 'Conference')\n # Ensure that the current user is the conference organizer\n if user_id != conf.organizerUserId:\n raise endpoints.UnauthorizedException(\n 'Only the conference organizer can create a new session')\n # Verify that the speaker exists\n speaker = _getEntityByWebsafeKey(request.websafeSpeakerKey, 'Speaker')\n # Ensure that the user submitted the required name property\n if not request.name:\n raise endpoints.BadRequestException(\n \"Session 'name' field required\")\n # Copy SessionForm/ProtoRPC Message into dict\n data = {\n field.name: getattr(request, field.name) for field in\n request.all_fields()\n }\n # Remove data that isn't destined for the Session entity\n del data['websafeConferenceKey']\n del data['websafeSpeakerKey']\n del data['websafeKey']\n # Add default values for those missing in the data model\n for df in SESSION_DEFAULTS:\n if data[df] in (None, []):\n data[df] = SESSION_DEFAULTS[df]\n # Ensure the string version of typeOfSession is what is stored\n # in the NDB model\n data['typeOfSession'] = str(data['typeOfSession'])\n # Convert date from string to Date object\n if data['date'] is not None:\n try:\n data['date'] = datetime.strptime(\n data['date'][:10], '%Y-%m-%d').date()\n except:\n raise endpoints.BadRequestException(\n \"Invalid 'date' value\")\n # Convert startTime from string to Time object\n if data['startTime'] is not None:\n try:\n data['startTime'] = datetime.strptime(\n data['startTime'], '%H:%M').time()\n except:\n raise endpoints.BadRequestException(\n \"Invalid 'startTime' value\")\n # Create Session\n session = Session(**data)\n session.conference = conf.key\n session.speaker = speaker.key\n session.put()\n # Add the session key to the speaker's sessions list\n speaker.sessions.append(session.key)\n speaker.put()\n # Add a task to task queue which checks if the speaker of this session\n # should be the new featured speaker\n taskqueue.add(params={'websafeSpeakerKey': request.websafeSpeakerKey,\n 'websafeConferenceKey': request.websafeConferenceKey},\n url='/tasks/update_featured_speaker'\n )\n # Return SessionForm object\n return self._copySessionToForm(session)","def conferences_choice(self):\n while self.choice != \"q\":\n self.choice = input(\"\\033[35m(c) for create \\n(m) for modify \\n\"\n \"(d) for delete \\n(s) for see \\n\"\n \"(q) for exit \\n(r) for return start page:\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n if self.choice == \"c\":\n title = input(\"\\033[33menter title:\\033[0m\")\n summary = input(\"\\033[33menter summary:\\033[0m\")\n date = input(\"\\033[33menter date:\\033[0m\")\n hour = input(\"\\033[33menter hour \\033[0m\")\n speaker = input(\"\\033[33menter speaker ID: \\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.create_conferences(title, summary, date, hour, speaker)\n\n\n if self.choice == \"m\":\n while column not in ['title', 'resume', 'date', 'hour', 'personid']:\n id = input(\"\\033[33menter confernces id :\\033[0m\")\n column = input(\n \"\\033[33mwhat do you want to modify \\n [title, resume, date, hour, personid] :\\033[0m\")\n datta = input(\"\\033[33menter new datta:\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.update_confernces(id, column, datta)\n\n\n if self.choice == \"d\":\n id = input(\"\\033[33menter conferences ID :\\033[0m\")\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n self.users.delete_conferences(id)\n\n\n if self.choice == \"s\":\n datta = self.conf.show_conferences()\n if datta:\n for elem in datta:\n print(elem)\n if self.choice == \"r\":\n os.system('cls' if os.name == 'nt' else 'clear')\n time.sleep(1)\n return\n if self.choice == \"q\":\n exit()","def update_c_mandatory_fields(request, campaign_id):\n # print(request.POST)\n campaign = Campaign.objects.get(id=campaign_id)\n form = CampaignForm(request.POST, instance = campaign)\n # print(form)\n if form.is_valid():\n form.save()\n return redirect('add_campaign_spec', id=campaign_id)\n else:\n # return redirect('clonecampaign', id=campaign_id)\n print(form.errors)\n return redirect(reverse('edit_campaign', kwargs={'campaign_id':campaign_id}))","def getConferencesToAttend(self, request):\n prof = self._getProfileFromUser() # get user Profile\n conf_keys = [\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\n ]\n conferences = ndb.get_multi(conf_keys)\n # Get organizers\n organisers = [\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\n ]\n profiles = ndb.get_multi(organisers)\n # Put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n # Return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\n for conf in conferences\n ]\n )","def create_event():\n event = None\n form = CreateEventForm()\n if form.validate_on_submit():\n venue = Venue.query.filter_by(address=form.address.data).first()\n if venue is None: # venue not already in db, need to add it\n venue_data = form.data\n venue_data[\"name\"] = venue_data[\"venue_name\"]\n venue_data[\"state\"] = CreateEventForm.convert_choice_to_value(form.state.data, \"STATES\")\n venue = Venue.create(**venue_data)\n event_type = EventType.query.get(form.event_type.data)\n event_category = EventCategory.query.get(form.category.data)\n start_time = CreateEventForm.convert_choice_to_value(form.start_time.data, \"TIMES\")\n end_time = CreateEventForm.convert_choice_to_value(form.end_time.data, \"TIMES\")\n event = Event(\n title=form.title.data,\n start_datetime=datetime.combine(form.start_date.data, start_time),\n end_datetime=datetime.combine(form.end_date.data, end_time),\n venue=venue,\n event_type=event_type,\n event_category=event_category,\n user=current_user._get_current_object(),\n )\n db.session.commit()\n return redirect(url_for(\"events.event_details\", id=event.id))\n return render_template(\"events/create_event.html\", form=form, event=event)","def _conferenceRegistration(self, request, reg=True):\n retval = None\n # Get user profile\n prof = self._getProfileFromUser()\n # Check if conference given in the websafeConferenceKey exists\n wsck = request.websafeConferenceKey\n conf = _getEntityByWebsafeKey(wsck, 'Conference')\n # Register\n if reg:\n # Check if user already registered, otherwise add\n if wsck in prof.conferenceKeysToAttend:\n raise ConflictException(\n \"You have already registered for this conference.\")\n # Check if seats available\n if conf.seatsAvailable <= 0:\n raise ConflictException(\n \"There are no seats available.\")\n # Register user, deduct one seat\n prof.conferenceKeysToAttend.append(wsck)\n conf.seatsAvailable -= 1\n retval = True\n # Unregister\n else:\n # Check if user already registered\n if wsck in prof.conferenceKeysToAttend:\n # Unregister user, add back one seat\n prof.conferenceKeysToAttend.remove(wsck)\n conf.seatsAvailable += 1\n retval = True\n else:\n retval = False\n # Update the datastore and return\n prof.put()\n conf.put()\n return BooleanMessage(data=retval)","def create_calendar(request):\n if request.method == 'POST':\n\n form = CalendarForm(request.POST)\n \n if form.is_valid():\n calendar = form.save(commit=False) # prvent form from saving since we need to link company\n calendar.company = request.user.company\n calendar.save()\n return redirect('appointment:calendar_list')\n else:\n form = CalendarForm()\n return render(request, 'calendar_form.html', {'form': form})","def participant_create(request):\n if request.method == 'POST':\n serializer = ParticipantSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n participant = get_object_or_404(Participant, email=serializer.data['email'])\n serializer = ParticipantSerializer(participant, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\n else:\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_OR_BAD_REQUEST}\n return Response(content, status=status.HTTP_400_BAD_REQUEST)","def _copySpeakerToForm(self, speaker):\n sf = SpeakerForm()\n for field in sf.all_fields():\n if hasattr(speaker, field.name):\n setattr(sf, field.name, getattr(speaker, field.name))\n elif field.name == \"websafeKey\":\n setattr(sf, field.name, speaker.key.urlsafe())\n sf.check_initialized()\n return sf","def conference_registration(self, request, reg=True):\n prof = self.profile_service.get_profile_from_user() # get user Profile\n\n # check if conf exists given websafeConfKey\n # get conference; check that it exists\n wsck = request.websafeConferenceKey\n conf = ndb.Key(urlsafe=wsck).get()\n if not conf:\n raise endpoints.NotFoundException(\n 'No conference found with key: %s' % wsck)\n\n # register\n if reg:\n # check if user already registered otherwise add\n if wsck in prof.conferenceKeysToAttend:\n raise ConflictException(\n \"You have already registered for this conference\")\n\n # check if seats avail\n if conf.seatsAvailable <= 0:\n raise ConflictException(\"There are no seats available.\")\n\n # register user, take away one seat\n prof.conferenceKeysToAttend.append(wsck)\n conf.seatsAvailable -= 1\n retval = True\n\n # unregister\n else:\n # check if user already registered\n if wsck in prof.conferenceKeysToAttend:\n\n # unregister user, add back one seat\n prof.conferenceKeysToAttend.remove(wsck)\n conf.seatsAvailable += 1\n retval = True\n else:\n retval = False\n\n # write things back to the datastore & return\n prof.put()\n conf.put()\n return BooleanMessage(data=retval)","def contact_create(request):\n\n if request.method == \"POST\":\n form = ContactForm(request.POST)\n profile_form = ContactProfileForm(request.POST)\n\n if form.is_valid() and profile_form.is_valid():\n contact = form.save()\n\n # Populate the required 'contact' field before saving\n profile = profile_form.save(commit=False)\n profile.contact = contact\n profile.save()\n\n messages.success(request, _(\"The contact %(name)s was successfully created\") % \n {'name': unicode(contact)})\n return HttpResponseRedirect(reverse(\"moderation.views.contact\", args=(contact.pk,)))\n else:\n form_initial = {'phone_number': request.GET.get('phone_number', '')}\n form = ContactForm(initial=form_initial)\n\n profile_initial = {}\n if \"facility\" in request.GET:\n facility = get_object_or_404(Facility, pk=request.GET['facility'])\n profile_initial['facility'] = facility.pk\n profile_form = ContactProfileForm(initial=profile_initial)\n\n return render_to_response(\"contact_create.html\", \n { 'form': form, \n 'profile_form': profile_form,\n },\n context_instance=RequestContext(request))","def event_register_participant(request, event_id, participant_id):\n if request.method == 'PUT':\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\n participant = get_object_or_404(Participant, pk=participant_id)\n try:\n attendance = Attendance(participant=participant, event=event, datetime_register=datetime.now())\n attendance.save()\n except Exception as e:\n print e\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_TO_EVENT}\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\n serializer = AttendanceSerializer(attendance)\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def create(self, *args, **kwargs):\n\n talk = kwargs.get('talk', None)\n comment = kwargs.get('comment', None)\n\n if talk is None and comment is None:\n raise ValidationError('Either talk or comment should not be null.')\n elif talk is not None and comment is not None:\n raise ValidationError('Either talk or comment should be null.')\n\n return super().create(*args, **kwargs)","def post(self):\n try:\n new_form = FORM_SCHEMA.load(request.json).data\n except ValidationError as err:\n APP.logger.error(err.args)\n return err.messages, status.HTTP_400_BAD_REQUEST\n\n add_new_form = Form(**new_form)\n DB.session.add(add_new_form)\n\n try:\n DB.session.commit()\n except IntegrityError as err:\n APP.logger.error(err.args)\n DB.session.rollback()\n return {'error': 'Already exists.'}, status.HTTP_400_BAD_REQUEST\n return Response(status=status.HTTP_201_CREATED)","def put():\n title = request.form[\"title\"]\n description = request.form[\"description\"]\n is_valid = request.form[\"is_valid\"]\n company_id = request.form[\"company_id\"]\n city_id = request.form[\"city_id\"]\n start_date = request.form[\"start_date\"]\n add_date = request.form[\"add_date\"]\n sector = request.form[\"sector\"]\n contract_type_id = request.form[\"contract_type_id\"]\n experience = request.form[\"experience\"]\n formation = request.form[\"formation\"]\n advertisement_id = request.form[\"advertisement_id\"]\n elementFromDB = Advertisements().get_one_element(request.form[\"advertisement_id\"])\n if elementFromDB is None:\n return abort(500, \"L'élément n'existe pas.\")\n else:\n try:\n elements = Advertisements().patch( title, description, is_valid, company_id, city_id, start_date, add_date, sector, contract_type_id, experience, formation, advertisement_id )\n return elements\n except Exception as identifier:\n return abort(500, identifier)","def create_patient_appointment():\n if request.method == 'POST':\n patient_email = request.form['patient_email']\n doctor_email = request.form['doctor_email']\n date = request.form['date']\n time = request.form['time']\n\n response = requests.post(server_url + 'patient/create_appointment', json={\n 'patient_email': patient_email,\n 'doctor_email': doctor_email,\n 'date': date,\n 'time': time\n })\n\n response = response.json()\n\n if response.get('Status') == \"DOCTOR_HAS_AN_APPOINTMENT_SELECTED_TIME_SLOT\":\n return render_template('patients/appointment_failed.html')\n elif response.get('Status') == \"DOCTOR_IS_NOT_AVAILABLE_AT_THAT_TIME\":\n return render_template('patients/appointment_failed.html')\n elif response.get('Status') == \"INVALID_PATIENT_EMAIL\":\n return render_template('patients/appointment_failed.html')\n elif response.get('Status') == \"INVALID_DOCTOR_EMAIL\":\n return render_template('patients/appointment_failed.html')\n else:\n referer = request.referrer\n return redirect(referer, code=302)\n else:\n return render_template('patients/dashboard.html')","def is_in_conference(self) -> bool:","def create_appointment_form(request, post):\n # string_date = \"{0}-{1}-{2}\".format(year, month, day)\n # date = datetime.datetime.strptime(string_date, '%Y-%m-%d').date()\n new_appointment = None\n date_string = post.get(\"date\") + \"-\" + post.get(\"time\")\n date = datetime.datetime.strptime(date_string, '%Y-%m-%d-%H:%M')\n the_user = request.user\n notes = post.get(\"notes\")\n\n if the_user.userprofile.is_doctor():\n patient_id = int(post.get(\"patient\", the_user.pk))\n patient = User.objects.get(pk=patient_id)\n doctor = User.objects.get(pk=the_user.id)\n new_appointment = Appointment.objects.create(date=date, doctor=doctor, patient=patient, notes=notes)\n\n elif request.user.userprofile.is_patient():\n doctor_id = int(post.get(\"doctor\", the_user.pk))\n doctor = User.objects.get(pk=doctor_id)\n patient = User.objects.get(pk=the_user.id)\n new_appointment = Appointment.objects.create(date=date, doctor=doctor, patient=patient, notes=notes)\n\n return new_appointment","def post(self):\n data = request.json\n return new_subscription(data=data)","def add_event(request, owner_type, owner_id):\n\n # Like before, get the request's context.\n context = RequestContext(request)\n event_added = False\n\n user = request.user\n # If it's a HTTP POST, we're interested in processing form data.\n if request.method == 'POST':\n verified_obj = verified_calendar(context, owner_type, owner_id, user)\n if not isinstance(verified_obj, HttpResponse):\n calendar, edit_priv = verified_obj\n else:\n return verified_obj\n\n # Attempt to grab information from the raw form information.\n event_form = EventForm(data=request.POST)\n if event_form.is_valid():\n # Save the event's form data to the database.\n event = event_form.save(commit=False)\n event.cal = calendar\n event.creator = user\n\n event.save()\n\n event_added = True\n\n #notify the subscribers\n created_event.send(sender=None, owner_type=owner_type, owner_id=owner_id,\n event=event, user=user)\n\n\n # Invalid form or forms - mistakes or something else?\n # Print problems to the terminal.\n # They'll also be shown to the user.\n else:\n print event_form.errors\n\n # Not a HTTP POST, so we render our form using the EventForm.\n # These forms will be blank, ready for user input.\n else:\n event_form = EventForm()\n\n # Render the template depending on the context.\n return render_to_response(\n 'scheduler/add_event.html', {'event_form': event_form, 'user' : user,\n 'event_added': event_added},\n context)","def newConferenceCitation(self, **attrlinks):\n return ConferenceCitation(self, **attrlinks)","def create(self, validated_data):\n if (AdoptionRequest.objects\n .filter(adoption_proposal=validated_data['adoption_proposal'])\n .exists()):\n return (AdoptionRequest.objects\n .get(adoption_proposal=validated_data['adoption_proposal']))\n else:\n adoption_request = AdoptionRequest.objects.create(**validated_data)\n person_devices = find_devices(adoption_request.adoption_proposal\n .owner.id)\n title = 'SOSFIDO'\n message = ('Se ha enviado una petición a tu propuesta de ' +\n 'adopción de tu mascota ' +\n adoption_request.adoption_proposal.pet_name)\n data_notification = {'proposal_id': adoption_request\n .adoption_proposal.id}\n image_notification = 'https://s3.amazonaws.com/uploads.hipchat.' + \\\n 'com/529035/4489842/wDKOTjEIqyr1pPX/' + \\\n 'logo_sosfido1.png'\n send_notification_mobile(person_devices, title, message,\n data_notification, image_notification)\n return adoption_request","async def get_conference_by_id(self, session_id):\n db = self['db_engine']\n async with db.acquire() as connection:\n query = select([Conference]).where(Conference.session_id == session_id).where(\n Conference.expired_at > datetime.now(timezone.utc))\n if conference := await(await connection.execute(query)).first():\n result = dict(conference)\n query = select([User.email, User.display_name]).where(User.id == conference.user_id)\n if user := await(await connection.execute(query)).first():\n result['user_data'] = UserData(conference.user_id, *user)\n return result","def updateAgenda():\n data = request.json\n if \"agenda_id\" in data and \"new_agenda\" in data:\n responseWrapper: ResponseWrapper = connectMongo.updateAgenda(data.get(\"agenda_id\"), data.get(\"new_agenda\"))\n if not responseWrapper.found:\n return jsonify(response=404, msg=\"Agenda not found\")\n return jsonify(response=200, agenda=responseWrapper.object.makeJson())\n else:\n return jsonify(response=400, msg=\"you didn't sent all the necessary information\")","def update_appointment(request,pk):\n appointment = AppointmentRequests.objects.get(id=pk)\n form = AppointmentUpdate(instance=appointment)\n if request.method == \"POST\":\n form = AppointmentUpdate(request.POST,instance=appointment)\n if form.is_valid():\n form.save()\n return redirect(\"dashboard\")\n else:\n messages.info(request,\"Invalid Data sent, Make sure you provided right data.\")\n return redirect(\"update_appointment\",pk=pk)\n else:\n return render(request,\"update_appointment.html\",{\"form\":form})","def put(self, patient_id):\n args = self.parser.parse_args()\n\n # Convert data_time and validate\n date_of_birth = utils.str2date(args.date_of_birth)\n if date_of_birth is None:\n abort()\n\n return_id = None\n with open_session() as session:\n record = Patient(args.first_name,\n args.last_name,\n args.gender,\n date_of_birth)\n\n session.add(record)\n session.flush()\n session.commit()\n\n return_id = record.id\n\n # Check for error handling\n if return_id is None:\n return gen_response(\"internal server error\")\n else:\n return gen_response({\"id\": return_id})","def update(self, request, hash_key, phone_number, partial, format=None):\n video = get_object_or_404(Video, hash_key=hash_key)\n \n try:\n user = User.objects.get_user_by_number(phone_number)\n except ValidationError as err:\n raise exceptions.ValidationError({'phone_number': err.messages})\n \n try:\n association = VideoUsers.objects.select_related(\n 'user', 'video').get(user=user, video=video)\n except VideoUsers.DoesNotExist:\n association = None\n \n serializer = self.get_serializer(association, data=request.data,\n partial=partial)\n serializer.is_valid(raise_exception=True)\n \n if association is None:\n # Create association object here with any properties in\n # serializer, such as\n # permission = serializer.data.get('permission')\n association = VideoUsers.objects.create(user=user, video=video)\n serializer = self.get_serializer(association)\n # log creation \n activity_send(request.user, verb='invite', \n object=user, target=video)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n association = serializer.save(force_update=True)\n return Response(serializer.data, status=status.HTTP_200_OK)","def insert_talk(request):\n try:\n is_loggedin, username = get_session_variables(request)\n # User is not logged in\n if not logged_in(request):\n return HttpResponseRedirect('/register/login')\n\n # User is logged in\n else:\n if request.method == 'POST':\n form = AddSpeakerForm(request.POST)\n\n # Invalid form imput\n if not form.is_valid():\n error = \"Invalid inputs\"\n return render_to_response('achievement/new_speaker.html', \\\n {'form':form, \\\n 'error':error, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n\n # Form is valid\n else:\n # Get the new achievement_id\n achievement_id = get_achievement_id(request)\t\n achievement_type = \"Speaker\"\n\n # Saving inputs\n achievement_obj = Achievement(achievement_id, \\\n achievement_type, \\\n username)\n achievement_obj.save()\n contribution_obj = form.save(commit = False)\n contribution_obj.achievement_id = achievement_obj\n contribution_obj.achieve_typ = achievement_type\n user_obj = get_object_or_404(User_info, username = username)\n contribution_obj.username = user_obj\n contribution_obj.save()\n return render_to_response('achievement/success.html', \\\n {'achievement_type':achievement_type, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n # Method is not POST\n else:\n return render_to_response('achievement/new_speaker.html', \\\n {'form': AddSpeakerForm, \\\n 'is_loggedin':is_loggedin, \\\n 'username':username}, \\\n RequestContext(request))\n except KeyError:\n return error_key(request)","def form_valid(self, form, cruiseday_form, participant_form, document_form, equipment_form, invoice_form):\r\n\r\n\t\tis_submitting = False\r\n\r\n\t\told_cruise = get_object_or_404(Cruise, pk=self.kwargs.get('pk'))\r\n\t\told_cruise_days_string = str(old_cruise.get_cruise_days())\r\n\r\n\t\tnew_cruise = form.save(commit=False)\r\n\t\tnew_cruise.information_approved = False\r\n\r\n\t\tself.object = form.save()\r\n\t\tcruiseday_form.instance = self.object\r\n\t\tcruiseday_form.save()\r\n\t\tparticipant_form.instance = self.object\r\n\t\tparticipant_form.save()\r\n\t\tdocument_form.instance = self.object\r\n\t\tdocument_form.save()\r\n\t\tequipment_form.instance = self.object\r\n\t\tequipment_form.save()\r\n\t\tinvoice_form.instance = self.object\r\n\t\tinvoice_form.save()\r\n\r\n\t\tnew_cruise.leader = self.request.user\r\n\t\ttry:\r\n\t\t\tnew_cruise.organization = new_cruise.leader.userdata.organization\r\n\t\texcept:\r\n\t\t\tpass\r\n\t\tform.cleaned_data[\"leader\"] = self.request.user\r\n\r\n\t\tnew_cruise.outdate_missing_information()\r\n\r\n\t\tif new_cruise.is_submitted and old_cruise_days_string != str(new_cruise.get_cruise_days()):\r\n\t\t\tnew_cruise.is_approved = False\r\n\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise ' + str(Cruise) + ' updated. Your cruise days were modified, so your cruise is now pending approval. You may track its approval status on the \"<a href=\"/user/cruises/submitted/\">Submitted Cruises</a>\" page.'))\r\n\t\t\tdelete_cruise_deadline_and_departure_notifications(new_cruise)\r\n\t\t\tset_date_dict_outdated()\r\n\t\telif new_cruise.is_submitted and old_cruise.information_approved:\r\n\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise ' + str(Cruise) + ' updated. Your cruise information was modified, so your cruise\\'s information is now pending approval. You may track its approval status on the \"<a href=\"/user/cruises/upcoming/\">Upcoming Cruises</a>\" page.'))\r\n\t\t\tdelete_cruise_departure_notifications(new_cruise)\r\n\r\n\t\t# check whether we're saving or submitting the form\r\n\t\telif hasattr(self, \"request\") and self.request.POST.get(\"save_cruise\"):\r\n\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully saved. You may edit and submit it on the \"<a href=\"/user/cruises/unsubmitted/\">Unsubmitted Cruises</a>\" page.'))\r\n\t\telif hasattr(self, \"request\") and self.request.POST.get(\"submit_cruise\"):\r\n\t\t\tis_submitting = True\r\n\r\n\t\t\tcruise_days = cruiseday_form.cleaned_data\r\n\t\t\tcruise_participants = participant_form.cleaned_data\r\n\t\t\tcruise_invoice = invoice_form.cleaned_data\r\n\r\n\t\t\tif (new_cruise.is_submittable(user=self.request.user, cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)):\r\n\t\t\t\tnew_cruise.is_submitted = True\r\n\t\t\t\tnew_cruise.submit_date = timezone.now()\r\n\t\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully submitted. You may track its approval status on the \"<a href=\"/user/cruises/submitted/\">Submitted Cruises</a>\" page.'))\r\n\t\t\telse:\r\n\t\t\t\tnew_cruise.is_submitted = False\r\n\t\t\t\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted:' + str(new_cruise.get_missing_information_string(cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)) + '<br>If you decide to do this later, you can get back to this cruise to review and add any missing or invalid information on the \"<a href=\"/user/cruises/unsubmitted/\">Unsubmitted Cruises</a>\" page.'))\r\n\t\telse:\r\n\t\t\tmessages.add_message(self.request, messages.ERROR, mark_safe('We were unable to determine the action you wished to take when saving the form. If you wished to send it in for approval and not just save it, please submit it below.'))\r\n\r\n\t\tnew_cruise.save()\r\n\r\n\t\tif old_cruise.information_approved:\r\n\t\t\tadmin_user_emails = [admin_user.email for admin_user in list(User.objects.filter(userdata__role='admin'))]\r\n\t\t\tsend_template_only_email(admin_user_emails, EmailTemplate.objects.get(title='Approved cruise updated'), cruise=old_cruise)\r\n\r\n\t\treturn HttpResponseRedirect(self.get_success_url(is_submitting, new_cruise))","def form_valid(self, form, cruiseday_form, participant_form, document_form, equipment_form, invoice_form):\r\n\r\n\t\tis_submitting = False\r\n\r\n\t\tCruise = form.save(commit=False)\r\n\t\t\r\n\t\tCruise.leader = self.request.user\r\n\t\ttry:\r\n\t\t\tCruise.organization = Cruise.leader.userdata.organization\r\n\t\texcept:\r\n\t\t\tpass\r\n\t\tform.cleaned_data[\"leader\"] = self.request.user\r\n\r\n\t\tif hasattr(self, \"request\"):\r\n\t\t\t# check whether we're saving or submitting the form\r\n\t\t\tif self.request.POST.get(\"save_cruise\"):\r\n\t\t\t\tCruise.is_submitted = False\r\n\t\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully saved. You may edit and submit it on the \"<a href=\"/user/cruises/unsubmitted/\">Unsubmitted Cruises</a>\" page.'))\r\n\t\t\telif self.request.POST.get(\"submit_cruise\"):\r\n\t\t\t\tis_submitting = True\r\n\r\n\t\t\t\tcruiseday_form = CruiseDayFormSet(self.request.POST)\r\n\t\t\t\tparticipant_form = ParticipantFormSet(self.request.POST)\r\n\t\t\t\tcruise_days = cruiseday_form.cleaned_data\r\n\t\t\t\tcruise_participants = participant_form.cleaned_data\r\n\t\t\t\tcruise_invoice = invoice_form.cleaned_data\r\n\t\t\t\tif (Cruise.is_submittable(user=self.request.user, cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)):\r\n\t\t\t\t\tCruise.is_submitted = True\r\n\t\t\t\t\tCruise.submit_date = timezone.now()\r\n\t\t\t\t\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully submitted. You may track its approval status on the \"<a href=\"/user/cruises/submitted/\">Submitted Cruises</a>\" page.'))\r\n\t\t\t\telse:\r\n\t\t\t\t\tCruise.is_submitted = False\r\n\t\t\t\t\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted:' + str(Cruise.get_missing_information_string(cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)) + '<br>If you decide to do this later, you can get back to this cruise to review and add any missing or invalid information on the \"<a href=\"/user/cruises/unsubmitted/\">Unsubmitted Cruises</a>\" page.'))\r\n\t\t\telse:\r\n\t\t\t\tCruise.is_submitted = False\r\n\t\t\t\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted: We were unable to determine the action you wished to take on submit. Please try to submit again below.'))\r\n\r\n\t\tCruise.save()\r\n\t\tself.object = form.save()\r\n\t\tcruiseday_form.instance = self.object\r\n\t\tcruiseday_form.save()\r\n\t\tparticipant_form.instance = self.object\r\n\t\tparticipant_form.save()\r\n\t\tdocument_form.instance = self.object\r\n\t\tdocument_form.save()\r\n\t\tequipment_form.instance = self.object\r\n\t\tequipment_form.save()\r\n\t\tinvoice_form.instance = self.object\r\n\t\tinvoice_form.save()\r\n\r\n\t\treturn HttpResponseRedirect(self.get_success_url(is_submitting, Cruise))","def conferences(self):\r\n return conferences.Conferences(self)","def profileupdaterequest_create(request):\n form = ProfileUpdateRequestForm()\n form_helper = bootstrap_helper_wider_labels\n page_title = 'Update Instructor Profile'\n\n if request.method == 'POST':\n form = ProfileUpdateRequestForm(request.POST)\n\n if form.is_valid():\n form.save()\n\n # TODO: email notification?\n\n context = {\n 'title': 'Thank you for updating your instructor profile',\n }\n return render(request,\n 'forms/profileupdate_confirm.html',\n context)\n else:\n messages.error(request, 'Fix errors below.')\n\n context = {\n 'title': page_title,\n 'form': form,\n 'form_helper': form_helper,\n }\n return render(request, 'forms/profileupdate.html', context)","def post(self):\r\n data = request.form\r\n return create(data=data)","def create_call_appointment_on_call_schedule_accept(sender, instance, **kwargs):\n try:\n obj = sender.objects.get(pk=instance.pk)\n if obj.completed != instance.completed:\n if instance.completed:\n CallAppointment.objects.create(\n time_slot=instance.confirmed_time\n )\n except ObjectDoesNotExist:\n pass","def put_record(uuid):\n\n record = collection[uuid]\n record.data.update(request.json or request.form.to_dict())\n record.save()\n\n return get_record(uuid)","def save():\n form_data = request.form.to_dict()\n if (form_data['release-at'] == 'Never'):\n form_data['release-at'] = None\n\n if not 'id' in form_data:\n r = requests.post(API_ROUTE, headers={'Auth': _auth()}, json=form_data)\n if r.status_code != requests.codes.created:\n return r.text, r.status_code\n else:\n r = requests.put(API_ROUTE + '/' + str(request.form['id']), headers={'Auth': _auth()}, json=form_data)\n if r.status_code != requests.codes.ok:\n return r.text, r.status_code\n\n return redirect(url_for('index'), code=278)","def create(self, request, *args, **kwargs):\n self.serializer_class = ConversationDetailSerializer\n return super(ConversationViewSet, self).create(request, *args, **kwargs)","def update(self, conversation):\n self.content_type = \"application/json\"\n self.method = \"PATCH\"\n entity = Conversation(json.loads(self.send(conversation).content))\n self._initialize_collection_properties(entity)\n return entity","def new_capteur_saving():\n f = None\n f = CapteurForm()\n if f.validate_on_submit():\n o = Capteur(\n name = f.get_name(),\n intervalle = f.get_interval(),\n tel = f.get_phoneNumber(),\n TypeMesure = f.get_typeMesure().get_id(),\n parterre = f.get_parterre().get_id())\n a = Actions(\n contenu = \"Ajout d'un nouveau capteur \"+f.get_name(),\n liste = 1\n )\n db.session.add(a)\n db.session.add(o)\n db.session.commit()\n return redirect(url_for('capteur_info', id = o.get_id()))\n return render_template(\n \"addCapteur.html\",\n form = f,\n title = \"Nouveau capteur\",\n param = \"create\")","def save(self):\n\n if self.object_id:\n # update event\n if not self._track_changes:\n return True # there's nothing to update\n url = self.build_url(\n self._endpoints.get('event').format(id=self.object_id))\n method = self.con.patch\n data = self.to_api_data(restrict_keys=self._track_changes)\n else:\n # new event\n if self.calendar_id:\n url = self.build_url(\n self._endpoints.get('event_calendar').format(\n id=self.calendar_id))\n else:\n url = self.build_url(self._endpoints.get('event_default'))\n method = self.con.post\n data = self.to_api_data()\n\n response = method(url, data=data)\n if not response:\n return False\n\n self._track_changes.clear() # clear the tracked changes\n\n if not self.object_id:\n # new event\n event = response.json()\n\n self.object_id = event.get(self._cc('id'), None)\n\n self.__created = event.get(self._cc('createdDateTime'), None)\n self.__modified = event.get(self._cc('lastModifiedDateTime'), None)\n\n self.__created = parse(self.__created).astimezone(\n self.protocol.timezone) if self.__created else None\n self.__modified = parse(self.__modified).astimezone(\n self.protocol.timezone) if self.__modified else None\n\n self.ical_uid = event.get(self._cc('iCalUId'), None)\n else:\n self.__modified = self.protocol.timezone.localize(dt.datetime.now())\n\n return True","def new():\n data = {}\n bai = request.values.get('bai', u\"\", type=unicode)\n if bai:\n # Add BAI information to form in order to keep connection between\n # a HEPName and an author profile.\n data[\"bai\"] = bai\n\n form = AuthorUpdateForm(data=data)\n ctx = {\n \"action\": url_for('.submitnew'),\n \"name\": \"authorUpdateForm\",\n \"id\": \"authorUpdateForm\",\n }\n\n return render_template('authors/forms/new_form.html', form=form, **ctx)","def delete_conference(request, pk):\n conference = ConferenceLog.objects.get(pk=pk)\n conference.delete()\n return redirect('readapi:index')","def _register_new_attendee(request, course_id):\n\n form = RegistrationForm(request.POST)\n is_test = (request.GET.get(\"env\") == settings.TEST_KEY)\n\n # Validate the form: the captcha field will automatically\n # check the input\n if not form.is_valid():\n # return defaults.bad_request(request,\n # SuspiciousOperation(\"Form not valid\"))\n pass\n\n name = request.POST[\"name\"]\n email = request.POST[\"email_attendee\"]\n attendee = None\n course_attendee = None\n course_event = get_object_or_404(CourseEvent, pk=course_id)\n gdpr = False\n marketing = False\n student = False\n amount = 0\n\n if course_event.course_type.level is not None:\n level = list(filter(lambda c: c[0] == course_event.course_type.level,\n CourseType.level_choices))[0][1]\n else:\n level = None\n\n if \"gdpr\" in request.POST and request.POST[\"gdpr\"] == \"on\":\n gdpr = True\n\n if \"marketing\" in request.POST and request.POST[\"marketing\"] == \"on\":\n marketing = True\n\n if \"student\" in request.POST and request.POST[\"student\"] == \"on\":\n student = True\n else:\n student = False\n\n existing_attendees = course_event.courseattendee_set.filter(\n attendee__email=email\n )\n\n if level is not None:\n title = \"{} - {}\".format(course_event.course_type.title, level)\n else:\n title = course_event.course_type.title\n\n if len(existing_attendees) > 0:\n context = {\n \"name\": existing_attendees[0].attendee.name,\n \"email\": existing_attendees[0].attendee.email,\n \"title\": title,\n \"google_tag\": settings.GOOGLE_TAG,\n }\n return render(request, \"already_registered.html\", context)\n\n # save attendee details only if attendee was registered\n attendee = None\n try:\n attendee = Attendee.objects.get(email=email)\n _update_attendee_by_email(email, marketing, gdpr, name)\n except ObjectDoesNotExist as e:\n attendee = _create_new_attende(name, email, gdpr, marketing)\n\n amount = 0\n\n if datetime.date.today() <= course_event.early_date:\n if student:\n amount = course_event.price_student\n else:\n amount = course_event.price_regular\n else:\n amount = course_event.price_late\n\n course_attendee = CourseAttendee(\n attendee=attendee,\n course=course_event,\n student=student,\n registration_date=datetime.date.today(),\n level=request.POST[\"level\"],\n note=request.POST[\"note\"],\n topics=request.POST[\"topics\"],\n next_topics=request.POST[\"next_topics\"],\n attended=False,\n amount=amount,\n token=uuid.uuid1()\n )\n\n attendee.courses.add(course_event)\n\n organisation = request.POST[\"organisation\"]\n if not organisation:\n organisation = attendee.name\n invoicemail = request.POST[\"invoicemail\"]\n if not invoicemail:\n invoicemail = request.POST[\"email_attendee\"]\n\n order = request.POST[\"order\"]\n invoice_detail = None\n if order:\n invoice_details = InvoiceDetail.objects.filter(\n order=order,\n name=organisation\n )\n if len(invoice_details) > 0:\n invoice_detail = invoice_details[0]\n\n if not invoice_detail:\n invoice_detail = InvoiceDetail(\n address=\"{street}\\n{zipcode} {city}\".format(\n street=request.POST[\"street\"],\n zipcode=request.POST[\"zip_code\"],\n city=request.POST[\"city\"]),\n name=organisation,\n ico=request.POST[\"ico\"],\n dic=request.POST[\"dic\"],\n order=request.POST[\"order\"],\n email=invoicemail\n )\n\n invoice_detail.save()\n course_attendee.invoice_detail = invoice_detail\n course_attendee.save()\n\n _send_mails(course_event, attendee, title, organisation, amount, is_test)\n\n context = {\n \"course_name\": title,\n \"course_date\": course_event.date,\n \"attendee\": attendee.name,\n \"mail\": attendee.email,\n \"course_id\": course_event.id,\n \"google_tag\": settings.GOOGLE_TAG,\n }\n\n return render(request, \"submitted.html\", context)","def query_form_data(self) -> FlaskResponse: # pylint: disable=no-self-use\n form_data = {}\n slice_id = request.args.get(\"slice_id\")\n if slice_id:\n slc = db.session.query(Slice).filter_by(id=slice_id).one_or_none()\n if slc:\n form_data = slc.form_data.copy()\n\n update_time_range(form_data)\n\n return json.dumps(form_data)","def event_create(req):\n try:\n utils.assert_keys(req.form, ['creator_id']+_event_args)\n event_id = db_conn.event_new(**req.form)\n json = {'event_id': event_id}\n except Exception as e:\n json = {'errors': [str(e)]}\n return req.Response(json=json)","def create_venue_form():\n form = VenueForm()\n return render_template('forms/new_venue.html', form=form)","def add_profile(self, request, *args, **kwargs):\n # Todo (mo): utilize self.get_serializer(instance=conversation, data=request.data)\n context = {\n 'conversation': self.get_object(),\n 'request': request\n }\n serializer = AddProfileSerializer(data=request.data, context=context)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def create_new_schedule():\n\n # collect all relevant information from form\n user_id = int(session['user_id'])\n user = User.query.filter_by(user_id=int(session['user_id'])).one()\n contact_form_value = request.form.get('contact_id')\n start_date_unicode = request.form.get('start_date')\n period = int(request.form.get('period'))\n\n # extracts email from contact_form_value string using re library\n contact_email = contact_form_value.partition('<')[-1].rpartition('>')[0]\n\n # pull contact from database\n contact = Contact.query.filter_by(email=contact_email).one()\n contact_id = contact.contact_id\n\n # turns start_date into datetime object using dateutil library\n start_date = parser.parse(start_date_unicode)\n\n # calculates send_date from start_date and period\n send_date = start_date + datetime.timedelta(days=period)\n\n # write scheduled message to database\n new_scheduled_msg = ScheduledMessage(user_id=user_id, \n contact_id=contact_id,\n send_date=send_date,\n sent=False)\n\n # set new period on contact in database\n contact.contact_period = period\n\n db.session.add(new_scheduled_msg)\n db.session.commit()\n\n print 'user_id:', user_id\n print 'contact_form_value:', contact_form_value\n print 'start_date:', start_date, 'type:', type(start_date)\n print 'contact_email:', contact_email\n print 'contact:', contact\n print 'contact_id:', contact.contact_id\n print 'period:', period\n print 'send_date:', send_date\n return jsonify({})","def create(self):\n errors, data = self.format_and_control(request.form, create=True)\n\n if len(errors) > 0:\n set_session_var('errors', dict(errors))\n return None\n\n # Create object\n instance = self.Component(**data)\n\n # Add object in database\n res = add_in_db(instance)\n\n if res != 'added':\n set_session_var('errors', str(res))\n else:\n set_session_var('success', res)\n\n if self.module_fn is not None:\n self.module_fn(instance, data)\n\n return instance","def clerk_create_appointment():\n if request.method == 'POST':\n patient_email = request.form['patient_email']\n doctor_email = request.form['doctor_email']\n date = request.form['date']\n time = request.form['time']\n\n response_clerk_create_appointment = requests.post(server_url + 'medical_clerk/create_appointment', json={\n 'patient_email': patient_email,\n 'doctor_email': doctor_email,\n 'date': date,\n 'time': time\n })\n response_clerk_create_appointment = response_clerk_create_appointment.json()\n\n if response_clerk_create_appointment.get('Status') == \"INVALID_DOCTOR_EMAIL\":\n return render_template('clerks/clerk_appointment_failed.html')\n elif response_clerk_create_appointment.get('Status') == \"INVALID_PATIENT_EMAIL\":\n return render_template('clerks/clerk_appointment_failed.html')\n elif response_clerk_create_appointment.get('Status') == \"DOCTOR_IS_NOT_AVAILABLE_AT_THAT_TIME\":\n return render_template('clerks/clerk_appointment_failed.html')\n elif response_clerk_create_appointment.get('Status') == \"DOCTOR_HAS_AN_APPOINTMENT_SELECTED_TIME_SLOT\":\n return render_template('clerks/clerk_appointment_failed.html')\n else:\n referer = request.referrer\n return redirect(referer, code=302)\n else:\n return render_template('clerks/home.html')","def save_capteur():\n f = None\n f = CapteurForm()\n a = get_capteur(f.get_id())\n na = a.get_name()\n if f.validate_on_submit():\n a.set_name(f.get_name())\n a.set_num(f.get_phoneNumber())\n a.set_interval(f.get_interval())\n if a.get_parterre() != f.get_parterre().get_id():\n a.set_parterre(f.get_parterre().get_id())\n if a.get_typeMesure() != f.get_typeMesure().get_id():\n a.set_typeMesure(f.get_typeMesure().get_id())\n ac = Actions(\n contenu = \"Modification du capteur \"+na+\" -> \"+a.get_name(),\n liste = 1\n )\n db.session.add(ac)\n db.session.commit()\n return redirect(url_for(\n \"capteur_info\",\n id = f.get_id()))\n f.next.data = \"save_capteur\"\n return render_template(\n \"addCapteur.html\",\n title = a.get_name()+\" - edit\",\n form = f,\n capteur=a,\n param = \"modif\")","def create_or_edit_event(request, calendar_id=None, event_id=None, redirect=None):\n instance = None\n if event_id:\n instance = get_object_or_404(Event, id=event_id)\n calendar = None\n if calendar_id is not None:\n calendar = get_object_or_404(Calendar, id=calendar_id)\n form = EventForm(data=request.POST or None, instance=instance, hour24=True)\n if form.is_valid():\n event = form.save(commit=False)\n if instance is None:\n event.creator = request.user\n event.save()\n if calendar is not None and instance is None:\n calendar.events.add(event)\n next = redirect or reverse('s_event', args=[event.id])\n if 'next' in request.GET:\n next = _check_next_url(request.GET['next']) or next\n return HttpResponseRedirect(next)\n return render_to_response('schedule/create_event.html', {\n \"form\": form,\n \"calendar\": calendar\n }, context_instance=RequestContext(request))","def get_room_request_from_form(form):\n room_request = RoomRequest(\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n relationship_to_patient=form.rel_to_patient.data,\n address_line_one=form.street_address.data,\n address_line_two=form.apt_st_address.data,\n city=form.city.data,\n state=form.state.data,\n zip_code=form.zipcode.data,\n country=form.country.data,\n primary_phone=form.phone_number.data,\n secondary_phone=form.alt_phone_number.data,\n email=form.email.data,\n primary_language=form.primary_language.data,\n secondary_language=form.secondary_language.data,\n previous_stay=form.stayed_before.data,\n patient_first_name=form.patient_first_name.data,\n patient_last_name=form.patient_last_name.data,\n patient_dob=form.patient_dob.data,\n patient_gender=form.patient_gender.data,\n patient_hospital=form.hospital.data,\n patient_hospital_department=form.hospital_department.data,\n patient_treatment_description=form.description.data,\n patient_diagnosis=form.diagnosis.data,\n patient_first_appt_date=form.first_appt_date.data,\n patient_check_in=form.check_in_date.data,\n patient_check_out=form.check_out_date.data,\n patient_treating_doctor=form.treating_doctor.data,\n patient_doctors_phone=form.doctor_phone_number.data,\n patient_social_worker=form.hospital_social_worker.data,\n patient_social_worker_phone=form.sw_phone_number.data,\n inpatient=form.in_or_out_patient.data,\n inpatient_prior=form.staying_prior_to_admission.data,\n vaccinated=form.vaccinated.data,\n comments=form.comments.data,\n wheelchair_access=form.wheelchair_access.data,\n full_bathroom=form.full_bathroom.data,\n pack_n_play=form.pack_n_play.data)\n\n guests = []\n for i in range(1, 6):\n guest_name = form[f'guest{i}_name'].data\n guest_dob = form[f'guest{i}_dob'].data\n guest_rel = form[f'guest{i}_rel_to_patient'].data\n guest_email = form[f'guest{i}_email'].data\n guest_guardian = form[f'guest{i}_guardian'].data\n if guest_name and guest_dob and guest_rel and guest_email and guest_guardian:\n guests.append(Guest(\n name=guest_name,\n dob=guest_dob,\n relationship_to_patient=guest_rel,\n email=guest_email,\n guardian=guest_guardian))\n room_request.guests = guests\n\n return room_request","async def get_conference(self, user_id):\n db = self['db_engine']\n async with db.acquire() as connection:\n query = select([Conference]).where(Conference.user_id == user_id).where(\n Conference.expired_at > datetime.now(timezone.utc))\n if conference := await(await connection.execute(query)).first():\n return conference","def conference_status(request, call_sid):\n resp = VoiceResponse()\n call_status = None\n if request.method == 'POST':\n call_status = request.POST.get('CallStatus', None)\n if request.method == 'GET':\n call_status = request.GET.get('CallStatus', None)\n if call_status == 'completed':\n client.calls.hangup(call_sid)\n if call_status == 'no-answer' or call_status == 'busy' or call_status == 'failed':\n call = client.calls(call_sid).update(\n method=\"POST\",\n url=BASE_URL + \"/NoAnswer?myCallSid=\"+call_sid\n )\n print(call.to)\n return HttpResponse(str(resp))","def getConferenceSessions(self, request):\n sessions = self._getConferenceSessions(request)\n # Return individual SessionForm object per Session\n return SessionForms(\n items=[self._copySessionToForm(session) for session in sessions]\n )","def get_form_from_room_request(room_request):\n form = RoomRequestForm(\n first_name=room_request.first_name,\n last_name=room_request.last_name,\n rel_to_patient=room_request.relationship_to_patient,\n street_address=room_request.address_line_one,\n apt_st_address=room_request.address_line_two,\n city=room_request.city,\n state=room_request.state,\n zipcode=room_request.zip_code,\n country=room_request.country,\n phone_number=room_request.primary_phone,\n alt_phone_number=room_request.secondary_phone,\n email=room_request.email,\n primary_language=room_request.primary_language,\n secondary_language=room_request.secondary_language,\n stayed_before=room_request.previous_stay,\n patient_first_name=room_request.patient_first_name,\n patient_last_name=room_request.patient_last_name,\n patient_dob=room_request.patient_dob,\n patient_gender=room_request.patient_gender,\n hospital=room_request.patient_hospital,\n hospital_department=room_request.patient_hospital_department,\n description=room_request.patient_treatment_description,\n diagnosis=room_request.patient_diagnosis,\n first_appt_date=room_request.patient_first_appt_date,\n check_in_date=room_request.patient_check_in,\n check_out_date=room_request.patient_check_out,\n treating_doctor=room_request.patient_treating_doctor,\n doctor_phone_number=room_request.patient_doctors_phone,\n hospital_social_worker=room_request.patient_social_worker,\n sw_phone_number=room_request.patient_social_worker_phone,\n in_or_out_patient=room_request.inpatient,\n staying_prior_to_admission=room_request.inpatient_prior,\n vaccinated=room_request.vaccinated,\n comments=room_request.comments,\n wheelchair_access=room_request.wheelchair_access,\n full_bathroom=room_request.full_bathroom,\n pack_n_play=room_request.pack_n_play)\n\n for i, guest in enumerate(room_request.guests):\n form[f'guest{i+1}_name'].data = guest.name\n form[f'guest{i+1}_dob'].data = guest.dob\n form[f'guest{i+1}_rel_to_patient'].data = guest.relationship_to_patient\n form[f'guest{i+1}_email'].data = guest.email\n form[f'guest{i+1}_guardian'].data = guest.guardian\n\n return form","def subscribe():\n form = SubscribeForm()\n if form.validate_on_submit():\n subscription = Subscription(email=form.email.data)\n db.session.add(subscription)\n db.session.commit()\n return redirect(url_for('main.index'))\n\n return render_template('subscribes.html', form=form)","def update(self, request, *args, **kwargs):\n instance = self.get_object()\n if instance.author != request.user:\n return Response(status=status.HTTP_403_FORBIDDEN)\n if request.user.role < User.ORGANIZER:\n return Response(\n {'error_message': _('Only Organizer can edit events.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'title' not in request.data:\n return Response(\n {'error_message': _('No title provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'city_id' not in request.data:\n return Response(\n {'error_message': _('No city provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'currency_id' not in request.data:\n return Response(\n {'error_message': _('No currency provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'min_price' not in request.data and 'max_price' not in request.data:\n return Response(\n {'error_message': _('Min_price or max_price should be provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'datetimes' not in request.data:\n if 'start_datetime' not in request.data:\n return Response(\n {'error_message': _('No start_datetime provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n if 'end_datetime' not in request.data:\n return Response(\n {'error_message': _('No end_datetime provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n start_datetime = dateutil.parser.parse(request.data.pop('start_datetime'))\n end_datetime = dateutil.parser.parse(request.data.pop('end_datetime'))\n\n start_time = datetime.datetime.strftime(start_datetime, settings.TIME_STRING_FIELD_FORMAT)\n end_time = datetime.datetime.strftime(end_datetime, settings.TIME_STRING_FIELD_FORMAT)\n request.data['datetimes'] = [{\n 'date': date_to_string(date, settings.DATE_STRING_FIELD_FORMAT),\n 'start_time': start_time,\n 'end_time': end_time,\n } for date in daterange(start_datetime.date(), end_datetime.date())]\n else:\n for item in request.data['datetimes']:\n if 'date' not in item or \\\n 'start_time' not in item or \\\n 'end_time' not in item:\n return Response(\n {'error_message': _('Wrong datetimes format provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n try:\n string_to_date(str(item['date']), settings.DATE_STRING_FIELD_FORMAT)\n string_to_time(item['start_time'], settings.TIME_STRING_FIELD_FORMAT)\n string_to_time(item['end_time'], settings.TIME_STRING_FIELD_FORMAT)\n except:\n return Response(\n {'error_message': _('Wrong datetimes format provided.')},\n status=status.HTTP_400_BAD_REQUEST\n )\n\n serializer = self.get_serializer(instance, data=request.data)\n if serializer.is_valid():\n self.perform_update(serializer)\n headers = self.get_success_headers(serializer.data)\n\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\n else:\n return Response(\n serializer.errors,\n status=status.HTTP_400_BAD_REQUEST\n )","def unregisterFromConference(self, request):\n return self._conferenceRegistration(request, reg=False)","def new_album():\n form = PriceForm(request.form)\n if request.method == 'POST' and form.validate():\n # save the album\n price = Price()\n save_changes(price, form, new=True)\n flash('Price created successfully!')\n return redirect('/')\n return render_template('new_album.html', form=form)","def post(self, request, pk):\n application = get_application(pk)\n\n data = request.data\n data[\"organisation\"] = get_request_user_organisation_id(request)\n\n serializer = PartySerializer(data=data, application_type=application.case_type.sub_type)\n\n if serializer.is_valid(raise_exception=True):\n if str_to_bool(data.get(\"validate_only\", False)):\n return JsonResponse(data={data[\"type\"]: serializer.initial_data}, status=status.HTTP_200_OK)\n\n # Save party and add to application\n serializer.save()\n\n try:\n party, removed_party = application.add_party(serializer.instance)\n except ApplicationException as exc:\n return JsonResponse(data={\"errors\": exc.data}, status=status.HTTP_400_BAD_REQUEST)\n\n # Audit\n if removed_party:\n audit_trail_service.create(\n actor=request.user,\n verb=AuditType.REMOVE_PARTY,\n target=application.get_case(),\n payload={\"party_type\": removed_party.type.replace(\"_\", \" \"), \"party_name\": removed_party.name},\n )\n audit_trail_service.create(\n actor=request.user,\n verb=AuditType.ADD_PARTY,\n target=application.get_case(),\n payload={\"party_type\": party.type, \"party_name\": party.name},\n )\n\n return JsonResponse(data={party.type: serializer.data}, status=status.HTTP_201_CREATED)","def create(self, request, *args, **kwargs):\n self.serializer_class = ConversationDetailSerializer\n return super(PublicChatViewSet, self).create(request, *args, **kwargs)","def create_volunteer(request, volunteer_id=None):\n volunteer = None\n try:\n volunteer = Volunteer.objects.get(id=volunteer_id)\n user = User.objects.get(id=volunteer.user_id)\n except Volunteer.DoesNotExist:\n vol_form = VolunteerForm()\n user_form = UserCreationForm()\n user = User()\n except User.DoesNotExist:\n user_form = UserCreationForm()\n\n if request.method == \"POST\":\n vol_form = VolunteerForm(request.POST, request.FILES)\n user_form = UserCreationForm(request.POST, request.FILES)\n\n if vol_form.is_valid() and user_form.is_valid():\n svol = vol_form.save()\n user = user_form.save()\n\n # associate the user with the volunteer\n svol.user = user\n svol.save()\n user = authenticate(username=user.username, password=user_form.clean_password2())\n login(request, user)\n\n return redirect(\"edit-volunteer-profile\", \n volunteer_id=svol.id, hide_contact_form=True)\n elif volunteer_id:\n vol_form = VolunteerForm(instance=volunteer)\n if user:\n user_form = UserCreationForm(instance=user)\n\n return render(request, 'edit-volunteer.html', \n {\"vol_form\" : vol_form, \"user_form\" : user_form })","def add_api():\n form = NewAPIForm()\n form.parameters.choices = get_all_params()\n\n if form.add_parameter.data:\n form.new_parameter.append_entry()\n\n if form.submit.data and form.validate_on_submit():\n new_api = Api(\n name=form.name.data,\n region=form.region.data,\n description=form.description.data,\n url=form.url.data\n )\n\n # Links the parameters to the new API.\n for param_data in form.parameters.data:\n param = Parameter.query.filter_by(name=param_data).first()\n if param is not None:\n # TODO: Ask users to give a param description when adding params.\n new_api.add_param(param, \"Parameter-description\")\n\n # Creates the new parameters and links them to the API.\n for new_param in form.new_parameter:\n param = Parameter(\n name=new_param.param_name.data,\n param_format=new_param.param_format.data,\n count=0\n )\n new_api.add_param(param, new_param.description)\n\n db.session.add(new_api)\n try:\n db.session.commit()\n flash('API {} successfully added'\n .format(new_api.name),\n 'form-success')\n return redirect(url_for('api.index'))\n except IntegrityError:\n db.session.rollback()\n flash('Database error occurred. Please try again.',\n 'form-error')\n return render_template('api/add.html', form=form)","def create_venue_submission():\n # TODO: insert form data as a new Venue record in the db, instead (DONE)\n # TODO: modify data to be the data object returned from db insertion\n\n try:\n name = request.form.get(\"name\")\n city = request.form.get(\"city\")\n state = request.form.get(\"state\")\n address = request.form.get(\"address\")\n phone = request.form.get(\"phone\")\n imageLink = request.form.get(\"image_link\")\n genres = request.form.getlist(\"genres\")\n facebookLink = request.form.get(\"facebook_link\")\n website = request.form.get(\"website\")\n seeking_talent = request.form.get(\"facebook_link\")\n seeking_description = request.form.get(\"facebook_link\")\n\n venue_to_add = Venue(\n name=name,\n city=city,\n state=state,\n address=address,\n phone=phone,\n image_link=imageLink,\n genres=genres,\n facebook_link=facebookLink,\n website=website,\n seeking_talent=seeking_talent,\n seeking_description=seeking_description\n )\n\n db.session.add(venue_to_add)\n db.session.commit()\n\n # on successful db insert, flash success\n flash(\"Venue \" + request.form[\"name\"] + \" was successfully listed!\")\n\n # TODO: on unsuccessful db insert, flash an error instead.\n # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\n except:\n flash(\"An error occurred. Venue \" + name + \" could not be listed.\")\n db.session.rollback()\n finally:\n db.session.close()\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n return render_template(\"pages/home.html\")","def update(self, instance, validated_data):\n instance.description = validated_data.get('description', instance.description)\n instance.tender_number = validated_data.get('tender_number', instance.tender_number)\n instance.start_date = validated_data.get('start_date', instance.start_date)\n instance.end_date = validated_data.get('end_date', instance.end_date)\n instance.save()\n return instance","def add_Capteur():\n f = None\n f = CapteurForm()\n return render_template(\n \"addCapteur.html\",\n form = f,\n title = \"Nouveau capteur\",\n param = \"create\")","def save_contract(bid):\n title = 'Create contract -- ' + bid\n doc = data_active.find_one({'bid': bid})\n if doc is None:\n # error exit from function\n web_logging.error('bid= {} not found in \"data_active\"'.format(bid))\n flash('bid= {} not found in \"data_active\"'.format(bid))\n return redirect(url_for('lists'))\n\n # take only 10 chars as number\n doc['phone'] = doc['phone'].replace('-', '')\n doc['phone'] = doc['phone'][len(doc['phone'])-10:]\n\n search_number = numbers.find_one({'numbers': {'$eq': doc['phone']}})\n contract = Contract()\n contract.from_lists(doc)\n\n if search_number is None:\n # no contacts with such number, call create new contact form\n form_doc = {'city': doc['location'], 'numbers': doc['phone'], 'comment': doc['comment'],\n 'loc_comments': doc['comment']}\n info = '--------- New Contact ------------'\n else:\n form_doc = Contact().to_form(search_number)\n info = '========= Contact already known, please check ======'\n\n form_doc.update(contract.as_dict())\n web_logging.debug('data for form= {}'.format(form_doc))\n form = Transaction(**form_doc)\n\n if form.validate_on_submit():\n contact_info = Contact()\n contact_info.from_form(form.data)\n\n contract_info = Contract()\n contract_info.from_form(form.data)\n\n if contact_info.contact_id is None:\n # contact is new\n contact_info.create_time = datetime.utcnow()\n contact_info.update_time = contact_info.create_time\n web_logging.debug('inserting contact_info= {}'.format(contact_info.as_dict()))\n web_logging.debug('inserting contract_info= {}'.format(contract_info))\n flash('inserting contact_info= {}, contract_info= {}'.format(contact_info.as_dict(), contract_info))\n result_contract = contract_info.mongo_insert()\n result_contact = contact_info.mongo_insert()\n # add contact id into document\n result_contract_upd = contracts.update_one({'_id': ObjectId(result_contract)},\n {'$set': {'contact': ObjectId(result_contact)}})\n result_contact_upd = numbers.update_one({'_id': ObjectId(result_contact)},\n {'$addToSet': {'contracts': ObjectId(result_contract)}})\n else:\n # contact already exists\n contact_info.update_time = datetime.utcnow()\n web_logging.debug('inserting contact_info= {}'.format(contact_info.as_dict()))\n web_logging.debug('inserting contract_info= {}'.format(contract_info))\n flash('updating contact_info= {}, creating contract_info= {}'.format(contact_info.as_dict(), contract_info))\n result_contract = contract_info.mongo_insert()\n result_contact = numbers.update_one({'_id': ObjectId(contact_info.contact_id)},\n {'$addToSet': {'contracts': ObjectId(result_contract)}})\n\n return redirect('/contracts')\n\n return render_template('contract.html', title=title, form=form, info=info)","def getSpeaker(self, request):\n # Get Speaker object from request; bail if not found\n speaker = _getEntityByWebsafeKey(request.websafeSpeakerKey,\n 'Speaker')\n # Return SpeakerForm\n return self._copySpeakerToForm(speaker)","def new(request):\n\n if request.method == 'POST':\n form = PublicChatForm(request.POST)\n if form.is_valid(): # All validation rules pass\n chat = form.save(commit=False)\n chat.posted_by = request.user\n\n chat.save()\n\n return HttpResponseRedirect('/chats/')\n else:\n form = PublicChatForm()\n\n return render_to_response('new.html', {\n 'form': form,\n }, context_instance=RequestContext(request))","def update(self, instance, validated_data):\n instance.email = validated_data.get('email', instance.email)\n instance.first_name = validated_data.get('first_name', instance.first_name)\n instance.last_name = validated_data.get('last_name', instance.last_name)\n instance.date_of_birth = validated_data.get('date_of_birth', instance.date_of_birth)\n instance.bio = validated_data.get('bio', instance.bio)\n instance.country = validated_data.get('country', instance.country)\n instance.avatar = validated_data.get('avatar', instance.avatar)\n instance.save()\n return instance","def create_or_update_resources(self, resource, form, session):\n if resource is None:\n # create new user\n user = self.User()\n session.add(user)\n else:\n # update existing user\n user = resource\n\n # update user\n user.name = form.name.data\n user.description = form.description.data\n user.email = form.email.data\n if form.password.data:\n user.set_password(form.password.data)\n user.failed_sign_in_count = form.failed_sign_in_count.data or 0\n\n if self.totp_enabled:\n if form.totp_secret.data:\n user.totp_secret = form.totp_secret.data\n else:\n user.totp_secret = None\n\n # update user info\n if form.user_info.data:\n # ignore crsf_token of subform\n user_info_data = form.user_info.data\n user_info_data.pop('csrf_token', None)\n\n if user_info_data:\n user_info = user.user_info\n if user_info is None:\n # create new user_info\n user_info = self.UserInfo()\n # assign to user\n user_info.user = user\n\n # update user info fields\n for field, value in user_info_data.items():\n setattr(user_info, field, value)\n\n # update groups\n self.update_collection(\n user.groups_collection, form.groups, self.Group, 'id', session\n )\n # update roles\n self.update_collection(\n user.roles_collection, form.roles, self.Role, 'id', session\n )","def booking_update(id):\n if request.method == \"POST\":\n month = request.form.get(\"month\")\n day = request.form.get(\"day\")\n year = request.form.get(\"year\")\n\n booking = Booking.query.filter_by(id=id).first()\n booking.first_name = request.form.get(\"first_name\")\n booking.last_name = request.form.get(\"last_name\")\n booking.phone = request.form.get(\"phone\")\n booking.email = request.form.get(\"email\")\n booking.num_participants = request.form.get(\"num_participants\")\n booking.booking_date = f\"{year}-{month}-{day}\"\n booking.collection_time = request.form.get(\"collection_time\")\n booking.return_time = request.form.get(\"return_time\")\n\n if request.form.get(\"rental_complete\") == \"True\":\n booking.rental_complete = True\n else:\n booking.rental_complete = False\n\n payment = Payment.query.filter_by(booking_id=id).first()\n payment.full_amount_due = float(request.form.get(\"full_amount_due\"))\n\n if request.form.get(\"remainder_due_check\") == \"paid\":\n payment.remainder_due = 0\n else:\n payment.remainder_due = request.form.get(\"remainder_due\")\n\n db.session.commit()\n\n return redirect(url_for(\"bookings.booking_index\"))\n\n booking = Booking.query.get(id)\n payment = Payment.query.filter_by(booking_id=id).first()\n booking_date = booking.booking_date.split(\"-\")\n date = {\"month\": booking_date[1], \"day\": booking_date[2],\n \"year\": booking_date[0]}\n return render_template(\"booking_update.html\", booking=booking,\n payment=payment, date=date)","def create_venue_submission():\n form = VenueForm(request.form)\n\n try:\n new_venue = Venue(\n name=form.name.data,\n city=form.city.data,\n state=form.state.data,\n address=form.address.data,\n phone=form.phone.data,\n genres=form.genres.data,\n facebook_link=form.facebook_link.data,\n image_link=form.image_link.data,\n website=form.website.data,\n seeking_talent=form.seeking_talent.data,\n seeking_description=form.seeking_description.data\n )\n\n db.session.add(new_venue)\n db.session.commit()\n\n flash('Venue ' + request.form['name'] + ' was successfully listed!', 'info')\n\n except Exception as ex:\n db.session.rollback()\n flash('Error occurred. Venue ' + request.form['name'] + ' could not be listed. ' + str(ex), 'danger')\n finally:\n db.session.close()\n\n return redirect(url_for('index'))","def createCourse():\n\tif request.method == 'POST':\n\t\tcname = request.form['cname']\n\t\tcourseterm = request.form['courseterm']\n\t\tcoursepoint = request.form['coursepoint']\n\t\tcoursetype = request.form['coursetype']\t\n\t\tcourseyear = request.form['courseyear']\t\n\t\ttname = request.form['tname']\t\n\t\terror = None\n\n\t\tif not cname:\n\t\t\terror = 'Course name is required.'\n\t\telif not courseterm:\n\t\t\terror = 'Course term is required'\n\t\telif not courseterm:\n\t\t\terror = 'Course point is required'\n\n\t\tif error is not None:\n\t\t\tflash(error)\n\t\telse:\n\t\t\tdb = get_db()\n\t\t\tcur = db.cursor()\n\t\t\tcur.execute(\n\t\t\t\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\n\t\t\t\t' VALUES (%s, %s, %s, %s, %s, %s)',\n\t\t\t\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\n\t\t\t)\n\t\t\tdb.commit()\n\t\t\treturn redirect(url_for('info.index'))\n\n\treturn render_template('info/createCourse.html')"],"string":"[\n \"def _createConferenceObject(self, request):\\n # preload necessary data items\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = getUserId(user)\\n\\n if not request.name:\\n raise endpoints.BadRequestException(\\\"Conference 'name' field required\\\")\\n\\n # copy ConferenceForm/ProtoRPC Message into dict\\n data = {field.name: getattr(request, field.name) for field in request.all_fields()}\\n del data['websafeKey']\\n del data['organizerDisplayName']\\n\\n # add default values for those missing (both data model & outbound Message)\\n for df in DEFAULTS:\\n if data[df] in (None, []):\\n data[df] = DEFAULTS[df]\\n setattr(request, df, DEFAULTS[df])\\n\\n # convert dates from strings to Date objects; set month based on start_date\\n if data['startDate']:\\n data['startDate'] = datetime.strptime(data['startDate'][:10], \\\"%Y-%m-%d\\\").date()\\n data['month'] = data['startDate'].month\\n else:\\n data['month'] = 0\\n if data['endDate']:\\n data['endDate'] = datetime.strptime(data['endDate'][:10], \\\"%Y-%m-%d\\\").date()\\n\\n # set seatsAvailable to be same as maxAttendees on creation\\n if data[\\\"maxAttendees\\\"] > 0:\\n data[\\\"seatsAvailable\\\"] = data[\\\"maxAttendees\\\"]\\n # generate Profile Key based on user ID and Conference\\n # ID based on Profile key get Conference key from ID\\n p_key = ndb.Key(Profile, user_id)\\n c_id = Conference.allocate_ids(size=1, parent=p_key)[0]\\n c_key = ndb.Key(Conference, c_id, parent=p_key)\\n data['key'] = c_key\\n data['organizerUserId'] = request.organizerUserId = user_id\\n\\n # create Conference, send email to organizer confirming\\n # creation of Conference & return (modified) ConferenceForm\\n Conference(**data).put()\\n taskqueue.add(params={'email': user.email(),\\n 'conferenceInfo': repr(request)},\\n url='/tasks/send_confirmation_email'\\n )\\n return request\",\n \"def updateConference(self, request):\\n return self._updateConferenceObject(request)\",\n \"def update_conference(self, request):\\n return self.conference_service.update_conference_object(request)\",\n \"def updateConference(self, request):\\n return self._updateConferenceObject(request)\",\n \"def get_conference(self, request):\\n # get Conference object from request; bail if not found\\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' %\\n request.websafeConferenceKey)\\n prof = conf.key.parent().get()\\n # return ConferenceForm\\n return self.conference_service.copy_conference_to_form(ConferenceForm(),\\n conf,\\n prof.displayName)\",\n \"def getConference(self, request):\\n # get Conference object from request; bail if not found\\n conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' % request.websafeConferenceKey)\\n prof = conf.key.parent().get()\\n # return ConferenceForm\\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\",\n \"def getConference(self, request):\\n # Get Conference object from request; bail if not found\\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\\n 'Conference')\\n prof = conf.key.parent().get()\\n # Return ConferenceForm\\n return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\",\n \"def _copyConferenceToForm(self, conf, displayName):\\n cf = ConferenceForm()\\n for field in cf.all_fields():\\n if hasattr(conf, field.name):\\n # convert Date to date string; just copy others\\n if field.name.endswith('Date'):\\n setattr(cf, field.name, str(getattr(conf, field.name)))\\n else:\\n setattr(cf, field.name, getattr(conf, field.name))\\n elif field.name == \\\"websafeKey\\\":\\n setattr(cf, field.name, conf.key.urlsafe())\\n if displayName:\\n setattr(cf, 'organizerDisplayName', displayName)\\n cf.check_initialized()\\n return cf\",\n \"def _copyConferenceToForm(self, conf, displayName):\\n cf = ConferenceForm()\\n for field in cf.all_fields():\\n if hasattr(conf, field.name):\\n # Convert Date to date string; just copy others\\n if field.name.endswith('Date'):\\n setattr(cf, field.name, str(getattr(conf, field.name)))\\n else:\\n setattr(cf, field.name, getattr(conf, field.name))\\n elif field.name == \\\"websafeKey\\\":\\n setattr(cf, field.name, conf.key.urlsafe())\\n if displayName:\\n setattr(cf, 'organizerDisplayName', displayName)\\n cf.check_initialized()\\n return cf\",\n \"def registerForConference(self, request):\\n return self._conferenceRegistration(request)\",\n \"def build_conference(self, data) -> dict:\\n builder = ConferenceBuilder()\\n builder.add_title(title=data.get(\\\"name\\\"), subtitle=data.get(\\\"subtitle\\\"))\\n builder.set_short_description(value=data.get(\\\"description\\\", \\\"\\\"))\\n builder.set_opening_date(get_value(data, \\\"dates[0]\\\"))\\n builder.set_closing_date(get_value(data, \\\"dates[1]\\\"))\\n builder.add_inspire_categories(data.get(\\\"field_of_interest\\\", []))\\n builder.add_public_note(value=data.get(\\\"additional_info\\\", \\\"\\\"))\\n builder.add_series(\\n name=data.get(\\\"series_name\\\"), number=data.get(\\\"series_number\\\")\\n )\\n for address in data.get(\\\"addresses\\\"):\\n builder.add_address(\\n cities=[address.get(\\\"city\\\")],\\n state=address.get(\\\"state\\\"),\\n place_name=address.get(\\\"venue\\\"),\\n country_code=country_name_to_code(address.get(\\\"country\\\")),\\n )\\n for contact in data.get(\\\"contacts\\\", []):\\n builder.add_contact(**contact)\\n for acr in data.get(\\\"acronyms\\\", []):\\n builder.add_acronym(acr)\\n for website in data.get(\\\"websites\\\", []):\\n builder.add_url(website)\\n for keyword in data.get(\\\"keywords\\\", []):\\n builder.add_keyword(value=keyword)\\n\\n builder.record[\\\"$schema\\\"] = url_for(\\n \\\"invenio_jsonschemas.get_schema\\\",\\n schema_path=\\\"records/conferences.json\\\",\\n _external=True,\\n )\\n\\n return builder.record\",\n \"def register_for_conference(self, request):\\n return self.conference_registration(request)\",\n \"def conference(self, sid):\\r\\n return conferences.Conference(self, sid)\",\n \"def get_conferences_created(self, request):\\n # make sure user is authed\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = self.auth.get_user_id(user)\\n\\n # create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(items=[\\n self.conference_service.copy_conference_to_form(ConferenceForm(),\\n conf,\\n prof.displayName)\\n for conf in confs])\",\n \"def create_conference_session(self, request):\\n user = endpoints.get_current_user()\\n\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n\\n self.session_service.create_conference_session(request, user)\\n return request\",\n \"def get_conferences_to_attend(self, request):\\n prof = self.profile_service.get_profile_from_user() # get user Profile\\n conf_keys = [ndb.Key(urlsafe=wsck) for wsck in\\n prof.conferenceKeysToAttend]\\n conferences = ndb.get_multi(conf_keys)\\n\\n # get organizers\\n organisers = [ndb.Key(Profile, conf.organizerUserId) for conf in\\n conferences]\\n profiles = ndb.get_multi(organisers)\\n\\n # put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(items=[\\n self.conference_service.copy_conference_to_form(\\n ConferenceForm(), conf, names[conf.organizerUserId]\\n ) for conf in conferences])\",\n \"def getConferencesCreated(self, request):\\n # make sure user is authed\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = getUserId(user)\\n\\n # create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\\n )\",\n \"def post(self):\\n json_data = request.get_json()\\n json_data[\\\"sender_id\\\"] = current_user.id\\n try:\\n new_campaign = self.schema.load(json_data)\\n except ValidationError as err:\\n return {\\\"message\\\": err.messages}, HTTPStatus.BAD_REQUEST\\n if Campaign.query.filter_by(mailchimp_id=new_campaign.mailchimp_id).first() is not None:\\n return {\\\"message\\\": \\\"Campaign already exists.\\\"}, HTTPStatus.CONFLICT\\n db.session.add(new_campaign)\\n db.session.commit()\\n return self.schema.dump(new_campaign), HTTPStatus.CREATED\",\n \"def ConferenceRecord(self, default={}):\\n return HEP.JSONReferenceObject(self.data.get('conference_record', default))\",\n \"def test_create_form_update_existing(self):\\n survey = self._create_test_survey()\\n assert survey is not None\\n\\n survey = SurveyForm.create(self.test_survey_name, self.test_form_update, update_if_exists=True)\\n assert survey is not None\\n\\n survey = SurveyForm.get(self.test_survey_name)\\n assert survey is not None\\n assert survey.form == self.test_form_update\",\n \"def getConferencesCreated(self, request):\\n # Make sure user is authenticated\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = user.email()\\n # Create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # Return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(\\n conf, getattr(prof, 'displayName')) for conf in confs\\n ]\\n )\",\n \"def create_appointment():\\n\\n form = AppointmentForm()\\n\\n if form.validate_on_submit():\\n\\n appointment = Appointment(\\n title = form.title.data,\\n description = form.description.data,\\n location = form.location.data,\\n start = form.start.data,\\n client = form.client.data,\\n user = current_user\\n )\\n\\n try:\\n db.session.add(appointment)\\n db.session.commit()\\n\\n flash('Successfully created the appointment.')\\n\\n return redirect(url_for('appointment.read_appointments'))\\n except:\\n flash('Error creating the appointment')\\n\\n return render_template('appointments/form.html.j2', form=form, title='Create appointment')\",\n \"def join_conference(request, conference_name):\\n resp = VoiceResponse()\\n resp.dial(hangupOnStar=True).conference(conference_name)\\n return HttpResponse(str(resp))\",\n \"def _createSpeakerObject(self, request):\\n # Preload necessary data items\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = user.email()\\n if not request.name:\\n raise endpoints.BadRequestException(\\n \\\"Speaker 'name' field required\\\")\\n # Copy SpeakerForm/ProtoRPC Message into dict\\n data = {\\n field.name: getattr(request, field.name) for field in\\n request.all_fields()\\n }\\n del data['websafeKey']\\n # Add default values for those missing (both data model and\\n # outbound Message)\\n for df in SPEAKER_DEFAULTS:\\n if data[df] in (None, []):\\n data[df] = SPEAKER_DEFAULTS[df]\\n # Create Speaker and return SpeakerForm\\n speaker = Speaker(**data)\\n speaker.put()\\n return self._copySpeakerToForm(speaker)\",\n \"def _createSessionObject(self, request):\\n # Preload necessary data items\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = user.email()\\n # Get the conference entity\\n conf = _getEntityByWebsafeKey(request.websafeConferenceKey,\\n 'Conference')\\n # Ensure that the current user is the conference organizer\\n if user_id != conf.organizerUserId:\\n raise endpoints.UnauthorizedException(\\n 'Only the conference organizer can create a new session')\\n # Verify that the speaker exists\\n speaker = _getEntityByWebsafeKey(request.websafeSpeakerKey, 'Speaker')\\n # Ensure that the user submitted the required name property\\n if not request.name:\\n raise endpoints.BadRequestException(\\n \\\"Session 'name' field required\\\")\\n # Copy SessionForm/ProtoRPC Message into dict\\n data = {\\n field.name: getattr(request, field.name) for field in\\n request.all_fields()\\n }\\n # Remove data that isn't destined for the Session entity\\n del data['websafeConferenceKey']\\n del data['websafeSpeakerKey']\\n del data['websafeKey']\\n # Add default values for those missing in the data model\\n for df in SESSION_DEFAULTS:\\n if data[df] in (None, []):\\n data[df] = SESSION_DEFAULTS[df]\\n # Ensure the string version of typeOfSession is what is stored\\n # in the NDB model\\n data['typeOfSession'] = str(data['typeOfSession'])\\n # Convert date from string to Date object\\n if data['date'] is not None:\\n try:\\n data['date'] = datetime.strptime(\\n data['date'][:10], '%Y-%m-%d').date()\\n except:\\n raise endpoints.BadRequestException(\\n \\\"Invalid 'date' value\\\")\\n # Convert startTime from string to Time object\\n if data['startTime'] is not None:\\n try:\\n data['startTime'] = datetime.strptime(\\n data['startTime'], '%H:%M').time()\\n except:\\n raise endpoints.BadRequestException(\\n \\\"Invalid 'startTime' value\\\")\\n # Create Session\\n session = Session(**data)\\n session.conference = conf.key\\n session.speaker = speaker.key\\n session.put()\\n # Add the session key to the speaker's sessions list\\n speaker.sessions.append(session.key)\\n speaker.put()\\n # Add a task to task queue which checks if the speaker of this session\\n # should be the new featured speaker\\n taskqueue.add(params={'websafeSpeakerKey': request.websafeSpeakerKey,\\n 'websafeConferenceKey': request.websafeConferenceKey},\\n url='/tasks/update_featured_speaker'\\n )\\n # Return SessionForm object\\n return self._copySessionToForm(session)\",\n \"def conferences_choice(self):\\n while self.choice != \\\"q\\\":\\n self.choice = input(\\\"\\\\033[35m(c) for create \\\\n(m) for modify \\\\n\\\"\\n \\\"(d) for delete \\\\n(s) for see \\\\n\\\"\\n \\\"(q) for exit \\\\n(r) for return start page:\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n if self.choice == \\\"c\\\":\\n title = input(\\\"\\\\033[33menter title:\\\\033[0m\\\")\\n summary = input(\\\"\\\\033[33menter summary:\\\\033[0m\\\")\\n date = input(\\\"\\\\033[33menter date:\\\\033[0m\\\")\\n hour = input(\\\"\\\\033[33menter hour \\\\033[0m\\\")\\n speaker = input(\\\"\\\\033[33menter speaker ID: \\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.create_conferences(title, summary, date, hour, speaker)\\n\\n\\n if self.choice == \\\"m\\\":\\n while column not in ['title', 'resume', 'date', 'hour', 'personid']:\\n id = input(\\\"\\\\033[33menter confernces id :\\\\033[0m\\\")\\n column = input(\\n \\\"\\\\033[33mwhat do you want to modify \\\\n [title, resume, date, hour, personid] :\\\\033[0m\\\")\\n datta = input(\\\"\\\\033[33menter new datta:\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.update_confernces(id, column, datta)\\n\\n\\n if self.choice == \\\"d\\\":\\n id = input(\\\"\\\\033[33menter conferences ID :\\\\033[0m\\\")\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n self.users.delete_conferences(id)\\n\\n\\n if self.choice == \\\"s\\\":\\n datta = self.conf.show_conferences()\\n if datta:\\n for elem in datta:\\n print(elem)\\n if self.choice == \\\"r\\\":\\n os.system('cls' if os.name == 'nt' else 'clear')\\n time.sleep(1)\\n return\\n if self.choice == \\\"q\\\":\\n exit()\",\n \"def update_c_mandatory_fields(request, campaign_id):\\n # print(request.POST)\\n campaign = Campaign.objects.get(id=campaign_id)\\n form = CampaignForm(request.POST, instance = campaign)\\n # print(form)\\n if form.is_valid():\\n form.save()\\n return redirect('add_campaign_spec', id=campaign_id)\\n else:\\n # return redirect('clonecampaign', id=campaign_id)\\n print(form.errors)\\n return redirect(reverse('edit_campaign', kwargs={'campaign_id':campaign_id}))\",\n \"def getConferencesToAttend(self, request):\\n prof = self._getProfileFromUser() # get user Profile\\n conf_keys = [\\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\\n ]\\n conferences = ndb.get_multi(conf_keys)\\n # Get organizers\\n organisers = [\\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\\n ]\\n profiles = ndb.get_multi(organisers)\\n # Put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n # Return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\\n for conf in conferences\\n ]\\n )\",\n \"def create_event():\\n event = None\\n form = CreateEventForm()\\n if form.validate_on_submit():\\n venue = Venue.query.filter_by(address=form.address.data).first()\\n if venue is None: # venue not already in db, need to add it\\n venue_data = form.data\\n venue_data[\\\"name\\\"] = venue_data[\\\"venue_name\\\"]\\n venue_data[\\\"state\\\"] = CreateEventForm.convert_choice_to_value(form.state.data, \\\"STATES\\\")\\n venue = Venue.create(**venue_data)\\n event_type = EventType.query.get(form.event_type.data)\\n event_category = EventCategory.query.get(form.category.data)\\n start_time = CreateEventForm.convert_choice_to_value(form.start_time.data, \\\"TIMES\\\")\\n end_time = CreateEventForm.convert_choice_to_value(form.end_time.data, \\\"TIMES\\\")\\n event = Event(\\n title=form.title.data,\\n start_datetime=datetime.combine(form.start_date.data, start_time),\\n end_datetime=datetime.combine(form.end_date.data, end_time),\\n venue=venue,\\n event_type=event_type,\\n event_category=event_category,\\n user=current_user._get_current_object(),\\n )\\n db.session.commit()\\n return redirect(url_for(\\\"events.event_details\\\", id=event.id))\\n return render_template(\\\"events/create_event.html\\\", form=form, event=event)\",\n \"def _conferenceRegistration(self, request, reg=True):\\n retval = None\\n # Get user profile\\n prof = self._getProfileFromUser()\\n # Check if conference given in the websafeConferenceKey exists\\n wsck = request.websafeConferenceKey\\n conf = _getEntityByWebsafeKey(wsck, 'Conference')\\n # Register\\n if reg:\\n # Check if user already registered, otherwise add\\n if wsck in prof.conferenceKeysToAttend:\\n raise ConflictException(\\n \\\"You have already registered for this conference.\\\")\\n # Check if seats available\\n if conf.seatsAvailable <= 0:\\n raise ConflictException(\\n \\\"There are no seats available.\\\")\\n # Register user, deduct one seat\\n prof.conferenceKeysToAttend.append(wsck)\\n conf.seatsAvailable -= 1\\n retval = True\\n # Unregister\\n else:\\n # Check if user already registered\\n if wsck in prof.conferenceKeysToAttend:\\n # Unregister user, add back one seat\\n prof.conferenceKeysToAttend.remove(wsck)\\n conf.seatsAvailable += 1\\n retval = True\\n else:\\n retval = False\\n # Update the datastore and return\\n prof.put()\\n conf.put()\\n return BooleanMessage(data=retval)\",\n \"def create_calendar(request):\\n if request.method == 'POST':\\n\\n form = CalendarForm(request.POST)\\n \\n if form.is_valid():\\n calendar = form.save(commit=False) # prvent form from saving since we need to link company\\n calendar.company = request.user.company\\n calendar.save()\\n return redirect('appointment:calendar_list')\\n else:\\n form = CalendarForm()\\n return render(request, 'calendar_form.html', {'form': form})\",\n \"def participant_create(request):\\n if request.method == 'POST':\\n serializer = ParticipantSerializer(data=request.data)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n else:\\n participant = get_object_or_404(Participant, email=serializer.data['email'])\\n serializer = ParticipantSerializer(participant, data=request.data, partial=True)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\\n else:\\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_OR_BAD_REQUEST}\\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\",\n \"def _copySpeakerToForm(self, speaker):\\n sf = SpeakerForm()\\n for field in sf.all_fields():\\n if hasattr(speaker, field.name):\\n setattr(sf, field.name, getattr(speaker, field.name))\\n elif field.name == \\\"websafeKey\\\":\\n setattr(sf, field.name, speaker.key.urlsafe())\\n sf.check_initialized()\\n return sf\",\n \"def conference_registration(self, request, reg=True):\\n prof = self.profile_service.get_profile_from_user() # get user Profile\\n\\n # check if conf exists given websafeConfKey\\n # get conference; check that it exists\\n wsck = request.websafeConferenceKey\\n conf = ndb.Key(urlsafe=wsck).get()\\n if not conf:\\n raise endpoints.NotFoundException(\\n 'No conference found with key: %s' % wsck)\\n\\n # register\\n if reg:\\n # check if user already registered otherwise add\\n if wsck in prof.conferenceKeysToAttend:\\n raise ConflictException(\\n \\\"You have already registered for this conference\\\")\\n\\n # check if seats avail\\n if conf.seatsAvailable <= 0:\\n raise ConflictException(\\\"There are no seats available.\\\")\\n\\n # register user, take away one seat\\n prof.conferenceKeysToAttend.append(wsck)\\n conf.seatsAvailable -= 1\\n retval = True\\n\\n # unregister\\n else:\\n # check if user already registered\\n if wsck in prof.conferenceKeysToAttend:\\n\\n # unregister user, add back one seat\\n prof.conferenceKeysToAttend.remove(wsck)\\n conf.seatsAvailable += 1\\n retval = True\\n else:\\n retval = False\\n\\n # write things back to the datastore & return\\n prof.put()\\n conf.put()\\n return BooleanMessage(data=retval)\",\n \"def contact_create(request):\\n\\n if request.method == \\\"POST\\\":\\n form = ContactForm(request.POST)\\n profile_form = ContactProfileForm(request.POST)\\n\\n if form.is_valid() and profile_form.is_valid():\\n contact = form.save()\\n\\n # Populate the required 'contact' field before saving\\n profile = profile_form.save(commit=False)\\n profile.contact = contact\\n profile.save()\\n\\n messages.success(request, _(\\\"The contact %(name)s was successfully created\\\") % \\n {'name': unicode(contact)})\\n return HttpResponseRedirect(reverse(\\\"moderation.views.contact\\\", args=(contact.pk,)))\\n else:\\n form_initial = {'phone_number': request.GET.get('phone_number', '')}\\n form = ContactForm(initial=form_initial)\\n\\n profile_initial = {}\\n if \\\"facility\\\" in request.GET:\\n facility = get_object_or_404(Facility, pk=request.GET['facility'])\\n profile_initial['facility'] = facility.pk\\n profile_form = ContactProfileForm(initial=profile_initial)\\n\\n return render_to_response(\\\"contact_create.html\\\", \\n { 'form': form, \\n 'profile_form': profile_form,\\n },\\n context_instance=RequestContext(request))\",\n \"def event_register_participant(request, event_id, participant_id):\\n if request.method == 'PUT':\\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\\n participant = get_object_or_404(Participant, pk=participant_id)\\n try:\\n attendance = Attendance(participant=participant, event=event, datetime_register=datetime.now())\\n attendance.save()\\n except Exception as e:\\n print e\\n content = {'detail': config.PARTICIPANT_ALREADY_REGISTERED_TO_EVENT}\\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\\n serializer = AttendanceSerializer(attendance)\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def create(self, *args, **kwargs):\\n\\n talk = kwargs.get('talk', None)\\n comment = kwargs.get('comment', None)\\n\\n if talk is None and comment is None:\\n raise ValidationError('Either talk or comment should not be null.')\\n elif talk is not None and comment is not None:\\n raise ValidationError('Either talk or comment should be null.')\\n\\n return super().create(*args, **kwargs)\",\n \"def post(self):\\n try:\\n new_form = FORM_SCHEMA.load(request.json).data\\n except ValidationError as err:\\n APP.logger.error(err.args)\\n return err.messages, status.HTTP_400_BAD_REQUEST\\n\\n add_new_form = Form(**new_form)\\n DB.session.add(add_new_form)\\n\\n try:\\n DB.session.commit()\\n except IntegrityError as err:\\n APP.logger.error(err.args)\\n DB.session.rollback()\\n return {'error': 'Already exists.'}, status.HTTP_400_BAD_REQUEST\\n return Response(status=status.HTTP_201_CREATED)\",\n \"def put():\\n title = request.form[\\\"title\\\"]\\n description = request.form[\\\"description\\\"]\\n is_valid = request.form[\\\"is_valid\\\"]\\n company_id = request.form[\\\"company_id\\\"]\\n city_id = request.form[\\\"city_id\\\"]\\n start_date = request.form[\\\"start_date\\\"]\\n add_date = request.form[\\\"add_date\\\"]\\n sector = request.form[\\\"sector\\\"]\\n contract_type_id = request.form[\\\"contract_type_id\\\"]\\n experience = request.form[\\\"experience\\\"]\\n formation = request.form[\\\"formation\\\"]\\n advertisement_id = request.form[\\\"advertisement_id\\\"]\\n elementFromDB = Advertisements().get_one_element(request.form[\\\"advertisement_id\\\"])\\n if elementFromDB is None:\\n return abort(500, \\\"L'élément n'existe pas.\\\")\\n else:\\n try:\\n elements = Advertisements().patch( title, description, is_valid, company_id, city_id, start_date, add_date, sector, contract_type_id, experience, formation, advertisement_id )\\n return elements\\n except Exception as identifier:\\n return abort(500, identifier)\",\n \"def create_patient_appointment():\\n if request.method == 'POST':\\n patient_email = request.form['patient_email']\\n doctor_email = request.form['doctor_email']\\n date = request.form['date']\\n time = request.form['time']\\n\\n response = requests.post(server_url + 'patient/create_appointment', json={\\n 'patient_email': patient_email,\\n 'doctor_email': doctor_email,\\n 'date': date,\\n 'time': time\\n })\\n\\n response = response.json()\\n\\n if response.get('Status') == \\\"DOCTOR_HAS_AN_APPOINTMENT_SELECTED_TIME_SLOT\\\":\\n return render_template('patients/appointment_failed.html')\\n elif response.get('Status') == \\\"DOCTOR_IS_NOT_AVAILABLE_AT_THAT_TIME\\\":\\n return render_template('patients/appointment_failed.html')\\n elif response.get('Status') == \\\"INVALID_PATIENT_EMAIL\\\":\\n return render_template('patients/appointment_failed.html')\\n elif response.get('Status') == \\\"INVALID_DOCTOR_EMAIL\\\":\\n return render_template('patients/appointment_failed.html')\\n else:\\n referer = request.referrer\\n return redirect(referer, code=302)\\n else:\\n return render_template('patients/dashboard.html')\",\n \"def is_in_conference(self) -> bool:\",\n \"def create_appointment_form(request, post):\\n # string_date = \\\"{0}-{1}-{2}\\\".format(year, month, day)\\n # date = datetime.datetime.strptime(string_date, '%Y-%m-%d').date()\\n new_appointment = None\\n date_string = post.get(\\\"date\\\") + \\\"-\\\" + post.get(\\\"time\\\")\\n date = datetime.datetime.strptime(date_string, '%Y-%m-%d-%H:%M')\\n the_user = request.user\\n notes = post.get(\\\"notes\\\")\\n\\n if the_user.userprofile.is_doctor():\\n patient_id = int(post.get(\\\"patient\\\", the_user.pk))\\n patient = User.objects.get(pk=patient_id)\\n doctor = User.objects.get(pk=the_user.id)\\n new_appointment = Appointment.objects.create(date=date, doctor=doctor, patient=patient, notes=notes)\\n\\n elif request.user.userprofile.is_patient():\\n doctor_id = int(post.get(\\\"doctor\\\", the_user.pk))\\n doctor = User.objects.get(pk=doctor_id)\\n patient = User.objects.get(pk=the_user.id)\\n new_appointment = Appointment.objects.create(date=date, doctor=doctor, patient=patient, notes=notes)\\n\\n return new_appointment\",\n \"def post(self):\\n data = request.json\\n return new_subscription(data=data)\",\n \"def add_event(request, owner_type, owner_id):\\n\\n # Like before, get the request's context.\\n context = RequestContext(request)\\n event_added = False\\n\\n user = request.user\\n # If it's a HTTP POST, we're interested in processing form data.\\n if request.method == 'POST':\\n verified_obj = verified_calendar(context, owner_type, owner_id, user)\\n if not isinstance(verified_obj, HttpResponse):\\n calendar, edit_priv = verified_obj\\n else:\\n return verified_obj\\n\\n # Attempt to grab information from the raw form information.\\n event_form = EventForm(data=request.POST)\\n if event_form.is_valid():\\n # Save the event's form data to the database.\\n event = event_form.save(commit=False)\\n event.cal = calendar\\n event.creator = user\\n\\n event.save()\\n\\n event_added = True\\n\\n #notify the subscribers\\n created_event.send(sender=None, owner_type=owner_type, owner_id=owner_id,\\n event=event, user=user)\\n\\n\\n # Invalid form or forms - mistakes or something else?\\n # Print problems to the terminal.\\n # They'll also be shown to the user.\\n else:\\n print event_form.errors\\n\\n # Not a HTTP POST, so we render our form using the EventForm.\\n # These forms will be blank, ready for user input.\\n else:\\n event_form = EventForm()\\n\\n # Render the template depending on the context.\\n return render_to_response(\\n 'scheduler/add_event.html', {'event_form': event_form, 'user' : user,\\n 'event_added': event_added},\\n context)\",\n \"def newConferenceCitation(self, **attrlinks):\\n return ConferenceCitation(self, **attrlinks)\",\n \"def create(self, validated_data):\\n if (AdoptionRequest.objects\\n .filter(adoption_proposal=validated_data['adoption_proposal'])\\n .exists()):\\n return (AdoptionRequest.objects\\n .get(adoption_proposal=validated_data['adoption_proposal']))\\n else:\\n adoption_request = AdoptionRequest.objects.create(**validated_data)\\n person_devices = find_devices(adoption_request.adoption_proposal\\n .owner.id)\\n title = 'SOSFIDO'\\n message = ('Se ha enviado una petición a tu propuesta de ' +\\n 'adopción de tu mascota ' +\\n adoption_request.adoption_proposal.pet_name)\\n data_notification = {'proposal_id': adoption_request\\n .adoption_proposal.id}\\n image_notification = 'https://s3.amazonaws.com/uploads.hipchat.' + \\\\\\n 'com/529035/4489842/wDKOTjEIqyr1pPX/' + \\\\\\n 'logo_sosfido1.png'\\n send_notification_mobile(person_devices, title, message,\\n data_notification, image_notification)\\n return adoption_request\",\n \"async def get_conference_by_id(self, session_id):\\n db = self['db_engine']\\n async with db.acquire() as connection:\\n query = select([Conference]).where(Conference.session_id == session_id).where(\\n Conference.expired_at > datetime.now(timezone.utc))\\n if conference := await(await connection.execute(query)).first():\\n result = dict(conference)\\n query = select([User.email, User.display_name]).where(User.id == conference.user_id)\\n if user := await(await connection.execute(query)).first():\\n result['user_data'] = UserData(conference.user_id, *user)\\n return result\",\n \"def updateAgenda():\\n data = request.json\\n if \\\"agenda_id\\\" in data and \\\"new_agenda\\\" in data:\\n responseWrapper: ResponseWrapper = connectMongo.updateAgenda(data.get(\\\"agenda_id\\\"), data.get(\\\"new_agenda\\\"))\\n if not responseWrapper.found:\\n return jsonify(response=404, msg=\\\"Agenda not found\\\")\\n return jsonify(response=200, agenda=responseWrapper.object.makeJson())\\n else:\\n return jsonify(response=400, msg=\\\"you didn't sent all the necessary information\\\")\",\n \"def update_appointment(request,pk):\\n appointment = AppointmentRequests.objects.get(id=pk)\\n form = AppointmentUpdate(instance=appointment)\\n if request.method == \\\"POST\\\":\\n form = AppointmentUpdate(request.POST,instance=appointment)\\n if form.is_valid():\\n form.save()\\n return redirect(\\\"dashboard\\\")\\n else:\\n messages.info(request,\\\"Invalid Data sent, Make sure you provided right data.\\\")\\n return redirect(\\\"update_appointment\\\",pk=pk)\\n else:\\n return render(request,\\\"update_appointment.html\\\",{\\\"form\\\":form})\",\n \"def put(self, patient_id):\\n args = self.parser.parse_args()\\n\\n # Convert data_time and validate\\n date_of_birth = utils.str2date(args.date_of_birth)\\n if date_of_birth is None:\\n abort()\\n\\n return_id = None\\n with open_session() as session:\\n record = Patient(args.first_name,\\n args.last_name,\\n args.gender,\\n date_of_birth)\\n\\n session.add(record)\\n session.flush()\\n session.commit()\\n\\n return_id = record.id\\n\\n # Check for error handling\\n if return_id is None:\\n return gen_response(\\\"internal server error\\\")\\n else:\\n return gen_response({\\\"id\\\": return_id})\",\n \"def update(self, request, hash_key, phone_number, partial, format=None):\\n video = get_object_or_404(Video, hash_key=hash_key)\\n \\n try:\\n user = User.objects.get_user_by_number(phone_number)\\n except ValidationError as err:\\n raise exceptions.ValidationError({'phone_number': err.messages})\\n \\n try:\\n association = VideoUsers.objects.select_related(\\n 'user', 'video').get(user=user, video=video)\\n except VideoUsers.DoesNotExist:\\n association = None\\n \\n serializer = self.get_serializer(association, data=request.data,\\n partial=partial)\\n serializer.is_valid(raise_exception=True)\\n \\n if association is None:\\n # Create association object here with any properties in\\n # serializer, such as\\n # permission = serializer.data.get('permission')\\n association = VideoUsers.objects.create(user=user, video=video)\\n serializer = self.get_serializer(association)\\n # log creation \\n activity_send(request.user, verb='invite', \\n object=user, target=video)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n\\n association = serializer.save(force_update=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def insert_talk(request):\\n try:\\n is_loggedin, username = get_session_variables(request)\\n # User is not logged in\\n if not logged_in(request):\\n return HttpResponseRedirect('/register/login')\\n\\n # User is logged in\\n else:\\n if request.method == 'POST':\\n form = AddSpeakerForm(request.POST)\\n\\n # Invalid form imput\\n if not form.is_valid():\\n error = \\\"Invalid inputs\\\"\\n return render_to_response('achievement/new_speaker.html', \\\\\\n {'form':form, \\\\\\n 'error':error, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n\\n # Form is valid\\n else:\\n # Get the new achievement_id\\n achievement_id = get_achievement_id(request)\\t\\n achievement_type = \\\"Speaker\\\"\\n\\n # Saving inputs\\n achievement_obj = Achievement(achievement_id, \\\\\\n achievement_type, \\\\\\n username)\\n achievement_obj.save()\\n contribution_obj = form.save(commit = False)\\n contribution_obj.achievement_id = achievement_obj\\n contribution_obj.achieve_typ = achievement_type\\n user_obj = get_object_or_404(User_info, username = username)\\n contribution_obj.username = user_obj\\n contribution_obj.save()\\n return render_to_response('achievement/success.html', \\\\\\n {'achievement_type':achievement_type, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n # Method is not POST\\n else:\\n return render_to_response('achievement/new_speaker.html', \\\\\\n {'form': AddSpeakerForm, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'username':username}, \\\\\\n RequestContext(request))\\n except KeyError:\\n return error_key(request)\",\n \"def form_valid(self, form, cruiseday_form, participant_form, document_form, equipment_form, invoice_form):\\r\\n\\r\\n\\t\\tis_submitting = False\\r\\n\\r\\n\\t\\told_cruise = get_object_or_404(Cruise, pk=self.kwargs.get('pk'))\\r\\n\\t\\told_cruise_days_string = str(old_cruise.get_cruise_days())\\r\\n\\r\\n\\t\\tnew_cruise = form.save(commit=False)\\r\\n\\t\\tnew_cruise.information_approved = False\\r\\n\\r\\n\\t\\tself.object = form.save()\\r\\n\\t\\tcruiseday_form.instance = self.object\\r\\n\\t\\tcruiseday_form.save()\\r\\n\\t\\tparticipant_form.instance = self.object\\r\\n\\t\\tparticipant_form.save()\\r\\n\\t\\tdocument_form.instance = self.object\\r\\n\\t\\tdocument_form.save()\\r\\n\\t\\tequipment_form.instance = self.object\\r\\n\\t\\tequipment_form.save()\\r\\n\\t\\tinvoice_form.instance = self.object\\r\\n\\t\\tinvoice_form.save()\\r\\n\\r\\n\\t\\tnew_cruise.leader = self.request.user\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tnew_cruise.organization = new_cruise.leader.userdata.organization\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tpass\\r\\n\\t\\tform.cleaned_data[\\\"leader\\\"] = self.request.user\\r\\n\\r\\n\\t\\tnew_cruise.outdate_missing_information()\\r\\n\\r\\n\\t\\tif new_cruise.is_submitted and old_cruise_days_string != str(new_cruise.get_cruise_days()):\\r\\n\\t\\t\\tnew_cruise.is_approved = False\\r\\n\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise ' + str(Cruise) + ' updated. Your cruise days were modified, so your cruise is now pending approval. You may track its approval status on the \\\"<a href=\\\"/user/cruises/submitted/\\\">Submitted Cruises</a>\\\" page.'))\\r\\n\\t\\t\\tdelete_cruise_deadline_and_departure_notifications(new_cruise)\\r\\n\\t\\t\\tset_date_dict_outdated()\\r\\n\\t\\telif new_cruise.is_submitted and old_cruise.information_approved:\\r\\n\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise ' + str(Cruise) + ' updated. Your cruise information was modified, so your cruise\\\\'s information is now pending approval. You may track its approval status on the \\\"<a href=\\\"/user/cruises/upcoming/\\\">Upcoming Cruises</a>\\\" page.'))\\r\\n\\t\\t\\tdelete_cruise_departure_notifications(new_cruise)\\r\\n\\r\\n\\t\\t# check whether we're saving or submitting the form\\r\\n\\t\\telif hasattr(self, \\\"request\\\") and self.request.POST.get(\\\"save_cruise\\\"):\\r\\n\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully saved. You may edit and submit it on the \\\"<a href=\\\"/user/cruises/unsubmitted/\\\">Unsubmitted Cruises</a>\\\" page.'))\\r\\n\\t\\telif hasattr(self, \\\"request\\\") and self.request.POST.get(\\\"submit_cruise\\\"):\\r\\n\\t\\t\\tis_submitting = True\\r\\n\\r\\n\\t\\t\\tcruise_days = cruiseday_form.cleaned_data\\r\\n\\t\\t\\tcruise_participants = participant_form.cleaned_data\\r\\n\\t\\t\\tcruise_invoice = invoice_form.cleaned_data\\r\\n\\r\\n\\t\\t\\tif (new_cruise.is_submittable(user=self.request.user, cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)):\\r\\n\\t\\t\\t\\tnew_cruise.is_submitted = True\\r\\n\\t\\t\\t\\tnew_cruise.submit_date = timezone.now()\\r\\n\\t\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully submitted. You may track its approval status on the \\\"<a href=\\\"/user/cruises/submitted/\\\">Submitted Cruises</a>\\\" page.'))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tnew_cruise.is_submitted = False\\r\\n\\t\\t\\t\\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted:' + str(new_cruise.get_missing_information_string(cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)) + '<br>If you decide to do this later, you can get back to this cruise to review and add any missing or invalid information on the \\\"<a href=\\\"/user/cruises/unsubmitted/\\\">Unsubmitted Cruises</a>\\\" page.'))\\r\\n\\t\\telse:\\r\\n\\t\\t\\tmessages.add_message(self.request, messages.ERROR, mark_safe('We were unable to determine the action you wished to take when saving the form. If you wished to send it in for approval and not just save it, please submit it below.'))\\r\\n\\r\\n\\t\\tnew_cruise.save()\\r\\n\\r\\n\\t\\tif old_cruise.information_approved:\\r\\n\\t\\t\\tadmin_user_emails = [admin_user.email for admin_user in list(User.objects.filter(userdata__role='admin'))]\\r\\n\\t\\t\\tsend_template_only_email(admin_user_emails, EmailTemplate.objects.get(title='Approved cruise updated'), cruise=old_cruise)\\r\\n\\r\\n\\t\\treturn HttpResponseRedirect(self.get_success_url(is_submitting, new_cruise))\",\n \"def form_valid(self, form, cruiseday_form, participant_form, document_form, equipment_form, invoice_form):\\r\\n\\r\\n\\t\\tis_submitting = False\\r\\n\\r\\n\\t\\tCruise = form.save(commit=False)\\r\\n\\t\\t\\r\\n\\t\\tCruise.leader = self.request.user\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tCruise.organization = Cruise.leader.userdata.organization\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tpass\\r\\n\\t\\tform.cleaned_data[\\\"leader\\\"] = self.request.user\\r\\n\\r\\n\\t\\tif hasattr(self, \\\"request\\\"):\\r\\n\\t\\t\\t# check whether we're saving or submitting the form\\r\\n\\t\\t\\tif self.request.POST.get(\\\"save_cruise\\\"):\\r\\n\\t\\t\\t\\tCruise.is_submitted = False\\r\\n\\t\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully saved. You may edit and submit it on the \\\"<a href=\\\"/user/cruises/unsubmitted/\\\">Unsubmitted Cruises</a>\\\" page.'))\\r\\n\\t\\t\\telif self.request.POST.get(\\\"submit_cruise\\\"):\\r\\n\\t\\t\\t\\tis_submitting = True\\r\\n\\r\\n\\t\\t\\t\\tcruiseday_form = CruiseDayFormSet(self.request.POST)\\r\\n\\t\\t\\t\\tparticipant_form = ParticipantFormSet(self.request.POST)\\r\\n\\t\\t\\t\\tcruise_days = cruiseday_form.cleaned_data\\r\\n\\t\\t\\t\\tcruise_participants = participant_form.cleaned_data\\r\\n\\t\\t\\t\\tcruise_invoice = invoice_form.cleaned_data\\r\\n\\t\\t\\t\\tif (Cruise.is_submittable(user=self.request.user, cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)):\\r\\n\\t\\t\\t\\t\\tCruise.is_submitted = True\\r\\n\\t\\t\\t\\t\\tCruise.submit_date = timezone.now()\\r\\n\\t\\t\\t\\t\\tmessages.add_message(self.request, messages.SUCCESS, mark_safe('Cruise successfully submitted. You may track its approval status on the \\\"<a href=\\\"/user/cruises/submitted/\\\">Submitted Cruises</a>\\\" page.'))\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tCruise.is_submitted = False\\r\\n\\t\\t\\t\\t\\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted:' + str(Cruise.get_missing_information_string(cleaned_data=form.cleaned_data, cruise_invoice=cruise_invoice, cruise_days=cruise_days, cruise_participants=cruise_participants)) + '<br>If you decide to do this later, you can get back to this cruise to review and add any missing or invalid information on the \\\"<a href=\\\"/user/cruises/unsubmitted/\\\">Unsubmitted Cruises</a>\\\" page.'))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tCruise.is_submitted = False\\r\\n\\t\\t\\t\\tmessages.add_message(self.request, messages.ERROR, mark_safe('Cruise could not be submitted: We were unable to determine the action you wished to take on submit. Please try to submit again below.'))\\r\\n\\r\\n\\t\\tCruise.save()\\r\\n\\t\\tself.object = form.save()\\r\\n\\t\\tcruiseday_form.instance = self.object\\r\\n\\t\\tcruiseday_form.save()\\r\\n\\t\\tparticipant_form.instance = self.object\\r\\n\\t\\tparticipant_form.save()\\r\\n\\t\\tdocument_form.instance = self.object\\r\\n\\t\\tdocument_form.save()\\r\\n\\t\\tequipment_form.instance = self.object\\r\\n\\t\\tequipment_form.save()\\r\\n\\t\\tinvoice_form.instance = self.object\\r\\n\\t\\tinvoice_form.save()\\r\\n\\r\\n\\t\\treturn HttpResponseRedirect(self.get_success_url(is_submitting, Cruise))\",\n \"def conferences(self):\\r\\n return conferences.Conferences(self)\",\n \"def profileupdaterequest_create(request):\\n form = ProfileUpdateRequestForm()\\n form_helper = bootstrap_helper_wider_labels\\n page_title = 'Update Instructor Profile'\\n\\n if request.method == 'POST':\\n form = ProfileUpdateRequestForm(request.POST)\\n\\n if form.is_valid():\\n form.save()\\n\\n # TODO: email notification?\\n\\n context = {\\n 'title': 'Thank you for updating your instructor profile',\\n }\\n return render(request,\\n 'forms/profileupdate_confirm.html',\\n context)\\n else:\\n messages.error(request, 'Fix errors below.')\\n\\n context = {\\n 'title': page_title,\\n 'form': form,\\n 'form_helper': form_helper,\\n }\\n return render(request, 'forms/profileupdate.html', context)\",\n \"def post(self):\\r\\n data = request.form\\r\\n return create(data=data)\",\n \"def create_call_appointment_on_call_schedule_accept(sender, instance, **kwargs):\\n try:\\n obj = sender.objects.get(pk=instance.pk)\\n if obj.completed != instance.completed:\\n if instance.completed:\\n CallAppointment.objects.create(\\n time_slot=instance.confirmed_time\\n )\\n except ObjectDoesNotExist:\\n pass\",\n \"def put_record(uuid):\\n\\n record = collection[uuid]\\n record.data.update(request.json or request.form.to_dict())\\n record.save()\\n\\n return get_record(uuid)\",\n \"def save():\\n form_data = request.form.to_dict()\\n if (form_data['release-at'] == 'Never'):\\n form_data['release-at'] = None\\n\\n if not 'id' in form_data:\\n r = requests.post(API_ROUTE, headers={'Auth': _auth()}, json=form_data)\\n if r.status_code != requests.codes.created:\\n return r.text, r.status_code\\n else:\\n r = requests.put(API_ROUTE + '/' + str(request.form['id']), headers={'Auth': _auth()}, json=form_data)\\n if r.status_code != requests.codes.ok:\\n return r.text, r.status_code\\n\\n return redirect(url_for('index'), code=278)\",\n \"def create(self, request, *args, **kwargs):\\n self.serializer_class = ConversationDetailSerializer\\n return super(ConversationViewSet, self).create(request, *args, **kwargs)\",\n \"def update(self, conversation):\\n self.content_type = \\\"application/json\\\"\\n self.method = \\\"PATCH\\\"\\n entity = Conversation(json.loads(self.send(conversation).content))\\n self._initialize_collection_properties(entity)\\n return entity\",\n \"def new_capteur_saving():\\n f = None\\n f = CapteurForm()\\n if f.validate_on_submit():\\n o = Capteur(\\n name = f.get_name(),\\n intervalle = f.get_interval(),\\n tel = f.get_phoneNumber(),\\n TypeMesure = f.get_typeMesure().get_id(),\\n parterre = f.get_parterre().get_id())\\n a = Actions(\\n contenu = \\\"Ajout d'un nouveau capteur \\\"+f.get_name(),\\n liste = 1\\n )\\n db.session.add(a)\\n db.session.add(o)\\n db.session.commit()\\n return redirect(url_for('capteur_info', id = o.get_id()))\\n return render_template(\\n \\\"addCapteur.html\\\",\\n form = f,\\n title = \\\"Nouveau capteur\\\",\\n param = \\\"create\\\")\",\n \"def save(self):\\n\\n if self.object_id:\\n # update event\\n if not self._track_changes:\\n return True # there's nothing to update\\n url = self.build_url(\\n self._endpoints.get('event').format(id=self.object_id))\\n method = self.con.patch\\n data = self.to_api_data(restrict_keys=self._track_changes)\\n else:\\n # new event\\n if self.calendar_id:\\n url = self.build_url(\\n self._endpoints.get('event_calendar').format(\\n id=self.calendar_id))\\n else:\\n url = self.build_url(self._endpoints.get('event_default'))\\n method = self.con.post\\n data = self.to_api_data()\\n\\n response = method(url, data=data)\\n if not response:\\n return False\\n\\n self._track_changes.clear() # clear the tracked changes\\n\\n if not self.object_id:\\n # new event\\n event = response.json()\\n\\n self.object_id = event.get(self._cc('id'), None)\\n\\n self.__created = event.get(self._cc('createdDateTime'), None)\\n self.__modified = event.get(self._cc('lastModifiedDateTime'), None)\\n\\n self.__created = parse(self.__created).astimezone(\\n self.protocol.timezone) if self.__created else None\\n self.__modified = parse(self.__modified).astimezone(\\n self.protocol.timezone) if self.__modified else None\\n\\n self.ical_uid = event.get(self._cc('iCalUId'), None)\\n else:\\n self.__modified = self.protocol.timezone.localize(dt.datetime.now())\\n\\n return True\",\n \"def new():\\n data = {}\\n bai = request.values.get('bai', u\\\"\\\", type=unicode)\\n if bai:\\n # Add BAI information to form in order to keep connection between\\n # a HEPName and an author profile.\\n data[\\\"bai\\\"] = bai\\n\\n form = AuthorUpdateForm(data=data)\\n ctx = {\\n \\\"action\\\": url_for('.submitnew'),\\n \\\"name\\\": \\\"authorUpdateForm\\\",\\n \\\"id\\\": \\\"authorUpdateForm\\\",\\n }\\n\\n return render_template('authors/forms/new_form.html', form=form, **ctx)\",\n \"def delete_conference(request, pk):\\n conference = ConferenceLog.objects.get(pk=pk)\\n conference.delete()\\n return redirect('readapi:index')\",\n \"def _register_new_attendee(request, course_id):\\n\\n form = RegistrationForm(request.POST)\\n is_test = (request.GET.get(\\\"env\\\") == settings.TEST_KEY)\\n\\n # Validate the form: the captcha field will automatically\\n # check the input\\n if not form.is_valid():\\n # return defaults.bad_request(request,\\n # SuspiciousOperation(\\\"Form not valid\\\"))\\n pass\\n\\n name = request.POST[\\\"name\\\"]\\n email = request.POST[\\\"email_attendee\\\"]\\n attendee = None\\n course_attendee = None\\n course_event = get_object_or_404(CourseEvent, pk=course_id)\\n gdpr = False\\n marketing = False\\n student = False\\n amount = 0\\n\\n if course_event.course_type.level is not None:\\n level = list(filter(lambda c: c[0] == course_event.course_type.level,\\n CourseType.level_choices))[0][1]\\n else:\\n level = None\\n\\n if \\\"gdpr\\\" in request.POST and request.POST[\\\"gdpr\\\"] == \\\"on\\\":\\n gdpr = True\\n\\n if \\\"marketing\\\" in request.POST and request.POST[\\\"marketing\\\"] == \\\"on\\\":\\n marketing = True\\n\\n if \\\"student\\\" in request.POST and request.POST[\\\"student\\\"] == \\\"on\\\":\\n student = True\\n else:\\n student = False\\n\\n existing_attendees = course_event.courseattendee_set.filter(\\n attendee__email=email\\n )\\n\\n if level is not None:\\n title = \\\"{} - {}\\\".format(course_event.course_type.title, level)\\n else:\\n title = course_event.course_type.title\\n\\n if len(existing_attendees) > 0:\\n context = {\\n \\\"name\\\": existing_attendees[0].attendee.name,\\n \\\"email\\\": existing_attendees[0].attendee.email,\\n \\\"title\\\": title,\\n \\\"google_tag\\\": settings.GOOGLE_TAG,\\n }\\n return render(request, \\\"already_registered.html\\\", context)\\n\\n # save attendee details only if attendee was registered\\n attendee = None\\n try:\\n attendee = Attendee.objects.get(email=email)\\n _update_attendee_by_email(email, marketing, gdpr, name)\\n except ObjectDoesNotExist as e:\\n attendee = _create_new_attende(name, email, gdpr, marketing)\\n\\n amount = 0\\n\\n if datetime.date.today() <= course_event.early_date:\\n if student:\\n amount = course_event.price_student\\n else:\\n amount = course_event.price_regular\\n else:\\n amount = course_event.price_late\\n\\n course_attendee = CourseAttendee(\\n attendee=attendee,\\n course=course_event,\\n student=student,\\n registration_date=datetime.date.today(),\\n level=request.POST[\\\"level\\\"],\\n note=request.POST[\\\"note\\\"],\\n topics=request.POST[\\\"topics\\\"],\\n next_topics=request.POST[\\\"next_topics\\\"],\\n attended=False,\\n amount=amount,\\n token=uuid.uuid1()\\n )\\n\\n attendee.courses.add(course_event)\\n\\n organisation = request.POST[\\\"organisation\\\"]\\n if not organisation:\\n organisation = attendee.name\\n invoicemail = request.POST[\\\"invoicemail\\\"]\\n if not invoicemail:\\n invoicemail = request.POST[\\\"email_attendee\\\"]\\n\\n order = request.POST[\\\"order\\\"]\\n invoice_detail = None\\n if order:\\n invoice_details = InvoiceDetail.objects.filter(\\n order=order,\\n name=organisation\\n )\\n if len(invoice_details) > 0:\\n invoice_detail = invoice_details[0]\\n\\n if not invoice_detail:\\n invoice_detail = InvoiceDetail(\\n address=\\\"{street}\\\\n{zipcode} {city}\\\".format(\\n street=request.POST[\\\"street\\\"],\\n zipcode=request.POST[\\\"zip_code\\\"],\\n city=request.POST[\\\"city\\\"]),\\n name=organisation,\\n ico=request.POST[\\\"ico\\\"],\\n dic=request.POST[\\\"dic\\\"],\\n order=request.POST[\\\"order\\\"],\\n email=invoicemail\\n )\\n\\n invoice_detail.save()\\n course_attendee.invoice_detail = invoice_detail\\n course_attendee.save()\\n\\n _send_mails(course_event, attendee, title, organisation, amount, is_test)\\n\\n context = {\\n \\\"course_name\\\": title,\\n \\\"course_date\\\": course_event.date,\\n \\\"attendee\\\": attendee.name,\\n \\\"mail\\\": attendee.email,\\n \\\"course_id\\\": course_event.id,\\n \\\"google_tag\\\": settings.GOOGLE_TAG,\\n }\\n\\n return render(request, \\\"submitted.html\\\", context)\",\n \"def query_form_data(self) -> FlaskResponse: # pylint: disable=no-self-use\\n form_data = {}\\n slice_id = request.args.get(\\\"slice_id\\\")\\n if slice_id:\\n slc = db.session.query(Slice).filter_by(id=slice_id).one_or_none()\\n if slc:\\n form_data = slc.form_data.copy()\\n\\n update_time_range(form_data)\\n\\n return json.dumps(form_data)\",\n \"def event_create(req):\\n try:\\n utils.assert_keys(req.form, ['creator_id']+_event_args)\\n event_id = db_conn.event_new(**req.form)\\n json = {'event_id': event_id}\\n except Exception as e:\\n json = {'errors': [str(e)]}\\n return req.Response(json=json)\",\n \"def create_venue_form():\\n form = VenueForm()\\n return render_template('forms/new_venue.html', form=form)\",\n \"def add_profile(self, request, *args, **kwargs):\\n # Todo (mo): utilize self.get_serializer(instance=conversation, data=request.data)\\n context = {\\n 'conversation': self.get_object(),\\n 'request': request\\n }\\n serializer = AddProfileSerializer(data=request.data, context=context)\\n serializer.is_valid(raise_exception=True)\\n serializer.save()\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def create_new_schedule():\\n\\n # collect all relevant information from form\\n user_id = int(session['user_id'])\\n user = User.query.filter_by(user_id=int(session['user_id'])).one()\\n contact_form_value = request.form.get('contact_id')\\n start_date_unicode = request.form.get('start_date')\\n period = int(request.form.get('period'))\\n\\n # extracts email from contact_form_value string using re library\\n contact_email = contact_form_value.partition('<')[-1].rpartition('>')[0]\\n\\n # pull contact from database\\n contact = Contact.query.filter_by(email=contact_email).one()\\n contact_id = contact.contact_id\\n\\n # turns start_date into datetime object using dateutil library\\n start_date = parser.parse(start_date_unicode)\\n\\n # calculates send_date from start_date and period\\n send_date = start_date + datetime.timedelta(days=period)\\n\\n # write scheduled message to database\\n new_scheduled_msg = ScheduledMessage(user_id=user_id, \\n contact_id=contact_id,\\n send_date=send_date,\\n sent=False)\\n\\n # set new period on contact in database\\n contact.contact_period = period\\n\\n db.session.add(new_scheduled_msg)\\n db.session.commit()\\n\\n print 'user_id:', user_id\\n print 'contact_form_value:', contact_form_value\\n print 'start_date:', start_date, 'type:', type(start_date)\\n print 'contact_email:', contact_email\\n print 'contact:', contact\\n print 'contact_id:', contact.contact_id\\n print 'period:', period\\n print 'send_date:', send_date\\n return jsonify({})\",\n \"def create(self):\\n errors, data = self.format_and_control(request.form, create=True)\\n\\n if len(errors) > 0:\\n set_session_var('errors', dict(errors))\\n return None\\n\\n # Create object\\n instance = self.Component(**data)\\n\\n # Add object in database\\n res = add_in_db(instance)\\n\\n if res != 'added':\\n set_session_var('errors', str(res))\\n else:\\n set_session_var('success', res)\\n\\n if self.module_fn is not None:\\n self.module_fn(instance, data)\\n\\n return instance\",\n \"def clerk_create_appointment():\\n if request.method == 'POST':\\n patient_email = request.form['patient_email']\\n doctor_email = request.form['doctor_email']\\n date = request.form['date']\\n time = request.form['time']\\n\\n response_clerk_create_appointment = requests.post(server_url + 'medical_clerk/create_appointment', json={\\n 'patient_email': patient_email,\\n 'doctor_email': doctor_email,\\n 'date': date,\\n 'time': time\\n })\\n response_clerk_create_appointment = response_clerk_create_appointment.json()\\n\\n if response_clerk_create_appointment.get('Status') == \\\"INVALID_DOCTOR_EMAIL\\\":\\n return render_template('clerks/clerk_appointment_failed.html')\\n elif response_clerk_create_appointment.get('Status') == \\\"INVALID_PATIENT_EMAIL\\\":\\n return render_template('clerks/clerk_appointment_failed.html')\\n elif response_clerk_create_appointment.get('Status') == \\\"DOCTOR_IS_NOT_AVAILABLE_AT_THAT_TIME\\\":\\n return render_template('clerks/clerk_appointment_failed.html')\\n elif response_clerk_create_appointment.get('Status') == \\\"DOCTOR_HAS_AN_APPOINTMENT_SELECTED_TIME_SLOT\\\":\\n return render_template('clerks/clerk_appointment_failed.html')\\n else:\\n referer = request.referrer\\n return redirect(referer, code=302)\\n else:\\n return render_template('clerks/home.html')\",\n \"def save_capteur():\\n f = None\\n f = CapteurForm()\\n a = get_capteur(f.get_id())\\n na = a.get_name()\\n if f.validate_on_submit():\\n a.set_name(f.get_name())\\n a.set_num(f.get_phoneNumber())\\n a.set_interval(f.get_interval())\\n if a.get_parterre() != f.get_parterre().get_id():\\n a.set_parterre(f.get_parterre().get_id())\\n if a.get_typeMesure() != f.get_typeMesure().get_id():\\n a.set_typeMesure(f.get_typeMesure().get_id())\\n ac = Actions(\\n contenu = \\\"Modification du capteur \\\"+na+\\\" -> \\\"+a.get_name(),\\n liste = 1\\n )\\n db.session.add(ac)\\n db.session.commit()\\n return redirect(url_for(\\n \\\"capteur_info\\\",\\n id = f.get_id()))\\n f.next.data = \\\"save_capteur\\\"\\n return render_template(\\n \\\"addCapteur.html\\\",\\n title = a.get_name()+\\\" - edit\\\",\\n form = f,\\n capteur=a,\\n param = \\\"modif\\\")\",\n \"def create_or_edit_event(request, calendar_id=None, event_id=None, redirect=None):\\n instance = None\\n if event_id:\\n instance = get_object_or_404(Event, id=event_id)\\n calendar = None\\n if calendar_id is not None:\\n calendar = get_object_or_404(Calendar, id=calendar_id)\\n form = EventForm(data=request.POST or None, instance=instance, hour24=True)\\n if form.is_valid():\\n event = form.save(commit=False)\\n if instance is None:\\n event.creator = request.user\\n event.save()\\n if calendar is not None and instance is None:\\n calendar.events.add(event)\\n next = redirect or reverse('s_event', args=[event.id])\\n if 'next' in request.GET:\\n next = _check_next_url(request.GET['next']) or next\\n return HttpResponseRedirect(next)\\n return render_to_response('schedule/create_event.html', {\\n \\\"form\\\": form,\\n \\\"calendar\\\": calendar\\n }, context_instance=RequestContext(request))\",\n \"def get_room_request_from_form(form):\\n room_request = RoomRequest(\\n first_name=form.first_name.data,\\n last_name=form.last_name.data,\\n relationship_to_patient=form.rel_to_patient.data,\\n address_line_one=form.street_address.data,\\n address_line_two=form.apt_st_address.data,\\n city=form.city.data,\\n state=form.state.data,\\n zip_code=form.zipcode.data,\\n country=form.country.data,\\n primary_phone=form.phone_number.data,\\n secondary_phone=form.alt_phone_number.data,\\n email=form.email.data,\\n primary_language=form.primary_language.data,\\n secondary_language=form.secondary_language.data,\\n previous_stay=form.stayed_before.data,\\n patient_first_name=form.patient_first_name.data,\\n patient_last_name=form.patient_last_name.data,\\n patient_dob=form.patient_dob.data,\\n patient_gender=form.patient_gender.data,\\n patient_hospital=form.hospital.data,\\n patient_hospital_department=form.hospital_department.data,\\n patient_treatment_description=form.description.data,\\n patient_diagnosis=form.diagnosis.data,\\n patient_first_appt_date=form.first_appt_date.data,\\n patient_check_in=form.check_in_date.data,\\n patient_check_out=form.check_out_date.data,\\n patient_treating_doctor=form.treating_doctor.data,\\n patient_doctors_phone=form.doctor_phone_number.data,\\n patient_social_worker=form.hospital_social_worker.data,\\n patient_social_worker_phone=form.sw_phone_number.data,\\n inpatient=form.in_or_out_patient.data,\\n inpatient_prior=form.staying_prior_to_admission.data,\\n vaccinated=form.vaccinated.data,\\n comments=form.comments.data,\\n wheelchair_access=form.wheelchair_access.data,\\n full_bathroom=form.full_bathroom.data,\\n pack_n_play=form.pack_n_play.data)\\n\\n guests = []\\n for i in range(1, 6):\\n guest_name = form[f'guest{i}_name'].data\\n guest_dob = form[f'guest{i}_dob'].data\\n guest_rel = form[f'guest{i}_rel_to_patient'].data\\n guest_email = form[f'guest{i}_email'].data\\n guest_guardian = form[f'guest{i}_guardian'].data\\n if guest_name and guest_dob and guest_rel and guest_email and guest_guardian:\\n guests.append(Guest(\\n name=guest_name,\\n dob=guest_dob,\\n relationship_to_patient=guest_rel,\\n email=guest_email,\\n guardian=guest_guardian))\\n room_request.guests = guests\\n\\n return room_request\",\n \"async def get_conference(self, user_id):\\n db = self['db_engine']\\n async with db.acquire() as connection:\\n query = select([Conference]).where(Conference.user_id == user_id).where(\\n Conference.expired_at > datetime.now(timezone.utc))\\n if conference := await(await connection.execute(query)).first():\\n return conference\",\n \"def conference_status(request, call_sid):\\n resp = VoiceResponse()\\n call_status = None\\n if request.method == 'POST':\\n call_status = request.POST.get('CallStatus', None)\\n if request.method == 'GET':\\n call_status = request.GET.get('CallStatus', None)\\n if call_status == 'completed':\\n client.calls.hangup(call_sid)\\n if call_status == 'no-answer' or call_status == 'busy' or call_status == 'failed':\\n call = client.calls(call_sid).update(\\n method=\\\"POST\\\",\\n url=BASE_URL + \\\"/NoAnswer?myCallSid=\\\"+call_sid\\n )\\n print(call.to)\\n return HttpResponse(str(resp))\",\n \"def getConferenceSessions(self, request):\\n sessions = self._getConferenceSessions(request)\\n # Return individual SessionForm object per Session\\n return SessionForms(\\n items=[self._copySessionToForm(session) for session in sessions]\\n )\",\n \"def get_form_from_room_request(room_request):\\n form = RoomRequestForm(\\n first_name=room_request.first_name,\\n last_name=room_request.last_name,\\n rel_to_patient=room_request.relationship_to_patient,\\n street_address=room_request.address_line_one,\\n apt_st_address=room_request.address_line_two,\\n city=room_request.city,\\n state=room_request.state,\\n zipcode=room_request.zip_code,\\n country=room_request.country,\\n phone_number=room_request.primary_phone,\\n alt_phone_number=room_request.secondary_phone,\\n email=room_request.email,\\n primary_language=room_request.primary_language,\\n secondary_language=room_request.secondary_language,\\n stayed_before=room_request.previous_stay,\\n patient_first_name=room_request.patient_first_name,\\n patient_last_name=room_request.patient_last_name,\\n patient_dob=room_request.patient_dob,\\n patient_gender=room_request.patient_gender,\\n hospital=room_request.patient_hospital,\\n hospital_department=room_request.patient_hospital_department,\\n description=room_request.patient_treatment_description,\\n diagnosis=room_request.patient_diagnosis,\\n first_appt_date=room_request.patient_first_appt_date,\\n check_in_date=room_request.patient_check_in,\\n check_out_date=room_request.patient_check_out,\\n treating_doctor=room_request.patient_treating_doctor,\\n doctor_phone_number=room_request.patient_doctors_phone,\\n hospital_social_worker=room_request.patient_social_worker,\\n sw_phone_number=room_request.patient_social_worker_phone,\\n in_or_out_patient=room_request.inpatient,\\n staying_prior_to_admission=room_request.inpatient_prior,\\n vaccinated=room_request.vaccinated,\\n comments=room_request.comments,\\n wheelchair_access=room_request.wheelchair_access,\\n full_bathroom=room_request.full_bathroom,\\n pack_n_play=room_request.pack_n_play)\\n\\n for i, guest in enumerate(room_request.guests):\\n form[f'guest{i+1}_name'].data = guest.name\\n form[f'guest{i+1}_dob'].data = guest.dob\\n form[f'guest{i+1}_rel_to_patient'].data = guest.relationship_to_patient\\n form[f'guest{i+1}_email'].data = guest.email\\n form[f'guest{i+1}_guardian'].data = guest.guardian\\n\\n return form\",\n \"def subscribe():\\n form = SubscribeForm()\\n if form.validate_on_submit():\\n subscription = Subscription(email=form.email.data)\\n db.session.add(subscription)\\n db.session.commit()\\n return redirect(url_for('main.index'))\\n\\n return render_template('subscribes.html', form=form)\",\n \"def update(self, request, *args, **kwargs):\\n instance = self.get_object()\\n if instance.author != request.user:\\n return Response(status=status.HTTP_403_FORBIDDEN)\\n if request.user.role < User.ORGANIZER:\\n return Response(\\n {'error_message': _('Only Organizer can edit events.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'title' not in request.data:\\n return Response(\\n {'error_message': _('No title provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'city_id' not in request.data:\\n return Response(\\n {'error_message': _('No city provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'currency_id' not in request.data:\\n return Response(\\n {'error_message': _('No currency provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'min_price' not in request.data and 'max_price' not in request.data:\\n return Response(\\n {'error_message': _('Min_price or max_price should be provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'datetimes' not in request.data:\\n if 'start_datetime' not in request.data:\\n return Response(\\n {'error_message': _('No start_datetime provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n if 'end_datetime' not in request.data:\\n return Response(\\n {'error_message': _('No end_datetime provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n start_datetime = dateutil.parser.parse(request.data.pop('start_datetime'))\\n end_datetime = dateutil.parser.parse(request.data.pop('end_datetime'))\\n\\n start_time = datetime.datetime.strftime(start_datetime, settings.TIME_STRING_FIELD_FORMAT)\\n end_time = datetime.datetime.strftime(end_datetime, settings.TIME_STRING_FIELD_FORMAT)\\n request.data['datetimes'] = [{\\n 'date': date_to_string(date, settings.DATE_STRING_FIELD_FORMAT),\\n 'start_time': start_time,\\n 'end_time': end_time,\\n } for date in daterange(start_datetime.date(), end_datetime.date())]\\n else:\\n for item in request.data['datetimes']:\\n if 'date' not in item or \\\\\\n 'start_time' not in item or \\\\\\n 'end_time' not in item:\\n return Response(\\n {'error_message': _('Wrong datetimes format provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n try:\\n string_to_date(str(item['date']), settings.DATE_STRING_FIELD_FORMAT)\\n string_to_time(item['start_time'], settings.TIME_STRING_FIELD_FORMAT)\\n string_to_time(item['end_time'], settings.TIME_STRING_FIELD_FORMAT)\\n except:\\n return Response(\\n {'error_message': _('Wrong datetimes format provided.')},\\n status=status.HTTP_400_BAD_REQUEST\\n )\\n\\n serializer = self.get_serializer(instance, data=request.data)\\n if serializer.is_valid():\\n self.perform_update(serializer)\\n headers = self.get_success_headers(serializer.data)\\n\\n return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\\n else:\\n return Response(\\n serializer.errors,\\n status=status.HTTP_400_BAD_REQUEST\\n )\",\n \"def unregisterFromConference(self, request):\\n return self._conferenceRegistration(request, reg=False)\",\n \"def new_album():\\n form = PriceForm(request.form)\\n if request.method == 'POST' and form.validate():\\n # save the album\\n price = Price()\\n save_changes(price, form, new=True)\\n flash('Price created successfully!')\\n return redirect('/')\\n return render_template('new_album.html', form=form)\",\n \"def post(self, request, pk):\\n application = get_application(pk)\\n\\n data = request.data\\n data[\\\"organisation\\\"] = get_request_user_organisation_id(request)\\n\\n serializer = PartySerializer(data=data, application_type=application.case_type.sub_type)\\n\\n if serializer.is_valid(raise_exception=True):\\n if str_to_bool(data.get(\\\"validate_only\\\", False)):\\n return JsonResponse(data={data[\\\"type\\\"]: serializer.initial_data}, status=status.HTTP_200_OK)\\n\\n # Save party and add to application\\n serializer.save()\\n\\n try:\\n party, removed_party = application.add_party(serializer.instance)\\n except ApplicationException as exc:\\n return JsonResponse(data={\\\"errors\\\": exc.data}, status=status.HTTP_400_BAD_REQUEST)\\n\\n # Audit\\n if removed_party:\\n audit_trail_service.create(\\n actor=request.user,\\n verb=AuditType.REMOVE_PARTY,\\n target=application.get_case(),\\n payload={\\\"party_type\\\": removed_party.type.replace(\\\"_\\\", \\\" \\\"), \\\"party_name\\\": removed_party.name},\\n )\\n audit_trail_service.create(\\n actor=request.user,\\n verb=AuditType.ADD_PARTY,\\n target=application.get_case(),\\n payload={\\\"party_type\\\": party.type, \\\"party_name\\\": party.name},\\n )\\n\\n return JsonResponse(data={party.type: serializer.data}, status=status.HTTP_201_CREATED)\",\n \"def create(self, request, *args, **kwargs):\\n self.serializer_class = ConversationDetailSerializer\\n return super(PublicChatViewSet, self).create(request, *args, **kwargs)\",\n \"def create_volunteer(request, volunteer_id=None):\\n volunteer = None\\n try:\\n volunteer = Volunteer.objects.get(id=volunteer_id)\\n user = User.objects.get(id=volunteer.user_id)\\n except Volunteer.DoesNotExist:\\n vol_form = VolunteerForm()\\n user_form = UserCreationForm()\\n user = User()\\n except User.DoesNotExist:\\n user_form = UserCreationForm()\\n\\n if request.method == \\\"POST\\\":\\n vol_form = VolunteerForm(request.POST, request.FILES)\\n user_form = UserCreationForm(request.POST, request.FILES)\\n\\n if vol_form.is_valid() and user_form.is_valid():\\n svol = vol_form.save()\\n user = user_form.save()\\n\\n # associate the user with the volunteer\\n svol.user = user\\n svol.save()\\n user = authenticate(username=user.username, password=user_form.clean_password2())\\n login(request, user)\\n\\n return redirect(\\\"edit-volunteer-profile\\\", \\n volunteer_id=svol.id, hide_contact_form=True)\\n elif volunteer_id:\\n vol_form = VolunteerForm(instance=volunteer)\\n if user:\\n user_form = UserCreationForm(instance=user)\\n\\n return render(request, 'edit-volunteer.html', \\n {\\\"vol_form\\\" : vol_form, \\\"user_form\\\" : user_form })\",\n \"def add_api():\\n form = NewAPIForm()\\n form.parameters.choices = get_all_params()\\n\\n if form.add_parameter.data:\\n form.new_parameter.append_entry()\\n\\n if form.submit.data and form.validate_on_submit():\\n new_api = Api(\\n name=form.name.data,\\n region=form.region.data,\\n description=form.description.data,\\n url=form.url.data\\n )\\n\\n # Links the parameters to the new API.\\n for param_data in form.parameters.data:\\n param = Parameter.query.filter_by(name=param_data).first()\\n if param is not None:\\n # TODO: Ask users to give a param description when adding params.\\n new_api.add_param(param, \\\"Parameter-description\\\")\\n\\n # Creates the new parameters and links them to the API.\\n for new_param in form.new_parameter:\\n param = Parameter(\\n name=new_param.param_name.data,\\n param_format=new_param.param_format.data,\\n count=0\\n )\\n new_api.add_param(param, new_param.description)\\n\\n db.session.add(new_api)\\n try:\\n db.session.commit()\\n flash('API {} successfully added'\\n .format(new_api.name),\\n 'form-success')\\n return redirect(url_for('api.index'))\\n except IntegrityError:\\n db.session.rollback()\\n flash('Database error occurred. Please try again.',\\n 'form-error')\\n return render_template('api/add.html', form=form)\",\n \"def create_venue_submission():\\n # TODO: insert form data as a new Venue record in the db, instead (DONE)\\n # TODO: modify data to be the data object returned from db insertion\\n\\n try:\\n name = request.form.get(\\\"name\\\")\\n city = request.form.get(\\\"city\\\")\\n state = request.form.get(\\\"state\\\")\\n address = request.form.get(\\\"address\\\")\\n phone = request.form.get(\\\"phone\\\")\\n imageLink = request.form.get(\\\"image_link\\\")\\n genres = request.form.getlist(\\\"genres\\\")\\n facebookLink = request.form.get(\\\"facebook_link\\\")\\n website = request.form.get(\\\"website\\\")\\n seeking_talent = request.form.get(\\\"facebook_link\\\")\\n seeking_description = request.form.get(\\\"facebook_link\\\")\\n\\n venue_to_add = Venue(\\n name=name,\\n city=city,\\n state=state,\\n address=address,\\n phone=phone,\\n image_link=imageLink,\\n genres=genres,\\n facebook_link=facebookLink,\\n website=website,\\n seeking_talent=seeking_talent,\\n seeking_description=seeking_description\\n )\\n\\n db.session.add(venue_to_add)\\n db.session.commit()\\n\\n # on successful db insert, flash success\\n flash(\\\"Venue \\\" + request.form[\\\"name\\\"] + \\\" was successfully listed!\\\")\\n\\n # TODO: on unsuccessful db insert, flash an error instead.\\n # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\\n except:\\n flash(\\\"An error occurred. Venue \\\" + name + \\\" could not be listed.\\\")\\n db.session.rollback()\\n finally:\\n db.session.close()\\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\\n return render_template(\\\"pages/home.html\\\")\",\n \"def update(self, instance, validated_data):\\n instance.description = validated_data.get('description', instance.description)\\n instance.tender_number = validated_data.get('tender_number', instance.tender_number)\\n instance.start_date = validated_data.get('start_date', instance.start_date)\\n instance.end_date = validated_data.get('end_date', instance.end_date)\\n instance.save()\\n return instance\",\n \"def add_Capteur():\\n f = None\\n f = CapteurForm()\\n return render_template(\\n \\\"addCapteur.html\\\",\\n form = f,\\n title = \\\"Nouveau capteur\\\",\\n param = \\\"create\\\")\",\n \"def save_contract(bid):\\n title = 'Create contract -- ' + bid\\n doc = data_active.find_one({'bid': bid})\\n if doc is None:\\n # error exit from function\\n web_logging.error('bid= {} not found in \\\"data_active\\\"'.format(bid))\\n flash('bid= {} not found in \\\"data_active\\\"'.format(bid))\\n return redirect(url_for('lists'))\\n\\n # take only 10 chars as number\\n doc['phone'] = doc['phone'].replace('-', '')\\n doc['phone'] = doc['phone'][len(doc['phone'])-10:]\\n\\n search_number = numbers.find_one({'numbers': {'$eq': doc['phone']}})\\n contract = Contract()\\n contract.from_lists(doc)\\n\\n if search_number is None:\\n # no contacts with such number, call create new contact form\\n form_doc = {'city': doc['location'], 'numbers': doc['phone'], 'comment': doc['comment'],\\n 'loc_comments': doc['comment']}\\n info = '--------- New Contact ------------'\\n else:\\n form_doc = Contact().to_form(search_number)\\n info = '========= Contact already known, please check ======'\\n\\n form_doc.update(contract.as_dict())\\n web_logging.debug('data for form= {}'.format(form_doc))\\n form = Transaction(**form_doc)\\n\\n if form.validate_on_submit():\\n contact_info = Contact()\\n contact_info.from_form(form.data)\\n\\n contract_info = Contract()\\n contract_info.from_form(form.data)\\n\\n if contact_info.contact_id is None:\\n # contact is new\\n contact_info.create_time = datetime.utcnow()\\n contact_info.update_time = contact_info.create_time\\n web_logging.debug('inserting contact_info= {}'.format(contact_info.as_dict()))\\n web_logging.debug('inserting contract_info= {}'.format(contract_info))\\n flash('inserting contact_info= {}, contract_info= {}'.format(contact_info.as_dict(), contract_info))\\n result_contract = contract_info.mongo_insert()\\n result_contact = contact_info.mongo_insert()\\n # add contact id into document\\n result_contract_upd = contracts.update_one({'_id': ObjectId(result_contract)},\\n {'$set': {'contact': ObjectId(result_contact)}})\\n result_contact_upd = numbers.update_one({'_id': ObjectId(result_contact)},\\n {'$addToSet': {'contracts': ObjectId(result_contract)}})\\n else:\\n # contact already exists\\n contact_info.update_time = datetime.utcnow()\\n web_logging.debug('inserting contact_info= {}'.format(contact_info.as_dict()))\\n web_logging.debug('inserting contract_info= {}'.format(contract_info))\\n flash('updating contact_info= {}, creating contract_info= {}'.format(contact_info.as_dict(), contract_info))\\n result_contract = contract_info.mongo_insert()\\n result_contact = numbers.update_one({'_id': ObjectId(contact_info.contact_id)},\\n {'$addToSet': {'contracts': ObjectId(result_contract)}})\\n\\n return redirect('/contracts')\\n\\n return render_template('contract.html', title=title, form=form, info=info)\",\n \"def getSpeaker(self, request):\\n # Get Speaker object from request; bail if not found\\n speaker = _getEntityByWebsafeKey(request.websafeSpeakerKey,\\n 'Speaker')\\n # Return SpeakerForm\\n return self._copySpeakerToForm(speaker)\",\n \"def new(request):\\n\\n if request.method == 'POST':\\n form = PublicChatForm(request.POST)\\n if form.is_valid(): # All validation rules pass\\n chat = form.save(commit=False)\\n chat.posted_by = request.user\\n\\n chat.save()\\n\\n return HttpResponseRedirect('/chats/')\\n else:\\n form = PublicChatForm()\\n\\n return render_to_response('new.html', {\\n 'form': form,\\n }, context_instance=RequestContext(request))\",\n \"def update(self, instance, validated_data):\\n instance.email = validated_data.get('email', instance.email)\\n instance.first_name = validated_data.get('first_name', instance.first_name)\\n instance.last_name = validated_data.get('last_name', instance.last_name)\\n instance.date_of_birth = validated_data.get('date_of_birth', instance.date_of_birth)\\n instance.bio = validated_data.get('bio', instance.bio)\\n instance.country = validated_data.get('country', instance.country)\\n instance.avatar = validated_data.get('avatar', instance.avatar)\\n instance.save()\\n return instance\",\n \"def create_or_update_resources(self, resource, form, session):\\n if resource is None:\\n # create new user\\n user = self.User()\\n session.add(user)\\n else:\\n # update existing user\\n user = resource\\n\\n # update user\\n user.name = form.name.data\\n user.description = form.description.data\\n user.email = form.email.data\\n if form.password.data:\\n user.set_password(form.password.data)\\n user.failed_sign_in_count = form.failed_sign_in_count.data or 0\\n\\n if self.totp_enabled:\\n if form.totp_secret.data:\\n user.totp_secret = form.totp_secret.data\\n else:\\n user.totp_secret = None\\n\\n # update user info\\n if form.user_info.data:\\n # ignore crsf_token of subform\\n user_info_data = form.user_info.data\\n user_info_data.pop('csrf_token', None)\\n\\n if user_info_data:\\n user_info = user.user_info\\n if user_info is None:\\n # create new user_info\\n user_info = self.UserInfo()\\n # assign to user\\n user_info.user = user\\n\\n # update user info fields\\n for field, value in user_info_data.items():\\n setattr(user_info, field, value)\\n\\n # update groups\\n self.update_collection(\\n user.groups_collection, form.groups, self.Group, 'id', session\\n )\\n # update roles\\n self.update_collection(\\n user.roles_collection, form.roles, self.Role, 'id', session\\n )\",\n \"def booking_update(id):\\n if request.method == \\\"POST\\\":\\n month = request.form.get(\\\"month\\\")\\n day = request.form.get(\\\"day\\\")\\n year = request.form.get(\\\"year\\\")\\n\\n booking = Booking.query.filter_by(id=id).first()\\n booking.first_name = request.form.get(\\\"first_name\\\")\\n booking.last_name = request.form.get(\\\"last_name\\\")\\n booking.phone = request.form.get(\\\"phone\\\")\\n booking.email = request.form.get(\\\"email\\\")\\n booking.num_participants = request.form.get(\\\"num_participants\\\")\\n booking.booking_date = f\\\"{year}-{month}-{day}\\\"\\n booking.collection_time = request.form.get(\\\"collection_time\\\")\\n booking.return_time = request.form.get(\\\"return_time\\\")\\n\\n if request.form.get(\\\"rental_complete\\\") == \\\"True\\\":\\n booking.rental_complete = True\\n else:\\n booking.rental_complete = False\\n\\n payment = Payment.query.filter_by(booking_id=id).first()\\n payment.full_amount_due = float(request.form.get(\\\"full_amount_due\\\"))\\n\\n if request.form.get(\\\"remainder_due_check\\\") == \\\"paid\\\":\\n payment.remainder_due = 0\\n else:\\n payment.remainder_due = request.form.get(\\\"remainder_due\\\")\\n\\n db.session.commit()\\n\\n return redirect(url_for(\\\"bookings.booking_index\\\"))\\n\\n booking = Booking.query.get(id)\\n payment = Payment.query.filter_by(booking_id=id).first()\\n booking_date = booking.booking_date.split(\\\"-\\\")\\n date = {\\\"month\\\": booking_date[1], \\\"day\\\": booking_date[2],\\n \\\"year\\\": booking_date[0]}\\n return render_template(\\\"booking_update.html\\\", booking=booking,\\n payment=payment, date=date)\",\n \"def create_venue_submission():\\n form = VenueForm(request.form)\\n\\n try:\\n new_venue = Venue(\\n name=form.name.data,\\n city=form.city.data,\\n state=form.state.data,\\n address=form.address.data,\\n phone=form.phone.data,\\n genres=form.genres.data,\\n facebook_link=form.facebook_link.data,\\n image_link=form.image_link.data,\\n website=form.website.data,\\n seeking_talent=form.seeking_talent.data,\\n seeking_description=form.seeking_description.data\\n )\\n\\n db.session.add(new_venue)\\n db.session.commit()\\n\\n flash('Venue ' + request.form['name'] + ' was successfully listed!', 'info')\\n\\n except Exception as ex:\\n db.session.rollback()\\n flash('Error occurred. Venue ' + request.form['name'] + ' could not be listed. ' + str(ex), 'danger')\\n finally:\\n db.session.close()\\n\\n return redirect(url_for('index'))\",\n \"def createCourse():\\n\\tif request.method == 'POST':\\n\\t\\tcname = request.form['cname']\\n\\t\\tcourseterm = request.form['courseterm']\\n\\t\\tcoursepoint = request.form['coursepoint']\\n\\t\\tcoursetype = request.form['coursetype']\\t\\n\\t\\tcourseyear = request.form['courseyear']\\t\\n\\t\\ttname = request.form['tname']\\t\\n\\t\\terror = None\\n\\n\\t\\tif not cname:\\n\\t\\t\\terror = 'Course name is required.'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course term is required'\\n\\t\\telif not courseterm:\\n\\t\\t\\terror = 'Course point is required'\\n\\n\\t\\tif error is not None:\\n\\t\\t\\tflash(error)\\n\\t\\telse:\\n\\t\\t\\tdb = get_db()\\n\\t\\t\\tcur = db.cursor()\\n\\t\\t\\tcur.execute(\\n\\t\\t\\t\\t'INSERT INTO course (cname, courseyear, coursetype, courseterm, coursepoint, tname)'\\n\\t\\t\\t\\t' VALUES (%s, %s, %s, %s, %s, %s)',\\n\\t\\t\\t\\t(cname, courseyear, coursetype, courseterm, coursepoint, tname)\\n\\t\\t\\t)\\n\\t\\t\\tdb.commit()\\n\\t\\t\\treturn redirect(url_for('info.index'))\\n\\n\\treturn render_template('info/createCourse.html')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7081421","0.7047886","0.6980478","0.6792568","0.67714655","0.6611755","0.6580865","0.61293185","0.60343635","0.59881353","0.5875531","0.57053417","0.5569745","0.5504398","0.5384232","0.53397477","0.5316019","0.52989537","0.51942","0.5168115","0.5145852","0.5060172","0.50315446","0.49873978","0.49795318","0.4978742","0.49707797","0.49236855","0.48550284","0.48450962","0.48008335","0.47607172","0.4751936","0.47017404","0.46912912","0.4683086","0.4676043","0.4662035","0.46475494","0.46465096","0.46138","0.46042165","0.45745158","0.45720395","0.4549827","0.45480058","0.45296115","0.45230398","0.4519711","0.45068017","0.44904244","0.44326892","0.4429339","0.44291028","0.441976","0.44092336","0.44084802","0.4390993","0.43898416","0.43885967","0.43827277","0.43676433","0.4364887","0.43640277","0.4361825","0.43573698","0.43429667","0.43386889","0.43302128","0.43133584","0.43071675","0.4304454","0.43030292","0.42986465","0.4298611","0.4297176","0.42902297","0.42819566","0.42713898","0.426906","0.42665076","0.42639422","0.425175","0.42456856","0.42444494","0.4237068","0.42344832","0.42121908","0.42116025","0.42100206","0.42066467","0.42063478","0.42061317","0.42043343","0.4201778","0.41986915","0.41981682","0.4180771","0.4168983","0.41687813"],"string":"[\n \"0.7081421\",\n \"0.7047886\",\n \"0.6980478\",\n \"0.6792568\",\n \"0.67714655\",\n \"0.6611755\",\n \"0.6580865\",\n \"0.61293185\",\n \"0.60343635\",\n \"0.59881353\",\n \"0.5875531\",\n \"0.57053417\",\n \"0.5569745\",\n \"0.5504398\",\n \"0.5384232\",\n \"0.53397477\",\n \"0.5316019\",\n \"0.52989537\",\n \"0.51942\",\n \"0.5168115\",\n \"0.5145852\",\n \"0.5060172\",\n \"0.50315446\",\n \"0.49873978\",\n \"0.49795318\",\n \"0.4978742\",\n \"0.49707797\",\n \"0.49236855\",\n \"0.48550284\",\n \"0.48450962\",\n \"0.48008335\",\n \"0.47607172\",\n \"0.4751936\",\n \"0.47017404\",\n \"0.46912912\",\n \"0.4683086\",\n \"0.4676043\",\n \"0.4662035\",\n \"0.46475494\",\n \"0.46465096\",\n \"0.46138\",\n \"0.46042165\",\n \"0.45745158\",\n \"0.45720395\",\n \"0.4549827\",\n \"0.45480058\",\n \"0.45296115\",\n \"0.45230398\",\n \"0.4519711\",\n \"0.45068017\",\n \"0.44904244\",\n \"0.44326892\",\n \"0.4429339\",\n \"0.44291028\",\n \"0.441976\",\n \"0.44092336\",\n \"0.44084802\",\n \"0.4390993\",\n \"0.43898416\",\n \"0.43885967\",\n \"0.43827277\",\n \"0.43676433\",\n \"0.4364887\",\n \"0.43640277\",\n \"0.4361825\",\n \"0.43573698\",\n \"0.43429667\",\n \"0.43386889\",\n \"0.43302128\",\n \"0.43133584\",\n \"0.43071675\",\n \"0.4304454\",\n \"0.43030292\",\n \"0.42986465\",\n \"0.4298611\",\n \"0.4297176\",\n \"0.42902297\",\n \"0.42819566\",\n \"0.42713898\",\n \"0.426906\",\n \"0.42665076\",\n \"0.42639422\",\n \"0.425175\",\n \"0.42456856\",\n \"0.42444494\",\n \"0.4237068\",\n \"0.42344832\",\n \"0.42121908\",\n \"0.42116025\",\n \"0.42100206\",\n \"0.42066467\",\n \"0.42063478\",\n \"0.42061317\",\n \"0.42043343\",\n \"0.4201778\",\n \"0.41986915\",\n \"0.41981682\",\n \"0.4180771\",\n \"0.4168983\",\n \"0.41687813\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68450546"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":18496,"cells":{"query":{"kind":"string","value":"Parse, check validity and format user supplied filters."},"document":{"kind":"string","value":"def _formatFilters(self, filters):\n formatted_filters = []\n inequality_field = None\n for f in filters:\n filtr = {\n field.name: getattr(f, field.name) for field in f.all_fields()\n }\n try:\n filtr[\"field\"] = FIELDS[filtr[\"field\"]]\n filtr[\"operator\"] = OPERATORS[filtr[\"operator\"]]\n except KeyError:\n raise endpoints.BadRequestException(\n \"Filter contains invalid field or operator.\")\n # Every operation except \"=\" is an inequality\n if filtr[\"operator\"] != \"=\":\n # Check if inequality operation has been used in previous\n # filters disallow the filter if inequality was performed on a\n # different field before. Track the field on which the\n # inequality operation is performed.\n if inequality_field and inequality_field != filtr[\"field\"]:\n raise endpoints.BadRequestException(\n \"Inequality filter is allowed on only one field.\")\n else:\n inequality_field = filtr[\"field\"]\n formatted_filters.append(filtr)\n return (inequality_field, formatted_filters)"},"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 handle_filters(filters: Optional[List[str]]) -> List[Dict[str, Any]]:\n filters_to_send = []\n for filter_ in filters or []:\n split_filter = filter_.split('=')\n if len(split_filter) != 2 or not split_filter[0] or not split_filter[1]:\n raise DemistoException('Filters should be in the format of \"filtername1=filtervalue1,filtername2=filtervalue2\". '\n f'The filter \"{filter_}\" doesn\\'t meet this requirement.')\n filters_to_send.append({'name': split_filter[0],\n 'operator': '=',\n 'value': split_filter[1]})\n return filters_to_send","def make_filter_specification(cls, filter_string):\n try:\n return parse_filter(filter_string)\n except ParseException as err:\n raise ValueError('Expression parameters have errors. %s' % err)","def parse_filters(func, *args, **kwargs):\n request = args[1]\n filters = request.query_params.get('filters', None)\n\n if not filters:\n return func(*args, **kwargs)\n cleaned_filters = {}\n try:\n filters = json.loads(filters)\n for field, operations in filters.items():\n for operation, value in operations.items():\n cleaned_filters['%s__%s' % (field, operation)] = value\n except (ValueError, AttributeError):\n return Response(\n {'errors': [\n \"Filters incorrectly formatted. Required format: \"\n \"{'filters': {'fieldname': { 'operation': 'value'}}\"\n ]},\n status=400\n )\n\n try:\n # NOTE(adriant): This feels dirty and unclear, but it works.\n # Positional argument 3 is filters, so we just replace it.\n args = list(args)\n args[2] = cleaned_filters\n return func(*args, **kwargs)\n except FieldError as e:\n return Response({'errors': [str(e)]}, status=400)","def parse_filters(func, *args, **kwargs):\n request = args[1]\n filters = request.query_params.get(\"filters\", None)\n\n if not filters:\n return func(*args, **kwargs)\n cleaned_filters = {}\n try:\n filters = json.loads(filters)\n for field, operations in filters.items():\n for operation, value in operations.items():\n cleaned_filters[\"%s__%s\" % (field, operation)] = value\n except (ValueError, AttributeError):\n return Response(\n {\n \"errors\": [\n \"Filters incorrectly formatted. Required format: \"\n \"{'filters': {'fieldname': { 'operation': 'value'}}\"\n ]\n },\n status=400,\n )\n\n try:\n # NOTE(adriant): This feels dirty and unclear, but it works.\n # Positional argument 3 is filters, so we just replace it.\n args = list(args)\n args[2] = cleaned_filters\n return func(*args, **kwargs)\n except FieldError as e:\n return Response({\"errors\": [str(e)]}, status=400)","def _ParseFilterOptions(self, options):\n names = [u'date_filters', u'filter_file']\n helpers_manager.ArgumentHelperManager.ParseOptions(\n options, self, names=names)\n\n extensions_string = self.ParseStringOption(options, u'extensions_string')\n self._ParseExtensionsString(extensions_string)\n\n names_string = getattr(options, u'names_string', None)\n self._ParseNamesString(names_string)\n\n signature_identifiers = getattr(options, u'signature_identifiers', None)\n try:\n self._ParseSignatureIdentifiers(\n self._data_location, signature_identifiers)\n except (IOError, ValueError) as exception:\n raise errors.BadConfigOption(exception)\n\n if self._filter_file:\n self.has_filters = True\n else:\n self.has_filters = self._filter_collection.HasFilters()","def _formatFilters(self, filters):\n formatted_filters = []\n inequality_field = None\n\n for f in filters:\n filtr = {field.name: getattr(f, field.name) for field in f.all_fields()}\n\n try:\n filtr[\"field\"] = FIELDS[filtr[\"field\"]]\n filtr[\"operator\"] = OPERATORS[filtr[\"operator\"]]\n except KeyError:\n raise endpoints.BadRequestException(\"Filter contains invalid field or operator.\")\n\n # Every operation except \"=\" is an inequality\n if filtr[\"operator\"] != \"=\":\n # check if inequality operation has been used in previous filters\n # disallow the filter if inequality was performed on a different field before\n # track the field on which the inequality operation is performed\n if inequality_field and inequality_field != filtr[\"field\"]:\n raise endpoints.BadRequestException(\"Inequality filter is allowed on only one field.\")\n else:\n inequality_field = filtr[\"field\"]\n\n formatted_filters.append(filtr)\n return (inequality_field, formatted_filters)","def parse_filters(filters_str):\n fltrs = []\n for part in str(filters_str).lower().split(\",\"):\n if part==\"blur\":\n fltrs.append(filters.blur(1))\n elif part==\"distort\":\n fltrs.append(filters.distort(18))\n\n return fltrs","def get_filters():\n \n \"\"\"\"\"\"\"\"\n \n \"\"\"Messeges to genrate filters\"\"\"\n\tnote_messege = 'In this project, we make use of Python to explore data related to bike share systems for three major cities in the United States\\n'\n welcome_messege = 'Hello! Let\\'s explore some US bikeshare data!\\n'\n enter_city_name_messege = 'Which city would you like to filter by? Chicago, New York City or Washington? '\n filter_definition_messege = '\\nWould you like to filter the data by - \\n1. Month\\n2. Day\\n3. Both\\n4. No Filter\\n\\nPlease choose the appropriate filter name.\\nNote: Incorrect filter name will result as \\'no filter selected\\' by the user.\\n'\n enter_filter_messege = 'Desired filter (e.g: Month, Day, Both or No Filter): '\n enter_month_name_messege = 'Enter month name (e.g: january, february, march, april, may or june): '\n enter_day_name_messege = 'Enter day of the week (e.g: monday, tuesday, wednesday, thursday, friday, saturday, sunday): '\n exception_messege = '\\nWarning! That is not a valid input.\\n'\n warning_city_name_messege = '\\nWarning! Invalid city name. Select city name from the following cities only - Chicago, New York City or Washington.' \n warning_month_name_messege = '\\nWarning! Invalid month name. Select month name from the following months only - january, february, march, april, may or june'\n warning_day_name_messege = '\\nWarning! Invalid day name. Select day name from the following days only - monday, tuesday, wednesday, thursday, friday, saturday, sunday'\n \"\"\"\"\"\"\"\"\n \n \"\"\"City, Month and Day List\"\"\"\n city_list = ['chicago', 'new york city', 'washington']\n month_list = ['january', 'february', 'march', 'april', 'may', 'june']\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n \"\"\"\"\"\"\"\"\n \n\tprint(note_messege)\n print(welcome_messege)\n \n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs \n while True:\n try:\n city = input(enter_city_name_messege)\n break\n except:\n print(exception_messege)\n \n while city.lower() not in city_list:\n while True:\n try: \n print(warning_city_name_messege)\n city = input(enter_city_name_messege)\n break\n except:\n print(exception_messege)\n \n print(filter_definition_messege)\n while True:\n try:\n filter_choice = input(enter_filter_messege)\n break\n except:\n print(exception_messege)\n while True: \n if filter_choice.lower() == 'month':\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = input(enter_month_name_messege) \n break\n except:\n print(exception_messege)\n while month.lower() not in month_list:\n while True:\n try: \n print(warning_month_name_messege)\n month = input(enter_month_name_messege) \n break\n except:\n print(exception_messege)\n day = 'all'\n break\n \n elif filter_choice.lower() == 'day':\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday) \n while True:\n try:\n day = input(enter_day_name_messege)\n break\n except:\n print(exception_messege)\n while day.lower() not in day_list:\n while True:\n try: \n print(warning_day_name_messege)\n day = input(enter_day_name_messege) \n break\n except:\n print(exception_messege)\n month = 'all'\n break\n \n elif filter_choice.lower() == 'both':\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = input(enter_month_name_messege)\n break\n except:\n print(exception_messege)\n while month.lower() not in month_list:\n while True:\n try: \n print(warning_month_name_messege)\n month = input(enter_month_name_messege) \n break\n except:\n print(exception_messege)\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = input(enter_day_name_messege)\n break\n except:\n print(exception_messege)\n while day.lower() not in day_list:\n while True:\n try: \n print(warning_day_name_messege)\n day = input(enter_day_name_messege) \n break\n except:\n print(exception_messege)\n break\n \n else:\n month = 'all'\n day = 'all'\n break\n \n\n print('-'*40)\n return city.lower(), month.lower(), day.lower()","def __parse__(self, filter):\n \n if filter == 'zipcode':\n # Return 5 digit zip or, if applicable, Concatenate 5 digit and \n # 4 digit zipcode\n if self.data['Mailing Zip 4']:\n return \"%s-%s\" %(str(self.data['Mailing Zip Code'])[:-2],\n str(self.data['Mailing Zip 4'])[:-2]\n )\n else:\n return str(self.data['Mailing Zip Code'])[:-2]\n elif filter == 'employee_count':\n # Convert employee count string to digit\n pattern = '.+to\\s([0-9]+)'\n try:\n return re.findall(\n pattern, self.data['Location Employee Size Range'])[0]\n except IndexError:\n pass\n elif filter == 'phone':\n # Regex phone number digits and concatenate\n number = ''.join(re.findall('[0-9]+', \n self.data['Phone Number Combined']))\n return number if len(number) == 10 else 0","def parse_queryset_filters(filters):\n OP_MAP = [\n (\">=\", \"__gte\"),\n (\"=>\", \"__gte\"),\n (\">\", \"__gt\"),\n (\"<=\", \"__lte\"),\n (\"=<\", \"__lte\"),\n (\"<\", \"__lt\"),\n (\"=\", \"\"),\n ]\n\n filter_dict = {}\n exclude_dict = {}\n for filter_str in filters.split(\",\"):\n if \"!=\" in filter_str:\n key, val = filter_str.split(\"!=\")\n exclude_dict[key] = parse_bool(val, strict=False)\n continue\n for op, new_op in OP_MAP:\n if op in filter_str:\n key, val = filter_str.split(op)\n filter_dict[key + new_op] = parse_bool(val, strict=False)\n break\n else:\n raise ValueError(\"Unknown filter expression: %s\" % filter_str)\n\n return (filter_dict, exclude_dict)","def get_filters():\r\n\r\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\r\n print(\"Hello! Let\\'s explore some US bikeshare data!\")\r\n while True:\r\n city = input(\"Would you like to see data for Chicago, New York City or Washington?\\n\").split(\": \")[0].lower()\r\n try:\r\n if city not in CITY_DATA:\r\n print(\"Sorry, {} is not a valid city. Please type again by entering either 'Chicago', 'New York City' OR 'Washington' again\".format(city))\r\n continue\r\n else:\r\n break\r\n except ValueError as e:\r\n print(\"Exception occurred: {}\".format(e))\r\n\r\n # TO DO: get user input for month (all, january, february, ... , june)\r\n while True:\r\n month = input(\"Which month? January, February, March, April or June?..Enter 'all' if no month filter\\n\").lower()\r\n try:\r\n months = ['january', 'february', 'march', 'april', 'may', 'june','all']\r\n if month not in months:\r\n print(\"Sorry, {} is not a valid month. Please type again by entering again\".format(month))\r\n continue\r\n else:\r\n break\r\n except ValueError as e:\r\n print(\"Exception occurred: {}\".format(e))\r\n\r\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\r\n while True:\r\n day_of_week = input(\"Which day? Enter the response in integer. Ex: 1 = Sunday..Enter 'all' if no day filter\\n\")\r\n try:\r\n if day_of_week.isnumeric():\r\n day_of_week = int(day_of_week)\r\n if day_of_week == 1:\r\n day = \"Sunday\"\r\n break\r\n elif day_of_week == 2:\r\n day = \"Monday\"\r\n break\r\n elif day_of_week == 3:\r\n day = \"Tuesday\"\r\n break\r\n elif day_of_week == 4:\r\n day = \"Wednesday\"\r\n break\r\n elif day_of_week == 5:\r\n day = \"Thursday\"\r\n break\r\n elif day_of_week == 6:\r\n day = \"Friday\"\r\n break\r\n elif day_of_week == 7:\r\n day = \"Saturday\"\r\n break\r\n else:\r\n print(\"Sorry, {} is not a valid day. Please type again by entering either the day of week in integer ex: 1 = Sunday\".format(day_of_week))\r\n continue\r\n else:\r\n if day_of_week == \"all\":\r\n day = \"all\"\r\n break\r\n else:\r\n continue\r\n except ValueError as e:\r\n print(\"Exception occurred: {}\".format(e))\r\n\r\n print('-'*40)\r\n return city, month, day","def parse_date_filter(filter_value):\n # Accepted date format is ISO 8601, YYYY-MM-DDTHH:MM:SSZ (no `Z` is accepted too)\n filter_data = {\n \"operator\": None,\n \"date1\": None,\n \"date2\": None\n }\n\n iso_date_group = r\"(\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?(Z|\\+\\d{2}:\\d{2})?)\"\n # Filter with a comparison operator (>, >=, <, <=) and a date (e.g. `>=YYYY-MM-DDTHH:MM:SS`)\n oper_comparison = r\"^(<=?|>=?)%s$\" % iso_date_group\n # Filter with a range operator (..) between two dates\n # (e.g. YYYY-MM-DDTHH:MM:SSZ..YYYY-MM-DDTHH:MM:SSZ)\n range_comparison = r\"^%s\\.{2}%s$\" % (iso_date_group, iso_date_group)\n\n oper_result = re.match(oper_comparison, filter_value)\n range_result = re.match(range_comparison, filter_value)\n\n if not oper_result and not range_result:\n raise ValueError('Filter format is not valid')\n\n if oper_result:\n filter_data['operator'] = oper_result.group(1)\n filter_data['date1'] = str_to_datetime(oper_result.group(2))\n\n if range_result:\n filter_data['operator'] = '..'\n filter_data['date1'] = str_to_datetime(range_result.group(1))\n filter_data['date2'] = str_to_datetime(range_result.group(4))\n\n if filter_data['date1'] > filter_data['date2']:\n range_msg = 'Date range is invalid. Upper bound must be greater than the lower bound'\n raise ValueError(range_msg)\n\n return filter_data","def test_parse_filter_params_success(self):\n filter_params = {\n \"resolution\": \"daily\",\n \"time_scope_value\": \"-10\",\n \"time_scope_units\": \"day\",\n \"region\": FAKE.word(),\n \"payer_tenant_id\": FAKE.uuid4(),\n \"product_service\": FAKE.word(),\n }\n serializer = OCIFilterSerializer(data=filter_params)\n self.assertTrue(serializer.is_valid())","def parse_filter(value):\n\n if value.endswith('+pol'):\n pol = True\n value = value[:-4]\n else:\n pol = False\n\n if value in ufti_filters:\n return (ufti_filters[value], pol)\n\n else:\n logger.warning('Filter ' + value + ' is not recognised')\n return (None, pol)","def get_filters():\n # Get the user to input their name. Characters, hypens, spaces and brackets are valid - numbers are not\n valid_characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ -_(){}[]&'\n while True:\n name = input(\"\\nPlease enter your name, using only characters, spaces and hypens: \")\n if all(char in valid_characters for char in name):\n print('\\nHello {}! Let\\'s explore some US bikeshare data!'.format(name.title()))\n break\n print(\"\\nNumbers are not valid inputs. Please try again.\")\n\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n # city_input = 'chicago'\n CITIES = ['chicago', 'new york city', 'washington']\n while True:\n city_input = input('\\nPlease choose the city you would like to see data for: Chicago, New York City, Washington.\\n').lower()\n if city_input in CITIES:\n print('\\nThank-you for selecting {}!'.format(city_input.title()))\n break\n else:\n print(\"\\nI'm not sure what city you are referring to. Please try again!\")\n\n # TO DO: get user input for month (all, january, february, ... , june)\n # month_input = 'all'\n MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\n while True:\n month_input = input('\\nPlease choose from one or ALL of the following months: January, February, March, April, May, June or All. \\n').lower()\n if month_input in MONTHS:\n print('\\nThank-you for selecting {}!'.format(month_input.title()))\n break\n print(\"\\nI'm not sure what month you are referring to. Please try again!\")\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n # day_input = 'Thursday'\n DAYS = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\n while True:\n day_input = input('\\nPlease choose from one or ALL of the following days: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or All. \\n').lower()\n if day_input in DAYS:\n print('\\nThank-you for selecting {}!'.format(day_input.title()))\n break\n print(\"\\nI'm not sure what day you are referring to. Please try again!\")\n\n print('-'*40)\n return city_input, month_input, day_input","def get_filters():\n # Declare initial value for month and day to handle if user not select one of them or both for filter\n month, day = '', ''\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n city = input('Would you like to see data for for Chicago, New York City, or Washington?\\n')\n validate_city = validate_input('city', city)\n if validate_city:\n print(validate_city)\n else:\n break\n \n while True:\n # get user input for filter (month, day, both, or none)\n filters = input(f'Would you like to filter the data by month, day, both, or not at all? '\n f'type \"none\" for no time filter.\\n')\n\n validate_filters = validate_input('filters', filters)\n if validate_filters:\n print(validate_filters)\n else:\n break\n \n filters = filters.lower()\n if filters != 'none':\n # get user input for month (all, january, february, ... , june)\n filters = filters.lower()\n if filters in ['both', 'month']:\n while True:\n month = input(f'Which month? January, February, Mrach, April, May, June, or All? '\n f'Please type out the full month name.\\n')\n\n validate_month = validate_input('month', month)\n if validate_month:\n print(validate_month)\n else:\n month = month.upper()\n break\n # get user input for day of week (all, monday, tuesday, ... sunday)\n if filters in ['both', 'day']:\n while True:\n day = input(f'Which day? Monday, Tuesday, Wednesday, Thursday, '\n f'Friday, Saturday, or All? Please type out the full day name.\\n')\n\n validate_day = validate_input('day', day)\n\n if validate_day:\n print(validate_day)\n else:\n break\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n\t try:\n\t city = input('Input city name (chicago, new york city, washington): ').lower()\n\t if city in cities:\n break\n\t except ValueError:\n\t print('That\\'s not a valid String!')\n\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n\t try:\n\t month = input('Input month name (all, january, february, ... , june): ').lower()\n\t if month in months:\n break\n\t except ValueError:\n\t print('That\\'s not a valid String!')\n\n # TO DO: get user input for day of week (all, monday, tuesday, ..., sunday)\n while True:\n\t try:\n\t day = input('Input day of week (all, monday, tuesday, ..., sunday): ').lower()\n\t if day in days:\n break\n\t except ValueError:\n\t print('That\\'s not a valid String!')\n\n print('-'*40)\n return city, month, day","def parse_for_filters(query_string):\n if ';' in query_string:\n strings = query_string.split(';')\n else:\n strings = query_string.split('&')\n\n filters = []\n leftovers = [] \n for string in strings:\n query = cgi.parse_qs(string)\n try:\n key, value = query.items()[0]\n\n try:\n argument = unicode(value[0], 'UTF-8')\n except TypeError:\n argument = value[0]\n\n func = FILTER_PARSERS[key](argument)\n filters.append(func)\n except(KeyError, IndexError):\n leftovers.append(string)\n\n leftovers = ';'.join(leftovers)\n return filters, leftovers","def check_request_type_filter(\n field_names: list,\n request_type_filter: dict,\n has_start_time: bool,\n has_end_time: bool,\n has_start_date: bool,\n has_end_date: bool,\n has_date: bool,\n has_SP: bool,\n has_year: bool,\n has_month: bool,\n has_week: bool\n):\n\n filter_str = f'\\n\\nFilter:\\n{request_type_filter}\\n\\nField Names:\\n{\", \".join(field_names)}'\n\n assert {(False, True): True, (False, False): False, (True, True): False, (True, False): False}[(has_year, has_month)] == False, 'Cannot provide a month without a year' + filter_str\n assert {(False, True): True, (False, False): False, (True, True): False, (True, False): False}[(has_year, has_week)] == False, 'Cannot provide a week without a year' + filter_str\n assert has_start_time + has_end_time != 1, 'Only one of start/end time was provided' + filter_str\n assert has_start_date + has_end_date != 1, 'Only one of start/end date was provided' + filter_str\n assert (has_SP + has_date != 1) or (has_start_date + has_end_date == 2), 'Only one of date/SP was provided' + filter_str\n assert sum(request_type_filter.values()) == 1, 'Request type could not be determined\\n\\nFilter' + filter_str\n\n return","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n \n #list to validate the input from user against these city names.\n city_list = ['chicago', 'new york city', 'washington'] \n month_list = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday','sunday', 'all']\n \n #Used stack overflow to be able to validate user inputs \n while True:\n city = input(\"Would you like to see data for Chicago, New York City or Washington? \").lower()\n if city not in city_list:\n print(\"This is not a valid city. Please enter: Chicago, New York City or Washington.\")\n else:\n break\n \n while True:\n filter_mon_day = input(\"Would you like to filter the data by month, day or both.? \").lower()\n if filter_mon_day not in ('month', 'day', 'both'):\n print(\"This is not a valid filter. Please enter: month, day or both.\")\n else:\n break\n \n # TO DO: get user input for month (all, january, february, ... , june)\n \n if filter_mon_day == \"month\":\n while True:\n month = input(\"Enter a valid month: January, February, March, April, May or June or all: \").lower()\n if month not in month_list:\n print(\"This is not a valid month. Please enter: January, February, March, April, May or June or all\") \n else:\n break\n \n day = \"all\"\n \n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n \n if filter_mon_day == \"day\":\n month = \"all\"\n while True:\n day = input(\"Enter a valid day: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all: \").lower()\n if day not in day_list:\n print(\"This is not a valid day. Please enter: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all\")\n else:\n break\n \n \n \n # TO DO: get user input for both month and day. \n \n if filter_mon_day == \"both\":\n #month = input(\"Enter a valid month: January, February, March, April, May or June or all: \").lower()\n while True:\n month = input(\"Enter a valid month: January, February, March, April, May or June or all: \").lower()\n if month not in month_list:\n print(\"This is not a valid month. Please enter: January, February, March, April, May or June or all\") \n else:\n break\n \n while True:\n day = input(\"Enter a valid day: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all: \").lower()\n if day not in day_list:\n print(\"This is not a valid day. Please enter: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all\")\n else:\n break \n \n\n print('-'*40)\n return city, month, day","def parse_filter(self, tokens):\n if not tokens:\n self._parser_state.error('missing filter')\n filters = []\n if tokens[0].type == 'LBRACE':\n opening_brace = tokens.pop(0)\n while tokens:\n filters.append(self._parse_single_filter(tokens))\n if not tokens or tokens[0].type != 'COMMA':\n break\n tokens.pop(0)\n if not tokens or tokens[0].type != 'RBRACE':\n self._parser_state.error('unclosed brace', token=opening_brace)\n tokens.pop(0)\n else:\n filters.append(self._parse_single_filter(tokens))\n return filters","def get_filters():\n print('Hello! Let\\'s explore some US bike-share data.\\n')\n\n # Gets city to filter data\n while True:\n city = input('Choose among the following cities for which you want to see the data: \\\n \\n1) Chicago\\n2) New York\\n3) Washington?\\n')\n city = city.title()\n\n # Decoding mnemonic inputs\n if city == '1' or city == 'Chi' or city == 'C':\n city = 'Chicago'\n elif city == '2' or city == 'Ny' or city == 'N Y' or city == 'Newyork' or city == 'N':\n city = 'New York'\n elif city == '3' or city == 'Wash' or city == 'W':\n city = 'Washington'\n\n # Asking user to input again if unexpected input else continue\n if city == 'Chicago' or city == 'New York' or city == 'Washington':\n print('\\nLooks like you want to explore the statistics of: ', city)\n break\n else:\n print('\\n******************INVALID INPUT*******************')\n print('Please select the city from the available options.')\n\n print('----------------------------------------------')\n # Asking user which filter to apply and accepting required values\n while True:\n filters = input('\\nWould you like to filter the data by:\\n' +\n '1) Month\\n2) Day \\n3) Both or\\n4) None at all?' +\n '\\nChoose among the available filters(1 - 4):\\n')\n filters = filters.title()\n\n options = ['1', '2', '3', '4', 'Month', 'Day', 'Both', 'None']\n\n # Taking care of invalid filters else getting required values of filters\n if filters not in options:\n print('\\n******************INVALID INPUT*******************')\n print('Please apply any of the following filters: Month, Day, Both, or None')\n else:\n # Decoding mnemonic inputs\n if filters not in options[4:8]:\n if filters == '1':\n filters = 'Month'\n elif filters == '2':\n filters = 'Day'\n elif filters == '3':\n filters = 'Both'\n elif filters == '4':\n filters = 'None'\n\n # Providing feedback to users\n print(\"Okay, We will apply the following filter: \", filters)\n print('----------------------------------------------')\n\n # Getting values for month and day according to selected filter\n month = get_month(filters)\n day = get_day(filters)\n break\n\n # Displaying the filters to data:\n print('\\n\\n****************CHOSEN FILTERS****************')\n print('City: {}\\nMonth: {}\\nDay: {}'.format(city, month, day))\n print('----------------------------------------------')\n\n return city, month, day, filters","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # TO DO: get user input for city (chicago, new york city, washington).\n while True:\n city = input(\n \"Please input one of the following: Chicago, New York City, or Washington: \"\n ).lower()\n if city not in ('chicago', 'new york city', 'washington'):\n print('That is not an option, please try again.')\n else:\n break\n\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n month = input(\n \"Please input one month from January to June or 'all': \"\n ).lower()\n if month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\n print('That is not an option, please try again.')\n else:\n break\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n day = input(\n \"Please input a day of the week from Monday to Sunday or 'all': \"\n ).lower()\n if day not in ('monday', 'tuesday', 'wednesday', 'thursday', 'friday',\n 'saturday', 'sunday', 'all'):\n print('That is not an option, please try again.')\n else:\n break\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # Get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n city = input('Would you like to see data for Chicago, New York City or Washington?')\n if city.lower() in CITY_DATA:\n break\n print('ERROR: City does not match. Please try again.')\n\n # Get user input for month (all, january, february, ... , june)\n while True:\n month = input(\"Type month (January, February, March, April, May or June) to filter by or type 'all' for no filter\")\n if month.lower() in MONTH_LIST or month.lower() == 'all':\n break\n print(\"ERROR: Input was not a month from January to June nor all. Please try again.\")\n\n # Get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n day = input(\"Type day of the week to filter or type 'all' for no filter\")\n if day.lower() in DAY_LIST or day.lower() == 'all':\n break\n print(\"ERROR: Input was not a day of the week nor all.\")\n\n print('-'*40)\n return city, month, day","def __init__( self, filters=None, prx=None ):\n\n if filters is None:\n if prx is None:\n\n self._filter_list = rts2_wwwapi.rts2comm().get_filters()\n\n elif type(filters) == list:\n self._filter_list = filters\n\n elif type(filters) == dict:\n raise TypeError(\"Filters are should not be a dict, it probably should be None\")\n # this assumes that the keywords of the dictionary are \n # the fitler names and the value is the filter number. \n\n\n #sort by filter number and reverse look up. \n # this doesn't work in python3\n #for key, value in sorted(filters.iteritems(), key=lambda (k,v): (v,k)):\n #self._filter_list.append( key )\n\n elif type(filters) == str or type(filters) == unicode:\n self._filter_list = str(filters).split()\n\n else:\n raise TypeError(\"Unexpected filter type {}, type must be string, unicode, list or dict\".format(type(filters)))","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n city = str(input('Would you like to see data for Chicago, New York, or Washington?\\n'))\n break\n except:\n print('\\nPlease input Chicago, New York, or Washington\\n')\n\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try:\n both = str(input('Would you like to filter the data by month, day, or not at all?\\n'))\n break\n except:\n print('\\nPlease input month, day or not\\n') \n if both == 'month':\n while True:\n try:\n month = str(input('Which month - January, February, March, April, May, or June?\\n'))\n break\n except:\n print('\\nPlease input a valid month\\n')\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = str(input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\n'))\n break\n except:\n print('\\nPlease input a valid day\\n')\n\n print('-'*40)\n return city, month, day","def get_filters():\n\n city = prompts.city_prompt.launch()\n\n _filter = prompts.filter_prompt.launch()\n\n if _filter == \"Month\":\n month = prompts.month_prompt.launch()\n day = \"All\"\n\n elif _filter == \"Day\":\n day = prompts.day_prompt.launch()\n month = \"All\"\n\n elif _filter == \"Both\":\n month = prompts.month_prompt.launch()\n day = prompts.day_prompt.launch()\n\n else:\n month, day = \"All\", \"All\"\n\n print(\"-\" * 40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n valid_input = False\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while not valid_input:\n city = input('\\n Please select a city, Available cities are chicago, new york city, washington.\\n')\n city = clean_input(city.lower())\n if city in ['chicago', 'new york city', 'washington']:\n valid_input = True\n else:\n print(\"Invalid city entered. Available cities are \" + ','.join(CITY_DATA.keys()))\n\n # get user input for month (all, january, february, ... , june)\n valid_input = False\n\n while not valid_input:\n month = input('\\n Please select a month, Available values are all, january, february, ..., june.\\n')\n month = clean_input(month.lower())\n if month in MONTHS.keys():\n valid_input = True\n else:\n print(\"Invalid month entered. Available months are \" + ','.join(MONTHS.keys()))\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n valid_input = False\n\n while not valid_input:\n day = input('\\n Please enter day, Available values are all, monday, tuesday, ... sunday.\\n')\n day = clean_input(day.lower())\n if day in DAYS.keys():\n valid_input = True\n else:\n print(\"Invalid day entered. Available days are \" + ','.join(DAYS.keys()))\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n cities = ['chicago', 'new york city', 'washington']\n city_good = 'n'\n while city_good == 'n':\n city = input(\"Enter the name of the city to analyze (Chicago, New York City, Washington): \").lower()\n if city in cities:\n print(Fore.GREEN + '{} it is!\\n'.format(city.capitalize()) + Style.RESET_ALL)\n city_good = 'y'\n else:\n print(Fore.RED + 'Error! \"{}\" is not a valid entry, please try again!\\n'.format(city) + Style.RESET_ALL)\n city_good = 'n'\n\n # TO DO: get user input for month (all, january, february, ... , june)\n months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\n month_good = 'n'\n while month_good == 'n':\n month = input(\"Enter the month to analyze (All, January, February, March, April, May, June): \").lower()\n if month in months:\n print(Fore.GREEN + '{} it is!\\n'.format(month.capitalize()) + Style.RESET_ALL)\n month_good = 'y'\n else:\n print(Fore.RED + 'Error! \"{}\" is not a valid entry, please try again!\\n'.format(month) + Style.RESET_ALL)\n month_good = 'n'\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n days = ['all', 'sunday', 'monday', 'tuesday', 'wednesday', 'thursday',\n 'friday', 'saturday']\n day_good = 'n'\n while day_good == 'n':\n day = input(\"Enter the day of the week to analyze (All, Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday): \").lower()\n if day in days:\n print(Fore.GREEN + '{} it is!\\n'.format(day.capitalize()) + Style.RESET_ALL)\n day_good = 'y'\n else:\n print(Fore.RED + 'Error! \"{}\" is not a valid entry, please try again!\\n'.format(day) + Style.RESET_ALL)\n day_good = 'n'\n\n print('='*70)\n return city, month, day","def _CheckFilter(self, filter, values):\n try:\n match = Query.FILTER_REGEX.match(filter)\n if not match:\n raise datastore_errors.BadFilterError(\n 'Could not parse filter string: %s' % str(filter))\n except TypeError:\n raise datastore_errors.BadFilterError(\n 'Could not parse filter string: %s' % str(filter))\n\n property = match.group(1)\n operator = match.group(3)\n if operator is None:\n operator = '='\n\n if isinstance(values, tuple):\n values = list(values)\n elif not isinstance(values, list):\n values = [values]\n if isinstance(values[0], datastore_types._RAW_PROPERTY_TYPES):\n raise datastore_errors.BadValueError(\n 'Filtering on %s properties is not supported.' % typename(values[0]))\n\n if operator in self.INEQUALITY_OPERATORS:\n if self.__inequality_prop and property != self.__inequality_prop:\n raise datastore_errors.BadFilterError(\n 'Only one property per query may have inequality filters (%s).' %\n ', '.join(self.INEQUALITY_OPERATORS))\n elif len(self.__orderings) >= 1 and self.__orderings[0][0] != property:\n raise datastore_errors.BadFilterError(\n 'Inequality operators (%s) must be on the same property as the '\n 'first sort order, if any sort orders are supplied' %\n ', '.join(self.INEQUALITY_OPERATORS))\n\n if (self.__kind is None and\n property != datastore_types._KEY_SPECIAL_PROPERTY):\n raise datastore_errors.BadFilterError(\n 'Only %s filters are allowed on kindless queries.' %\n datastore_types._KEY_SPECIAL_PROPERTY)\n\n if property in datastore_types._SPECIAL_PROPERTIES:\n if property == datastore_types._KEY_SPECIAL_PROPERTY:\n for value in values:\n if not isinstance(value, Key):\n raise datastore_errors.BadFilterError(\n '%s filter value must be a Key; received %s (a %s)' %\n (datastore_types._KEY_SPECIAL_PROPERTY, value, typename(value)))\n\n return match","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!\\n')\n #get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n city = input('Which city do you want to explore? chicago, new york, washington\\n')\n if city.lower() in CITY_DATA:\n break\n else:\n print('Enter valid city name..\\n')\n except ValueError:\n \n print(\"Provide a string value...\\n\")\n continue\n\n #get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = input('Which month do you want to explore? all, january, february, ... , etc\\n')\n if month.lower() in MONTHS or month == 'all':\n break\n else:\n print('Enter valid month name..\\n')\n except ValueError:\n \n print(\"Provide a string value...\\n\")\n continue\n\n\n #get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = input('Which day do you want to explore? all, saturday, sunday, ... , etc\\n')\n if day.lower() in DAYS or day == 'all':\n break\n else:\n print('Enter valid day name..\\n')\n except ValueError:\n \n print(\"Provide a string value...\\n\")\n continue\n\n print('-'*40)\n return city.lower(), month.lower(), day.lower()","def filter(self, filters):","def get_filters():\n allowed_cities = ('chicago','new york','washington')\n\n allowed_time_filters = ('month','day','both','none')\n\n allowed_months = ('january','february','march','april','may','june')\n\n allowed_days = ('mon','tue','wed','thur','fri','sat','sun')\n\n weekday_dict = {\"Mon\": 'Monday',\"Tue\": 'Tuesday',\"Wed\": 'Wednesday',\"Thur\": 'Thursday',\"Fri\": 'Friday',\"Sat\": 'Saturday',\"Sun\": 'Sunday'}\n\n print('\\nHello! Let\\'s explore some US bikeshare data! \\n')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n print(\"Would you like to see data for Chicago, New York, or Washington cities? \\n\")\n city = input(\"Enter city : \\n\").rstrip().lstrip()\n while city.lower() not in allowed_cities:\n print('You need to enter one of the following cities: Chicago, New York or Washington.\\n')\n city = input(\"Enter city : \\n\")\n else:\n user_filters.append(city.title())\n\n print(\"Would you like to filter {}'s data by month, day, both or not at all? Type none for no time filter.\\n\".format(str(user_filters[0])))\n\n time_filter_option = input('Enter your time filtering option: \\n').rstrip().lstrip()\n\n while time_filter_option not in allowed_time_filters:\n print('You need to enter valid time filtering choice eg by month, day, both or not at all? Type none for no time filter.')\n time_filter_option = input('Enter your time filtering option: ').rstrip().lstrip()\n\n if time_filter_option.lower() == 'both':\n # get user input for month (all, january, february, ... , june)\n month = input(\"Enter month : \").rstrip().lstrip()\n while month.lower() not in allowed_months:\n print('You need to enter valid month name, January through June.')\n month = input(\"Enter month : \").rstrip().lstrip()\n else:\n user_filters.append(month.title())\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n day = input(\"Enter weekday eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \").title().rstrip().lstrip()\n while day.lower() not in allowed_days:\n print('You need to enter weekday abbreviations eg Mon, Tue, Wed, Thur, Fri, Sat, Sun.')\n day = input(\"Enter weekday abbreviation eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \").title().rstrip().lstrip()\n else:\n user_filters.append(weekday_dict[day].title())\n\n elif time_filter_option.lower() == 'month':\n month = input(\"Enter month name, January through June: \").rstrip().lstrip()\n while month.lower() not in allowed_months:\n print('You need to enter valid month name, January through June.')\n month = input(\"Enter month : \").rstrip().lstrip()\n else:\n user_filters.append(month.title())\n user_filters.append('all')\n\n elif time_filter_option.lower() == 'day':\n day = input(\"Enter weekday eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \").title().rstrip().lstrip()\n while day.lower() not in allowed_days:\n print('You need to enter weekday abbreviations eg Mon, Tue, Wed, Thur, Fri, Sat, Sun.')\n day = input(\"Enter weekday abbreviation eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \").title().rstrip().lstrip()\n else:\n user_filters.append('all')\n user_filters.append(weekday_dict[day].title())\n\n else:\n user_filters.append('all')\n user_filters.append('all')\n\n # Assign filter values selected by user to variables city, month, and day respectively.\n city = user_filters[0].lower()\n month = user_filters[1].lower()\n day = user_filters[2].lower()\n print()\n print('-'*80)\n print(\"\\tFOR CITY OF '{}', YOU HAVE CHOSEN THE FOLLOWING TIME FILTERS:\".format(city.upper()))\n print(\"\\tMonth: '{}'\".format(month.upper()))\n print(\"\\tDay: '{}'\".format(day.upper()))\n print('-'*80)\n\n return city, month, day","def update_filters(self, filters: str) -> None:\r\n\r\n log.debug(f'Updating filters to {filters}')\r\n\r\n parts = filters.split('&')\r\n\r\n for part in parts:\r\n value, key = part.split('=')\r\n\r\n if value == 'iv':\r\n self.__payload['prevMinIV'] = self.__payload['minIV']\r\n self.__payload['minIV'] = key.strip()\r\n elif value == 'exiv':\r\n self.__payload['exMinIV'] = key.strip()\r\n else:\r\n log.debug(f'Dont know filter: \"{part}\", ignoring...')\r\n\r\n self.__filters_string = filters","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n city = handle_invalid_inputs(question_1, cities)\n \n # get the user input of the filter_choosed (month, day, both, or not at all(none))\n\n filter_choosed = handle_invalid_inputs(question_2, filters)\n \n # if filter_choosed == \"month\"\n if filter_choosed == \"month\":\n # get user input for month (all, january, february, ... , june)\n month = get_month()\n day = \"all\"\n \n # if filter_choosed == \"day\"\n if filter_choosed == \"day\":\n # get user input for day of week (all, monday, tuesday, ... sunday)\n day = get_day()\n month = \"all\"\n \n # if filter_choosed == \"both\"\n if filter_choosed == \"both\":\n # get user input for day of week and month\n month, day = get_both()\n \n # if filter_choosed == none\n if filter_choosed == \"none\":\n month = \"all\"\n day = \"all\"\n\n print('-'*40)\n return city, month, day, filter_choosed","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data! Including data from Chicago, Washington, and New York City')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n city = input(\"Please choose a city you want to learn more about from the following: Chicago, New York City or Washington. \\n> \").lower(\n ).replace(\" \", \"\")\n if city in cities_data:\n break\n else:\n print(\"\\n\\t\" + city + \" isn't valid, please enter a valid city.\")\n\n # get user input for month (all, january, february, ... , june)\n while True:\n month = input(\"Please choose a month or type 'all' to apply no month filter.\\n (Choices: all, january, february, march, april, may or june)\\n>\").lower(\n ).replace(\" \", \"\")\n if month in months_data or month == \"all\":\n break\n else:\n print(\"\\n\\t ERROR: \" + month + \" isn't valid, please enter a month or 'all'.\")\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n day = input(\"Please choose a day of the week or type 'all' to apply no day filter.\\n (Choices: all, monday, tuesday, wednesday, thursday, friday, saturday or sunday)\\n>\").lower().replace(\" \", \"\")\n if day in days_data or day == \"all\":\n break\n else:\n print(\"\\n\\t ERROR: \" + day + \" isn't valid, please enter a week day name or 'all'.\")\n\n print('-'*40)\n return city, month, day","def get_filters():\n\n print('\\nHello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city = input(\"Which city would you like to explore new york city, chicago or washington? \\n \").lower().strip()\n while city not in ('new york city', 'chicago', 'washington'):\n city = input(\"invalid city please enter a valid city \\n \").lower().strip()\n\n # TO DO: get user input for month (all, january, february, ... , june)\n\n month = input(\"Which month would you like to filter january, february, march, april, may, june or 'all' \\n\").lower().strip()\n while month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\n month = input(\"invalid month please enter a valid month \\n\").lower().strip()\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n day = input(\n \" which day would you like to filter sunday, monday, tuesday, wednesday, thursday, friday, Saturday or all.\\n\").lower().strip()\n while day not in ('sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'all'):\n day = input(\"invalid day please enter a valid day \\n\").lower().strip()\n\n print('-' * 40)\n return city, month, day","def get_filters():\n\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # ADD : available analysis parameters\n cities_list=['chicago','new york city','washington']\n months_list=['all','january','february','march','april','may','june']\n days_list=['all','monday','tuesday','wednesday','thursday','friday','saturday','sunday']\n\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city=''\n while city not in cities_list:\n city=str(input(\"Enter the name of the city to analyze: \")).lower()\n if city not in cities_list:\n print(\"!Warning : cities available for analysis : {}\".format(cities_list))\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month=''\n while month not in months_list:\n month=str(input(\"Enter the month to analyze (enter 'all' if you want all the months): \")).lower()\n if month not in months_list:\n print(\"!Warning : months available for analysis : {}\".format(months_list))\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day=''\n while day not in days_list:\n day=str(input(\"Enter the day to analyze (enter 'all' if you want all the days): \")).lower()\n if day not in days_list:\n print(\"!Warning : days available for analysis : {}\".format(days_list))\n\n print('-'*40)\n return city, month, day","def create_filter_from_args(self, args: dict) -> Filter:\n keys = set(args.keys())\n filter_args = {}\n\n if \"name\" in keys:\n value = args.get('name')\n if value != \"\":\n filter_args.update({\"text_filter\": args.get('name')})\n if \"product_in\" in keys:\n value = args.get('product_in')\n if value != \"\":\n filter_args.update({\"product_in\": 'true' if value == \"yes\" else 'false'})\n if \"human_in\" in keys:\n value = args.get('human_in')\n if value != \"\":\n filter_args.update({\"human_in\": 'true' if value == \"yes\" else 'false'})\n if \"institutional\" in keys:\n value = args.get('institutional')\n if value != \"\":\n filter_args.update({\"institutional\": 'true' if value == \"yes\" else 'false'})\n if \"format\" in keys:\n value = args.get('format')\n if value != \"\":\n filter_args.update({\"picture_format\": 'true' if value == \"vertical\" else 'false'})\n if \"credit\" in keys:\n value = args.get('credit')\n if value != \"\":\n filter_args.update({\"author_credits\": value})\n if \"limited_use\" in keys:\n value = args.get('limited_use')\n if value != \"\":\n filter_args.update({\"limited_usage\": 'true' if value == \"yes\" else 'false'})\n if \"tags\" in keys:\n value = args.get('tags')\n if value != \"\":\n filter_args.update({\"limited_usage\": value.split(';')})\n\n f = Filter(**filter_args)\n return f","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). Use a while loop to handle invalid inputs\n city = \"\"\n while (city.title() != \"Chicago\" and city.title() != \"New York\" and city.title() != \"Washington\"):\n city = input(\"Would you like to see data for Chicago, New York, or Washington? \")\n \n print(\"Looks like you want to hear about {}! If this is not true, restart the program now!\".format(city.title()))\n \n filter_type = \"\"\n filter_types = (\"none\", \"month\", \"day\", \"both\")\n while (filter_type.lower() not in filter_types):\n filter_type = input(\"Would you like to filter the data by month, day, both or not at all? Type \\\"none\\\" for no time filters. \")\n \n # get user input for month (all, january, february, ... , june)\n if (filter_type == \"month\" or filter_type == \"both\"):\n if (filter_type == \"month\"):\n day = \"all\"\n month = \"\"\n months = (\"all\", \"january\", \"february\", \"march\", \"april\", \"may\", \"june\")\n while (month.lower() not in months):\n month = input(\"Which month? January, February, March, April, May, or June? \")\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n if (filter_type == \"day\" or filter_type == \"both\"):\n if (filter_type == \"day\"):\n month = \"all\"\n day = \"no\"\n days = (\"all\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\")\n while (day.lower() != \"all\" and (not any(day.title() in d for d in days) or (day.upper() == 'T' or day.upper() == 'S'))):\n day = input(\"Which day? Please type a day M, Tu, W, Th, F, Sa, Su. \")\n day = [d for d in days if d.startswith(day.title())][0]\n \n if (filter_type == \"none\"):\n month = \"all\"\n day = \"all\"\n \n print('-'*40)\n return city.lower(), month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n while True:\n try:\n city = input('What city would you like to view: ').lower()\n if city in cities:\n break\n else:\n print('That is not a valid city. Try New York City, Chicago, or Washington')\n except:\n print('There was an unknown error')\n finally:\n print('\\nAttempted Input\\n')\n\n # get user input for month (all, january, february, ... , june) make sure these are integers\n while True:\n try:\n month = input(\"Filter on what month (select all for all months) \"\n \"give as an integer: \")\n if month in str(range(1,7)):\n break\n elif month.lower() == 'all':\n break\n else:\n print('That is not a valid month. Try an integer 1-7')\n except:\n print('There was an unknown error')\n finally:\n print('\\nAttempted Input\\n')\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = input(\"Filter on what day of the week(select all for all days): \")\n if day in str(range(1,8)):\n break\n elif day.lower() == 'all':\n break\n else:\n print('That is not a valid day. Try an integer 1-7')\n except:\n print('There was an unknown error')\n finally:\n print('\\nAttempted Input\\n')\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n #Invalid input is administered to by using a while loop.\n while True:\n city=input(\"Choose a city name between Chicago, New York City or Washington:!\").lower()\n if city not in CITY_DATA:\n print(\"\\n Not a valid city\\n\")\n continue\n else:\n break\n\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try\n month=str(input('Enter name of one month(from January to June) to filter by or \"all\" ,for no filter :')).lower()\n months=['january', 'february', 'march', 'april', 'may', 'june']\n if month == 'january':\n month = months[0]\n elif month == 'february':\n month = months[1]\n elif month == 'march':\n month = months[2]\n elif month == 'april':\n month = months[3]\n elif month == 'may':\n month = months[4]\n elif month == 'june':\n month = months[5]\n elif month == 'all':\n print('all')\n else:\n raise(Exception)\n\t\t\texcept Exception as error:\n print('Invalid Input!,please restart again!.')","def get_filters() -> List[Tuple[str, Callable]]:\n return [\n ('group_files', group_files),\n ('timesince', timesince),\n ('just_updated', just_updated),\n ('get_category_name', get_category_name),\n ('process_status_display', process_status_display),\n ('compilation_status_display', compilation_status_display),\n ('duration', duration),\n ('tidy_filesize', tidy_filesize),\n ('asdict', asdict),\n ('compilation_log_display', compilation_log_display)\n ]","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city = input('Would you like to see data for Chicago, New York City, or Washington? ')\n city = city.lower()\n while city not in CITY_DATA:\n city = input('Not a valid name! Try again!')\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('Which month? January, February, March, April, May, or June? Type \"all\" for all months ')\n month = month.lower()\n while month not in months:\n if month == 'all':\n break\n else:\n month = input('Not a valid month! Try again!')\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('Which day of week? Type \"all\" for every weekday ')\n day = day.lower()\n while day not in days:\n if day == 'all':\n break\n else:\n day = input('Not a valid day! Try again!')\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n \n cities = ['Chicago', 'New York', 'Washington']\n filterValues = ['Month', 'Day', 'Both', 'None']\n months = ['January', 'February', 'March', 'April', 'May', 'June'] \n days = ['1', '2', '3', '4', '5', '6', '7']\n \n \n print('What city do you want to look into ? Chicago, New York or Washington?')\n city = get_valid_input(cities)\n \n # TO DO: get user input for month (all, january, february, ... , june)\n \n print('Do you want to filter the data by Month, Day, Both or not at all? Type \"None\" for no time filter!')\n filter = get_valid_input(filterValues)\n \n if filter == 'month':\n print('Which month? January, February, March, April, May or June?')\n month = get_valid_input(months)\n day = 'all'\n \n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n elif filter == 'day':\n print('Which day? Monday(1), Tuesday(2), ..., ? Please enter number!')\n day = int(get_valid_input(days))-1\n month = 'all'\n \n elif filter == 'both':\n print('Which month? January, February, March, April, May or June?')\n month = get_valid_input(months)\n print('Which day? Monday(1), Tuesday(2), ..., ? Please enter number!')\n day = int(get_valid_input(days))-1\n \n elif filter == 'none':\n month = 'all'\n day = 'all'\n \n else:\n return -1\n \n if month !='all': \n month = [element.lower() for element in months].index(month) + 1\n \n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n city = ''\n month = ''\n day = ''\n valid_cities = ['chicago', 'new york city', 'washington']\n valid_months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\n valid_days = ['all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday','sunday']\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while not city:\n text = input('Which city would you like to view data for? Enter \"Chicago\", \"New York City\", or \"Washington\": ' ).lower()\n if text in valid_cities:\n city = text\n else:\n print('That entry is not a valid selection, please try again.')\n # get user input for month (all, january, february, ... , june)\n #limited filter options to available date range in the data set\n while not month:\n text = input('Data is available for January through June. Which month would you like to view data for? Enter \"all\" or a specific month eg \"January\" : ' ).lower()\n if text in valid_months:\n month = text\n else:\n print('That entry is not a valid, please try again.')\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while not day:\n text = input('Which day of the week would you like to view data for? Enter \"all\" or a specific day such as \"Tuesday\": ').lower()\n if text in valid_days:\n day = text\n else:\n print('That entry is not a valid, please try again.')\n\n print('-'*40)\n return city, month, day","def load_filter_file(self, filter_path):\n logger.debug(\"Adding filter file {}\", filter_path)\n try:\n with open(filter_path, \"r\") as filter_file:\n try:\n json_filter_data = json.load(filter_file)\n except Exception as err:\n msg = \"Unable to parse filter file {} as a json file. {!r}\".format(\n filter_path, err)\n logger.debug(msg)\n raise errors.ParserError(msg)\n except IOError:\n raise errors.ParserError(\n \"Unable to access filter path '{}'\".format(filter_path))\n\n if \"version\" not in json_filter_data:\n raise errors.ParserError(\n \"Loading filter-file {} failed. Missing 'version' key.\".format(\n filter_path))\n\n if \"filters\" not in json_filter_data:\n raise errors.ParserError(\n \"Loading filter-file {} failed. Missing 'filters' key.\".format(\n filter_path))\n\n if not isinstance(json_filter_data[\"version\"], dict):\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Expecting value of 'version' entry to be a dictionary \"\n \"but instead its a {}.\".format(filter_path,\n type(json_filter_data[\"version\"])))\n\n version_info = json_filter_data[\"version\"]\n\n if \"major\" not in version_info:\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Missing 'major' key in 'version' value.\".format(filter_path))\n\n if \"minor\" not in version_info:\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Missing 'minor' key in 'version' value.\".format(filter_path))\n\n if not isinstance(version_info[\"major\"], int):\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Expecting int for major version found {} instead.\".format(\n filter_path, type(version_info[\"major\"])))\n\n if not isinstance(version_info[\"minor\"], int):\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Expecting int for minor version found {} instead.\".format(\n filter_path, type(version_info[\"minor\"])))\n\n if version_info[\"major\"] != FILTER_JSON_FORMAT_MAJOR_VERSION:\n raise errors.ParserError(\n \"Loading filter-file {} failed. \"\n \"Found unexpected major version in JSON filter file.\".format(\n filter_path))\n\n self._add_filters(json_filter_data[\"filters\"], filter_path)","def get_filters():\n print('Welcome! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n while True:\n try:\n city = input(\"Enter the name of the city you want to explore the data of (chicago, new york city, or washington): \")\n if city not in dict.keys(CITY_DATA):\n raise Exception ('Invalid city name')\n print (\"You have selected: {}.\\n\".format(city.title()))\n break\n except Exception :\n print (\"Invalid name of city, please choose chicago, new york city, or washington (writtent in this format)\\n\")\n\n # TO DO: get user input for month (all, january, february, ... , june)\n\n while True:\n valid_months_names =('all', 'january', 'february', 'march', 'april', 'may', 'june')\n try:\n month = input(\"Enter the month that you wish to explore (use 'all' for the entire timeframe): \")\n if month not in valid_months_names:\n raise Exception ('Invalid month name')\n print (\"You have selected: {}.\\n\".format(month.title()))\n break\n except Exception :\n print (\"Invalid month: Please choose from all, january, february, march, april, may, june\\n\")\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n while True:\n valid_day_names =('all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday')\n try:\n day = input(\"Enter the day that you wish to explore (use 'all' for exploring all days): \")\n if day not in valid_day_names:\n raise Exception ('Invalid day name')\n print (\"You have selected: {}.\\n\".format(day.title()))\n break\n except Exception :\n print (\"Invalid day: Please choose from all, monday, tuesday, wednesday, thursday, friday, saturday, sunday\\n\")\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city = input(\"Would you like to see data for Chicago, New York City or Washington?\\n\").lower()\n while city not in CITY_DATA.keys():\n print(\"Invalid input of City\\n\")\n city = input(\"Would you like to see data for Chicago, New York City or Washington?\\n\").lower() \n\n # TO DO: get user input for month (all, january, february, ... , june)\n filter = input(\"Would you like to filter the data by month, day, both, or not at all? Type \\\"none\\\" for no time filter\\n\").lower()\n filter_list= ['month', 'day', 'both', 'none']\n \n while filter not in filter_list:\n print(\"Invalid input\\n\")\n filter = input(\"Would you like to filter the data by month, day, both, or not at all? Type \\\"none\\\" for no time filter\\n\").lower()\n \n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n if filter == 'month':\n month = input(\"\\nWhich month? January, February, March, April, May, or June?\\n\").title()\n while month not in month_list:\n print(\"Invalid month input\\n\")\n month = input(\"\\nWhich month? January, February, March, April, May, or June?\\n\").title()\n day = 0\n elif filter == 'day':\n day = input(\"\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\n\").title()\n while day not in day_list:\n print(\"Invalid day input\\n\")\n day = input(\"\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\n\").title()\n month = 0\n \n elif filter == 'both':\n month = input(\"\\nWhich month? January, February, March, April, May, or June?\\n\").title()\n while month not in month_list:\n print(\"Invalid month input\\n\")\n month = input(\"\\nWhich month? January, February, March, April, May, or June?\\n\").title()\n\n day = input(\"\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\n\").title()\n while day not in day_list:\n print(\"Invalid day input\\n\")\n day = input(\"\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\n\").title()\n \n elif filter =='none':\n month =0\n day = 0\n \n print('-'*40)\n print('output {} {} {}'.format(city, month, day))\n \n if month !=0:\n month = month_list.index(month)+1\n\n return city, month, day","def test_parse_filter_params_no_time(self):\n filter_params = {\n \"region\": FAKE.word(),\n \"payer_tenant_id\": FAKE.uuid4(),\n \"instance_type\": FAKE.word(),\n }\n serializer = OCIFilterSerializer(data=filter_params)\n self.assertTrue(serializer.is_valid())","def get_filters():\n filters = {}\n print('Hello Friend! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n city = input (\"Which city's data would you like to look at? (Chicago, New York City or Washington)\")\n break\n except (ValueError, KeyboardInterrpt, TypeError):\n print(\"Please enter either Chicago, New York City or Washington\")\n finally:\n filters['city_name'] = city\n print(\"Looking at data for {}\".format(city))\n\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = input (\"Which month's (January, February, March, April, May, June or all) data would you like to look at?\")\n break\n except (ValueError, KeyboardInterrpt, TypeError):\n print(\"Please enter either all, January, February, March, April, May or June.\")\n finally:\n filters['month_name'] = month\n print(\"Looking at data for {}\".format(month))\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = input (\"Which day of the week would you like to look at data for?\")\n break\n except (ValueError, KeyboardInterrpt, TypeError):\n print(\"Please enter either Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or ALL.\")\n finally:\n filters['day_name'] = day\n print(\"Looking at data for {}\".format(day))\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington).\n city = get_city()\n # ask if they want to filter by day or month\n filter = wanna_filter()\n if filter == 'day':\n day = get_day()\n month = ''\n return city, month, day\n elif filter == 'month':\n month = get_month()\n day = ''\n return city, month, day\n else:\n day = ''\n month = ''\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n cities = ['chicago', 'new york city', 'washington']\n city = input(\"Would you like to see data for Chicago, New York, or Washington? Please enter: \").lower()\n if city == 'new york':\n city += ' city'\n break\n elif city in cities:\n break\n else:\n print('It seems the spelling of the city is not correct...Please re-enter the city name.')\n except: # other error situations e.g. none stings being input; but not sure which type(s) of errors should be specified here \n print('It seems like an invalid input...Please re-enter the city.')\n\n # get user input for month (all, january, february, ... , june)\n # get user input for day of week (all, monday, tuesday, ... sunday)\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n while True:\n choice = input(\"Would you like to filter by month, day, both, or neither (no filter)? Please enter your choice: \").lower()\n if choice == 'month' or choice == 'by month':\n while True:\n month = input(\"Which month - January, February, March, April, May, or June? Please enter: \").lower()\n day = 'all'\n if month in months:\n break\n else:\n print('The input is not valid. Please input the correct month.')\n break\n elif choice == 'day' or choice == 'by day':\n while True:\n day = input(\"Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Please enter: \").lower()\n month = 'all'\n if day in days:\n break\n else:\n print('The input is not valid. Please input the correct day name.')\n break\n elif choice == 'both' or choice == 'by both':\n while True:\n month = input(\"Which month - January, February, March, April, May, or June? Please enter: \").lower()\n day = input(\"Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Please enter: \").lower()\n if (month in months) and (day in days):\n break\n else:\n print('It seems at least one of the inputs is not valid. Please input the correct month and/or day name.')\n break\n elif choice == 'neither' or choice == 'no filter':\n month = 'all'\n day = 'all'\n break\n else:\n print('The input is not valid. Please make sure the choice input is in correct format.')\n\n print('-'*40)\n return city, month, day","def set_filter():\n try:\n #=======================================================================\n # isofilter=[arg.partition('=')[-1] for arg in argv if 'atomfilter=' in arg][0][1:-1].split(',')\n #=======================================================================\n isofilter = config.arg('atomfilter')[1:-1].split(',')\n isofilter = [f.split(':') for f in isofilter]\n for f in isofilter:\n if len(f) < 2:\n f.append('True')\n if len(f) < 3:\n f.append('True')\n if len(f) < 4:\n f.append('None')\n except:\n isofilter = [['element', 'H', 'True', 'None']]\n try:\n #=======================================================================\n # isopartnerfilter=[arg.partition('=')[-1] for arg in argv if 'partnerfilter=' in arg][0][1:-1].split(',')\n #=======================================================================\n isopartnerfilter = config.arg('partnerfilter')[1:-1].split(',')\n isopartnerfilter = [f.split(':') for f in isopartnerfilter]\n for f in isopartnerfilter:\n if len(f) < 2:\n f.append('True')\n if len(f) < 3:\n f.append('True')\n if len(f) < 4:\n f.append('None')\n except:\n isopartnerfilter = [['None', 'None', 'None', 'None']]\n return isofilter, isopartnerfilter\n isofilterlist = []\n isopartnerfilterlist = []\n for i in xrange(len(isofilter) / 2):\n isofilterlist.append(tuple(isofilter[2 * i:2 * i + 2]))\n for i in xrange(len(isopartnerfilter) / 2):\n isopartnerfilterlist.append(tuple(isopartnerfilter[2 * i:2 * i + 2]))\n\n return [isofilterlist, isopartnerfilterlist]","def get_filters():\r\n print('Hello! Let\\'s explore some US bikeshare data!\\n')\r\n\r\n # ref https://stackabuse.com/getting-user-input-in-python/\r\n\r\n # Get user input for city (chicago, new york city, washington).\r\n cities = ['Chicago', 'New York city', 'Washington']\r\n city = get_user_input(cities,\"city\")\r\n\r\n # Get user input for month (all, january, february, ... , june)\r\n months = ['All', 'Jan', 'Feb', 'Mar', 'Apr', 'Jun']\r\n month = get_user_input(months,\"month\")\r\n\r\n # Get user input for day of week (all, monday, tuesday, ... sunday)\r\n days = ['All', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\r\n day = get_user_input(days,\"day\")\r\n\r\n print('-'*40)\r\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n text_OK = False\n city_conf_text = 'Chosen city is {}? Enter \"y\" to confirm. '\n while True:\n city = input('\\nFor which city would you like to run an evaluation? Chicago (CH), New York City (NY) or Washington (WA)? \\n\\\nPlease Enter abbreviation. ')\n if city[0:2].lower() == 'ch':\n city = 'chicago'\n text_OK = True\n elif city[0:2].lower() == 'ny':\n city = 'new york city'\n text_OK = True\n elif city[0:2].lower() == 'wa':\n city = 'washington'\n text_OK = True\n else:\n print('\\nInput for city selection could not be evaluated. Please enter valid abbreviation and check for typing errors. ')\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n if text_OK == True:\n conf_input = input(city_conf_text.format(city.title()))\n if conf_input.lower() == 'y':\n break\n else:\n text_OK = False\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n\n # get user input for month (all, january, february, ... , june)\n text_OK = False\n month_conf_text = 'Chosen month(s) is {}? Enter \"y\" to confirm. '\n while True:\n month = input('\\nFor which month(s) would you like to run an evaluation?\\n\\\nThe following months are available: January (Jan), February (Feb), March (Mar), April (Apr), May (May), June (Jun) or all (all). \\n\\\nPlease enter abbreviation. ')\n # months list transferred to main program head in order to make it available for call of other functions\n for i in range(len(months)):\n if month[0:3].lower() == months[i][0:3]:\n month_name = months[i].title()\n month = i+1\n text_OK = True\n break\n if text_OK == True:\n conf_input = input(month_conf_text.format(month_name.title()))\n if conf_input.lower() == 'y':\n break\n else:\n text_OK = False\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n else:\n print('\\nInput for month selection could not be evaluated. Please check for typing errors. ')\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n text_OK = False\n day_conf_text = 'Chosen day(s) of week is {}? Enter \"y\" to confirm. '\n day_except_text = 'Please enter an integer number. '\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\n while True:\n day = input('\\nFor which day(s) would you like to run an evaluation?\\n\\\nPlease enter specific day as number (Monday = 1 ... Sunday = 7) or enter \"8\" to select all. ')\n try:\n for i in range(len(days)):\n if (int(day)-1) == i:\n week_day = days[int(day)-1].title()\n text_OK = True\n break\n if text_OK == True:\n conf_input = input(day_conf_text.format(week_day.title()))\n if conf_input.lower() == 'y':\n if day == '5':\n print(\"Thank god it's friday !\")\n time.sleep(2)\n break\n else:\n text_OK = False\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n else:\n print('\\nInput for day selection could not be evaluated. Please check for typing errors.')\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n except Exception as e:\n print('Exception occurred: {}'.format(e))\n print(day_except_text)\n conf_request = input(cancel_text)\n if conf_request.lower() == 'n':\n return\n print('-'*40)\n return city, month, week_day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city=''\n month=''\n day=''\n city_list = ['chicago', 'new york city', 'washington']\n while city.lower() not in city_list:\n city = input(\"Enter a city: \") \n if city.lower() not in city_list:\n print('Sorry, Invalid city name. Please enter a city of Chicago, New York, or Washington.')\n\n # TO DO: get user input for month (all, january, february, ... , june)\n months_dict = {'january': 1, 'february': 2, 'march': 3, 'april': 4, 'may': 5, 'june': 6}\n while month.lower() not in months_dict.keys(): \n month = input(\"Enter a month: \") \n if month.lower() not in months_dict.keys():\n print('Sorry, Invalid month name. Please enter a month between January and June') \n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday'] \n while day.lower() not in day_list: \n day = input(\"Enter a weekday: \") \n if day.lower() not in day_list:\n print('Sorry, Invalid weekday name. Please enter a weekday name') \n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s look at some US bikeshare data!')\n # I want to get user input for the city choice using a while loop to handle inputs that are not part of CITY_DATA.\n # I want to get user input for the city choice using a while loop to handle inputs that are not part of CITY_DATA.\n while True:\n city = input(\"\\nDo you want analyze data from Chicago, New York City, or Washington? Answer with the name of the requested city!\\n\").lower()\n if city in CITY_DATA:\n break\n else:\n print(\"\\nPlease enter a valid city name (Chicago, New York City, or Washington.\\n\")\n\n # I want to get user input for the month filter using a while loop to handle inputs that are not part of MONTH_DATA.\n while True:\n month = input(\"\\nWhich month do you want to filter? January, Feburary, March, April, May, or June? Type all if you do not want to filter by month.\\n\").lower()\n if month in MONTH_DATA:\n break\n else:\n print(\"\\nPlease enter a valid month or all if you do not want to filter.\\n\")\n\n # I want to get user input for the weekday filter using a while loop to handle inputs that are not part of DAY_DATA.\n while True:\n day = input(\"\\nWhich weekday do you want to see? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Type all if you do not want to filter by weekday.\\n\").lower()\n if day in DAY_DATA:\n break\n else:\n print(\"\\nPlease enter a valid weekday or all if you do not want to filter.\\n\")\n\n print('-'*40)\n return city, month, day","def _process_alert_source_filters(query, filters):\n if filters:\n if not is_valid_model_filters(models.AlertSource, filters):\n return\n query = query.filter_by(**filters)\n\n return query","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # User input for cities (Chicago, New York City, Washington), using while loop to ensure valid city input, normalized with lower()\n while True:\n city = input(\"\\nFor which city would you like to see data: Chicago, New York City, or Washington? \\n\").lower()\n if city not in ('chicago', 'new york city', 'washington'):\n print(\"Please enter either Chicago, New York City, or Washington. Invalid entry.\")\n continue\n else:\n break\n\n # User input for month between January and June, or all, using while loop to ensure valid month input, normalized with lower()\n while True:\n month = input(\"\\nFor which month would you like to see data: January, February, March, April, May, June, or all? \\n\").lower()\n if month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\n print(\"Please enter a month between January and June. Invalid entry.\")\n continue\n else:\n break\n\n # User input for day of the week, or all, normalized with lower()\n while True:\n day = input(\"\\nFor which day would you like to see data: \"\n \"Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, or all?\\n\").lower()\n if day not in ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all'):\n print(\"Please enter full name of a day of the week. Invalid entry\")\n continue\n else:\n break\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('\\nHello! Let\\'s explore some US bikeshare data!\\n')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while 1:\n city = input('Would you like to see data for Chicago | CH, New York City | NYC, or Washington | WA?\\n').lower()\n print()\n if city=='chicago' or city=='ch':\n city='chicago'\n if city=='new york city' or city=='nyc':\n city='new york city'\n if city=='washington' or city=='wa':\n city='washington'\n if city not in CITY_DATA:\n print(\"\\nI'm sorry, I'm not sure which city you're referring to. Let's try again.\")\n continue\n city = CITY_DATA[city]\n break\n\n # TO DO: get user input for month (january, february, ... , june)\n while 1:\n select = input('Would you like to filter the data by month and/or day? Enter yes or no. Type \"no\" for no time filter.\\n').lower()\n print()\n if select=='yes':\n select=True\n elif select=='no':\n select=False\n else:\n print(\"\\nI'm sorry, you did not enter a valid selection. Let's try again.\")\n continue\n break\n\n # TO DO: get user input for day of week (monday, tuesday, ... sunday)\n while 1:\n if select:\n filter=input('Would you like to filter the data by month, day, or both\\n').lower()\n print()\n if filter=='month':\n month = input('Which month? January, February, March, April, May, or June?\\n').lower()\n print()\n if month not in MONTH_LIST:\n print(\"\\nI'm sorry, I'm not sure which month you're trying to filter by. Let's try again.\")\n continue\n month = MONTH_LIST[month]\n day='all'\n elif filter=='day':\n day = input('Which day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\n').lower()\n print()\n if day not in WEEK_LIST:\n print(\"\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\")\n continue\n day = WEEK_LIST[day]\n month='all'\n elif filter=='both':\n month = input('Which month? January, February, March, April, May, or June?\\n').lower()\n print()\n if month not in MONTH_LIST:\n print(\"\\nI'm sorry, I'm not sure which month you're trying to filter by. Let's try again.\")\n continue\n month = MONTH_LIST[month]\n day = input('And which day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday\\n').lower()\n print()\n if day not in WEEK_LIST:\n print(\"\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\")\n continue\n day = WEEK_LIST[day]\n else:\n print(\"\\nI'm sorry, I'm not sure which time period you're trying to filter by. Let's try again.\")\n continue\n break\n else:\n day='all'\n month='all'\n break\n\n\n print('-'*40)\n return city, month, day","def __init__(self, config, *parse_list):\n super(ParsingFilter, self).__init__()\n self.config = config\n try:\n if (\n self.config[\"filter\"][\"whitelist\"]\n and self.config[\"filter\"][\"blacklist\"]\n ):\n _LOGGER.warning(\n _(\n \"Both whitelist and blacklist filters found in configuration. \"\n \"Only one can be used at a time - only the whitelist filter will be used.\"\n )\n )\n self.parse_list = [\n logging.Filter(name) for name in parse_list[0][\"whitelist\"]\n ]\n except KeyError:\n self.parse_list = parse_list[0].get(\"whitelist\") or parse_list[0].get(\n \"blacklist\"\n )\n\n self.parse_list = [logging.Filter(name) for name in self.parse_list]","def get_filters():\n\n print(\"Hello! Let's explore some US bikeshare data!\")\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n city = input(\"Would you like to analyze Chicago, New York City or Washington? \").lower()\n while city == 'chicago' or 'new york city' or 'washington':\n print(\"You have selected: \", city)\n break\n else:\n print(\"Invalid input. Start Over!\")\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('\\nWhich month do you want to analyze? ').lower()\n while month == 'january' or 'february' or 'march' or 'april' or 'may' or 'june' or 'july' or 'august' or 'september' or 'october' or 'november' or 'december' or 'all':\n print(\"You would like to analyze: \", month)\n break\n else:\n print(\"Invalid input. Try again.\")\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('\\nWhich day of the week are you interested in? ').lower()\n while day == 'sunday' or 'monday' or 'tuesday' or 'wednesday' or 'thursday' or 'friday' or 'saturday'or 'all':\n print(\"You are interested in: \", day)\n break\n else:\n print(\"Invalid input. Try again.\")\n\n return city, month, day","def filter(self, filters):\r\n # because http.Request needs params to be a dict of strings to strings\r\n # (roughly) and since BitBucket wants repeated parameters to express\r\n # OR, we'll do the quoting by hand ourselves\r\n def flatten_conditions(filters):\r\n for key, val in filters.items():\r\n if isinstance(val, (list, tuple)):\r\n for v in val:\r\n yield (port.to_b(key), port.to_b(v))\r\n else:\r\n yield (port.to_b(key), port.to_b(val))\r\n\r\n to_encode = tuple(flatten_conditions(filters))\r\n qs = port.urlencode(to_encode)\r\n\r\n url = '{0}/?{1}'.format(self.get_url(), qs)\r\n return http.Request('GET', url), parsers.parse_json","def _validate_submission_type(filters: dict) -> None:\n legacy_submission_type = filters.get(\"submission_type\", ...)\n submission_types = filters.get(\"submission_types\", ...)\n\n if submission_types == ... and legacy_submission_type == ...:\n raise InvalidParameterException(\"Missing required filter: submission_types\")\n\n elif submission_types == ... and legacy_submission_type != ...:\n del filters[\"submission_type\"]\n if isinstance(legacy_submission_type, list):\n raise InvalidParameterException(\"Use filter `submission_types` to request multiple submission types\")\n else:\n submission_types = [legacy_submission_type]\n else:\n if not isinstance(submission_types, list):\n submission_types = [submission_types]\n\n if len(submission_types) == 0:\n msg = f\"Provide at least one value in submission_types: {' '.join(VALID_ACCOUNT_SUBMISSION_TYPES)}\"\n raise InvalidParameterException(msg)\n\n if any(True for submission_type in submission_types if submission_type not in VALID_ACCOUNT_SUBMISSION_TYPES):\n msg = f\"Invalid value in submission_types. Options: [{', '.join(VALID_ACCOUNT_SUBMISSION_TYPES)}]\"\n raise InvalidParameterException(msg)\n\n filters[\"submission_types\"] = list(set(submission_types))","def get_filters():\n print('Hello! I am Python 3.8! Let\\'s explore some US bikeshare data!')\n print()\n # TO DO: get user input for city (chicago, new york city, washington).\n # HINT: Use a while loop to handle invalid inputs\n while 1:\n print('Which city should we have a look?')\n city = input('1 = Chicago\\n'\n '2 = New York City\\n'\n '3 = Washington\\n'\n '\\n').lower()\n print('*'*40)\n print()\n if city in ('1', 'ch'):\n city = 'chicago'\n print(\"Chicago City! Here We go !!!\")\n if city in ('2', 'ny', 'nyc'):\n city = 'new york city'\n print(\"New York City! Here We go !!!\")\n if city in ('3', 'wa', 'washington dc'):\n city = 'washington'\n print(\"Washington! Here We go !!!\")\n if city not in CITY_DATA:\n print('Please enter a valid city')\n continue\n city = CITY_DATA[city]\n break\n # TO DO: get user input for month (all, january, february, ... , june)\n # TO DO: get user input for day of week (all, monday, tuesday, ...sunday)\n\n while 1:\n m_choice = input('Do you want to filter the data ?\\n'\n 'Yes\\n'\n 'No\\n'\n '\\n').lower()\n print()\n if m_choice in ('yes', 'y', 'yus'):\n print('*'*40)\n print('\\nThe data is now being filtered ...')\n m_choice = True\n elif m_choice in ('no', 'n', 'nope'):\n m_choice = False\n else:\n print('*'*40)\n print('You did not enter a valid choice. Let\\'s try again. ')\n print('*'*40)\n continue\n break\n\n while 1:\n if m_choice:\n filter = input('You can filter by:\\n'\n '\\n'\n 'Month\\n'\n 'Day\\n'\n 'Both\\n'\n '\\n').lower()\n print()\n print('*'*40)\n if filter == 'month':\n print('Which month would you like to see?')\n month = input('jan = January \\n'\n 'feb = February\\n'\n 'mar = March\\n'\n 'apr = April\\n'\n 'may = May\\n'\n 'jun = June \\n'\n '\\n').lower()\n print('*'*40)\n print()\n if month not in MONTH_DATA:\n print('Sorry I did not understand that input.'\n 'Could you try again?')\n continue\n month = MONTH_DATA[month]\n day = 'all'\n elif filter == 'day':\n print('Which day would you like to see? ')\n day = input('mon = Monday\\n'\n 'tues = Tuesday\\n'\n 'wed = Wednesday\\n'\n 'thur = Thursday\\n'\n 'fri = Friday\\n'\n 'sat = Saturday\\n'\n 'sun = Sunday\\n'\n '\\n').lower()\n print('*'*40)\n print()\n if day not in WEEK_DATA:\n print('Sorry I did not understand that input.'\n 'Could you try again?')\n continue\n day = WEEK_DATA[day]\n month = 'all'\n elif filter == 'both':\n print('Which month would you like to see?')\n month = input('jan = January \\n'\n 'feb = February\\n'\n 'mar = March\\n'\n 'apr = April\\n'\n 'may = May\\n'\n 'jun = June \\n'\n '\\n').lower()\n print('*'*40)\n print()\n if month not in MONTH_DATA:\n print('Sorry I did not understand that input.'\n 'Could you try again?')\n continue\n month = MONTH_DATA[month]\n print('Which day of the week would you like to see?')\n day = input('mon = Monday\\n'\n 'tues = Tuesday\\n'\n 'wed = Wednesday\\n'\n 'thur = Thursday\\n'\n 'fri = Friday\\n'\n 'sat = Saturday\\n'\n 'sun = Sunday\\n'\n '\\n').lower()\n print('*'*40)\n print()\n if day not in WEEK_DATA:\n print('Sorry I did not understand that input.'\n 'Could you try again?')\n continue\n day = WEEK_DATA[day]\n else:\n print('Sorry I did not understand that input.'\n 'Could you try again?')\n continue\n break\n else:\n day = 'all'\n month = 'all'\n break\n\n print('*'*40)\n return city, month, day","def _parse_filter_list(filter: Optional[List]) -> Any:\n ret = None\n if filter:\n ret = set(filter)\n return ret","def _convert_filters_type(\n filter: List[Tuple[str, PredicateType, SupportedFilterType]],\n schema: StateSchema,\n) -> List[Tuple[str, SupportedFilterType]]:\n new_filter = []\n if dataclasses.is_dataclass(schema):\n schema = {field.name: field.type for field in fields(schema)}\n else:\n schema = schema.schema_dict()\n\n for col, predicate, val in filter:\n if col in schema:\n column_type = schema[col]\n try:\n isinstance(val, column_type)\n except TypeError:\n # Calling `isinstance` to the Literal type raises a TypeError.\n # Ignore this case.\n pass\n else:\n if isinstance(val, column_type):\n # Do nothing.\n pass\n elif column_type is int or column_type == \"integer\":\n try:\n val = convert_string_to_type(val, int)\n except ValueError:\n raise ValueError(\n f\"Invalid filter `--filter {col} {val}` for a int type \"\n \"column. Please provide an integer filter \"\n f\"`--filter {col} [int]`\"\n )\n elif column_type is float or column_type == \"number\":\n try:\n val = convert_string_to_type(\n val,\n float,\n )\n except ValueError:\n raise ValueError(\n f\"Invalid filter `--filter {col} {val}` for a float \"\n \"type column. Please provide an integer filter \"\n f\"`--filter {col} [float]`\"\n )\n elif column_type is bool or column_type == \"boolean\":\n try:\n val = convert_string_to_type(val, bool)\n except ValueError:\n raise ValueError(\n f\"Invalid filter `--filter {col} {val}` for a boolean \"\n \"type column. Please provide \"\n f\"`--filter {col} [True|true|1]` for True or \"\n f\"`--filter {col} [False|false|0]` for False.\"\n )\n new_filter.append((col, predicate, val))\n return new_filter","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n city = str(input('Would you like to see data for Chicago, New York or Washington?\\n').lower())\n while city not in CITY_DATA.keys():\n city = str(input('Sorry, it looks like an invalid input. Please try again: Chicago, New York or Washington?\\n').lower())\n\n # TO DO: get user input for month (all, january, february, ... , june)\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n filter_list = ['month', 'weekday', 'both', 'none']\n filter_input = str(input('Would you like to filter the data by month, weekday, both or not at all? Type \"none\" if you don\\'t want to filter anything at all.\\n').lower())\n\n while filter_input not in filter_list:\n filter_input = str(input('Sorry, it looks like an invalid input. Please try again: month, weekday, both or none?\\n').lower())\n\n month = 'all'\n day = 'all'\n\n if filter_input == 'none':\n month = 'all'\n day = 'all'\n elif filter_input == 'month':\n month = str(input('What month would you like to look at? Jan, Feb, Mar, Apr, May, Jun or all?\\n').lower())\n while month not in months:\n month = str(input('Sorry, it looks like an invalid input. Please try again: Jan, Feb, Mar, Apr, May, Jun or all?\\n').lower())\n elif filter_input == 'weekday':\n day = str(input('What weekday would you like to look at? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\n').lower())\n while day not in weekdays:\n day = str(input('Sorry, it looks like an invalid input. Please try again: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\n').lower())\n elif filter_input == 'both':\n month = str(input('What month would you like to look at? Jan, Feb, Mar, Apr, May, Jun or all?\\n').lower())\n while month not in months:\n month = str(input('Sorry, it looks like an invalid input. Please try again: Jan, Feb, Mar, Apr, May, Jun or all?\\n').lower())\n day = str(input('What weekday would you like to look at? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\n').lower())\n while day not in weekdays:\n day = str(input('Sorry, it looks like an invalid input. Please try again: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\n').lower())\n\n print('-'*40)\n return filter_input, city, month, day","def std_filters():\n kwargs = {\n \"sentence_filters\":[punctuation_filter],\n \"word_filters\":[small_word_filter, stopword_filter, stemming_filter]\n }\n return kwargs","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 properties[_param] = req.params.get(param)\n\n if 'changes-since' in filters:\n isotime = filters['changes-since']\n try:\n filters['changes-since'] = timeutils.parse_isotime(isotime)\n except ValueError:\n raise exc.HTTPBadRequest(_(\"Unrecognized changes-since value\"))\n\n if 'protected' in filters:\n value = self._get_bool(filters['protected'])\n if value is None:\n raise exc.HTTPBadRequest(_(\"protected must be True, or \"\n \"False\"))\n\n filters['protected'] = value\n\n # only allow admins to filter on 'deleted'\n if req.context.is_admin:\n deleted_filter = self._parse_deleted_filter(req)\n if deleted_filter is not None:\n filters['deleted'] = deleted_filter\n elif 'changes-since' not in filters:\n filters['deleted'] = False\n elif 'changes-since' not in filters:\n filters['deleted'] = False\n\n if properties:\n filters['properties'] = properties\n\n return filters","def get_filters():\r\n print('Welcome to Bikeshare data Analysis...')\r\n try:\r\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\r\n while True:\r\n try:\r\n cit_num = int(input('Enter city you would like to analyze \\n1 : Chicago\\n2 : Newyork\\n3 : Washington :\\n'))\r\n except ValueError:\r\n print(\"Please enter a number value\\n\")\r\n continue\r\n if cit_num not in (1,2,3):\r\n print(\"Please enter a valid number that refers to cities\\n\")\r\n continue\r\n else:\r\n break\r\n\r\n city = LST_CITY[cit_num - 1].lower()\r\n # TO DO: get user input for month (all, january, february, ... , june)\r\n while True:\r\n try:\r\n month_num = int(input('Enter month of the year you would like to analyze \\n1 : JAN\\n2 : FEB\\n3 : MAR\\n4 : APR\\n5 : MAY\\n6 : JUN\\n7 : WHOLE WEEK\\n'))\r\n except ValueError:\r\n print(\"Please enter a num value\\n\")\r\n continue\r\n if month_num not in range(1,8):\r\n print(\"Please enter a valid month...\\n\")\r\n continue\r\n else:\r\n break\r\n month = VALID_MONTHS[month_num-1]\r\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\r\n while True:\r\n try:\r\n day_num = int(input('Enter day of the week you would like to analyze \\n1 : MON\\n2 : TUE\\n3 : WED\\n4 : THU\\n5 : FRI\\n6 : SAT\\n7 : SUN\\n8 : ALL MONTHS\\n'))\r\n except ValueError:\r\n print(\"Please enter a num value\\n\")\r\n continue\r\n if day_num not in range(1, 9):\r\n print(\"Please enter a valid month number...\\n\")\r\n continue\r\n else:\r\n break\r\n day = VALID_DAYS[day_num-1]\r\n\r\n print('\\n'+'*'*20)\r\n print('Your selections are stated below\\nCity : {} , Month : {} , Day : {} '.format(city.title(),month.title(),day.title()))\r\n print('*'*20)\r\n \r\n return city, month, day ,cit_num , month_num , day_num\r\n \r\n except Exception as e:\r\n print('An exception has been occurred : {}'.format(e))","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n print('-'*40)\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n city = input('Which city do you like to analyze? Please choose a city from chicago, '\n 'new york city, or washington: ').lower()\n if city not in CITY_DATA.keys():\n raise ValueError\n break\n except ValueError:\n print('That\\'s not a valid input, please enter a city name again.\\n')\n\n print('-'*40)\n # get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = input('Which month do you like to analyze? Please enter a month name from january to june, '\n 'or \"all\" to select all months: ').lower()\n if month not in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:\n raise ValueError\n break\n except ValueError:\n print('That\\'s not a valid input, please enter a month name again, or \"all\" to select all months.\\n')\n\n print('-'*40)\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = input('Which day of week do you like to analyze? Please enter a weekday name from monday to sunday, '\n 'or \"all\" to select the entire week: ').lower()\n if day not in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']:\n raise ValueError\n break\n except ValueError:\n print('That\\'s not a valid input, please enter a weekday name again, or \"all\" to select the entire week.\\n')\n\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington).\n # HINT: Use a while loop to handle invalid inputs\n while True:\n try:\n city = str(input(\"You may see stats from either Chicago, New York City, or Washington. Please enter your chosen city! \").lower())\n except KeyboardInterrupt:\n print(\"Bye!\")\n except:\n error_mess()\n continue\n\n if city.lower() not in ('chicago', 'new york city', 'washington'):\n error_mess()\n else:\n break\n # TO DO: get user input for month (all, january, february, ... , june)\n while True:\n try:\n month = str(input(\"For which month would you like to see {}'s data? If you would like to see an entire calendar year, please specify 'all months'. \".format(city.title())))\n except KeyboardInterrupt:\n print(\"Bye!\")\n except:\n error_mess()\n continue\n\n if month.lower() not in ('january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december', 'all months'):\n error_mess()\n else:\n break\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n try:\n day = int(input(\"For which day of {} would you like to see data? If you would like to see every day, please specify '0'. \".format(month.title())))\n except KeyboardInterrupt:\n print(\"Bye!\")\n except:\n error_mess()\n continue\n\n if day not in range(31):\n error_mess()\n else:\n break\n\n print('-'*50)\n return city, month, day","def get_filters(self):","def _validate_filter(filter):\n\n if filter.HasField('composite_filter'):\n for sub_filter in filter.composite_filter.filters:\n _validate_filter(sub_filter)\n elif filter.HasField('property_filter'):\n if filter.property_filter.op in UNSUPPORTED_OPERATORS:\n raise ValueError('Query cannot have any inequality filters.')\n else:\n pass","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # Gets user input for city, allowing for 1 choice of 3 cities. Solves for case and entries not in dataset.\n while True:\n city = input('Please select one of the following cities: Chicago, New York City or Washington.\\n').lower()\n if city not in city_names:\n print('Invalid entry, please enter one of the three cities above.')\n else:\n break\n\n # Gets user input for month, either selection of signle month in dataset or all. Solves for case and entries not in dataset.\n while True:\n month = input('Please select one month from the following, or all: January, February, March, April, May, June, or all.\\n').lower()\n if month not in month_names:\n print('Invalid entry, please enter one of the months listed above or all.')\n else:\n break\n\n # Gets user input for day, either selection of single day in dataset or all. Solves for case and entries not in dataset.\n while True:\n day = input('Please select a day of the week or all: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or all.\\n').lower()\n if day not in day_names:\n print('Invalid entry, please enter one of the days listed above or all.')\n else:\n break\n\n print('-'*40)\n return city, month, day","def _add_filters(self, filters):\n self._env.filters['dateformat'] = dateformat\n self._env.filters.update(filters or {})","def get_filters():\r\n print('Hello! Let\\'s explore some US bikeshare data!')\r\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\r\n #Take the inputs from the user\r\n city = input(\"\\nPlease enter a city from the following: \\n1.chicago\\n2.new york city\\n3.washington\\n\").lower()\r\n\r\n #while loop to make sure that the user entered the correct name of city\r\n while (city not in CITY_DATA.keys()):\r\n city = input(\"\\nplease enter a city from the following: \\n1.chicago\\n2.new york city\\n3.washington\\n\").lower()\r\n\r\n # TO DO: get user input for month (all, january, february, ... , june)\r\n #\r\n months = ['All','January', 'February', 'March', 'April', 'May', 'June']\r\n month = input('\\nPlease enter month from the folloiwng to filter by:\\n\\njanuary, february, march, april, may, june\\n\\nnote:type \"all\" for no month filtering\\n').title()\r\n #while loop to make sure that the user entered the correct name of month\r\n while (month not in months):\r\n month = input('Please enter month from the folloiwng to filter by\\njanuary, february, march, april, may, june\\ntype \"all\" for no month filtering\\n').title()\r\n\r\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\r\n\r\n days = ['All' , 'Monday' , 'Tuesday' , 'Wednesday' , 'Thursday' , 'Friday' , 'Saturday' , 'Sunday']\r\n day = input('\\nPlease enter day of the week to filter by\\ntype \"all\" for no day filtering\\n').title()\r\n #while loop to make sure that the user entered the day name in the correct way\r\n while (day not in days):\r\n day = input('\\nPlease enter a correct day of the week to filter by\\ntype \"all\" for no day filtering\\n').title()\r\n\r\n print('\\nyour choice was :\\ncity: {}\\nmonth: {}\\nday: {}\\n'.format(city,month,day))\r\n print('-'*40)\r\n\r\n return city, month, day","def load_filters(self, filters):\n if not filters:\n return\n if not isinstance(filters, list):\n raise errors.ParserError(\"Expecting 'filters' value to be a list \"\n \"but instead its a {}.\".format(type(filters)))\n for filter_path in filters:\n if not os.path.exists(filter_path):\n raise errors.ParserError(\n \"Filter path '{}' doesn't exist\".format(filter_path))\n elif os.path.isdir(filter_path):\n self._load_filter_directory(filter_path)\n else:\n self.load_filter_file(filter_path)","def test_parse_filter(self):\n old_type = Filter(\n 'filter[student.school.title]', [\n Relationship('student', PersonSchema(), None),\n Relationship('school', StudentSchema(), None)],\n Attribute('title', SchoolSchema(), None), ('eq', ['test']))\n new_type = self.driver.parse(old_type)\n \n assert new_type.source == old_type\n assert old_type.relationships != new_type.relationships\n assert isinstance(new_type.relationships[0], Mapper)\n assert old_type.attribute != new_type.attribute\n assert isinstance(new_type.attribute, Column)\n assert old_type.value == new_type.value","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n# TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city_abbreviations = ['chi', 'ny', 'w']\n while True:\n city = input('Kindly specify a city by typing chicago or new york city or washington: \\n\\n').lower()\n if city in CITY_DATA.keys():\n break\n elif city.lower() == 'new york': #if the user forgot to add city to new york as it's a common mistake between users\n print('\\nPlease notify that the city you typed called new york city, So retype it again right this time: \\n')\n elif city in city_abbreviations: #if the user input was abbreviations of the name as it's a common mistake between users\n print('\\nPlease notify that city abbreviation\\'s is not allowed, Retype city full name!\\n')\n elif city.lower() == 'newyorkcity': #if user's input was newyorkcity without any spaces as it's a common mistake between users\n print('\\nPlease notify that the city you typed called new york city with spaces between words, So retype it again right this time: \\n')\n else: #if user printed any other things like wrong name or used speical chracters like space _ + = ~\n print('\\nThats invalid input....\\n\\nplease choose one of the three cities chicago or new york city or washington.\\n') \n# TO DO: get user input for month (all, january, february, ... , june)\n months = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\n months_abbreviations = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug']\n while True:\n month = input('\\n\\nTo filter data by a particuler month, please type the month or all for not filtering by month: \\n-january\\n-february\\n-march\\n-april\\n-may\\n-june\\n-all\\n\\n').lower()\n if month in months:\n break\n elif month in months_abbreviations:\n print('\\nPlease notify that months abbreviation\\'s is not allowed, Retype month full name!\\n')\n else: #if the user input was abbreviations of the name as it's a common mistake between users \n print('\\nThats invalid input....\\n\\n\\nplease choose one of the six months listed to filter with or use no filter\\n')\n# TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday','saturday', 'sunday', 'all']\n days_abbreviations = ['mon', 'tu', 'tue', 'tues', 'wed', 'th', 'thu', 'thur', 'thurs', 'fri', 'sat', 'sun']\n while True:\n day = input('\\n\\nTo filter data by a particuler day, please type the day or all for not filtering by day: \\n-saturday\\n-sunday\\n-monday\\n-tuesday\\n-wednesday\\n-thursday\\n-friday\\n-all\\n\\n').lower()\n if day in days:\n break\n elif day in days_abbreviations: #if the user input was abbreviations of the name as it's a common mistake between users\n print('\\nPlease notify that day abbreviation\\'s is not allowed, Retype day full name!\\n') \n else:\n print('\\nThats invalid input....\\n\\n\\nplease choose one of the seven days listed to filter with or use no filter\\n') \n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n city = get_city_from_user()\n print('-' * 10)\n\n # get user input for month (all, january, february, ... , june)\n month = get_month_from_user()\n print('-' * 10)\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n day = get_day_of_week_from_user()\n print('-' * 40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:# to ensure that there is no invalid input\n city = input('Which city do you want to explore Chicago, New York or Washington? \\n> ').lower()\n if city in CITIES:\n break\n else:\n print(\"You entered invalied City.\")\n\n # get user input for month (all, january, february, ... , june)\n while True:# to ensure that there is no invalid input\n month = input('Please choose which month (january, february, march, april, may, june) or just type all for all months provided \\n').lower()\n if month in MONTHS:\n break\n if month == 'all':\n break\n else:\n print(\"You entered invalied month.\")\n\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while True:# to ensure that there is no invalid input\n day = input('Please choose which day (sunday, monday, tuesday, wednesday, thursday, friday, saturday) or just type all for all months provided \\n ').lower()\n if day in DAYS:\n break\n if DAYS == 'all':\n break\n else:\n print(\"You entered invalied day.\")\n print('-'*50)\n return city, month, day","def _extract_filter_type_and_value(data):\n if data.startswith(\"in:\"):\n value = list(six.text_type(data[3:]).split(\",\"))\n filter_type = 'in'\n elif data.startswith(\"nin:\"):\n value = list(six.text_type(data[4:]).split(\",\"))\n filter_type = 'nin'\n elif data.startswith(\"neq:\"):\n value = six.text_type(data[4:])\n filter_type = 'neq'\n elif data.startswith(\"gt:\"):\n value = six.text_type(data[3:])\n filter_type = 'gt'\n elif data.startswith(\"gte:\"):\n value = six.text_type(data[4:])\n filter_type = 'gte'\n elif data.startswith(\"lt:\"):\n value = six.text_type(data[3:])\n filter_type = 'lt'\n elif data.startswith(\"lte:\"):\n value = six.text_type(data[4:])\n filter_type = 'lte'\n elif data.startswith(\"eq:\"):\n value = six.text_type(data[3:])\n filter_type = 'eq'\n elif data.startswith(\"has:\"):\n value = six.text_type(data[4:])\n filter_type = 'has'\n else:\n value = data\n filter_type = 'eq'\n\n return filter_type, value","def ParseFilterFile(input_lines):\n # Strip comments and whitespace from each line and filter non-empty lines.\n stripped_lines = (l.split('#', 1)[0].strip() for l in input_lines)\n filter_lines = [l for l in stripped_lines if l]\n\n # Split the tests into positive and negative patterns (gtest treats\n # every pattern after the first '-' sign as an exclusion).\n positive_patterns = [l for l in filter_lines if l[0] != '-']\n negative_patterns = [l[1:] for l in filter_lines if l[0] == '-']\n return positive_patterns, negative_patterns","def parse_message_filter(message_filter):\n if '#' not in message_filter:\n message_filter += '#*'\n appname, instanceid = message_filter.split('#', 1)\n return collections.namedtuple('ParseResult', 'filter appname instanceid')(\n message_filter, appname, instanceid\n )","def filter_format(filter_dict, query_data, filter_type, index): \r\n \r\n filter_list = ''\r\n count = 0 \r\n \r\n while query_data[index] != 'PRESENT\\n': \r\n if filter_type in query_data[index]: \r\n count += 1\r\n filter_keyword = query_data[index].strip(filter_type)\r\n filter_list = (filter_keyword.strip('\\n'))\r\n \r\n index += 1 \r\n \r\n if count > 0: \r\n filter_dict[filter_type] = filter_list\r\n return filter_dict","def get_filters():\n print('Hello! My name is Damir. Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n try:\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\n while city not in CITY_DATA:\n print('No results found, please check your spelling.').lower()\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\n while month not in MONTH_LIST:\n print('No results found, please check your spelling.').lower()\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\n\n print('your choice was: ', month)\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('Which day of the week would you like to request data for? or all?').lower()\n while day not in DAYS_LIST:\n print('No results found, please check your spelling.').lower()\n day = input('Which day of the week would you like to request data for? or all?').lower()\n\n print('your choice was: ', day)\n\n return city, month, day\n except Exception as e:\n print('An error with your inputs occured: {}'.format(e))\n print('-'*40)","def get_filters():\n print('Hello! My name is Damir. Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n try:\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\n while city not in CITY_DATA:\n print('No results found, please check your spelling.').lower()\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\n while month not in MONTH_LIST:\n print('No results found, please check your spelling.').lower()\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\n\n print('your choice was: ', month)\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('Which day of the week would you like to request data for? or all?').lower()\n while day not in DAYS_LIST:\n print('No results found, please check your spelling.').lower()\n day = input('Which day of the week would you like to request data for? or all?').lower()\n\n print('your choice was: ', day)\n\n return city, month, day\n except Exception as e:\n print('An error with your inputs occured: {}'.format(e))\n print('-'*40)","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n # improving user input for city\n city = input(\"\\nWhat city do you intend to filter by? New York City, Chicago or Washington?\\n\").lower()\n ### The following checks will ensure that the user inputs one of the expected cities.\n while(True):\n if(city == 'chicago' or city == 'new york city' or city == 'washington' or city == 'all'):\n break\n else:\n city = input('Enter Correct city: ').lower()\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('\\nWhich or the following months will you want to filter by? January, February, March, April, May, or June?\\n').lower()\n \n # Here we validate user input for month to ensure that it is one of the expected values.\n while(True):\n if(month == 'january' or month == 'february' or month == 'march' or month == 'april' or month == 'may' or month == 'june' or month == 'all'):\n break\n else:\n month = input('Enter a valid month\\n').lower()\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n \n day = input('Which or the following days ? monday, tuesday, wednesday, thursday, friday, saturday , sunday or all will you want to display the data for?\\n').lower()\n \n # Ensuring that the correct value is inputted for day of the week.\n while(True):\n \n if(day == 'monday' or day == 'tuesday' or day == 'wednesday' or day == 'thursday' or day == 'friday' or day == 'saturday' or day == 'sunday' or day == 'all'):\n break\n else:\n day = input('Enter Correct day: ').lower()\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! My name is Damir. Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n try:\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\n while city not in CITY_DATA:\n print('No results found, please check your spelling.').lower()\n city = input('Which city would you like to review data from? chicago, new york city or washington?').lower()\n\n # TO DO: get user input for month (all, january, february, ... , june)\n month = input('Which month you would like to review the data from january to june? or all?').lower()\n while month not in MONTH_LIST:\n print('No results found, please check your spelling.').lower()\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\n\n print('your choice was: ', month)\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('Which day of the week would you like to request data for? or all?').lower()\n while day not in DAYS_LIST:\n print('No results found, please check your spelling.').lower()\n day = input('Which day of the week would you like to request data for? or all?').lower()\n\n print('The day you chose was: ', day)\n\n return city, month, day\n except Exception as e:\n print('An error with your inputs occured: {}'.format(e))\n print('-'*40)","def get_filters():\n print('\\nHello! Let\\'s explore some US bikeshare data!')\n\n # get user input for city (chicago, new york city, washington).\n city=input('Please, introduce the name of the city you want to obtain information about [chicago/new york city/washington]: ')\n city=city.lower()\n #Let us now check that it is actually a valid input. We give 3 chances before exiting the program\n i=0\n while (city != 'chicago') and (city != 'new york city') and (city != 'washington'):\n city=input('Please, introduce a valid city [chicago/new york city/washington]: ')\n i += 1\n if i==3:\n sys.exit()\n\n # get user input for month (all, january, february, ... , june)\n ans_month=input('Do you want to filter by month? [y/n]: ')\n if ans_month=='n':\n month='all'\n elif ans_month=='y':\n month=input('Which month? [january/february/.../june]: ')\n month=month.lower()\n #we check that it is actually a valid input. We give 3 chances before exiting the program:\n i=0\n set_months=['january','february','march','april','may','june']\n while month not in set_months:\n month=input('Please, introduce a valid month [january/february/.../june]: ')\n i += 1\n if i==3:\n sys.exit()\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n ans_day=input('Do you want to filter by day of week? [y/n]: ')\n if ans_day=='n':\n day='all'\n elif ans_day=='y':\n day=input('Which day? [monday/tuesday/.../sunday]: ')\n day=day.lower()\n #check that it is actually a valid input. We give 3 chances before exiting the program:\n i=0\n set_days=['monday','tuesday','wednesday','thursday','friday','saturday','sunday']\n while day not in set_days:\n day=input('Please, introduce a valid day [monday/tuesday/.../sunday]: ')\n i += 1\n if i==3:\n sys.exit()\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n # Get the names of the cities \n city = ''\n while city not in CITY_DATA.keys():\n print(\"\\nPlease enter the name of the city to analyze\")\n print(\"\\nValid values are Chicago, New York City, Washington. Note the city name is not case sensitive\")\n city = input(\"\\nCity: \").lower()\n\n if city not in CITY_DATA.keys():\n print(\"\\nInvalid or unknown city specified.\")\n \n print(f\"\\nYou selected '{city.title()}' as city\")\n\n # TO DO: get user input for month (all, january, february, ... , june)\n\n month = ''\n MONTH_OPTS = ['january','february','march','april','may','june','all']\n while month not in MONTH_OPTS:\n print(\"\\nPlease enter the month(s) for which to get data to analyze\")\n print(\"\\nOptions are january to june\")\n print(\"\\nYou can also enter 'all' to view data for all months\")\n month = input(\"\\nMonth: \").lower()\n\n if month not in MONTH_OPTS:\n print(\"\\nInvalid or unknown month specified.\")\n \n print(f\"\\nYou selected '{month.title()}' as month\")\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n DAY_OPTS = ['all','monday','tuesday','wednesday','thursday','friday','saturday','sunday']\n day = ''\n while day not in DAY_OPTS:\n print(\"\\nPlease enter the day(s) for which to get data to analyze\")\n print(\"\\nOptions are monday to sunday\")\n print(\"\\nYou can also enter 'all' to view data for all days\")\n day = input(\"\\nDay: \").lower()\n\n if day not in DAY_OPTS:\n print(\"\\nInvalid or unknown day specified. Retrying ...\")\n \n print(f\"\\nYou selected '{day.title()}' as day\")\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('.'*40)\n print('Hello! Let\\'s explore some US bikeshare data!\\n')\n\n # user input for city (chicago, new york city, washington)\n while True:\n city = input(\n '\\nWhich city\\'s data will you like to explore? Chicago, New York City, or Washington?\\n').lower()\n\n if city in CITY_DATA:\n confirmation = input(\n '\\nYou have opted to view data for {}. If this was an error, enter no to restart, else enter yes to continue.\\n'.format(city.title())).lower()\n if confirmation == 'yes':\n break\n else:\n print(\n 'Sorry, we do not have that data, type a city name from the 3 options provided.\\n')\n\n # user input for month (all, january, february, ... , june)\n while True:\n month = input(\n '\\nWhat month will you like to explore? (Enter Jan for January, Feb for February, etc.)\\nIf you don\\'t want to filter by month, enter all.\\n').lower()\n\n if month in MONTHS:\n month = MONTHS[month]\n break\n elif month == 'all':\n break\n else:\n print('\\nOops! That\\'s not in the options\\n')\n\n # user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n day = input(\n '\\nWould you like to see data for a particular day of the week?\\nIf yes, enter the day. (Enter Mon for Monday, Tue for Tuesday, etc.) If no, just type all.\\n').lower()\n\n if day in DAYS:\n day = DAYS[day]\n break\n elif day == 'all':\n break\n else:\n print('\\nOops! That\\'s not in the options\\n')\n\n if month != 'all' or day != 'all':\n print('\\nYou have chosen to explore US bikeshare data for \\ncity: {}, month: {}, day: {}'.format(\n city, month, day).title())\n\n print('-'*40)\n return city, month, day","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n city_temp = ''\n while city_temp.lower() not in CITY_DATA:\n city_temp = str(input('For which city would you like to see data? Chicago, New York, or Washington?\\n').lower()) \n if city_temp.lower() in CITY_DATA:\n city = city_temp\n break\n else:\n print('This city is not available.')\n continue\n \n # TO DO: get user input for month (all, january, february, ... , june)\n month_temp = ''\n while month_temp.lower() not in MONTH_DATA:\n month_temp = input('For which month would you like to see the data?\\nPlease enter any month from January to June or enter all to see data for all the months available\\n')\n if month_temp.lower() in MONTH_DATA:\n month = month_temp.lower()\n else:\n print('This month is not available or does not exist.') # Error message\n\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n day_temp = ''\n while day_temp.lower() not in DAY_DATA:\n day_temp = input ('For which day of the week would you like to see the data?\\nPlease enter any day from Monday to Sunday or enter all to see data for all the days\\n')\n if day_temp.lower() in DAY_DATA:\n day = day_temp.lower()\n else:\n print('This day of the week does not exist') # Error message\n \n print('-'*40)\n return city, month, day","def extract_filters(self):\n self.filters = self.controller.filters\n\n self.extract_core_stats()\n self.extract_abilities()\n # goes through and adds all list-based filters\n for filterType, elements in self.filters.items():\n if type(elements) == list and len(elements) > 0:\n self.extract_filter_list(filterType, elements)","def filter(self, *args):\n exps = []\n\n #this ones can be improved. some are not getting extracted\n if \"retweets\" in args:\n exps.append(re.compile(\"^RT ?(?<=^|(?<=[^a-zA-Z0-9-_\\.]))@([A-Za-z]+[A-Za-z0-9-_]+):\"))\n if \"emoticons\" in args:\n exps.append(\"emoticons\")\n if \"flags\" in args:\n exps.append(re.compile(u\"[\\U0001F1E6-\\U0001F1FF]\"))\n if \"handles\" in args:\n # Handles at start of string\n exps.append(re.compile(\"^\\s*((?<=^|(?<=[^\\S]))@([\\S]+)\\s*)*\"))\n # Handles at end of string\n exps.append(re.compile(\"\\s+((?<=^|(?<=[^\\S]))@(\\S+)\\s*)*$\"))\n if \"urls\" in args:\n exps.append(re.compile(\"(https?|ftp)://[^\\s/$.?#].[^\\s]*\"))\n if \"hashtags\" in args:\n # Hastags at start of string\n exps.append(re.compile(\"^\\s*((?<=^|(?<=[^\\S]))#([\\S]+)\\s*)*\"))\n # Hashtags at end of string\n exps.append(re.compile(\"\\s+((?<=^|(?<=[^\\S]))#(\\S+)\\s*)*$\"))\n\n\n # Use all filters\n if \"*\" in args and not exps:\n return self.filter(\"retweets\", \"emoticons\", \"flags\", \"handles\", \"urls\", \"hashtags\")\n\n filtering_text = self.raw_text\n\n for expression in exps:\n if expression == \"emoticons\":\n filtering_text = ''.join(c for c in filtering_text if c not in emoji.UNICODE_EMOJI)\n else:\n filtering_text = re.sub(expression, \"\", filtering_text)\n\n # Remove extra spaces\n self.clean_text = re.sub(r\"\\s\\s+\", ' ', filtering_text.strip())\n return self.clean_text","def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n city = input(\"Please enter a city ('chicago', 'new york city' or 'washington'): \").lower()\n if city in ['chicago', 'new york city', 'washington']:\n break\n else:\n print('Invalid input! Please try again.')\n\n # get user input for month (all, january, february, ... , june)\n while True:\n month = input(\"Please enter a month ('january', 'february', 'march', 'april', 'may', 'june') or enter 'all': \").lower()\n if (month in months) or (month == 'all'):\n break\n else:\n print('Invalid Input! Please try again.')\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n while True:\n weekdays = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n day = input(\"Please enter a weekday ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday') or 'all': \").lower()\n if (day in weekdays) or (day =='all'):\n break\n else:\n print('Invalid input! Please try again.')\n\n print('\\nInput Successful!\\nCity: {}\\nMonth: {}\\nWeekday: {}'.format(city, month, day)) #quick summary of all input for the user.\n print('-'*40)\n return city, month, day"],"string":"[\n \"def handle_filters(filters: Optional[List[str]]) -> List[Dict[str, Any]]:\\n filters_to_send = []\\n for filter_ in filters or []:\\n split_filter = filter_.split('=')\\n if len(split_filter) != 2 or not split_filter[0] or not split_filter[1]:\\n raise DemistoException('Filters should be in the format of \\\"filtername1=filtervalue1,filtername2=filtervalue2\\\". '\\n f'The filter \\\"{filter_}\\\" doesn\\\\'t meet this requirement.')\\n filters_to_send.append({'name': split_filter[0],\\n 'operator': '=',\\n 'value': split_filter[1]})\\n return filters_to_send\",\n \"def make_filter_specification(cls, filter_string):\\n try:\\n return parse_filter(filter_string)\\n except ParseException as err:\\n raise ValueError('Expression parameters have errors. %s' % err)\",\n \"def parse_filters(func, *args, **kwargs):\\n request = args[1]\\n filters = request.query_params.get('filters', None)\\n\\n if not filters:\\n return func(*args, **kwargs)\\n cleaned_filters = {}\\n try:\\n filters = json.loads(filters)\\n for field, operations in filters.items():\\n for operation, value in operations.items():\\n cleaned_filters['%s__%s' % (field, operation)] = value\\n except (ValueError, AttributeError):\\n return Response(\\n {'errors': [\\n \\\"Filters incorrectly formatted. Required format: \\\"\\n \\\"{'filters': {'fieldname': { 'operation': 'value'}}\\\"\\n ]},\\n status=400\\n )\\n\\n try:\\n # NOTE(adriant): This feels dirty and unclear, but it works.\\n # Positional argument 3 is filters, so we just replace it.\\n args = list(args)\\n args[2] = cleaned_filters\\n return func(*args, **kwargs)\\n except FieldError as e:\\n return Response({'errors': [str(e)]}, status=400)\",\n \"def parse_filters(func, *args, **kwargs):\\n request = args[1]\\n filters = request.query_params.get(\\\"filters\\\", None)\\n\\n if not filters:\\n return func(*args, **kwargs)\\n cleaned_filters = {}\\n try:\\n filters = json.loads(filters)\\n for field, operations in filters.items():\\n for operation, value in operations.items():\\n cleaned_filters[\\\"%s__%s\\\" % (field, operation)] = value\\n except (ValueError, AttributeError):\\n return Response(\\n {\\n \\\"errors\\\": [\\n \\\"Filters incorrectly formatted. Required format: \\\"\\n \\\"{'filters': {'fieldname': { 'operation': 'value'}}\\\"\\n ]\\n },\\n status=400,\\n )\\n\\n try:\\n # NOTE(adriant): This feels dirty and unclear, but it works.\\n # Positional argument 3 is filters, so we just replace it.\\n args = list(args)\\n args[2] = cleaned_filters\\n return func(*args, **kwargs)\\n except FieldError as e:\\n return Response({\\\"errors\\\": [str(e)]}, status=400)\",\n \"def _ParseFilterOptions(self, options):\\n names = [u'date_filters', u'filter_file']\\n helpers_manager.ArgumentHelperManager.ParseOptions(\\n options, self, names=names)\\n\\n extensions_string = self.ParseStringOption(options, u'extensions_string')\\n self._ParseExtensionsString(extensions_string)\\n\\n names_string = getattr(options, u'names_string', None)\\n self._ParseNamesString(names_string)\\n\\n signature_identifiers = getattr(options, u'signature_identifiers', None)\\n try:\\n self._ParseSignatureIdentifiers(\\n self._data_location, signature_identifiers)\\n except (IOError, ValueError) as exception:\\n raise errors.BadConfigOption(exception)\\n\\n if self._filter_file:\\n self.has_filters = True\\n else:\\n self.has_filters = self._filter_collection.HasFilters()\",\n \"def _formatFilters(self, filters):\\n formatted_filters = []\\n inequality_field = None\\n\\n for f in filters:\\n filtr = {field.name: getattr(f, field.name) for field in f.all_fields()}\\n\\n try:\\n filtr[\\\"field\\\"] = FIELDS[filtr[\\\"field\\\"]]\\n filtr[\\\"operator\\\"] = OPERATORS[filtr[\\\"operator\\\"]]\\n except KeyError:\\n raise endpoints.BadRequestException(\\\"Filter contains invalid field or operator.\\\")\\n\\n # Every operation except \\\"=\\\" is an inequality\\n if filtr[\\\"operator\\\"] != \\\"=\\\":\\n # check if inequality operation has been used in previous filters\\n # disallow the filter if inequality was performed on a different field before\\n # track the field on which the inequality operation is performed\\n if inequality_field and inequality_field != filtr[\\\"field\\\"]:\\n raise endpoints.BadRequestException(\\\"Inequality filter is allowed on only one field.\\\")\\n else:\\n inequality_field = filtr[\\\"field\\\"]\\n\\n formatted_filters.append(filtr)\\n return (inequality_field, formatted_filters)\",\n \"def parse_filters(filters_str):\\n fltrs = []\\n for part in str(filters_str).lower().split(\\\",\\\"):\\n if part==\\\"blur\\\":\\n fltrs.append(filters.blur(1))\\n elif part==\\\"distort\\\":\\n fltrs.append(filters.distort(18))\\n\\n return fltrs\",\n \"def get_filters():\\n \\n \\\"\\\"\\\"\\\"\\\"\\\"\\\"\\\"\\n \\n \\\"\\\"\\\"Messeges to genrate filters\\\"\\\"\\\"\\n\\tnote_messege = 'In this project, we make use of Python to explore data related to bike share systems for three major cities in the United States\\\\n'\\n welcome_messege = 'Hello! Let\\\\'s explore some US bikeshare data!\\\\n'\\n enter_city_name_messege = 'Which city would you like to filter by? Chicago, New York City or Washington? '\\n filter_definition_messege = '\\\\nWould you like to filter the data by - \\\\n1. Month\\\\n2. Day\\\\n3. Both\\\\n4. No Filter\\\\n\\\\nPlease choose the appropriate filter name.\\\\nNote: Incorrect filter name will result as \\\\'no filter selected\\\\' by the user.\\\\n'\\n enter_filter_messege = 'Desired filter (e.g: Month, Day, Both or No Filter): '\\n enter_month_name_messege = 'Enter month name (e.g: january, february, march, april, may or june): '\\n enter_day_name_messege = 'Enter day of the week (e.g: monday, tuesday, wednesday, thursday, friday, saturday, sunday): '\\n exception_messege = '\\\\nWarning! That is not a valid input.\\\\n'\\n warning_city_name_messege = '\\\\nWarning! Invalid city name. Select city name from the following cities only - Chicago, New York City or Washington.' \\n warning_month_name_messege = '\\\\nWarning! Invalid month name. Select month name from the following months only - january, february, march, april, may or june'\\n warning_day_name_messege = '\\\\nWarning! Invalid day name. Select day name from the following days only - monday, tuesday, wednesday, thursday, friday, saturday, sunday'\\n \\\"\\\"\\\"\\\"\\\"\\\"\\\"\\\"\\n \\n \\\"\\\"\\\"City, Month and Day List\\\"\\\"\\\"\\n city_list = ['chicago', 'new york city', 'washington']\\n month_list = ['january', 'february', 'march', 'april', 'may', 'june']\\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\\n \\\"\\\"\\\"\\\"\\\"\\\"\\\"\\\"\\n \\n\\tprint(note_messege)\\n print(welcome_messege)\\n \\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs \\n while True:\\n try:\\n city = input(enter_city_name_messege)\\n break\\n except:\\n print(exception_messege)\\n \\n while city.lower() not in city_list:\\n while True:\\n try: \\n print(warning_city_name_messege)\\n city = input(enter_city_name_messege)\\n break\\n except:\\n print(exception_messege)\\n \\n print(filter_definition_messege)\\n while True:\\n try:\\n filter_choice = input(enter_filter_messege)\\n break\\n except:\\n print(exception_messege)\\n while True: \\n if filter_choice.lower() == 'month':\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = input(enter_month_name_messege) \\n break\\n except:\\n print(exception_messege)\\n while month.lower() not in month_list:\\n while True:\\n try: \\n print(warning_month_name_messege)\\n month = input(enter_month_name_messege) \\n break\\n except:\\n print(exception_messege)\\n day = 'all'\\n break\\n \\n elif filter_choice.lower() == 'day':\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday) \\n while True:\\n try:\\n day = input(enter_day_name_messege)\\n break\\n except:\\n print(exception_messege)\\n while day.lower() not in day_list:\\n while True:\\n try: \\n print(warning_day_name_messege)\\n day = input(enter_day_name_messege) \\n break\\n except:\\n print(exception_messege)\\n month = 'all'\\n break\\n \\n elif filter_choice.lower() == 'both':\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = input(enter_month_name_messege)\\n break\\n except:\\n print(exception_messege)\\n while month.lower() not in month_list:\\n while True:\\n try: \\n print(warning_month_name_messege)\\n month = input(enter_month_name_messege) \\n break\\n except:\\n print(exception_messege)\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = input(enter_day_name_messege)\\n break\\n except:\\n print(exception_messege)\\n while day.lower() not in day_list:\\n while True:\\n try: \\n print(warning_day_name_messege)\\n day = input(enter_day_name_messege) \\n break\\n except:\\n print(exception_messege)\\n break\\n \\n else:\\n month = 'all'\\n day = 'all'\\n break\\n \\n\\n print('-'*40)\\n return city.lower(), month.lower(), day.lower()\",\n \"def __parse__(self, filter):\\n \\n if filter == 'zipcode':\\n # Return 5 digit zip or, if applicable, Concatenate 5 digit and \\n # 4 digit zipcode\\n if self.data['Mailing Zip 4']:\\n return \\\"%s-%s\\\" %(str(self.data['Mailing Zip Code'])[:-2],\\n str(self.data['Mailing Zip 4'])[:-2]\\n )\\n else:\\n return str(self.data['Mailing Zip Code'])[:-2]\\n elif filter == 'employee_count':\\n # Convert employee count string to digit\\n pattern = '.+to\\\\s([0-9]+)'\\n try:\\n return re.findall(\\n pattern, self.data['Location Employee Size Range'])[0]\\n except IndexError:\\n pass\\n elif filter == 'phone':\\n # Regex phone number digits and concatenate\\n number = ''.join(re.findall('[0-9]+', \\n self.data['Phone Number Combined']))\\n return number if len(number) == 10 else 0\",\n \"def parse_queryset_filters(filters):\\n OP_MAP = [\\n (\\\">=\\\", \\\"__gte\\\"),\\n (\\\"=>\\\", \\\"__gte\\\"),\\n (\\\">\\\", \\\"__gt\\\"),\\n (\\\"<=\\\", \\\"__lte\\\"),\\n (\\\"=<\\\", \\\"__lte\\\"),\\n (\\\"<\\\", \\\"__lt\\\"),\\n (\\\"=\\\", \\\"\\\"),\\n ]\\n\\n filter_dict = {}\\n exclude_dict = {}\\n for filter_str in filters.split(\\\",\\\"):\\n if \\\"!=\\\" in filter_str:\\n key, val = filter_str.split(\\\"!=\\\")\\n exclude_dict[key] = parse_bool(val, strict=False)\\n continue\\n for op, new_op in OP_MAP:\\n if op in filter_str:\\n key, val = filter_str.split(op)\\n filter_dict[key + new_op] = parse_bool(val, strict=False)\\n break\\n else:\\n raise ValueError(\\\"Unknown filter expression: %s\\\" % filter_str)\\n\\n return (filter_dict, exclude_dict)\",\n \"def get_filters():\\r\\n\\r\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\r\\n print(\\\"Hello! Let\\\\'s explore some US bikeshare data!\\\")\\r\\n while True:\\r\\n city = input(\\\"Would you like to see data for Chicago, New York City or Washington?\\\\n\\\").split(\\\": \\\")[0].lower()\\r\\n try:\\r\\n if city not in CITY_DATA:\\r\\n print(\\\"Sorry, {} is not a valid city. Please type again by entering either 'Chicago', 'New York City' OR 'Washington' again\\\".format(city))\\r\\n continue\\r\\n else:\\r\\n break\\r\\n except ValueError as e:\\r\\n print(\\\"Exception occurred: {}\\\".format(e))\\r\\n\\r\\n # TO DO: get user input for month (all, january, february, ... , june)\\r\\n while True:\\r\\n month = input(\\\"Which month? January, February, March, April or June?..Enter 'all' if no month filter\\\\n\\\").lower()\\r\\n try:\\r\\n months = ['january', 'february', 'march', 'april', 'may', 'june','all']\\r\\n if month not in months:\\r\\n print(\\\"Sorry, {} is not a valid month. Please type again by entering again\\\".format(month))\\r\\n continue\\r\\n else:\\r\\n break\\r\\n except ValueError as e:\\r\\n print(\\\"Exception occurred: {}\\\".format(e))\\r\\n\\r\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\r\\n while True:\\r\\n day_of_week = input(\\\"Which day? Enter the response in integer. Ex: 1 = Sunday..Enter 'all' if no day filter\\\\n\\\")\\r\\n try:\\r\\n if day_of_week.isnumeric():\\r\\n day_of_week = int(day_of_week)\\r\\n if day_of_week == 1:\\r\\n day = \\\"Sunday\\\"\\r\\n break\\r\\n elif day_of_week == 2:\\r\\n day = \\\"Monday\\\"\\r\\n break\\r\\n elif day_of_week == 3:\\r\\n day = \\\"Tuesday\\\"\\r\\n break\\r\\n elif day_of_week == 4:\\r\\n day = \\\"Wednesday\\\"\\r\\n break\\r\\n elif day_of_week == 5:\\r\\n day = \\\"Thursday\\\"\\r\\n break\\r\\n elif day_of_week == 6:\\r\\n day = \\\"Friday\\\"\\r\\n break\\r\\n elif day_of_week == 7:\\r\\n day = \\\"Saturday\\\"\\r\\n break\\r\\n else:\\r\\n print(\\\"Sorry, {} is not a valid day. Please type again by entering either the day of week in integer ex: 1 = Sunday\\\".format(day_of_week))\\r\\n continue\\r\\n else:\\r\\n if day_of_week == \\\"all\\\":\\r\\n day = \\\"all\\\"\\r\\n break\\r\\n else:\\r\\n continue\\r\\n except ValueError as e:\\r\\n print(\\\"Exception occurred: {}\\\".format(e))\\r\\n\\r\\n print('-'*40)\\r\\n return city, month, day\",\n \"def parse_date_filter(filter_value):\\n # Accepted date format is ISO 8601, YYYY-MM-DDTHH:MM:SSZ (no `Z` is accepted too)\\n filter_data = {\\n \\\"operator\\\": None,\\n \\\"date1\\\": None,\\n \\\"date2\\\": None\\n }\\n\\n iso_date_group = r\\\"(\\\\d{4}-\\\\d{2}-\\\\d{2}T\\\\d{2}:\\\\d{2}:\\\\d{2}(\\\\.\\\\d+)?(Z|\\\\+\\\\d{2}:\\\\d{2})?)\\\"\\n # Filter with a comparison operator (>, >=, <, <=) and a date (e.g. `>=YYYY-MM-DDTHH:MM:SS`)\\n oper_comparison = r\\\"^(<=?|>=?)%s$\\\" % iso_date_group\\n # Filter with a range operator (..) between two dates\\n # (e.g. YYYY-MM-DDTHH:MM:SSZ..YYYY-MM-DDTHH:MM:SSZ)\\n range_comparison = r\\\"^%s\\\\.{2}%s$\\\" % (iso_date_group, iso_date_group)\\n\\n oper_result = re.match(oper_comparison, filter_value)\\n range_result = re.match(range_comparison, filter_value)\\n\\n if not oper_result and not range_result:\\n raise ValueError('Filter format is not valid')\\n\\n if oper_result:\\n filter_data['operator'] = oper_result.group(1)\\n filter_data['date1'] = str_to_datetime(oper_result.group(2))\\n\\n if range_result:\\n filter_data['operator'] = '..'\\n filter_data['date1'] = str_to_datetime(range_result.group(1))\\n filter_data['date2'] = str_to_datetime(range_result.group(4))\\n\\n if filter_data['date1'] > filter_data['date2']:\\n range_msg = 'Date range is invalid. Upper bound must be greater than the lower bound'\\n raise ValueError(range_msg)\\n\\n return filter_data\",\n \"def test_parse_filter_params_success(self):\\n filter_params = {\\n \\\"resolution\\\": \\\"daily\\\",\\n \\\"time_scope_value\\\": \\\"-10\\\",\\n \\\"time_scope_units\\\": \\\"day\\\",\\n \\\"region\\\": FAKE.word(),\\n \\\"payer_tenant_id\\\": FAKE.uuid4(),\\n \\\"product_service\\\": FAKE.word(),\\n }\\n serializer = OCIFilterSerializer(data=filter_params)\\n self.assertTrue(serializer.is_valid())\",\n \"def parse_filter(value):\\n\\n if value.endswith('+pol'):\\n pol = True\\n value = value[:-4]\\n else:\\n pol = False\\n\\n if value in ufti_filters:\\n return (ufti_filters[value], pol)\\n\\n else:\\n logger.warning('Filter ' + value + ' is not recognised')\\n return (None, pol)\",\n \"def get_filters():\\n # Get the user to input their name. Characters, hypens, spaces and brackets are valid - numbers are not\\n valid_characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ -_(){}[]&'\\n while True:\\n name = input(\\\"\\\\nPlease enter your name, using only characters, spaces and hypens: \\\")\\n if all(char in valid_characters for char in name):\\n print('\\\\nHello {}! Let\\\\'s explore some US bikeshare data!'.format(name.title()))\\n break\\n print(\\\"\\\\nNumbers are not valid inputs. Please try again.\\\")\\n\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n # city_input = 'chicago'\\n CITIES = ['chicago', 'new york city', 'washington']\\n while True:\\n city_input = input('\\\\nPlease choose the city you would like to see data for: Chicago, New York City, Washington.\\\\n').lower()\\n if city_input in CITIES:\\n print('\\\\nThank-you for selecting {}!'.format(city_input.title()))\\n break\\n else:\\n print(\\\"\\\\nI'm not sure what city you are referring to. Please try again!\\\")\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n # month_input = 'all'\\n MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\\n while True:\\n month_input = input('\\\\nPlease choose from one or ALL of the following months: January, February, March, April, May, June or All. \\\\n').lower()\\n if month_input in MONTHS:\\n print('\\\\nThank-you for selecting {}!'.format(month_input.title()))\\n break\\n print(\\\"\\\\nI'm not sure what month you are referring to. Please try again!\\\")\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n # day_input = 'Thursday'\\n DAYS = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\\n while True:\\n day_input = input('\\\\nPlease choose from one or ALL of the following days: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or All. \\\\n').lower()\\n if day_input in DAYS:\\n print('\\\\nThank-you for selecting {}!'.format(day_input.title()))\\n break\\n print(\\\"\\\\nI'm not sure what day you are referring to. Please try again!\\\")\\n\\n print('-'*40)\\n return city_input, month_input, day_input\",\n \"def get_filters():\\n # Declare initial value for month and day to handle if user not select one of them or both for filter\\n month, day = '', ''\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n city = input('Would you like to see data for for Chicago, New York City, or Washington?\\\\n')\\n validate_city = validate_input('city', city)\\n if validate_city:\\n print(validate_city)\\n else:\\n break\\n \\n while True:\\n # get user input for filter (month, day, both, or none)\\n filters = input(f'Would you like to filter the data by month, day, both, or not at all? '\\n f'type \\\"none\\\" for no time filter.\\\\n')\\n\\n validate_filters = validate_input('filters', filters)\\n if validate_filters:\\n print(validate_filters)\\n else:\\n break\\n \\n filters = filters.lower()\\n if filters != 'none':\\n # get user input for month (all, january, february, ... , june)\\n filters = filters.lower()\\n if filters in ['both', 'month']:\\n while True:\\n month = input(f'Which month? January, February, Mrach, April, May, June, or All? '\\n f'Please type out the full month name.\\\\n')\\n\\n validate_month = validate_input('month', month)\\n if validate_month:\\n print(validate_month)\\n else:\\n month = month.upper()\\n break\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n if filters in ['both', 'day']:\\n while True:\\n day = input(f'Which day? Monday, Tuesday, Wednesday, Thursday, '\\n f'Friday, Saturday, or All? Please type out the full day name.\\\\n')\\n\\n validate_day = validate_input('day', day)\\n\\n if validate_day:\\n print(validate_day)\\n else:\\n break\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n\\t try:\\n\\t city = input('Input city name (chicago, new york city, washington): ').lower()\\n\\t if city in cities:\\n break\\n\\t except ValueError:\\n\\t print('That\\\\'s not a valid String!')\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n\\t try:\\n\\t month = input('Input month name (all, january, february, ... , june): ').lower()\\n\\t if month in months:\\n break\\n\\t except ValueError:\\n\\t print('That\\\\'s not a valid String!')\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ..., sunday)\\n while True:\\n\\t try:\\n\\t day = input('Input day of week (all, monday, tuesday, ..., sunday): ').lower()\\n\\t if day in days:\\n break\\n\\t except ValueError:\\n\\t print('That\\\\'s not a valid String!')\\n\\n print('-'*40)\\n return city, month, day\",\n \"def parse_for_filters(query_string):\\n if ';' in query_string:\\n strings = query_string.split(';')\\n else:\\n strings = query_string.split('&')\\n\\n filters = []\\n leftovers = [] \\n for string in strings:\\n query = cgi.parse_qs(string)\\n try:\\n key, value = query.items()[0]\\n\\n try:\\n argument = unicode(value[0], 'UTF-8')\\n except TypeError:\\n argument = value[0]\\n\\n func = FILTER_PARSERS[key](argument)\\n filters.append(func)\\n except(KeyError, IndexError):\\n leftovers.append(string)\\n\\n leftovers = ';'.join(leftovers)\\n return filters, leftovers\",\n \"def check_request_type_filter(\\n field_names: list,\\n request_type_filter: dict,\\n has_start_time: bool,\\n has_end_time: bool,\\n has_start_date: bool,\\n has_end_date: bool,\\n has_date: bool,\\n has_SP: bool,\\n has_year: bool,\\n has_month: bool,\\n has_week: bool\\n):\\n\\n filter_str = f'\\\\n\\\\nFilter:\\\\n{request_type_filter}\\\\n\\\\nField Names:\\\\n{\\\", \\\".join(field_names)}'\\n\\n assert {(False, True): True, (False, False): False, (True, True): False, (True, False): False}[(has_year, has_month)] == False, 'Cannot provide a month without a year' + filter_str\\n assert {(False, True): True, (False, False): False, (True, True): False, (True, False): False}[(has_year, has_week)] == False, 'Cannot provide a week without a year' + filter_str\\n assert has_start_time + has_end_time != 1, 'Only one of start/end time was provided' + filter_str\\n assert has_start_date + has_end_date != 1, 'Only one of start/end date was provided' + filter_str\\n assert (has_SP + has_date != 1) or (has_start_date + has_end_date == 2), 'Only one of date/SP was provided' + filter_str\\n assert sum(request_type_filter.values()) == 1, 'Request type could not be determined\\\\n\\\\nFilter' + filter_str\\n\\n return\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n \\n #list to validate the input from user against these city names.\\n city_list = ['chicago', 'new york city', 'washington'] \\n month_list = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday','sunday', 'all']\\n \\n #Used stack overflow to be able to validate user inputs \\n while True:\\n city = input(\\\"Would you like to see data for Chicago, New York City or Washington? \\\").lower()\\n if city not in city_list:\\n print(\\\"This is not a valid city. Please enter: Chicago, New York City or Washington.\\\")\\n else:\\n break\\n \\n while True:\\n filter_mon_day = input(\\\"Would you like to filter the data by month, day or both.? \\\").lower()\\n if filter_mon_day not in ('month', 'day', 'both'):\\n print(\\\"This is not a valid filter. Please enter: month, day or both.\\\")\\n else:\\n break\\n \\n # TO DO: get user input for month (all, january, february, ... , june)\\n \\n if filter_mon_day == \\\"month\\\":\\n while True:\\n month = input(\\\"Enter a valid month: January, February, March, April, May or June or all: \\\").lower()\\n if month not in month_list:\\n print(\\\"This is not a valid month. Please enter: January, February, March, April, May or June or all\\\") \\n else:\\n break\\n \\n day = \\\"all\\\"\\n \\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n \\n if filter_mon_day == \\\"day\\\":\\n month = \\\"all\\\"\\n while True:\\n day = input(\\\"Enter a valid day: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all: \\\").lower()\\n if day not in day_list:\\n print(\\\"This is not a valid day. Please enter: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all\\\")\\n else:\\n break\\n \\n \\n \\n # TO DO: get user input for both month and day. \\n \\n if filter_mon_day == \\\"both\\\":\\n #month = input(\\\"Enter a valid month: January, February, March, April, May or June or all: \\\").lower()\\n while True:\\n month = input(\\\"Enter a valid month: January, February, March, April, May or June or all: \\\").lower()\\n if month not in month_list:\\n print(\\\"This is not a valid month. Please enter: January, February, March, April, May or June or all\\\") \\n else:\\n break\\n \\n while True:\\n day = input(\\\"Enter a valid day: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all: \\\").lower()\\n if day not in day_list:\\n print(\\\"This is not a valid day. Please enter: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday or all\\\")\\n else:\\n break \\n \\n\\n print('-'*40)\\n return city, month, day\",\n \"def parse_filter(self, tokens):\\n if not tokens:\\n self._parser_state.error('missing filter')\\n filters = []\\n if tokens[0].type == 'LBRACE':\\n opening_brace = tokens.pop(0)\\n while tokens:\\n filters.append(self._parse_single_filter(tokens))\\n if not tokens or tokens[0].type != 'COMMA':\\n break\\n tokens.pop(0)\\n if not tokens or tokens[0].type != 'RBRACE':\\n self._parser_state.error('unclosed brace', token=opening_brace)\\n tokens.pop(0)\\n else:\\n filters.append(self._parse_single_filter(tokens))\\n return filters\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bike-share data.\\\\n')\\n\\n # Gets city to filter data\\n while True:\\n city = input('Choose among the following cities for which you want to see the data: \\\\\\n \\\\n1) Chicago\\\\n2) New York\\\\n3) Washington?\\\\n')\\n city = city.title()\\n\\n # Decoding mnemonic inputs\\n if city == '1' or city == 'Chi' or city == 'C':\\n city = 'Chicago'\\n elif city == '2' or city == 'Ny' or city == 'N Y' or city == 'Newyork' or city == 'N':\\n city = 'New York'\\n elif city == '3' or city == 'Wash' or city == 'W':\\n city = 'Washington'\\n\\n # Asking user to input again if unexpected input else continue\\n if city == 'Chicago' or city == 'New York' or city == 'Washington':\\n print('\\\\nLooks like you want to explore the statistics of: ', city)\\n break\\n else:\\n print('\\\\n******************INVALID INPUT*******************')\\n print('Please select the city from the available options.')\\n\\n print('----------------------------------------------')\\n # Asking user which filter to apply and accepting required values\\n while True:\\n filters = input('\\\\nWould you like to filter the data by:\\\\n' +\\n '1) Month\\\\n2) Day \\\\n3) Both or\\\\n4) None at all?' +\\n '\\\\nChoose among the available filters(1 - 4):\\\\n')\\n filters = filters.title()\\n\\n options = ['1', '2', '3', '4', 'Month', 'Day', 'Both', 'None']\\n\\n # Taking care of invalid filters else getting required values of filters\\n if filters not in options:\\n print('\\\\n******************INVALID INPUT*******************')\\n print('Please apply any of the following filters: Month, Day, Both, or None')\\n else:\\n # Decoding mnemonic inputs\\n if filters not in options[4:8]:\\n if filters == '1':\\n filters = 'Month'\\n elif filters == '2':\\n filters = 'Day'\\n elif filters == '3':\\n filters = 'Both'\\n elif filters == '4':\\n filters = 'None'\\n\\n # Providing feedback to users\\n print(\\\"Okay, We will apply the following filter: \\\", filters)\\n print('----------------------------------------------')\\n\\n # Getting values for month and day according to selected filter\\n month = get_month(filters)\\n day = get_day(filters)\\n break\\n\\n # Displaying the filters to data:\\n print('\\\\n\\\\n****************CHOSEN FILTERS****************')\\n print('City: {}\\\\nMonth: {}\\\\nDay: {}'.format(city, month, day))\\n print('----------------------------------------------')\\n\\n return city, month, day, filters\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # TO DO: get user input for city (chicago, new york city, washington).\\n while True:\\n city = input(\\n \\\"Please input one of the following: Chicago, New York City, or Washington: \\\"\\n ).lower()\\n if city not in ('chicago', 'new york city', 'washington'):\\n print('That is not an option, please try again.')\\n else:\\n break\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n month = input(\\n \\\"Please input one month from January to June or 'all': \\\"\\n ).lower()\\n if month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\\n print('That is not an option, please try again.')\\n else:\\n break\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n day = input(\\n \\\"Please input a day of the week from Monday to Sunday or 'all': \\\"\\n ).lower()\\n if day not in ('monday', 'tuesday', 'wednesday', 'thursday', 'friday',\\n 'saturday', 'sunday', 'all'):\\n print('That is not an option, please try again.')\\n else:\\n break\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # Get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n city = input('Would you like to see data for Chicago, New York City or Washington?')\\n if city.lower() in CITY_DATA:\\n break\\n print('ERROR: City does not match. Please try again.')\\n\\n # Get user input for month (all, january, february, ... , june)\\n while True:\\n month = input(\\\"Type month (January, February, March, April, May or June) to filter by or type 'all' for no filter\\\")\\n if month.lower() in MONTH_LIST or month.lower() == 'all':\\n break\\n print(\\\"ERROR: Input was not a month from January to June nor all. Please try again.\\\")\\n\\n # Get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n day = input(\\\"Type day of the week to filter or type 'all' for no filter\\\")\\n if day.lower() in DAY_LIST or day.lower() == 'all':\\n break\\n print(\\\"ERROR: Input was not a day of the week nor all.\\\")\\n\\n print('-'*40)\\n return city, month, day\",\n \"def __init__( self, filters=None, prx=None ):\\n\\n if filters is None:\\n if prx is None:\\n\\n self._filter_list = rts2_wwwapi.rts2comm().get_filters()\\n\\n elif type(filters) == list:\\n self._filter_list = filters\\n\\n elif type(filters) == dict:\\n raise TypeError(\\\"Filters are should not be a dict, it probably should be None\\\")\\n # this assumes that the keywords of the dictionary are \\n # the fitler names and the value is the filter number. \\n\\n\\n #sort by filter number and reverse look up. \\n # this doesn't work in python3\\n #for key, value in sorted(filters.iteritems(), key=lambda (k,v): (v,k)):\\n #self._filter_list.append( key )\\n\\n elif type(filters) == str or type(filters) == unicode:\\n self._filter_list = str(filters).split()\\n\\n else:\\n raise TypeError(\\\"Unexpected filter type {}, type must be string, unicode, list or dict\\\".format(type(filters)))\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n city = str(input('Would you like to see data for Chicago, New York, or Washington?\\\\n'))\\n break\\n except:\\n print('\\\\nPlease input Chicago, New York, or Washington\\\\n')\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n both = str(input('Would you like to filter the data by month, day, or not at all?\\\\n'))\\n break\\n except:\\n print('\\\\nPlease input month, day or not\\\\n') \\n if both == 'month':\\n while True:\\n try:\\n month = str(input('Which month - January, February, March, April, May, or June?\\\\n'))\\n break\\n except:\\n print('\\\\nPlease input a valid month\\\\n')\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = str(input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\\\n'))\\n break\\n except:\\n print('\\\\nPlease input a valid day\\\\n')\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n\\n city = prompts.city_prompt.launch()\\n\\n _filter = prompts.filter_prompt.launch()\\n\\n if _filter == \\\"Month\\\":\\n month = prompts.month_prompt.launch()\\n day = \\\"All\\\"\\n\\n elif _filter == \\\"Day\\\":\\n day = prompts.day_prompt.launch()\\n month = \\\"All\\\"\\n\\n elif _filter == \\\"Both\\\":\\n month = prompts.month_prompt.launch()\\n day = prompts.day_prompt.launch()\\n\\n else:\\n month, day = \\\"All\\\", \\\"All\\\"\\n\\n print(\\\"-\\\" * 40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n valid_input = False\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while not valid_input:\\n city = input('\\\\n Please select a city, Available cities are chicago, new york city, washington.\\\\n')\\n city = clean_input(city.lower())\\n if city in ['chicago', 'new york city', 'washington']:\\n valid_input = True\\n else:\\n print(\\\"Invalid city entered. Available cities are \\\" + ','.join(CITY_DATA.keys()))\\n\\n # get user input for month (all, january, february, ... , june)\\n valid_input = False\\n\\n while not valid_input:\\n month = input('\\\\n Please select a month, Available values are all, january, february, ..., june.\\\\n')\\n month = clean_input(month.lower())\\n if month in MONTHS.keys():\\n valid_input = True\\n else:\\n print(\\\"Invalid month entered. Available months are \\\" + ','.join(MONTHS.keys()))\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n valid_input = False\\n\\n while not valid_input:\\n day = input('\\\\n Please enter day, Available values are all, monday, tuesday, ... sunday.\\\\n')\\n day = clean_input(day.lower())\\n if day in DAYS.keys():\\n valid_input = True\\n else:\\n print(\\\"Invalid day entered. Available days are \\\" + ','.join(DAYS.keys()))\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n cities = ['chicago', 'new york city', 'washington']\\n city_good = 'n'\\n while city_good == 'n':\\n city = input(\\\"Enter the name of the city to analyze (Chicago, New York City, Washington): \\\").lower()\\n if city in cities:\\n print(Fore.GREEN + '{} it is!\\\\n'.format(city.capitalize()) + Style.RESET_ALL)\\n city_good = 'y'\\n else:\\n print(Fore.RED + 'Error! \\\"{}\\\" is not a valid entry, please try again!\\\\n'.format(city) + Style.RESET_ALL)\\n city_good = 'n'\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\\n month_good = 'n'\\n while month_good == 'n':\\n month = input(\\\"Enter the month to analyze (All, January, February, March, April, May, June): \\\").lower()\\n if month in months:\\n print(Fore.GREEN + '{} it is!\\\\n'.format(month.capitalize()) + Style.RESET_ALL)\\n month_good = 'y'\\n else:\\n print(Fore.RED + 'Error! \\\"{}\\\" is not a valid entry, please try again!\\\\n'.format(month) + Style.RESET_ALL)\\n month_good = 'n'\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n days = ['all', 'sunday', 'monday', 'tuesday', 'wednesday', 'thursday',\\n 'friday', 'saturday']\\n day_good = 'n'\\n while day_good == 'n':\\n day = input(\\\"Enter the day of the week to analyze (All, Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday): \\\").lower()\\n if day in days:\\n print(Fore.GREEN + '{} it is!\\\\n'.format(day.capitalize()) + Style.RESET_ALL)\\n day_good = 'y'\\n else:\\n print(Fore.RED + 'Error! \\\"{}\\\" is not a valid entry, please try again!\\\\n'.format(day) + Style.RESET_ALL)\\n day_good = 'n'\\n\\n print('='*70)\\n return city, month, day\",\n \"def _CheckFilter(self, filter, values):\\n try:\\n match = Query.FILTER_REGEX.match(filter)\\n if not match:\\n raise datastore_errors.BadFilterError(\\n 'Could not parse filter string: %s' % str(filter))\\n except TypeError:\\n raise datastore_errors.BadFilterError(\\n 'Could not parse filter string: %s' % str(filter))\\n\\n property = match.group(1)\\n operator = match.group(3)\\n if operator is None:\\n operator = '='\\n\\n if isinstance(values, tuple):\\n values = list(values)\\n elif not isinstance(values, list):\\n values = [values]\\n if isinstance(values[0], datastore_types._RAW_PROPERTY_TYPES):\\n raise datastore_errors.BadValueError(\\n 'Filtering on %s properties is not supported.' % typename(values[0]))\\n\\n if operator in self.INEQUALITY_OPERATORS:\\n if self.__inequality_prop and property != self.__inequality_prop:\\n raise datastore_errors.BadFilterError(\\n 'Only one property per query may have inequality filters (%s).' %\\n ', '.join(self.INEQUALITY_OPERATORS))\\n elif len(self.__orderings) >= 1 and self.__orderings[0][0] != property:\\n raise datastore_errors.BadFilterError(\\n 'Inequality operators (%s) must be on the same property as the '\\n 'first sort order, if any sort orders are supplied' %\\n ', '.join(self.INEQUALITY_OPERATORS))\\n\\n if (self.__kind is None and\\n property != datastore_types._KEY_SPECIAL_PROPERTY):\\n raise datastore_errors.BadFilterError(\\n 'Only %s filters are allowed on kindless queries.' %\\n datastore_types._KEY_SPECIAL_PROPERTY)\\n\\n if property in datastore_types._SPECIAL_PROPERTIES:\\n if property == datastore_types._KEY_SPECIAL_PROPERTY:\\n for value in values:\\n if not isinstance(value, Key):\\n raise datastore_errors.BadFilterError(\\n '%s filter value must be a Key; received %s (a %s)' %\\n (datastore_types._KEY_SPECIAL_PROPERTY, value, typename(value)))\\n\\n return match\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!\\\\n')\\n #get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n city = input('Which city do you want to explore? chicago, new york, washington\\\\n')\\n if city.lower() in CITY_DATA:\\n break\\n else:\\n print('Enter valid city name..\\\\n')\\n except ValueError:\\n \\n print(\\\"Provide a string value...\\\\n\\\")\\n continue\\n\\n #get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = input('Which month do you want to explore? all, january, february, ... , etc\\\\n')\\n if month.lower() in MONTHS or month == 'all':\\n break\\n else:\\n print('Enter valid month name..\\\\n')\\n except ValueError:\\n \\n print(\\\"Provide a string value...\\\\n\\\")\\n continue\\n\\n\\n #get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = input('Which day do you want to explore? all, saturday, sunday, ... , etc\\\\n')\\n if day.lower() in DAYS or day == 'all':\\n break\\n else:\\n print('Enter valid day name..\\\\n')\\n except ValueError:\\n \\n print(\\\"Provide a string value...\\\\n\\\")\\n continue\\n\\n print('-'*40)\\n return city.lower(), month.lower(), day.lower()\",\n \"def filter(self, filters):\",\n \"def get_filters():\\n allowed_cities = ('chicago','new york','washington')\\n\\n allowed_time_filters = ('month','day','both','none')\\n\\n allowed_months = ('january','february','march','april','may','june')\\n\\n allowed_days = ('mon','tue','wed','thur','fri','sat','sun')\\n\\n weekday_dict = {\\\"Mon\\\": 'Monday',\\\"Tue\\\": 'Tuesday',\\\"Wed\\\": 'Wednesday',\\\"Thur\\\": 'Thursday',\\\"Fri\\\": 'Friday',\\\"Sat\\\": 'Saturday',\\\"Sun\\\": 'Sunday'}\\n\\n print('\\\\nHello! Let\\\\'s explore some US bikeshare data! \\\\n')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n print(\\\"Would you like to see data for Chicago, New York, or Washington cities? \\\\n\\\")\\n city = input(\\\"Enter city : \\\\n\\\").rstrip().lstrip()\\n while city.lower() not in allowed_cities:\\n print('You need to enter one of the following cities: Chicago, New York or Washington.\\\\n')\\n city = input(\\\"Enter city : \\\\n\\\")\\n else:\\n user_filters.append(city.title())\\n\\n print(\\\"Would you like to filter {}'s data by month, day, both or not at all? Type none for no time filter.\\\\n\\\".format(str(user_filters[0])))\\n\\n time_filter_option = input('Enter your time filtering option: \\\\n').rstrip().lstrip()\\n\\n while time_filter_option not in allowed_time_filters:\\n print('You need to enter valid time filtering choice eg by month, day, both or not at all? Type none for no time filter.')\\n time_filter_option = input('Enter your time filtering option: ').rstrip().lstrip()\\n\\n if time_filter_option.lower() == 'both':\\n # get user input for month (all, january, february, ... , june)\\n month = input(\\\"Enter month : \\\").rstrip().lstrip()\\n while month.lower() not in allowed_months:\\n print('You need to enter valid month name, January through June.')\\n month = input(\\\"Enter month : \\\").rstrip().lstrip()\\n else:\\n user_filters.append(month.title())\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input(\\\"Enter weekday eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \\\").title().rstrip().lstrip()\\n while day.lower() not in allowed_days:\\n print('You need to enter weekday abbreviations eg Mon, Tue, Wed, Thur, Fri, Sat, Sun.')\\n day = input(\\\"Enter weekday abbreviation eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \\\").title().rstrip().lstrip()\\n else:\\n user_filters.append(weekday_dict[day].title())\\n\\n elif time_filter_option.lower() == 'month':\\n month = input(\\\"Enter month name, January through June: \\\").rstrip().lstrip()\\n while month.lower() not in allowed_months:\\n print('You need to enter valid month name, January through June.')\\n month = input(\\\"Enter month : \\\").rstrip().lstrip()\\n else:\\n user_filters.append(month.title())\\n user_filters.append('all')\\n\\n elif time_filter_option.lower() == 'day':\\n day = input(\\\"Enter weekday eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \\\").title().rstrip().lstrip()\\n while day.lower() not in allowed_days:\\n print('You need to enter weekday abbreviations eg Mon, Tue, Wed, Thur, Fri, Sat, Sun.')\\n day = input(\\\"Enter weekday abbreviation eg Mon, Tue, Wed, Thur, Fri, Sat, Sun: \\\").title().rstrip().lstrip()\\n else:\\n user_filters.append('all')\\n user_filters.append(weekday_dict[day].title())\\n\\n else:\\n user_filters.append('all')\\n user_filters.append('all')\\n\\n # Assign filter values selected by user to variables city, month, and day respectively.\\n city = user_filters[0].lower()\\n month = user_filters[1].lower()\\n day = user_filters[2].lower()\\n print()\\n print('-'*80)\\n print(\\\"\\\\tFOR CITY OF '{}', YOU HAVE CHOSEN THE FOLLOWING TIME FILTERS:\\\".format(city.upper()))\\n print(\\\"\\\\tMonth: '{}'\\\".format(month.upper()))\\n print(\\\"\\\\tDay: '{}'\\\".format(day.upper()))\\n print('-'*80)\\n\\n return city, month, day\",\n \"def update_filters(self, filters: str) -> None:\\r\\n\\r\\n log.debug(f'Updating filters to {filters}')\\r\\n\\r\\n parts = filters.split('&')\\r\\n\\r\\n for part in parts:\\r\\n value, key = part.split('=')\\r\\n\\r\\n if value == 'iv':\\r\\n self.__payload['prevMinIV'] = self.__payload['minIV']\\r\\n self.__payload['minIV'] = key.strip()\\r\\n elif value == 'exiv':\\r\\n self.__payload['exMinIV'] = key.strip()\\r\\n else:\\r\\n log.debug(f'Dont know filter: \\\"{part}\\\", ignoring...')\\r\\n\\r\\n self.__filters_string = filters\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n\\n city = handle_invalid_inputs(question_1, cities)\\n \\n # get the user input of the filter_choosed (month, day, both, or not at all(none))\\n\\n filter_choosed = handle_invalid_inputs(question_2, filters)\\n \\n # if filter_choosed == \\\"month\\\"\\n if filter_choosed == \\\"month\\\":\\n # get user input for month (all, january, february, ... , june)\\n month = get_month()\\n day = \\\"all\\\"\\n \\n # if filter_choosed == \\\"day\\\"\\n if filter_choosed == \\\"day\\\":\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n day = get_day()\\n month = \\\"all\\\"\\n \\n # if filter_choosed == \\\"both\\\"\\n if filter_choosed == \\\"both\\\":\\n # get user input for day of week and month\\n month, day = get_both()\\n \\n # if filter_choosed == none\\n if filter_choosed == \\\"none\\\":\\n month = \\\"all\\\"\\n day = \\\"all\\\"\\n\\n print('-'*40)\\n return city, month, day, filter_choosed\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data! Including data from Chicago, Washington, and New York City')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n city = input(\\\"Please choose a city you want to learn more about from the following: Chicago, New York City or Washington. \\\\n> \\\").lower(\\n ).replace(\\\" \\\", \\\"\\\")\\n if city in cities_data:\\n break\\n else:\\n print(\\\"\\\\n\\\\t\\\" + city + \\\" isn't valid, please enter a valid city.\\\")\\n\\n # get user input for month (all, january, february, ... , june)\\n while True:\\n month = input(\\\"Please choose a month or type 'all' to apply no month filter.\\\\n (Choices: all, january, february, march, april, may or june)\\\\n>\\\").lower(\\n ).replace(\\\" \\\", \\\"\\\")\\n if month in months_data or month == \\\"all\\\":\\n break\\n else:\\n print(\\\"\\\\n\\\\t ERROR: \\\" + month + \\\" isn't valid, please enter a month or 'all'.\\\")\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n day = input(\\\"Please choose a day of the week or type 'all' to apply no day filter.\\\\n (Choices: all, monday, tuesday, wednesday, thursday, friday, saturday or sunday)\\\\n>\\\").lower().replace(\\\" \\\", \\\"\\\")\\n if day in days_data or day == \\\"all\\\":\\n break\\n else:\\n print(\\\"\\\\n\\\\t ERROR: \\\" + day + \\\" isn't valid, please enter a week day name or 'all'.\\\")\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n\\n print('\\\\nHello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city = input(\\\"Which city would you like to explore new york city, chicago or washington? \\\\n \\\").lower().strip()\\n while city not in ('new york city', 'chicago', 'washington'):\\n city = input(\\\"invalid city please enter a valid city \\\\n \\\").lower().strip()\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n\\n month = input(\\\"Which month would you like to filter january, february, march, april, may, june or 'all' \\\\n\\\").lower().strip()\\n while month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\\n month = input(\\\"invalid month please enter a valid month \\\\n\\\").lower().strip()\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n\\n day = input(\\n \\\" which day would you like to filter sunday, monday, tuesday, wednesday, thursday, friday, Saturday or all.\\\\n\\\").lower().strip()\\n while day not in ('sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'all'):\\n day = input(\\\"invalid day please enter a valid day \\\\n\\\").lower().strip()\\n\\n print('-' * 40)\\n return city, month, day\",\n \"def get_filters():\\n\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # ADD : available analysis parameters\\n cities_list=['chicago','new york city','washington']\\n months_list=['all','january','february','march','april','may','june']\\n days_list=['all','monday','tuesday','wednesday','thursday','friday','saturday','sunday']\\n\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city=''\\n while city not in cities_list:\\n city=str(input(\\\"Enter the name of the city to analyze: \\\")).lower()\\n if city not in cities_list:\\n print(\\\"!Warning : cities available for analysis : {}\\\".format(cities_list))\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month=''\\n while month not in months_list:\\n month=str(input(\\\"Enter the month to analyze (enter 'all' if you want all the months): \\\")).lower()\\n if month not in months_list:\\n print(\\\"!Warning : months available for analysis : {}\\\".format(months_list))\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day=''\\n while day not in days_list:\\n day=str(input(\\\"Enter the day to analyze (enter 'all' if you want all the days): \\\")).lower()\\n if day not in days_list:\\n print(\\\"!Warning : days available for analysis : {}\\\".format(days_list))\\n\\n print('-'*40)\\n return city, month, day\",\n \"def create_filter_from_args(self, args: dict) -> Filter:\\n keys = set(args.keys())\\n filter_args = {}\\n\\n if \\\"name\\\" in keys:\\n value = args.get('name')\\n if value != \\\"\\\":\\n filter_args.update({\\\"text_filter\\\": args.get('name')})\\n if \\\"product_in\\\" in keys:\\n value = args.get('product_in')\\n if value != \\\"\\\":\\n filter_args.update({\\\"product_in\\\": 'true' if value == \\\"yes\\\" else 'false'})\\n if \\\"human_in\\\" in keys:\\n value = args.get('human_in')\\n if value != \\\"\\\":\\n filter_args.update({\\\"human_in\\\": 'true' if value == \\\"yes\\\" else 'false'})\\n if \\\"institutional\\\" in keys:\\n value = args.get('institutional')\\n if value != \\\"\\\":\\n filter_args.update({\\\"institutional\\\": 'true' if value == \\\"yes\\\" else 'false'})\\n if \\\"format\\\" in keys:\\n value = args.get('format')\\n if value != \\\"\\\":\\n filter_args.update({\\\"picture_format\\\": 'true' if value == \\\"vertical\\\" else 'false'})\\n if \\\"credit\\\" in keys:\\n value = args.get('credit')\\n if value != \\\"\\\":\\n filter_args.update({\\\"author_credits\\\": value})\\n if \\\"limited_use\\\" in keys:\\n value = args.get('limited_use')\\n if value != \\\"\\\":\\n filter_args.update({\\\"limited_usage\\\": 'true' if value == \\\"yes\\\" else 'false'})\\n if \\\"tags\\\" in keys:\\n value = args.get('tags')\\n if value != \\\"\\\":\\n filter_args.update({\\\"limited_usage\\\": value.split(';')})\\n\\n f = Filter(**filter_args)\\n return f\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). Use a while loop to handle invalid inputs\\n city = \\\"\\\"\\n while (city.title() != \\\"Chicago\\\" and city.title() != \\\"New York\\\" and city.title() != \\\"Washington\\\"):\\n city = input(\\\"Would you like to see data for Chicago, New York, or Washington? \\\")\\n \\n print(\\\"Looks like you want to hear about {}! If this is not true, restart the program now!\\\".format(city.title()))\\n \\n filter_type = \\\"\\\"\\n filter_types = (\\\"none\\\", \\\"month\\\", \\\"day\\\", \\\"both\\\")\\n while (filter_type.lower() not in filter_types):\\n filter_type = input(\\\"Would you like to filter the data by month, day, both or not at all? Type \\\\\\\"none\\\\\\\" for no time filters. \\\")\\n \\n # get user input for month (all, january, february, ... , june)\\n if (filter_type == \\\"month\\\" or filter_type == \\\"both\\\"):\\n if (filter_type == \\\"month\\\"):\\n day = \\\"all\\\"\\n month = \\\"\\\"\\n months = (\\\"all\\\", \\\"january\\\", \\\"february\\\", \\\"march\\\", \\\"april\\\", \\\"may\\\", \\\"june\\\")\\n while (month.lower() not in months):\\n month = input(\\\"Which month? January, February, March, April, May, or June? \\\")\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n if (filter_type == \\\"day\\\" or filter_type == \\\"both\\\"):\\n if (filter_type == \\\"day\\\"):\\n month = \\\"all\\\"\\n day = \\\"no\\\"\\n days = (\\\"all\\\", \\\"Monday\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\\"Friday\\\", \\\"Saturday\\\", \\\"Sunday\\\")\\n while (day.lower() != \\\"all\\\" and (not any(day.title() in d for d in days) or (day.upper() == 'T' or day.upper() == 'S'))):\\n day = input(\\\"Which day? Please type a day M, Tu, W, Th, F, Sa, Su. \\\")\\n day = [d for d in days if d.startswith(day.title())][0]\\n \\n if (filter_type == \\\"none\\\"):\\n month = \\\"all\\\"\\n day = \\\"all\\\"\\n \\n print('-'*40)\\n return city.lower(), month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n\\n while True:\\n try:\\n city = input('What city would you like to view: ').lower()\\n if city in cities:\\n break\\n else:\\n print('That is not a valid city. Try New York City, Chicago, or Washington')\\n except:\\n print('There was an unknown error')\\n finally:\\n print('\\\\nAttempted Input\\\\n')\\n\\n # get user input for month (all, january, february, ... , june) make sure these are integers\\n while True:\\n try:\\n month = input(\\\"Filter on what month (select all for all months) \\\"\\n \\\"give as an integer: \\\")\\n if month in str(range(1,7)):\\n break\\n elif month.lower() == 'all':\\n break\\n else:\\n print('That is not a valid month. Try an integer 1-7')\\n except:\\n print('There was an unknown error')\\n finally:\\n print('\\\\nAttempted Input\\\\n')\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = input(\\\"Filter on what day of the week(select all for all days): \\\")\\n if day in str(range(1,8)):\\n break\\n elif day.lower() == 'all':\\n break\\n else:\\n print('That is not a valid day. Try an integer 1-7')\\n except:\\n print('There was an unknown error')\\n finally:\\n print('\\\\nAttempted Input\\\\n')\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n #Invalid input is administered to by using a while loop.\\n while True:\\n city=input(\\\"Choose a city name between Chicago, New York City or Washington:!\\\").lower()\\n if city not in CITY_DATA:\\n print(\\\"\\\\n Not a valid city\\\\n\\\")\\n continue\\n else:\\n break\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try\\n month=str(input('Enter name of one month(from January to June) to filter by or \\\"all\\\" ,for no filter :')).lower()\\n months=['january', 'february', 'march', 'april', 'may', 'june']\\n if month == 'january':\\n month = months[0]\\n elif month == 'february':\\n month = months[1]\\n elif month == 'march':\\n month = months[2]\\n elif month == 'april':\\n month = months[3]\\n elif month == 'may':\\n month = months[4]\\n elif month == 'june':\\n month = months[5]\\n elif month == 'all':\\n print('all')\\n else:\\n raise(Exception)\\n\\t\\t\\texcept Exception as error:\\n print('Invalid Input!,please restart again!.')\",\n \"def get_filters() -> List[Tuple[str, Callable]]:\\n return [\\n ('group_files', group_files),\\n ('timesince', timesince),\\n ('just_updated', just_updated),\\n ('get_category_name', get_category_name),\\n ('process_status_display', process_status_display),\\n ('compilation_status_display', compilation_status_display),\\n ('duration', duration),\\n ('tidy_filesize', tidy_filesize),\\n ('asdict', asdict),\\n ('compilation_log_display', compilation_log_display)\\n ]\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city = input('Would you like to see data for Chicago, New York City, or Washington? ')\\n city = city.lower()\\n while city not in CITY_DATA:\\n city = input('Not a valid name! Try again!')\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('Which month? January, February, March, April, May, or June? Type \\\"all\\\" for all months ')\\n month = month.lower()\\n while month not in months:\\n if month == 'all':\\n break\\n else:\\n month = input('Not a valid month! Try again!')\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input('Which day of week? Type \\\"all\\\" for every weekday ')\\n day = day.lower()\\n while day not in days:\\n if day == 'all':\\n break\\n else:\\n day = input('Not a valid day! Try again!')\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n \\n cities = ['Chicago', 'New York', 'Washington']\\n filterValues = ['Month', 'Day', 'Both', 'None']\\n months = ['January', 'February', 'March', 'April', 'May', 'June'] \\n days = ['1', '2', '3', '4', '5', '6', '7']\\n \\n \\n print('What city do you want to look into ? Chicago, New York or Washington?')\\n city = get_valid_input(cities)\\n \\n # TO DO: get user input for month (all, january, february, ... , june)\\n \\n print('Do you want to filter the data by Month, Day, Both or not at all? Type \\\"None\\\" for no time filter!')\\n filter = get_valid_input(filterValues)\\n \\n if filter == 'month':\\n print('Which month? January, February, March, April, May or June?')\\n month = get_valid_input(months)\\n day = 'all'\\n \\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n\\n elif filter == 'day':\\n print('Which day? Monday(1), Tuesday(2), ..., ? Please enter number!')\\n day = int(get_valid_input(days))-1\\n month = 'all'\\n \\n elif filter == 'both':\\n print('Which month? January, February, March, April, May or June?')\\n month = get_valid_input(months)\\n print('Which day? Monday(1), Tuesday(2), ..., ? Please enter number!')\\n day = int(get_valid_input(days))-1\\n \\n elif filter == 'none':\\n month = 'all'\\n day = 'all'\\n \\n else:\\n return -1\\n \\n if month !='all': \\n month = [element.lower() for element in months].index(month) + 1\\n \\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n city = ''\\n month = ''\\n day = ''\\n valid_cities = ['chicago', 'new york city', 'washington']\\n valid_months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\\n valid_days = ['all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday','sunday']\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while not city:\\n text = input('Which city would you like to view data for? Enter \\\"Chicago\\\", \\\"New York City\\\", or \\\"Washington\\\": ' ).lower()\\n if text in valid_cities:\\n city = text\\n else:\\n print('That entry is not a valid selection, please try again.')\\n # get user input for month (all, january, february, ... , june)\\n #limited filter options to available date range in the data set\\n while not month:\\n text = input('Data is available for January through June. Which month would you like to view data for? Enter \\\"all\\\" or a specific month eg \\\"January\\\" : ' ).lower()\\n if text in valid_months:\\n month = text\\n else:\\n print('That entry is not a valid, please try again.')\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while not day:\\n text = input('Which day of the week would you like to view data for? Enter \\\"all\\\" or a specific day such as \\\"Tuesday\\\": ').lower()\\n if text in valid_days:\\n day = text\\n else:\\n print('That entry is not a valid, please try again.')\\n\\n print('-'*40)\\n return city, month, day\",\n \"def load_filter_file(self, filter_path):\\n logger.debug(\\\"Adding filter file {}\\\", filter_path)\\n try:\\n with open(filter_path, \\\"r\\\") as filter_file:\\n try:\\n json_filter_data = json.load(filter_file)\\n except Exception as err:\\n msg = \\\"Unable to parse filter file {} as a json file. {!r}\\\".format(\\n filter_path, err)\\n logger.debug(msg)\\n raise errors.ParserError(msg)\\n except IOError:\\n raise errors.ParserError(\\n \\\"Unable to access filter path '{}'\\\".format(filter_path))\\n\\n if \\\"version\\\" not in json_filter_data:\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. Missing 'version' key.\\\".format(\\n filter_path))\\n\\n if \\\"filters\\\" not in json_filter_data:\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. Missing 'filters' key.\\\".format(\\n filter_path))\\n\\n if not isinstance(json_filter_data[\\\"version\\\"], dict):\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Expecting value of 'version' entry to be a dictionary \\\"\\n \\\"but instead its a {}.\\\".format(filter_path,\\n type(json_filter_data[\\\"version\\\"])))\\n\\n version_info = json_filter_data[\\\"version\\\"]\\n\\n if \\\"major\\\" not in version_info:\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Missing 'major' key in 'version' value.\\\".format(filter_path))\\n\\n if \\\"minor\\\" not in version_info:\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Missing 'minor' key in 'version' value.\\\".format(filter_path))\\n\\n if not isinstance(version_info[\\\"major\\\"], int):\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Expecting int for major version found {} instead.\\\".format(\\n filter_path, type(version_info[\\\"major\\\"])))\\n\\n if not isinstance(version_info[\\\"minor\\\"], int):\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Expecting int for minor version found {} instead.\\\".format(\\n filter_path, type(version_info[\\\"minor\\\"])))\\n\\n if version_info[\\\"major\\\"] != FILTER_JSON_FORMAT_MAJOR_VERSION:\\n raise errors.ParserError(\\n \\\"Loading filter-file {} failed. \\\"\\n \\\"Found unexpected major version in JSON filter file.\\\".format(\\n filter_path))\\n\\n self._add_filters(json_filter_data[\\\"filters\\\"], filter_path)\",\n \"def get_filters():\\n print('Welcome! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n\\n while True:\\n try:\\n city = input(\\\"Enter the name of the city you want to explore the data of (chicago, new york city, or washington): \\\")\\n if city not in dict.keys(CITY_DATA):\\n raise Exception ('Invalid city name')\\n print (\\\"You have selected: {}.\\\\n\\\".format(city.title()))\\n break\\n except Exception :\\n print (\\\"Invalid name of city, please choose chicago, new york city, or washington (writtent in this format)\\\\n\\\")\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n\\n while True:\\n valid_months_names =('all', 'january', 'february', 'march', 'april', 'may', 'june')\\n try:\\n month = input(\\\"Enter the month that you wish to explore (use 'all' for the entire timeframe): \\\")\\n if month not in valid_months_names:\\n raise Exception ('Invalid month name')\\n print (\\\"You have selected: {}.\\\\n\\\".format(month.title()))\\n break\\n except Exception :\\n print (\\\"Invalid month: Please choose from all, january, february, march, april, may, june\\\\n\\\")\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n\\n while True:\\n valid_day_names =('all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday')\\n try:\\n day = input(\\\"Enter the day that you wish to explore (use 'all' for exploring all days): \\\")\\n if day not in valid_day_names:\\n raise Exception ('Invalid day name')\\n print (\\\"You have selected: {}.\\\\n\\\".format(day.title()))\\n break\\n except Exception :\\n print (\\\"Invalid day: Please choose from all, monday, tuesday, wednesday, thursday, friday, saturday, sunday\\\\n\\\")\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city = input(\\\"Would you like to see data for Chicago, New York City or Washington?\\\\n\\\").lower()\\n while city not in CITY_DATA.keys():\\n print(\\\"Invalid input of City\\\\n\\\")\\n city = input(\\\"Would you like to see data for Chicago, New York City or Washington?\\\\n\\\").lower() \\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n filter = input(\\\"Would you like to filter the data by month, day, both, or not at all? Type \\\\\\\"none\\\\\\\" for no time filter\\\\n\\\").lower()\\n filter_list= ['month', 'day', 'both', 'none']\\n \\n while filter not in filter_list:\\n print(\\\"Invalid input\\\\n\\\")\\n filter = input(\\\"Would you like to filter the data by month, day, both, or not at all? Type \\\\\\\"none\\\\\\\" for no time filter\\\\n\\\").lower()\\n \\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n if filter == 'month':\\n month = input(\\\"\\\\nWhich month? January, February, March, April, May, or June?\\\\n\\\").title()\\n while month not in month_list:\\n print(\\\"Invalid month input\\\\n\\\")\\n month = input(\\\"\\\\nWhich month? January, February, March, April, May, or June?\\\\n\\\").title()\\n day = 0\\n elif filter == 'day':\\n day = input(\\\"\\\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\\\n\\\").title()\\n while day not in day_list:\\n print(\\\"Invalid day input\\\\n\\\")\\n day = input(\\\"\\\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\\\n\\\").title()\\n month = 0\\n \\n elif filter == 'both':\\n month = input(\\\"\\\\nWhich month? January, February, March, April, May, or June?\\\\n\\\").title()\\n while month not in month_list:\\n print(\\\"Invalid month input\\\\n\\\")\\n month = input(\\\"\\\\nWhich month? January, February, March, April, May, or June?\\\\n\\\").title()\\n\\n day = input(\\\"\\\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\\\n\\\").title()\\n while day not in day_list:\\n print(\\\"Invalid day input\\\\n\\\")\\n day = input(\\\"\\\\nWhich day? Monday, Tuesday, Wednesday, Thrusday, Friday, Saturday, Sunday?\\\\n\\\").title()\\n \\n elif filter =='none':\\n month =0\\n day = 0\\n \\n print('-'*40)\\n print('output {} {} {}'.format(city, month, day))\\n \\n if month !=0:\\n month = month_list.index(month)+1\\n\\n return city, month, day\",\n \"def test_parse_filter_params_no_time(self):\\n filter_params = {\\n \\\"region\\\": FAKE.word(),\\n \\\"payer_tenant_id\\\": FAKE.uuid4(),\\n \\\"instance_type\\\": FAKE.word(),\\n }\\n serializer = OCIFilterSerializer(data=filter_params)\\n self.assertTrue(serializer.is_valid())\",\n \"def get_filters():\\n filters = {}\\n print('Hello Friend! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n city = input (\\\"Which city's data would you like to look at? (Chicago, New York City or Washington)\\\")\\n break\\n except (ValueError, KeyboardInterrpt, TypeError):\\n print(\\\"Please enter either Chicago, New York City or Washington\\\")\\n finally:\\n filters['city_name'] = city\\n print(\\\"Looking at data for {}\\\".format(city))\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = input (\\\"Which month's (January, February, March, April, May, June or all) data would you like to look at?\\\")\\n break\\n except (ValueError, KeyboardInterrpt, TypeError):\\n print(\\\"Please enter either all, January, February, March, April, May or June.\\\")\\n finally:\\n filters['month_name'] = month\\n print(\\\"Looking at data for {}\\\".format(month))\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = input (\\\"Which day of the week would you like to look at data for?\\\")\\n break\\n except (ValueError, KeyboardInterrpt, TypeError):\\n print(\\\"Please enter either Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or ALL.\\\")\\n finally:\\n filters['day_name'] = day\\n print(\\\"Looking at data for {}\\\".format(day))\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington).\\n city = get_city()\\n # ask if they want to filter by day or month\\n filter = wanna_filter()\\n if filter == 'day':\\n day = get_day()\\n month = ''\\n return city, month, day\\n elif filter == 'month':\\n month = get_month()\\n day = ''\\n return city, month, day\\n else:\\n day = ''\\n month = ''\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n cities = ['chicago', 'new york city', 'washington']\\n city = input(\\\"Would you like to see data for Chicago, New York, or Washington? Please enter: \\\").lower()\\n if city == 'new york':\\n city += ' city'\\n break\\n elif city in cities:\\n break\\n else:\\n print('It seems the spelling of the city is not correct...Please re-enter the city name.')\\n except: # other error situations e.g. none stings being input; but not sure which type(s) of errors should be specified here \\n print('It seems like an invalid input...Please re-enter the city.')\\n\\n # get user input for month (all, january, february, ... , june)\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\\n while True:\\n choice = input(\\\"Would you like to filter by month, day, both, or neither (no filter)? Please enter your choice: \\\").lower()\\n if choice == 'month' or choice == 'by month':\\n while True:\\n month = input(\\\"Which month - January, February, March, April, May, or June? Please enter: \\\").lower()\\n day = 'all'\\n if month in months:\\n break\\n else:\\n print('The input is not valid. Please input the correct month.')\\n break\\n elif choice == 'day' or choice == 'by day':\\n while True:\\n day = input(\\\"Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Please enter: \\\").lower()\\n month = 'all'\\n if day in days:\\n break\\n else:\\n print('The input is not valid. Please input the correct day name.')\\n break\\n elif choice == 'both' or choice == 'by both':\\n while True:\\n month = input(\\\"Which month - January, February, March, April, May, or June? Please enter: \\\").lower()\\n day = input(\\\"Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Please enter: \\\").lower()\\n if (month in months) and (day in days):\\n break\\n else:\\n print('It seems at least one of the inputs is not valid. Please input the correct month and/or day name.')\\n break\\n elif choice == 'neither' or choice == 'no filter':\\n month = 'all'\\n day = 'all'\\n break\\n else:\\n print('The input is not valid. Please make sure the choice input is in correct format.')\\n\\n print('-'*40)\\n return city, month, day\",\n \"def set_filter():\\n try:\\n #=======================================================================\\n # isofilter=[arg.partition('=')[-1] for arg in argv if 'atomfilter=' in arg][0][1:-1].split(',')\\n #=======================================================================\\n isofilter = config.arg('atomfilter')[1:-1].split(',')\\n isofilter = [f.split(':') for f in isofilter]\\n for f in isofilter:\\n if len(f) < 2:\\n f.append('True')\\n if len(f) < 3:\\n f.append('True')\\n if len(f) < 4:\\n f.append('None')\\n except:\\n isofilter = [['element', 'H', 'True', 'None']]\\n try:\\n #=======================================================================\\n # isopartnerfilter=[arg.partition('=')[-1] for arg in argv if 'partnerfilter=' in arg][0][1:-1].split(',')\\n #=======================================================================\\n isopartnerfilter = config.arg('partnerfilter')[1:-1].split(',')\\n isopartnerfilter = [f.split(':') for f in isopartnerfilter]\\n for f in isopartnerfilter:\\n if len(f) < 2:\\n f.append('True')\\n if len(f) < 3:\\n f.append('True')\\n if len(f) < 4:\\n f.append('None')\\n except:\\n isopartnerfilter = [['None', 'None', 'None', 'None']]\\n return isofilter, isopartnerfilter\\n isofilterlist = []\\n isopartnerfilterlist = []\\n for i in xrange(len(isofilter) / 2):\\n isofilterlist.append(tuple(isofilter[2 * i:2 * i + 2]))\\n for i in xrange(len(isopartnerfilter) / 2):\\n isopartnerfilterlist.append(tuple(isopartnerfilter[2 * i:2 * i + 2]))\\n\\n return [isofilterlist, isopartnerfilterlist]\",\n \"def get_filters():\\r\\n print('Hello! Let\\\\'s explore some US bikeshare data!\\\\n')\\r\\n\\r\\n # ref https://stackabuse.com/getting-user-input-in-python/\\r\\n\\r\\n # Get user input for city (chicago, new york city, washington).\\r\\n cities = ['Chicago', 'New York city', 'Washington']\\r\\n city = get_user_input(cities,\\\"city\\\")\\r\\n\\r\\n # Get user input for month (all, january, february, ... , june)\\r\\n months = ['All', 'Jan', 'Feb', 'Mar', 'Apr', 'Jun']\\r\\n month = get_user_input(months,\\\"month\\\")\\r\\n\\r\\n # Get user input for day of week (all, monday, tuesday, ... sunday)\\r\\n days = ['All', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\\r\\n day = get_user_input(days,\\\"day\\\")\\r\\n\\r\\n print('-'*40)\\r\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n text_OK = False\\n city_conf_text = 'Chosen city is {}? Enter \\\"y\\\" to confirm. '\\n while True:\\n city = input('\\\\nFor which city would you like to run an evaluation? Chicago (CH), New York City (NY) or Washington (WA)? \\\\n\\\\\\nPlease Enter abbreviation. ')\\n if city[0:2].lower() == 'ch':\\n city = 'chicago'\\n text_OK = True\\n elif city[0:2].lower() == 'ny':\\n city = 'new york city'\\n text_OK = True\\n elif city[0:2].lower() == 'wa':\\n city = 'washington'\\n text_OK = True\\n else:\\n print('\\\\nInput for city selection could not be evaluated. Please enter valid abbreviation and check for typing errors. ')\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n if text_OK == True:\\n conf_input = input(city_conf_text.format(city.title()))\\n if conf_input.lower() == 'y':\\n break\\n else:\\n text_OK = False\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n\\n # get user input for month (all, january, february, ... , june)\\n text_OK = False\\n month_conf_text = 'Chosen month(s) is {}? Enter \\\"y\\\" to confirm. '\\n while True:\\n month = input('\\\\nFor which month(s) would you like to run an evaluation?\\\\n\\\\\\nThe following months are available: January (Jan), February (Feb), March (Mar), April (Apr), May (May), June (Jun) or all (all). \\\\n\\\\\\nPlease enter abbreviation. ')\\n # months list transferred to main program head in order to make it available for call of other functions\\n for i in range(len(months)):\\n if month[0:3].lower() == months[i][0:3]:\\n month_name = months[i].title()\\n month = i+1\\n text_OK = True\\n break\\n if text_OK == True:\\n conf_input = input(month_conf_text.format(month_name.title()))\\n if conf_input.lower() == 'y':\\n break\\n else:\\n text_OK = False\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n else:\\n print('\\\\nInput for month selection could not be evaluated. Please check for typing errors. ')\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n text_OK = False\\n day_conf_text = 'Chosen day(s) of week is {}? Enter \\\"y\\\" to confirm. '\\n day_except_text = 'Please enter an integer number. '\\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\\n while True:\\n day = input('\\\\nFor which day(s) would you like to run an evaluation?\\\\n\\\\\\nPlease enter specific day as number (Monday = 1 ... Sunday = 7) or enter \\\"8\\\" to select all. ')\\n try:\\n for i in range(len(days)):\\n if (int(day)-1) == i:\\n week_day = days[int(day)-1].title()\\n text_OK = True\\n break\\n if text_OK == True:\\n conf_input = input(day_conf_text.format(week_day.title()))\\n if conf_input.lower() == 'y':\\n if day == '5':\\n print(\\\"Thank god it's friday !\\\")\\n time.sleep(2)\\n break\\n else:\\n text_OK = False\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n else:\\n print('\\\\nInput for day selection could not be evaluated. Please check for typing errors.')\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n except Exception as e:\\n print('Exception occurred: {}'.format(e))\\n print(day_except_text)\\n conf_request = input(cancel_text)\\n if conf_request.lower() == 'n':\\n return\\n print('-'*40)\\n return city, month, week_day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city=''\\n month=''\\n day=''\\n city_list = ['chicago', 'new york city', 'washington']\\n while city.lower() not in city_list:\\n city = input(\\\"Enter a city: \\\") \\n if city.lower() not in city_list:\\n print('Sorry, Invalid city name. Please enter a city of Chicago, New York, or Washington.')\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n months_dict = {'january': 1, 'february': 2, 'march': 3, 'april': 4, 'may': 5, 'june': 6}\\n while month.lower() not in months_dict.keys(): \\n month = input(\\\"Enter a month: \\\") \\n if month.lower() not in months_dict.keys():\\n print('Sorry, Invalid month name. Please enter a month between January and June') \\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday'] \\n while day.lower() not in day_list: \\n day = input(\\\"Enter a weekday: \\\") \\n if day.lower() not in day_list:\\n print('Sorry, Invalid weekday name. Please enter a weekday name') \\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s look at some US bikeshare data!')\\n # I want to get user input for the city choice using a while loop to handle inputs that are not part of CITY_DATA.\\n # I want to get user input for the city choice using a while loop to handle inputs that are not part of CITY_DATA.\\n while True:\\n city = input(\\\"\\\\nDo you want analyze data from Chicago, New York City, or Washington? Answer with the name of the requested city!\\\\n\\\").lower()\\n if city in CITY_DATA:\\n break\\n else:\\n print(\\\"\\\\nPlease enter a valid city name (Chicago, New York City, or Washington.\\\\n\\\")\\n\\n # I want to get user input for the month filter using a while loop to handle inputs that are not part of MONTH_DATA.\\n while True:\\n month = input(\\\"\\\\nWhich month do you want to filter? January, Feburary, March, April, May, or June? Type all if you do not want to filter by month.\\\\n\\\").lower()\\n if month in MONTH_DATA:\\n break\\n else:\\n print(\\\"\\\\nPlease enter a valid month or all if you do not want to filter.\\\\n\\\")\\n\\n # I want to get user input for the weekday filter using a while loop to handle inputs that are not part of DAY_DATA.\\n while True:\\n day = input(\\\"\\\\nWhich weekday do you want to see? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? Type all if you do not want to filter by weekday.\\\\n\\\").lower()\\n if day in DAY_DATA:\\n break\\n else:\\n print(\\\"\\\\nPlease enter a valid weekday or all if you do not want to filter.\\\\n\\\")\\n\\n print('-'*40)\\n return city, month, day\",\n \"def _process_alert_source_filters(query, filters):\\n if filters:\\n if not is_valid_model_filters(models.AlertSource, filters):\\n return\\n query = query.filter_by(**filters)\\n\\n return query\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # User input for cities (Chicago, New York City, Washington), using while loop to ensure valid city input, normalized with lower()\\n while True:\\n city = input(\\\"\\\\nFor which city would you like to see data: Chicago, New York City, or Washington? \\\\n\\\").lower()\\n if city not in ('chicago', 'new york city', 'washington'):\\n print(\\\"Please enter either Chicago, New York City, or Washington. Invalid entry.\\\")\\n continue\\n else:\\n break\\n\\n # User input for month between January and June, or all, using while loop to ensure valid month input, normalized with lower()\\n while True:\\n month = input(\\\"\\\\nFor which month would you like to see data: January, February, March, April, May, June, or all? \\\\n\\\").lower()\\n if month not in ('january', 'february', 'march', 'april', 'may', 'june', 'all'):\\n print(\\\"Please enter a month between January and June. Invalid entry.\\\")\\n continue\\n else:\\n break\\n\\n # User input for day of the week, or all, normalized with lower()\\n while True:\\n day = input(\\\"\\\\nFor which day would you like to see data: \\\"\\n \\\"Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday, or all?\\\\n\\\").lower()\\n if day not in ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all'):\\n print(\\\"Please enter full name of a day of the week. Invalid entry\\\")\\n continue\\n else:\\n break\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('\\\\nHello! Let\\\\'s explore some US bikeshare data!\\\\n')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while 1:\\n city = input('Would you like to see data for Chicago | CH, New York City | NYC, or Washington | WA?\\\\n').lower()\\n print()\\n if city=='chicago' or city=='ch':\\n city='chicago'\\n if city=='new york city' or city=='nyc':\\n city='new york city'\\n if city=='washington' or city=='wa':\\n city='washington'\\n if city not in CITY_DATA:\\n print(\\\"\\\\nI'm sorry, I'm not sure which city you're referring to. Let's try again.\\\")\\n continue\\n city = CITY_DATA[city]\\n break\\n\\n # TO DO: get user input for month (january, february, ... , june)\\n while 1:\\n select = input('Would you like to filter the data by month and/or day? Enter yes or no. Type \\\"no\\\" for no time filter.\\\\n').lower()\\n print()\\n if select=='yes':\\n select=True\\n elif select=='no':\\n select=False\\n else:\\n print(\\\"\\\\nI'm sorry, you did not enter a valid selection. Let's try again.\\\")\\n continue\\n break\\n\\n # TO DO: get user input for day of week (monday, tuesday, ... sunday)\\n while 1:\\n if select:\\n filter=input('Would you like to filter the data by month, day, or both\\\\n').lower()\\n print()\\n if filter=='month':\\n month = input('Which month? January, February, March, April, May, or June?\\\\n').lower()\\n print()\\n if month not in MONTH_LIST:\\n print(\\\"\\\\nI'm sorry, I'm not sure which month you're trying to filter by. Let's try again.\\\")\\n continue\\n month = MONTH_LIST[month]\\n day='all'\\n elif filter=='day':\\n day = input('Which day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\\\n').lower()\\n print()\\n if day not in WEEK_LIST:\\n print(\\\"\\\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\\\")\\n continue\\n day = WEEK_LIST[day]\\n month='all'\\n elif filter=='both':\\n month = input('Which month? January, February, March, April, May, or June?\\\\n').lower()\\n print()\\n if month not in MONTH_LIST:\\n print(\\\"\\\\nI'm sorry, I'm not sure which month you're trying to filter by. Let's try again.\\\")\\n continue\\n month = MONTH_LIST[month]\\n day = input('And which day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday\\\\n').lower()\\n print()\\n if day not in WEEK_LIST:\\n print(\\\"\\\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\\\")\\n continue\\n day = WEEK_LIST[day]\\n else:\\n print(\\\"\\\\nI'm sorry, I'm not sure which time period you're trying to filter by. Let's try again.\\\")\\n continue\\n break\\n else:\\n day='all'\\n month='all'\\n break\\n\\n\\n print('-'*40)\\n return city, month, day\",\n \"def __init__(self, config, *parse_list):\\n super(ParsingFilter, self).__init__()\\n self.config = config\\n try:\\n if (\\n self.config[\\\"filter\\\"][\\\"whitelist\\\"]\\n and self.config[\\\"filter\\\"][\\\"blacklist\\\"]\\n ):\\n _LOGGER.warning(\\n _(\\n \\\"Both whitelist and blacklist filters found in configuration. \\\"\\n \\\"Only one can be used at a time - only the whitelist filter will be used.\\\"\\n )\\n )\\n self.parse_list = [\\n logging.Filter(name) for name in parse_list[0][\\\"whitelist\\\"]\\n ]\\n except KeyError:\\n self.parse_list = parse_list[0].get(\\\"whitelist\\\") or parse_list[0].get(\\n \\\"blacklist\\\"\\n )\\n\\n self.parse_list = [logging.Filter(name) for name in self.parse_list]\",\n \"def get_filters():\\n\\n print(\\\"Hello! Let's explore some US bikeshare data!\\\")\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n\\n city = input(\\\"Would you like to analyze Chicago, New York City or Washington? \\\").lower()\\n while city == 'chicago' or 'new york city' or 'washington':\\n print(\\\"You have selected: \\\", city)\\n break\\n else:\\n print(\\\"Invalid input. Start Over!\\\")\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('\\\\nWhich month do you want to analyze? ').lower()\\n while month == 'january' or 'february' or 'march' or 'april' or 'may' or 'june' or 'july' or 'august' or 'september' or 'october' or 'november' or 'december' or 'all':\\n print(\\\"You would like to analyze: \\\", month)\\n break\\n else:\\n print(\\\"Invalid input. Try again.\\\")\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input('\\\\nWhich day of the week are you interested in? ').lower()\\n while day == 'sunday' or 'monday' or 'tuesday' or 'wednesday' or 'thursday' or 'friday' or 'saturday'or 'all':\\n print(\\\"You are interested in: \\\", day)\\n break\\n else:\\n print(\\\"Invalid input. Try again.\\\")\\n\\n return city, month, day\",\n \"def filter(self, filters):\\r\\n # because http.Request needs params to be a dict of strings to strings\\r\\n # (roughly) and since BitBucket wants repeated parameters to express\\r\\n # OR, we'll do the quoting by hand ourselves\\r\\n def flatten_conditions(filters):\\r\\n for key, val in filters.items():\\r\\n if isinstance(val, (list, tuple)):\\r\\n for v in val:\\r\\n yield (port.to_b(key), port.to_b(v))\\r\\n else:\\r\\n yield (port.to_b(key), port.to_b(val))\\r\\n\\r\\n to_encode = tuple(flatten_conditions(filters))\\r\\n qs = port.urlencode(to_encode)\\r\\n\\r\\n url = '{0}/?{1}'.format(self.get_url(), qs)\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def _validate_submission_type(filters: dict) -> None:\\n legacy_submission_type = filters.get(\\\"submission_type\\\", ...)\\n submission_types = filters.get(\\\"submission_types\\\", ...)\\n\\n if submission_types == ... and legacy_submission_type == ...:\\n raise InvalidParameterException(\\\"Missing required filter: submission_types\\\")\\n\\n elif submission_types == ... and legacy_submission_type != ...:\\n del filters[\\\"submission_type\\\"]\\n if isinstance(legacy_submission_type, list):\\n raise InvalidParameterException(\\\"Use filter `submission_types` to request multiple submission types\\\")\\n else:\\n submission_types = [legacy_submission_type]\\n else:\\n if not isinstance(submission_types, list):\\n submission_types = [submission_types]\\n\\n if len(submission_types) == 0:\\n msg = f\\\"Provide at least one value in submission_types: {' '.join(VALID_ACCOUNT_SUBMISSION_TYPES)}\\\"\\n raise InvalidParameterException(msg)\\n\\n if any(True for submission_type in submission_types if submission_type not in VALID_ACCOUNT_SUBMISSION_TYPES):\\n msg = f\\\"Invalid value in submission_types. Options: [{', '.join(VALID_ACCOUNT_SUBMISSION_TYPES)}]\\\"\\n raise InvalidParameterException(msg)\\n\\n filters[\\\"submission_types\\\"] = list(set(submission_types))\",\n \"def get_filters():\\n print('Hello! I am Python 3.8! Let\\\\'s explore some US bikeshare data!')\\n print()\\n # TO DO: get user input for city (chicago, new york city, washington).\\n # HINT: Use a while loop to handle invalid inputs\\n while 1:\\n print('Which city should we have a look?')\\n city = input('1 = Chicago\\\\n'\\n '2 = New York City\\\\n'\\n '3 = Washington\\\\n'\\n '\\\\n').lower()\\n print('*'*40)\\n print()\\n if city in ('1', 'ch'):\\n city = 'chicago'\\n print(\\\"Chicago City! Here We go !!!\\\")\\n if city in ('2', 'ny', 'nyc'):\\n city = 'new york city'\\n print(\\\"New York City! Here We go !!!\\\")\\n if city in ('3', 'wa', 'washington dc'):\\n city = 'washington'\\n print(\\\"Washington! Here We go !!!\\\")\\n if city not in CITY_DATA:\\n print('Please enter a valid city')\\n continue\\n city = CITY_DATA[city]\\n break\\n # TO DO: get user input for month (all, january, february, ... , june)\\n # TO DO: get user input for day of week (all, monday, tuesday, ...sunday)\\n\\n while 1:\\n m_choice = input('Do you want to filter the data ?\\\\n'\\n 'Yes\\\\n'\\n 'No\\\\n'\\n '\\\\n').lower()\\n print()\\n if m_choice in ('yes', 'y', 'yus'):\\n print('*'*40)\\n print('\\\\nThe data is now being filtered ...')\\n m_choice = True\\n elif m_choice in ('no', 'n', 'nope'):\\n m_choice = False\\n else:\\n print('*'*40)\\n print('You did not enter a valid choice. Let\\\\'s try again. ')\\n print('*'*40)\\n continue\\n break\\n\\n while 1:\\n if m_choice:\\n filter = input('You can filter by:\\\\n'\\n '\\\\n'\\n 'Month\\\\n'\\n 'Day\\\\n'\\n 'Both\\\\n'\\n '\\\\n').lower()\\n print()\\n print('*'*40)\\n if filter == 'month':\\n print('Which month would you like to see?')\\n month = input('jan = January \\\\n'\\n 'feb = February\\\\n'\\n 'mar = March\\\\n'\\n 'apr = April\\\\n'\\n 'may = May\\\\n'\\n 'jun = June \\\\n'\\n '\\\\n').lower()\\n print('*'*40)\\n print()\\n if month not in MONTH_DATA:\\n print('Sorry I did not understand that input.'\\n 'Could you try again?')\\n continue\\n month = MONTH_DATA[month]\\n day = 'all'\\n elif filter == 'day':\\n print('Which day would you like to see? ')\\n day = input('mon = Monday\\\\n'\\n 'tues = Tuesday\\\\n'\\n 'wed = Wednesday\\\\n'\\n 'thur = Thursday\\\\n'\\n 'fri = Friday\\\\n'\\n 'sat = Saturday\\\\n'\\n 'sun = Sunday\\\\n'\\n '\\\\n').lower()\\n print('*'*40)\\n print()\\n if day not in WEEK_DATA:\\n print('Sorry I did not understand that input.'\\n 'Could you try again?')\\n continue\\n day = WEEK_DATA[day]\\n month = 'all'\\n elif filter == 'both':\\n print('Which month would you like to see?')\\n month = input('jan = January \\\\n'\\n 'feb = February\\\\n'\\n 'mar = March\\\\n'\\n 'apr = April\\\\n'\\n 'may = May\\\\n'\\n 'jun = June \\\\n'\\n '\\\\n').lower()\\n print('*'*40)\\n print()\\n if month not in MONTH_DATA:\\n print('Sorry I did not understand that input.'\\n 'Could you try again?')\\n continue\\n month = MONTH_DATA[month]\\n print('Which day of the week would you like to see?')\\n day = input('mon = Monday\\\\n'\\n 'tues = Tuesday\\\\n'\\n 'wed = Wednesday\\\\n'\\n 'thur = Thursday\\\\n'\\n 'fri = Friday\\\\n'\\n 'sat = Saturday\\\\n'\\n 'sun = Sunday\\\\n'\\n '\\\\n').lower()\\n print('*'*40)\\n print()\\n if day not in WEEK_DATA:\\n print('Sorry I did not understand that input.'\\n 'Could you try again?')\\n continue\\n day = WEEK_DATA[day]\\n else:\\n print('Sorry I did not understand that input.'\\n 'Could you try again?')\\n continue\\n break\\n else:\\n day = 'all'\\n month = 'all'\\n break\\n\\n print('*'*40)\\n return city, month, day\",\n \"def _parse_filter_list(filter: Optional[List]) -> Any:\\n ret = None\\n if filter:\\n ret = set(filter)\\n return ret\",\n \"def _convert_filters_type(\\n filter: List[Tuple[str, PredicateType, SupportedFilterType]],\\n schema: StateSchema,\\n) -> List[Tuple[str, SupportedFilterType]]:\\n new_filter = []\\n if dataclasses.is_dataclass(schema):\\n schema = {field.name: field.type for field in fields(schema)}\\n else:\\n schema = schema.schema_dict()\\n\\n for col, predicate, val in filter:\\n if col in schema:\\n column_type = schema[col]\\n try:\\n isinstance(val, column_type)\\n except TypeError:\\n # Calling `isinstance` to the Literal type raises a TypeError.\\n # Ignore this case.\\n pass\\n else:\\n if isinstance(val, column_type):\\n # Do nothing.\\n pass\\n elif column_type is int or column_type == \\\"integer\\\":\\n try:\\n val = convert_string_to_type(val, int)\\n except ValueError:\\n raise ValueError(\\n f\\\"Invalid filter `--filter {col} {val}` for a int type \\\"\\n \\\"column. Please provide an integer filter \\\"\\n f\\\"`--filter {col} [int]`\\\"\\n )\\n elif column_type is float or column_type == \\\"number\\\":\\n try:\\n val = convert_string_to_type(\\n val,\\n float,\\n )\\n except ValueError:\\n raise ValueError(\\n f\\\"Invalid filter `--filter {col} {val}` for a float \\\"\\n \\\"type column. Please provide an integer filter \\\"\\n f\\\"`--filter {col} [float]`\\\"\\n )\\n elif column_type is bool or column_type == \\\"boolean\\\":\\n try:\\n val = convert_string_to_type(val, bool)\\n except ValueError:\\n raise ValueError(\\n f\\\"Invalid filter `--filter {col} {val}` for a boolean \\\"\\n \\\"type column. Please provide \\\"\\n f\\\"`--filter {col} [True|true|1]` for True or \\\"\\n f\\\"`--filter {col} [False|false|0]` for False.\\\"\\n )\\n new_filter.append((col, predicate, val))\\n return new_filter\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n city = str(input('Would you like to see data for Chicago, New York or Washington?\\\\n').lower())\\n while city not in CITY_DATA.keys():\\n city = str(input('Sorry, it looks like an invalid input. Please try again: Chicago, New York or Washington?\\\\n').lower())\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n\\n filter_list = ['month', 'weekday', 'both', 'none']\\n filter_input = str(input('Would you like to filter the data by month, weekday, both or not at all? Type \\\"none\\\" if you don\\\\'t want to filter anything at all.\\\\n').lower())\\n\\n while filter_input not in filter_list:\\n filter_input = str(input('Sorry, it looks like an invalid input. Please try again: month, weekday, both or none?\\\\n').lower())\\n\\n month = 'all'\\n day = 'all'\\n\\n if filter_input == 'none':\\n month = 'all'\\n day = 'all'\\n elif filter_input == 'month':\\n month = str(input('What month would you like to look at? Jan, Feb, Mar, Apr, May, Jun or all?\\\\n').lower())\\n while month not in months:\\n month = str(input('Sorry, it looks like an invalid input. Please try again: Jan, Feb, Mar, Apr, May, Jun or all?\\\\n').lower())\\n elif filter_input == 'weekday':\\n day = str(input('What weekday would you like to look at? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\\\n').lower())\\n while day not in weekdays:\\n day = str(input('Sorry, it looks like an invalid input. Please try again: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\\\n').lower())\\n elif filter_input == 'both':\\n month = str(input('What month would you like to look at? Jan, Feb, Mar, Apr, May, Jun or all?\\\\n').lower())\\n while month not in months:\\n month = str(input('Sorry, it looks like an invalid input. Please try again: Jan, Feb, Mar, Apr, May, Jun or all?\\\\n').lower())\\n day = str(input('What weekday would you like to look at? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\\\n').lower())\\n while day not in weekdays:\\n day = str(input('Sorry, it looks like an invalid input. Please try again: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all?\\\\n').lower())\\n\\n print('-'*40)\\n return filter_input, city, month, day\",\n \"def std_filters():\\n kwargs = {\\n \\\"sentence_filters\\\":[punctuation_filter],\\n \\\"word_filters\\\":[small_word_filter, stopword_filter, stemming_filter]\\n }\\n return kwargs\",\n \"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 properties[_param] = req.params.get(param)\\n\\n if 'changes-since' in filters:\\n isotime = filters['changes-since']\\n try:\\n filters['changes-since'] = timeutils.parse_isotime(isotime)\\n except ValueError:\\n raise exc.HTTPBadRequest(_(\\\"Unrecognized changes-since value\\\"))\\n\\n if 'protected' in filters:\\n value = self._get_bool(filters['protected'])\\n if value is None:\\n raise exc.HTTPBadRequest(_(\\\"protected must be True, or \\\"\\n \\\"False\\\"))\\n\\n filters['protected'] = value\\n\\n # only allow admins to filter on 'deleted'\\n if req.context.is_admin:\\n deleted_filter = self._parse_deleted_filter(req)\\n if deleted_filter is not None:\\n filters['deleted'] = deleted_filter\\n elif 'changes-since' not in filters:\\n filters['deleted'] = False\\n elif 'changes-since' not in filters:\\n filters['deleted'] = False\\n\\n if properties:\\n filters['properties'] = properties\\n\\n return filters\",\n \"def get_filters():\\r\\n print('Welcome to Bikeshare data Analysis...')\\r\\n try:\\r\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\r\\n while True:\\r\\n try:\\r\\n cit_num = int(input('Enter city you would like to analyze \\\\n1 : Chicago\\\\n2 : Newyork\\\\n3 : Washington :\\\\n'))\\r\\n except ValueError:\\r\\n print(\\\"Please enter a number value\\\\n\\\")\\r\\n continue\\r\\n if cit_num not in (1,2,3):\\r\\n print(\\\"Please enter a valid number that refers to cities\\\\n\\\")\\r\\n continue\\r\\n else:\\r\\n break\\r\\n\\r\\n city = LST_CITY[cit_num - 1].lower()\\r\\n # TO DO: get user input for month (all, january, february, ... , june)\\r\\n while True:\\r\\n try:\\r\\n month_num = int(input('Enter month of the year you would like to analyze \\\\n1 : JAN\\\\n2 : FEB\\\\n3 : MAR\\\\n4 : APR\\\\n5 : MAY\\\\n6 : JUN\\\\n7 : WHOLE WEEK\\\\n'))\\r\\n except ValueError:\\r\\n print(\\\"Please enter a num value\\\\n\\\")\\r\\n continue\\r\\n if month_num not in range(1,8):\\r\\n print(\\\"Please enter a valid month...\\\\n\\\")\\r\\n continue\\r\\n else:\\r\\n break\\r\\n month = VALID_MONTHS[month_num-1]\\r\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\r\\n while True:\\r\\n try:\\r\\n day_num = int(input('Enter day of the week you would like to analyze \\\\n1 : MON\\\\n2 : TUE\\\\n3 : WED\\\\n4 : THU\\\\n5 : FRI\\\\n6 : SAT\\\\n7 : SUN\\\\n8 : ALL MONTHS\\\\n'))\\r\\n except ValueError:\\r\\n print(\\\"Please enter a num value\\\\n\\\")\\r\\n continue\\r\\n if day_num not in range(1, 9):\\r\\n print(\\\"Please enter a valid month number...\\\\n\\\")\\r\\n continue\\r\\n else:\\r\\n break\\r\\n day = VALID_DAYS[day_num-1]\\r\\n\\r\\n print('\\\\n'+'*'*20)\\r\\n print('Your selections are stated below\\\\nCity : {} , Month : {} , Day : {} '.format(city.title(),month.title(),day.title()))\\r\\n print('*'*20)\\r\\n \\r\\n return city, month, day ,cit_num , month_num , day_num\\r\\n \\r\\n except Exception as e:\\r\\n print('An exception has been occurred : {}'.format(e))\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n print('-'*40)\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n city = input('Which city do you like to analyze? Please choose a city from chicago, '\\n 'new york city, or washington: ').lower()\\n if city not in CITY_DATA.keys():\\n raise ValueError\\n break\\n except ValueError:\\n print('That\\\\'s not a valid input, please enter a city name again.\\\\n')\\n\\n print('-'*40)\\n # get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = input('Which month do you like to analyze? Please enter a month name from january to june, '\\n 'or \\\"all\\\" to select all months: ').lower()\\n if month not in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:\\n raise ValueError\\n break\\n except ValueError:\\n print('That\\\\'s not a valid input, please enter a month name again, or \\\"all\\\" to select all months.\\\\n')\\n\\n print('-'*40)\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = input('Which day of week do you like to analyze? Please enter a weekday name from monday to sunday, '\\n 'or \\\"all\\\" to select the entire week: ').lower()\\n if day not in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']:\\n raise ValueError\\n break\\n except ValueError:\\n print('That\\\\'s not a valid input, please enter a weekday name again, or \\\"all\\\" to select the entire week.\\\\n')\\n\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington).\\n # HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\\n city = str(input(\\\"You may see stats from either Chicago, New York City, or Washington. Please enter your chosen city! \\\").lower())\\n except KeyboardInterrupt:\\n print(\\\"Bye!\\\")\\n except:\\n error_mess()\\n continue\\n\\n if city.lower() not in ('chicago', 'new york city', 'washington'):\\n error_mess()\\n else:\\n break\\n # TO DO: get user input for month (all, january, february, ... , june)\\n while True:\\n try:\\n month = str(input(\\\"For which month would you like to see {}'s data? If you would like to see an entire calendar year, please specify 'all months'. \\\".format(city.title())))\\n except KeyboardInterrupt:\\n print(\\\"Bye!\\\")\\n except:\\n error_mess()\\n continue\\n\\n if month.lower() not in ('january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december', 'all months'):\\n error_mess()\\n else:\\n break\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n try:\\n day = int(input(\\\"For which day of {} would you like to see data? If you would like to see every day, please specify '0'. \\\".format(month.title())))\\n except KeyboardInterrupt:\\n print(\\\"Bye!\\\")\\n except:\\n error_mess()\\n continue\\n\\n if day not in range(31):\\n error_mess()\\n else:\\n break\\n\\n print('-'*50)\\n return city, month, day\",\n \"def get_filters(self):\",\n \"def _validate_filter(filter):\\n\\n if filter.HasField('composite_filter'):\\n for sub_filter in filter.composite_filter.filters:\\n _validate_filter(sub_filter)\\n elif filter.HasField('property_filter'):\\n if filter.property_filter.op in UNSUPPORTED_OPERATORS:\\n raise ValueError('Query cannot have any inequality filters.')\\n else:\\n pass\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # Gets user input for city, allowing for 1 choice of 3 cities. Solves for case and entries not in dataset.\\n while True:\\n city = input('Please select one of the following cities: Chicago, New York City or Washington.\\\\n').lower()\\n if city not in city_names:\\n print('Invalid entry, please enter one of the three cities above.')\\n else:\\n break\\n\\n # Gets user input for month, either selection of signle month in dataset or all. Solves for case and entries not in dataset.\\n while True:\\n month = input('Please select one month from the following, or all: January, February, March, April, May, June, or all.\\\\n').lower()\\n if month not in month_names:\\n print('Invalid entry, please enter one of the months listed above or all.')\\n else:\\n break\\n\\n # Gets user input for day, either selection of single day in dataset or all. Solves for case and entries not in dataset.\\n while True:\\n day = input('Please select a day of the week or all: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or all.\\\\n').lower()\\n if day not in day_names:\\n print('Invalid entry, please enter one of the days listed above or all.')\\n else:\\n break\\n\\n print('-'*40)\\n return city, month, day\",\n \"def _add_filters(self, filters):\\n self._env.filters['dateformat'] = dateformat\\n self._env.filters.update(filters or {})\",\n \"def get_filters():\\r\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\r\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\r\\n #Take the inputs from the user\\r\\n city = input(\\\"\\\\nPlease enter a city from the following: \\\\n1.chicago\\\\n2.new york city\\\\n3.washington\\\\n\\\").lower()\\r\\n\\r\\n #while loop to make sure that the user entered the correct name of city\\r\\n while (city not in CITY_DATA.keys()):\\r\\n city = input(\\\"\\\\nplease enter a city from the following: \\\\n1.chicago\\\\n2.new york city\\\\n3.washington\\\\n\\\").lower()\\r\\n\\r\\n # TO DO: get user input for month (all, january, february, ... , june)\\r\\n #\\r\\n months = ['All','January', 'February', 'March', 'April', 'May', 'June']\\r\\n month = input('\\\\nPlease enter month from the folloiwng to filter by:\\\\n\\\\njanuary, february, march, april, may, june\\\\n\\\\nnote:type \\\"all\\\" for no month filtering\\\\n').title()\\r\\n #while loop to make sure that the user entered the correct name of month\\r\\n while (month not in months):\\r\\n month = input('Please enter month from the folloiwng to filter by\\\\njanuary, february, march, april, may, june\\\\ntype \\\"all\\\" for no month filtering\\\\n').title()\\r\\n\\r\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\r\\n\\r\\n days = ['All' , 'Monday' , 'Tuesday' , 'Wednesday' , 'Thursday' , 'Friday' , 'Saturday' , 'Sunday']\\r\\n day = input('\\\\nPlease enter day of the week to filter by\\\\ntype \\\"all\\\" for no day filtering\\\\n').title()\\r\\n #while loop to make sure that the user entered the day name in the correct way\\r\\n while (day not in days):\\r\\n day = input('\\\\nPlease enter a correct day of the week to filter by\\\\ntype \\\"all\\\" for no day filtering\\\\n').title()\\r\\n\\r\\n print('\\\\nyour choice was :\\\\ncity: {}\\\\nmonth: {}\\\\nday: {}\\\\n'.format(city,month,day))\\r\\n print('-'*40)\\r\\n\\r\\n return city, month, day\",\n \"def load_filters(self, filters):\\n if not filters:\\n return\\n if not isinstance(filters, list):\\n raise errors.ParserError(\\\"Expecting 'filters' value to be a list \\\"\\n \\\"but instead its a {}.\\\".format(type(filters)))\\n for filter_path in filters:\\n if not os.path.exists(filter_path):\\n raise errors.ParserError(\\n \\\"Filter path '{}' doesn't exist\\\".format(filter_path))\\n elif os.path.isdir(filter_path):\\n self._load_filter_directory(filter_path)\\n else:\\n self.load_filter_file(filter_path)\",\n \"def test_parse_filter(self):\\n old_type = Filter(\\n 'filter[student.school.title]', [\\n Relationship('student', PersonSchema(), None),\\n Relationship('school', StudentSchema(), None)],\\n Attribute('title', SchoolSchema(), None), ('eq', ['test']))\\n new_type = self.driver.parse(old_type)\\n \\n assert new_type.source == old_type\\n assert old_type.relationships != new_type.relationships\\n assert isinstance(new_type.relationships[0], Mapper)\\n assert old_type.attribute != new_type.attribute\\n assert isinstance(new_type.attribute, Column)\\n assert old_type.value == new_type.value\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n# TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city_abbreviations = ['chi', 'ny', 'w']\\n while True:\\n city = input('Kindly specify a city by typing chicago or new york city or washington: \\\\n\\\\n').lower()\\n if city in CITY_DATA.keys():\\n break\\n elif city.lower() == 'new york': #if the user forgot to add city to new york as it's a common mistake between users\\n print('\\\\nPlease notify that the city you typed called new york city, So retype it again right this time: \\\\n')\\n elif city in city_abbreviations: #if the user input was abbreviations of the name as it's a common mistake between users\\n print('\\\\nPlease notify that city abbreviation\\\\'s is not allowed, Retype city full name!\\\\n')\\n elif city.lower() == 'newyorkcity': #if user's input was newyorkcity without any spaces as it's a common mistake between users\\n print('\\\\nPlease notify that the city you typed called new york city with spaces between words, So retype it again right this time: \\\\n')\\n else: #if user printed any other things like wrong name or used speical chracters like space _ + = ~\\n print('\\\\nThats invalid input....\\\\n\\\\nplease choose one of the three cities chicago or new york city or washington.\\\\n') \\n# TO DO: get user input for month (all, january, february, ... , june)\\n months = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\\n months_abbreviations = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug']\\n while True:\\n month = input('\\\\n\\\\nTo filter data by a particuler month, please type the month or all for not filtering by month: \\\\n-january\\\\n-february\\\\n-march\\\\n-april\\\\n-may\\\\n-june\\\\n-all\\\\n\\\\n').lower()\\n if month in months:\\n break\\n elif month in months_abbreviations:\\n print('\\\\nPlease notify that months abbreviation\\\\'s is not allowed, Retype month full name!\\\\n')\\n else: #if the user input was abbreviations of the name as it's a common mistake between users \\n print('\\\\nThats invalid input....\\\\n\\\\n\\\\nplease choose one of the six months listed to filter with or use no filter\\\\n')\\n# TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday','saturday', 'sunday', 'all']\\n days_abbreviations = ['mon', 'tu', 'tue', 'tues', 'wed', 'th', 'thu', 'thur', 'thurs', 'fri', 'sat', 'sun']\\n while True:\\n day = input('\\\\n\\\\nTo filter data by a particuler day, please type the day or all for not filtering by day: \\\\n-saturday\\\\n-sunday\\\\n-monday\\\\n-tuesday\\\\n-wednesday\\\\n-thursday\\\\n-friday\\\\n-all\\\\n\\\\n').lower()\\n if day in days:\\n break\\n elif day in days_abbreviations: #if the user input was abbreviations of the name as it's a common mistake between users\\n print('\\\\nPlease notify that day abbreviation\\\\'s is not allowed, Retype day full name!\\\\n') \\n else:\\n print('\\\\nThats invalid input....\\\\n\\\\n\\\\nplease choose one of the seven days listed to filter with or use no filter\\\\n') \\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n city = get_city_from_user()\\n print('-' * 10)\\n\\n # get user input for month (all, january, february, ... , june)\\n month = get_month_from_user()\\n print('-' * 10)\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n day = get_day_of_week_from_user()\\n print('-' * 40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:# to ensure that there is no invalid input\\n city = input('Which city do you want to explore Chicago, New York or Washington? \\\\n> ').lower()\\n if city in CITIES:\\n break\\n else:\\n print(\\\"You entered invalied City.\\\")\\n\\n # get user input for month (all, january, february, ... , june)\\n while True:# to ensure that there is no invalid input\\n month = input('Please choose which month (january, february, march, april, may, june) or just type all for all months provided \\\\n').lower()\\n if month in MONTHS:\\n break\\n if month == 'all':\\n break\\n else:\\n print(\\\"You entered invalied month.\\\")\\n\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:# to ensure that there is no invalid input\\n day = input('Please choose which day (sunday, monday, tuesday, wednesday, thursday, friday, saturday) or just type all for all months provided \\\\n ').lower()\\n if day in DAYS:\\n break\\n if DAYS == 'all':\\n break\\n else:\\n print(\\\"You entered invalied day.\\\")\\n print('-'*50)\\n return city, month, day\",\n \"def _extract_filter_type_and_value(data):\\n if data.startswith(\\\"in:\\\"):\\n value = list(six.text_type(data[3:]).split(\\\",\\\"))\\n filter_type = 'in'\\n elif data.startswith(\\\"nin:\\\"):\\n value = list(six.text_type(data[4:]).split(\\\",\\\"))\\n filter_type = 'nin'\\n elif data.startswith(\\\"neq:\\\"):\\n value = six.text_type(data[4:])\\n filter_type = 'neq'\\n elif data.startswith(\\\"gt:\\\"):\\n value = six.text_type(data[3:])\\n filter_type = 'gt'\\n elif data.startswith(\\\"gte:\\\"):\\n value = six.text_type(data[4:])\\n filter_type = 'gte'\\n elif data.startswith(\\\"lt:\\\"):\\n value = six.text_type(data[3:])\\n filter_type = 'lt'\\n elif data.startswith(\\\"lte:\\\"):\\n value = six.text_type(data[4:])\\n filter_type = 'lte'\\n elif data.startswith(\\\"eq:\\\"):\\n value = six.text_type(data[3:])\\n filter_type = 'eq'\\n elif data.startswith(\\\"has:\\\"):\\n value = six.text_type(data[4:])\\n filter_type = 'has'\\n else:\\n value = data\\n filter_type = 'eq'\\n\\n return filter_type, value\",\n \"def ParseFilterFile(input_lines):\\n # Strip comments and whitespace from each line and filter non-empty lines.\\n stripped_lines = (l.split('#', 1)[0].strip() for l in input_lines)\\n filter_lines = [l for l in stripped_lines if l]\\n\\n # Split the tests into positive and negative patterns (gtest treats\\n # every pattern after the first '-' sign as an exclusion).\\n positive_patterns = [l for l in filter_lines if l[0] != '-']\\n negative_patterns = [l[1:] for l in filter_lines if l[0] == '-']\\n return positive_patterns, negative_patterns\",\n \"def parse_message_filter(message_filter):\\n if '#' not in message_filter:\\n message_filter += '#*'\\n appname, instanceid = message_filter.split('#', 1)\\n return collections.namedtuple('ParseResult', 'filter appname instanceid')(\\n message_filter, appname, instanceid\\n )\",\n \"def filter_format(filter_dict, query_data, filter_type, index): \\r\\n \\r\\n filter_list = ''\\r\\n count = 0 \\r\\n \\r\\n while query_data[index] != 'PRESENT\\\\n': \\r\\n if filter_type in query_data[index]: \\r\\n count += 1\\r\\n filter_keyword = query_data[index].strip(filter_type)\\r\\n filter_list = (filter_keyword.strip('\\\\n'))\\r\\n \\r\\n index += 1 \\r\\n \\r\\n if count > 0: \\r\\n filter_dict[filter_type] = filter_list\\r\\n return filter_dict\",\n \"def get_filters():\\n print('Hello! My name is Damir. Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n try:\\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\\n while city not in CITY_DATA:\\n print('No results found, please check your spelling.').lower()\\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\\n while month not in MONTH_LIST:\\n print('No results found, please check your spelling.').lower()\\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\\n\\n print('your choice was: ', month)\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n while day not in DAYS_LIST:\\n print('No results found, please check your spelling.').lower()\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n\\n print('your choice was: ', day)\\n\\n return city, month, day\\n except Exception as e:\\n print('An error with your inputs occured: {}'.format(e))\\n print('-'*40)\",\n \"def get_filters():\\n print('Hello! My name is Damir. Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n try:\\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\\n while city not in CITY_DATA:\\n print('No results found, please check your spelling.').lower()\\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\\n while month not in MONTH_LIST:\\n print('No results found, please check your spelling.').lower()\\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\\n\\n print('your choice was: ', month)\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n while day not in DAYS_LIST:\\n print('No results found, please check your spelling.').lower()\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n\\n print('your choice was: ', day)\\n\\n return city, month, day\\n except Exception as e:\\n print('An error with your inputs occured: {}'.format(e))\\n print('-'*40)\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n # improving user input for city\\n city = input(\\\"\\\\nWhat city do you intend to filter by? New York City, Chicago or Washington?\\\\n\\\").lower()\\n ### The following checks will ensure that the user inputs one of the expected cities.\\n while(True):\\n if(city == 'chicago' or city == 'new york city' or city == 'washington' or city == 'all'):\\n break\\n else:\\n city = input('Enter Correct city: ').lower()\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('\\\\nWhich or the following months will you want to filter by? January, February, March, April, May, or June?\\\\n').lower()\\n \\n # Here we validate user input for month to ensure that it is one of the expected values.\\n while(True):\\n if(month == 'january' or month == 'february' or month == 'march' or month == 'april' or month == 'may' or month == 'june' or month == 'all'):\\n break\\n else:\\n month = input('Enter a valid month\\\\n').lower()\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n \\n day = input('Which or the following days ? monday, tuesday, wednesday, thursday, friday, saturday , sunday or all will you want to display the data for?\\\\n').lower()\\n \\n # Ensuring that the correct value is inputted for day of the week.\\n while(True):\\n \\n if(day == 'monday' or day == 'tuesday' or day == 'wednesday' or day == 'thursday' or day == 'friday' or day == 'saturday' or day == 'sunday' or day == 'all'):\\n break\\n else:\\n day = input('Enter Correct day: ').lower()\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! My name is Damir. Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n try:\\n city = input('Which city would you like to analyze data from? chicago, new york city or washington?').lower()\\n while city not in CITY_DATA:\\n print('No results found, please check your spelling.').lower()\\n city = input('Which city would you like to review data from? chicago, new york city or washington?').lower()\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n month = input('Which month you would like to review the data from january to june? or all?').lower()\\n while month not in MONTH_LIST:\\n print('No results found, please check your spelling.').lower()\\n month = input('Which month you would like to analyze the data from january to june? or all?').lower()\\n\\n print('your choice was: ', month)\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n while day not in DAYS_LIST:\\n print('No results found, please check your spelling.').lower()\\n day = input('Which day of the week would you like to request data for? or all?').lower()\\n\\n print('The day you chose was: ', day)\\n\\n return city, month, day\\n except Exception as e:\\n print('An error with your inputs occured: {}'.format(e))\\n print('-'*40)\",\n \"def get_filters():\\n print('\\\\nHello! Let\\\\'s explore some US bikeshare data!')\\n\\n # get user input for city (chicago, new york city, washington).\\n city=input('Please, introduce the name of the city you want to obtain information about [chicago/new york city/washington]: ')\\n city=city.lower()\\n #Let us now check that it is actually a valid input. We give 3 chances before exiting the program\\n i=0\\n while (city != 'chicago') and (city != 'new york city') and (city != 'washington'):\\n city=input('Please, introduce a valid city [chicago/new york city/washington]: ')\\n i += 1\\n if i==3:\\n sys.exit()\\n\\n # get user input for month (all, january, february, ... , june)\\n ans_month=input('Do you want to filter by month? [y/n]: ')\\n if ans_month=='n':\\n month='all'\\n elif ans_month=='y':\\n month=input('Which month? [january/february/.../june]: ')\\n month=month.lower()\\n #we check that it is actually a valid input. We give 3 chances before exiting the program:\\n i=0\\n set_months=['january','february','march','april','may','june']\\n while month not in set_months:\\n month=input('Please, introduce a valid month [january/february/.../june]: ')\\n i += 1\\n if i==3:\\n sys.exit()\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n ans_day=input('Do you want to filter by day of week? [y/n]: ')\\n if ans_day=='n':\\n day='all'\\n elif ans_day=='y':\\n day=input('Which day? [monday/tuesday/.../sunday]: ')\\n day=day.lower()\\n #check that it is actually a valid input. We give 3 chances before exiting the program:\\n i=0\\n set_days=['monday','tuesday','wednesday','thursday','friday','saturday','sunday']\\n while day not in set_days:\\n day=input('Please, introduce a valid day [monday/tuesday/.../sunday]: ')\\n i += 1\\n if i==3:\\n sys.exit()\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n\\n # Get the names of the cities \\n city = ''\\n while city not in CITY_DATA.keys():\\n print(\\\"\\\\nPlease enter the name of the city to analyze\\\")\\n print(\\\"\\\\nValid values are Chicago, New York City, Washington. Note the city name is not case sensitive\\\")\\n city = input(\\\"\\\\nCity: \\\").lower()\\n\\n if city not in CITY_DATA.keys():\\n print(\\\"\\\\nInvalid or unknown city specified.\\\")\\n \\n print(f\\\"\\\\nYou selected '{city.title()}' as city\\\")\\n\\n # TO DO: get user input for month (all, january, february, ... , june)\\n\\n month = ''\\n MONTH_OPTS = ['january','february','march','april','may','june','all']\\n while month not in MONTH_OPTS:\\n print(\\\"\\\\nPlease enter the month(s) for which to get data to analyze\\\")\\n print(\\\"\\\\nOptions are january to june\\\")\\n print(\\\"\\\\nYou can also enter 'all' to view data for all months\\\")\\n month = input(\\\"\\\\nMonth: \\\").lower()\\n\\n if month not in MONTH_OPTS:\\n print(\\\"\\\\nInvalid or unknown month specified.\\\")\\n \\n print(f\\\"\\\\nYou selected '{month.title()}' as month\\\")\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n DAY_OPTS = ['all','monday','tuesday','wednesday','thursday','friday','saturday','sunday']\\n day = ''\\n while day not in DAY_OPTS:\\n print(\\\"\\\\nPlease enter the day(s) for which to get data to analyze\\\")\\n print(\\\"\\\\nOptions are monday to sunday\\\")\\n print(\\\"\\\\nYou can also enter 'all' to view data for all days\\\")\\n day = input(\\\"\\\\nDay: \\\").lower()\\n\\n if day not in DAY_OPTS:\\n print(\\\"\\\\nInvalid or unknown day specified. Retrying ...\\\")\\n \\n print(f\\\"\\\\nYou selected '{day.title()}' as day\\\")\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('.'*40)\\n print('Hello! Let\\\\'s explore some US bikeshare data!\\\\n')\\n\\n # user input for city (chicago, new york city, washington)\\n while True:\\n city = input(\\n '\\\\nWhich city\\\\'s data will you like to explore? Chicago, New York City, or Washington?\\\\n').lower()\\n\\n if city in CITY_DATA:\\n confirmation = input(\\n '\\\\nYou have opted to view data for {}. If this was an error, enter no to restart, else enter yes to continue.\\\\n'.format(city.title())).lower()\\n if confirmation == 'yes':\\n break\\n else:\\n print(\\n 'Sorry, we do not have that data, type a city name from the 3 options provided.\\\\n')\\n\\n # user input for month (all, january, february, ... , june)\\n while True:\\n month = input(\\n '\\\\nWhat month will you like to explore? (Enter Jan for January, Feb for February, etc.)\\\\nIf you don\\\\'t want to filter by month, enter all.\\\\n').lower()\\n\\n if month in MONTHS:\\n month = MONTHS[month]\\n break\\n elif month == 'all':\\n break\\n else:\\n print('\\\\nOops! That\\\\'s not in the options\\\\n')\\n\\n # user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n day = input(\\n '\\\\nWould you like to see data for a particular day of the week?\\\\nIf yes, enter the day. (Enter Mon for Monday, Tue for Tuesday, etc.) If no, just type all.\\\\n').lower()\\n\\n if day in DAYS:\\n day = DAYS[day]\\n break\\n elif day == 'all':\\n break\\n else:\\n print('\\\\nOops! That\\\\'s not in the options\\\\n')\\n\\n if month != 'all' or day != 'all':\\n print('\\\\nYou have chosen to explore US bikeshare data for \\\\ncity: {}, month: {}, day: {}'.format(\\n city, month, day).title())\\n\\n print('-'*40)\\n return city, month, day\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n city_temp = ''\\n while city_temp.lower() not in CITY_DATA:\\n city_temp = str(input('For which city would you like to see data? Chicago, New York, or Washington?\\\\n').lower()) \\n if city_temp.lower() in CITY_DATA:\\n city = city_temp\\n break\\n else:\\n print('This city is not available.')\\n continue\\n \\n # TO DO: get user input for month (all, january, february, ... , june)\\n month_temp = ''\\n while month_temp.lower() not in MONTH_DATA:\\n month_temp = input('For which month would you like to see the data?\\\\nPlease enter any month from January to June or enter all to see data for all the months available\\\\n')\\n if month_temp.lower() in MONTH_DATA:\\n month = month_temp.lower()\\n else:\\n print('This month is not available or does not exist.') # Error message\\n\\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\\n day_temp = ''\\n while day_temp.lower() not in DAY_DATA:\\n day_temp = input ('For which day of the week would you like to see the data?\\\\nPlease enter any day from Monday to Sunday or enter all to see data for all the days\\\\n')\\n if day_temp.lower() in DAY_DATA:\\n day = day_temp.lower()\\n else:\\n print('This day of the week does not exist') # Error message\\n \\n print('-'*40)\\n return city, month, day\",\n \"def extract_filters(self):\\n self.filters = self.controller.filters\\n\\n self.extract_core_stats()\\n self.extract_abilities()\\n # goes through and adds all list-based filters\\n for filterType, elements in self.filters.items():\\n if type(elements) == list and len(elements) > 0:\\n self.extract_filter_list(filterType, elements)\",\n \"def filter(self, *args):\\n exps = []\\n\\n #this ones can be improved. some are not getting extracted\\n if \\\"retweets\\\" in args:\\n exps.append(re.compile(\\\"^RT ?(?<=^|(?<=[^a-zA-Z0-9-_\\\\.]))@([A-Za-z]+[A-Za-z0-9-_]+):\\\"))\\n if \\\"emoticons\\\" in args:\\n exps.append(\\\"emoticons\\\")\\n if \\\"flags\\\" in args:\\n exps.append(re.compile(u\\\"[\\\\U0001F1E6-\\\\U0001F1FF]\\\"))\\n if \\\"handles\\\" in args:\\n # Handles at start of string\\n exps.append(re.compile(\\\"^\\\\s*((?<=^|(?<=[^\\\\S]))@([\\\\S]+)\\\\s*)*\\\"))\\n # Handles at end of string\\n exps.append(re.compile(\\\"\\\\s+((?<=^|(?<=[^\\\\S]))@(\\\\S+)\\\\s*)*$\\\"))\\n if \\\"urls\\\" in args:\\n exps.append(re.compile(\\\"(https?|ftp)://[^\\\\s/$.?#].[^\\\\s]*\\\"))\\n if \\\"hashtags\\\" in args:\\n # Hastags at start of string\\n exps.append(re.compile(\\\"^\\\\s*((?<=^|(?<=[^\\\\S]))#([\\\\S]+)\\\\s*)*\\\"))\\n # Hashtags at end of string\\n exps.append(re.compile(\\\"\\\\s+((?<=^|(?<=[^\\\\S]))#(\\\\S+)\\\\s*)*$\\\"))\\n\\n\\n # Use all filters\\n if \\\"*\\\" in args and not exps:\\n return self.filter(\\\"retweets\\\", \\\"emoticons\\\", \\\"flags\\\", \\\"handles\\\", \\\"urls\\\", \\\"hashtags\\\")\\n\\n filtering_text = self.raw_text\\n\\n for expression in exps:\\n if expression == \\\"emoticons\\\":\\n filtering_text = ''.join(c for c in filtering_text if c not in emoji.UNICODE_EMOJI)\\n else:\\n filtering_text = re.sub(expression, \\\"\\\", filtering_text)\\n\\n # Remove extra spaces\\n self.clean_text = re.sub(r\\\"\\\\s\\\\s+\\\", ' ', filtering_text.strip())\\n return self.clean_text\",\n \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n city = input(\\\"Please enter a city ('chicago', 'new york city' or 'washington'): \\\").lower()\\n if city in ['chicago', 'new york city', 'washington']:\\n break\\n else:\\n print('Invalid input! Please try again.')\\n\\n # get user input for month (all, january, february, ... , june)\\n while True:\\n month = input(\\\"Please enter a month ('january', 'february', 'march', 'april', 'may', 'june') or enter 'all': \\\").lower()\\n if (month in months) or (month == 'all'):\\n break\\n else:\\n print('Invalid Input! Please try again.')\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n while True:\\n weekdays = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\\n day = input(\\\"Please enter a weekday ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday') or 'all': \\\").lower()\\n if (day in weekdays) or (day =='all'):\\n break\\n else:\\n print('Invalid input! Please try again.')\\n\\n print('\\\\nInput Successful!\\\\nCity: {}\\\\nMonth: {}\\\\nWeekday: {}'.format(city, month, day)) #quick summary of all input for the user.\\n print('-'*40)\\n return city, month, day\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6897771","0.6654421","0.6647934","0.6647418","0.6594005","0.65434283","0.65231794","0.64544934","0.6430273","0.6366135","0.62607723","0.6208928","0.6146251","0.6066933","0.6063317","0.6019137","0.60165054","0.59925497","0.5987568","0.59434175","0.594083","0.5938775","0.5911863","0.58939856","0.5887174","0.58707756","0.5862375","0.5855801","0.58480835","0.58312404","0.5830552","0.58292973","0.58253825","0.58233315","0.58044106","0.5798372","0.5796016","0.5790959","0.57902586","0.57891756","0.57879686","0.5770541","0.5770043","0.5767301","0.57595396","0.5759193","0.5753551","0.57520103","0.57408273","0.5713356","0.5709295","0.57044226","0.5696847","0.5690794","0.56867725","0.56831497","0.5682125","0.5679762","0.5673346","0.56718373","0.5666412","0.5659904","0.5654814","0.56528336","0.5641902","0.563638","0.5634962","0.56335175","0.5625916","0.5624605","0.56226546","0.56226206","0.56207883","0.5620019","0.56176734","0.56045365","0.5602316","0.5590561","0.5577402","0.5574399","0.55736274","0.5571384","0.5571268","0.5559571","0.5545248","0.5538131","0.55376875","0.55338633","0.55325216","0.552445","0.552445","0.55166936","0.55158615","0.55144703","0.55131847","0.55129147","0.55092657","0.5500264","0.55001056","0.5496569"],"string":"[\n \"0.6897771\",\n \"0.6654421\",\n \"0.6647934\",\n \"0.6647418\",\n \"0.6594005\",\n \"0.65434283\",\n \"0.65231794\",\n \"0.64544934\",\n \"0.6430273\",\n \"0.6366135\",\n \"0.62607723\",\n \"0.6208928\",\n \"0.6146251\",\n \"0.6066933\",\n \"0.6063317\",\n \"0.6019137\",\n \"0.60165054\",\n \"0.59925497\",\n \"0.5987568\",\n \"0.59434175\",\n \"0.594083\",\n \"0.5938775\",\n \"0.5911863\",\n \"0.58939856\",\n \"0.5887174\",\n \"0.58707756\",\n \"0.5862375\",\n \"0.5855801\",\n \"0.58480835\",\n \"0.58312404\",\n \"0.5830552\",\n \"0.58292973\",\n \"0.58253825\",\n \"0.58233315\",\n \"0.58044106\",\n \"0.5798372\",\n \"0.5796016\",\n \"0.5790959\",\n \"0.57902586\",\n \"0.57891756\",\n \"0.57879686\",\n \"0.5770541\",\n \"0.5770043\",\n \"0.5767301\",\n \"0.57595396\",\n \"0.5759193\",\n \"0.5753551\",\n \"0.57520103\",\n \"0.57408273\",\n \"0.5713356\",\n \"0.5709295\",\n \"0.57044226\",\n \"0.5696847\",\n \"0.5690794\",\n \"0.56867725\",\n \"0.56831497\",\n \"0.5682125\",\n \"0.5679762\",\n \"0.5673346\",\n \"0.56718373\",\n \"0.5666412\",\n \"0.5659904\",\n \"0.5654814\",\n \"0.56528336\",\n \"0.5641902\",\n \"0.563638\",\n \"0.5634962\",\n \"0.56335175\",\n \"0.5625916\",\n \"0.5624605\",\n \"0.56226546\",\n \"0.56226206\",\n \"0.56207883\",\n \"0.5620019\",\n \"0.56176734\",\n \"0.56045365\",\n \"0.5602316\",\n \"0.5590561\",\n \"0.5577402\",\n \"0.5574399\",\n \"0.55736274\",\n \"0.5571384\",\n \"0.5571268\",\n \"0.5559571\",\n \"0.5545248\",\n \"0.5538131\",\n \"0.55376875\",\n \"0.55338633\",\n \"0.55325216\",\n \"0.552445\",\n \"0.552445\",\n \"0.55166936\",\n \"0.55158615\",\n \"0.55144703\",\n \"0.55131847\",\n \"0.55129147\",\n \"0.55092657\",\n \"0.5500264\",\n \"0.55001056\",\n \"0.5496569\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65210766"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":18497,"cells":{"query":{"kind":"string","value":"Retrieve all conferences matching one or more given topics."},"document":{"kind":"string","value":"def _getConferencesByTopicSearch(self, request):\n # Generate list of filters from the topic arguments\n filters = [Conference.topics == topic for topic in request.topics]\n if not filters:\n raise endpoints.BadRequestException(\n 'At least one topic must be specified'\n )\n # Retrieve all conferences matching one or more of the topic filters\n conferences = Conference.query(\n ndb.OR(*filters)).order(Conference.name).fetch()\n return conferences"},"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 getConferencesByTopicSearch(self, request):\n conferences = self._getConferencesByTopicSearch(request)\n # Need to fetch organiser displayName from profiles\n # Get all keys and use get_multi for speed\n organisers = [\n (ndb.Key(Profile, conf.organizerUserId)) for conf in conferences\n ]\n profiles = ndb.get_multi(organisers)\n # Put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n # Return individual ConferenceForm object per Conference\n # Return individual ConferenceForm object per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\n for conf in conferences\n ]\n )","def get_all_subscriptions_by_topic(self, topic, next_token=None):\r\n params = {'ContentType' : 'JSON',\r\n 'TopicArn' : topic}\r\n if next_token:\r\n params['NextToken'] = next_token\r\n response = self.make_request('ListSubscriptionsByTopic', params,\r\n '/', 'GET')\r\n body = response.read()\r\n if response.status == 200:\r\n return json.loads(body)\r\n else:\r\n boto.log.error('%s %s' % (response.status, response.reason))\r\n boto.log.error('%s' % body)\r\n raise self.ResponseError(response.status, response.reason, body)","def get_subscriptions(self, topic_name):\r\n resp = self._make_request('get',\r\n 'topics/%s/subscriptions' % topic_name)\r\n return resp.json()","def resolve_topics(self, info, **kwargs):\n return Topic.objects.all()","def _get_all_filtered_channels(self, topics_without_signature):\n mpe_address = self.get_mpe_address()\n event_signature = self.ident.w3.sha3(\n text=\"ChannelOpen(uint256,uint256,address,address,address,bytes32,uint256,uint256)\").hex()\n topics = [event_signature] + topics_without_signature\n logs = self.ident.w3.eth.getLogs(\n {\"fromBlock\": self.args.from_block, \"address\": mpe_address, \"topics\": topics})\n abi = get_contract_def(\"MultiPartyEscrow\")\n event_abi = abi_get_element_by_name(abi, \"ChannelOpen\")\n channels_ids = [get_event_data(event_abi, l)[\n \"args\"][\"channelId\"] for l in logs]\n return channels_ids","def get_topics():\n topics, _ = base_query(db_session)\n return jsonify([p.serialize for p in topics])","def schools_by_topic(mongo_collection, topic):\n res = []\n returned_values = mongo_collection.find({\"topics\": {\"$all\": [topic]}})\n for value in returned_values:\n res.append(value)\n return res","def get_conferences():\n conferences = []\n for confId in range(1, 8):\n c = requests.get(CONFERENCES_URL.format(confId)).json()\n conf_list = c.get('conferences')\n if conf_list is None or len(conf_list) == 0:\n continue\n conferences.append(Conference.from_json(conf_list[0]))\n return conferences","def list_topics(project_id):\n project_path = f\"projects/{project_id}\"\n for topic in PUBLISHER_CLIENT.list_topics(request={\"project\": project_path}):\n print(topic)","def check_exists(cls, topics):\n\t\tresult = []\n\t\tfor known_feed in cls.get([cls.create_key(url) for url in set(topics)]):\n\t\t\tif known_feed is not None:\n\t\t\t\tresult.append(known_feed.topic)\n\t\treturn result","def listen(self, topics):\n logging.debug(f'Listen to {list(map(lambda x: x.name, topics))}')\n\n for topic in map(lambda x: x.name, topics):\n try:\n self.subscribe(topic)\n logging.debug(f'Subscribed the {topic} topic')\n except Exception:\n logging.debug(f\"Can't subscribe the {topic} topic\")","def get_topics(self, tags=None):\r\n params = {}\r\n if tags:\r\n params['tags'] = ','.join(tags)\r\n resp = self._make_request('get', 'topics', params=params)\r\n return resp.json()","def get_subscribers(cls, topic, count, starting_at_callback=None):\n\t\tquery = cls.all()\n\t\tquery.filter('topic_hash =', utils.sha1_hash(topic))\n\t\tquery.filter('subscription_state = ', cls.STATE_VERIFIED)\n\t\tif starting_at_callback:\n\t\t\tquery.filter('callback_hash >=', utils.sha1_hash(starting_at_callback))\n\t\tquery.order('callback_hash')\n\n\t\treturn query.fetch(count)","def get_topics(self):\n try:\n with self.__db_lock:\n sql = \"SELECT * FROM 'topics' ORDER BY 'name' ASC\"\n self.__cursor.execute(sql)\n topics = self.__cursor.fetchall()\n if topics is None or len(topics) == 0:\n return []\n return [topic[1] for topic in topics]\n except Exception as e:\n logging.error(\n \"Exception when trying to get topics list: {}\".format(e))\n return []","def schools_by_topic(mongo_collection, topic):\n return mongo_collection.find({\"topics\": {\"$in\": [topic]}})","def get_confs(req_topics=None, start_year=None, end_year=None):\n\n req_topics = req_topics or ','.join(topics.keys())\n start_year = start_year or min_year\n end_year = end_year or max_year\n\n # validate topics\n parsed_topics = req_topics.split(',')\n\n for topic in parsed_topics:\n if topic not in topics.keys():\n raise ValueError('Topic \\'{topic}\\' not valid'.format(topic=topic))\n\n # validate startYear\n try:\n start_year = int(start_year)\n except ValueError:\n raise ValueError('startYear must be valid integer.')\n\n if start_year > max_year or start_year < min_year:\n raise ValueError('startYear must be value between {min} and {max}.'.format(min=min_year, max=max_year))\n\n # validate endYear\n try:\n end_year = int(end_year)\n except ValueError:\n raise ValueError('endYear must be valid integer.')\n\n if end_year > max_year or end_year < min_year:\n raise ValueError('endYear must be value between {min} and {max}.'.format(min=min_year, max=max_year))\n\n if start_year > end_year:\n raise ValueError(\"startYear must not be greater than endYear.\")\n\n # aggregate data\n data = list()\n\n for year in range(start_year, end_year+1):\n for topic in parsed_topics:\n tmp = fetch(topic, year)\n tmp = normalize_confs(tmp, topic, year)\n data.extend(tmp)\n return data","def _get_subscriptions(self, topic_arn):\n return self.conn.get_all_subscriptions_by_topic(topic_arn)['ListSubscriptionsByTopicResponse']['ListSubscriptionsByTopicResult']['Subscriptions']","def topics(self, project: str) -> list:\n assert self.exists(project), f'Project {project} inesistente'\n\n cursor = self.projects(\n {\n 'url': project\n }\n )\n try:\n return cursor.next()['topics']\n except StopIteration:\n return []","def get_teacher_topic_all():\n topic_data = query_db(\n \"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \"\n \"ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n topics = []\n for topic in topic_data:\n topic_dict_teacher = {}\n topic_dict_teacher[\"id\"] = topic[0]\n topic_dict_teacher[\"name\"] = flask.escape(str(topic[1]))\n topic_dict_teacher[\"class\"] = flask.escape(str(topic[2]))\n topics.append(topic_dict_teacher)\n return topics","def describe_topics(self, topics=None):\n return self._client.describe_topics(topics)","def get_topics(self):\r\n return [x[0] for x in get_published_topics()]","def extract_queries(self, path_topics=\"../topics-rnd5.xml\"):\n \n topic_queries = []\n with open(path_topics, \"r\") as f:\n for line in f:\n match = re.match(\".*<query>([^<]*)<\\/query>.*\", line)\n if match:\n topic_queries.append(match.group(1))\n if len(topic_queries) != 50:\n sys.exit(\"There should be 50 topics, found {}\".format(\n len(topic_queries)))\n \n return topic_queries","def get_all_subscriptions(cls, **kwargs):\n return Subscription.query.filter(**kwargs).all()","async def subscribe(topics, on_close=None, on_error=None):\n loop = asyncio.get_event_loop()\n session = aiohttp.ClientSession()\n async with session.ws_connect('wss://api.huobi.pro/ws') as ws:\n keys = {\n topic: uuid.uuid4().hex\n for topic in topics\n }\n keyed_channels = {\n v: topics[k]\n for k, v in keys.items()\n }\n subscribed_chanels = {}\n for topic, config in topics.items():\n payload = {\n 'sub': topic,\n 'id': keys[topic]\n }\n await ws.send_str(encode_ws_payload(payload))\n async for msg in ws:\n if msg.type == aiohttp.WSMsgType.BINARY:\n data = decode_ws_payload(msg.data)\n\n ping = data.get('ping')\n if ping:\n reply = encode_ws_payload({'pong': ping})\n await ws.send_str(\n reply\n )\n\n subbed = data.get('subbed')\n if subbed:\n if data.get('status') == 'ok':\n subscribed_chanels[subbed] = keyed_channels[data['id']]\n\n ch = data.get('ch')\n if ch:\n cb = subscribed_chanels[ch].get('callback', lambda _: None)\n if asyncio.iscoroutinefunction(cb):\n await cb(data)\n else:\n loop.run_in_executor(None, partial(cb, data))\n elif msg.type == aiohttp.WSMsgType.CLOSED:\n if on_close:\n return on_close()\n return\n elif msg.type == aiohttp.WSMsgType.ERROR:\n if on_error:\n return on_error()\n return","def conferences(self):\r\n return conferences.Conferences(self)","def list(self, topic, **options):\n pass","def test_get_full_topic_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.get_full_topic_courses(topic_id, course_id)","def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = response.get(\"Items\")\n return questions","def topics(ctx):\n pass","def topics(self):\n # use get_model to avoid circular dependency\n topic_model = apps.get_model('tags', 'Topic')\n return topic_model.objects.filter(tag__in=self.tags.all()).distinct()","def test_list_topic_entries_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.list_topic_entries_courses(topic_id, course_id)","def add_topics(self, project: str, *topics: str):\n assert self.exists(project), f'Project {project} inesistente'\n\n return self.collection.find_one_and_update(\n {'url': project},\n {\n '$addToSet': {\n 'topics': {\n '$each': topics,\n }\n }\n }\n )","def show_all_subjects(self):\n self.load_subjects_in_twSubjects([self.pj[SUBJECTS][x][\"name\"] for x in self.pj[SUBJECTS]])","def get_subscriptions(self, private_key, client_id):\n return self._samp_hub.getSubscriptions(private_key, client_id)","def _get_cloudwatch_subscriptions(self):\n return self._get_subscriptions(self.cloudwatch_arn)","def get_topics(self):\n return self.client.cluster.topics()","def remove_topics(self, project: str, *topics: str):\n assert self.exists(project), f'Project {project} inesistente'\n\n return self.collection.find_one_and_update(\n {\n 'url': project\n },\n {\n '$pull': {\n 'topics': {\n '$in': topics,\n }\n }\n }\n )","def _request_search(self, query, topics, pages=1):\n\n query = query.replace(' ', '+')\n url = 'https://google.com/search?q={}'.format(query)\n\n user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36\"\n\n headers = {'user-agent': user_agent}\n resp = requests.get(url, headers=headers)\n\n if resp.status_code == 200:\n soup = BeautifulSoup(resp.content, \"html.parser\")\n else:\n raise Exception(\"Something's fucky\")\n\n items = []\n\n for result in soup.find_all('div', class_='rc'):\n anchors = result.find_all('a')\n if anchors:\n link = anchors[0]['href']\n title = result.find('h3').text\n\n topic_words = list({topic.text.lower() for topic in result.find_all('em') if topic.text.lower() in topics})\n\n item = {\n 'title': title,\n 'url': link,\n 'topics': topic_words\n }\n items.append(item)\n else:\n raise Exception('No anchor to extract text from')\n\n return items","def topics(self):\n return topics.Topics(self)","def subscribed_topics(self):\n return self.manager.subscribed_topics","def add_subscription_topics(self, topics: List[str]) -> None:\n self.log.debug(f\"Adding {topics} to {self.topics}\")\n self.topics.extend(topics)","def get_queryset(self):\n return models.ProfileTopic.objects.filter(\n profile__pk=self.kwargs.get(\"pk\")\n )","def topics(self):\r\n return topics.Topics(self)","def getSubscriptionTopics(self) -> List[str]:\n return self.cpp.getSubscriptionTopics()","def test_get_single_topic_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.get_single_topic_courses(topic_id, course_id)","def list(cls, **kwargs):\n response = Yola().list_subscriptions(**kwargs)\n return [cls(**sub) for sub in response['results']]","def list_subscriptions(\n connection, project_id, fields=None, offset=0, limit=-1, error_msg=None\n):\n return connection.get(\n url=f'{connection.base_url}/api/subscriptions',\n params={'offset': offset, 'limit': limit, 'fields': fields},\n headers={'X-MSTR-ProjectID': project_id},\n )","def list_courses_subjects(self, all=False):\n q = {'facet': 'true',\n 'facet.field': 'course_subject',\n 'facet.mincount': '1',\n 'facet.sort': 'index', # Sort alphabetically\n 'rows': '0', # we don't need any actual document\n }\n if all:\n q['q'] = '*:*'\n else:\n q['q'] = 'NOT presentation_start:[* TO NOW]'\n results = searcher.search(q, start=0, count=1000) # Do not paginate\n subjects = subjects_facet_to_subjects_domain(results)\n return subjects","def search_by_topic(query):\n data = {'query': str(query)}\n url = r'http://www.reddit.com/api/subreddits_by_topic.json'\n response = r.get(url, data=data)\n return response.json()['data']","def get_subscriptions(self):\n url = '{}/v2/subscriptions'.format(self.url)\n r = requests.get(url, headers=self.headers_v2)\n return r.json()","def topics(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"topics\")","def subscribedQueries(self):\n return map(Query.get, self.subscriptions)","def test_wiki_topics(self):\n t1 = TopicFactory(slug='doesnotexist')\n t2 = TopicFactory(slug='extant')\n t3 = TopicFactory(slug='tagged')\n\n doc = DocumentFactory(locale=u'en-US', category=10)\n doc.topics.add(t2)\n RevisionFactory(document=doc, is_approved=True)\n\n doc = DocumentFactory(locale=u'en-US', category=10)\n doc.topics.add(t2)\n doc.topics.add(t3)\n RevisionFactory(document=doc, is_approved=True)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n ([t2.slug, t3.slug], 1),\n )\n\n qs = {'a': 1, 'w': 1, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def get_queryset(self, request):\n qs = super(TopicAdmin, self).get_queryset(request)\n if request.user.is_superuser:\n return qs\n return qs.filter(id__in=request.user.profile.topics.all())","def search(self, text=None, topic_ids=None, per_page=None, page=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/{1}'.format(self.get_url(), 'search')\r\n return http.Request('GET', url, params), parsers.parse_json","def getConferencesToAttend(self, request):\n prof = self._getProfileFromUser() # get user Profile\n conf_keys = [\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\n ]\n conferences = ndb.get_multi(conf_keys)\n # Get organizers\n organisers = [\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\n ]\n profiles = ndb.get_multi(organisers)\n # Put display names in a dict for easier fetching\n names = {}\n for profile in profiles:\n names[profile.key.id()] = profile.displayName\n # Return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\n for conf in conferences\n ]\n )","def subscribers(id):\n return core.query(schema.streamBySubscribers, id)","def topic_list(request, forum_id, pagination_id=1):\n\ttry:\n\t\ttopics = Topic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1'))\n\t\tforum_name = Forum.objects.get(id=forum_id)\n\t\tforum_name = forum_name.forum_name\n\texcept:\n\t\treturn HttpResponseRedirect('/forum/')\n\treturn object_list(\n\t\trequest,\n\t\tTopic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1')),\n\t\tpaginate_by = 10,\n\t\tallow_empty = True,\n\t\tpage = pagination_id,\n\t\textra_context = {'forum': forum_id, 'perms': list_perms(request), 'forum_name': forum_name},\n\t\ttemplate_name = 'myghtyboard/topics_list.html')","def test_list_discussion_topics_courses(self):\r\n course_id = None # Change me!!\r\n\r\n r = self.client.list_discussion_topics_courses(course_id, exclude_context_module_locked_topics=None, include=None, only_announcements=None, order_by=None, scope=None, search_term=None)","def deleteTopics(self, topics):\n if not topics:\n return\n topicsCArraySize = len(topics)\n topicsCArray = internals.new_topicPtrArray(topicsCArraySize)\n try:\n for i, topic in enumerate(topics):\n internals.topicPtrArray_setitem(topicsCArray,\n i,\n get_handle(topic))\n _ExceptionUtil.raiseOnError(\n internals.blpapi_ProviderSession_deleteTopics(\n self.__handle,\n topicsCArray,\n topicsCArraySize))\n finally:\n internals.delete_topicPtrArray(topicsCArray)","def get_subscriptions(self):\n return self.subscriptions.all()","def topics(self):\r\n return Topics(self)","def terminateSubscriptionsOnTopics(self, topics, message=None):\n if not topics:\n return\n topicsCArraySize = len(topics)\n topicsCArray = internals.new_topicPtrArray(topicsCArraySize)\n try:\n for i, topic in enumerate(topics):\n internals.topicPtrArray_setitem(topicsCArray,\n i,\n get_handle(topic))\n _ExceptionUtil.raiseOnError(\n internals.blpapi_ProviderSession_terminateSubscriptionsOnTopics(\n self.__handle,\n topicsCArray,\n topicsCArraySize,\n message))\n finally:\n internals.delete_topicPtrArray(topicsCArray)","def test_get_posts_by_topic(self):\n data = {'topic': self.topic.id}\n response = self.client.get(reverse('api:posts-list'), data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data.get('results')), 2)","def load_metadata_for_topics(self, *topics):\n topics = tuple(_coerce_topic(t) for t in topics)\n log.debug(\"%r: load_metadata_for_topics: %r\", self, topics)\n fetch_all_metadata = not topics\n\n # create the request\n requestId = self._next_id()\n request = KafkaCodec.encode_metadata_request(self._clientIdBytes,\n requestId, topics)\n\n # Callbacks for the request deferred...\n def _handleMetadataResponse(response):\n # Decode the response\n (brokers, topics) = \\\n KafkaCodec.decode_metadata_response(response)\n log.debug(\"%r: Broker/Topic metadata: %r/%r\",\n self, brokers, topics)\n\n # If we fetched the metadata for all topics, then store away the\n # received metadata for diagnostics.\n if fetch_all_metadata:\n self._brokers = brokers\n self._topics = topics\n\n # Iff we were fetching for all topics, and we got at least one\n # broker back, then remove brokers when we update our brokers\n ok_to_remove = (fetch_all_metadata and len(brokers))\n # Take the metadata we got back, update our self.clients, and\n # if needed disconnect or connect from/to old/new brokers\n self._update_brokers(\n [(nativeString(b.host), b.port) for b in brokers.values()],\n remove=ok_to_remove,\n )\n\n # Now loop through all the topics/partitions in the response\n # and setup our cache/data-structures\n for topic, topic_metadata in topics.items():\n _, topic_error, partitions = topic_metadata\n self.reset_topic_metadata(topic)\n self.topic_errors[topic] = topic_error\n if not partitions:\n log.warning('No partitions for %s, Err:%d',\n topic, topic_error)\n continue\n\n self.topic_partitions[topic] = []\n for partition, meta in partitions.items():\n self.topic_partitions[topic].append(partition)\n topic_part = TopicAndPartition(topic, partition)\n self.partition_meta[topic_part] = meta\n if meta.leader == -1:\n log.warning('No leader for topic %s partition %s',\n topic, partition)\n self.topics_to_brokers[topic_part] = None\n else:\n self.topics_to_brokers[\n topic_part] = brokers[meta.leader]\n self.topic_partitions[topic] = sorted(\n self.topic_partitions[topic])\n return True\n\n def _handleMetadataErr(err):\n # This should maybe do more cleanup?\n if err.check(t_CancelledError, CancelledError):\n # Eat the error\n return None\n log.error(\"Failed to retrieve metadata:%s\", err)\n raise KafkaUnavailableError(\n \"Unable to load metadata from configured \"\n \"hosts: {!r}\".format(err))\n\n # Send the request, add the handlers\n d = self._send_broker_unaware_request(requestId, request)\n d.addCallbacks(_handleMetadataResponse, _handleMetadataErr)\n return d","def get_topics_articles(topic_id):\n articles = db_session.query(Article).filter_by(topic_id=topic_id)\n return jsonify([p.serialize for p in articles])","def list_subscriptions_async(\n future_session: \"FuturesSession\",\n connection,\n project_id,\n fields=None,\n offset=0,\n limit=-1,\n):\n params = {'offset': offset, 'limit': limit, 'fields': fields}\n url = f'{connection.base_url}/api/subscriptions'\n headers = {'X-MSTR-ProjectID': project_id}\n\n return future_session.get(url=url, headers=headers, params=params)","def getGroupTopics(group_id): \r\n return Group.getGroupTopics(group_id)","def get_documents(self, subtypes=None, refresh=False):\n search = ScopusSearch('au-id({})'.format(self.identifier), refresh)\n if subtypes:\n return [p for p in search.results if p.subtype in subtypes]\n else:\n return search.results","def getAllSubscriptions(self):\n return self.request(\n \"getAllSubscriptions\",\n )","def test_list_entries_courses(self):\r\n course_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.list_entries_courses(topic_id, course_id, ids=None)","def load_channels(archived=False):\n if not settings.SLACK_TOKEN:\n return {'ok': False, 'error': 'config_error'}\n\n client = WebClient(token=settings.SLACK_TOKEN)\n\n try:\n response = client.conversations_list(exclude_archived=not archived)\n assert response['ok'] is True\n\n channels = []\n for channel in response['channels']:\n channels.append((channel['id'], channel['name']))\n\n return {'ok': True, 'channels': channels}\n except SlackApiError as e:\n assert e.response['ok'] is False\n return e.response","def get_queryset(self):\n return models.ProfileItem.objects.filter(topic=self.kwargs.get(\"pk\"))","def topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"topics\")","def list_subscriptions(self, user_token):\n response = _request('GET',\n url=self.url_v1('/user/subscriptions'),\n user_agent=self.user_agent,\n user_token=user_token,\n )\n _raise_for_status(response)\n\n return response.json()['topics']","def test_question_topics(self):\n p = ProductFactory()\n t1 = TopicFactory(slug='doesnotexist', product=p)\n t2 = TopicFactory(slug='cookies', product=p)\n t3 = TopicFactory(slug='sync', product=p)\n\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t3)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n )\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def get_topic_terms_df(self, topics):\n labels = list(topics.keys())\n \n topic_terms = []\n for topic, top_n_words in topics.items():\n top_n_words = sorted(top_n_words, key=lambda x: x[1], reverse=True)[:self.num_words]\n terms = [term for term, c_tf_idf in top_n_words]\n terms = \", \".join(terms)\n topic_terms.append(terms)\n\n topic_terms_df = pd.DataFrame()\n topic_terms_df['id'] = labels\n topic_terms_df['Topic terms'] = topic_terms\n return topic_terms_df","def test_get_all_topics(mock_send_message_json):\n assert OranDmaap.get_all_topics_url == f\"{BASE_URL}/topics/listAll\"","def get_tweets_by_topic(topic, start_date, end_date):\n try:\n query = f\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\"\n logger.info(f'QUERY: {query}') \n with MysqlCursor() as cur:\n cur.execute(query)\n tweets = cur.fetchall()\n columns = [col[0] for col in cur.description]\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \n return tweets, columns\n \n except Exception as ex:\n logger.exception(ex)\n return f'Exception: {ex}'","def get_subscribed_clients(self, private_key, mtype):\n return self._samp_hub.getSubscribedClients(private_key, mtype)","def get_subscriptions(self, use_threading=False):\r\n \r\n if self._subscriptions is None:\r\n if use_threading:\r\n self.load_subscriptions_threaded()\r\n else:\r\n self._subscriptions = []\r\n for page in range(self._subscription_pages):\r\n self._load_subscriptions(page=page+1)\r\n return self._subscriptions","def fetchAllThreads(self):\n\n catalog = getToolByName(self.context, 'portal_catalog')\n return [ dict(url=thread.getURL(),\n title=thread.Title,\n author=thread.Creator,\n createDate = thread.created.strftime('%Y/%m/%d'),)\n for thread in \n catalog({'object_provides': Itopic.__identifier__,\n 'path': dict(query='/'.join(self.context.getPhysicalPath()),\n depth=1),\n 'sort_on': 'sortable_title'})\n ]","def print_topics(self, time=0, top_terms=20):\n return [self.print_topic(topic, time, top_terms) for topic in range(self.num_topics)]","def test_get_full_topic_groups(self):\r\n group_id = None # Change me!!\r\n topic_id = None # Change me!!\r\n\r\n r = self.client.get_full_topic_groups(group_id, topic_id)","def topics(self):\r\n return contents.Topics(self)","def receive_many(self, channels):\n if not channels:\n raise ValueError(\"Cannot receive on empty channel list!\")\n res = (None, None)\n while True:\n message = self.pubsub.get_message() #peek at subscribed channels\n if message and message.get('type',None) in ['message','pmessage']:\n try:\n res = (message.get('channel','')[len(self.prefix + self.sub_prefix):],json.loads(message.get('data',None)))\n break\n except:\n #log error and ignore it!!!\n pass \n result = self.connection.blpop([self.prefix + channel for channel in channels], timeout=1)\n if result:\n content = self.connection.get(result[1])\n if content is None:\n continue\n res = (result[0][len(self.prefix):], json.loads(content))\n break\n else:\n break\n return res","def test_topics_for_products(self):\n desktop_topics = topics_for(product=self.desktop)\n eq_(len(desktop_topics), 3)\n\n mobile_topics = topics_for(product=self.mobile)\n eq_(len(mobile_topics), 2)","def GetSubscriptions(self):\n\n return self.__GetJson(\"/subscriptions\", True)","def validate_new_curriculum_topics(self, curriculum_topics):\n\n for cur in curriculum_topics:\n # check to make sure its in the general topics table\n self.db_cursor.execute(\"\"\"SELECT COUNT(*) FROM Topic WHERE name = %s\"\"\", (cur,))\n ct = self.db_cursor.fetchone()\n ct = ct[0]\n if ct == 0:\n print(\"topic does not exist, we must create new one or cancel\") # todo\n\n return True","def get_all_subscriptions(self, next_token=None):\r\n params = {'ContentType' : 'JSON'}\r\n if next_token:\r\n params['NextToken'] = next_token\r\n response = self.make_request('ListSubscriptions', params, '/', 'GET')\r\n body = response.read()\r\n if response.status == 200:\r\n return json.loads(body)\r\n else:\r\n boto.log.error('%s %s' % (response.status, response.reason))\r\n boto.log.error('%s' % body)\r\n raise self.ResponseError(response.status, response.reason, body)","def get_cloudwatch_email_subscriptions(self):\n return map(lambda subscription: subscription['Endpoint'], self._get_cloudwatch_subscriptions())","def get_subscriptions(self, client_id):\n return self.hub.get_subscriptions(self.get_private_key(), client_id)","def filter_content_by_topic(self, topic, content_type=None):\n\n if content_type is None:\n is_article, is_podcast, is_video = True, True, True\n else:\n is_article, is_podcast, is_video = self.get_content_type(content_type)\n\n try:\n response = {\"status\": \"ok\"}\n if is_article:\n db_articles = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_ARTICLES])\n response[\"articles\"] = list(db_articles.find({\"category\": topic}, {'_id': 0}))\n if is_podcast:\n db_podcasts = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_PODCASTS])\n response[\"podcasts\"] = list(db_podcasts.find({\"category\": topic}, {'_id': 0}))\n if is_video:\n db_videos = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_VIDEOS])\n response[\"videos\"] = list(db_videos.find({\"category\": topic}, {'_id': 0}))\n return response\n except:\n return {\"status\": \"bad\"}","def topics(self):\r\n return ProjectTopics(self)","def get_core_topics_paged_get(self, categoryFilter, locales, page, quickDate, sort):\n # TODO: Assuming first server is good - need to make fallback logic\n return self.session.get_any(\"{base}{request_url}\".format(base=self.servers[0],\n request_url=F\"/Forum/GetCoreTopicsPaged/{page}/{sort}/{quickDate}/{categoryFilter}/\"))","def test_topic_retrieval_authenticated(self):\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.free_token.key)\n response = self.client.get('/topics/', format='json')\n data = json.loads(response.content)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(data['count'],3)\n self.assertTrue({'name': 'Topic 1', 'description': 'The first topic.'} in data['results'])\n self.assertTrue({'name': 'Topic 2', 'description': 'The second topic.'} in data['results'])","def get_speakers(self, request):\n return self.speaker_service.get_speakers()","def subscribe(self, topic_type, tx_filter=None):\n\n request = protos.RequestSubscribe(type=topic_type, filter=tx_filter)\n res = self.stub.subscribe(request)\n for r in res:\n yield r","def getConferencesCreated(self, request):\n # make sure user is authed\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n # create ancestor query for all key matches for this user\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n prof = ndb.Key(Profile, user_id).get()\n # return set of ConferenceForm objects per Conference\n return ConferenceForms(\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\n )","def test_get_topics(self):\n\n for m in self.models:\n\n topics = m.topics\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertEqual(topics.num_rows(), 25)\n self.assertEqual(topics.num_columns(), 2)\n z = m.topics[\"topic_probabilities\"]\n for k in range(m.num_topics):\n self.assertTrue(\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\n \"Returned probabilities do not sum to 1.\",\n )\n\n # Make sure returned object is an SFrame of the right size\n topics = m.get_topics()\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertTrue(\n topics.num_columns() == 3,\n \"Returned SFrame should have a topic, word, and probs.\",\n )\n\n # Make sure that requesting a single topic returns only that topic\n num_words = 8\n topics = m.get_topics([5], num_words=num_words)\n self.assertTrue(\n all(topics[\"topic\"] == 5), \"Returned topics do not have the right id.\"\n )\n self.assertEqual(topics.num_rows(), num_words)\n topics = m.get_topics([2, 4], num_words=num_words)\n self.assertEqual(set(list(topics[\"topic\"])), set([2, 4]))\n self.assertEqual(topics.num_rows(), num_words + num_words)\n\n # Make sure the cumulative probability of the returned words is\n # is less than the cutoff we provided.\n # A cutoff of 1.0 should return num_words for every topic.\n cutoff = 1.0\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\n totals = topics.groupby(\n \"topic\", {\"total_score\": turicreate.aggregate.SUM(\"score\")}\n )\n self.assertTrue(\n all(totals[\"total_score\"] <= (cutoff + DELTA)),\n \"More words were returned than expected for this cutoff.\",\n )\n\n # Make sure we raise errors for bad input\n with self.assertRaises(ValueError):\n m.get_topics([-1])\n with self.assertRaises(ValueError):\n m.get_topics([10000])\n with self.assertRaises(ToolkitError):\n topics = m.get_topics(output_type=\"other\")\n\n # Test getting topic_words\n topic_words = m.get_topics(output_type=\"topic_words\", num_words=5)\n self.assertEqual(type(topic_words), turicreate.SFrame)\n\n # Test words are sorted correctly for the first topic\n # TODO: Make this more deterministic.\n\n # topic_probs = m.get_topics(num_words=5)\n # expected = [w for w in topic_probs['word'][:5]]\n # observed = topic_words['words'][0]\n # self.assertEqual(observed[0], expected[0])"],"string":"[\n \"def getConferencesByTopicSearch(self, request):\\n conferences = self._getConferencesByTopicSearch(request)\\n # Need to fetch organiser displayName from profiles\\n # Get all keys and use get_multi for speed\\n organisers = [\\n (ndb.Key(Profile, conf.organizerUserId)) for conf in conferences\\n ]\\n profiles = ndb.get_multi(organisers)\\n # Put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n # Return individual ConferenceForm object per Conference\\n # Return individual ConferenceForm object per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\\n for conf in conferences\\n ]\\n )\",\n \"def get_all_subscriptions_by_topic(self, topic, next_token=None):\\r\\n params = {'ContentType' : 'JSON',\\r\\n 'TopicArn' : topic}\\r\\n if next_token:\\r\\n params['NextToken'] = next_token\\r\\n response = self.make_request('ListSubscriptionsByTopic', params,\\r\\n '/', 'GET')\\r\\n body = response.read()\\r\\n if response.status == 200:\\r\\n return json.loads(body)\\r\\n else:\\r\\n boto.log.error('%s %s' % (response.status, response.reason))\\r\\n boto.log.error('%s' % body)\\r\\n raise self.ResponseError(response.status, response.reason, body)\",\n \"def get_subscriptions(self, topic_name):\\r\\n resp = self._make_request('get',\\r\\n 'topics/%s/subscriptions' % topic_name)\\r\\n return resp.json()\",\n \"def resolve_topics(self, info, **kwargs):\\n return Topic.objects.all()\",\n \"def _get_all_filtered_channels(self, topics_without_signature):\\n mpe_address = self.get_mpe_address()\\n event_signature = self.ident.w3.sha3(\\n text=\\\"ChannelOpen(uint256,uint256,address,address,address,bytes32,uint256,uint256)\\\").hex()\\n topics = [event_signature] + topics_without_signature\\n logs = self.ident.w3.eth.getLogs(\\n {\\\"fromBlock\\\": self.args.from_block, \\\"address\\\": mpe_address, \\\"topics\\\": topics})\\n abi = get_contract_def(\\\"MultiPartyEscrow\\\")\\n event_abi = abi_get_element_by_name(abi, \\\"ChannelOpen\\\")\\n channels_ids = [get_event_data(event_abi, l)[\\n \\\"args\\\"][\\\"channelId\\\"] for l in logs]\\n return channels_ids\",\n \"def get_topics():\\n topics, _ = base_query(db_session)\\n return jsonify([p.serialize for p in topics])\",\n \"def schools_by_topic(mongo_collection, topic):\\n res = []\\n returned_values = mongo_collection.find({\\\"topics\\\": {\\\"$all\\\": [topic]}})\\n for value in returned_values:\\n res.append(value)\\n return res\",\n \"def get_conferences():\\n conferences = []\\n for confId in range(1, 8):\\n c = requests.get(CONFERENCES_URL.format(confId)).json()\\n conf_list = c.get('conferences')\\n if conf_list is None or len(conf_list) == 0:\\n continue\\n conferences.append(Conference.from_json(conf_list[0]))\\n return conferences\",\n \"def list_topics(project_id):\\n project_path = f\\\"projects/{project_id}\\\"\\n for topic in PUBLISHER_CLIENT.list_topics(request={\\\"project\\\": project_path}):\\n print(topic)\",\n \"def check_exists(cls, topics):\\n\\t\\tresult = []\\n\\t\\tfor known_feed in cls.get([cls.create_key(url) for url in set(topics)]):\\n\\t\\t\\tif known_feed is not None:\\n\\t\\t\\t\\tresult.append(known_feed.topic)\\n\\t\\treturn result\",\n \"def listen(self, topics):\\n logging.debug(f'Listen to {list(map(lambda x: x.name, topics))}')\\n\\n for topic in map(lambda x: x.name, topics):\\n try:\\n self.subscribe(topic)\\n logging.debug(f'Subscribed the {topic} topic')\\n except Exception:\\n logging.debug(f\\\"Can't subscribe the {topic} topic\\\")\",\n \"def get_topics(self, tags=None):\\r\\n params = {}\\r\\n if tags:\\r\\n params['tags'] = ','.join(tags)\\r\\n resp = self._make_request('get', 'topics', params=params)\\r\\n return resp.json()\",\n \"def get_subscribers(cls, topic, count, starting_at_callback=None):\\n\\t\\tquery = cls.all()\\n\\t\\tquery.filter('topic_hash =', utils.sha1_hash(topic))\\n\\t\\tquery.filter('subscription_state = ', cls.STATE_VERIFIED)\\n\\t\\tif starting_at_callback:\\n\\t\\t\\tquery.filter('callback_hash >=', utils.sha1_hash(starting_at_callback))\\n\\t\\tquery.order('callback_hash')\\n\\n\\t\\treturn query.fetch(count)\",\n \"def get_topics(self):\\n try:\\n with self.__db_lock:\\n sql = \\\"SELECT * FROM 'topics' ORDER BY 'name' ASC\\\"\\n self.__cursor.execute(sql)\\n topics = self.__cursor.fetchall()\\n if topics is None or len(topics) == 0:\\n return []\\n return [topic[1] for topic in topics]\\n except Exception as e:\\n logging.error(\\n \\\"Exception when trying to get topics list: {}\\\".format(e))\\n return []\",\n \"def schools_by_topic(mongo_collection, topic):\\n return mongo_collection.find({\\\"topics\\\": {\\\"$in\\\": [topic]}})\",\n \"def get_confs(req_topics=None, start_year=None, end_year=None):\\n\\n req_topics = req_topics or ','.join(topics.keys())\\n start_year = start_year or min_year\\n end_year = end_year or max_year\\n\\n # validate topics\\n parsed_topics = req_topics.split(',')\\n\\n for topic in parsed_topics:\\n if topic not in topics.keys():\\n raise ValueError('Topic \\\\'{topic}\\\\' not valid'.format(topic=topic))\\n\\n # validate startYear\\n try:\\n start_year = int(start_year)\\n except ValueError:\\n raise ValueError('startYear must be valid integer.')\\n\\n if start_year > max_year or start_year < min_year:\\n raise ValueError('startYear must be value between {min} and {max}.'.format(min=min_year, max=max_year))\\n\\n # validate endYear\\n try:\\n end_year = int(end_year)\\n except ValueError:\\n raise ValueError('endYear must be valid integer.')\\n\\n if end_year > max_year or end_year < min_year:\\n raise ValueError('endYear must be value between {min} and {max}.'.format(min=min_year, max=max_year))\\n\\n if start_year > end_year:\\n raise ValueError(\\\"startYear must not be greater than endYear.\\\")\\n\\n # aggregate data\\n data = list()\\n\\n for year in range(start_year, end_year+1):\\n for topic in parsed_topics:\\n tmp = fetch(topic, year)\\n tmp = normalize_confs(tmp, topic, year)\\n data.extend(tmp)\\n return data\",\n \"def _get_subscriptions(self, topic_arn):\\n return self.conn.get_all_subscriptions_by_topic(topic_arn)['ListSubscriptionsByTopicResponse']['ListSubscriptionsByTopicResult']['Subscriptions']\",\n \"def topics(self, project: str) -> list:\\n assert self.exists(project), f'Project {project} inesistente'\\n\\n cursor = self.projects(\\n {\\n 'url': project\\n }\\n )\\n try:\\n return cursor.next()['topics']\\n except StopIteration:\\n return []\",\n \"def get_teacher_topic_all():\\n topic_data = query_db(\\n \\\"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \\\"\\n \\\"ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n topics = []\\n for topic in topic_data:\\n topic_dict_teacher = {}\\n topic_dict_teacher[\\\"id\\\"] = topic[0]\\n topic_dict_teacher[\\\"name\\\"] = flask.escape(str(topic[1]))\\n topic_dict_teacher[\\\"class\\\"] = flask.escape(str(topic[2]))\\n topics.append(topic_dict_teacher)\\n return topics\",\n \"def describe_topics(self, topics=None):\\n return self._client.describe_topics(topics)\",\n \"def get_topics(self):\\r\\n return [x[0] for x in get_published_topics()]\",\n \"def extract_queries(self, path_topics=\\\"../topics-rnd5.xml\\\"):\\n \\n topic_queries = []\\n with open(path_topics, \\\"r\\\") as f:\\n for line in f:\\n match = re.match(\\\".*<query>([^<]*)<\\\\/query>.*\\\", line)\\n if match:\\n topic_queries.append(match.group(1))\\n if len(topic_queries) != 50:\\n sys.exit(\\\"There should be 50 topics, found {}\\\".format(\\n len(topic_queries)))\\n \\n return topic_queries\",\n \"def get_all_subscriptions(cls, **kwargs):\\n return Subscription.query.filter(**kwargs).all()\",\n \"async def subscribe(topics, on_close=None, on_error=None):\\n loop = asyncio.get_event_loop()\\n session = aiohttp.ClientSession()\\n async with session.ws_connect('wss://api.huobi.pro/ws') as ws:\\n keys = {\\n topic: uuid.uuid4().hex\\n for topic in topics\\n }\\n keyed_channels = {\\n v: topics[k]\\n for k, v in keys.items()\\n }\\n subscribed_chanels = {}\\n for topic, config in topics.items():\\n payload = {\\n 'sub': topic,\\n 'id': keys[topic]\\n }\\n await ws.send_str(encode_ws_payload(payload))\\n async for msg in ws:\\n if msg.type == aiohttp.WSMsgType.BINARY:\\n data = decode_ws_payload(msg.data)\\n\\n ping = data.get('ping')\\n if ping:\\n reply = encode_ws_payload({'pong': ping})\\n await ws.send_str(\\n reply\\n )\\n\\n subbed = data.get('subbed')\\n if subbed:\\n if data.get('status') == 'ok':\\n subscribed_chanels[subbed] = keyed_channels[data['id']]\\n\\n ch = data.get('ch')\\n if ch:\\n cb = subscribed_chanels[ch].get('callback', lambda _: None)\\n if asyncio.iscoroutinefunction(cb):\\n await cb(data)\\n else:\\n loop.run_in_executor(None, partial(cb, data))\\n elif msg.type == aiohttp.WSMsgType.CLOSED:\\n if on_close:\\n return on_close()\\n return\\n elif msg.type == aiohttp.WSMsgType.ERROR:\\n if on_error:\\n return on_error()\\n return\",\n \"def conferences(self):\\r\\n return conferences.Conferences(self)\",\n \"def list(self, topic, **options):\\n pass\",\n \"def test_get_full_topic_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.get_full_topic_courses(topic_id, course_id)\",\n \"def get_questions_of_topic(topic):\\n\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n fe = Attr(\\\"TopicId\\\").eq(topic.get(\\\"TopicId\\\"))\\n response = question_table.scan(FilterExpression=fe)\\n questions = response.get(\\\"Items\\\")\\n return questions\",\n \"def topics(ctx):\\n pass\",\n \"def topics(self):\\n # use get_model to avoid circular dependency\\n topic_model = apps.get_model('tags', 'Topic')\\n return topic_model.objects.filter(tag__in=self.tags.all()).distinct()\",\n \"def test_list_topic_entries_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_topic_entries_courses(topic_id, course_id)\",\n \"def add_topics(self, project: str, *topics: str):\\n assert self.exists(project), f'Project {project} inesistente'\\n\\n return self.collection.find_one_and_update(\\n {'url': project},\\n {\\n '$addToSet': {\\n 'topics': {\\n '$each': topics,\\n }\\n }\\n }\\n )\",\n \"def show_all_subjects(self):\\n self.load_subjects_in_twSubjects([self.pj[SUBJECTS][x][\\\"name\\\"] for x in self.pj[SUBJECTS]])\",\n \"def get_subscriptions(self, private_key, client_id):\\n return self._samp_hub.getSubscriptions(private_key, client_id)\",\n \"def _get_cloudwatch_subscriptions(self):\\n return self._get_subscriptions(self.cloudwatch_arn)\",\n \"def get_topics(self):\\n return self.client.cluster.topics()\",\n \"def remove_topics(self, project: str, *topics: str):\\n assert self.exists(project), f'Project {project} inesistente'\\n\\n return self.collection.find_one_and_update(\\n {\\n 'url': project\\n },\\n {\\n '$pull': {\\n 'topics': {\\n '$in': topics,\\n }\\n }\\n }\\n )\",\n \"def _request_search(self, query, topics, pages=1):\\n\\n query = query.replace(' ', '+')\\n url = 'https://google.com/search?q={}'.format(query)\\n\\n user_agent = \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36\\\"\\n\\n headers = {'user-agent': user_agent}\\n resp = requests.get(url, headers=headers)\\n\\n if resp.status_code == 200:\\n soup = BeautifulSoup(resp.content, \\\"html.parser\\\")\\n else:\\n raise Exception(\\\"Something's fucky\\\")\\n\\n items = []\\n\\n for result in soup.find_all('div', class_='rc'):\\n anchors = result.find_all('a')\\n if anchors:\\n link = anchors[0]['href']\\n title = result.find('h3').text\\n\\n topic_words = list({topic.text.lower() for topic in result.find_all('em') if topic.text.lower() in topics})\\n\\n item = {\\n 'title': title,\\n 'url': link,\\n 'topics': topic_words\\n }\\n items.append(item)\\n else:\\n raise Exception('No anchor to extract text from')\\n\\n return items\",\n \"def topics(self):\\n return topics.Topics(self)\",\n \"def subscribed_topics(self):\\n return self.manager.subscribed_topics\",\n \"def add_subscription_topics(self, topics: List[str]) -> None:\\n self.log.debug(f\\\"Adding {topics} to {self.topics}\\\")\\n self.topics.extend(topics)\",\n \"def get_queryset(self):\\n return models.ProfileTopic.objects.filter(\\n profile__pk=self.kwargs.get(\\\"pk\\\")\\n )\",\n \"def topics(self):\\r\\n return topics.Topics(self)\",\n \"def getSubscriptionTopics(self) -> List[str]:\\n return self.cpp.getSubscriptionTopics()\",\n \"def test_get_single_topic_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.get_single_topic_courses(topic_id, course_id)\",\n \"def list(cls, **kwargs):\\n response = Yola().list_subscriptions(**kwargs)\\n return [cls(**sub) for sub in response['results']]\",\n \"def list_subscriptions(\\n connection, project_id, fields=None, offset=0, limit=-1, error_msg=None\\n):\\n return connection.get(\\n url=f'{connection.base_url}/api/subscriptions',\\n params={'offset': offset, 'limit': limit, 'fields': fields},\\n headers={'X-MSTR-ProjectID': project_id},\\n )\",\n \"def list_courses_subjects(self, all=False):\\n q = {'facet': 'true',\\n 'facet.field': 'course_subject',\\n 'facet.mincount': '1',\\n 'facet.sort': 'index', # Sort alphabetically\\n 'rows': '0', # we don't need any actual document\\n }\\n if all:\\n q['q'] = '*:*'\\n else:\\n q['q'] = 'NOT presentation_start:[* TO NOW]'\\n results = searcher.search(q, start=0, count=1000) # Do not paginate\\n subjects = subjects_facet_to_subjects_domain(results)\\n return subjects\",\n \"def search_by_topic(query):\\n data = {'query': str(query)}\\n url = r'http://www.reddit.com/api/subreddits_by_topic.json'\\n response = r.get(url, data=data)\\n return response.json()['data']\",\n \"def get_subscriptions(self):\\n url = '{}/v2/subscriptions'.format(self.url)\\n r = requests.get(url, headers=self.headers_v2)\\n return r.json()\",\n \"def topics(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"topics\\\")\",\n \"def subscribedQueries(self):\\n return map(Query.get, self.subscriptions)\",\n \"def test_wiki_topics(self):\\n t1 = TopicFactory(slug='doesnotexist')\\n t2 = TopicFactory(slug='extant')\\n t3 = TopicFactory(slug='tagged')\\n\\n doc = DocumentFactory(locale=u'en-US', category=10)\\n doc.topics.add(t2)\\n RevisionFactory(document=doc, is_approved=True)\\n\\n doc = DocumentFactory(locale=u'en-US', category=10)\\n doc.topics.add(t2)\\n doc.topics.add(t3)\\n RevisionFactory(document=doc, is_approved=True)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n ([t2.slug, t3.slug], 1),\\n )\\n\\n qs = {'a': 1, 'w': 1, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def get_queryset(self, request):\\n qs = super(TopicAdmin, self).get_queryset(request)\\n if request.user.is_superuser:\\n return qs\\n return qs.filter(id__in=request.user.profile.topics.all())\",\n \"def search(self, text=None, topic_ids=None, per_page=None, page=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/{1}'.format(self.get_url(), 'search')\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def getConferencesToAttend(self, request):\\n prof = self._getProfileFromUser() # get user Profile\\n conf_keys = [\\n ndb.Key(urlsafe=wsck) for wsck in prof.conferenceKeysToAttend\\n ]\\n conferences = ndb.get_multi(conf_keys)\\n # Get organizers\\n organisers = [\\n ndb.Key(Profile, conf.organizerUserId) for conf in conferences\\n ]\\n profiles = ndb.get_multi(organisers)\\n # Put display names in a dict for easier fetching\\n names = {}\\n for profile in profiles:\\n names[profile.key.id()] = profile.displayName\\n # Return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[\\n self._copyConferenceToForm(conf, names[conf.organizerUserId])\\n for conf in conferences\\n ]\\n )\",\n \"def subscribers(id):\\n return core.query(schema.streamBySubscribers, id)\",\n \"def topic_list(request, forum_id, pagination_id=1):\\n\\ttry:\\n\\t\\ttopics = Topic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1'))\\n\\t\\tforum_name = Forum.objects.get(id=forum_id)\\n\\t\\tforum_name = forum_name.forum_name\\n\\texcept:\\n\\t\\treturn HttpResponseRedirect('/forum/')\\n\\treturn object_list(\\n\\t\\trequest,\\n\\t\\tTopic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1')),\\n\\t\\tpaginate_by = 10,\\n\\t\\tallow_empty = True,\\n\\t\\tpage = pagination_id,\\n\\t\\textra_context = {'forum': forum_id, 'perms': list_perms(request), 'forum_name': forum_name},\\n\\t\\ttemplate_name = 'myghtyboard/topics_list.html')\",\n \"def test_list_discussion_topics_courses(self):\\r\\n course_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_discussion_topics_courses(course_id, exclude_context_module_locked_topics=None, include=None, only_announcements=None, order_by=None, scope=None, search_term=None)\",\n \"def deleteTopics(self, topics):\\n if not topics:\\n return\\n topicsCArraySize = len(topics)\\n topicsCArray = internals.new_topicPtrArray(topicsCArraySize)\\n try:\\n for i, topic in enumerate(topics):\\n internals.topicPtrArray_setitem(topicsCArray,\\n i,\\n get_handle(topic))\\n _ExceptionUtil.raiseOnError(\\n internals.blpapi_ProviderSession_deleteTopics(\\n self.__handle,\\n topicsCArray,\\n topicsCArraySize))\\n finally:\\n internals.delete_topicPtrArray(topicsCArray)\",\n \"def get_subscriptions(self):\\n return self.subscriptions.all()\",\n \"def topics(self):\\r\\n return Topics(self)\",\n \"def terminateSubscriptionsOnTopics(self, topics, message=None):\\n if not topics:\\n return\\n topicsCArraySize = len(topics)\\n topicsCArray = internals.new_topicPtrArray(topicsCArraySize)\\n try:\\n for i, topic in enumerate(topics):\\n internals.topicPtrArray_setitem(topicsCArray,\\n i,\\n get_handle(topic))\\n _ExceptionUtil.raiseOnError(\\n internals.blpapi_ProviderSession_terminateSubscriptionsOnTopics(\\n self.__handle,\\n topicsCArray,\\n topicsCArraySize,\\n message))\\n finally:\\n internals.delete_topicPtrArray(topicsCArray)\",\n \"def test_get_posts_by_topic(self):\\n data = {'topic': self.topic.id}\\n response = self.client.get(reverse('api:posts-list'), data)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(len(response.data.get('results')), 2)\",\n \"def load_metadata_for_topics(self, *topics):\\n topics = tuple(_coerce_topic(t) for t in topics)\\n log.debug(\\\"%r: load_metadata_for_topics: %r\\\", self, topics)\\n fetch_all_metadata = not topics\\n\\n # create the request\\n requestId = self._next_id()\\n request = KafkaCodec.encode_metadata_request(self._clientIdBytes,\\n requestId, topics)\\n\\n # Callbacks for the request deferred...\\n def _handleMetadataResponse(response):\\n # Decode the response\\n (brokers, topics) = \\\\\\n KafkaCodec.decode_metadata_response(response)\\n log.debug(\\\"%r: Broker/Topic metadata: %r/%r\\\",\\n self, brokers, topics)\\n\\n # If we fetched the metadata for all topics, then store away the\\n # received metadata for diagnostics.\\n if fetch_all_metadata:\\n self._brokers = brokers\\n self._topics = topics\\n\\n # Iff we were fetching for all topics, and we got at least one\\n # broker back, then remove brokers when we update our brokers\\n ok_to_remove = (fetch_all_metadata and len(brokers))\\n # Take the metadata we got back, update our self.clients, and\\n # if needed disconnect or connect from/to old/new brokers\\n self._update_brokers(\\n [(nativeString(b.host), b.port) for b in brokers.values()],\\n remove=ok_to_remove,\\n )\\n\\n # Now loop through all the topics/partitions in the response\\n # and setup our cache/data-structures\\n for topic, topic_metadata in topics.items():\\n _, topic_error, partitions = topic_metadata\\n self.reset_topic_metadata(topic)\\n self.topic_errors[topic] = topic_error\\n if not partitions:\\n log.warning('No partitions for %s, Err:%d',\\n topic, topic_error)\\n continue\\n\\n self.topic_partitions[topic] = []\\n for partition, meta in partitions.items():\\n self.topic_partitions[topic].append(partition)\\n topic_part = TopicAndPartition(topic, partition)\\n self.partition_meta[topic_part] = meta\\n if meta.leader == -1:\\n log.warning('No leader for topic %s partition %s',\\n topic, partition)\\n self.topics_to_brokers[topic_part] = None\\n else:\\n self.topics_to_brokers[\\n topic_part] = brokers[meta.leader]\\n self.topic_partitions[topic] = sorted(\\n self.topic_partitions[topic])\\n return True\\n\\n def _handleMetadataErr(err):\\n # This should maybe do more cleanup?\\n if err.check(t_CancelledError, CancelledError):\\n # Eat the error\\n return None\\n log.error(\\\"Failed to retrieve metadata:%s\\\", err)\\n raise KafkaUnavailableError(\\n \\\"Unable to load metadata from configured \\\"\\n \\\"hosts: {!r}\\\".format(err))\\n\\n # Send the request, add the handlers\\n d = self._send_broker_unaware_request(requestId, request)\\n d.addCallbacks(_handleMetadataResponse, _handleMetadataErr)\\n return d\",\n \"def get_topics_articles(topic_id):\\n articles = db_session.query(Article).filter_by(topic_id=topic_id)\\n return jsonify([p.serialize for p in articles])\",\n \"def list_subscriptions_async(\\n future_session: \\\"FuturesSession\\\",\\n connection,\\n project_id,\\n fields=None,\\n offset=0,\\n limit=-1,\\n):\\n params = {'offset': offset, 'limit': limit, 'fields': fields}\\n url = f'{connection.base_url}/api/subscriptions'\\n headers = {'X-MSTR-ProjectID': project_id}\\n\\n return future_session.get(url=url, headers=headers, params=params)\",\n \"def getGroupTopics(group_id): \\r\\n return Group.getGroupTopics(group_id)\",\n \"def get_documents(self, subtypes=None, refresh=False):\\n search = ScopusSearch('au-id({})'.format(self.identifier), refresh)\\n if subtypes:\\n return [p for p in search.results if p.subtype in subtypes]\\n else:\\n return search.results\",\n \"def getAllSubscriptions(self):\\n return self.request(\\n \\\"getAllSubscriptions\\\",\\n )\",\n \"def test_list_entries_courses(self):\\r\\n course_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_entries_courses(topic_id, course_id, ids=None)\",\n \"def load_channels(archived=False):\\n if not settings.SLACK_TOKEN:\\n return {'ok': False, 'error': 'config_error'}\\n\\n client = WebClient(token=settings.SLACK_TOKEN)\\n\\n try:\\n response = client.conversations_list(exclude_archived=not archived)\\n assert response['ok'] is True\\n\\n channels = []\\n for channel in response['channels']:\\n channels.append((channel['id'], channel['name']))\\n\\n return {'ok': True, 'channels': channels}\\n except SlackApiError as e:\\n assert e.response['ok'] is False\\n return e.response\",\n \"def get_queryset(self):\\n return models.ProfileItem.objects.filter(topic=self.kwargs.get(\\\"pk\\\"))\",\n \"def topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"topics\\\")\",\n \"def list_subscriptions(self, user_token):\\n response = _request('GET',\\n url=self.url_v1('/user/subscriptions'),\\n user_agent=self.user_agent,\\n user_token=user_token,\\n )\\n _raise_for_status(response)\\n\\n return response.json()['topics']\",\n \"def test_question_topics(self):\\n p = ProductFactory()\\n t1 = TopicFactory(slug='doesnotexist', product=p)\\n t2 = TopicFactory(slug='cookies', product=p)\\n t3 = TopicFactory(slug='sync', product=p)\\n\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t3)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n )\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def get_topic_terms_df(self, topics):\\n labels = list(topics.keys())\\n \\n topic_terms = []\\n for topic, top_n_words in topics.items():\\n top_n_words = sorted(top_n_words, key=lambda x: x[1], reverse=True)[:self.num_words]\\n terms = [term for term, c_tf_idf in top_n_words]\\n terms = \\\", \\\".join(terms)\\n topic_terms.append(terms)\\n\\n topic_terms_df = pd.DataFrame()\\n topic_terms_df['id'] = labels\\n topic_terms_df['Topic terms'] = topic_terms\\n return topic_terms_df\",\n \"def test_get_all_topics(mock_send_message_json):\\n assert OranDmaap.get_all_topics_url == f\\\"{BASE_URL}/topics/listAll\\\"\",\n \"def get_tweets_by_topic(topic, start_date, end_date):\\n try:\\n query = f\\\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\\\"\\n logger.info(f'QUERY: {query}') \\n with MysqlCursor() as cur:\\n cur.execute(query)\\n tweets = cur.fetchall()\\n columns = [col[0] for col in cur.description]\\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \\n return tweets, columns\\n \\n except Exception as ex:\\n logger.exception(ex)\\n return f'Exception: {ex}'\",\n \"def get_subscribed_clients(self, private_key, mtype):\\n return self._samp_hub.getSubscribedClients(private_key, mtype)\",\n \"def get_subscriptions(self, use_threading=False):\\r\\n \\r\\n if self._subscriptions is None:\\r\\n if use_threading:\\r\\n self.load_subscriptions_threaded()\\r\\n else:\\r\\n self._subscriptions = []\\r\\n for page in range(self._subscription_pages):\\r\\n self._load_subscriptions(page=page+1)\\r\\n return self._subscriptions\",\n \"def fetchAllThreads(self):\\n\\n catalog = getToolByName(self.context, 'portal_catalog')\\n return [ dict(url=thread.getURL(),\\n title=thread.Title,\\n author=thread.Creator,\\n createDate = thread.created.strftime('%Y/%m/%d'),)\\n for thread in \\n catalog({'object_provides': Itopic.__identifier__,\\n 'path': dict(query='/'.join(self.context.getPhysicalPath()),\\n depth=1),\\n 'sort_on': 'sortable_title'})\\n ]\",\n \"def print_topics(self, time=0, top_terms=20):\\n return [self.print_topic(topic, time, top_terms) for topic in range(self.num_topics)]\",\n \"def test_get_full_topic_groups(self):\\r\\n group_id = None # Change me!!\\r\\n topic_id = None # Change me!!\\r\\n\\r\\n r = self.client.get_full_topic_groups(group_id, topic_id)\",\n \"def topics(self):\\r\\n return contents.Topics(self)\",\n \"def receive_many(self, channels):\\n if not channels:\\n raise ValueError(\\\"Cannot receive on empty channel list!\\\")\\n res = (None, None)\\n while True:\\n message = self.pubsub.get_message() #peek at subscribed channels\\n if message and message.get('type',None) in ['message','pmessage']:\\n try:\\n res = (message.get('channel','')[len(self.prefix + self.sub_prefix):],json.loads(message.get('data',None)))\\n break\\n except:\\n #log error and ignore it!!!\\n pass \\n result = self.connection.blpop([self.prefix + channel for channel in channels], timeout=1)\\n if result:\\n content = self.connection.get(result[1])\\n if content is None:\\n continue\\n res = (result[0][len(self.prefix):], json.loads(content))\\n break\\n else:\\n break\\n return res\",\n \"def test_topics_for_products(self):\\n desktop_topics = topics_for(product=self.desktop)\\n eq_(len(desktop_topics), 3)\\n\\n mobile_topics = topics_for(product=self.mobile)\\n eq_(len(mobile_topics), 2)\",\n \"def GetSubscriptions(self):\\n\\n return self.__GetJson(\\\"/subscriptions\\\", True)\",\n \"def validate_new_curriculum_topics(self, curriculum_topics):\\n\\n for cur in curriculum_topics:\\n # check to make sure its in the general topics table\\n self.db_cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM Topic WHERE name = %s\\\"\\\"\\\", (cur,))\\n ct = self.db_cursor.fetchone()\\n ct = ct[0]\\n if ct == 0:\\n print(\\\"topic does not exist, we must create new one or cancel\\\") # todo\\n\\n return True\",\n \"def get_all_subscriptions(self, next_token=None):\\r\\n params = {'ContentType' : 'JSON'}\\r\\n if next_token:\\r\\n params['NextToken'] = next_token\\r\\n response = self.make_request('ListSubscriptions', params, '/', 'GET')\\r\\n body = response.read()\\r\\n if response.status == 200:\\r\\n return json.loads(body)\\r\\n else:\\r\\n boto.log.error('%s %s' % (response.status, response.reason))\\r\\n boto.log.error('%s' % body)\\r\\n raise self.ResponseError(response.status, response.reason, body)\",\n \"def get_cloudwatch_email_subscriptions(self):\\n return map(lambda subscription: subscription['Endpoint'], self._get_cloudwatch_subscriptions())\",\n \"def get_subscriptions(self, client_id):\\n return self.hub.get_subscriptions(self.get_private_key(), client_id)\",\n \"def filter_content_by_topic(self, topic, content_type=None):\\n\\n if content_type is None:\\n is_article, is_podcast, is_video = True, True, True\\n else:\\n is_article, is_podcast, is_video = self.get_content_type(content_type)\\n\\n try:\\n response = {\\\"status\\\": \\\"ok\\\"}\\n if is_article:\\n db_articles = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_ARTICLES])\\n response[\\\"articles\\\"] = list(db_articles.find({\\\"category\\\": topic}, {'_id': 0}))\\n if is_podcast:\\n db_podcasts = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_PODCASTS])\\n response[\\\"podcasts\\\"] = list(db_podcasts.find({\\\"category\\\": topic}, {'_id': 0}))\\n if is_video:\\n db_videos = self.db_connector.connect_to_collection(self.db_config[MONGODB][COLLECTION_VIDEOS])\\n response[\\\"videos\\\"] = list(db_videos.find({\\\"category\\\": topic}, {'_id': 0}))\\n return response\\n except:\\n return {\\\"status\\\": \\\"bad\\\"}\",\n \"def topics(self):\\r\\n return ProjectTopics(self)\",\n \"def get_core_topics_paged_get(self, categoryFilter, locales, page, quickDate, sort):\\n # TODO: Assuming first server is good - need to make fallback logic\\n return self.session.get_any(\\\"{base}{request_url}\\\".format(base=self.servers[0],\\n request_url=F\\\"/Forum/GetCoreTopicsPaged/{page}/{sort}/{quickDate}/{categoryFilter}/\\\"))\",\n \"def test_topic_retrieval_authenticated(self):\\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.free_token.key)\\n response = self.client.get('/topics/', format='json')\\n data = json.loads(response.content)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(data['count'],3)\\n self.assertTrue({'name': 'Topic 1', 'description': 'The first topic.'} in data['results'])\\n self.assertTrue({'name': 'Topic 2', 'description': 'The second topic.'} in data['results'])\",\n \"def get_speakers(self, request):\\n return self.speaker_service.get_speakers()\",\n \"def subscribe(self, topic_type, tx_filter=None):\\n\\n request = protos.RequestSubscribe(type=topic_type, filter=tx_filter)\\n res = self.stub.subscribe(request)\\n for r in res:\\n yield r\",\n \"def getConferencesCreated(self, request):\\n # make sure user is authed\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException('Authorization required')\\n user_id = getUserId(user)\\n\\n # create ancestor query for all key matches for this user\\n confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\\n prof = ndb.Key(Profile, user_id).get()\\n # return set of ConferenceForm objects per Conference\\n return ConferenceForms(\\n items=[self._copyConferenceToForm(conf, getattr(prof, 'displayName')) for conf in confs]\\n )\",\n \"def test_get_topics(self):\\n\\n for m in self.models:\\n\\n topics = m.topics\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertEqual(topics.num_rows(), 25)\\n self.assertEqual(topics.num_columns(), 2)\\n z = m.topics[\\\"topic_probabilities\\\"]\\n for k in range(m.num_topics):\\n self.assertTrue(\\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\\n \\\"Returned probabilities do not sum to 1.\\\",\\n )\\n\\n # Make sure returned object is an SFrame of the right size\\n topics = m.get_topics()\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertTrue(\\n topics.num_columns() == 3,\\n \\\"Returned SFrame should have a topic, word, and probs.\\\",\\n )\\n\\n # Make sure that requesting a single topic returns only that topic\\n num_words = 8\\n topics = m.get_topics([5], num_words=num_words)\\n self.assertTrue(\\n all(topics[\\\"topic\\\"] == 5), \\\"Returned topics do not have the right id.\\\"\\n )\\n self.assertEqual(topics.num_rows(), num_words)\\n topics = m.get_topics([2, 4], num_words=num_words)\\n self.assertEqual(set(list(topics[\\\"topic\\\"])), set([2, 4]))\\n self.assertEqual(topics.num_rows(), num_words + num_words)\\n\\n # Make sure the cumulative probability of the returned words is\\n # is less than the cutoff we provided.\\n # A cutoff of 1.0 should return num_words for every topic.\\n cutoff = 1.0\\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\\n totals = topics.groupby(\\n \\\"topic\\\", {\\\"total_score\\\": turicreate.aggregate.SUM(\\\"score\\\")}\\n )\\n self.assertTrue(\\n all(totals[\\\"total_score\\\"] <= (cutoff + DELTA)),\\n \\\"More words were returned than expected for this cutoff.\\\",\\n )\\n\\n # Make sure we raise errors for bad input\\n with self.assertRaises(ValueError):\\n m.get_topics([-1])\\n with self.assertRaises(ValueError):\\n m.get_topics([10000])\\n with self.assertRaises(ToolkitError):\\n topics = m.get_topics(output_type=\\\"other\\\")\\n\\n # Test getting topic_words\\n topic_words = m.get_topics(output_type=\\\"topic_words\\\", num_words=5)\\n self.assertEqual(type(topic_words), turicreate.SFrame)\\n\\n # Test words are sorted correctly for the first topic\\n # TODO: Make this more deterministic.\\n\\n # topic_probs = m.get_topics(num_words=5)\\n # expected = [w for w in topic_probs['word'][:5]]\\n # observed = topic_words['words'][0]\\n # self.assertEqual(observed[0], expected[0])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6711613","0.6230136","0.61121726","0.60039115","0.5985098","0.5940796","0.58975244","0.58861005","0.5882982","0.5880553","0.5836876","0.58033025","0.57397395","0.5726651","0.5705699","0.5686112","0.56527764","0.56140727","0.54820526","0.5472352","0.54712605","0.53320074","0.5298793","0.5282408","0.5267355","0.5241664","0.5239345","0.52040243","0.51683694","0.51324224","0.5132087","0.51036483","0.51024044","0.5089826","0.50810206","0.50707656","0.5045029","0.5026609","0.50242895","0.5018048","0.50071496","0.5003375","0.5003369","0.4956228","0.4947688","0.49446687","0.49261212","0.49169797","0.48942623","0.4885435","0.4879033","0.48723584","0.48717862","0.48714942","0.4865386","0.48550725","0.48453966","0.48451662","0.48366064","0.48346323","0.48280737","0.48231262","0.4819161","0.48170632","0.4815823","0.479898","0.4796844","0.4791071","0.47763398","0.4768584","0.4765588","0.47651374","0.47375062","0.47241065","0.47168544","0.47144443","0.47111958","0.4695463","0.4693845","0.46878472","0.46819243","0.467741","0.46734202","0.46712178","0.46658522","0.46637484","0.46619973","0.46618629","0.4660981","0.46580517","0.4652288","0.46506158","0.46320686","0.4630503","0.46225047","0.46209887","0.46071228","0.46017197","0.46007326","0.46000087"],"string":"[\n \"0.6711613\",\n \"0.6230136\",\n \"0.61121726\",\n \"0.60039115\",\n \"0.5985098\",\n \"0.5940796\",\n \"0.58975244\",\n \"0.58861005\",\n \"0.5882982\",\n \"0.5880553\",\n \"0.5836876\",\n \"0.58033025\",\n \"0.57397395\",\n \"0.5726651\",\n \"0.5705699\",\n \"0.5686112\",\n \"0.56527764\",\n \"0.56140727\",\n \"0.54820526\",\n \"0.5472352\",\n \"0.54712605\",\n \"0.53320074\",\n \"0.5298793\",\n \"0.5282408\",\n \"0.5267355\",\n \"0.5241664\",\n \"0.5239345\",\n \"0.52040243\",\n \"0.51683694\",\n \"0.51324224\",\n \"0.5132087\",\n \"0.51036483\",\n \"0.51024044\",\n \"0.5089826\",\n \"0.50810206\",\n \"0.50707656\",\n \"0.5045029\",\n \"0.5026609\",\n \"0.50242895\",\n \"0.5018048\",\n \"0.50071496\",\n \"0.5003375\",\n \"0.5003369\",\n \"0.4956228\",\n \"0.4947688\",\n \"0.49446687\",\n \"0.49261212\",\n \"0.49169797\",\n \"0.48942623\",\n \"0.4885435\",\n \"0.4879033\",\n \"0.48723584\",\n \"0.48717862\",\n \"0.48714942\",\n \"0.4865386\",\n \"0.48550725\",\n \"0.48453966\",\n \"0.48451662\",\n \"0.48366064\",\n \"0.48346323\",\n \"0.48280737\",\n \"0.48231262\",\n \"0.4819161\",\n \"0.48170632\",\n \"0.4815823\",\n \"0.479898\",\n \"0.4796844\",\n \"0.4791071\",\n \"0.47763398\",\n \"0.4768584\",\n \"0.4765588\",\n \"0.47651374\",\n \"0.47375062\",\n \"0.47241065\",\n \"0.47168544\",\n \"0.47144443\",\n \"0.47111958\",\n \"0.4695463\",\n \"0.4693845\",\n \"0.46878472\",\n \"0.46819243\",\n \"0.467741\",\n \"0.46734202\",\n \"0.46712178\",\n \"0.46658522\",\n \"0.46637484\",\n \"0.46619973\",\n \"0.46618629\",\n \"0.4660981\",\n \"0.46580517\",\n \"0.4652288\",\n \"0.46506158\",\n \"0.46320686\",\n \"0.4630503\",\n \"0.46225047\",\n \"0.46209887\",\n \"0.46071228\",\n \"0.46017197\",\n \"0.46007326\",\n \"0.46000087\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.790961"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18498,"cells":{"query":{"kind":"string","value":"Return formatted query from the submitted filters."},"document":{"kind":"string","value":"def _getQuery(self, request):\n q = Conference.query()\n inequality_filter, filters = self._formatFilters(request.filters)\n # If exists, sort on inequality filter first\n if not inequality_filter:\n q = q.order(Conference.name)\n else:\n q = q.order(ndb.GenericProperty(inequality_filter))\n q = q.order(Conference.name)\n for filtr in filters:\n if filtr[\"field\"] in [\"month\", \"maxAttendees\"]:\n try:\n filtr[\"value\"] = int(filtr[\"value\"])\n except ValueError:\n raise endpoints.BadRequestException(\n \"Non-integer in integer field.\")\n formatted_query = ndb.query.FilterNode(\n filtr[\"field\"], filtr[\"operator\"], filtr[\"value\"])\n q = q.filter(formatted_query)\n return q"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_query(self):\n columns = ','.join(['\"{}\"'.format(x) for x in self.columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self.table)\n filter_params = []\n if self.filters:\n filter_sql, filter_params = filter_postgis(self.filters)\n query += ' WHERE {}'.format(filter_sql)\n query += ';'\n return str(text(query)), filter_params","def __get_filter_regex(self) -> str:\n return '(' + ')|('.join(self.__QUERY_FILTERS) + ')'","def _formatFilters(self, filters):\n formatted_filters = []\n inequality_field = None\n for f in filters:\n filtr = {\n field.name: getattr(f, field.name) for field in f.all_fields()\n }\n try:\n filtr[\"field\"] = FIELDS[filtr[\"field\"]]\n filtr[\"operator\"] = OPERATORS[filtr[\"operator\"]]\n except KeyError:\n raise endpoints.BadRequestException(\n \"Filter contains invalid field or operator.\")\n # Every operation except \"=\" is an inequality\n if filtr[\"operator\"] != \"=\":\n # Check if inequality operation has been used in previous\n # filters disallow the filter if inequality was performed on a\n # different field before. Track the field on which the\n # inequality operation is performed.\n if inequality_field and inequality_field != filtr[\"field\"]:\n raise endpoints.BadRequestException(\n \"Inequality filter is allowed on only one field.\")\n else:\n inequality_field = filtr[\"field\"]\n formatted_filters.append(filtr)\n return (inequality_field, formatted_filters)","def get_query(self):\n columns = ','.join(['\"{}\"'.format(x) for x in self._columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self._table)\n filter_params = []\n if self._filters:\n filter_sql, filter_params = filter_postgis(self._filters)\n query += ' WHERE {}'.format(filter_sql)\n query += ';'\n return str(text(query)), filter_params","def _formatFilters(self, filters):\n formatted_filters = []\n inequality_field = None\n\n for f in filters:\n filtr = {field.name: getattr(f, field.name) for field in f.all_fields()}\n\n try:\n filtr[\"field\"] = FIELDS[filtr[\"field\"]]\n filtr[\"operator\"] = OPERATORS[filtr[\"operator\"]]\n except KeyError:\n raise endpoints.BadRequestException(\"Filter contains invalid field or operator.\")\n\n # Every operation except \"=\" is an inequality\n if filtr[\"operator\"] != \"=\":\n # check if inequality operation has been used in previous filters\n # disallow the filter if inequality was performed on a different field before\n # track the field on which the inequality operation is performed\n if inequality_field and inequality_field != filtr[\"field\"]:\n raise endpoints.BadRequestException(\"Inequality filter is allowed on only one field.\")\n else:\n inequality_field = filtr[\"field\"]\n\n formatted_filters.append(filtr)\n return (inequality_field, formatted_filters)","def get_raw_query(self):\n # create a raw query\n if len(self.criteria) > 1:\n # more than one criteria, create a AND query\n raw_query = {\"$and\": self.criteria}\n else:\n # one criteria, raw query is the criteria\n raw_query = self.criteria[0]\n\n return raw_query","def make_sql_condition_string(self, field, condition, value, filter_type):\n\n if field == 'no filters': \n return ''\n else: \n if filter_type in ('metric', 'attribute', 'event'):\n # for < (less than) filters on metrics, we need to use a \"having\" filter with an aggregation (only MAX for now) on the metric\n if (filter_type == 'metric'):\n return 'AND ' + 'MAX(cumul_filter_metric)' + ' ' + condition + ' ' + value + ''\n else:\n return 'AND ' + field + ' ' + condition + ' ' + value + ''\n else:\n raise NotImplementedError()","def _build_filter_part(self, cls, filters, order_by=None, select=None):\r\n import types\r\n query_parts = []\r\n\r\n order_by_filtered = False\r\n\r\n if order_by:\r\n if order_by[0] == \"-\":\r\n order_by_method = \"DESC\";\r\n order_by = order_by[1:]\r\n else:\r\n order_by_method = \"ASC\";\r\n\r\n if select:\r\n if order_by and order_by in select:\r\n order_by_filtered = True\r\n query_parts.append(\"(%s)\" % select)\r\n\r\n if isinstance(filters, str) or isinstance(filters, unicode):\r\n query = \"WHERE %s AND `__type__` = '%s'\" % (filters, cls.__name__)\r\n if order_by in [\"__id__\", \"itemName()\"]:\r\n query += \" ORDER BY itemName() %s\" % order_by_method\r\n elif order_by != None:\r\n query += \" ORDER BY `%s` %s\" % (order_by, order_by_method)\r\n return query\r\n\r\n for filter in filters:\r\n filter_parts = []\r\n filter_props = filter[0]\r\n if type(filter_props) != list:\r\n filter_props = [filter_props]\r\n for filter_prop in filter_props:\r\n (name, op) = filter_prop.strip().split(\" \", 1)\r\n value = filter[1]\r\n property = cls.find_property(name)\r\n if name == order_by:\r\n order_by_filtered = True\r\n if types.TypeType(value) == types.ListType:\r\n filter_parts_sub = []\r\n for val in value:\r\n val = self.encode_value(property, val)\r\n if isinstance(val, list):\r\n for v in val:\r\n filter_parts_sub.append(self._build_filter(property, name, op, v))\r\n else:\r\n filter_parts_sub.append(self._build_filter(property, name, op, val))\r\n filter_parts.append(\"(%s)\" % (\" OR \".join(filter_parts_sub)))\r\n else:\r\n val = self.encode_value(property, value)\r\n if isinstance(val, list):\r\n for v in val:\r\n filter_parts.append(self._build_filter(property, name, op, v))\r\n else:\r\n filter_parts.append(self._build_filter(property, name, op, val))\r\n query_parts.append(\"(%s)\" % (\" or \".join(filter_parts)))\r\n\r\n\r\n type_query = \"(`__type__` = '%s'\" % cls.__name__\r\n for subclass in self._get_all_decendents(cls).keys():\r\n type_query += \" or `__type__` = '%s'\" % subclass\r\n type_query +=\")\"\r\n query_parts.append(type_query)\r\n\r\n order_by_query = \"\"\r\n\r\n if order_by:\r\n if not order_by_filtered:\r\n query_parts.append(\"`%s` LIKE '%%'\" % order_by)\r\n if order_by in [\"__id__\", \"itemName()\"]:\r\n order_by_query = \" ORDER BY itemName() %s\" % order_by_method\r\n else:\r\n order_by_query = \" ORDER BY `%s` %s\" % (order_by, order_by_method)\r\n\r\n if len(query_parts) > 0:\r\n return \"WHERE %s %s\" % (\" AND \".join(query_parts), order_by_query)\r\n else:\r\n return \"\"","def make_filter_string(cls, filter_specification):\n registry = get_current_registry()\n visitor_cls = registry.getUtility(IFilterSpecificationVisitor,\n name=EXPRESSION_KINDS.CQL)\n visitor = visitor_cls()\n filter_specification.accept(visitor)\n return str(visitor.expression)","def _getQuery(self, request):\n q = Conference.query()\n inequality_filter, filters = self._formatFilters(request.filters)\n\n # If exists, sort on inequality filter first\n if not inequality_filter:\n q = q.order(Conference.name)\n else:\n q = q.order(ndb.GenericProperty(inequality_filter))\n q = q.order(Conference.name)\n\n for filtr in filters:\n if filtr[\"field\"] in [\"month\", \"maxAttendees\"]:\n filtr[\"value\"] = int(filtr[\"value\"])\n formatted_query = ndb.query.FilterNode(filtr[\"field\"], filtr[\"operator\"], filtr[\"value\"])\n q = q.filter(formatted_query)\n return q","def get_filter(self) -> str:\n\n return \";;\".join(self.filters)","def format(query: str) -> str:\n return query.format(**Constants.to_dict())","def format_query(query):\n query = query.encode('utf-8')\n # all queries must start with 'c '!\n if not query.startswith('c '):\n query = 'c ' + query.strip()\n # check if outcome of query is set to '?'\n if query[-1] != '?':\n query = query + ' ?'\n return query + '\\n'","def _custom_filter(self, query):\r\n return query","def __str__(self):\n query = []\n\n if self.root:\n query.append('query')\n\n if self.alias is not None:\n query.append(self.alias + ':')\n\n if self.name is not None:\n query.append(str(self.name))\n\n if self.filters:\n query.append('(')\n for k,v in self.filters.items():\n query.append('{k}:\"{v}\"'.format(k=k, v=v))\n query.append(')')\n\n if self.children:\n query.append('{')\n if self.children:\n for idx, f in enumerate(self.children.values()):\n query.append(str(f))\n if idx < len(self.children) - 1:\n query.append(',')\n\n query.append('}')\n\n return ''.join(query)","def getQuery(self):\n # Get a list of object managers, each of which containing the\n # corresponding view and filter set filter field objects of all\n # available filter set classes.\n filter_field_managers = [\n manager for manager in self.getFilterSetFilterFieldManagers()\n ] + [\n manager for manager in self.view.getViewFilterFieldManagers()\n ]\n\n # Create an OR query for all filter fields of the same class\n or_queries = []\n for manager in filter_field_managers:\n filter_fields = manager.all()\n if filter_fields:\n or_queries.append(\n reduce(lambda x, y: x | y, [\n filter_field.getRecordFilter()\n for filter_field in filter_fields\n ]\n )\n )\n\n # If there are different filter field OR queries, combine those\n # queries as one AND query\n if or_queries:\n return reduce(lambda x, y: x & y, [\n or_query for or_query in or_queries\n ]\n )\n # If the filter set does not have any filter fields, we return an empty\n # query, which is equivalent to querying all objects, e.g.:\n # `View.objects.all() == View.objects.filter(Q())`\n else:\n return Q()","def _build_query(self, **query):\n\n available_fields = list(self.model._available_fields.keys())\n\n q_str = ''\n for key, val in list(query.items()):\n # Get the field and the operator from the query\n key_field, key_sep, key_oper = key.partition('__')\n\n if key_field == 'pk' and 'id' not in query:\n key_field = 'id'\n\n if key_field in available_fields:\n if q_str != '':\n q_str += ' AND '\n\n if_cstm = ''\n if key_field.endswith('_c'):\n if_cstm = '_cstm'\n\n field = self.model._table + if_cstm + '.' + key_field\n\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\n q_str += '%s = \"%s\"' % (field, val)\n elif key_oper == 'contains':\n q_str += '%s LIKE \"%%%s%%\"' % (field, val)\n elif key_oper == 'startswith':\n q_str += '%s LIKE \"%s%%\"' % (field, val)\n elif key_oper == 'in':\n q_str += '%s IN (' % field\n for elem in val:\n q_str += \"'%s',\" % elem\n q_str = q_str.rstrip(',')\n q_str += ')'\n elif key_oper == 'gt':\n q_str += '%s > \"%s\"' % (field, val)\n elif key_oper == 'gte':\n q_str += '%s >= \"%s\"' % (field, val)\n elif key_oper == 'lt':\n q_str += '%s < \"%s\"' % (field, val)\n elif key_oper == 'lte':\n q_str += '%s <= \"%s\"' % (field, val)\n else:\n raise LookupError('Unsupported operator')\n\n return q_str","def get_query(self):\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\n for prop, value in self.FILTERS:\n q.filter(\"%s =\" % prop, value)\n if self.ancestor:\n q.ancestor(self.ancestor)\n q.order(self.ORDER_BY)\n return q","def _get_query(self):\n\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\n\n query_string = \"select \" + \",\".join(ENDPOINT_QUERY_FIELDS[endpoint])\n\n # ZOQL does not support the `order by` sorting\n query_string = query_string + \" from {} where UpdatedDate > '{}'\".format(\n endpoint,\n marker\n )\n\n return query_string","def build_query_structure(self):\n query_list = list()\n filter_list = list()\n for key, val in self.q_dict.items():\n if key in self.es_query_keys:\n query_list.append(\n {\"match\": {\".\".join(key.split(\"_\")): val[0]}})\n elif key in self.es_date_keys:\n filter_list.append(\n {\"range\": {\".\".join(key.split(\"_\")): val}})\n elif \":\" in val[0]:\n #for handling queries like dd_dct=gte:1\n range_val = val[0].split(\":\")\n filter_list.append({\"range\": {\".\".join(key.split(\"_\")): {\n range_val[0]: int(range_val[1])}}})\n else:\n filter_list.append(\n {\"terms\": {\".\".join(key.split(\"_\")): val}})\n return query_list, filter_list","def build_query(self):\r\n\r\n # this filter is required\r\n query = Metric.query.filter(Metric.metric_type == self.metric_type)\r\n\r\n # get query parameters (parameters which are not here are ignored)\r\n is_active = request.args.get('is_active')\r\n frequency = request.args.get('frequency')\r\n threshold_type = request.args.get('threshold_type')\r\n sort = request.args.get('sort')\r\n\r\n # process each parameter, and if valid add it as a query condition\r\n if is_active is not None:\r\n is_active = is_active.lower() == 'true'\r\n query = Metric.query.filter_by(is_active=is_active)\r\n if frequency is not None:\r\n try:\r\n frequency = Frequency.from_name(frequency)\r\n except ValueError as e:\r\n msg = f\"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}\"\r\n abort(400, message=msg)\r\n query = query.filter_by(frequency=frequency)\r\n if threshold_type is not None:\r\n try:\r\n threshold_type = ThresholdType.from_name(threshold_type)\r\n except ValueError as e:\r\n msg = f\"Invalid 'threshold_type': {threshold_type}. Use one of \" \\\r\n f\"{ThresholdType.values()}\"\r\n abort(400, message=msg)\r\n query = query.filter_by(threshold_type=threshold_type)\r\n if sort is not None and sort.lstrip(\"-\") == 'metric_id':\r\n query = query.order_by(Metric.metric_id.desc())\r\n else:\r\n query = query.order_by(Metric.metric_id)\r\n\r\n return query","def _query_builder(self,\n start_date=None,\n end_date=None,\n clipper=None\n ):\n search_string='format=json&lang=fr&q='\n if (start_date is not None):\n if(end_date is not None):\n search_string+='entre+'+start_date+'+et+'+end_date\n else:\n search_string+=start_date\n elif(end_date is not None): \n search_string+=end_date\n \n if(clipper.query is not None):\n query=clipper.query.replace(' ','+')\n search_string+='+'+query\n if(clipper.bbox is not None):\n search_string+='&box='+clipper.bbox\n\n return search_string","def build_query(self):\r\n # build query from base class add required field for joining with parent\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\r\n\r\n # get the remaining query parameters\r\n asset_class = request.args.get('asset_class')\r\n model_name = request.args.get('model_name')\r\n pricing_library = request.args.get('pricing_library')\r\n\r\n # process each parameter and, if valid, add as a query condition\r\n if asset_class is not None:\r\n query = query.filter(QuantModelMetric.asset_class == asset_class)\r\n if model_name is not None:\r\n query = query.filter(QuantModelMetric.model_name == model_name)\r\n if pricing_library is not None:\r\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\r\n return query","def _build_where_clause(**kwds_filter):\n clause = []\n params = []\n items = kwds_filter.items()\n items = sorted(items, key=lambda x: x[0]) # Ordered by key.\n for key, val in items:\n if nonstringiter(val):\n clause.append(key + ' IN (%s)' % (', '.join('?' * len(val))))\n for x in val:\n params.append(x)\n else:\n clause.append(key + '=?')\n params.append(val)\n\n clause = ' AND '.join(clause) if clause else ''\n return clause, params","def create_search_query(words, *filters):\n if not filters:\n filters = filters + ('title',)\n\n query = ''\n\n for filter in filters:\n query += filter + ' LIKE \"%'\n\n for letter in words:\n if letter == '%':\n letter = '%\" AND ' + filter + ' LIKE \"%'\n elif letter == ' ':\n letter = '%\" OR ' + filter + ' LIKE \"%'\n\n query += letter\n\n if filter == filters[-1]:\n query += '%\"'\n else:\n query += '%\" OR '\n\n return query","def _build_where_clause(**kwds_filter):\n clause = []\n params = []\n items = kwds_filter.items()\n items = sorted(items, key=lambda x: x[0]) # Ordered by key.\n for key, val in items:\n if _is_nsiterable(val):\n clause.append(key + ' IN (%s)' % (', '.join('?' * len(val))))\n for x in val:\n params.append(x)\n else:\n clause.append(key + '=?')\n params.append(val)\n\n clause = ' AND '.join(clause) if clause else ''\n return clause, params","def _build_query_filters(self, query: dict, filters: list) -> dict:\n\n for filter_tuple in filters:\n if not isinstance(filter_tuple, tuple) or len(filter_tuple) != 3:\n LOG.error(\"polling_filters tuple %s : invalid format or does not contain 3 elements - skipping this filter\", filter_tuple)\n continue\n if isinstance(filter_tuple[2], list) :\n # If \"value\" is a list of values then create a rule (json object) for each \n # value and use \"OR\" condition.\n condition = {'condition': \"OR\",\n 'rules': []}\n for value in filter_tuple[2]:\n rule = {}\n # Prepend fieldname with \"table.\" string\n rule['field'] = f\"table.{filter_tuple[0]}\"\n rule['operator'] = filter_tuple[1]\n rule['value'] = value\n condition['rules'].append(rule)\n query['rules'].append(condition)\n else:\n # Create a single rule for this tuple\n rule = {}\n field_name = f\"table.{filter_tuple[0]}\"\n rule['field'] = field_name\n rule['operator'] = filter_tuple[1]\n rule['value'] = filter_tuple[2]\n query['rules'].append(rule)\n return query","def get_current_filters(self) -> str:\r\n return self.__filters_string","def create_filter_query(self, collection_name: str, field: str, filter_type: str, filter_values: Union[List[str], str]=None):\n if filter_type == 'contains':\n # return [{'field' : field, 'filter_type' : 'contains', \"condition\":\"==\", \"condition_value\": filter_values}]\n return [{'field': field, 'filter_type': 'regexp', 'condition': '==', 'condition_value': '.*' + str(filter_values) + '.*'}]\n if filter_type == 'exact_match':\n return [{'field' : field, 'filter_type' : 'exact_match', \"condition\":\"==\", \"condition_value\": filter_values}]\n if filter_type == 'categories':\n return [{'field' : field, 'filter_type' : 'categories', \"condition\":\"==\", \"condition_value\": filter_values}]\n if filter_type == 'exists':\n if filter_values is None or filter_values == '==':\n return [{'field' : field, 'filter_type' : 'exists', \"condition\":\"==\", \"condition_value\":\" \"}]\n elif filter_values == '!=':\n return [{'field' : field, 'filter_type' : 'exists', \"condition\":\"!=\", \"condition_value\":\" \"}]\n if filter_type == '<=' or filter_type == '>=' or filter_type == '>' or filter_type == '<' or filter_type == '==':\n if self.collection_schema(collection_name)[field] == 'date':\n return [{'field' : field, 'filter_type' : 'date', \"condition\":filter_type, \"condition_value\": filter_values}]\n elif self.collection_schema(collection_name)[field] == 'numeric':\n return [{'field' : field, 'filter_type' : 'numeric', \"condition\":filter_type, \"condition_value\":filter_values}]\n else:\n raise ValueError(f\"{filter_type} has not been defined. Please choose one of contains/exact_match/exists/categories/>=/<=/>/<.\")","def build_filters(self, view, filters=None):\n query_builder = self.get_query_builder(backend=self, view=view)\n return query_builder.build_query(**(filters if filters else {}))","def get_where_clause(self, params: Dict) -> str:\n return ''","def _valid_filter_query(self):\n if self._output_invalid_imeis:\n valid_filter_sql = sql.SQL('TRUE')\n else:\n valid_filter_sql = sql.SQL('is_valid IS TRUE')\n return valid_filter_sql","def filter(self, **kwargs):\n kwargs['query'] += ' FROM {0}'\n return kwargs","def query(self, paramstyle=None):\n s = []\n for x in self.items:\n if isinstance(x, SQLParam):\n x = x.get_marker(paramstyle)\n s.append(safestr(x))\n else:\n x = safestr(x)\n # automatically escape % characters in the query\n # For backward compatability, ignore escaping when the query looks already escaped\n if paramstyle in ['format', 'pyformat']:\n if '%' in x and '%%' not in x:\n x = x.replace('%', '%%')\n s.append(x)\n return \"\".join(s)","def to_string(self):\n filter_string = '('\n variable_length = len(self.values) - 1\n for index in range(0, variable_length + 1):\n filter_string += '&' + self.values[index]\n # append or for all but the last\n if index < variable_length:\n filter_string += self.combination_operator\n filter_string += ')'\n\n return filter_string","def __format_conditional_filters(field: dict) -> dict:\n if \"conditionalFormats\" in field:\n for cf in field[\"conditionalFormats\"]:\n if \"filter\" in cf and isinstance(\n cf[\"filter\"], QueryFilter\n ): # Supports one QueryFilter without list form\n cf[\"filter\"] = encode_conditional_format_filter(cf[\"filter\"])\n\n elif \"filter\" in cf and isinstance(cf[\"filter\"], list): # Supports list of QueryFilters\n filters = []\n for query_filter in cf[\"filter\"]:\n filters.append(encode_conditional_format_filter(query_filter))\n if len(filters) > 2:\n raise Exception(\"Too many QueryFilters given for one conditional format.\")\n cf[\"filter\"] = filters[0] + \"&\" + filters[1] if len(filters) == 2 else filters[0]\n\n return field","def _build_filters(self, criteria: Q):\n composed_query = query.Q()\n\n if criteria.connector == criteria.AND:\n for child in criteria.children:\n if isinstance(child, Q):\n composed_query = composed_query & self._build_filters(child)\n else:\n stripped_key, lookup_class = self.provider._extract_lookup(child[0])\n lookup = lookup_class(stripped_key, child[1])\n if criteria.negated:\n composed_query = composed_query & ~lookup.as_expression()\n else:\n composed_query = composed_query & lookup.as_expression()\n else:\n for child in criteria.children:\n if isinstance(child, Q):\n composed_query = composed_query | self._build_filters(child)\n else:\n stripped_key, lookup_class = self.provider._extract_lookup(child[0])\n lookup = lookup_class(stripped_key, child[1])\n if criteria.negated:\n composed_query = composed_query | ~lookup.as_expression()\n else:\n composed_query = composed_query | lookup.as_expression()\n\n return composed_query","def get_query(self, q, request):\r\n \r\n return self.model.objects.filter(filter__icontains=q).order_by('filter')[:50]","def format_query(query, option=''):\n q_tmp = []\n for s in query:\n if option != 'url' or s.isalnum():\n q_tmp.append(s)\n else:\n q_tmp.append('\\\"' + s.lower() + '\\\"')\n\n if option == 'file':\n sep = '_'\n else:\n sep = ' '\n q = sep.join(q_tmp)\n if option == 'url':\n q = urllib2.quote('\\'' + q + '\\'')\n return q","async def getQueryFilters(self, ):\n payload = {}\n \n\n # Parameter validation\n schema = CatalogValidator.getQueryFilters()\n schema.dump(schema.load(payload))\n \n\n url_with_params = await create_url_with_params(self._conf.domain, f\"/service/platform/catalog/v1.0/company/{self._conf.companyId}/application/{self.applicationId}/collections/query-options/\", \"\"\"{\"required\":[{\"in\":\"path\",\"name\":\"company_id\",\"description\":\"A `company_id` is a unique identifier for a particular seller account.\",\"schema\":{\"type\":\"string\"},\"required\":true},{\"in\":\"path\",\"name\":\"application_id\",\"description\":\"A `application_id` is a unique identifier for a particular sale channel.\",\"schema\":{\"type\":\"string\"},\"required\":true}],\"optional\":[],\"query\":[],\"headers\":[],\"path\":[{\"in\":\"path\",\"name\":\"company_id\",\"description\":\"A `company_id` is a unique identifier for a particular seller account.\",\"schema\":{\"type\":\"string\"},\"required\":true},{\"in\":\"path\",\"name\":\"application_id\",\"description\":\"A `application_id` is a unique identifier for a particular sale channel.\",\"schema\":{\"type\":\"string\"},\"required\":true}]}\"\"\", )\n query_string = await create_query_string()\n headers = {\n \"Authorization\": \"Bearer \" + await self._conf.getAccessToken()\n }\n for h in self._conf.extraHeaders:\n headers.update(h)\n exclude_headers = []\n for key, val in headers.items():\n if not key.startswith(\"x-fp-\"):\n exclude_headers.append(key)\n return await AiohttpHelper().aiohttp_request(\"GET\", url_with_params, headers=get_headers_with_signature(self._conf.domain, \"get\", await create_url_without_domain(f\"/service/platform/catalog/v1.0/company/{self._conf.companyId}/application/{self.applicationId}/collections/query-options/\", ), query_string, headers, \"\", exclude_headers=exclude_headers), data=\"\")","def parse_filter_expr(expr: dict) -> str:\n if not hasattr(expr, 'name'):\n if type(expr) is BNode:\n return f\"?v_{expr}\"\n else:\n return expr.n3()\n else:\n if expr.name == 'RelationalExpression':\n return f\"({parse_filter_expr(expr.expr)} {expr.op} {parse_filter_expr(expr.other)})\"\n elif expr.name == 'AdditiveExpression':\n expression = parse_filter_expr(expr.expr)\n for i in range(len(expr.op)):\n expression = f\"({expression} {expr.op[i]} {parse_filter_expr(expr.other[i])})\"\n return expression\n elif expr.name == 'ConditionalAndExpression':\n expression = parse_filter_expr(expr.expr)\n for other in expr.other:\n expression = f\"({expression} && {parse_filter_expr(other)})\"\n return expression\n elif expr.name == 'ConditionalOrExpression':\n expression = parse_filter_expr(expr.expr)\n for other in expr.other:\n expression = f\"({expression} || {parse_filter_expr(other)})\"\n return expression\n elif expr.name.startswith('Builtin_'):\n return f\"{expr.name[8:]}({parse_filter_expr(expr.arg)})\"\n raise UnsupportedSPARQL(f\"Unsupported SPARQL FILTER expression: {expr.name}\")","def build_query_filters(**kwargs):\n queryTarget = kwargs.get(\"queryTarget\", None)\n targetSubtreeClass = kwargs.get(\"targetSubtreeClass\", None)\n queryTargetFilter = kwargs.get(\"queryTargetFilter\", None)\n rspSubtree = kwargs.get(\"rspSubtree\", None)\n rspSubtreeClass = kwargs.get(\"rspSubtreeClass\", None)\n rspSubtreeInclude = kwargs.get(\"rspSubtreeInclude\", None)\n rspPropInclude = kwargs.get(\"rspPropInclude\", None)\n orderBy = kwargs.get(\"orderBy\", None)\n opts = \"\"\n if queryTarget is not None:\n opts+= \"&query-target=%s\" % queryTarget\n if targetSubtreeClass is not None:\n opts+= \"&target-subtree-class=%s\" % targetSubtreeClass\n if queryTargetFilter is not None:\n opts+= \"&query-target-filter=%s\" % queryTargetFilter\n if rspSubtree is not None:\n opts+= \"&rsp-subtree=%s\" % rspSubtree\n if rspSubtreeClass is not None:\n opts+= \"&rsp-subtree-class=%s\" % rspSubtreeClass\n if rspSubtreeInclude is not None:\n opts+= \"&rsp-subtree-include=%s\" % rspSubtreeInclude\n if rspPropInclude is not None:\n opts+= \"&rsp-prop-include=%s\" % rspPropInclude\n if orderBy is not None:\n opts+= \"&order-by=%s\" % orderBy\n\n if len(opts)>0: opts = \"?%s\" % opts.strip(\"&\")\n return opts","def build_query_filters(**kwargs):\n queryTarget = kwargs.get(\"queryTarget\", None)\n targetSubtreeClass = kwargs.get(\"targetSubtreeClass\", None)\n queryTargetFilter = kwargs.get(\"queryTargetFilter\", None)\n rspSubtree = kwargs.get(\"rspSubtree\", None)\n rspSubtreeInclude = kwargs.get(\"rspSubtreeInclude\", None)\n rspPropInclude = kwargs.get(\"rspPropInclude\", None)\n opts = \"\"\n if queryTarget is not None:\n opts+= \"&query-target=%s\" % queryTarget\n if targetSubtreeClass is not None:\n opts+= \"&target-subtree-class=%s\" % targetSubtreeClass\n if queryTargetFilter is not None:\n opts+= \"&query-target-filter=%s\" % queryTargetFilter\n if rspSubtree is not None:\n opts+= \"&rsp-subtree=%s\" % rspSubtree\n if rspSubtreeInclude is not None:\n opts+= \"&rsp-subtree-include=%s\" % rspSubtreeInclude\n if rspPropInclude is not None:\n opts+= \"&rsp-prop-include=%s\" % rspPropInclude\n\n if len(opts)>0: opts = \"?%s\" % opts.strip(\"&\")\n return opts","def _scryfall_query_builder(colors, query_type, deck_format):\n base_query = consts.BASE_QUERIES[deck_format]\n color_query = consts.COLOR_QUERIES[colors]\n query_params = consts.QUERIES[deck_format][query_type]['query'] % {'colors': color_query}\n\n full_query = ' '.join([base_query, query_params])\n\n return full_query","def build_query(self):\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\r\n algorithm = request.args.get('algorithm')\r\n if algorithm is not None:\r\n query = query.filter(MlModelMetric.algorithm == algorithm)\r\n return query","def filter(self, filters):\r\n # because http.Request needs params to be a dict of strings to strings\r\n # (roughly) and since BitBucket wants repeated parameters to express\r\n # OR, we'll do the quoting by hand ourselves\r\n def flatten_conditions(filters):\r\n for key, val in filters.items():\r\n if isinstance(val, (list, tuple)):\r\n for v in val:\r\n yield (port.to_b(key), port.to_b(v))\r\n else:\r\n yield (port.to_b(key), port.to_b(val))\r\n\r\n to_encode = tuple(flatten_conditions(filters))\r\n qs = port.urlencode(to_encode)\r\n\r\n url = '{0}/?{1}'.format(self.get_url(), qs)\r\n return http.Request('GET', url), parsers.parse_json","def filter_format(filter_dict, query_data, filter_type, index): \r\n \r\n filter_list = ''\r\n count = 0 \r\n \r\n while query_data[index] != 'PRESENT\\n': \r\n if filter_type in query_data[index]: \r\n count += 1\r\n filter_keyword = query_data[index].strip(filter_type)\r\n filter_list = (filter_keyword.strip('\\n'))\r\n \r\n index += 1 \r\n \r\n if count > 0: \r\n filter_dict[filter_type] = filter_list\r\n return filter_dict","def asSQL(self) -> str:\n # To modify this method, pay attentin to SQL injection.\n # For example, if `self.x` is assumed to be integer\n # but is not guaranteed to be,\n # `:d` must always be specified in format strings:\n # `expressions.append(f\"x > {self.x:d}\")`\n expressions = []\n if self.date_start is not None:\n if self.date_start.tzinfo is None:\n datestr = self.date_start.isoformat()\n else:\n datestr = self.date_start.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\n expressions.append(f\"pfs_visit.issued_at >= '{datestr}'\")\n if self.date_end is not None:\n if self.date_end.tzinfo is None:\n datestr = self.date_end.isoformat()\n else:\n datestr = self.date_end.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\n expressions.append(f\"pfs_visit.issued_at < '{datestr}'\")\n if self.visit_start is not None:\n expressions.append(f\"pfs_visit.pfs_visit_id >= '{self.visit_start:d}'\")\n if self.visit_end is not None:\n expressions.append(f\"pfs_visit.pfs_visit_id < '{self.visit_end:d}'\")\n\n if expressions:\n return \"(\" + \" AND \".join(expressions) + \")\"\n else:\n return \"TRUE\"","def metadata_filter_as_string(metadata_filter):\n if not isinstance(metadata_filter, dict):\n return metadata_filter\n\n additional = metadata_filter.get('additional', [])\n if additional == 'all':\n entries = ['all']\n else:\n entries = [key for key in additional if key not in _JUPYTEXT_CELL_METADATA]\n\n excluded = metadata_filter.get('excluded', [])\n if excluded == 'all':\n entries.append('-all')\n else:\n entries.extend(['-' + e for e in excluded])\n\n return ','.join(entries)","def get_query(self, query_args):\n num_query_template = Template(self.query_spec.numerator_template)\n num_query = num_query_template.substitute(**query_args)\n den_query_template = Template(self.query_spec.denominator_template)\n den_query = den_query_template.substitute(**query_args)\n query = f\"({num_query}) / ({den_query})\"\n logger.debug(f\"Query: {query}\")\n return query","def filter_query(self, request, query, view):\n\n if not request.params:\n return query\n\n querystring_params = self.parse_query_string(request.params)\n query, filter_list = self.build_filter_list(querystring_params, query, view)\n\n return self.apply_filter(query, filter_list)","def conditional_format(\n query_filter: Union[str, QueryFilter, List[QueryFilter]],\n bold: bool = False,\n italic: bool = False,\n strike_through: bool = False,\n text_color: str = \"\",\n background_color: str = \"\",\n) -> ConditionalFormat:\n filter_str = query_filter\n\n if isinstance(query_filter, QueryFilter):\n filter_str = encode_conditional_format_filter(query_filter)\n elif isinstance(query_filter, list):\n if len(query_filter) > 2:\n raise Exception(\"Too many QueryFilters given for one conditional format.\")\n\n if not all([isinstance(qf, QueryFilter) for qf in query_filter]):\n raise Exception(\n \"Please pass QueryFilter objects when updating a conditional format using a list of filters.\"\n )\n\n string_filters = [encode_conditional_format_filter(f) for f in query_filter]\n filter_str = (\n string_filters[0] + \"&\" + string_filters[1]\n if len(query_filter) == 2\n else string_filters[0]\n )\n\n return ConditionalFormat(\n background_color=background_color,\n bold=bold,\n filter=filter_str,\n italic=italic,\n strike_through=strike_through,\n text_color=text_color,\n )","def create_query_body(self, **kwargs):\n\n query = \"\"\n\n for key, value in kwargs.items():\n if value is True and key != 'nsfw':\n query += '&has=%s' % key[:-1]\n\n if key == 'nsfw':\n query += '&include_nsfw=%s' % str(value).lower()\n\n return query","def _process_alert_source_filters(query, filters):\n if filters:\n if not is_valid_model_filters(models.AlertSource, filters):\n return\n query = query.filter_by(**filters)\n\n return query","def _get_filter_args(self, filter):\n kwargs = {}\n for arg in filter.args[1:]:\n kwargs[arg] = input(f'Type the {arg.replace(\"_\", \" \").title()}: ')\n return kwargs","def summary_parameters(self):\n return {'filters': ', '.join(self.getOption('filters'))}","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n level = self.request.GET.get(\"level\")\n description = self.request.GET.get(\"description\")\n status = self.request.GET.get(\"status\")\n job_tag = self.request.GET.get(\"job_tag\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(program_id=query) |\n Q(category=query) |\n Q(title__icontains=query) |\n #Q(category__category_name__icontains=query) |\n Q(description__icontains=query) |\n Q(job_tag__tag__in=str(query).split(\",\"))\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(level__icontains=int(query)), Q.OR)\n\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n else:\n if program_id:\n q_object.add(\n Q(program_id=program_id),\n Q.AND)\n\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n if description:\n q_object.add(\n Q(description__icontains=description), Q.AND)\n\n if job_tag:\n q_object.add(\n Q(job_tag__tag__in=str(job_tag).split(\",\")),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n return q_object","def as_sql(self, qn, connection):\n lhs, lhs_params = self.process_lhs(qn, connection)\n rhs, rhs_params = self.process_rhs(qn, connection)\n\n field = lhs\n # JSONField formats query values for the database by wrapping them psycopg2's\n # JsonAdapter, but we need the raw Python dict so that we can parse the\n # query tree. Intercept the query parameter (it'll always be the first\n # element in the parameter list, since the custom jsonb filter only accepts one argument)\n # and revert it back to a Python dict for tree parsing.\n tree = rhs_params[0].adapted\n\n return FilterTree(tree, field).sql()","def get_filter_name(self):\n pass","def toQueryString(self):\n return self.__str__()","def apply_filters(self, query, model_class, filters, whitelist=None,\n nested_conditions=None, stack_size_limit=100,\n convert_key_names_func=str, gettext=None):\n return apply_mql_filters(\n query,\n model_class,\n filters=filters,\n nested_conditions=nested_conditions,\n whitelist=whitelist,\n stack_size_limit=stack_size_limit,\n convert_key_names_func=convert_key_names_func,\n gettext=gettext\n )","def _create_query_str(data):\n params = []\n for name, value in data.items():\n params.append(name + '=' + str(value))\n\n return '?' + '&'.join(params)","def get_filter_parameters(self):\n if not self.should_filter():\n return []\n\n fields = []\n for filter_backend in self.view.filter_backends:\n fields += self.get_filter_backend_parameters(filter_backend())\n\n return fields","def _filter_params(self):\n default_params = self.get_default_params()\n complete_params = dict(self.get_default_params())\n complete_params.update(self.params)\n\n return utils.format_dictionary(complete_params)","def build_where_statement(attr_converter):\n statements = []\n values = []\n # iterate over the querystring params\n for attr, val in request.args.iteritems():\n try:\n statements.append(attr_converter[attr]['converter'](\n attr_converter[attr]['column']))\n values.append(val) # add after the possible keyerror\n except KeyError:\n # TODO: handle invalid params somehow or continue to ignore\n pass\n if statements:\n return 'WHERE '+' AND '.join(statements), values\n return '', []","def _get_query(request):\n query = request.GET.get(\"query\", \"\")\n date = request.GET.get(\"date\", \"\")\n timestamp = request.GET.get(\"timestamp\", None)\n sort = request.GET.get(\"sort\", \"top\").lower()\n filter = request.GET.get(\"filter\", \"following\").lower()\n\n if timestamp:\n t = parse(timestamp, ignoretz=True)\n timestamp = pytz.utc.localize(t)\n else:\n timestamp = timezone.now()\n\n start_time = ''\n end_time = ''\n\n if date:\n start_time, end_time = DateRangeParser().parse(date)\n\n get_dict = {\n \"query\": query,\n \"filter\": filter,\n \"sort\": sort,\n \"start_time\": start_time,\n \"end_time\": end_time,\n \"username\": request.GET.get(\"username\", \"\"),\n \"orderBy\": request.GET.get(\"orderBy\", \"start_time\"),\n \"direction\": request.GET.get(\"direction\", \"\"),\n \"template\": request.GET.get(\"template\", \"\"),\n \"type\": request.GET.get(\"type\", \"\"),\n \"page\": request.GET.get(\"page\", 1),\n 'timestamp': timestamp,\n }\n\n return get_dict, query, date, sort, filter","def _format_queries(self, body):\n for query in body:\n if \"bindVars\" in query:\n query[\"bind_vars\"] = query.pop(\"bindVars\")\n if \"runTime\" in query:\n query[\"runtime\"] = query.pop(\"runTime\")\n return body","def filter_query(self, query):\n\n if self.state:\n query = query.filter(self.model_class.state == self.state)\n if self.term:\n term = '%{}%'.format(self.term)\n query = query.filter(\n or_(\n *[column.ilike(term) for column in self.term_columns]\n )\n )\n if self.user_ids:\n query = query.filter(self.model_class.user_id.in_(self.user_ids))\n if self.group_ids:\n query = query.filter(self.model_class.group_id.in_(self.group_ids))\n if self.issues:\n query = query.filter(self.model_class._issues.has_any(self.issues))\n if self.categories:\n query = query.filter(\n self.model_class._categories.has_any(self.categories)\n )\n if self.organizations:\n query = query.filter(\n self.model_class._organizations.has_any(self.organizations)\n )\n\n return query","def generateSQLFilter(sql_filter, es_type, category):\n query_string = \"Category = ? AND (\"\n insertion_list = [category]\n\n i = 0\n while i < len(sql_filter):\n insertion_list.append(sql_filter[i]) # append values to be inserted into SQL statement (? substitution)\n if (i + 1) != 1: # NOT first loop\n query_string += \" OR \"\n query_string += es_type + \" = ?\"\n i += 1\n\n query_string += \")\"\n #\n #\n\n return query_string, insertion_list","def filter(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"filter\")","def default_filters(self, query) -> object:\n user = self.request.user\n model = self.model\n custom_filter = self.request.params.get('_custom_filter')\n if 'g:professionals' in user.groups and custom_filter == 'pool':\n # disable security for this custom filter\n self.enable_security = False\n professional = Professional.get(user.id)\n pool_ids = [item.id for item in professional.pools]\n query = query.filter(\n model.pool_id.in_(pool_ids),\n model.state == 'published'\n )\n elif custom_filter == 'late_first_submission':\n config_delta = timedelta(seconds=int(LATE_SUBMISSION_SECONDS))\n date_limit = datetime_utcnow() - config_delta\n query = query.filter(\n model.scheduled_datetime <= date_limit,\n model.state == 'awaiting_assets',\n model.last_approval_date.is_(None)\n )\n elif custom_filter == 'late_re_submission':\n config_delta = timedelta(seconds=int(LATE_SUBMISSION_SECONDS))\n date_limit = datetime_utcnow() - config_delta\n query = query.filter(\n model.last_approval_date <= date_limit,\n model.state == 'awaiting_assets',\n model.submission_path.isnot(None),\n )\n return query","def get_filters():\n\n city = prompts.city_prompt.launch()\n\n _filter = prompts.filter_prompt.launch()\n\n if _filter == \"Month\":\n month = prompts.month_prompt.launch()\n day = \"All\"\n\n elif _filter == \"Day\":\n day = prompts.day_prompt.launch()\n month = \"All\"\n\n elif _filter == \"Both\":\n month = prompts.month_prompt.launch()\n day = prompts.day_prompt.launch()\n\n else:\n month, day = \"All\", \"All\"\n\n print(\"-\" * 40)\n return city, month, day","def __getSelectionFilter(self):\n \n selectionPairs = []\n selectionPairs.append(('field','field'))\n selectionPairs.append(('spw','spw'))\n selectionPairs.append(('polarization','correlation'))\n selectionPairs.append(('baseline','antenna'))\n selectionPairs.append(('time','timerange'))\n selectionPairs.append(('scan','scan'))\n selectionPairs.append(('uvdist','uvrange'))\n selectionPairs.append(('scanintent','intent'))\n selectionPairs.append(('observation','observation'))\n return self.__generateFilter(selectionPairs)","def random_filter_query(\n self, collection_name: str, text_filters: int = 1, numeric_filters: int = 0\n ):\n schema = self.collection_schema(collection_name)\n facets = self.facets(collection_name)\n full_filter_query = {\"text\": [], \"numeric\": []}\n for f, t in schema.items():\n if t == \"text\":\n if isinstance(facets[f], list):\n full_filter_query[\"text\"].append(\n {\n \"field\": f,\n \"filter_type\": \"text\",\n \"condition_value\": random.sample(facets[f], 1)[0][f],\n \"condition\": \"==\",\n }\n )\n elif t == \"numeric\":\n if isinstance(facets[f], dict):\n full_filter_query[\"numeric\"].append(\n {\n \"field\": f,\n \"filter_type\": \"date\",\n \"condition_value\": (facets[f][\"max\"] - facets[f][\"min\"])\n / 2,\n \"condition\": \">=\",\n }\n )\n return random.sample(full_filter_query[\"text\"], text_filters) + random.sample(\n full_filter_query[\"numeric\"], numeric_filters\n )","def filters_show():\n log = slog()\n log.title_set('Filters applied')\n if self.args['table3D']: log.render3D()\n log('Input directory: %s\\n' % self.str_inputDir)\n log('Output directory: %s\\n' % self.str_outputDir)\n for filter in ['file', 'dir']:\n log('%sFilter: ' % filter)\n sl_ffilter = ['%s %s' % (x, self.args['%sFilterLogic' % filter]) \\\n for x in self.args['%sFilter' % filter].split(',')]\n str_ffilter = ' '.join(sl_ffilter)\n sl_ffilter = str_ffilter.split()\n str_ffilter = ' '.join(sl_ffilter[:-1])\n log('%s\\n ' % str_ffilter)\n return log","def format_query_for_displaying(query):\n keys = query.keys()\n result = ''\n for k in keys:\n v = query[k]\n print(\"Checking v=%r and k=%r\" % (v, k))\n result += '[%s] ' % k\n if v and type(v) == dict:\n kk = v.keys()\n r = ''\n for l in kk:\n m = v[l]\n r += str(m)\n r += ' '\n result += r\n else:\n print(\"result: [%s] v:[%s]\" % (result, str(v)))\n result += str(v)\n result += '\\n'\n if result == '':\n result = 'JUST VISITED THE SEARCH PAGE.'\n return result","def get_grouped_filters(filters):\n grouped_filters = Q()\n\n if 'keyword' in filters:\n grouped_filters |= Q(keywords__name__in=[k.lower() for k in filters['keyword'].split(',')])\n filters.pop('keyword')\n\n pipe_map = lambda x, y: x | y\n if 'title__icontains' in filters:\n title_filters = [Q(title__icontains=f) for f in filters.pop('title__icontains')]\n grouped_filters |= reduce(pipe_map, title_filters)\n if 'description__icontains' in filters:\n d_filters = [Q(description__icontains= f) for f in filters.pop('description__icontains')]\n grouped_filters |= reduce(pipe_map, d_filters)\n if 'story_text__icontains' in filters:\n grouped_filters |= Q(story_text__exact=\"\")\n story_filters = [Q(story_text__icontains=f) for f in filters.pop('story_text__icontains')]\n grouped_filters |= reduce(pipe_map, story_filters)\n\n return grouped_filters","def query(self):\n q = dict(self._query)\n fworker_check = [{\"spec._fworker\": {\"$exists\": False}}, {\"spec._fworker\": None}, {\"spec._fworker\": self.name}]\n if \"$or\" in q:\n q[\"$and\"] = q.get(\"$and\", [])\n q[\"$and\"].extend([{\"$or\": q.pop(\"$or\")}, {\"$or\": fworker_check}])\n else:\n q[\"$or\"] = fworker_check\n if self.category and isinstance(self.category, str):\n if self.category == \"__none__\":\n q[\"spec._category\"] = {\"$exists\": False}\n else:\n q[\"spec._category\"] = self.category\n elif self.category: # category is list of str\n q[\"spec._category\"] = {\"$in\": self.category}\n\n return q","def _filterDictToStr(self, filterDict):\n values = []\n for key, vals in filterDict.items():\n if key not in ('contentRating', 'label', 'contentRating!', 'label!'):\n raise BadRequest(f'Unknown filter key: {key}')\n values.append(f\"{key}={'%2C'.join(vals)}\")\n return '|'.join(values)","def _get_queries(args):\n if args.mode == '2DSEQ':\n queries = [\"@type=='2dseq'\", \"@is_spectroscopy==True\", \"@is_complex==True\"]\n elif args.mode == 'FID':\n queries = [\"@type=='fid'\", \"@is_spectroscopy==True\"]\n return queries + args.query","def get_query():\n query = \"\"\"{\n repository(name: \"flux\", owner: \"fluxcd\") {\n forkCount\n issues {\n totalCount\n }\n pullRequests {\n totalCount\n }\n releases {\n totalCount\n }\n stargazers {\n totalCount\n }\n watchers {\n totalCount\n }\n }\n}\n \"\"\"\n return query","def sql(self):\n rule_specs = []\n\n patterns = {}\n pattern_specs = []\n\n # It's safe to unpack `self.get_rules` because it can only\n # return A) an empty list or B) a list of two-tuples with two elements in\n # them (the path and the rule for each query directive).\n for path, rule in self.rules:\n # Don't parse if this is not a properly registered rule type.\n if not self.is_rule(rule):\n pass\n rule_type = rule['_rule_type']\n sql_tuple = self.sql_generators[rule_type](path, rule)\n if sql_tuple is not None:\n rule_specs.append(sql_tuple)\n\n # The check on 'pattern' here allows us to apply a pattern filter on top of others\n if 'pattern' in rule:\n match_multiple = (rule['_rule_type'] == 'containment_multiple')\n for pattern in self.split_search_pattern(rule['pattern']):\n sql_tuple = FilterTree.text_similarity_filter(path, pattern, match_multiple)\n # add to the list of rules generated for this pattern (one per field)\n patterns.setdefault(pattern, []).append(sql_tuple)\n\n rule_string = ' AND '.join([rule[0] for rule in rule_specs])\n\n pattern_rules = patterns.values()\n pattern_strings = []\n\n # check if any of the fields for this string pattern match\n for rule_list in pattern_rules:\n pattern_strings.append(' OR '.join([rule[0] for rule in rule_list]))\n pattern_specs += rule_list\n\n # check that record has a match for all of the string patterns in some field\n pattern_string = '(' + ') AND ('.join(pattern_strings) + ')' if pattern_strings else ''\n\n if rule_string != '' and pattern_string != '':\n filter_string = '(' + (' AND ('.join([rule_string, pattern_string])) + ')' + ')'\n elif rule_string != '' or pattern_string != '':\n filter_string = '(' + ''.join([rule_string, pattern_string]) + ')'\n else:\n filter_string = ''\n\n # flatten the rule_paths\n rule_paths_first = ([rule[1] for rule in rule_specs] +\n [rule[1] for rule in pattern_specs])\n rule_paths = [item for sublist in rule_paths_first\n for item in sublist]\n\n outcome = (filter_string, tuple(rule_paths))\n return outcome","def filters():\n return {\"reform_vlans\": FilterModule.reform_vlans}","def __generate_search_query(self) -> None:\n if self.query_accuracy < 100:\n if self.title is not None and self.title != '' and self.artist is not None and self.artist != '':\n # Use the title and the artist name to find more information about the song.\n query: str = self.title + ' ' + self.artist\n query = re.sub(self.__get_filter_regex(), '', query)\n self.query = query\n # Remove unnecessary information in order to get a simpler query version.\n self.minimal_query = re.sub(r'\\([\\s\\S]+\\)', '', query).strip()\n self.query_accuracy = 100\n return\n if self.query_accuracy < 50:\n # No title nor artist name available, use the filename as search query.\n filename: str = os.path.basename(self.original_path)\n filename = os.path.splitext(filename)[0]\n query: str = filename.lower()\n query = re.sub(self.__get_filter_regex(), '', query)\n query = query.replace('_', ' ')\n query = query.strip()\n self.query = query\n self.minimal_query = re.sub(r'\\([\\s\\S]+\\)', '', query).strip()\n self.query_accuracy = 50","def _process_model_like_filter(model, query, filters):\n if query is None:\n return query\n\n if filters:\n for key in sorted(filters):\n column_attr = getattr(model, key)\n if 'property' == type(column_attr).__name__:\n continue\n value = filters[key]\n if not (isinstance(value, (str, int))):\n continue\n query = query.filter(\n column_attr.op('LIKE')(u'%%%s%%' % value))\n return query","def filter_by_query_params(self, request):\n items = self\n company = request.GET.get('company', None)\n main_contractor = request.GET.get('main_contractor', None)\n main_sub_contractor = request.GET.get('main_sub_contractor', None)\n client = request.GET.get('client', None)\n q = request.GET.get('q', None)\n sort_by = request.GET.get('sort_by', None)\n str = request.GET.get('str', None)\n\n # filter\n if main_contractor:\n items = items.filter(main_contractor=main_contractor).distinct()\n if main_sub_contractor:\n items = items.filter(main_sub_contractor=main_sub_contractor).distinct()\n if client:\n items = items.filter(client=client).distinct()\n if company:\n items = items.filter(companies_linked__in=[company]).distinct()\n # sort\n if q == 'asc' and sort_by:\n items = items.order_by(sort_by).distinct()\n\n if q == 'des' and sort_by:\n items = items.order_by('-' + sort_by).distinct()\n\n if str:\n # str = str.strip().lower()\n items = items.filter(Q(reference_no__icontains=str) |\n Q(name__icontains=str)).distinct()\n return items","def encoded_query_str(request):\n return updated_query_str(request)","def _create_filter_object(form_data: Dict) -> Q:\n filter_object = Q(title__icontains=form_data[\"title\"])\n filter_object &= Q(author__icontains=form_data[\"author\"])\n filter_object &= Q(\n publication_language__icontains=form_data[\"publication_language\"]\n )\n if form_data[\"publication_date_start\"]:\n filter_object &= Q(\n publication_date__gte=form_data[\"publication_date_start\"]\n )\n if form_data[\"publication_date_end\"]:\n filter_object &= Q(publication_date__lte=form_data[\"publication_date_end\"])\n return filter_object","def fetch_querydict(self):\n query = dict()\n query[\"filtered\"] = dict()\n if self.q_dict and isinstance(self.q_dict, dict):\n query_list, filter_list = self.build_query_structure()\n if query_list:\n query[\"filtered\"][\"query\"] = {\"bool\": {\"must\": query_list}}\n if filter_list:\n query[\"filtered\"][\"filter\"] = {\"bool\": {\"must\": filter_list}}\n return query","def format_search_filter(self, term: event_search.SearchFilter) -> Optional[WhereType]:\n name = term.key.name\n\n converted_filter = self.convert_search_filter_to_condition(\n event_search.SearchFilter(\n # We want to use group_id elsewhere so shouldn't be removed from the dataset\n # but if a user has a tag with the same name we want to make sure that works\n event_search.SearchKey(\"tags[group_id]\" if name == \"group_id\" else name),\n term.operator,\n term.value,\n )\n )\n return converted_filter if converted_filter else None","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(title__icontains=query) \n ), Q.OR)\n\n else:\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n return q_object","def std_filters():\n kwargs = {\n \"sentence_filters\":[punctuation_filter],\n \"word_filters\":[small_word_filter, stopword_filter, stemming_filter]\n }\n return kwargs","def build_query(self):\n query = self.action.build_query()\n if self.sandboxoption != None:\n query['Sandbox'] = self.sandboxoption\n else:\n query['Sandbox'] = '0'\n return query","def get_search_query(self):\n q = None\n if 'q' in self.request.GET and self.request.GET['q']:\n q = self.request.GET['q'].strip()\n return q","def as_sql(self):\n distinct = 'DISTINCT ' if self._distinct else ''\n final = ' FINAL' if self._final else ''\n table_name = '`%s`' % self._model_cls.table_name()\n if self._model_cls.is_system_model():\n table_name = '`system`.' + table_name\n params = (distinct, self.select_fields_as_sql(), table_name, final)\n sql = u'SELECT %s%s\\nFROM %s%s' % params\n\n if self._prewhere_q and not self._prewhere_q.is_empty:\n sql += '\\nPREWHERE ' + self.conditions_as_sql(prewhere=True)\n\n if self._where_q and not self._where_q.is_empty:\n sql += '\\nWHERE ' + self.conditions_as_sql(prewhere=False)\n\n if self._grouping_fields:\n sql += '\\nGROUP BY %s' % comma_join('`%s`' % field for field in self._grouping_fields)\n\n if self._grouping_with_totals:\n sql += ' WITH TOTALS'\n\n if self._order_by:\n sql += '\\nORDER BY ' + self.order_by_as_sql()\n\n if self._limit_by:\n sql += '\\nLIMIT %d, %d' % self._limit_by\n sql += ' BY %s' % comma_join(string_or_func(field) for field in self._limit_by_fields)\n\n if self._limits:\n sql += '\\nLIMIT %d, %d' % self._limits\n\n return sql","def pp_query(query):\n print(format_query(query))","def query_text(self) -> str:\n return self._query_text","async def get_filter(self, **kwargs: Any) -> str:\n return self._telescope.filter_name","def normalize_query(self):\n query_lower = self.user_input.lower()\n # Remove punctuation\n query_no_punctuation = re.sub(r\"[!#$%&'()*+,-./:;<=>?@\\^_`{|}~]+\\ *\", \" \", query_lower)\n # Remove accent from all the words\n self.query_no_accent = ''.join((c for c in \\\n unicodedata.normalize('NFD', query_no_punctuation) \\\n if unicodedata.category(c) != 'Mn'))\n\n return query_lower, query_no_punctuation, self.query_no_accent","def get_params(self):\n params = super(JoinFilterQuery, self).get_params()\n params.append(('fq', self._query))\n\n return params"],"string":"[\n \"def get_query(self):\\n columns = ','.join(['\\\"{}\\\"'.format(x) for x in self.columns])\\n query = 'SELECT {} FROM \\\"{}\\\"'.format(columns, self.table)\\n filter_params = []\\n if self.filters:\\n filter_sql, filter_params = filter_postgis(self.filters)\\n query += ' WHERE {}'.format(filter_sql)\\n query += ';'\\n return str(text(query)), filter_params\",\n \"def __get_filter_regex(self) -> str:\\n return '(' + ')|('.join(self.__QUERY_FILTERS) + ')'\",\n \"def _formatFilters(self, filters):\\n formatted_filters = []\\n inequality_field = None\\n for f in filters:\\n filtr = {\\n field.name: getattr(f, field.name) for field in f.all_fields()\\n }\\n try:\\n filtr[\\\"field\\\"] = FIELDS[filtr[\\\"field\\\"]]\\n filtr[\\\"operator\\\"] = OPERATORS[filtr[\\\"operator\\\"]]\\n except KeyError:\\n raise endpoints.BadRequestException(\\n \\\"Filter contains invalid field or operator.\\\")\\n # Every operation except \\\"=\\\" is an inequality\\n if filtr[\\\"operator\\\"] != \\\"=\\\":\\n # Check if inequality operation has been used in previous\\n # filters disallow the filter if inequality was performed on a\\n # different field before. Track the field on which the\\n # inequality operation is performed.\\n if inequality_field and inequality_field != filtr[\\\"field\\\"]:\\n raise endpoints.BadRequestException(\\n \\\"Inequality filter is allowed on only one field.\\\")\\n else:\\n inequality_field = filtr[\\\"field\\\"]\\n formatted_filters.append(filtr)\\n return (inequality_field, formatted_filters)\",\n \"def get_query(self):\\n columns = ','.join(['\\\"{}\\\"'.format(x) for x in self._columns])\\n query = 'SELECT {} FROM \\\"{}\\\"'.format(columns, self._table)\\n filter_params = []\\n if self._filters:\\n filter_sql, filter_params = filter_postgis(self._filters)\\n query += ' WHERE {}'.format(filter_sql)\\n query += ';'\\n return str(text(query)), filter_params\",\n \"def _formatFilters(self, filters):\\n formatted_filters = []\\n inequality_field = None\\n\\n for f in filters:\\n filtr = {field.name: getattr(f, field.name) for field in f.all_fields()}\\n\\n try:\\n filtr[\\\"field\\\"] = FIELDS[filtr[\\\"field\\\"]]\\n filtr[\\\"operator\\\"] = OPERATORS[filtr[\\\"operator\\\"]]\\n except KeyError:\\n raise endpoints.BadRequestException(\\\"Filter contains invalid field or operator.\\\")\\n\\n # Every operation except \\\"=\\\" is an inequality\\n if filtr[\\\"operator\\\"] != \\\"=\\\":\\n # check if inequality operation has been used in previous filters\\n # disallow the filter if inequality was performed on a different field before\\n # track the field on which the inequality operation is performed\\n if inequality_field and inequality_field != filtr[\\\"field\\\"]:\\n raise endpoints.BadRequestException(\\\"Inequality filter is allowed on only one field.\\\")\\n else:\\n inequality_field = filtr[\\\"field\\\"]\\n\\n formatted_filters.append(filtr)\\n return (inequality_field, formatted_filters)\",\n \"def get_raw_query(self):\\n # create a raw query\\n if len(self.criteria) > 1:\\n # more than one criteria, create a AND query\\n raw_query = {\\\"$and\\\": self.criteria}\\n else:\\n # one criteria, raw query is the criteria\\n raw_query = self.criteria[0]\\n\\n return raw_query\",\n \"def make_sql_condition_string(self, field, condition, value, filter_type):\\n\\n if field == 'no filters': \\n return ''\\n else: \\n if filter_type in ('metric', 'attribute', 'event'):\\n # for < (less than) filters on metrics, we need to use a \\\"having\\\" filter with an aggregation (only MAX for now) on the metric\\n if (filter_type == 'metric'):\\n return 'AND ' + 'MAX(cumul_filter_metric)' + ' ' + condition + ' ' + value + ''\\n else:\\n return 'AND ' + field + ' ' + condition + ' ' + value + ''\\n else:\\n raise NotImplementedError()\",\n \"def _build_filter_part(self, cls, filters, order_by=None, select=None):\\r\\n import types\\r\\n query_parts = []\\r\\n\\r\\n order_by_filtered = False\\r\\n\\r\\n if order_by:\\r\\n if order_by[0] == \\\"-\\\":\\r\\n order_by_method = \\\"DESC\\\";\\r\\n order_by = order_by[1:]\\r\\n else:\\r\\n order_by_method = \\\"ASC\\\";\\r\\n\\r\\n if select:\\r\\n if order_by and order_by in select:\\r\\n order_by_filtered = True\\r\\n query_parts.append(\\\"(%s)\\\" % select)\\r\\n\\r\\n if isinstance(filters, str) or isinstance(filters, unicode):\\r\\n query = \\\"WHERE %s AND `__type__` = '%s'\\\" % (filters, cls.__name__)\\r\\n if order_by in [\\\"__id__\\\", \\\"itemName()\\\"]:\\r\\n query += \\\" ORDER BY itemName() %s\\\" % order_by_method\\r\\n elif order_by != None:\\r\\n query += \\\" ORDER BY `%s` %s\\\" % (order_by, order_by_method)\\r\\n return query\\r\\n\\r\\n for filter in filters:\\r\\n filter_parts = []\\r\\n filter_props = filter[0]\\r\\n if type(filter_props) != list:\\r\\n filter_props = [filter_props]\\r\\n for filter_prop in filter_props:\\r\\n (name, op) = filter_prop.strip().split(\\\" \\\", 1)\\r\\n value = filter[1]\\r\\n property = cls.find_property(name)\\r\\n if name == order_by:\\r\\n order_by_filtered = True\\r\\n if types.TypeType(value) == types.ListType:\\r\\n filter_parts_sub = []\\r\\n for val in value:\\r\\n val = self.encode_value(property, val)\\r\\n if isinstance(val, list):\\r\\n for v in val:\\r\\n filter_parts_sub.append(self._build_filter(property, name, op, v))\\r\\n else:\\r\\n filter_parts_sub.append(self._build_filter(property, name, op, val))\\r\\n filter_parts.append(\\\"(%s)\\\" % (\\\" OR \\\".join(filter_parts_sub)))\\r\\n else:\\r\\n val = self.encode_value(property, value)\\r\\n if isinstance(val, list):\\r\\n for v in val:\\r\\n filter_parts.append(self._build_filter(property, name, op, v))\\r\\n else:\\r\\n filter_parts.append(self._build_filter(property, name, op, val))\\r\\n query_parts.append(\\\"(%s)\\\" % (\\\" or \\\".join(filter_parts)))\\r\\n\\r\\n\\r\\n type_query = \\\"(`__type__` = '%s'\\\" % cls.__name__\\r\\n for subclass in self._get_all_decendents(cls).keys():\\r\\n type_query += \\\" or `__type__` = '%s'\\\" % subclass\\r\\n type_query +=\\\")\\\"\\r\\n query_parts.append(type_query)\\r\\n\\r\\n order_by_query = \\\"\\\"\\r\\n\\r\\n if order_by:\\r\\n if not order_by_filtered:\\r\\n query_parts.append(\\\"`%s` LIKE '%%'\\\" % order_by)\\r\\n if order_by in [\\\"__id__\\\", \\\"itemName()\\\"]:\\r\\n order_by_query = \\\" ORDER BY itemName() %s\\\" % order_by_method\\r\\n else:\\r\\n order_by_query = \\\" ORDER BY `%s` %s\\\" % (order_by, order_by_method)\\r\\n\\r\\n if len(query_parts) > 0:\\r\\n return \\\"WHERE %s %s\\\" % (\\\" AND \\\".join(query_parts), order_by_query)\\r\\n else:\\r\\n return \\\"\\\"\",\n \"def make_filter_string(cls, filter_specification):\\n registry = get_current_registry()\\n visitor_cls = registry.getUtility(IFilterSpecificationVisitor,\\n name=EXPRESSION_KINDS.CQL)\\n visitor = visitor_cls()\\n filter_specification.accept(visitor)\\n return str(visitor.expression)\",\n \"def _getQuery(self, request):\\n q = Conference.query()\\n inequality_filter, filters = self._formatFilters(request.filters)\\n\\n # If exists, sort on inequality filter first\\n if not inequality_filter:\\n q = q.order(Conference.name)\\n else:\\n q = q.order(ndb.GenericProperty(inequality_filter))\\n q = q.order(Conference.name)\\n\\n for filtr in filters:\\n if filtr[\\\"field\\\"] in [\\\"month\\\", \\\"maxAttendees\\\"]:\\n filtr[\\\"value\\\"] = int(filtr[\\\"value\\\"])\\n formatted_query = ndb.query.FilterNode(filtr[\\\"field\\\"], filtr[\\\"operator\\\"], filtr[\\\"value\\\"])\\n q = q.filter(formatted_query)\\n return q\",\n \"def get_filter(self) -> str:\\n\\n return \\\";;\\\".join(self.filters)\",\n \"def format(query: str) -> str:\\n return query.format(**Constants.to_dict())\",\n \"def format_query(query):\\n query = query.encode('utf-8')\\n # all queries must start with 'c '!\\n if not query.startswith('c '):\\n query = 'c ' + query.strip()\\n # check if outcome of query is set to '?'\\n if query[-1] != '?':\\n query = query + ' ?'\\n return query + '\\\\n'\",\n \"def _custom_filter(self, query):\\r\\n return query\",\n \"def __str__(self):\\n query = []\\n\\n if self.root:\\n query.append('query')\\n\\n if self.alias is not None:\\n query.append(self.alias + ':')\\n\\n if self.name is not None:\\n query.append(str(self.name))\\n\\n if self.filters:\\n query.append('(')\\n for k,v in self.filters.items():\\n query.append('{k}:\\\"{v}\\\"'.format(k=k, v=v))\\n query.append(')')\\n\\n if self.children:\\n query.append('{')\\n if self.children:\\n for idx, f in enumerate(self.children.values()):\\n query.append(str(f))\\n if idx < len(self.children) - 1:\\n query.append(',')\\n\\n query.append('}')\\n\\n return ''.join(query)\",\n \"def getQuery(self):\\n # Get a list of object managers, each of which containing the\\n # corresponding view and filter set filter field objects of all\\n # available filter set classes.\\n filter_field_managers = [\\n manager for manager in self.getFilterSetFilterFieldManagers()\\n ] + [\\n manager for manager in self.view.getViewFilterFieldManagers()\\n ]\\n\\n # Create an OR query for all filter fields of the same class\\n or_queries = []\\n for manager in filter_field_managers:\\n filter_fields = manager.all()\\n if filter_fields:\\n or_queries.append(\\n reduce(lambda x, y: x | y, [\\n filter_field.getRecordFilter()\\n for filter_field in filter_fields\\n ]\\n )\\n )\\n\\n # If there are different filter field OR queries, combine those\\n # queries as one AND query\\n if or_queries:\\n return reduce(lambda x, y: x & y, [\\n or_query for or_query in or_queries\\n ]\\n )\\n # If the filter set does not have any filter fields, we return an empty\\n # query, which is equivalent to querying all objects, e.g.:\\n # `View.objects.all() == View.objects.filter(Q())`\\n else:\\n return Q()\",\n \"def _build_query(self, **query):\\n\\n available_fields = list(self.model._available_fields.keys())\\n\\n q_str = ''\\n for key, val in list(query.items()):\\n # Get the field and the operator from the query\\n key_field, key_sep, key_oper = key.partition('__')\\n\\n if key_field == 'pk' and 'id' not in query:\\n key_field = 'id'\\n\\n if key_field in available_fields:\\n if q_str != '':\\n q_str += ' AND '\\n\\n if_cstm = ''\\n if key_field.endswith('_c'):\\n if_cstm = '_cstm'\\n\\n field = self.model._table + if_cstm + '.' + key_field\\n\\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\\n q_str += '%s = \\\"%s\\\"' % (field, val)\\n elif key_oper == 'contains':\\n q_str += '%s LIKE \\\"%%%s%%\\\"' % (field, val)\\n elif key_oper == 'startswith':\\n q_str += '%s LIKE \\\"%s%%\\\"' % (field, val)\\n elif key_oper == 'in':\\n q_str += '%s IN (' % field\\n for elem in val:\\n q_str += \\\"'%s',\\\" % elem\\n q_str = q_str.rstrip(',')\\n q_str += ')'\\n elif key_oper == 'gt':\\n q_str += '%s > \\\"%s\\\"' % (field, val)\\n elif key_oper == 'gte':\\n q_str += '%s >= \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lt':\\n q_str += '%s < \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lte':\\n q_str += '%s <= \\\"%s\\\"' % (field, val)\\n else:\\n raise LookupError('Unsupported operator')\\n\\n return q_str\",\n \"def get_query(self):\\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\\n for prop, value in self.FILTERS:\\n q.filter(\\\"%s =\\\" % prop, value)\\n if self.ancestor:\\n q.ancestor(self.ancestor)\\n q.order(self.ORDER_BY)\\n return q\",\n \"def _get_query(self):\\n\\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\\n\\n query_string = \\\"select \\\" + \\\",\\\".join(ENDPOINT_QUERY_FIELDS[endpoint])\\n\\n # ZOQL does not support the `order by` sorting\\n query_string = query_string + \\\" from {} where UpdatedDate > '{}'\\\".format(\\n endpoint,\\n marker\\n )\\n\\n return query_string\",\n \"def build_query_structure(self):\\n query_list = list()\\n filter_list = list()\\n for key, val in self.q_dict.items():\\n if key in self.es_query_keys:\\n query_list.append(\\n {\\\"match\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val[0]}})\\n elif key in self.es_date_keys:\\n filter_list.append(\\n {\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n elif \\\":\\\" in val[0]:\\n #for handling queries like dd_dct=gte:1\\n range_val = val[0].split(\\\":\\\")\\n filter_list.append({\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): {\\n range_val[0]: int(range_val[1])}}})\\n else:\\n filter_list.append(\\n {\\\"terms\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n return query_list, filter_list\",\n \"def build_query(self):\\r\\n\\r\\n # this filter is required\\r\\n query = Metric.query.filter(Metric.metric_type == self.metric_type)\\r\\n\\r\\n # get query parameters (parameters which are not here are ignored)\\r\\n is_active = request.args.get('is_active')\\r\\n frequency = request.args.get('frequency')\\r\\n threshold_type = request.args.get('threshold_type')\\r\\n sort = request.args.get('sort')\\r\\n\\r\\n # process each parameter, and if valid add it as a query condition\\r\\n if is_active is not None:\\r\\n is_active = is_active.lower() == 'true'\\r\\n query = Metric.query.filter_by(is_active=is_active)\\r\\n if frequency is not None:\\r\\n try:\\r\\n frequency = Frequency.from_name(frequency)\\r\\n except ValueError as e:\\r\\n msg = f\\\"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}\\\"\\r\\n abort(400, message=msg)\\r\\n query = query.filter_by(frequency=frequency)\\r\\n if threshold_type is not None:\\r\\n try:\\r\\n threshold_type = ThresholdType.from_name(threshold_type)\\r\\n except ValueError as e:\\r\\n msg = f\\\"Invalid 'threshold_type': {threshold_type}. Use one of \\\" \\\\\\r\\n f\\\"{ThresholdType.values()}\\\"\\r\\n abort(400, message=msg)\\r\\n query = query.filter_by(threshold_type=threshold_type)\\r\\n if sort is not None and sort.lstrip(\\\"-\\\") == 'metric_id':\\r\\n query = query.order_by(Metric.metric_id.desc())\\r\\n else:\\r\\n query = query.order_by(Metric.metric_id)\\r\\n\\r\\n return query\",\n \"def _query_builder(self,\\n start_date=None,\\n end_date=None,\\n clipper=None\\n ):\\n search_string='format=json&lang=fr&q='\\n if (start_date is not None):\\n if(end_date is not None):\\n search_string+='entre+'+start_date+'+et+'+end_date\\n else:\\n search_string+=start_date\\n elif(end_date is not None): \\n search_string+=end_date\\n \\n if(clipper.query is not None):\\n query=clipper.query.replace(' ','+')\\n search_string+='+'+query\\n if(clipper.bbox is not None):\\n search_string+='&box='+clipper.bbox\\n\\n return search_string\",\n \"def build_query(self):\\r\\n # build query from base class add required field for joining with parent\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\\r\\n\\r\\n # get the remaining query parameters\\r\\n asset_class = request.args.get('asset_class')\\r\\n model_name = request.args.get('model_name')\\r\\n pricing_library = request.args.get('pricing_library')\\r\\n\\r\\n # process each parameter and, if valid, add as a query condition\\r\\n if asset_class is not None:\\r\\n query = query.filter(QuantModelMetric.asset_class == asset_class)\\r\\n if model_name is not None:\\r\\n query = query.filter(QuantModelMetric.model_name == model_name)\\r\\n if pricing_library is not None:\\r\\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\\r\\n return query\",\n \"def _build_where_clause(**kwds_filter):\\n clause = []\\n params = []\\n items = kwds_filter.items()\\n items = sorted(items, key=lambda x: x[0]) # Ordered by key.\\n for key, val in items:\\n if nonstringiter(val):\\n clause.append(key + ' IN (%s)' % (', '.join('?' * len(val))))\\n for x in val:\\n params.append(x)\\n else:\\n clause.append(key + '=?')\\n params.append(val)\\n\\n clause = ' AND '.join(clause) if clause else ''\\n return clause, params\",\n \"def create_search_query(words, *filters):\\n if not filters:\\n filters = filters + ('title',)\\n\\n query = ''\\n\\n for filter in filters:\\n query += filter + ' LIKE \\\"%'\\n\\n for letter in words:\\n if letter == '%':\\n letter = '%\\\" AND ' + filter + ' LIKE \\\"%'\\n elif letter == ' ':\\n letter = '%\\\" OR ' + filter + ' LIKE \\\"%'\\n\\n query += letter\\n\\n if filter == filters[-1]:\\n query += '%\\\"'\\n else:\\n query += '%\\\" OR '\\n\\n return query\",\n \"def _build_where_clause(**kwds_filter):\\n clause = []\\n params = []\\n items = kwds_filter.items()\\n items = sorted(items, key=lambda x: x[0]) # Ordered by key.\\n for key, val in items:\\n if _is_nsiterable(val):\\n clause.append(key + ' IN (%s)' % (', '.join('?' * len(val))))\\n for x in val:\\n params.append(x)\\n else:\\n clause.append(key + '=?')\\n params.append(val)\\n\\n clause = ' AND '.join(clause) if clause else ''\\n return clause, params\",\n \"def _build_query_filters(self, query: dict, filters: list) -> dict:\\n\\n for filter_tuple in filters:\\n if not isinstance(filter_tuple, tuple) or len(filter_tuple) != 3:\\n LOG.error(\\\"polling_filters tuple %s : invalid format or does not contain 3 elements - skipping this filter\\\", filter_tuple)\\n continue\\n if isinstance(filter_tuple[2], list) :\\n # If \\\"value\\\" is a list of values then create a rule (json object) for each \\n # value and use \\\"OR\\\" condition.\\n condition = {'condition': \\\"OR\\\",\\n 'rules': []}\\n for value in filter_tuple[2]:\\n rule = {}\\n # Prepend fieldname with \\\"table.\\\" string\\n rule['field'] = f\\\"table.{filter_tuple[0]}\\\"\\n rule['operator'] = filter_tuple[1]\\n rule['value'] = value\\n condition['rules'].append(rule)\\n query['rules'].append(condition)\\n else:\\n # Create a single rule for this tuple\\n rule = {}\\n field_name = f\\\"table.{filter_tuple[0]}\\\"\\n rule['field'] = field_name\\n rule['operator'] = filter_tuple[1]\\n rule['value'] = filter_tuple[2]\\n query['rules'].append(rule)\\n return query\",\n \"def get_current_filters(self) -> str:\\r\\n return self.__filters_string\",\n \"def create_filter_query(self, collection_name: str, field: str, filter_type: str, filter_values: Union[List[str], str]=None):\\n if filter_type == 'contains':\\n # return [{'field' : field, 'filter_type' : 'contains', \\\"condition\\\":\\\"==\\\", \\\"condition_value\\\": filter_values}]\\n return [{'field': field, 'filter_type': 'regexp', 'condition': '==', 'condition_value': '.*' + str(filter_values) + '.*'}]\\n if filter_type == 'exact_match':\\n return [{'field' : field, 'filter_type' : 'exact_match', \\\"condition\\\":\\\"==\\\", \\\"condition_value\\\": filter_values}]\\n if filter_type == 'categories':\\n return [{'field' : field, 'filter_type' : 'categories', \\\"condition\\\":\\\"==\\\", \\\"condition_value\\\": filter_values}]\\n if filter_type == 'exists':\\n if filter_values is None or filter_values == '==':\\n return [{'field' : field, 'filter_type' : 'exists', \\\"condition\\\":\\\"==\\\", \\\"condition_value\\\":\\\" \\\"}]\\n elif filter_values == '!=':\\n return [{'field' : field, 'filter_type' : 'exists', \\\"condition\\\":\\\"!=\\\", \\\"condition_value\\\":\\\" \\\"}]\\n if filter_type == '<=' or filter_type == '>=' or filter_type == '>' or filter_type == '<' or filter_type == '==':\\n if self.collection_schema(collection_name)[field] == 'date':\\n return [{'field' : field, 'filter_type' : 'date', \\\"condition\\\":filter_type, \\\"condition_value\\\": filter_values}]\\n elif self.collection_schema(collection_name)[field] == 'numeric':\\n return [{'field' : field, 'filter_type' : 'numeric', \\\"condition\\\":filter_type, \\\"condition_value\\\":filter_values}]\\n else:\\n raise ValueError(f\\\"{filter_type} has not been defined. Please choose one of contains/exact_match/exists/categories/>=/<=/>/<.\\\")\",\n \"def build_filters(self, view, filters=None):\\n query_builder = self.get_query_builder(backend=self, view=view)\\n return query_builder.build_query(**(filters if filters else {}))\",\n \"def get_where_clause(self, params: Dict) -> str:\\n return ''\",\n \"def _valid_filter_query(self):\\n if self._output_invalid_imeis:\\n valid_filter_sql = sql.SQL('TRUE')\\n else:\\n valid_filter_sql = sql.SQL('is_valid IS TRUE')\\n return valid_filter_sql\",\n \"def filter(self, **kwargs):\\n kwargs['query'] += ' FROM {0}'\\n return kwargs\",\n \"def query(self, paramstyle=None):\\n s = []\\n for x in self.items:\\n if isinstance(x, SQLParam):\\n x = x.get_marker(paramstyle)\\n s.append(safestr(x))\\n else:\\n x = safestr(x)\\n # automatically escape % characters in the query\\n # For backward compatability, ignore escaping when the query looks already escaped\\n if paramstyle in ['format', 'pyformat']:\\n if '%' in x and '%%' not in x:\\n x = x.replace('%', '%%')\\n s.append(x)\\n return \\\"\\\".join(s)\",\n \"def to_string(self):\\n filter_string = '('\\n variable_length = len(self.values) - 1\\n for index in range(0, variable_length + 1):\\n filter_string += '&' + self.values[index]\\n # append or for all but the last\\n if index < variable_length:\\n filter_string += self.combination_operator\\n filter_string += ')'\\n\\n return filter_string\",\n \"def __format_conditional_filters(field: dict) -> dict:\\n if \\\"conditionalFormats\\\" in field:\\n for cf in field[\\\"conditionalFormats\\\"]:\\n if \\\"filter\\\" in cf and isinstance(\\n cf[\\\"filter\\\"], QueryFilter\\n ): # Supports one QueryFilter without list form\\n cf[\\\"filter\\\"] = encode_conditional_format_filter(cf[\\\"filter\\\"])\\n\\n elif \\\"filter\\\" in cf and isinstance(cf[\\\"filter\\\"], list): # Supports list of QueryFilters\\n filters = []\\n for query_filter in cf[\\\"filter\\\"]:\\n filters.append(encode_conditional_format_filter(query_filter))\\n if len(filters) > 2:\\n raise Exception(\\\"Too many QueryFilters given for one conditional format.\\\")\\n cf[\\\"filter\\\"] = filters[0] + \\\"&\\\" + filters[1] if len(filters) == 2 else filters[0]\\n\\n return field\",\n \"def _build_filters(self, criteria: Q):\\n composed_query = query.Q()\\n\\n if criteria.connector == criteria.AND:\\n for child in criteria.children:\\n if isinstance(child, Q):\\n composed_query = composed_query & self._build_filters(child)\\n else:\\n stripped_key, lookup_class = self.provider._extract_lookup(child[0])\\n lookup = lookup_class(stripped_key, child[1])\\n if criteria.negated:\\n composed_query = composed_query & ~lookup.as_expression()\\n else:\\n composed_query = composed_query & lookup.as_expression()\\n else:\\n for child in criteria.children:\\n if isinstance(child, Q):\\n composed_query = composed_query | self._build_filters(child)\\n else:\\n stripped_key, lookup_class = self.provider._extract_lookup(child[0])\\n lookup = lookup_class(stripped_key, child[1])\\n if criteria.negated:\\n composed_query = composed_query | ~lookup.as_expression()\\n else:\\n composed_query = composed_query | lookup.as_expression()\\n\\n return composed_query\",\n \"def get_query(self, q, request):\\r\\n \\r\\n return self.model.objects.filter(filter__icontains=q).order_by('filter')[:50]\",\n \"def format_query(query, option=''):\\n q_tmp = []\\n for s in query:\\n if option != 'url' or s.isalnum():\\n q_tmp.append(s)\\n else:\\n q_tmp.append('\\\\\\\"' + s.lower() + '\\\\\\\"')\\n\\n if option == 'file':\\n sep = '_'\\n else:\\n sep = ' '\\n q = sep.join(q_tmp)\\n if option == 'url':\\n q = urllib2.quote('\\\\'' + q + '\\\\'')\\n return q\",\n \"async def getQueryFilters(self, ):\\n payload = {}\\n \\n\\n # Parameter validation\\n schema = CatalogValidator.getQueryFilters()\\n schema.dump(schema.load(payload))\\n \\n\\n url_with_params = await create_url_with_params(self._conf.domain, f\\\"/service/platform/catalog/v1.0/company/{self._conf.companyId}/application/{self.applicationId}/collections/query-options/\\\", \\\"\\\"\\\"{\\\"required\\\":[{\\\"in\\\":\\\"path\\\",\\\"name\\\":\\\"company_id\\\",\\\"description\\\":\\\"A `company_id` is a unique identifier for a particular seller account.\\\",\\\"schema\\\":{\\\"type\\\":\\\"string\\\"},\\\"required\\\":true},{\\\"in\\\":\\\"path\\\",\\\"name\\\":\\\"application_id\\\",\\\"description\\\":\\\"A `application_id` is a unique identifier for a particular sale channel.\\\",\\\"schema\\\":{\\\"type\\\":\\\"string\\\"},\\\"required\\\":true}],\\\"optional\\\":[],\\\"query\\\":[],\\\"headers\\\":[],\\\"path\\\":[{\\\"in\\\":\\\"path\\\",\\\"name\\\":\\\"company_id\\\",\\\"description\\\":\\\"A `company_id` is a unique identifier for a particular seller account.\\\",\\\"schema\\\":{\\\"type\\\":\\\"string\\\"},\\\"required\\\":true},{\\\"in\\\":\\\"path\\\",\\\"name\\\":\\\"application_id\\\",\\\"description\\\":\\\"A `application_id` is a unique identifier for a particular sale channel.\\\",\\\"schema\\\":{\\\"type\\\":\\\"string\\\"},\\\"required\\\":true}]}\\\"\\\"\\\", )\\n query_string = await create_query_string()\\n headers = {\\n \\\"Authorization\\\": \\\"Bearer \\\" + await self._conf.getAccessToken()\\n }\\n for h in self._conf.extraHeaders:\\n headers.update(h)\\n exclude_headers = []\\n for key, val in headers.items():\\n if not key.startswith(\\\"x-fp-\\\"):\\n exclude_headers.append(key)\\n return await AiohttpHelper().aiohttp_request(\\\"GET\\\", url_with_params, headers=get_headers_with_signature(self._conf.domain, \\\"get\\\", await create_url_without_domain(f\\\"/service/platform/catalog/v1.0/company/{self._conf.companyId}/application/{self.applicationId}/collections/query-options/\\\", ), query_string, headers, \\\"\\\", exclude_headers=exclude_headers), data=\\\"\\\")\",\n \"def parse_filter_expr(expr: dict) -> str:\\n if not hasattr(expr, 'name'):\\n if type(expr) is BNode:\\n return f\\\"?v_{expr}\\\"\\n else:\\n return expr.n3()\\n else:\\n if expr.name == 'RelationalExpression':\\n return f\\\"({parse_filter_expr(expr.expr)} {expr.op} {parse_filter_expr(expr.other)})\\\"\\n elif expr.name == 'AdditiveExpression':\\n expression = parse_filter_expr(expr.expr)\\n for i in range(len(expr.op)):\\n expression = f\\\"({expression} {expr.op[i]} {parse_filter_expr(expr.other[i])})\\\"\\n return expression\\n elif expr.name == 'ConditionalAndExpression':\\n expression = parse_filter_expr(expr.expr)\\n for other in expr.other:\\n expression = f\\\"({expression} && {parse_filter_expr(other)})\\\"\\n return expression\\n elif expr.name == 'ConditionalOrExpression':\\n expression = parse_filter_expr(expr.expr)\\n for other in expr.other:\\n expression = f\\\"({expression} || {parse_filter_expr(other)})\\\"\\n return expression\\n elif expr.name.startswith('Builtin_'):\\n return f\\\"{expr.name[8:]}({parse_filter_expr(expr.arg)})\\\"\\n raise UnsupportedSPARQL(f\\\"Unsupported SPARQL FILTER expression: {expr.name}\\\")\",\n \"def build_query_filters(**kwargs):\\n queryTarget = kwargs.get(\\\"queryTarget\\\", None)\\n targetSubtreeClass = kwargs.get(\\\"targetSubtreeClass\\\", None)\\n queryTargetFilter = kwargs.get(\\\"queryTargetFilter\\\", None)\\n rspSubtree = kwargs.get(\\\"rspSubtree\\\", None)\\n rspSubtreeClass = kwargs.get(\\\"rspSubtreeClass\\\", None)\\n rspSubtreeInclude = kwargs.get(\\\"rspSubtreeInclude\\\", None)\\n rspPropInclude = kwargs.get(\\\"rspPropInclude\\\", None)\\n orderBy = kwargs.get(\\\"orderBy\\\", None)\\n opts = \\\"\\\"\\n if queryTarget is not None:\\n opts+= \\\"&query-target=%s\\\" % queryTarget\\n if targetSubtreeClass is not None:\\n opts+= \\\"&target-subtree-class=%s\\\" % targetSubtreeClass\\n if queryTargetFilter is not None:\\n opts+= \\\"&query-target-filter=%s\\\" % queryTargetFilter\\n if rspSubtree is not None:\\n opts+= \\\"&rsp-subtree=%s\\\" % rspSubtree\\n if rspSubtreeClass is not None:\\n opts+= \\\"&rsp-subtree-class=%s\\\" % rspSubtreeClass\\n if rspSubtreeInclude is not None:\\n opts+= \\\"&rsp-subtree-include=%s\\\" % rspSubtreeInclude\\n if rspPropInclude is not None:\\n opts+= \\\"&rsp-prop-include=%s\\\" % rspPropInclude\\n if orderBy is not None:\\n opts+= \\\"&order-by=%s\\\" % orderBy\\n\\n if len(opts)>0: opts = \\\"?%s\\\" % opts.strip(\\\"&\\\")\\n return opts\",\n \"def build_query_filters(**kwargs):\\n queryTarget = kwargs.get(\\\"queryTarget\\\", None)\\n targetSubtreeClass = kwargs.get(\\\"targetSubtreeClass\\\", None)\\n queryTargetFilter = kwargs.get(\\\"queryTargetFilter\\\", None)\\n rspSubtree = kwargs.get(\\\"rspSubtree\\\", None)\\n rspSubtreeInclude = kwargs.get(\\\"rspSubtreeInclude\\\", None)\\n rspPropInclude = kwargs.get(\\\"rspPropInclude\\\", None)\\n opts = \\\"\\\"\\n if queryTarget is not None:\\n opts+= \\\"&query-target=%s\\\" % queryTarget\\n if targetSubtreeClass is not None:\\n opts+= \\\"&target-subtree-class=%s\\\" % targetSubtreeClass\\n if queryTargetFilter is not None:\\n opts+= \\\"&query-target-filter=%s\\\" % queryTargetFilter\\n if rspSubtree is not None:\\n opts+= \\\"&rsp-subtree=%s\\\" % rspSubtree\\n if rspSubtreeInclude is not None:\\n opts+= \\\"&rsp-subtree-include=%s\\\" % rspSubtreeInclude\\n if rspPropInclude is not None:\\n opts+= \\\"&rsp-prop-include=%s\\\" % rspPropInclude\\n\\n if len(opts)>0: opts = \\\"?%s\\\" % opts.strip(\\\"&\\\")\\n return opts\",\n \"def _scryfall_query_builder(colors, query_type, deck_format):\\n base_query = consts.BASE_QUERIES[deck_format]\\n color_query = consts.COLOR_QUERIES[colors]\\n query_params = consts.QUERIES[deck_format][query_type]['query'] % {'colors': color_query}\\n\\n full_query = ' '.join([base_query, query_params])\\n\\n return full_query\",\n \"def build_query(self):\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\\r\\n algorithm = request.args.get('algorithm')\\r\\n if algorithm is not None:\\r\\n query = query.filter(MlModelMetric.algorithm == algorithm)\\r\\n return query\",\n \"def filter(self, filters):\\r\\n # because http.Request needs params to be a dict of strings to strings\\r\\n # (roughly) and since BitBucket wants repeated parameters to express\\r\\n # OR, we'll do the quoting by hand ourselves\\r\\n def flatten_conditions(filters):\\r\\n for key, val in filters.items():\\r\\n if isinstance(val, (list, tuple)):\\r\\n for v in val:\\r\\n yield (port.to_b(key), port.to_b(v))\\r\\n else:\\r\\n yield (port.to_b(key), port.to_b(val))\\r\\n\\r\\n to_encode = tuple(flatten_conditions(filters))\\r\\n qs = port.urlencode(to_encode)\\r\\n\\r\\n url = '{0}/?{1}'.format(self.get_url(), qs)\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def filter_format(filter_dict, query_data, filter_type, index): \\r\\n \\r\\n filter_list = ''\\r\\n count = 0 \\r\\n \\r\\n while query_data[index] != 'PRESENT\\\\n': \\r\\n if filter_type in query_data[index]: \\r\\n count += 1\\r\\n filter_keyword = query_data[index].strip(filter_type)\\r\\n filter_list = (filter_keyword.strip('\\\\n'))\\r\\n \\r\\n index += 1 \\r\\n \\r\\n if count > 0: \\r\\n filter_dict[filter_type] = filter_list\\r\\n return filter_dict\",\n \"def asSQL(self) -> str:\\n # To modify this method, pay attentin to SQL injection.\\n # For example, if `self.x` is assumed to be integer\\n # but is not guaranteed to be,\\n # `:d` must always be specified in format strings:\\n # `expressions.append(f\\\"x > {self.x:d}\\\")`\\n expressions = []\\n if self.date_start is not None:\\n if self.date_start.tzinfo is None:\\n datestr = self.date_start.isoformat()\\n else:\\n datestr = self.date_start.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\\n expressions.append(f\\\"pfs_visit.issued_at >= '{datestr}'\\\")\\n if self.date_end is not None:\\n if self.date_end.tzinfo is None:\\n datestr = self.date_end.isoformat()\\n else:\\n datestr = self.date_end.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\\n expressions.append(f\\\"pfs_visit.issued_at < '{datestr}'\\\")\\n if self.visit_start is not None:\\n expressions.append(f\\\"pfs_visit.pfs_visit_id >= '{self.visit_start:d}'\\\")\\n if self.visit_end is not None:\\n expressions.append(f\\\"pfs_visit.pfs_visit_id < '{self.visit_end:d}'\\\")\\n\\n if expressions:\\n return \\\"(\\\" + \\\" AND \\\".join(expressions) + \\\")\\\"\\n else:\\n return \\\"TRUE\\\"\",\n \"def metadata_filter_as_string(metadata_filter):\\n if not isinstance(metadata_filter, dict):\\n return metadata_filter\\n\\n additional = metadata_filter.get('additional', [])\\n if additional == 'all':\\n entries = ['all']\\n else:\\n entries = [key for key in additional if key not in _JUPYTEXT_CELL_METADATA]\\n\\n excluded = metadata_filter.get('excluded', [])\\n if excluded == 'all':\\n entries.append('-all')\\n else:\\n entries.extend(['-' + e for e in excluded])\\n\\n return ','.join(entries)\",\n \"def get_query(self, query_args):\\n num_query_template = Template(self.query_spec.numerator_template)\\n num_query = num_query_template.substitute(**query_args)\\n den_query_template = Template(self.query_spec.denominator_template)\\n den_query = den_query_template.substitute(**query_args)\\n query = f\\\"({num_query}) / ({den_query})\\\"\\n logger.debug(f\\\"Query: {query}\\\")\\n return query\",\n \"def filter_query(self, request, query, view):\\n\\n if not request.params:\\n return query\\n\\n querystring_params = self.parse_query_string(request.params)\\n query, filter_list = self.build_filter_list(querystring_params, query, view)\\n\\n return self.apply_filter(query, filter_list)\",\n \"def conditional_format(\\n query_filter: Union[str, QueryFilter, List[QueryFilter]],\\n bold: bool = False,\\n italic: bool = False,\\n strike_through: bool = False,\\n text_color: str = \\\"\\\",\\n background_color: str = \\\"\\\",\\n) -> ConditionalFormat:\\n filter_str = query_filter\\n\\n if isinstance(query_filter, QueryFilter):\\n filter_str = encode_conditional_format_filter(query_filter)\\n elif isinstance(query_filter, list):\\n if len(query_filter) > 2:\\n raise Exception(\\\"Too many QueryFilters given for one conditional format.\\\")\\n\\n if not all([isinstance(qf, QueryFilter) for qf in query_filter]):\\n raise Exception(\\n \\\"Please pass QueryFilter objects when updating a conditional format using a list of filters.\\\"\\n )\\n\\n string_filters = [encode_conditional_format_filter(f) for f in query_filter]\\n filter_str = (\\n string_filters[0] + \\\"&\\\" + string_filters[1]\\n if len(query_filter) == 2\\n else string_filters[0]\\n )\\n\\n return ConditionalFormat(\\n background_color=background_color,\\n bold=bold,\\n filter=filter_str,\\n italic=italic,\\n strike_through=strike_through,\\n text_color=text_color,\\n )\",\n \"def create_query_body(self, **kwargs):\\n\\n query = \\\"\\\"\\n\\n for key, value in kwargs.items():\\n if value is True and key != 'nsfw':\\n query += '&has=%s' % key[:-1]\\n\\n if key == 'nsfw':\\n query += '&include_nsfw=%s' % str(value).lower()\\n\\n return query\",\n \"def _process_alert_source_filters(query, filters):\\n if filters:\\n if not is_valid_model_filters(models.AlertSource, filters):\\n return\\n query = query.filter_by(**filters)\\n\\n return query\",\n \"def _get_filter_args(self, filter):\\n kwargs = {}\\n for arg in filter.args[1:]:\\n kwargs[arg] = input(f'Type the {arg.replace(\\\"_\\\", \\\" \\\").title()}: ')\\n return kwargs\",\n \"def summary_parameters(self):\\n return {'filters': ', '.join(self.getOption('filters'))}\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n level = self.request.GET.get(\\\"level\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n status = self.request.GET.get(\\\"status\\\")\\n job_tag = self.request.GET.get(\\\"job_tag\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(program_id=query) |\\n Q(category=query) |\\n Q(title__icontains=query) |\\n #Q(category__category_name__icontains=query) |\\n Q(description__icontains=query) |\\n Q(job_tag__tag__in=str(query).split(\\\",\\\"))\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(query)), Q.OR)\\n\\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n else:\\n if program_id:\\n q_object.add(\\n Q(program_id=program_id),\\n Q.AND)\\n\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n if description:\\n q_object.add(\\n Q(description__icontains=description), Q.AND)\\n\\n if job_tag:\\n q_object.add(\\n Q(job_tag__tag__in=str(job_tag).split(\\\",\\\")),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n return q_object\",\n \"def as_sql(self, qn, connection):\\n lhs, lhs_params = self.process_lhs(qn, connection)\\n rhs, rhs_params = self.process_rhs(qn, connection)\\n\\n field = lhs\\n # JSONField formats query values for the database by wrapping them psycopg2's\\n # JsonAdapter, but we need the raw Python dict so that we can parse the\\n # query tree. Intercept the query parameter (it'll always be the first\\n # element in the parameter list, since the custom jsonb filter only accepts one argument)\\n # and revert it back to a Python dict for tree parsing.\\n tree = rhs_params[0].adapted\\n\\n return FilterTree(tree, field).sql()\",\n \"def get_filter_name(self):\\n pass\",\n \"def toQueryString(self):\\n return self.__str__()\",\n \"def apply_filters(self, query, model_class, filters, whitelist=None,\\n nested_conditions=None, stack_size_limit=100,\\n convert_key_names_func=str, gettext=None):\\n return apply_mql_filters(\\n query,\\n model_class,\\n filters=filters,\\n nested_conditions=nested_conditions,\\n whitelist=whitelist,\\n stack_size_limit=stack_size_limit,\\n convert_key_names_func=convert_key_names_func,\\n gettext=gettext\\n )\",\n \"def _create_query_str(data):\\n params = []\\n for name, value in data.items():\\n params.append(name + '=' + str(value))\\n\\n return '?' + '&'.join(params)\",\n \"def get_filter_parameters(self):\\n if not self.should_filter():\\n return []\\n\\n fields = []\\n for filter_backend in self.view.filter_backends:\\n fields += self.get_filter_backend_parameters(filter_backend())\\n\\n return fields\",\n \"def _filter_params(self):\\n default_params = self.get_default_params()\\n complete_params = dict(self.get_default_params())\\n complete_params.update(self.params)\\n\\n return utils.format_dictionary(complete_params)\",\n \"def build_where_statement(attr_converter):\\n statements = []\\n values = []\\n # iterate over the querystring params\\n for attr, val in request.args.iteritems():\\n try:\\n statements.append(attr_converter[attr]['converter'](\\n attr_converter[attr]['column']))\\n values.append(val) # add after the possible keyerror\\n except KeyError:\\n # TODO: handle invalid params somehow or continue to ignore\\n pass\\n if statements:\\n return 'WHERE '+' AND '.join(statements), values\\n return '', []\",\n \"def _get_query(request):\\n query = request.GET.get(\\\"query\\\", \\\"\\\")\\n date = request.GET.get(\\\"date\\\", \\\"\\\")\\n timestamp = request.GET.get(\\\"timestamp\\\", None)\\n sort = request.GET.get(\\\"sort\\\", \\\"top\\\").lower()\\n filter = request.GET.get(\\\"filter\\\", \\\"following\\\").lower()\\n\\n if timestamp:\\n t = parse(timestamp, ignoretz=True)\\n timestamp = pytz.utc.localize(t)\\n else:\\n timestamp = timezone.now()\\n\\n start_time = ''\\n end_time = ''\\n\\n if date:\\n start_time, end_time = DateRangeParser().parse(date)\\n\\n get_dict = {\\n \\\"query\\\": query,\\n \\\"filter\\\": filter,\\n \\\"sort\\\": sort,\\n \\\"start_time\\\": start_time,\\n \\\"end_time\\\": end_time,\\n \\\"username\\\": request.GET.get(\\\"username\\\", \\\"\\\"),\\n \\\"orderBy\\\": request.GET.get(\\\"orderBy\\\", \\\"start_time\\\"),\\n \\\"direction\\\": request.GET.get(\\\"direction\\\", \\\"\\\"),\\n \\\"template\\\": request.GET.get(\\\"template\\\", \\\"\\\"),\\n \\\"type\\\": request.GET.get(\\\"type\\\", \\\"\\\"),\\n \\\"page\\\": request.GET.get(\\\"page\\\", 1),\\n 'timestamp': timestamp,\\n }\\n\\n return get_dict, query, date, sort, filter\",\n \"def _format_queries(self, body):\\n for query in body:\\n if \\\"bindVars\\\" in query:\\n query[\\\"bind_vars\\\"] = query.pop(\\\"bindVars\\\")\\n if \\\"runTime\\\" in query:\\n query[\\\"runtime\\\"] = query.pop(\\\"runTime\\\")\\n return body\",\n \"def filter_query(self, query):\\n\\n if self.state:\\n query = query.filter(self.model_class.state == self.state)\\n if self.term:\\n term = '%{}%'.format(self.term)\\n query = query.filter(\\n or_(\\n *[column.ilike(term) for column in self.term_columns]\\n )\\n )\\n if self.user_ids:\\n query = query.filter(self.model_class.user_id.in_(self.user_ids))\\n if self.group_ids:\\n query = query.filter(self.model_class.group_id.in_(self.group_ids))\\n if self.issues:\\n query = query.filter(self.model_class._issues.has_any(self.issues))\\n if self.categories:\\n query = query.filter(\\n self.model_class._categories.has_any(self.categories)\\n )\\n if self.organizations:\\n query = query.filter(\\n self.model_class._organizations.has_any(self.organizations)\\n )\\n\\n return query\",\n \"def generateSQLFilter(sql_filter, es_type, category):\\n query_string = \\\"Category = ? AND (\\\"\\n insertion_list = [category]\\n\\n i = 0\\n while i < len(sql_filter):\\n insertion_list.append(sql_filter[i]) # append values to be inserted into SQL statement (? substitution)\\n if (i + 1) != 1: # NOT first loop\\n query_string += \\\" OR \\\"\\n query_string += es_type + \\\" = ?\\\"\\n i += 1\\n\\n query_string += \\\")\\\"\\n #\\n #\\n\\n return query_string, insertion_list\",\n \"def filter(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"filter\\\")\",\n \"def default_filters(self, query) -> object:\\n user = self.request.user\\n model = self.model\\n custom_filter = self.request.params.get('_custom_filter')\\n if 'g:professionals' in user.groups and custom_filter == 'pool':\\n # disable security for this custom filter\\n self.enable_security = False\\n professional = Professional.get(user.id)\\n pool_ids = [item.id for item in professional.pools]\\n query = query.filter(\\n model.pool_id.in_(pool_ids),\\n model.state == 'published'\\n )\\n elif custom_filter == 'late_first_submission':\\n config_delta = timedelta(seconds=int(LATE_SUBMISSION_SECONDS))\\n date_limit = datetime_utcnow() - config_delta\\n query = query.filter(\\n model.scheduled_datetime <= date_limit,\\n model.state == 'awaiting_assets',\\n model.last_approval_date.is_(None)\\n )\\n elif custom_filter == 'late_re_submission':\\n config_delta = timedelta(seconds=int(LATE_SUBMISSION_SECONDS))\\n date_limit = datetime_utcnow() - config_delta\\n query = query.filter(\\n model.last_approval_date <= date_limit,\\n model.state == 'awaiting_assets',\\n model.submission_path.isnot(None),\\n )\\n return query\",\n \"def get_filters():\\n\\n city = prompts.city_prompt.launch()\\n\\n _filter = prompts.filter_prompt.launch()\\n\\n if _filter == \\\"Month\\\":\\n month = prompts.month_prompt.launch()\\n day = \\\"All\\\"\\n\\n elif _filter == \\\"Day\\\":\\n day = prompts.day_prompt.launch()\\n month = \\\"All\\\"\\n\\n elif _filter == \\\"Both\\\":\\n month = prompts.month_prompt.launch()\\n day = prompts.day_prompt.launch()\\n\\n else:\\n month, day = \\\"All\\\", \\\"All\\\"\\n\\n print(\\\"-\\\" * 40)\\n return city, month, day\",\n \"def __getSelectionFilter(self):\\n \\n selectionPairs = []\\n selectionPairs.append(('field','field'))\\n selectionPairs.append(('spw','spw'))\\n selectionPairs.append(('polarization','correlation'))\\n selectionPairs.append(('baseline','antenna'))\\n selectionPairs.append(('time','timerange'))\\n selectionPairs.append(('scan','scan'))\\n selectionPairs.append(('uvdist','uvrange'))\\n selectionPairs.append(('scanintent','intent'))\\n selectionPairs.append(('observation','observation'))\\n return self.__generateFilter(selectionPairs)\",\n \"def random_filter_query(\\n self, collection_name: str, text_filters: int = 1, numeric_filters: int = 0\\n ):\\n schema = self.collection_schema(collection_name)\\n facets = self.facets(collection_name)\\n full_filter_query = {\\\"text\\\": [], \\\"numeric\\\": []}\\n for f, t in schema.items():\\n if t == \\\"text\\\":\\n if isinstance(facets[f], list):\\n full_filter_query[\\\"text\\\"].append(\\n {\\n \\\"field\\\": f,\\n \\\"filter_type\\\": \\\"text\\\",\\n \\\"condition_value\\\": random.sample(facets[f], 1)[0][f],\\n \\\"condition\\\": \\\"==\\\",\\n }\\n )\\n elif t == \\\"numeric\\\":\\n if isinstance(facets[f], dict):\\n full_filter_query[\\\"numeric\\\"].append(\\n {\\n \\\"field\\\": f,\\n \\\"filter_type\\\": \\\"date\\\",\\n \\\"condition_value\\\": (facets[f][\\\"max\\\"] - facets[f][\\\"min\\\"])\\n / 2,\\n \\\"condition\\\": \\\">=\\\",\\n }\\n )\\n return random.sample(full_filter_query[\\\"text\\\"], text_filters) + random.sample(\\n full_filter_query[\\\"numeric\\\"], numeric_filters\\n )\",\n \"def filters_show():\\n log = slog()\\n log.title_set('Filters applied')\\n if self.args['table3D']: log.render3D()\\n log('Input directory: %s\\\\n' % self.str_inputDir)\\n log('Output directory: %s\\\\n' % self.str_outputDir)\\n for filter in ['file', 'dir']:\\n log('%sFilter: ' % filter)\\n sl_ffilter = ['%s %s' % (x, self.args['%sFilterLogic' % filter]) \\\\\\n for x in self.args['%sFilter' % filter].split(',')]\\n str_ffilter = ' '.join(sl_ffilter)\\n sl_ffilter = str_ffilter.split()\\n str_ffilter = ' '.join(sl_ffilter[:-1])\\n log('%s\\\\n ' % str_ffilter)\\n return log\",\n \"def format_query_for_displaying(query):\\n keys = query.keys()\\n result = ''\\n for k in keys:\\n v = query[k]\\n print(\\\"Checking v=%r and k=%r\\\" % (v, k))\\n result += '[%s] ' % k\\n if v and type(v) == dict:\\n kk = v.keys()\\n r = ''\\n for l in kk:\\n m = v[l]\\n r += str(m)\\n r += ' '\\n result += r\\n else:\\n print(\\\"result: [%s] v:[%s]\\\" % (result, str(v)))\\n result += str(v)\\n result += '\\\\n'\\n if result == '':\\n result = 'JUST VISITED THE SEARCH PAGE.'\\n return result\",\n \"def get_grouped_filters(filters):\\n grouped_filters = Q()\\n\\n if 'keyword' in filters:\\n grouped_filters |= Q(keywords__name__in=[k.lower() for k in filters['keyword'].split(',')])\\n filters.pop('keyword')\\n\\n pipe_map = lambda x, y: x | y\\n if 'title__icontains' in filters:\\n title_filters = [Q(title__icontains=f) for f in filters.pop('title__icontains')]\\n grouped_filters |= reduce(pipe_map, title_filters)\\n if 'description__icontains' in filters:\\n d_filters = [Q(description__icontains= f) for f in filters.pop('description__icontains')]\\n grouped_filters |= reduce(pipe_map, d_filters)\\n if 'story_text__icontains' in filters:\\n grouped_filters |= Q(story_text__exact=\\\"\\\")\\n story_filters = [Q(story_text__icontains=f) for f in filters.pop('story_text__icontains')]\\n grouped_filters |= reduce(pipe_map, story_filters)\\n\\n return grouped_filters\",\n \"def query(self):\\n q = dict(self._query)\\n fworker_check = [{\\\"spec._fworker\\\": {\\\"$exists\\\": False}}, {\\\"spec._fworker\\\": None}, {\\\"spec._fworker\\\": self.name}]\\n if \\\"$or\\\" in q:\\n q[\\\"$and\\\"] = q.get(\\\"$and\\\", [])\\n q[\\\"$and\\\"].extend([{\\\"$or\\\": q.pop(\\\"$or\\\")}, {\\\"$or\\\": fworker_check}])\\n else:\\n q[\\\"$or\\\"] = fworker_check\\n if self.category and isinstance(self.category, str):\\n if self.category == \\\"__none__\\\":\\n q[\\\"spec._category\\\"] = {\\\"$exists\\\": False}\\n else:\\n q[\\\"spec._category\\\"] = self.category\\n elif self.category: # category is list of str\\n q[\\\"spec._category\\\"] = {\\\"$in\\\": self.category}\\n\\n return q\",\n \"def _filterDictToStr(self, filterDict):\\n values = []\\n for key, vals in filterDict.items():\\n if key not in ('contentRating', 'label', 'contentRating!', 'label!'):\\n raise BadRequest(f'Unknown filter key: {key}')\\n values.append(f\\\"{key}={'%2C'.join(vals)}\\\")\\n return '|'.join(values)\",\n \"def _get_queries(args):\\n if args.mode == '2DSEQ':\\n queries = [\\\"@type=='2dseq'\\\", \\\"@is_spectroscopy==True\\\", \\\"@is_complex==True\\\"]\\n elif args.mode == 'FID':\\n queries = [\\\"@type=='fid'\\\", \\\"@is_spectroscopy==True\\\"]\\n return queries + args.query\",\n \"def get_query():\\n query = \\\"\\\"\\\"{\\n repository(name: \\\"flux\\\", owner: \\\"fluxcd\\\") {\\n forkCount\\n issues {\\n totalCount\\n }\\n pullRequests {\\n totalCount\\n }\\n releases {\\n totalCount\\n }\\n stargazers {\\n totalCount\\n }\\n watchers {\\n totalCount\\n }\\n }\\n}\\n \\\"\\\"\\\"\\n return query\",\n \"def sql(self):\\n rule_specs = []\\n\\n patterns = {}\\n pattern_specs = []\\n\\n # It's safe to unpack `self.get_rules` because it can only\\n # return A) an empty list or B) a list of two-tuples with two elements in\\n # them (the path and the rule for each query directive).\\n for path, rule in self.rules:\\n # Don't parse if this is not a properly registered rule type.\\n if not self.is_rule(rule):\\n pass\\n rule_type = rule['_rule_type']\\n sql_tuple = self.sql_generators[rule_type](path, rule)\\n if sql_tuple is not None:\\n rule_specs.append(sql_tuple)\\n\\n # The check on 'pattern' here allows us to apply a pattern filter on top of others\\n if 'pattern' in rule:\\n match_multiple = (rule['_rule_type'] == 'containment_multiple')\\n for pattern in self.split_search_pattern(rule['pattern']):\\n sql_tuple = FilterTree.text_similarity_filter(path, pattern, match_multiple)\\n # add to the list of rules generated for this pattern (one per field)\\n patterns.setdefault(pattern, []).append(sql_tuple)\\n\\n rule_string = ' AND '.join([rule[0] for rule in rule_specs])\\n\\n pattern_rules = patterns.values()\\n pattern_strings = []\\n\\n # check if any of the fields for this string pattern match\\n for rule_list in pattern_rules:\\n pattern_strings.append(' OR '.join([rule[0] for rule in rule_list]))\\n pattern_specs += rule_list\\n\\n # check that record has a match for all of the string patterns in some field\\n pattern_string = '(' + ') AND ('.join(pattern_strings) + ')' if pattern_strings else ''\\n\\n if rule_string != '' and pattern_string != '':\\n filter_string = '(' + (' AND ('.join([rule_string, pattern_string])) + ')' + ')'\\n elif rule_string != '' or pattern_string != '':\\n filter_string = '(' + ''.join([rule_string, pattern_string]) + ')'\\n else:\\n filter_string = ''\\n\\n # flatten the rule_paths\\n rule_paths_first = ([rule[1] for rule in rule_specs] +\\n [rule[1] for rule in pattern_specs])\\n rule_paths = [item for sublist in rule_paths_first\\n for item in sublist]\\n\\n outcome = (filter_string, tuple(rule_paths))\\n return outcome\",\n \"def filters():\\n return {\\\"reform_vlans\\\": FilterModule.reform_vlans}\",\n \"def __generate_search_query(self) -> None:\\n if self.query_accuracy < 100:\\n if self.title is not None and self.title != '' and self.artist is not None and self.artist != '':\\n # Use the title and the artist name to find more information about the song.\\n query: str = self.title + ' ' + self.artist\\n query = re.sub(self.__get_filter_regex(), '', query)\\n self.query = query\\n # Remove unnecessary information in order to get a simpler query version.\\n self.minimal_query = re.sub(r'\\\\([\\\\s\\\\S]+\\\\)', '', query).strip()\\n self.query_accuracy = 100\\n return\\n if self.query_accuracy < 50:\\n # No title nor artist name available, use the filename as search query.\\n filename: str = os.path.basename(self.original_path)\\n filename = os.path.splitext(filename)[0]\\n query: str = filename.lower()\\n query = re.sub(self.__get_filter_regex(), '', query)\\n query = query.replace('_', ' ')\\n query = query.strip()\\n self.query = query\\n self.minimal_query = re.sub(r'\\\\([\\\\s\\\\S]+\\\\)', '', query).strip()\\n self.query_accuracy = 50\",\n \"def _process_model_like_filter(model, query, filters):\\n if query is None:\\n return query\\n\\n if filters:\\n for key in sorted(filters):\\n column_attr = getattr(model, key)\\n if 'property' == type(column_attr).__name__:\\n continue\\n value = filters[key]\\n if not (isinstance(value, (str, int))):\\n continue\\n query = query.filter(\\n column_attr.op('LIKE')(u'%%%s%%' % value))\\n return query\",\n \"def filter_by_query_params(self, request):\\n items = self\\n company = request.GET.get('company', None)\\n main_contractor = request.GET.get('main_contractor', None)\\n main_sub_contractor = request.GET.get('main_sub_contractor', None)\\n client = request.GET.get('client', None)\\n q = request.GET.get('q', None)\\n sort_by = request.GET.get('sort_by', None)\\n str = request.GET.get('str', None)\\n\\n # filter\\n if main_contractor:\\n items = items.filter(main_contractor=main_contractor).distinct()\\n if main_sub_contractor:\\n items = items.filter(main_sub_contractor=main_sub_contractor).distinct()\\n if client:\\n items = items.filter(client=client).distinct()\\n if company:\\n items = items.filter(companies_linked__in=[company]).distinct()\\n # sort\\n if q == 'asc' and sort_by:\\n items = items.order_by(sort_by).distinct()\\n\\n if q == 'des' and sort_by:\\n items = items.order_by('-' + sort_by).distinct()\\n\\n if str:\\n # str = str.strip().lower()\\n items = items.filter(Q(reference_no__icontains=str) |\\n Q(name__icontains=str)).distinct()\\n return items\",\n \"def encoded_query_str(request):\\n return updated_query_str(request)\",\n \"def _create_filter_object(form_data: Dict) -> Q:\\n filter_object = Q(title__icontains=form_data[\\\"title\\\"])\\n filter_object &= Q(author__icontains=form_data[\\\"author\\\"])\\n filter_object &= Q(\\n publication_language__icontains=form_data[\\\"publication_language\\\"]\\n )\\n if form_data[\\\"publication_date_start\\\"]:\\n filter_object &= Q(\\n publication_date__gte=form_data[\\\"publication_date_start\\\"]\\n )\\n if form_data[\\\"publication_date_end\\\"]:\\n filter_object &= Q(publication_date__lte=form_data[\\\"publication_date_end\\\"])\\n return filter_object\",\n \"def fetch_querydict(self):\\n query = dict()\\n query[\\\"filtered\\\"] = dict()\\n if self.q_dict and isinstance(self.q_dict, dict):\\n query_list, filter_list = self.build_query_structure()\\n if query_list:\\n query[\\\"filtered\\\"][\\\"query\\\"] = {\\\"bool\\\": {\\\"must\\\": query_list}}\\n if filter_list:\\n query[\\\"filtered\\\"][\\\"filter\\\"] = {\\\"bool\\\": {\\\"must\\\": filter_list}}\\n return query\",\n \"def format_search_filter(self, term: event_search.SearchFilter) -> Optional[WhereType]:\\n name = term.key.name\\n\\n converted_filter = self.convert_search_filter_to_condition(\\n event_search.SearchFilter(\\n # We want to use group_id elsewhere so shouldn't be removed from the dataset\\n # but if a user has a tag with the same name we want to make sure that works\\n event_search.SearchKey(\\\"tags[group_id]\\\" if name == \\\"group_id\\\" else name),\\n term.operator,\\n term.value,\\n )\\n )\\n return converted_filter if converted_filter else None\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(title__icontains=query) \\n ), Q.OR)\\n\\n else:\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n return q_object\",\n \"def std_filters():\\n kwargs = {\\n \\\"sentence_filters\\\":[punctuation_filter],\\n \\\"word_filters\\\":[small_word_filter, stopword_filter, stemming_filter]\\n }\\n return kwargs\",\n \"def build_query(self):\\n query = self.action.build_query()\\n if self.sandboxoption != None:\\n query['Sandbox'] = self.sandboxoption\\n else:\\n query['Sandbox'] = '0'\\n return query\",\n \"def get_search_query(self):\\n q = None\\n if 'q' in self.request.GET and self.request.GET['q']:\\n q = self.request.GET['q'].strip()\\n return q\",\n \"def as_sql(self):\\n distinct = 'DISTINCT ' if self._distinct else ''\\n final = ' FINAL' if self._final else ''\\n table_name = '`%s`' % self._model_cls.table_name()\\n if self._model_cls.is_system_model():\\n table_name = '`system`.' + table_name\\n params = (distinct, self.select_fields_as_sql(), table_name, final)\\n sql = u'SELECT %s%s\\\\nFROM %s%s' % params\\n\\n if self._prewhere_q and not self._prewhere_q.is_empty:\\n sql += '\\\\nPREWHERE ' + self.conditions_as_sql(prewhere=True)\\n\\n if self._where_q and not self._where_q.is_empty:\\n sql += '\\\\nWHERE ' + self.conditions_as_sql(prewhere=False)\\n\\n if self._grouping_fields:\\n sql += '\\\\nGROUP BY %s' % comma_join('`%s`' % field for field in self._grouping_fields)\\n\\n if self._grouping_with_totals:\\n sql += ' WITH TOTALS'\\n\\n if self._order_by:\\n sql += '\\\\nORDER BY ' + self.order_by_as_sql()\\n\\n if self._limit_by:\\n sql += '\\\\nLIMIT %d, %d' % self._limit_by\\n sql += ' BY %s' % comma_join(string_or_func(field) for field in self._limit_by_fields)\\n\\n if self._limits:\\n sql += '\\\\nLIMIT %d, %d' % self._limits\\n\\n return sql\",\n \"def pp_query(query):\\n print(format_query(query))\",\n \"def query_text(self) -> str:\\n return self._query_text\",\n \"async def get_filter(self, **kwargs: Any) -> str:\\n return self._telescope.filter_name\",\n \"def normalize_query(self):\\n query_lower = self.user_input.lower()\\n # Remove punctuation\\n query_no_punctuation = re.sub(r\\\"[!#$%&'()*+,-./:;<=>?@\\\\^_`{|}~]+\\\\ *\\\", \\\" \\\", query_lower)\\n # Remove accent from all the words\\n self.query_no_accent = ''.join((c for c in \\\\\\n unicodedata.normalize('NFD', query_no_punctuation) \\\\\\n if unicodedata.category(c) != 'Mn'))\\n\\n return query_lower, query_no_punctuation, self.query_no_accent\",\n \"def get_params(self):\\n params = super(JoinFilterQuery, self).get_params()\\n params.append(('fq', self._query))\\n\\n return params\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67433846","0.67309666","0.66847456","0.6650939","0.6648182","0.64935666","0.6440689","0.63192457","0.6306208","0.628635","0.62689066","0.62631875","0.6200151","0.6186324","0.6155119","0.61444587","0.61128426","0.60982704","0.6098094","0.6062554","0.6021452","0.6014566","0.5986219","0.59845895","0.59746563","0.59621084","0.5945128","0.59444183","0.5932418","0.59311604","0.592014","0.5912474","0.58914024","0.586455","0.58379805","0.5836004","0.5808254","0.58042604","0.5797387","0.5758045","0.5744383","0.57418984","0.57342327","0.572395","0.5722323","0.57162","0.57109874","0.5705742","0.5700704","0.5695809","0.56803334","0.5677685","0.56758595","0.566844","0.5665444","0.5659052","0.5658861","0.5652961","0.5633588","0.562992","0.5618409","0.5604712","0.5592987","0.5590313","0.55897546","0.5586914","0.5586082","0.5575644","0.55743665","0.5569207","0.5559975","0.55588305","0.5551717","0.5551348","0.55499756","0.554058","0.55386853","0.55307204","0.5512407","0.550232","0.5502289","0.55002034","0.5496686","0.54964215","0.54913664","0.54836905","0.54793614","0.5475219","0.5464314","0.54624724","0.5457779","0.54575616","0.5454635","0.54434294","0.5442673","0.5422229","0.5413872","0.5410456","0.540831","0.54080325"],"string":"[\n \"0.67433846\",\n \"0.67309666\",\n \"0.66847456\",\n \"0.6650939\",\n \"0.6648182\",\n \"0.64935666\",\n \"0.6440689\",\n \"0.63192457\",\n \"0.6306208\",\n \"0.628635\",\n \"0.62689066\",\n \"0.62631875\",\n \"0.6200151\",\n \"0.6186324\",\n \"0.6155119\",\n \"0.61444587\",\n \"0.61128426\",\n \"0.60982704\",\n \"0.6098094\",\n \"0.6062554\",\n \"0.6021452\",\n \"0.6014566\",\n \"0.5986219\",\n \"0.59845895\",\n \"0.59746563\",\n \"0.59621084\",\n \"0.5945128\",\n \"0.59444183\",\n \"0.5932418\",\n \"0.59311604\",\n \"0.592014\",\n \"0.5912474\",\n \"0.58914024\",\n \"0.586455\",\n \"0.58379805\",\n \"0.5836004\",\n \"0.5808254\",\n \"0.58042604\",\n \"0.5797387\",\n \"0.5758045\",\n \"0.5744383\",\n \"0.57418984\",\n \"0.57342327\",\n \"0.572395\",\n \"0.5722323\",\n \"0.57162\",\n \"0.57109874\",\n \"0.5705742\",\n \"0.5700704\",\n \"0.5695809\",\n \"0.56803334\",\n \"0.5677685\",\n \"0.56758595\",\n \"0.566844\",\n \"0.5665444\",\n \"0.5659052\",\n \"0.5658861\",\n \"0.5652961\",\n \"0.5633588\",\n \"0.562992\",\n \"0.5618409\",\n \"0.5604712\",\n \"0.5592987\",\n \"0.5590313\",\n \"0.55897546\",\n \"0.5586914\",\n \"0.5586082\",\n \"0.5575644\",\n \"0.55743665\",\n \"0.5569207\",\n \"0.5559975\",\n \"0.55588305\",\n \"0.5551717\",\n \"0.5551348\",\n \"0.55499756\",\n \"0.554058\",\n \"0.55386853\",\n \"0.55307204\",\n \"0.5512407\",\n \"0.550232\",\n \"0.5502289\",\n \"0.55002034\",\n \"0.5496686\",\n \"0.54964215\",\n \"0.54913664\",\n \"0.54836905\",\n \"0.54793614\",\n \"0.5475219\",\n \"0.5464314\",\n \"0.54624724\",\n \"0.5457779\",\n \"0.54575616\",\n \"0.5454635\",\n \"0.54434294\",\n \"0.5442673\",\n \"0.5422229\",\n \"0.5413872\",\n \"0.5410456\",\n \"0.540831\",\n \"0.54080325\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62215513"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":18499,"cells":{"query":{"kind":"string","value":"Return Announcement from memcache."},"document":{"kind":"string","value":"def getAnnouncement(self, request):\n announcement = memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\"\n return StringMessage(data=announcement)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_announcement(self, request):\n return StringMessage(\n data=memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\")","def _cacheAnnouncement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5,\n Conference.seatsAvailable > 0)\n ).fetch(projection=[Conference.name])\n if confs:\n # If there are conferences close to being sold out,\n # format announcement and set it in memcache\n announcement = '%s %s' % (\n 'Last chance to attend! The following conferences '\n 'are nearly sold out:',\n ', '.join(conf.name for conf in confs))\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\n else:\n # If there are no sold out conferences,\n # delete the memcache announcements entry\n announcement = \"\"\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\n return announcement","def cache_announcement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5, Conference.seatsAvailable > 0\n )).fetch(projection=[Conference.name])\n\n if confs:\n # If there are almost sold out conferences,\n # format announcement and set it in memcache\n announcement = ANNOUNCEMENT_TPL % (\n ', '.join(conf.name for conf in confs))\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\n else:\n # If there are no sold out conferences,\n # delete the memcache announcements entry\n announcement = \"\"\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\n\n return announcement","def get_announcement(self, cid):\n cid = cid.upper()\n query = \"SELECT * from announcement where cid = %s\"\n inputs = (cid, )\n result = self.database_manager.execute_query(query, inputs)\n if result:\n announcement = \"Announcement for {} ({}): {}\".format(result[0][0], result[0][3], result[0][2])\n else:\n announcement = \"No announcement for this {}\".format(cid)\n return announcement","def memcacheGetRecord(self, key):\n\n pickled = self.memcacheGet(key)\n return self.unpickleRecord(pickled) if pickled is not None else None","def _cache_get(self, metric_name):\n pass","def _memcache_get(*args, **kwargs):\n return ndb.get_context().memcache_get(*args, **kwargs)","def get(self):\n return {'status': 'success', 'count': Announcement.query.count()}, 200","def get_cache(self, key):\n return self.r.get(key)","def get(self, key):\n return self.cache_data.get(key)","def __getitem__(self, key):\n self._remove_expired()\n\n cache_entry = self._d.get(key, None)\n log.debug(\"__getitem__: {}\".format(cache_entry))\n\n return cache_entry","def get(key):\n return Cache.cache_connector.get(key)","def get_cache(self):\n return self.cache","def _cache_get(self, metric_name):\n encoded_metric_name = self._encode(metric_name)\n with self.__env.begin(self.__metric_to_metadata_db, write=False) as txn:\n payload = txn.get(encoded_metric_name)\n\n if payload == self._EMPTY:\n return None, True\n\n if payload is not None:\n payload = self._decode(payload)\n\n if not payload:\n # cache miss\n return None, False\n\n # found something in the cache\n split = self.__split_payload(payload)\n\n if split is None:\n # invalid string => evict from cache\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\n txn.delete(key=encoded_metric_name)\n return None, False\n\n # valid value => get id and metadata string\n # TODO: optimization: id is a UUID (known length)\n id_str, metadata_str, timestamp = split\n try:\n id = uuid.UUID(id_str)\n except Exception as e:\n logging.debug(str(e))\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\n txn.delete(key=encoded_metric_name)\n return None, False\n\n # if the timestamp expired evict it in order to force\n # its recreation for the next time\n if self.__expired_timestamp(timestamp):\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\n txn.delete(key=encoded_metric_name)\n\n metadata = self.metadata_from_str(metadata_str)\n return bg_metric.Metric(metric_name, id, metadata), True","def get(self, key):\n # Initialize key variables\n result = self.cache.get(key)\n\n # Return\n return result","def cache_get(self, key: str) -> Optional[bytes]:\n if self.cache is not None:\n return self.cache.get(key)\n return None","def get(self):\n CACHE_KEY = 'topics'\n if not memcache.get(CACHE_KEY):\n logging.info('Populating cache.')\n topics = Topic.all().order('name')\n topic_list = []\n for topic in topics:\n topic_list.append(topic.ToDict())\n memcache.add(CACHE_KEY, simplejson.dumps(topic_list), 600)\n logging.info('Using cache.')\n logging.info(memcache.get(CACHE_KEY))\n self.response.headers['Content-Type'] = 'application/json'\n self.response.out.write(memcache.get(CACHE_KEY))","def get(self, key):\n if key is None:\n return None\n return self.cache_data.get(key, None)","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def _retrieveCachedData(self):","def memcacheGet(self, key):\n\n key = base64.b64encode(key)\n try:\n value = self._getMemcacheClient().get(key)\n except MemcacheError:\n log.error(\"Could not read from memcache, retrying\")\n try:\n value = self._getMemcacheClient(refresh=True).get(key)\n except MemcacheError:\n log.error(\"Could not read from memcache again, giving up\")\n del self.memcacheClient\n raise DirectoryMemcacheError(\"Failed to read from memcache\")\n return value","def get(self, key):\n if key:\n return self.cache_data.get(key)\n else:\n return None","def cache(self):\n return self.payload.setdefault(self._CACHE_ATTRIBUTE, {})","def from_cache(cls, cached_token):\n return cls(\n access_token=cached_token[\"access_token\"],\n expires_in=int(cached_token[\"expires_in\"]),\n expires_at=int(cached_token[\"expires_at\"]),\n token_type=cached_token[\"token_type\"],\n audience=cached_token[\"audience\"],\n )","def get(self, cache_id):\n return self.certificates.get(cache_id)","def getData(self, local_cache):","def fetch(self,key):\n try:\n return self.__content[key]\n except KeyError:\n return None","def GetEntityViaMemcache(entity_key):\n entity = memcache.get(entity_key)\n if entity is not None:\n return entity\n key = ndb.Key(urlsafe=entity_key)\n entity = key.get()\n if entity is not None:\n memcache.set(entity_key, entity)\n return entity","def get(self):\n CACHE_KEY = 'sources'\n if not memcache.get(CACHE_KEY):\n logging.info('Populating cache.')\n feeds = Feed.all().order('name')\n feed_list = []\n for feed in feeds:\n feed_list.append(feed.ToDict())\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\n logging.info('Using cache.')\n logging.info(memcache.get(CACHE_KEY))\n self.response.headers['Content-Type'] = 'application/json'\n self.response.out.write(memcache.get(CACHE_KEY))","def get(self, key):\n if key and key in self.cache_data.keys():\n return self.cache_data[key]\n else:\n return None","def get(self, key):\n if key and key in self.cache_data.keys():\n return self.cache_data[key]\n else:\n return None","def get(self, key):\n raise NotImplementedError(\"get must be implemented in your cache class\")","def get_news_with_cache(symbol):\n cache_dict = open_cache(filename=NEWS_CACHE)\n # if already cached\n if symbol in cache_dict:\n today = datetime.date.today()\n update = cache_dict[symbol][\"Fetched\"]\n update = datetime.datetime.strptime(update, '%Y-%m-%d').date()\n delta = today - update\n # if cache is old, get news again\n if delta.days >= 1:\n cache_dict[symbol] = get_news(symbol)\n save_cache(cache_dict=cache_dict, filename=NEWS_CACHE)\n # if never cached, get news\n else:\n cache_dict[symbol] = get_news(symbol)\n save_cache(cache_dict=cache_dict, filename=NEWS_CACHE)\n return cache_dict[symbol]","def cache_data(self):\n # Initialize key variables\n result = self.data['cache_data']\n return result","def fetchPrice(self, token):\n i = 0\n cache = self.cache\n cacheLen = len(self.cache)\n stamp = time.time()\n minStamp = stamp - self.maxCacheAge\n data = None\n while True:\n if i >= cacheLen:\n break\n cacheToken, cacheStamp, cacheData = cache[i]\n if cacheStamp < minStamp:\n print(\"CMClient: expired cache data for %s\" % cacheToken)\n cache.pop(i)\n cacheLen -= 1\n continue\n if token == cacheToken:\n data = cacheData\n i += 1\n if data:\n print(\"CMClient: returning cached data for %s\" % token)\n return data\n data = helpers.getUriAsJson(self.tickerTemplate % token)\n cache.insert(0, (token, stamp, data))\n self.saveSettings()\n print(\"CMClient: returning new data for %s\" % token)\n return data","def get(self, key):\n if key and key in self.cache_data:\n return self.cache_data[key]\n return None","def _get_cached_response(self, message_id, from_node):\n return self.operator.get_message_item(message_id, from_node)","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def get(self, key):\n if self.dexists('ttl', key) and int(dt.now().strftime('%s')) >= self.dget('ttl', key):\n self.rem(key)\n return None\n return super(MyCache, self).get(key)","def get(self, key):\n if key is None or key not in self.cache_data:\n return None\n else:\n for x in self.LRU:\n if x.key == key:\n x.age = 0\n else:\n x.age += 1\n return self.cache_data[key]","def __getitem__(self, key):\n if self.contains(key):\n return self._cache[key][0]\n raise CacheMissException(key)","def get_from_cache(self, subject_id):\n with self.cache.open_for_read(subject_id) as cache_file:\n chunks = utils.chunkiter(cache_file)\n for chunk in chunks:\n yield chunk","async def fetch_announcement(self, community_id: int, announcement_id: int) -> Optional[Announcement]:\n announcement_url = self._api_communities_url + str(community_id) + \"/notices/\" + str(announcement_id)\n async with self.web_session.get(announcement_url, headers=self._headers) as resp:\n if self.check_status(resp.status, announcement_url):\n data = await resp.json()\n return create_announcement_object(data)","async def get() -> list:\n if _cache is None:\n await _update()\n return _cache","def getCache(self, key):\n return self._cache.get(key, None)","def get(self):\n return GlobalNews.retrieve()","def get(self, key):\n return self._cache[key]","async def _get(self, key, encoding=\"utf-8\"):\n return SimpleMemoryBackend._cache.get(key)","def get_cache(self):\n return self._instance._cache[self.name]","def get_greetings(self):\n greetings = memcache.get(\"greetings\")\n if greetings is not None:\n return greetings\n else:\n greetings = self.render_greetings()\n if not memcache.add(\"greetings\", greetings, 10):\n logging.error(\"Memcache set failed.\")\n return greetings","def _cache_get(self, metric_name):\n try:\n with self._lock:\n metric = self.__cache.get(metric_name, False)\n except KeyError:\n # When metrics expire, we still get a KeyError.\n metric = False\n if metric is False:\n return None, False\n else:\n return metric, True","def get_detail_post_public_by_url(url):\n cached_data = cache.get(KEY_CACHE_CATEGORY_POST_DETAIL_DISPLAY_BY_URL + url)\n if not cached_data:\n # Get post in DB\n post = CategoryPostDao.get_post_detail_display_by_url(url)\n\n # Set list post into cache\n cache.set(KEY_CACHE_CATEGORY_POST_DETAIL_DISPLAY_BY_URL + url, post, settings.CACHE_TIME)\n cached_data = post \n return cached_data","def get(self, timeout=0):\n id = self.redis.brpop(self.feed_ids, timeout)\n if id is None:\n raise Empty\n id = id[1]\n\n pipe = self.redis.pipeline()\n pipe.zadd(self.feed_claimed, **{id: int(time.time()*1000)})\n pipe.hget(self.feed_items, id)\n result = pipe.execute()\n \n return id, result[1]","def __getitem__(self,idx):\n try:\n return self._cache[idx]\n except:\n pass\n\n try:\n # return full data entry as list\n out = self._data[idx]\n self._cache[idx] = out\n return out\n except:\n try:\n # return data entry with specified key word\n out = self._data[idx[0]][self._header[self._alias[idx[1]]]]\n self._cache[idx] = out\n return out\n except:\n pass","def getCacheContents(self):\n return self._cache","def get(self, key):\n if key is None or key not in self.cache_data:\n return None\n\n # modify the time and change the next newer value\n self.timesKey[key] = self.time\n self.time += 1\n\n return self.cache_data[key]","def display_memcache_info(request):\n # pylint: disable-msg=E1101\n return utility.respond(request, 'admin/memcache_info',\n {'memcache_info': memcache.get_stats()})","async def fetch_cached_patrons():\n return await self.conn.fetch(\"SELECT userid, super FROM patreon.cache\")","def get(self, request):\n announcement_id = request.GET.get(\"id\")\n if announcement_id:\n try:\n announcement = Announcement.objects.get(id=announcement_id)\n return self.success(AnnouncementSerializer(announcement).data)\n except Announcement.DoesNotExist:\n return self.error(\"Announcement does not exist\")\n announcement = Announcement.objects.all().order_by(\"-create_time\")\n if request.GET.get(\"visible\") == \"true\":\n announcement = announcement.filter(visible=True)\n return self.success(self.paginate_data(request, announcement, AnnouncementSerializer))","def get_from_cache(item_id, memory_cache, db_cache_table, db_cache_table_id_field=\"id\"):\n # try to retrieve from local in-memory cache\n rv, expires_at = memory_cache.get(item_id, (None, 0))\n if expires_at > expiry:\n return rv\n\n # try to retrieve from database cache\n if hasattr(flask.current_app, \"db\"): # we don't have db in startup\n with flask.current_app.db.session as session:\n cache = (\n session.query(db_cache_table)\n .filter(\n getattr(db_cache_table, db_cache_table_id_field, None) == item_id\n )\n .first()\n )\n if cache and cache.expires_at and cache.expires_at > expiry:\n rv = dict(cache)\n\n # store in memory cache\n memory_cache[item_id] = rv, cache.expires_at\n return rv","def readMail(m, msgNum):#Read a particular email\n resp, data = m.fetch(msgNum, \"(RFC822)\")\n email_body = data[0][1]\n mail = email.message_from_string(email_body)\n #temp = m.store(emailid,'+FLAGS', '\\\\Seen')\n m.expunge()\n\n\n return mail","def __getitem__(self,key):\n result = None\n # check if it's tin the cache first\n if key in self._cache:\n result = self._cache[key]\n else:\n # it's not in the cache so retrieve it\n result = self._get_from_tree(key)\n # remove None values\n result = [x for x in result if x is not None]\n self._cache[key] = result\n\n return result","def public_timeline(TTL=10):\n keyName = \"public_timeline\"\n ############### REDIS SESSION CODE #####################\n\n if redis_obj.get(keyName):\n print(\"** Messages from Redis Cache **\")\n key = pickle.loads(redis_obj.get(keyName))\n return render_template('timeline.html', messages=key)\n else:\n messages = public_timeline_query()\n print(\"** Messages from mongoDB **\")\n redis_obj.setex(keyName, pickle.dumps(messages), 60)\n return render_template('timeline.html', messages=messages)","def get(self, key):\n if key is None or key not in self.cache_data.keys():\n return\n self.count += 1\n self.key_tracker.update({key: self.count})\n return self.cache_data.get(key)","def get_ip_from_acache(key):\n return str(acache[key]._dict.keys()[0])","def newpublic_timeline(TTL=60):\n\n message_json = \"\"\n hash = hashlib.sha224(message_json).hexdigest()\n key = \"public_timeline_key:\" + hash\n # print \"Created Key\\t : %s\" % key\n\n############### REDIS SESSION CODE #####################\n\n # Check if data is in cache.\n if (R_SERVER.get(key)):\n print \"** Messages returned from Redis Cache **\"\n return cPickle.loads(R_SERVER.get(key))\n else:\n print \"** Messages returned from MongoDB **\"\n messages = public_timeline_query()\n data = []\n # print messages\n for row in messages:\n data.append({'user': row['username'], 'message': row['text'],\n 'pub_date': format_datetime(row['pub_date'])})\n\n public_timeline_JSON = jsonify(\n messages=data, Status_code=status.HTTP_200_OK)\n R_SERVER.set(key, cPickle.dumps(public_timeline_JSON))\n R_SERVER.expire(key, TTL)\n return public_timeline_JSON","def get(self, key):\n # type: (str) -> str\n return self.__cache_get(key)","def _get_cache(self, course_version_guid):\n if self.request_cache is None:\n return None\n\n return self.request_cache.data.setdefault('course_cache', {}).get(course_version_guid)","def get(self, key):\n with self._lock:\n self._check_expire()\n\n obj = self._obj_cache[key]\n self._log.debug(\"getting object '%s' (type=%s). \"\n \"updating access time.\",\n key, type(obj))\n self._obj_last_access[key] = time.time()\n return obj","def testCacheProtobuf(self):\n\n from google.appengine.ext import db\n\n class MyModel(db.Model):\n name = db.StringProperty()\n\n entity = MyModel(name=\"foobar\")\n\n os.environ['APPLICATION_ID'] = 'app'\n memcache.set('protobuf', db.model_to_protobuf(entity).Encode())\n\n encoded_entity = memcache.get('protobuf')\n cached_entity = db.model_from_protobuf(encoded_entity)\n assert cached_entity.name == 'foobar'","def testExpirationTime(self):\n\n bye = \"Good bye!\"\n memcache.add('bye', bye, 1)\n assert memcache.get('bye') == bye\n time.sleep(2)\n assert memcache.get('bye') == None","def _get_cache_ttl(self, request, response):\n return None # use default ttl","def fetch(self, activity):\n return None, None","def do_cache(*args, **kws):\n resp = self.response\n out = resp.out\n namespace = ''\n if self.cache_nsfuncs.get(func, None):\n namespace = self.cache_nsfuncs[func](self.request)\n p = urlsplit(self.request.url)[2]\n c = memcache.get(p, namespace)\n if c:\n # in case cache is found, use it \n # instead of rendering by calling function.\n out.write(c['body'])\n for k, i in c['hdr'].items():\n resp.headers[k] = i\n return\n\n r = func(*args, **kws)\n expire = self.cache_expires.get(func, 0)\n if expire == 0:\n return\n out.seek(0)\n try:\n p = urlsplit(self.request.url)[2]\n memcache.set(p, {'hdr':resp.headers,'body':out.read()},\n expire, namespace=namespace)\n logging.debug('%s is cahed' % p)\n except:\n memcache.flush_all()\n logging.debug('memcache is flashed.')","def get_meta(bill_content):\n\n title = bill_content.BillTitle.find(name='Title', language='en').text\n\n if 'amend' in title.lower():\n amend = True\n else:\n amend = False\n\n # rarely, no published version of a bill is available\n publication_tags = [t for t in bill_content.find_all('Publication')\n if t.find(name='Title', language='en').text == 'Royal Assent']\n if len(publication_tags) == 1:\n publication_id = publication_tags[0]['id']\n else:\n publication_id = None\n\n # all other metadata appear to be consistently present\n date = bill_content.Events.LastMajorStageEvent.Event['date']\n session = bill_content.ParliamentSession['parliamentNumber']\n subtype = bill_content.BillType.find(name='Title', language='en').text\n sponsor = bill_content.SponsorAffiliation.Person.FullName.text\n sponsor_party = bill_content.SponsorAffiliation.PoliticalParty.find(name='Title', language='en').text\n majority_party = bill_content.PrimeMinister.PoliticalParty.find(name='Title', language='en').text\n\n committee_tags = bill_content.find_all(name='Committee', accronym=True)\n committee_names = [t['accronym'] for t in committee_tags]\n committee_data = {c: committee_names.count(c) for c in set(committee_names)}\n\n metadata = _format_meta_entry(country=u'canada',\n title=title,\n id=publication_id,\n date=date,\n session=session,\n type=u'annual',\n subtype=subtype,\n amendment=amend,\n sponsor=sponsor,\n sponsor_party=sponsor_party,\n majority_party=majority_party,\n hearings=committee_data)\n\n return metadata","def get(self):\n announcements = Announcement.query.all()\n announcements = announcements_schema.dump(announcements)\n\n if not announcements:\n return {'status': 'success', 'announcements': announcements}, 206 # Partial Content Served\n\n return {'status': 'success', 'announcements': announcements}, 200","def get_cached_polling(self, obj):\n return self._cache[obj.pk]","def get_from_cache(self, url):\n cache_key, cache_lookup = self.get_cacheable_info(url)\n\n cache_timeout = self.cache_timeouts.get(cache_key,\n self.default_cache_timeout)\n\n data, access_time = MEM_CACHE[cache_key].get(cache_lookup, (None, 0))\n if data and time.time() - access_time < cache_timeout:\n return data\n return False","def get_cache(self, key, default=None):\n batch_number, value = self._cache.get(key, (None, None))\n if batch_number == self.model.batch_number:\n return value\n else:\n return default","def get(self, key):\n if key is None or key not in self.cache_data:\n return None\n else:\n for x in self.LFU:\n if x.key == key:\n x.age = 0\n x.freq += 1\n else:\n x.age += 1\n return self.cache_data[key]","def get_movie(self, movie_id):\n try:\n movie = cache.get(movie_id)\n except KeyError:\n movie = self.hit('/movie/{}'.format(movie_id), append_to_response='credits')\n cache.set(movie_id, movie)\n return movie","def _get_cache(self, course_version_guid):\r\n if not hasattr(self.thread_cache, 'course_cache'):\r\n self.thread_cache.course_cache = {}\r\n system = self.thread_cache.course_cache\r\n return system.get(course_version_guid)","def memcache_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"memcache_version\")","def _get_cached_manifest(self, revid):\n return self.manifests_lru[revid]","def _cache_get(self, objId, methodname):\n self._lock.acquire()\n entry = self.cache.get((objId, methodname))\n if entry is None: \n self._lock.release()\n return None\n if time.time() - entry[0] > CACHE_TIMEOUT: \n del self.cache[(objId, methodname)]\n self._lock.release()\n return None\n self._lock.release()\n return entry[1]","def test_retrieve_memcache_ok(self):\n content = 'Grecian Urn'\n namespace = 'default'\n request = self.store_request(namespace, content)\n embedded = validate(\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\n self.call_api('store_inline', self.message_to_dict(request), 200)\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\n digest=embedded['d'], namespace=handlers_endpoints_v1.Namespace())\n response = self.call_api(\n 'retrieve', self.message_to_dict(retrieve_request), 200)\n retrieved = response.json\n self.assertEqual(content, base64.b64decode(retrieved[u'content']))","def get(cls, obj: Model):\n return cache.get(cls._construct_key(obj))","def getCacheMessages(self):\n return self.db.getCacheMsgs()","def get_content_caching(self, loadbalancer):\n return loadbalancer.content_caching","def get(self, key):\n try:\n\n item = self._item_to_dict(self.client.get_item(**self._prepare_get_request(str(key))))\n\n # If item is empty, nothing in cache\n if not item:\n return None\n\n # If current time beyond expiry, nothing to return\n if time()>float(item[self._expiry_field.name]):\n return None\n\n return self.load_object(b64decode(item.get(self._value_field.name)))\n\n except Exception as e:\n logging.info('Error getting object from DynamoDB table %s (%s): %s',self.table_name,e.__class__.__name__,e)\n return None","def issue(self, check_cache=True):\n if not check_cache or not hasattr(self, '_issue'):\n issue_data = self.campaigns.values_list(\n 'issue__pk', 'issue__name').filter(\n issue__pk__isnull=False).order_by('issue__name').first()\n if issue_data:\n self._issue = Issue.objects.get(pk=issue_data[0])\n else:\n self._issue = None\n return self._issue","def _cache_msg(cls, sender_id, recipient_id, text, chat_id=None):\n # todo make this method async\n message = Message.new(sender_id, recipient_id, text, chat_id)\n return message","def construct_a_rr_from_cache(dn):\n for super_domain in dn:\n if super_domain in acache:\n ce = acache[super_domain]\n ip = ce._dict.keys()[0].toNetwork()\n now = int(time())\n ttl = ce._dict.values()[0]._expiration - now\n rr_a = RR_A(DomainName(super_domain), ttl, ip)\n return RR_A(DomainName(super_domain), ttl, ip)\n return None","def _get(self, *args, **kwargs):\n return self.deserialize_notifications(self.request.session.get(self.session_key)), True","def _get_cache(self, request):\n if self._cache is None:\n if hasattr(request.registry, 'cache'):\n cache_ttl = float(fxa_conf(request, 'cache_ttl_seconds'))\n oauth_cache = TokenVerificationCache(request.registry.cache,\n ttl=cache_ttl)\n self._cache = oauth_cache\n\n return self._cache","def from_cache(self, cache_key=None, pk=None):\n if pk:\n cache_key = self._cache_key(pk)\n # if cache_key is none, the mangler will generate a MD5 from the query\n return FromCache(self.label, cache_key)"],"string":"[\n \"def get_announcement(self, request):\\n return StringMessage(\\n data=memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \\\"\\\")\",\n \"def _cacheAnnouncement():\\n confs = Conference.query(ndb.AND(\\n Conference.seatsAvailable <= 5,\\n Conference.seatsAvailable > 0)\\n ).fetch(projection=[Conference.name])\\n if confs:\\n # If there are conferences close to being sold out,\\n # format announcement and set it in memcache\\n announcement = '%s %s' % (\\n 'Last chance to attend! The following conferences '\\n 'are nearly sold out:',\\n ', '.join(conf.name for conf in confs))\\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\\n else:\\n # If there are no sold out conferences,\\n # delete the memcache announcements entry\\n announcement = \\\"\\\"\\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\\n return announcement\",\n \"def cache_announcement():\\n confs = Conference.query(ndb.AND(\\n Conference.seatsAvailable <= 5, Conference.seatsAvailable > 0\\n )).fetch(projection=[Conference.name])\\n\\n if confs:\\n # If there are almost sold out conferences,\\n # format announcement and set it in memcache\\n announcement = ANNOUNCEMENT_TPL % (\\n ', '.join(conf.name for conf in confs))\\n memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\\n else:\\n # If there are no sold out conferences,\\n # delete the memcache announcements entry\\n announcement = \\\"\\\"\\n memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\\n\\n return announcement\",\n \"def get_announcement(self, cid):\\n cid = cid.upper()\\n query = \\\"SELECT * from announcement where cid = %s\\\"\\n inputs = (cid, )\\n result = self.database_manager.execute_query(query, inputs)\\n if result:\\n announcement = \\\"Announcement for {} ({}): {}\\\".format(result[0][0], result[0][3], result[0][2])\\n else:\\n announcement = \\\"No announcement for this {}\\\".format(cid)\\n return announcement\",\n \"def memcacheGetRecord(self, key):\\n\\n pickled = self.memcacheGet(key)\\n return self.unpickleRecord(pickled) if pickled is not None else None\",\n \"def _cache_get(self, metric_name):\\n pass\",\n \"def _memcache_get(*args, **kwargs):\\n return ndb.get_context().memcache_get(*args, **kwargs)\",\n \"def get(self):\\n return {'status': 'success', 'count': Announcement.query.count()}, 200\",\n \"def get_cache(self, key):\\n return self.r.get(key)\",\n \"def get(self, key):\\n return self.cache_data.get(key)\",\n \"def __getitem__(self, key):\\n self._remove_expired()\\n\\n cache_entry = self._d.get(key, None)\\n log.debug(\\\"__getitem__: {}\\\".format(cache_entry))\\n\\n return cache_entry\",\n \"def get(key):\\n return Cache.cache_connector.get(key)\",\n \"def get_cache(self):\\n return self.cache\",\n \"def _cache_get(self, metric_name):\\n encoded_metric_name = self._encode(metric_name)\\n with self.__env.begin(self.__metric_to_metadata_db, write=False) as txn:\\n payload = txn.get(encoded_metric_name)\\n\\n if payload == self._EMPTY:\\n return None, True\\n\\n if payload is not None:\\n payload = self._decode(payload)\\n\\n if not payload:\\n # cache miss\\n return None, False\\n\\n # found something in the cache\\n split = self.__split_payload(payload)\\n\\n if split is None:\\n # invalid string => evict from cache\\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\\n txn.delete(key=encoded_metric_name)\\n return None, False\\n\\n # valid value => get id and metadata string\\n # TODO: optimization: id is a UUID (known length)\\n id_str, metadata_str, timestamp = split\\n try:\\n id = uuid.UUID(id_str)\\n except Exception as e:\\n logging.debug(str(e))\\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\\n txn.delete(key=encoded_metric_name)\\n return None, False\\n\\n # if the timestamp expired evict it in order to force\\n # its recreation for the next time\\n if self.__expired_timestamp(timestamp):\\n with self.__env.begin(self.__metric_to_metadata_db, write=True) as txn:\\n txn.delete(key=encoded_metric_name)\\n\\n metadata = self.metadata_from_str(metadata_str)\\n return bg_metric.Metric(metric_name, id, metadata), True\",\n \"def get(self, key):\\n # Initialize key variables\\n result = self.cache.get(key)\\n\\n # Return\\n return result\",\n \"def cache_get(self, key: str) -> Optional[bytes]:\\n if self.cache is not None:\\n return self.cache.get(key)\\n return None\",\n \"def get(self):\\n CACHE_KEY = 'topics'\\n if not memcache.get(CACHE_KEY):\\n logging.info('Populating cache.')\\n topics = Topic.all().order('name')\\n topic_list = []\\n for topic in topics:\\n topic_list.append(topic.ToDict())\\n memcache.add(CACHE_KEY, simplejson.dumps(topic_list), 600)\\n logging.info('Using cache.')\\n logging.info(memcache.get(CACHE_KEY))\\n self.response.headers['Content-Type'] = 'application/json'\\n self.response.out.write(memcache.get(CACHE_KEY))\",\n \"def get(self, key):\\n if key is None:\\n return None\\n return self.cache_data.get(key, None)\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def _retrieveCachedData(self):\",\n \"def memcacheGet(self, key):\\n\\n key = base64.b64encode(key)\\n try:\\n value = self._getMemcacheClient().get(key)\\n except MemcacheError:\\n log.error(\\\"Could not read from memcache, retrying\\\")\\n try:\\n value = self._getMemcacheClient(refresh=True).get(key)\\n except MemcacheError:\\n log.error(\\\"Could not read from memcache again, giving up\\\")\\n del self.memcacheClient\\n raise DirectoryMemcacheError(\\\"Failed to read from memcache\\\")\\n return value\",\n \"def get(self, key):\\n if key:\\n return self.cache_data.get(key)\\n else:\\n return None\",\n \"def cache(self):\\n return self.payload.setdefault(self._CACHE_ATTRIBUTE, {})\",\n \"def from_cache(cls, cached_token):\\n return cls(\\n access_token=cached_token[\\\"access_token\\\"],\\n expires_in=int(cached_token[\\\"expires_in\\\"]),\\n expires_at=int(cached_token[\\\"expires_at\\\"]),\\n token_type=cached_token[\\\"token_type\\\"],\\n audience=cached_token[\\\"audience\\\"],\\n )\",\n \"def get(self, cache_id):\\n return self.certificates.get(cache_id)\",\n \"def getData(self, local_cache):\",\n \"def fetch(self,key):\\n try:\\n return self.__content[key]\\n except KeyError:\\n return None\",\n \"def GetEntityViaMemcache(entity_key):\\n entity = memcache.get(entity_key)\\n if entity is not None:\\n return entity\\n key = ndb.Key(urlsafe=entity_key)\\n entity = key.get()\\n if entity is not None:\\n memcache.set(entity_key, entity)\\n return entity\",\n \"def get(self):\\n CACHE_KEY = 'sources'\\n if not memcache.get(CACHE_KEY):\\n logging.info('Populating cache.')\\n feeds = Feed.all().order('name')\\n feed_list = []\\n for feed in feeds:\\n feed_list.append(feed.ToDict())\\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\\n logging.info('Using cache.')\\n logging.info(memcache.get(CACHE_KEY))\\n self.response.headers['Content-Type'] = 'application/json'\\n self.response.out.write(memcache.get(CACHE_KEY))\",\n \"def get(self, key):\\n if key and key in self.cache_data.keys():\\n return self.cache_data[key]\\n else:\\n return None\",\n \"def get(self, key):\\n if key and key in self.cache_data.keys():\\n return self.cache_data[key]\\n else:\\n return None\",\n \"def get(self, key):\\n raise NotImplementedError(\\\"get must be implemented in your cache class\\\")\",\n \"def get_news_with_cache(symbol):\\n cache_dict = open_cache(filename=NEWS_CACHE)\\n # if already cached\\n if symbol in cache_dict:\\n today = datetime.date.today()\\n update = cache_dict[symbol][\\\"Fetched\\\"]\\n update = datetime.datetime.strptime(update, '%Y-%m-%d').date()\\n delta = today - update\\n # if cache is old, get news again\\n if delta.days >= 1:\\n cache_dict[symbol] = get_news(symbol)\\n save_cache(cache_dict=cache_dict, filename=NEWS_CACHE)\\n # if never cached, get news\\n else:\\n cache_dict[symbol] = get_news(symbol)\\n save_cache(cache_dict=cache_dict, filename=NEWS_CACHE)\\n return cache_dict[symbol]\",\n \"def cache_data(self):\\n # Initialize key variables\\n result = self.data['cache_data']\\n return result\",\n \"def fetchPrice(self, token):\\n i = 0\\n cache = self.cache\\n cacheLen = len(self.cache)\\n stamp = time.time()\\n minStamp = stamp - self.maxCacheAge\\n data = None\\n while True:\\n if i >= cacheLen:\\n break\\n cacheToken, cacheStamp, cacheData = cache[i]\\n if cacheStamp < minStamp:\\n print(\\\"CMClient: expired cache data for %s\\\" % cacheToken)\\n cache.pop(i)\\n cacheLen -= 1\\n continue\\n if token == cacheToken:\\n data = cacheData\\n i += 1\\n if data:\\n print(\\\"CMClient: returning cached data for %s\\\" % token)\\n return data\\n data = helpers.getUriAsJson(self.tickerTemplate % token)\\n cache.insert(0, (token, stamp, data))\\n self.saveSettings()\\n print(\\\"CMClient: returning new data for %s\\\" % token)\\n return data\",\n \"def get(self, key):\\n if key and key in self.cache_data:\\n return self.cache_data[key]\\n return None\",\n \"def _get_cached_response(self, message_id, from_node):\\n return self.operator.get_message_item(message_id, from_node)\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def get(self, key):\\n if self.dexists('ttl', key) and int(dt.now().strftime('%s')) >= self.dget('ttl', key):\\n self.rem(key)\\n return None\\n return super(MyCache, self).get(key)\",\n \"def get(self, key):\\n if key is None or key not in self.cache_data:\\n return None\\n else:\\n for x in self.LRU:\\n if x.key == key:\\n x.age = 0\\n else:\\n x.age += 1\\n return self.cache_data[key]\",\n \"def __getitem__(self, key):\\n if self.contains(key):\\n return self._cache[key][0]\\n raise CacheMissException(key)\",\n \"def get_from_cache(self, subject_id):\\n with self.cache.open_for_read(subject_id) as cache_file:\\n chunks = utils.chunkiter(cache_file)\\n for chunk in chunks:\\n yield chunk\",\n \"async def fetch_announcement(self, community_id: int, announcement_id: int) -> Optional[Announcement]:\\n announcement_url = self._api_communities_url + str(community_id) + \\\"/notices/\\\" + str(announcement_id)\\n async with self.web_session.get(announcement_url, headers=self._headers) as resp:\\n if self.check_status(resp.status, announcement_url):\\n data = await resp.json()\\n return create_announcement_object(data)\",\n \"async def get() -> list:\\n if _cache is None:\\n await _update()\\n return _cache\",\n \"def getCache(self, key):\\n return self._cache.get(key, None)\",\n \"def get(self):\\n return GlobalNews.retrieve()\",\n \"def get(self, key):\\n return self._cache[key]\",\n \"async def _get(self, key, encoding=\\\"utf-8\\\"):\\n return SimpleMemoryBackend._cache.get(key)\",\n \"def get_cache(self):\\n return self._instance._cache[self.name]\",\n \"def get_greetings(self):\\n greetings = memcache.get(\\\"greetings\\\")\\n if greetings is not None:\\n return greetings\\n else:\\n greetings = self.render_greetings()\\n if not memcache.add(\\\"greetings\\\", greetings, 10):\\n logging.error(\\\"Memcache set failed.\\\")\\n return greetings\",\n \"def _cache_get(self, metric_name):\\n try:\\n with self._lock:\\n metric = self.__cache.get(metric_name, False)\\n except KeyError:\\n # When metrics expire, we still get a KeyError.\\n metric = False\\n if metric is False:\\n return None, False\\n else:\\n return metric, True\",\n \"def get_detail_post_public_by_url(url):\\n cached_data = cache.get(KEY_CACHE_CATEGORY_POST_DETAIL_DISPLAY_BY_URL + url)\\n if not cached_data:\\n # Get post in DB\\n post = CategoryPostDao.get_post_detail_display_by_url(url)\\n\\n # Set list post into cache\\n cache.set(KEY_CACHE_CATEGORY_POST_DETAIL_DISPLAY_BY_URL + url, post, settings.CACHE_TIME)\\n cached_data = post \\n return cached_data\",\n \"def get(self, timeout=0):\\n id = self.redis.brpop(self.feed_ids, timeout)\\n if id is None:\\n raise Empty\\n id = id[1]\\n\\n pipe = self.redis.pipeline()\\n pipe.zadd(self.feed_claimed, **{id: int(time.time()*1000)})\\n pipe.hget(self.feed_items, id)\\n result = pipe.execute()\\n \\n return id, result[1]\",\n \"def __getitem__(self,idx):\\n try:\\n return self._cache[idx]\\n except:\\n pass\\n\\n try:\\n # return full data entry as list\\n out = self._data[idx]\\n self._cache[idx] = out\\n return out\\n except:\\n try:\\n # return data entry with specified key word\\n out = self._data[idx[0]][self._header[self._alias[idx[1]]]]\\n self._cache[idx] = out\\n return out\\n except:\\n pass\",\n \"def getCacheContents(self):\\n return self._cache\",\n \"def get(self, key):\\n if key is None or key not in self.cache_data:\\n return None\\n\\n # modify the time and change the next newer value\\n self.timesKey[key] = self.time\\n self.time += 1\\n\\n return self.cache_data[key]\",\n \"def display_memcache_info(request):\\n # pylint: disable-msg=E1101\\n return utility.respond(request, 'admin/memcache_info',\\n {'memcache_info': memcache.get_stats()})\",\n \"async def fetch_cached_patrons():\\n return await self.conn.fetch(\\\"SELECT userid, super FROM patreon.cache\\\")\",\n \"def get(self, request):\\n announcement_id = request.GET.get(\\\"id\\\")\\n if announcement_id:\\n try:\\n announcement = Announcement.objects.get(id=announcement_id)\\n return self.success(AnnouncementSerializer(announcement).data)\\n except Announcement.DoesNotExist:\\n return self.error(\\\"Announcement does not exist\\\")\\n announcement = Announcement.objects.all().order_by(\\\"-create_time\\\")\\n if request.GET.get(\\\"visible\\\") == \\\"true\\\":\\n announcement = announcement.filter(visible=True)\\n return self.success(self.paginate_data(request, announcement, AnnouncementSerializer))\",\n \"def get_from_cache(item_id, memory_cache, db_cache_table, db_cache_table_id_field=\\\"id\\\"):\\n # try to retrieve from local in-memory cache\\n rv, expires_at = memory_cache.get(item_id, (None, 0))\\n if expires_at > expiry:\\n return rv\\n\\n # try to retrieve from database cache\\n if hasattr(flask.current_app, \\\"db\\\"): # we don't have db in startup\\n with flask.current_app.db.session as session:\\n cache = (\\n session.query(db_cache_table)\\n .filter(\\n getattr(db_cache_table, db_cache_table_id_field, None) == item_id\\n )\\n .first()\\n )\\n if cache and cache.expires_at and cache.expires_at > expiry:\\n rv = dict(cache)\\n\\n # store in memory cache\\n memory_cache[item_id] = rv, cache.expires_at\\n return rv\",\n \"def readMail(m, msgNum):#Read a particular email\\n resp, data = m.fetch(msgNum, \\\"(RFC822)\\\")\\n email_body = data[0][1]\\n mail = email.message_from_string(email_body)\\n #temp = m.store(emailid,'+FLAGS', '\\\\\\\\Seen')\\n m.expunge()\\n\\n\\n return mail\",\n \"def __getitem__(self,key):\\n result = None\\n # check if it's tin the cache first\\n if key in self._cache:\\n result = self._cache[key]\\n else:\\n # it's not in the cache so retrieve it\\n result = self._get_from_tree(key)\\n # remove None values\\n result = [x for x in result if x is not None]\\n self._cache[key] = result\\n\\n return result\",\n \"def public_timeline(TTL=10):\\n keyName = \\\"public_timeline\\\"\\n ############### REDIS SESSION CODE #####################\\n\\n if redis_obj.get(keyName):\\n print(\\\"** Messages from Redis Cache **\\\")\\n key = pickle.loads(redis_obj.get(keyName))\\n return render_template('timeline.html', messages=key)\\n else:\\n messages = public_timeline_query()\\n print(\\\"** Messages from mongoDB **\\\")\\n redis_obj.setex(keyName, pickle.dumps(messages), 60)\\n return render_template('timeline.html', messages=messages)\",\n \"def get(self, key):\\n if key is None or key not in self.cache_data.keys():\\n return\\n self.count += 1\\n self.key_tracker.update({key: self.count})\\n return self.cache_data.get(key)\",\n \"def get_ip_from_acache(key):\\n return str(acache[key]._dict.keys()[0])\",\n \"def newpublic_timeline(TTL=60):\\n\\n message_json = \\\"\\\"\\n hash = hashlib.sha224(message_json).hexdigest()\\n key = \\\"public_timeline_key:\\\" + hash\\n # print \\\"Created Key\\\\t : %s\\\" % key\\n\\n############### REDIS SESSION CODE #####################\\n\\n # Check if data is in cache.\\n if (R_SERVER.get(key)):\\n print \\\"** Messages returned from Redis Cache **\\\"\\n return cPickle.loads(R_SERVER.get(key))\\n else:\\n print \\\"** Messages returned from MongoDB **\\\"\\n messages = public_timeline_query()\\n data = []\\n # print messages\\n for row in messages:\\n data.append({'user': row['username'], 'message': row['text'],\\n 'pub_date': format_datetime(row['pub_date'])})\\n\\n public_timeline_JSON = jsonify(\\n messages=data, Status_code=status.HTTP_200_OK)\\n R_SERVER.set(key, cPickle.dumps(public_timeline_JSON))\\n R_SERVER.expire(key, TTL)\\n return public_timeline_JSON\",\n \"def get(self, key):\\n # type: (str) -> str\\n return self.__cache_get(key)\",\n \"def _get_cache(self, course_version_guid):\\n if self.request_cache is None:\\n return None\\n\\n return self.request_cache.data.setdefault('course_cache', {}).get(course_version_guid)\",\n \"def get(self, key):\\n with self._lock:\\n self._check_expire()\\n\\n obj = self._obj_cache[key]\\n self._log.debug(\\\"getting object '%s' (type=%s). \\\"\\n \\\"updating access time.\\\",\\n key, type(obj))\\n self._obj_last_access[key] = time.time()\\n return obj\",\n \"def testCacheProtobuf(self):\\n\\n from google.appengine.ext import db\\n\\n class MyModel(db.Model):\\n name = db.StringProperty()\\n\\n entity = MyModel(name=\\\"foobar\\\")\\n\\n os.environ['APPLICATION_ID'] = 'app'\\n memcache.set('protobuf', db.model_to_protobuf(entity).Encode())\\n\\n encoded_entity = memcache.get('protobuf')\\n cached_entity = db.model_from_protobuf(encoded_entity)\\n assert cached_entity.name == 'foobar'\",\n \"def testExpirationTime(self):\\n\\n bye = \\\"Good bye!\\\"\\n memcache.add('bye', bye, 1)\\n assert memcache.get('bye') == bye\\n time.sleep(2)\\n assert memcache.get('bye') == None\",\n \"def _get_cache_ttl(self, request, response):\\n return None # use default ttl\",\n \"def fetch(self, activity):\\n return None, None\",\n \"def do_cache(*args, **kws):\\n resp = self.response\\n out = resp.out\\n namespace = ''\\n if self.cache_nsfuncs.get(func, None):\\n namespace = self.cache_nsfuncs[func](self.request)\\n p = urlsplit(self.request.url)[2]\\n c = memcache.get(p, namespace)\\n if c:\\n # in case cache is found, use it \\n # instead of rendering by calling function.\\n out.write(c['body'])\\n for k, i in c['hdr'].items():\\n resp.headers[k] = i\\n return\\n\\n r = func(*args, **kws)\\n expire = self.cache_expires.get(func, 0)\\n if expire == 0:\\n return\\n out.seek(0)\\n try:\\n p = urlsplit(self.request.url)[2]\\n memcache.set(p, {'hdr':resp.headers,'body':out.read()},\\n expire, namespace=namespace)\\n logging.debug('%s is cahed' % p)\\n except:\\n memcache.flush_all()\\n logging.debug('memcache is flashed.')\",\n \"def get_meta(bill_content):\\n\\n title = bill_content.BillTitle.find(name='Title', language='en').text\\n\\n if 'amend' in title.lower():\\n amend = True\\n else:\\n amend = False\\n\\n # rarely, no published version of a bill is available\\n publication_tags = [t for t in bill_content.find_all('Publication')\\n if t.find(name='Title', language='en').text == 'Royal Assent']\\n if len(publication_tags) == 1:\\n publication_id = publication_tags[0]['id']\\n else:\\n publication_id = None\\n\\n # all other metadata appear to be consistently present\\n date = bill_content.Events.LastMajorStageEvent.Event['date']\\n session = bill_content.ParliamentSession['parliamentNumber']\\n subtype = bill_content.BillType.find(name='Title', language='en').text\\n sponsor = bill_content.SponsorAffiliation.Person.FullName.text\\n sponsor_party = bill_content.SponsorAffiliation.PoliticalParty.find(name='Title', language='en').text\\n majority_party = bill_content.PrimeMinister.PoliticalParty.find(name='Title', language='en').text\\n\\n committee_tags = bill_content.find_all(name='Committee', accronym=True)\\n committee_names = [t['accronym'] for t in committee_tags]\\n committee_data = {c: committee_names.count(c) for c in set(committee_names)}\\n\\n metadata = _format_meta_entry(country=u'canada',\\n title=title,\\n id=publication_id,\\n date=date,\\n session=session,\\n type=u'annual',\\n subtype=subtype,\\n amendment=amend,\\n sponsor=sponsor,\\n sponsor_party=sponsor_party,\\n majority_party=majority_party,\\n hearings=committee_data)\\n\\n return metadata\",\n \"def get(self):\\n announcements = Announcement.query.all()\\n announcements = announcements_schema.dump(announcements)\\n\\n if not announcements:\\n return {'status': 'success', 'announcements': announcements}, 206 # Partial Content Served\\n\\n return {'status': 'success', 'announcements': announcements}, 200\",\n \"def get_cached_polling(self, obj):\\n return self._cache[obj.pk]\",\n \"def get_from_cache(self, url):\\n cache_key, cache_lookup = self.get_cacheable_info(url)\\n\\n cache_timeout = self.cache_timeouts.get(cache_key,\\n self.default_cache_timeout)\\n\\n data, access_time = MEM_CACHE[cache_key].get(cache_lookup, (None, 0))\\n if data and time.time() - access_time < cache_timeout:\\n return data\\n return False\",\n \"def get_cache(self, key, default=None):\\n batch_number, value = self._cache.get(key, (None, None))\\n if batch_number == self.model.batch_number:\\n return value\\n else:\\n return default\",\n \"def get(self, key):\\n if key is None or key not in self.cache_data:\\n return None\\n else:\\n for x in self.LFU:\\n if x.key == key:\\n x.age = 0\\n x.freq += 1\\n else:\\n x.age += 1\\n return self.cache_data[key]\",\n \"def get_movie(self, movie_id):\\n try:\\n movie = cache.get(movie_id)\\n except KeyError:\\n movie = self.hit('/movie/{}'.format(movie_id), append_to_response='credits')\\n cache.set(movie_id, movie)\\n return movie\",\n \"def _get_cache(self, course_version_guid):\\r\\n if not hasattr(self.thread_cache, 'course_cache'):\\r\\n self.thread_cache.course_cache = {}\\r\\n system = self.thread_cache.course_cache\\r\\n return system.get(course_version_guid)\",\n \"def memcache_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"memcache_version\\\")\",\n \"def _get_cached_manifest(self, revid):\\n return self.manifests_lru[revid]\",\n \"def _cache_get(self, objId, methodname):\\n self._lock.acquire()\\n entry = self.cache.get((objId, methodname))\\n if entry is None: \\n self._lock.release()\\n return None\\n if time.time() - entry[0] > CACHE_TIMEOUT: \\n del self.cache[(objId, methodname)]\\n self._lock.release()\\n return None\\n self._lock.release()\\n return entry[1]\",\n \"def test_retrieve_memcache_ok(self):\\n content = 'Grecian Urn'\\n namespace = 'default'\\n request = self.store_request(namespace, content)\\n embedded = validate(\\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\\n self.call_api('store_inline', self.message_to_dict(request), 200)\\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\\n digest=embedded['d'], namespace=handlers_endpoints_v1.Namespace())\\n response = self.call_api(\\n 'retrieve', self.message_to_dict(retrieve_request), 200)\\n retrieved = response.json\\n self.assertEqual(content, base64.b64decode(retrieved[u'content']))\",\n \"def get(cls, obj: Model):\\n return cache.get(cls._construct_key(obj))\",\n \"def getCacheMessages(self):\\n return self.db.getCacheMsgs()\",\n \"def get_content_caching(self, loadbalancer):\\n return loadbalancer.content_caching\",\n \"def get(self, key):\\n try:\\n\\n item = self._item_to_dict(self.client.get_item(**self._prepare_get_request(str(key))))\\n\\n # If item is empty, nothing in cache\\n if not item:\\n return None\\n\\n # If current time beyond expiry, nothing to return\\n if time()>float(item[self._expiry_field.name]):\\n return None\\n\\n return self.load_object(b64decode(item.get(self._value_field.name)))\\n\\n except Exception as e:\\n logging.info('Error getting object from DynamoDB table %s (%s): %s',self.table_name,e.__class__.__name__,e)\\n return None\",\n \"def issue(self, check_cache=True):\\n if not check_cache or not hasattr(self, '_issue'):\\n issue_data = self.campaigns.values_list(\\n 'issue__pk', 'issue__name').filter(\\n issue__pk__isnull=False).order_by('issue__name').first()\\n if issue_data:\\n self._issue = Issue.objects.get(pk=issue_data[0])\\n else:\\n self._issue = None\\n return self._issue\",\n \"def _cache_msg(cls, sender_id, recipient_id, text, chat_id=None):\\n # todo make this method async\\n message = Message.new(sender_id, recipient_id, text, chat_id)\\n return message\",\n \"def construct_a_rr_from_cache(dn):\\n for super_domain in dn:\\n if super_domain in acache:\\n ce = acache[super_domain]\\n ip = ce._dict.keys()[0].toNetwork()\\n now = int(time())\\n ttl = ce._dict.values()[0]._expiration - now\\n rr_a = RR_A(DomainName(super_domain), ttl, ip)\\n return RR_A(DomainName(super_domain), ttl, ip)\\n return None\",\n \"def _get(self, *args, **kwargs):\\n return self.deserialize_notifications(self.request.session.get(self.session_key)), True\",\n \"def _get_cache(self, request):\\n if self._cache is None:\\n if hasattr(request.registry, 'cache'):\\n cache_ttl = float(fxa_conf(request, 'cache_ttl_seconds'))\\n oauth_cache = TokenVerificationCache(request.registry.cache,\\n ttl=cache_ttl)\\n self._cache = oauth_cache\\n\\n return self._cache\",\n \"def from_cache(self, cache_key=None, pk=None):\\n if pk:\\n cache_key = self._cache_key(pk)\\n # if cache_key is none, the mangler will generate a MD5 from the query\\n return FromCache(self.label, cache_key)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74955446","0.7363284","0.705641","0.58456796","0.5752198","0.56402856","0.56222874","0.5593154","0.5495106","0.5467809","0.54591787","0.54124725","0.5330662","0.53072345","0.5287989","0.527153","0.52640253","0.523533","0.52221245","0.52221245","0.52177227","0.51978356","0.51977575","0.5173499","0.5169746","0.5145714","0.5130637","0.51244736","0.5121278","0.51201475","0.51143","0.51143","0.509042","0.5059328","0.50588036","0.50243545","0.5023202","0.50167817","0.50151515","0.50151515","0.50151515","0.50151515","0.5014119","0.49979547","0.49866608","0.49768794","0.49670532","0.49590966","0.49547115","0.49495262","0.4949358","0.49451765","0.4920801","0.4911986","0.49109748","0.48885304","0.48852813","0.485798","0.48535404","0.48511207","0.48377827","0.4836961","0.48324394","0.48258927","0.48201796","0.48200718","0.48146603","0.4814224","0.48089892","0.4793584","0.47919056","0.478729","0.4785857","0.47783846","0.47728932","0.47616875","0.47474444","0.4728502","0.47259688","0.4716954","0.47116977","0.47095948","0.47088248","0.47085404","0.47084308","0.4695849","0.46915212","0.46888694","0.46882704","0.4677474","0.46675014","0.4651825","0.4650247","0.46481863","0.46457964","0.46406353","0.46378237","0.46374685","0.46364513","0.46354064"],"string":"[\n \"0.74955446\",\n \"0.7363284\",\n \"0.705641\",\n \"0.58456796\",\n \"0.5752198\",\n \"0.56402856\",\n \"0.56222874\",\n \"0.5593154\",\n \"0.5495106\",\n \"0.5467809\",\n \"0.54591787\",\n \"0.54124725\",\n \"0.5330662\",\n \"0.53072345\",\n \"0.5287989\",\n \"0.527153\",\n \"0.52640253\",\n \"0.523533\",\n \"0.52221245\",\n \"0.52221245\",\n \"0.52177227\",\n \"0.51978356\",\n \"0.51977575\",\n \"0.5173499\",\n \"0.5169746\",\n \"0.5145714\",\n \"0.5130637\",\n \"0.51244736\",\n \"0.5121278\",\n \"0.51201475\",\n \"0.51143\",\n \"0.51143\",\n \"0.509042\",\n \"0.5059328\",\n \"0.50588036\",\n \"0.50243545\",\n \"0.5023202\",\n \"0.50167817\",\n \"0.50151515\",\n \"0.50151515\",\n \"0.50151515\",\n \"0.50151515\",\n \"0.5014119\",\n \"0.49979547\",\n \"0.49866608\",\n \"0.49768794\",\n \"0.49670532\",\n \"0.49590966\",\n \"0.49547115\",\n \"0.49495262\",\n \"0.4949358\",\n \"0.49451765\",\n \"0.4920801\",\n \"0.4911986\",\n \"0.49109748\",\n \"0.48885304\",\n \"0.48852813\",\n \"0.485798\",\n \"0.48535404\",\n \"0.48511207\",\n \"0.48377827\",\n \"0.4836961\",\n \"0.48324394\",\n \"0.48258927\",\n \"0.48201796\",\n \"0.48200718\",\n \"0.48146603\",\n \"0.4814224\",\n \"0.48089892\",\n \"0.4793584\",\n \"0.47919056\",\n \"0.478729\",\n \"0.4785857\",\n \"0.47783846\",\n \"0.47728932\",\n \"0.47616875\",\n \"0.47474444\",\n \"0.4728502\",\n \"0.47259688\",\n \"0.4716954\",\n \"0.47116977\",\n \"0.47095948\",\n \"0.47088248\",\n \"0.47085404\",\n \"0.47084308\",\n \"0.4695849\",\n \"0.46915212\",\n \"0.46888694\",\n \"0.46882704\",\n \"0.4677474\",\n \"0.46675014\",\n \"0.4651825\",\n \"0.4650247\",\n \"0.46481863\",\n \"0.46457964\",\n \"0.46406353\",\n \"0.46378237\",\n \"0.46374685\",\n \"0.46364513\",\n \"0.46354064\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78246075"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":184,"numItemsPerPage":100,"numTotalItems":95772,"offset":18400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjI2OTgyNCwianRpIjoiOGMzNWM2MDAtN2I0Zi00NTBkLWI2MjktZmUzOTQyOTkzODY3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyNzM0MjQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.kNBWolfMEI89ZXF4CrFSZxaRCVdNUjRtJUbl2tx8oaIIOMc-xbWW9q6SWGYbTWrwucMeDg5z8HDK0jl9Re5HAQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Implementing set buy sell deal account functionality | def set_buy_sell_deal_account(self, account_list):
self.multiple_items_selection_from_kendo_dropdown(self.buy_sell_deal_account_dropdown_locator, account_list)
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Trading(Seller,Buyer):\n if Seller.has_sold == False:\n if Buyer.like_buy >= Seller.like_sell:\n Seller.has_sold = True\n Buyer.has_bought = True\n Seller.sold_objects += 1\n Buyer.bought_objects += 1\n print('A trade has been made')\n e... | [
"0.6445233",
"0.6413449",
"0.6269509",
"0.61719674",
"0.61377376",
"0.60943884",
"0.6090972",
"0.60790056",
"0.6075911",
"0.60327494",
"0.6020972",
"0.60124266",
"0.59813756",
"0.59656405",
"0.59601235",
"0.59371483",
"0.5922628",
"0.5903026",
"0.58998966",
"0.58923525",
"0.5... | 0.74209785 | 0 |
Implementing set vendor functionality | def set_vendor(self, vendor_list):
self.multiple_items_selection_from_kendo_dropdown(self.vendor_dropdown_locator, vendor_list)
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vendor(self, vendor):\n\n self._vendor = vendor",
"def vendor_list():\n return ['nxos', 'eos', 'cumulus']",
"def vendorid(self, vendorid):\n\n self._vendorid = vendorid",
"def vendor(self):\n return self._vendor",
"def set_vendor_specific(self, vendor_dict):\n\n ds = xr.D... | [
"0.7222287",
"0.6403538",
"0.630762",
"0.62553227",
"0.62217903",
"0.620997",
"0.61020625",
"0.6084132",
"0.59344214",
"0.5922468",
"0.58317804",
"0.5783661",
"0.5765108",
"0.5727027",
"0.57049346",
"0.5685642",
"0.5679784",
"0.5652456",
"0.56487244",
"0.56167257",
"0.5575269... | 0.6906274 | 1 |
Implementing set customer functionality | def set_customer(self, customer_list):
self.multiple_items_selection_from_kendo_dropdown(self.customer_dropdown_locator, customer_list)
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def customer(self, customer):\n\n self._customer = customer",
"def customer(self, customer):\n\n self._customer = customer",
"def store_customer(self, name):\n pass",
"def __init__(self):\n self.customers = set() # when a customer is registered it is added to the set",
"def _set... | [
"0.72094655",
"0.72094655",
"0.70021456",
"0.6832083",
"0.68292725",
"0.6733106",
"0.66695607",
"0.6500562",
"0.6478323",
"0.6275709",
"0.6192777",
"0.6181375",
"0.617908",
"0.61465466",
"0.61266625",
"0.61073637",
"0.61073637",
"0.61073637",
"0.61073637",
"0.610736",
"0.6078... | 0.686508 | 3 |
Implementing click buy and sell deal create button functionality | def click_buy_and_sell_deal_create_button(self):
self.click_element(self.save_vendor_profile_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_bulk_edit_button(self):\n self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True)",
"def click_request_new_deal_button(self):\n self.click_element(self.request_new_deal_button_locator)",
"async def _vis_buy(self, ctx, *args):\n if has_post_perm... | [
"0.688306",
"0.63619286",
"0.6302916",
"0.62475187",
"0.62253845",
"0.6183532",
"0.6155078",
"0.61283386",
"0.6127857",
"0.6119427",
"0.610177",
"0.60882276",
"0.60569257",
"0.60507286",
"0.6028283",
"0.60277146",
"0.60111076",
"0.6001032",
"0.5991747",
"0.5989623",
"0.598505... | 0.7983313 | 0 |
Implementing get buy and sell deal name functionality | def get_buy_and_sell_deal_name(self):
global buy_and_sell_deal
return buy_and_sell_deal | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_buy_and_sell_deal_name(self, buy_and_sell_deal_prefix):\n global buy_and_sell_deal\n buy_and_sell_deal = buy_and_sell_deal_prefix + self.random_string_generator(size=4)\n self.set_value_into_input_field(self.buy_and_sell_deal_textbox_locator, buy_and_sell_deal)",
"def buy_commodity(u... | [
"0.6696783",
"0.6224842",
"0.61127037",
"0.5885977",
"0.58507454",
"0.5779713",
"0.5724441",
"0.57045215",
"0.56503594",
"0.562514",
"0.5597653",
"0.5589393",
"0.5585691",
"0.553237",
"0.5506362",
"0.5504986",
"0.5480954",
"0.5470787",
"0.54650915",
"0.5447669",
"0.5447065",
... | 0.8101306 | 0 |
Implementing click buy and sell deal save button functionality | def click_buy_and_sell_deal_save_button(self):
self.click_element(self.save_vendor_profile_locator, True)
try:
self.wait().until(EC.visibility_of_element_located(self.confirmation_popup_locator), 'confirmation popup locator not found before specified time out')
self.click_element... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_create_button(self):\n self.click_element(self.save_vendor_profile_locator)",
"def click_buy_and_sell_deal_bulk_edit_button(self):\n self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True)",
"def sell():\n\n # if user reached route via GET return ... | [
"0.7297728",
"0.715392",
"0.6314758",
"0.6237061",
"0.62175316",
"0.6208546",
"0.6186244",
"0.61780924",
"0.6158734",
"0.61187",
"0.6107623",
"0.61025167",
"0.6087814",
"0.60843027",
"0.6078598",
"0.6073275",
"0.6062028",
"0.60600346",
"0.6055075",
"0.60517657",
"0.6045317",
... | 0.7188054 | 1 |
Implementing set start date functionality | def set_start_date(self, start_date):
self.set_value_into_input_field(self.start_date_inputbox_locator, start_date) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_start_date(self, date):\n pass",
"def start_date(self, start_date):\n self._start_date = start_date",
"def start_date(self, start_date):\n \n self._start_date = start_date",
"def start_date(self, start_date):\n\n self._start_date = start_date",
"def start_date(sel... | [
"0.86141276",
"0.80457026",
"0.7868022",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7836363",
"0.7739041",
"0.749863",
"0.74370885",
"0.7398391",
"0.729659",
"0.7160086",
"0.70503116",
"0.70105284",
"0.69417137",... | 0.80691314 | 1 |
Implementing set buy and sell management grid settings functionality | def set_buy_and_sell_management_grid_settings(self, grid_settings):
self.set_grid_settings(self.buy_and_sell_management_grid_div_id, grid_settings) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_vendor_price_lists_grid_settings(self, grid_settings):\n self.set_grid_settings(self.view_price_list_div_id, grid_settings)",
"def set_vendor_price_list_detail_grid_settings(self, grid_settings):\n self.wait_for_ajax_spinner_load(300)\n self.set_grid_settings(self.vendor_price_list_d... | [
"0.6398558",
"0.604387",
"0.59920263",
"0.5793335",
"0.56829715",
"0.56757975",
"0.5573747",
"0.5565601",
"0.5519586",
"0.54784703",
"0.54700017",
"0.54370415",
"0.5375229",
"0.53743625",
"0.5347664",
"0.53292626",
"0.53240794",
"0.52728754",
"0.52641976",
"0.52620107",
"0.52... | 0.79892987 | 0 |
Implementing sort buy and sell management grid column functionality | def sort_buy_and_sell_management_grid_column(self, column_name, descending_order):
self.sort_grid_column(self.buy_and_sell_management_grid_div_id, column_name, descending_order) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def OnColumnClick(self, event):\r\n columns = self.data.getParam('columns')\r\n self.SortItems(columns[event.GetColumn()],'INVERT')",
"def sort_vendor_price_lists_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.view_price_list_div_id, column_name, descending_or... | [
"0.6722339",
"0.65786284",
"0.6452577",
"0.6041004",
"0.6005217",
"0.5994471",
"0.5969092",
"0.5801106",
"0.5778006",
"0.57584304",
"0.57022667",
"0.55251795",
"0.5473172",
"0.5456213",
"0.5440098",
"0.54038495",
"0.53848374",
"0.5352643",
"0.5340399",
"0.5331021",
"0.5279471... | 0.70848614 | 0 |
Implementing get all rows of specific column from buy and sell management grid functionality | def get_all_rows_of_specific_column_from_buy_and_sell_management_grid(self, column_name):
self.all_row_data = self.get_all_rows_of_specific_column_from_grid(self.buy_and_sell_management_grid_div_id, column_name, self.get_grid_row_count(self.buy_and_sell_management_grid_count_span_locator, 2))
return sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_rows_of_rate_increase_violation_column_from_vendor_price_list_detail_grid(self, column_name):\n self.all_row_data = []\n self.wait_for_ajax_spinner_load()\n row_count = len(self.wait().until(EC.presence_of_all_elements_located(self.vendor_price_list_detail_row_locator), 'vendor pri... | [
"0.6735556",
"0.6720651",
"0.67186683",
"0.6640583",
"0.65773517",
"0.6404575",
"0.6303615",
"0.5945554",
"0.5850497",
"0.58364195",
"0.57948834",
"0.57733727",
"0.57243025",
"0.56459594",
"0.56439763",
"0.5600407",
"0.5567689",
"0.547445",
"0.5463407",
"0.54597545",
"0.54290... | 0.7284325 | 0 |
Implementing drag buy and sell management grid column functionality | def drag_buy_and_sell_management_grid_column(self, source_column, destination_column):
self.drag_grid_column(self.buy_and_sell_management_grid_div_id, source_column, destination_column) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drag_vendor_price_lists_grid_column(self, source_column, destination_column):\n self.drag_grid_column(self.view_price_list_div_id, source_column, destination_column)",
"def drag_vendor_price_list_detail_grid_column(self, source_column, destination_column):\n self.wait_for_ajax_spinner_load(300)... | [
"0.68649036",
"0.6444064",
"0.63804555",
"0.63166463",
"0.62582725",
"0.61420965",
"0.61303335",
"0.59818137",
"0.5775787",
"0.56232387",
"0.5621364",
"0.5609542",
"0.5536238",
"0.55356735",
"0.53799516",
"0.5366732",
"0.53493655",
"0.5324431",
"0.52932245",
"0.525854",
"0.52... | 0.7623682 | 0 |
Implementing get buy and sell management grid column names by order functionality | def get_buy_and_sell_management_grid_column_names_by_order(self):
self.column_name_list = self.get_grid_column_names_by_order(self.buy_and_sell_management_grid_div_id)
return self.column_name_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vendor_price_lists_grid_column_names_by_order(self):\n self.column_name_list = self.get_grid_column_names_by_order(self.view_price_list_div_id)\n return self.column_name_list",
"def get_vendor_price_list_detail_grid_column_names_by_order(self):\n self.wait_for_ajax_spinner_load(300)\... | [
"0.7173671",
"0.7164694",
"0.6961585",
"0.67937434",
"0.66460264",
"0.64222735",
"0.63047063",
"0.6221156",
"0.61231023",
"0.5842353",
"0.58139414",
"0.57718784",
"0.5757523",
"0.56256413",
"0.5614401",
"0.56115836",
"0.5588756",
"0.55571264",
"0.55410564",
"0.55111706",
"0.5... | 0.75991935 | 0 |
Implementing click buy and sell management grid export to excel button functionality | def click_buy_and_sell_management_grid_export_to_excel_button(self):
self.click_grid_export_to_excel_button(self.buy_and_sell_management_grid_div_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_vendor_price_list_detail_rates_grid_export_to_excel_button(self):\n self.click_grid_export_to_excel_button(self.vendor_price_list_detail_rates_grid_div_id)",
"def click_vendor_price_list_detail_reference_rates_grid_export_to_excel_button(self):\n self.click_grid_export_to_excel_button(sel... | [
"0.750306",
"0.74308896",
"0.7306206",
"0.7060795",
"0.68989515",
"0.68989515",
"0.6491007",
"0.6120849",
"0.591677",
"0.5913262",
"0.58993304",
"0.56994367",
"0.569883",
"0.56973416",
"0.5684809",
"0.56796527",
"0.56629676",
"0.5620759",
"0.5603418",
"0.55868685",
"0.5576812... | 0.86254066 | 0 |
Implementing select buy and sell deal management grid first row checkbox functionality | def select_buy_and_sell_deal_management_grid_first_row_checkbox(self):
self.select_grid_row_checkbox(self.buy_and_sell_management_grid_div_id, None, self.buy_and_sell_management_grid_checkbox_column_number, True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_target_buy_list_overrides_grid_row_checkbox(self):\n for i in range(2):\n target_buy_list_overrides_grid_checkbox_locator = (By.XPATH, \"(//div[@data-grid-name='%s']/descendant::input[@type='checkbox'])[%s]\" % (self.target_buy_list_overrides_data_grid_name, str(i + 2)))\n s... | [
"0.63728535",
"0.6276276",
"0.62166536",
"0.61831915",
"0.6053796",
"0.59584445",
"0.5842777",
"0.5797149",
"0.57607424",
"0.5707139",
"0.5612971",
"0.5531448",
"0.5506551",
"0.5497594",
"0.5481814",
"0.54728425",
"0.54640925",
"0.5431332",
"0.538145",
"0.5380802",
"0.5379458... | 0.80763716 | 0 |
Implementing click buy and sell deal bulk edit button functionality | def click_buy_and_sell_deal_bulk_edit_button(self):
self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edit_tools(self, e):\n #GETTING SELECTION\n\n self.selected_item = self.user_inventory.selection()\n self.select_name = self.user_inventory.item([i for i in self.selected_item], \"values\")[0]\n self.select_entdate = self.user_inventory.item([i for i in self.selected_item], \"values... | [
"0.66338634",
"0.66057515",
"0.6131386",
"0.60525894",
"0.59958154",
"0.58884054",
"0.58270955",
"0.5821739",
"0.57709527",
"0.5718494",
"0.57100034",
"0.5659034",
"0.56481487",
"0.56460947",
"0.5622122",
"0.55970424",
"0.55949754",
"0.5559805",
"0.5537368",
"0.5531731",
"0.5... | 0.8322212 | 0 |
Implementing set bulk edit end date functionality | def set_bulk_edit_end_date(self, end_date, future_date):
if future_date is True:
end_date = self.get_date(future_date=True, number_of_days_to_add=30)
self.set_value_into_input_field(self.bulk_edit_end_date_inputbox_locator, end_date) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_bulk_add_end_date(self, end_date):\n global bulk_add_begin_date\n if end_date != \"\" and datetime.datetime.strptime(end_date, \"%m/%d/%Y\") > bulk_add_begin_date:\n self.set_value_into_input_field(self.bulk_add_end_date_inputbox_locator, str(end_date))\n elif end_date == \"... | [
"0.7402478",
"0.7031963",
"0.67778796",
"0.6563116",
"0.64243233",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63864547",
"0.63433135",
"0.629675",
"0.6284176",
"0.61299545",
"0.604149",
"0.6033477",
"0.59961516",
"0.59... | 0.74479747 | 0 |
Implementing click bulk edit submit button functionality | def click_bulk_edit_submit_button(self):
self.click_element(self.bulk_edit_submit_button_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_bulk_edit_submit_button(self):\n self.click_element(self.bulk_edit_submit_button_locator, True)",
"def click_bulk_edit_button(self):\n self.click_element(self.bulk_edit_button_locator, True)",
"def click_statement_fields_bulk_edit_button(self):\n self.click_element(self.statement... | [
"0.80722886",
"0.7676083",
"0.72128063",
"0.6668404",
"0.6572236",
"0.65535694",
"0.63675964",
"0.63469726",
"0.63170797",
"0.62994254",
"0.6182403",
"0.61548156",
"0.61191946",
"0.6086712",
"0.60528755",
"0.60109603",
"0.59184855",
"0.5897436",
"0.58945984",
"0.58505696",
"0... | 0.8029734 | 1 |
Implementing click save changes button functionality | def click_save_changes_button(self):
self.click_element(self.save_changes_button_locator, True)
try:
self.wait().until(EC.visibility_of_element_located(self.confirmation_popup_locator), 'confirmation popup locator not found before specified time out')
self.click_element(self.ok_b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onSaveNotesButton(self, button):\n pass",
"def save():\n pass",
"def save_edits(self, value):\r\n if value != self._save:\r\n self._save = value",
"def save():",
"def save_edits(self):\r\n return self._save",
"def save(self):\n pass",
"def save(self):\n... | [
"0.7248518",
"0.7177743",
"0.7092814",
"0.7066338",
"0.7051386",
"0.70364714",
"0.70364714",
"0.70364714",
"0.70364714",
"0.70364714",
"0.7032507",
"0.70250934",
"0.6993248",
"0.6992968",
"0.69803596",
"0.6938146",
"0.68485695",
"0.68485695",
"0.68437153",
"0.68347204",
"0.68... | 0.68267846 | 20 |
Implementing set multiple status functionality | def set_multiple_status(self, status_list):
self.multiple_items_selection_from_kendo_dropdown(self.status_kendo_dropdown_locator, status_list)
self.wait_for_ajax_spinner_load()
buy_page_title_element = self.wait().until(EC.element_to_be_clickable(self.buy_page_title_locator), 'buy page title loc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_status(self, action, status):\n raise NotImplementedError(\"Base class: cannot be called directly\")",
"def change_status(self, status):\n for option in status:\n response = requests.patch(self.base_url + 'settings', headers=self.headers, json={option: status.get(option)})\n ... | [
"0.7171014",
"0.6825423",
"0.6711225",
"0.65869707",
"0.65354705",
"0.65178156",
"0.6515212",
"0.65148544",
"0.6439463",
"0.63568485",
"0.63568485",
"0.63568485",
"0.6324592",
"0.6317906",
"0.6298237",
"0.62834007",
"0.62388676",
"0.6228493",
"0.6176957",
"0.61687696",
"0.615... | 0.6786368 | 2 |
Implementing click buy and sell deal management grid first row inline action button functionality | def click_buy_and_sell_deal_management_grid_first_row_inline_action_button(self):
self.click_inline_action_button(self.buy_and_sell_management_grid_div_id, None, self.buy_and_sell_management_grid_inline_action_column_number, True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_bulk_edit_button(self):\n self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True)",
"def click_target_buy_policy_grid_first_row_inline_action_button(self):\n self.click_inline_action_button(self.target_buy_policies_grid_div_id, None, column_number=2, fi... | [
"0.7165491",
"0.69654083",
"0.69372815",
"0.69077474",
"0.66696733",
"0.6508445",
"0.6368367",
"0.6347989",
"0.63158387",
"0.627581",
"0.6269957",
"0.61755913",
"0.61733496",
"0.6149201",
"0.6137883",
"0.6119374",
"0.58740306",
"0.5799829",
"0.57629937",
"0.57190955",
"0.5663... | 0.8185726 | 0 |
Implementing is account label displayed correctly functionality | def is_account_label_displayed_correctly(self):
is_displayed_correctly = False
if self.get_text_from_element(self.account_label_locator) == self.account_label:
is_displayed_correctly = True
return is_displayed_correctly | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_account_manager_label_displayed_correctly(self):\n is_displayed_correctly = False\n if self.get_text_from_element(self.account_manager_label_locator) == self.account_manager_label:\n is_displayed_correctly = True\n return is_displayed_correctly",
"def Label(self) -> str:",
... | [
"0.6368619",
"0.6344278",
"0.6144431",
"0.61396897",
"0.6069333",
"0.5957378",
"0.591541",
"0.5847986",
"0.5835125",
"0.5799273",
"0.5767016",
"0.57317346",
"0.5720798",
"0.5717911",
"0.5690789",
"0.5643161",
"0.5638978",
"0.5619242",
"0.5607045",
"0.5593206",
"0.55726165",
... | 0.74997246 | 0 |
Implementing is account manager label displayed correctly functionality | def is_account_manager_label_displayed_correctly(self):
is_displayed_correctly = False
if self.get_text_from_element(self.account_manager_label_locator) == self.account_manager_label:
is_displayed_correctly = True
return is_displayed_correctly | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_account_label_displayed_correctly(self):\n is_displayed_correctly = False\n if self.get_text_from_element(self.account_label_locator) == self.account_label:\n is_displayed_correctly = True\n return is_displayed_correctly",
"def _add_user_label(self):\n user_label = tk.La... | [
"0.660459",
"0.6117637",
"0.5810706",
"0.579244",
"0.5719326",
"0.5605203",
"0.55454856",
"0.55295485",
"0.5480238",
"0.5474781",
"0.5458673",
"0.5429711",
"0.541652",
"0.54080826",
"0.53880817",
"0.5370094",
"0.53372633",
"0.533329",
"0.53238416",
"0.5259748",
"0.5249081",
... | 0.74036354 | 0 |
Implementing click vendor grid add destination button functionality | def click_vendor_grid_add_destination_button(self):
self.click_element(self.vendor_grid_add_destination_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_customer_grid_add_destination_button(self):\n self.click_element(self.customer_grid_add_destination_locator)",
"def set_destination_to_add_destination(self, destination):\n self.multiple_items_selection_from_kendo_dropdown(self.destination_multiple_kendo_dropdown_locator, destination)\n ... | [
"0.8016771",
"0.646783",
"0.62058467",
"0.59663063",
"0.5802453",
"0.5654677",
"0.558464",
"0.55153704",
"0.5512954",
"0.54436165",
"0.5412148",
"0.5398465",
"0.5385228",
"0.5373527",
"0.5345784",
"0.53445095",
"0.53421956",
"0.53135604",
"0.5309377",
"0.5302662",
"0.52934057... | 0.8421994 | 0 |
Implementing set source to add destination functionality | def set_source_to_add_destination(self, source_name):
self.single_selection_from_static_kendo_dropdown(self.source_kendo_dropdown_arrow_locator, source_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_destination(self):\n pass",
"def set_source(self, source):\n self.data['source'] = source",
"def attach(self, destination): \r\n self.destination= destination",
"def source(self, other):\n raise NotImplementedError",
"def set_destination_to_add_destination(self, d... | [
"0.7595484",
"0.68353415",
"0.6798214",
"0.67796534",
"0.67778414",
"0.67471397",
"0.67426056",
"0.6698542",
"0.64227223",
"0.64126635",
"0.63714236",
"0.6344426",
"0.6306878",
"0.628486",
"0.6280195",
"0.6280195",
"0.62796885",
"0.62796885",
"0.62796885",
"0.62796885",
"0.62... | 0.71872395 | 1 |
Implementing set route class to add destination functionality | def set_route_class_to_add_destination(self, route_class):
self.multiple_items_selection_from_kendo_dropdown(self.route_class_multiple_kendo_dropdown_locator, route_class)
self.click_element(self.new_destination_header_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def route(self):\n pass",
"def add_destination(self):\n pass",
"def add_routes(self):\n pass",
"def route(self, destination):\n assert destination in list(Destination), \"{} value expected, got {}\".format(Destination, destination)\n return self.destinations[destination... | [
"0.76027405",
"0.7224743",
"0.6823121",
"0.67958796",
"0.6792756",
"0.6775756",
"0.67167026",
"0.6405978",
"0.6373856",
"0.62698567",
"0.6204093",
"0.613918",
"0.6112342",
"0.60995054",
"0.6053411",
"0.6032961",
"0.60155857",
"0.6005509",
"0.5994421",
"0.59790593",
"0.5962053... | 0.6548088 | 7 |
Implementing set destination to add destination functionality | def set_destination_to_add_destination(self, destination):
self.multiple_items_selection_from_kendo_dropdown(self.destination_multiple_kendo_dropdown_locator, destination)
self.click_element(self.new_destination_header_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_destination(self):\n pass",
"def attach(self, destination): \r\n self.destination= destination",
"def attach(self, destination): \r\n self.destination=destination",
"def destination(self, destination):\n\n self._destination = destination",
"def do_destinati... | [
"0.8411674",
"0.7463169",
"0.7400204",
"0.7207934",
"0.70057255",
"0.6809444",
"0.6675045",
"0.66749346",
"0.66615766",
"0.6569928",
"0.65571934",
"0.6486881",
"0.64785564",
"0.64413476",
"0.6326941",
"0.631957",
"0.63072693",
"0.630284",
"0.61267376",
"0.6122438",
"0.6116038... | 0.7659271 | 1 |
Implementing set deal rate to add destination functionality | def set_deal_rate_to_add_destination(self, deal_rate):
self.set_value_into_input_field(self.deal_rate_textbox_locator, deal_rate, use_win32com=True)
self.click_element(self.new_destination_header_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_rates_destination(self):\n self.set_value_into_input_field(self.rates_tab_destination_textbox_locator, self.buy_page_excel_data_dictionary[\"Destination\"])",
"def __call__(self, rate:'kW'):\n self.rate = rate\n self.cost = self.price * rate",
"def _do_set_rate(self, rate):\n ... | [
"0.66945755",
"0.6256623",
"0.58632624",
"0.56280166",
"0.5582999",
"0.5560925",
"0.5540805",
"0.5522839",
"0.5397975",
"0.5368135",
"0.5368135",
"0.53364104",
"0.5259004",
"0.5257495",
"0.52336127",
"0.52244794",
"0.5215375",
"0.5210986",
"0.5177353",
"0.51745504",
"0.516315... | 0.74631596 | 0 |
Implementing set deal rate to add destination functionality | def set_deal_volume_to_add_destination(self, deal_volume):
self.set_value_into_input_field(self.deal_volume_textbox_locator, deal_volume, use_win32com=True)
self.click_element(self.new_destination_header_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_deal_rate_to_add_destination(self, deal_rate):\n self.set_value_into_input_field(self.deal_rate_textbox_locator, deal_rate, use_win32com=True)\n self.click_element(self.new_destination_header_locator)",
"def set_rates_destination(self):\n self.set_value_into_input_field(self.rates_ta... | [
"0.7464668",
"0.66951454",
"0.6258276",
"0.5864363",
"0.56300765",
"0.558504",
"0.55624336",
"0.55420876",
"0.5526853",
"0.5398804",
"0.5370449",
"0.5370449",
"0.533766",
"0.5262465",
"0.525798",
"0.5234379",
"0.5225543",
"0.52159494",
"0.5212933",
"0.517884",
"0.51761097",
... | 0.49938697 | 37 |
Implementing click customer grid add destination button functionality | def click_customer_grid_add_destination_button(self):
self.click_element(self.customer_grid_add_destination_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_vendor_grid_add_destination_button(self):\n self.click_element(self.vendor_grid_add_destination_locator)",
"def set_destination_to_add_destination(self, destination):\n self.multiple_items_selection_from_kendo_dropdown(self.destination_multiple_kendo_dropdown_locator, destination)\n ... | [
"0.80682164",
"0.6506563",
"0.611456",
"0.56259584",
"0.5613796",
"0.56112826",
"0.55190563",
"0.55092216",
"0.5477043",
"0.54032",
"0.5402372",
"0.53741306",
"0.53735375",
"0.5371906",
"0.5339226",
"0.52918637",
"0.5284191",
"0.5281223",
"0.52697504",
"0.52324903",
"0.520353... | 0.8646041 | 0 |
Implementing click request deal button functionality | def click_request_deal_button(self):
self.click_element(self.request_deal_button_locator)
try:
self.wait().until(EC.visibility_of_element_located(self.confirmation_popup_locator), 'confirmation popup locator not found before specified time out')
self.click_element(self.ok_button_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_request_new_deal_button(self):\n self.click_element(self.request_new_deal_button_locator)",
"def click(self):\r\n pass",
"def on_click(self) -> None:\n pass",
"def on_click ( self, object ):\n pass",
"def btn_follow_clicked(self, widget, data=None):\n print \"fo... | [
"0.7253511",
"0.70641905",
"0.67269415",
"0.6564448",
"0.62736565",
"0.6219062",
"0.6219062",
"0.6219062",
"0.6219062",
"0.61424035",
"0.61223346",
"0.61172473",
"0.60978913",
"0.60978913",
"0.60953057",
"0.6004954",
"0.5973172",
"0.59452313",
"0.59452313",
"0.58775026",
"0.5... | 0.6527855 | 4 |
Implementing click add destination save button functionality | def click_add_destination_save_button(self):
self.click_element(self.add_destination_save_button_locator, True)
try:
self.wait().until(EC.visibility_of_element_located(self.confirmation_popup_locator), 'confirmation popup locator not found before specified time out')
self.click_e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_vendor_grid_add_destination_button(self):\n self.click_element(self.vendor_grid_add_destination_locator)",
"def add_destination(self):\n pass",
"def click_customer_grid_add_destination_button(self):\n self.click_element(self.customer_grid_add_destination_locator)",
"def set_des... | [
"0.6717724",
"0.67038506",
"0.6494957",
"0.64152914",
"0.6392956",
"0.62395084",
"0.6176817",
"0.6160083",
"0.6086105",
"0.6084194",
"0.60714966",
"0.6068572",
"0.60467327",
"0.60467327",
"0.60467327",
"0.60467327",
"0.60467327",
"0.60350305",
"0.599849",
"0.5995645",
"0.5976... | 0.712687 | 0 |
Implementing compare vendor price list detail rates grid destination functionality | def compare_vendor_price_list_detail_rates_grid_destination(self):
self.buy_page_excel_data_dictionary = self.get_excel_data_dictionary()
is_compared = False
rates_grid_destination = self.get_specific_column_value_from_grid(self.vendor_price_list_detail_rates_grid_div_id, 1, self.destination_col... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_vendor_price_list_detail_rates_grid_rate(self):\n self.buy_page_excel_data_dictionary = self.get_excel_data_dictionary()\n is_compared = False\n rates_grid_rate_column_element = self.wait().until(EC.presence_of_element_located(self.rates_grid_rate_column_locator))\n if self.... | [
"0.7282243",
"0.66426474",
"0.63583803",
"0.6355037",
"0.5928445",
"0.58797383",
"0.57747257",
"0.57692534",
"0.5715802",
"0.567752",
"0.5522854",
"0.5438436",
"0.54336697",
"0.54138416",
"0.5389457",
"0.5359198",
"0.5304087",
"0.52684253",
"0.5221472",
"0.5190703",
"0.518978... | 0.7443731 | 0 |
Implementing compare vendor price list detail dial digits grid destination functionality | def compare_vendor_price_list_detail_dial_digits_grid_destination(self):
self.buy_page_excel_data_dictionary = self.get_excel_data_dictionary()
is_compared = False
dial_digits_grid_destination = self.get_specific_column_value_from_grid(self.vendor_price_list_detail_dial_digits_grid_div_id, 1, se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_vendor_price_list_detail_rates_grid_destination(self):\n self.buy_page_excel_data_dictionary = self.get_excel_data_dictionary()\n is_compared = False\n rates_grid_destination = self.get_specific_column_value_from_grid(self.vendor_price_list_detail_rates_grid_div_id, 1, self.destina... | [
"0.69088066",
"0.66299367",
"0.6117149",
"0.6081349",
"0.60306233",
"0.5784268",
"0.5770761",
"0.5732137",
"0.5709735",
"0.54425746",
"0.53622025",
"0.53092676",
"0.5295223",
"0.5292526",
"0.5235184",
"0.5194165",
"0.51922876",
"0.51855004",
"0.5147623",
"0.51423776",
"0.5084... | 0.7398431 | 0 |
Implementing compare vendor price list detail rates grid rate functionality | def compare_vendor_price_list_detail_rates_grid_rate(self):
self.buy_page_excel_data_dictionary = self.get_excel_data_dictionary()
is_compared = False
rates_grid_rate_column_element = self.wait().until(EC.presence_of_element_located(self.rates_grid_rate_column_locator))
if self.buy_page_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_rates_with_excel_data(self):\n is_compared = True\n self.grid_row_data.clear()\n self.grid_row_data.update({\"Rate1\": \"\", \"Rate2\": \"\", \"Rate3\": \"\", \"Begin Date\": \"\", \"Rating Method\": \"\"})\n self.buy_page_excel_data_dictionary[\"Rating Method\"] = self.buy_... | [
"0.63370734",
"0.60845774",
"0.60595584",
"0.602486",
"0.58634794",
"0.5762692",
"0.5760641",
"0.5752293",
"0.57230544",
"0.56713235",
"0.56500137",
"0.5597601",
"0.5579603",
"0.55513275",
"0.5518962",
"0.551297",
"0.5499407",
"0.5466408",
"0.546308",
"0.54610395",
"0.5433152... | 0.7548665 | 0 |
Implementing get reference rates tab analyzed rate functionality | def get_reference_rates_tab_analyzed_rate(self):
return self.get_text_from_element(self.reference_rates_tab_analyzed_tab_column_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rates(self):\n raise NotImplementedError(\"Must be implemented by subclass.\")",
"def get_current_rate(self):\n pass",
"def select_reference_rates_tab(self):\n self.select_static_tab(self.reference_rates_tab_locator, True)",
"def calculateDataRate(self):\n pass",
"def rate(self)... | [
"0.6990552",
"0.66156983",
"0.6201779",
"0.61440897",
"0.6085955",
"0.59836334",
"0.598278",
"0.58565754",
"0.57975405",
"0.5742415",
"0.5740475",
"0.56852716",
"0.56798387",
"0.56295514",
"0.5606371",
"0.56015044",
"0.5600266",
"0.5587584",
"0.5586825",
"0.55694264",
"0.5565... | 0.7216807 | 0 |
Implementing get reference rates tab effective date functionality | def get_reference_rates_tab_effective_date(self):
return self.get_specific_column_value_from_grid(self.vendor_price_list_detail_reference_rates_grid_div_id, 1, column_name=self.effective_date_column_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rate_dates(self, common_object):\n if common_object.IsKindOf(acm.FCashFlow):\n start_date = common_object.StartDate()\n elif common_object.IsKindOf(acm.FReset):\n start_date = common_object.Day()\n else:\n message = \"Rate dates for {0} object are not defi... | [
"0.61450714",
"0.6118674",
"0.59151864",
"0.570882",
"0.54793197",
"0.5440474",
"0.5394293",
"0.53898215",
"0.53862345",
"0.5362355",
"0.53242594",
"0.5290705",
"0.5275283",
"0.5234759",
"0.5224288",
"0.52232665",
"0.5206688",
"0.52063805",
"0.51902455",
"0.5173077",
"0.51618... | 0.8118622 | 0 |
Implementing is target buy policies tab displayed functionality | def is_target_buy_policies_tab_displayed(self):
return self.is_element_visible(self.target_buy_policies_tab_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_target_buy_policies_tab(self):\n self.select_static_tab(self.target_buy_policies_tab_locator, 'target buy policies tab locator not found before specified time out')",
"def click_buy_and_sell_deal_management_link(self):\n self.select_static_tab(self.buy_and_sell_deal_management_locator, m... | [
"0.70824105",
"0.6161778",
"0.6106716",
"0.5919944",
"0.57483226",
"0.57384634",
"0.5682125",
"0.5653956",
"0.5572523",
"0.55241346",
"0.55241346",
"0.5510905",
"0.5445419",
"0.54413915",
"0.5437206",
"0.5395717",
"0.5387464",
"0.53720695",
"0.53677285",
"0.5356148",
"0.53158... | 0.750019 | 0 |
Implementing select target buy policies tab functionality | def select_target_buy_policies_tab(self):
self.select_static_tab(self.target_buy_policies_tab_locator, 'target buy policies tab locator not found before specified time out') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_edit_target_buy_policy_button(self):\n self.click_element(self.edit_target_buy_policy_button_locator)",
"def set_target_buy_policy_type(self, type):\n self.single_selection_from_kendo_dropdown(self.target_buy_policy_type_kendo_dropdown_locator, type)",
"def select_buy_dashboard_tab(self... | [
"0.6704803",
"0.6659049",
"0.62752223",
"0.612902",
"0.6105099",
"0.60682184",
"0.5983011",
"0.59394455",
"0.59090525",
"0.58154",
"0.579811",
"0.5768202",
"0.57265127",
"0.57265127",
"0.5684236",
"0.56841564",
"0.55653906",
"0.55247366",
"0.55149025",
"0.5494688",
"0.5454117... | 0.76746756 | 0 |
Implementing is button displayed functionality | def is_button_displayed(self, button_name):
button_locator = (By.XPATH, "//a[contains(@class, 'k-button') and contains(text(), '%s')]" % button_name)
return self.is_element_visible(button_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_main_buttons(self):\n pass",
"def _test_display_up_button(self):\n return (self.product_displays.top_index > 0)",
"def display(self):\n\t\tprint('The button in the window was clicked!')",
"def is_visible(self):",
"def isButtonPressed() -> bool:\n pass",
"def exec_(self):\n ... | [
"0.71268463",
"0.7112893",
"0.70808524",
"0.6994791",
"0.6799946",
"0.6710357",
"0.6702696",
"0.665377",
"0.66383374",
"0.66172266",
"0.65960544",
"0.6558776",
"0.6558776",
"0.6558776",
"0.65109396",
"0.64982855",
"0.6454811",
"0.6441927",
"0.6415605",
"0.6379587",
"0.6377989... | 0.70637447 | 3 |
Implementing is target buy policies grid column present functionality | def is_target_buy_policies_grid_column_present(self, column_name):
column_locator = (By.XPATH, "//div[contains(@id, '%s')]/descendant::th[@data-title='%s']" % (self.target_buy_policies_grid_div_id, column_name))
return self.is_element_present(column_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_target_buy_policy_grid_row_details(self):\n self.grid_row_data.clear()\n self.grid_row_data = {\"Status\":\"\", \"Target Buy Policy Type\":\"\", \"Call Type\":\"\", \"Route Class\":\"\"}\n return self.get_grid_row_details(self.target_buy_policies_grid_div_id, self.grid_row_data, data_g... | [
"0.65470636",
"0.5555432",
"0.5375789",
"0.53040683",
"0.5265732",
"0.5251825",
"0.52483046",
"0.5247218",
"0.52333355",
"0.5223402",
"0.5211076",
"0.51632774",
"0.5090065",
"0.50898117",
"0.5086829",
"0.5062219",
"0.5055388",
"0.50354195",
"0.50350845",
"0.50128216",
"0.5012... | 0.6384165 | 1 |
Implementing click target buy policies grid first row inline action button functionality | def click_target_buy_policies_grid_first_row_inline_action_button(self):
self.click_inline_action_button(self.target_buy_policies_grid_div_id, None, self.target_buy_policies_grid_inline_action_column_number, first_row=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_target_buy_policy_grid_first_row_inline_action_button(self):\n self.click_inline_action_button(self.target_buy_policies_grid_div_id, None, column_number=2, first_row=True)\n self.wait_for_ajax_spinner_load()",
"def click_buy_and_sell_deal_management_grid_first_row_inline_action_button(sel... | [
"0.8105151",
"0.794805",
"0.73137873",
"0.7264641",
"0.7102072",
"0.7087844",
"0.70674783",
"0.7059369",
"0.7035884",
"0.70264035",
"0.68555933",
"0.6739181",
"0.67389554",
"0.6670417",
"0.65871423",
"0.6563748",
"0.6447696",
"0.64369726",
"0.6267657",
"0.61293465",
"0.599655... | 0.81812817 | 0 |
Implementing is inline action item present functionality | def is_inline_action_item_present(self, item_name):
inline_action_item_locator = (By.XPATH, "//ul[@id='Actions_listbox']/li[text()='%s']" % item_name)
return self.is_element_present(inline_action_item_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def action(self,item):\r\n pass",
"def _action(self):\n pass",
"def action(self):\n pass",
"def action(self):\n pass",
"def onShowed(self):\n self.parent.actionTagTwo=\"\"\n pass",
"def __actions__(self, obj):\n primary_fields = self.__provider__.get_prima... | [
"0.7179313",
"0.6199474",
"0.6192554",
"0.6192554",
"0.6180556",
"0.61662686",
"0.6146612",
"0.6127133",
"0.604029",
"0.6010713",
"0.6008397",
"0.6008368",
"0.58624053",
"0.5829709",
"0.5829346",
"0.5829346",
"0.5772425",
"0.57424104",
"0.5731849",
"0.56981766",
"0.56816936",... | 0.70441365 | 1 |
Implementing click create new target buy policy button functionality | def click_create_new_target_buy_policy_button(self):
self.click_element(self.create_new_target_buy_policy_button_locator, script_executor=True, error_message='create new target buy policy button locator not found before specified time out') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_create_button(self):\n self.click_element(self.save_vendor_profile_locator)",
"def click_edit_target_buy_policy_button(self):\n self.click_element(self.edit_target_buy_policy_button_locator)",
"def click_save_target_buy_policy_button(self):\n self.click_element(... | [
"0.7408507",
"0.7157741",
"0.6822164",
"0.6584997",
"0.65837705",
"0.627723",
"0.6117332",
"0.6084861",
"0.60220706",
"0.59835017",
"0.5927379",
"0.5761761",
"0.57569414",
"0.57379687",
"0.5736414",
"0.5709918",
"0.5691938",
"0.56675476",
"0.5583201",
"0.5517948",
"0.5517535"... | 0.7672479 | 0 |
Implementing set target buy policy name functionality | def set_target_buy_policy_name(self, prefix):
global target_buy_policy
target_buy_policy = (prefix + self.random_string_generator(size=4))
self.set_value_into_input_field(self.target_buy_policy_input_field_locator, value=target_buy_policy) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_target_buy_policy_abbreviation(self, prefix):\n self.set_value_into_input_field(self.target_buy_policy_abbreviation_input_field_locator, value=(prefix + self.random_string_generator(size=4)))",
"def use(target, name):\n return \"You use the \" + name + \" to pay \" + target.name",
"def se... | [
"0.70902216",
"0.65581495",
"0.6447595",
"0.63000184",
"0.6270888",
"0.6120589",
"0.6120589",
"0.6109877",
"0.604",
"0.5914904",
"0.5862381",
"0.58405066",
"0.5839304",
"0.5839304",
"0.56366056",
"0.55916286",
"0.5582298",
"0.556557",
"0.55630815",
"0.5550732",
"0.55316234",
... | 0.79451764 | 0 |
Implementing get target buy policy functionality | def get_target_buy_policy(self):
global target_buy_policy
return target_buy_policy | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def policy(agent):",
"def select_target_buy_policies_tab(self):\n self.select_static_tab(self.target_buy_policies_tab_locator, 'target buy policies tab locator not found before specified time out')",
"def getAction(self, state):\n # Pick Action\n legalActions = self.getLegalActions(state)\n act... | [
"0.62555337",
"0.6185256",
"0.61499",
"0.61307913",
"0.60904336",
"0.6055906",
"0.595635",
"0.5928054",
"0.5914833",
"0.5885227",
"0.58382344",
"0.5782354",
"0.5782354",
"0.57409716",
"0.5726041",
"0.56711364",
"0.5637255",
"0.563183",
"0.56234705",
"0.5622425",
"0.5594815",
... | 0.7827906 | 0 |
Implementing set target buy policy abbreviation functionality | def set_target_buy_policy_abbreviation(self, prefix):
self.set_value_into_input_field(self.target_buy_policy_abbreviation_input_field_locator, value=(prefix + self.random_string_generator(size=4))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_target_buy_policy_name(self, prefix):\n global target_buy_policy\n target_buy_policy = (prefix + self.random_string_generator(size=4))\n self.set_value_into_input_field(self.target_buy_policy_input_field_locator, value=target_buy_policy)",
"def set_target_buy_policy_type(self, type):... | [
"0.6439152",
"0.5870092",
"0.5696178",
"0.5516288",
"0.53760064",
"0.53070027",
"0.52721477",
"0.51333565",
"0.51169074",
"0.51167685",
"0.50744283",
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"0.49748594",
"0.49631476",
"0.49574703",
"0.49414706",
"0.49384534",
"0.49269494",
"... | 0.70045894 | 0 |
Implementing set route class to create new target buy policy functionality | def set_route_class_to_create_new_target_buy_policy(self, route_class):
self.single_selection_from_static_kendo_dropdown(self.route_class_single_kendo_dropdown_arrow_locator, route_class) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, source_node, source_gate_name, target_node, target_slot_name, weight=1):\n self.link(source_node, source_gate_name, target_node, target_slot_name, weight)",
"def policy(agent):",
"def apply_policy(self, policy):\n tenant_name = policy['tenant_name']\n fw_id = policy['fw_... | [
"0.57578945",
"0.56873965",
"0.5348911",
"0.5346214",
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"0.51925033",
"0.51838106",
"0.5182945",
"0.5146547",
"0.5137112",
"0.51151454",
"0.50... | 0.66891956 | 0 |
Implementing click include cli checkbox functionality | def click_include_cli_checkbox(self):
self.click_element(self.include_cli_checkbox_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_guarantee_cli_checkbox(self):\n self.click_element(self.guarantee_cli_checkbox_locator)",
"def click_automate_generation_checkbox(self):\n self.click_element(self.automate_generation_checkbox_locator)",
"def CheckBoxClicked(self,chkb):\r\n\r\n print(\"{} Selecionado.\", format(ch... | [
"0.7180335",
"0.633742",
"0.6186127",
"0.61447966",
"0.60904425",
"0.6061627",
"0.6051627",
"0.597381",
"0.59416974",
"0.59316313",
"0.5918221",
"0.58853",
"0.58538675",
"0.58538675",
"0.5849901",
"0.5836241",
"0.58296305",
"0.58069646",
"0.5802643",
"0.57914114",
"0.57344997... | 0.82027 | 0 |
Implementing click automate generation checkbox functionality | def click_automate_generation_checkbox(self):
self.click_element(self.automate_generation_checkbox_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_guarantee_cli_checkbox(self):\n self.click_element(self.guarantee_cli_checkbox_locator)",
"def on_checkBox_kongtoukai_clicked(self, checked):\n # TODO: not implemented yet\n raise NotImplementedError",
"def _add_checkbox(self, text, state_changed, tooltip, checked=True,\n ... | [
"0.67904496",
"0.6535885",
"0.65002245",
"0.6481565",
"0.6431148",
"0.63907903",
"0.63848615",
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"0.6037659",
"0.6002369",
"0.591591",
"0.5890406",
"0.58776736",
"0.58606"... | 0.7924865 | 0 |
Implementing click save target buy policy button functionality | def click_save_target_buy_policy_button(self):
self.click_element(self.save_target_buy_policy_button_locator, error_message='save target buy policy button locator not found before specified time out')
self.click_element(self.confirmation_popup_locator, error_message='success message locator not found be... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_save_edited_target_buy_policy_button(self):\n self.click_element(self.save_edited_target_buy_policy_button_locator)\n self.click_element(self.confirmation_popup_locator, error_message='success message locator not found before specified time out')\n self.click_element(self.ok_button_l... | [
"0.7729644",
"0.72661537",
"0.6927698",
"0.69045794",
"0.6451391",
"0.6321213",
"0.6035302",
"0.6026688",
"0.5929195",
"0.5800937",
"0.5781004",
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"0.56754345",
"0.5626255",
"0.56152767",
"0.5538557",
"0.552185",
"0.55135155",
"0.5491271"... | 0.7788845 | 0 |
Implementing click target buy overrides button functionality | def click_target_buy_overrides_button(self):
self.click_element(self.target_buy_overrides_button_locator, script_executor=True, error_message='target buy overrides button locator not found before specified time out')
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click(self):\r\n pass",
"def clickOverride():\n\n pass",
"def on_click(self) -> None:\n pass",
"def click_buy_page_inline_action_button(self, vendor):\n self.click_inline_action_button(self.vendors_div_id, vendor, self.grid_column_number)",
"def click_edit_target_buy_policy_butt... | [
"0.70341754",
"0.68537515",
"0.6533549",
"0.62906474",
"0.62827915",
"0.62779593",
"0.6274922",
"0.62407196",
"0.6194346",
"0.6173009",
"0.6167551",
"0.6162474",
"0.6103205",
"0.60976714",
"0.6089431",
"0.60790944",
"0.6078329",
"0.60737765",
"0.6058033",
"0.60538644",
"0.605... | 0.62780917 | 5 |
Implementing is target buy list overrides screen loaded functionality | def is_target_buy_list_overrides_screen_loaded(self):
return self.is_element_visible(self.target_buy_list_overrides_page_header_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listctrl_switch_loadout(self, source):\n if source == self.active_loadout:\n return False\n\n self.gear_slots[self.active_loadout.name] = self.create_blrevive_weapon()\n\n if source.name in self.gear_slots:\n self.load_blrevive_weapon(self.gear_slots[source.name])\n\n... | [
"0.6001915",
"0.5983745",
"0.58766633",
"0.5687606",
"0.567117",
"0.56279045",
"0.5524678",
"0.5523005",
"0.54764086",
"0.542335",
"0.5407171",
"0.53916734",
"0.5346383",
"0.531239",
"0.5288447",
"0.52460074",
"0.524296",
"0.523115",
"0.5228215",
"0.5227995",
"0.5221563",
"... | 0.72665113 | 0 |
Implementing is expected dropdown list control visible functionality | def is_expected_dropdown_list_control_visible_in_target_buy_overrides_screen(self, expected_list_control_name):
list_control_locator = (By.XPATH, "//select[@name='%s']" %(expected_list_control_name))
return self.is_element_present(list_control_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_view_dropdown_visible(self):\n return self.is_element_visible(self.view_dropdown_locator)",
"def is_dropdown_expanded(self):\n return self.dropdown.element(class_name=\"dropdown-focus\").exists",
"def is_visible(self):",
"def is_discrepancy_dropdown_visible(self):\n return self.is_ele... | [
"0.6721152",
"0.62846535",
"0.6274534",
"0.62375015",
"0.62283164",
"0.62241983",
"0.6187858",
"0.60083485",
"0.5904933",
"0.58766943",
"0.5875189",
"0.57837415",
"0.576537",
"0.5752084",
"0.57505965",
"0.5628871",
"0.56188196",
"0.5590547",
"0.5584492",
"0.55437773",
"0.5531... | 0.699368 | 0 |
Implementing is destination input textbox visible functionality | def is_destination_input_textbox_visible(self):
return self.is_element_visible(self.destination_input_textbox_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_destination_textbox_visible(self):\n return self.is_element_visible(self.reference_rates_destination_textbox_locator)",
"def is_visible(self):",
"def visible(self, show):",
"def ev_textinput(self, event: TextInput) -> None:",
"def show(self): \n InputItem.show(self)\n self.input... | [
"0.7082717",
"0.6056446",
"0.58619386",
"0.5751972",
"0.5742402",
"0.5742402",
"0.5723534",
"0.5681438",
"0.5681438",
"0.5649161",
"0.55968016",
"0.5532368",
"0.5513671",
"0.547241",
"0.5431309",
"0.53958184",
"0.53434575",
"0.53434575",
"0.5325499",
"0.52807295",
"0.5265454"... | 0.788917 | 0 |
Implementing is expected grid column present functionality | def is_expected_grid_column_present(self, expected_column_name):
grid_column_locator = (By.XPATH, "//th[@data-title='%s']" %(expected_column_name))
return self.is_element_present(grid_column_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def columns(self):\n \n pass",
"def is_specific_column_present(self, grid_div_id, column_name):\n specific_column_locator = (By.XPATH, \"//div[contains(@id, '%s')]/descendant::th[@data-field='%s']\" % (grid_div_id, column_name))\n return self.is_element_present(specific_column_locator)",
... | [
"0.6262402",
"0.6219727",
"0.6189594",
"0.61687696",
"0.6151684",
"0.6127107",
"0.6107462",
"0.610121",
"0.60943484",
"0.6070248",
"0.60317606",
"0.5992439",
"0.59751236",
"0.5942852",
"0.59316164",
"0.5924672",
"0.591986",
"0.5913953",
"0.58789116",
"0.5877305",
"0.5858077",... | 0.711884 | 0 |
Implementing select target buy list overrides grid row checkbox functionality | def select_target_buy_list_overrides_grid_row_checkbox(self):
for i in range(2):
target_buy_list_overrides_grid_checkbox_locator = (By.XPATH, "(//div[@data-grid-name='%s']/descendant::input[@type='checkbox'])[%s]" % (self.target_buy_list_overrides_data_grid_name, str(i + 2)))
self.click_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_vendor_price_list_grid_row_checkbox(self):\n for row in range(0,2):\n column_locator = (By.XPATH, \"//div[@id='%s']/descendant::th[@data-title='%s']\" % (self.vendor_price_list_grid_div_id, self.price_list_column_name))\n column_element = self.wait().until(EC.presence_of_ele... | [
"0.6595565",
"0.6579554",
"0.64243436",
"0.6204201",
"0.61319244",
"0.59698147",
"0.5958927",
"0.595838",
"0.59310436",
"0.59297544",
"0.5927377",
"0.58760035",
"0.5861238",
"0.58487624",
"0.58487624",
"0.58440185",
"0.58437294",
"0.5766345",
"0.5711271",
"0.56679034",
"0.566... | 0.7345537 | 0 |
Implementing click target buy list overrides bulk edit button functionality | def click_target_buy_list_overrides_bulk_edit_button(self):
self.click_element(self.target_buy_list_overrides_bulk_edit_button_locator, error_message='target buy list overrides bulk edit button locator not found before specified time out') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_bulk_edit_button(self):\n self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True)",
"def click_bulk_edit_button(self):\n self.click_element(self.bulk_edit_button_locator, True)",
"def click_statement_fields_bulk_edit_button(self):\n self.click_... | [
"0.7743176",
"0.72924215",
"0.67232466",
"0.6643152",
"0.6601634",
"0.6551515",
"0.6347435",
"0.6223986",
"0.614489",
"0.6128534",
"0.60816276",
"0.58114606",
"0.5806517",
"0.5763261",
"0.57512677",
"0.57141906",
"0.56977916",
"0.56793827",
"0.5675783",
"0.5646641",
"0.557232... | 0.69044256 | 2 |
Implementing is target buy details section present in vendor profile page functionality | def is_target_buy_details_section_present_in_vendor_profile_page(self):
return self.is_element_present(self.target_buy_details_section_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_specific_field_on_target_buy_details_section_present(self, field_name):\n field_locator = (By.XPATH, \"//div[contains(@id, 'divcustomerProfileDetails_')]/descendant::label[text()='%s']\" % field_name)\n return self.is_element_present(field_locator)",
"def is_target_buy_policies_tab_displayed... | [
"0.64984775",
"0.59864783",
"0.5873134",
"0.5831746",
"0.57233775",
"0.56565744",
"0.56486034",
"0.56228536",
"0.5586765",
"0.5521409",
"0.546854",
"0.54677045",
"0.5465543",
"0.5402681",
"0.53863895",
"0.53619283",
"0.53444225",
"0.52923024",
"0.52921027",
"0.5267506",
"0.52... | 0.76688296 | 0 |
Implementing is specific field on target buy details section present functionality | def is_specific_field_on_target_buy_details_section_present(self, field_name):
field_locator = (By.XPATH, "//div[contains(@id, 'divcustomerProfileDetails_')]/descendant::label[text()='%s']" % field_name)
return self.is_element_present(field_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_target_buy_details_section_present_in_vendor_profile_page(self):\n return self.is_element_present(self.target_buy_details_section_locator)",
"def get_details(self):",
"def enable_details(self):\n self.cs.detail = True",
"def purchase(self, item_type):",
"def input_payment_details(self)... | [
"0.6218292",
"0.58803385",
"0.57047385",
"0.55989105",
"0.5592674",
"0.5399362",
"0.5347198",
"0.531534",
"0.5296534",
"0.5213632",
"0.5194612",
"0.5192922",
"0.5177318",
"0.51669794",
"0.5142594",
"0.5140994",
"0.5122961",
"0.5118239",
"0.5090551",
"0.5051972",
"0.50412834",... | 0.6910262 | 0 |
Implementing set target buy distribution link code functionality | def set_target_buy_distribution_link_code(self):
self.set_value_into_input_field(self.target_buy_distribution_link_code_textbox_locator, self.random_string_generator(6))
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_link(\n meta: Dict,\n link: Optional[Union[type(None), str, bool, KEChainPages]] = None,\n link_value: Optional[CardWidgetLinkValue] = None,\n link_target: Optional[Union[str, LinkTargets]] = LinkTargets.SAME_TAB,\n **kwargs,\n) -> Dict:\n meta[\"linkTarget\"] = check_enum(link_target, L... | [
"0.57581264",
"0.57360834",
"0.5704888",
"0.57009506",
"0.56327444",
"0.5612569",
"0.5577209",
"0.5577209",
"0.55430907",
"0.5542682",
"0.5497914",
"0.5482014",
"0.5472685",
"0.5469825",
"0.54515964",
"0.54339606",
"0.54190713",
"0.5416186",
"0.5416186",
"0.54143345",
"0.5406... | 0.7068939 | 0 |
Implementing set target buy distribution receiving member functionality | def set_target_buy_distribution_receiving_member(self, member_prefix):
member_name = member_prefix + self.random_string_generator(size=4)
self.set_value_into_input_field(self.target_buy_distribution_receiving_member_textbox_locator, member_name)
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def target_sell_price(self):\n return super(Player, self).target_sell_price",
"async def _vis_buy(self, ctx, *args):\n if has_post_permission(ctx.guild.id, ctx.channel.id):\n number, item = ch.parse_number_and_name(args)\n if item:\n await ctx.send(vis_helpers.s... | [
"0.5813365",
"0.57899123",
"0.5756613",
"0.57438093",
"0.5740053",
"0.5614278",
"0.56005937",
"0.5541609",
"0.5541609",
"0.55083424",
"0.550767",
"0.54989284",
"0.54829466",
"0.5476783",
"0.5433824",
"0.5425738",
"0.54128516",
"0.54126567",
"0.5404454",
"0.53718203",
"0.53610... | 0.68895245 | 0 |
Implementing set target buy list functionality | def set_target_buy_list(self, item_name, is_first_item):
if is_first_item is True:
self.single_selection_from_static_kendo_dropdown(self.target_buy_list_kendo_dropdown_arrow_locator, first_item=True)
else:
self.single_selection_from_static_kendo_dropdown(self.target_buy_list_kend... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_target_stocks_list(self, list_of_stocks):\n self.target_stocks = list_of_stocks",
"def setBuyList(self, buyList):\n parsedBuyList = []\n for bought in buyList:\n if hasattr(bought, \"unitType\"):\n parsedBuyList.append(bought)\n elif isinstance(bo... | [
"0.6495771",
"0.62618667",
"0.60916185",
"0.5946068",
"0.5715191",
"0.567597",
"0.56602305",
"0.5574808",
"0.5572295",
"0.5452745",
"0.5394016",
"0.5347834",
"0.5309349",
"0.53008556",
"0.5293746",
"0.5289541",
"0.527092",
"0.5261056",
"0.5248171",
"0.5235941",
"0.51916426",
... | 0.66756034 | 0 |
Implementing set target buy list number plan functionality | def set_target_buy_list_number_plan(self, item_name, is_first_item):
if is_first_item is True:
self.single_selection_from_static_kendo_dropdown(self.target_buy_list_number_plan_kendo_dropdown_arrow_locator, first_item=True)
else:
self.single_selection_from_static_kendo_dropdown(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plans():",
"def set_target_buy_list(self, item_name, is_first_item):\n if is_first_item is True:\n self.single_selection_from_static_kendo_dropdown(self.target_buy_list_kendo_dropdown_arrow_locator, first_item=True)\n else:\n self.single_selection_from_static_kendo_dropdow... | [
"0.57999843",
"0.5784943",
"0.5556903",
"0.55306053",
"0.53336537",
"0.533109",
"0.5294177",
"0.5224904",
"0.5156525",
"0.51557237",
"0.5153142",
"0.514415",
"0.5124864",
"0.51236206",
"0.51054424",
"0.51054424",
"0.51011217",
"0.50561494",
"0.5020014",
"0.5019802",
"0.501817... | 0.69643253 | 0 |
Implementing set target buy policy type functionality | def set_target_buy_policy_type(self, type):
self.single_selection_from_kendo_dropdown(self.target_buy_policy_type_kendo_dropdown_locator, type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_target_buy_policy_name(self, prefix):\n global target_buy_policy\n target_buy_policy = (prefix + self.random_string_generator(size=4))\n self.set_value_into_input_field(self.target_buy_policy_input_field_locator, value=target_buy_policy)",
"def get_target_buy_policy(self):\n g... | [
"0.6160238",
"0.6142805",
"0.5944419",
"0.5795669",
"0.577346",
"0.5734563",
"0.56611264",
"0.5657722",
"0.5645191",
"0.5593953",
"0.5516085",
"0.5394442",
"0.53024334",
"0.5253677",
"0.51763785",
"0.5144148",
"0.51351786",
"0.51129127",
"0.50925",
"0.5092124",
"0.5087189",
... | 0.8053538 | 0 |
Implementing get target buy policy grid row details functionality | def get_target_buy_policy_grid_row_details(self):
self.grid_row_data.clear()
self.grid_row_data = {"Status":"", "Target Buy Policy Type":"", "Call Type":"", "Route Class":""}
return self.get_grid_row_details(self.target_buy_policies_grid_div_id, self.grid_row_data, data_grid_name=self.target_buy... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_outbound_statement_grid_row_data(self):\n self.grid_row_details_dictionary.clear()\n self.grid_row_details_dictionary = {\"Bill Account\": \"\", \"Begin Date\": \"\", \"End Date\": \"\", \"Amount ( EUR )\": \"\"}\n self.outbound_statement_grid_row_data = self.get_grid_row_details(self.... | [
"0.575327",
"0.56671774",
"0.54983485",
"0.5497504",
"0.54278195",
"0.53913313",
"0.5331615",
"0.5325904",
"0.5314808",
"0.53105533",
"0.5297771",
"0.52571106",
"0.52271354",
"0.52199143",
"0.519113",
"0.5143383",
"0.5140523",
"0.5123071",
"0.51150763",
"0.50886685",
"0.50832... | 0.8331571 | 0 |
Implementing click target buy policy grid first row inline action button functionality | def click_target_buy_policy_grid_first_row_inline_action_button(self):
self.click_inline_action_button(self.target_buy_policies_grid_div_id, None, column_number=2, first_row=True)
self.wait_for_ajax_spinner_load() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_target_buy_policies_grid_first_row_inline_action_button(self):\n self.click_inline_action_button(self.target_buy_policies_grid_div_id, None, self.target_buy_policies_grid_inline_action_column_number, first_row=True)",
"def click_buy_and_sell_deal_management_grid_first_row_inline_action_button(se... | [
"0.8202686",
"0.80335456",
"0.74357104",
"0.74149716",
"0.72845346",
"0.72829384",
"0.72605",
"0.72499174",
"0.7216867",
"0.7202817",
"0.70367014",
"0.69755983",
"0.68936044",
"0.68760645",
"0.6781373",
"0.6636045",
"0.6578328",
"0.6401699",
"0.636651",
"0.6061604",
"0.602482... | 0.8149763 | 1 |
Implementing click edit target buy policy button functionality | def click_edit_target_buy_policy_button(self):
self.click_element(self.edit_target_buy_policy_button_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_buy_and_sell_deal_bulk_edit_button(self):\n self.click_element(self.buy_and_sell_deal_bulk_edit_button_lcoator, True)",
"def click_cancel_edited_target_buy_policy_button(self):\n self.click_element(self.cancel_edited_target_buy_policy_button_locator)",
"def click_save_edited_target_buy_... | [
"0.72049874",
"0.71058124",
"0.6989213",
"0.6368108",
"0.631195",
"0.6140118",
"0.61324686",
"0.60960543",
"0.6026939",
"0.58555514",
"0.585286",
"0.57779235",
"0.5776125",
"0.5776095",
"0.5768035",
"0.57594347",
"0.55886054",
"0.55702305",
"0.5557164",
"0.55496216",
"0.55267... | 0.8397314 | 0 |
Implementing click save edited target buy policy button functionality | def click_save_edited_target_buy_policy_button(self):
self.click_element(self.save_edited_target_buy_policy_button_locator)
self.click_element(self.confirmation_popup_locator, error_message='success message locator not found before specified time out')
self.click_element(self.ok_button_locator, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_edit_target_buy_policy_button(self):\n self.click_element(self.edit_target_buy_policy_button_locator)",
"def click_save_target_buy_policy_button(self):\n self.click_element(self.save_target_buy_policy_button_locator, error_message='save target buy policy button locator not found before sp... | [
"0.7632212",
"0.7429682",
"0.6681149",
"0.6559746",
"0.6546259",
"0.6481419",
"0.6439012",
"0.5897788",
"0.5761528",
"0.5743864",
"0.5709328",
"0.5687823",
"0.5661773",
"0.5654063",
"0.56287414",
"0.5625229",
"0.5610592",
"0.5592306",
"0.5590449",
"0.55888796",
"0.5561194",
... | 0.78032136 | 0 |
Implementing click cancel edited target buy policy button functionality | def click_cancel_edited_target_buy_policy_button(self):
self.click_element(self.cancel_edited_target_buy_policy_button_locator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_edit_target_buy_policy_button(self):\n self.click_element(self.edit_target_buy_policy_button_locator)",
"def click_cancel(self):\n self.click_element(self.cancel_button_selector)",
"def click_save_edited_target_buy_policy_button(self):\n self.click_element(self.save_edited_target... | [
"0.7026351",
"0.68475145",
"0.66155964",
"0.6442423",
"0.6418752",
"0.638268",
"0.63307065",
"0.63213784",
"0.6320915",
"0.6320915",
"0.6320915",
"0.6234889",
"0.623244",
"0.62140983",
"0.6192419",
"0.61831707",
"0.61803746",
"0.6167173",
"0.61546427",
"0.61361396",
"0.613442... | 0.8608011 | 0 |
Class to organize the parameters that allow Experiments to be fairly compared | def __init__(
self,
train_dataset, # TODO: Allow providing separate (train_input, train_target) dfs
environment_params_path=None,
*,
results_path=None,
metrics=None,
holdout_dataset=None, # TODO: Allow providing separate (holdout_input, holdout_target) dfs
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.param_names = []\n self.param_values = []\n self.param_settings = []\n self.result = []\n self.best_params = None\n self.best_score = None\n self.max_reps = 5\n self.num_values = False\n self.algorithm_done = False",
"def p... | [
"0.67828584",
"0.6636928",
"0.6538981",
"0.6537098",
"0.6494992",
"0.64949113",
"0.64759845",
"0.64688396",
"0.63895583",
"0.6306166",
"0.63048327",
"0.624608",
"0.6226568",
"0.62020916",
"0.6191166",
"0.6190334",
"0.616863",
"0.6154092",
"0.6141812",
"0.6094788",
"0.6088222"... | 0.0 | -1 |
Execute all methods required to validate the environment and run Experiments | def environment_workflow(self):
self.update_custom_environment_params()
self.validate_parameters()
self.format_result_paths()
self.generate_cross_experiment_key()
G.log("Cross-Experiment Key: '{!s}'".format(self.cross_experiment_key)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n logging.info('running experiment...')\n self._prepare()\n self._load_data()\n self._run()\n self._evaluate()\n self._summarise()\n return True",
"def _problem_run_experiments_initialise(self):\n pass",
"def run_experiment():\n pass",
"de... | [
"0.7124226",
"0.70106345",
"0.69870996",
"0.69620895",
"0.67463315",
"0.6622042",
"0.65565574",
"0.6546434",
"0.6503521",
"0.6474704",
"0.6466486",
"0.6463572",
"0.6450513",
"0.6409788",
"0.6367203",
"0.6358608",
"0.6325544",
"0.6299639",
"0.62983626",
"0.6247161",
"0.6224909... | 0.6235772 | 20 |
Ensure the provided parameters are valid and properly formatted | def validate_parameters(self):
#################### metrics_params/metrics ####################
if (self.metrics is not None) and ("metrics" in self.metrics_params.keys()):
raise ValueError(
"`metrics` may be provided as a kwarg, or as a `metrics_params` key, but NOT BOTH. Re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(self):\n if self.params.get(\"format\"):\n if self.params[\"format\"] not in formats:\n raise ValueError(f\"format must be one of {formats}: {self.dt}\")\n for p in self.required:\n if not self.params.get(p):\n raise ValueError(f\"{p} m... | [
"0.74783075",
"0.7428857",
"0.729624",
"0.7253054",
"0.71651715",
"0.6975634",
"0.69456136",
"0.680637",
"0.6644833",
"0.66441953",
"0.66431385",
"0.6640662",
"0.661862",
"0.65815324",
"0.6571818",
"0.651624",
"0.65139985",
"0.6503205",
"0.64675975",
"0.6463459",
"0.6452657",... | 0.0 | -1 |
Remove paths contained in file_blacklist, and format others to prepare for saving results | def format_result_paths(self):
if self.file_blacklist == "ALL" or self.results_path is None:
return
# Blacklist the prediction files for any datasets that were not given
if self.holdout_dataset is None:
self.file_blacklist.append("predictions_holdout")
if self.te... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveFileListLocal(dataset,filelist,blacklist=[ ],tag=\"\"):\n if '/pnfs/' in dataset:\n tag += \"_pnfs\"\n dataset = '__'.join(dataset.split('/')[-3:])\n filename = \"filelist/filelist_%s%s.txt\"%(dataset.replace('/','__'),tag)\n with open(filename,'w+') as file:\n for line in filelist:\... | [
"0.6709962",
"0.6574502",
"0.64943206",
"0.6401187",
"0.6375883",
"0.62250584",
"0.614118",
"0.6117632",
"0.6113509",
"0.59959865",
"0.59959865",
"0.59959865",
"0.5943409",
"0.5885366",
"0.57896334",
"0.56836295",
"0.5682206",
"0.5677871",
"0.56692576",
"0.56671333",
"0.56568... | 0.53216684 | 49 |
Try to update null parameters from environment_params_path, or DEFAULT_PARAMS | def update_custom_environment_params(self):
allowed_parameter_keys = [
k for k, v in signature(Environment).parameters.items() if v.kind == v.KEYWORD_ONLY
]
for k in allowed_parameter_keys:
if getattr(self, k) is None:
setattr(self, k, self.DEFAULT_PARAMS... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def overwrite_hyperparams(self):\n try:\n default_hyperparams = self.hyperparams\n for key in default_hyperparams:\n try:\n flag = self.FLAGS[key]\n param_value = flag.value\n if param_value is not None:\n ... | [
"0.6267459",
"0.60361874",
"0.6005112",
"0.59332246",
"0.5914079",
"0.58198893",
"0.578207",
"0.57676196",
"0.57331485",
"0.57331485",
"0.56856215",
"0.5666313",
"0.5595226",
"0.5546338",
"0.55456305",
"0.5544343",
"0.55403894",
"0.55393416",
"0.5523232",
"0.5497067",
"0.5467... | 0.6903751 | 0 |
Generate a key to describe the current Environment's crossexperiment parameters | def generate_cross_experiment_key(self):
parameters = dict(
metrics_params=self.metrics_params,
cv_params=self.cv_params,
target_column=self.target_column,
id_column=self.id_column,
do_predict_proba=self.do_predict_proba,
prediction_formatt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _course_key(self):\r\n return \"slashes:{org}+{number}+{run}\".format(**self._course_dict)",
"def _key(\n service=None, # type: Optional[str]\n env=None, # type: Optional[str]\n ):\n # type: (...) -> str\n service = service or \"\"\n env = env or \"\"\n r... | [
"0.6446606",
"0.61374927",
"0.60621893",
"0.5975565",
"0.5934612",
"0.5891562",
"0.5889384",
"0.5880724",
"0.58526766",
"0.5810926",
"0.57407105",
"0.5730642",
"0.5688418",
"0.56631047",
"0.56109715",
"0.55618995",
"0.5547926",
"0.5547926",
"0.5547926",
"0.5547926",
"0.554792... | 0.70411134 | 0 |
Initialize reporting for the Environment and Experiments conducted during its lifetime | def initialize_reporting(self):
reporting_params = self.reporting_params
reporting_params["heartbeat_path"] = self.result_paths["current_heartbeat"]
reporting_handler = ReportingHandler(**reporting_params)
#################### Make Unified Logging Globally Available ####################... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _problem_run_experiments_initialise(self):\n pass",
"def experiment_init(self):\n pass",
"def _initialize_reporter(self):\n self._reporter.open(mode='w')\n self._reporter.write_thermodynamic_states(self._thermodynamic_states,\n self._... | [
"0.705591",
"0.70373553",
"0.694236",
"0.68620825",
"0.64724773",
"0.6324107",
"0.61711204",
"0.6154614",
"0.61451995",
"0.613628",
"0.60897315",
"0.60636425",
"0.6048451",
"0.60439444",
"0.6031022",
"0.60182476",
"0.5934517",
"0.5891018",
"0.5856779",
"0.5835391",
"0.5813859... | 0.694977 | 2 |
Get a `DatasetSentinel` representing an Experiment's `fold_train_input` Returns | def train_input(self):
return DatasetSentinel("train_input", **self._dataset_sentinel_helper()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())",
"def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())",
"def validation_target(self):\n return DatasetSentinel(\"validation_targe... | [
"0.7197259",
"0.7058317",
"0.6581115",
"0.63265425",
"0.62734795",
"0.61224383",
"0.61224383",
"0.609924",
"0.59627235",
"0.5961343",
"0.58399856",
"0.5539047",
"0.55349284",
"0.5525194",
"0.54774237",
"0.54644793",
"0.5406239",
"0.540372",
"0.5370397",
"0.5369286",
"0.535446... | 0.81839544 | 0 |
Get a `DatasetSentinel` representing an Experiment's `fold_train_target` Returns | def train_target(self):
return DatasetSentinel("train_target", **self._dataset_sentinel_helper()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())",
"def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())",
"def holdout_target(self):\n return DatasetSentinel(\n \"holdo... | [
"0.72998613",
"0.713837",
"0.66336054",
"0.6162712",
"0.5902305",
"0.57828236",
"0.5423811",
"0.5414221",
"0.53984654",
"0.5350862",
"0.52556276",
"0.52556276",
"0.5214808",
"0.5211418",
"0.51941895",
"0.51866794",
"0.5174885",
"0.5166052",
"0.51568496",
"0.5156731",
"0.51362... | 0.81183374 | 0 |
Get a `DatasetSentinel` representing an Experiment's `fold_validation_input` Returns | def validation_input(self):
return DatasetSentinel("validation_input", **self._dataset_sentinel_helper()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())",
"def validation_target(self):\n return DatasetSentinel(\"validation_target\", **self._dataset_sentinel_helper())",
"def holdout_input(self):\n return DatasetSentinel(\n \"holdou... | [
"0.7417075",
"0.7251253",
"0.6297536",
"0.6207659",
"0.5739387",
"0.5739387",
"0.57109046",
"0.5591265",
"0.55240226",
"0.54016316",
"0.53963184",
"0.5390611",
"0.5354384",
"0.5352668",
"0.52997166",
"0.50982046",
"0.509454",
"0.5057065",
"0.50273144",
"0.49092582",
"0.488593... | 0.79688627 | 0 |
Get a `DatasetSentinel` representing an Experiment's `fold_validation_target` Returns | def validation_target(self):
return DatasetSentinel("validation_target", **self._dataset_sentinel_helper()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())",
"def validation_input(self):\n return DatasetSentinel(\"validation_input\", **self._dataset_sentinel_helper())",
"def train_input(self):\n return DatasetSentinel(\"train_input\", **self.... | [
"0.7272191",
"0.7040783",
"0.65106875",
"0.6453621",
"0.58669484",
"0.52230227",
"0.52210194",
"0.5189654",
"0.5189253",
"0.5166722",
"0.50729924",
"0.5023478",
"0.4998633",
"0.4998633",
"0.49692518",
"0.4958584",
"0.4885863",
"0.4881906",
"0.48708826",
"0.4870653",
"0.486606... | 0.80776197 | 0 |
Get a `DatasetSentinel` representing an Experiment's `holdout_input_data` Returns | def holdout_input(self):
return DatasetSentinel(
"holdout_input", self.cross_experiment_key.parameters["holdout_dataset"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def holdout_target(self):\n return DatasetSentinel(\n \"holdout_target\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )",
"def train_input(self):\n return DatasetSentinel(\"train_input\", **self._dataset_sentinel_helper())",
"def validation_input(self):\n ... | [
"0.7605484",
"0.70850253",
"0.65761745",
"0.62563837",
"0.58847857",
"0.5119277",
"0.49333444",
"0.48865077",
"0.48654017",
"0.48275244",
"0.48057696",
"0.47976065",
"0.47965127",
"0.4744823",
"0.47400033",
"0.47386026",
"0.47028476",
"0.4699324",
"0.46962723",
"0.4694681",
"... | 0.8473368 | 0 |
Get a `DatasetSentinel` representing an Experiment's `holdout_target_data` Returns | def holdout_target(self):
return DatasetSentinel(
"holdout_target", self.cross_experiment_key.parameters["holdout_dataset"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def holdout_input(self):\n return DatasetSentinel(\n \"holdout_input\", self.cross_experiment_key.parameters[\"holdout_dataset\"]\n )",
"def train_target(self):\n return DatasetSentinel(\"train_target\", **self._dataset_sentinel_helper())",
"def validation_target(self):\n ... | [
"0.7770308",
"0.7121358",
"0.64517236",
"0.6015566",
"0.54749423",
"0.5369748",
"0.5230045",
"0.52188426",
"0.5049947",
"0.49974164",
"0.49770722",
"0.49149367",
"0.48960745",
"0.48660335",
"0.4858429",
"0.47984567",
"0.47667906",
"0.47544086",
"0.47335964",
"0.47325793",
"0.... | 0.84920114 | 0 |
Helper method for retrieving train/validation sentinel parameters | def _dataset_sentinel_helper(self):
params = self.cross_experiment_key.parameters
return dict(
dataset_hash=params["train_dataset"],
cv_type=params["cross_experiment_params"].get(
"cv_type", params["cross_experiment_params"].get("cross_validation_type", None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default_training_params():\n N_EPOCHS = 100\n BATCH_SIZE = 64\n EPSILON = 0.0001\n return N_EPOCHS, BATCH_SIZE, EPSILON",
"def set_training_params(use_defaults):\n if use_defaults:\n n_epochs, batch_size, epsilon = default_training_params()\n return n_epochs, batch_size, epsilon\... | [
"0.64764184",
"0.6429467",
"0.6267135",
"0.6226991",
"0.59285235",
"0.5904875",
"0.59016514",
"0.57753325",
"0.5774506",
"0.55555415",
"0.55554277",
"0.5521391",
"0.5516484",
"0.5502349",
"0.5497063",
"0.54911923",
"0.54711896",
"0.54711896",
"0.5470737",
"0.54651886",
"0.544... | 0.5830468 | 7 |
Create `holdout_set` (if necessary) by loading a DataFrame from a .csv file, or by separating `train_set`, and return the updated (`train_set`, `holdout_set`) pair | def define_holdout_set(
train_set: pd.DataFrame,
holdout_set: Union[pd.DataFrame, callable, str, None],
target_column: Union[str, List[str]],
) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
#################### Update `holdout_set` ####################
if callable(holdout_set):
train_set, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_train_set(addition_to_filename = ''):\n df = f.create_df()\n print('original df size is ', df.shape)\n print('original df columns ', df.columns)\n\n df = f.calculate_missing_prices_for_train_set(df)\n print('df size after averaging price ', df.shape)\n df = f.downcast_dtypes(df)\n d... | [
"0.6308423",
"0.6136544",
"0.60832155",
"0.60303485",
"0.59481156",
"0.5926183",
"0.58851606",
"0.5858881",
"0.5792204",
"0.57012284",
"0.56984967",
"0.569597",
"0.56708777",
"0.5638815",
"0.56349504",
"0.56267476",
"0.56263214",
"0.5608293",
"0.55986565",
"0.55914485",
"0.55... | 0.6827967 | 0 |
Validate contents of blacklist. For most values, the corresponding file is saved upon completion of the experiment. See the "Notes" section below for details on some special cases | def validate_file_blacklist(blacklist):
valid_values = [
# 'checkpoint',
"description",
"heartbeat",
"predictions_holdout",
"predictions_in_fold",
"predictions_oof",
"predictions_test",
"script_backup",
"tested_keys",
"current_heartbeat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_blacklist(experiment):\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\n return blacklist",
"def load_blacklist(experiment):\n blacklist = np.loadtxt('../Slip_Property_Data/%s_blacklist.txt'%experiment)\n return blacklist",
"def save_blacklist(blacklist, outpu... | [
"0.6533181",
"0.6533181",
"0.6494651",
"0.64306563",
"0.6377956",
"0.61511934",
"0.6121788",
"0.5941797",
"0.5909957",
"0.5882721",
"0.5851798",
"0.5822298",
"0.56683594",
"0.5648593",
"0.5628515",
"0.55158484",
"0.54681826",
"0.54313976",
"0.5406495",
"0.5349667",
"0.5342094... | 0.804985 | 0 |
Get the scsi data, and vtd mapping data and output in the fomat viosname,lunid,disk,vtdname,partitionid | def getVIOSmappings(vios,csvFile=''):
""" We need the cec this vios is running to be able to map the lpar names """
cec = nim_attribute(vios,"mgmt_profile").split()[2]
""" ssh comms all ok?? """
isOK = ssh_output(vios,"echo OK")
if isOK == None or isOK == False:
error_message("Unable to c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_vscsi_attributes(self,objects):\n print(\"\\n\")\n print((\"LocalPartitionID\".ljust(35),\":\",objects.LocalPartitionID.value()))\n print((\"VirtualSlotNumber\".ljust(35),\":\",objects.VirtualSlotNumber.value()))\n print((\"RequiredAdapter\".ljust(35),\":\",objects.RequiredAda... | [
"0.6226752",
"0.62065095",
"0.613328",
"0.5941464",
"0.58446807",
"0.5758732",
"0.57254314",
"0.5669364",
"0.5603092",
"0.5584567",
"0.55693907",
"0.5547329",
"0.553814",
"0.55317974",
"0.55209994",
"0.5405666",
"0.5393464",
"0.5392122",
"0.5388479",
"0.5382682",
"0.53686225"... | 0.69272226 | 0 |
Return possible map states | def get_moves(self): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_state_map(self):\n if self.is_po:\n state_map = self.curr_map\n else:\n state_map = self.grid_map\n state_map[self.curr_pos] = 0.5\n state_map[self.goal_pos] = 0.7\n return state_map",
"def states(self):\n knownstates = set(self.keys())\n ... | [
"0.71087664",
"0.7074518",
"0.677178",
"0.67380196",
"0.655753",
"0.6495594",
"0.6494033",
"0.6443897",
"0.64307165",
"0.6419317",
"0.6412042",
"0.6395079",
"0.63540417",
"0.6328602",
"0.63154495",
"0.6303637",
"0.62966526",
"0.6289538",
"0.6258314",
"0.6251944",
"0.6178415",... | 0.0 | -1 |
Return a hashable state | def get_state_hash(self):
return tuple([tuple([x for x in row if x not in [' ', '#']]) for row in self.map[1:-1]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_hashable_state(self):\n raise AIError(\"Must be implemented in child class!\")",
"def __hash__(self):\n return hash((self.name, self.state))",
"def __hash__(self):\n if getattr(self, \"_immutable\", False):\n return hash((tuple(self.states()), tuple(self.transitions())))... | [
"0.8085476",
"0.79855424",
"0.7687602",
"0.75003594",
"0.69587237",
"0.68687993",
"0.6838884",
"0.68151635",
"0.68011165",
"0.6758",
"0.6713541",
"0.6698997",
"0.6691076",
"0.668013",
"0.6669868",
"0.66635925",
"0.66240686",
"0.6620361",
"0.6606971",
"0.6604862",
"0.6591645",... | 0.676722 | 9 |
Make an instance of the api and attempts to authenticate the user. Return the authenticated api. | def authenticate():
# We are uploading and then downloading so we want Musicmanager
api = Musicmanager()
# Attempt to authenticate and log in
logged_in = api.login()
# If login() returns false, you have not performed oauth yet, or did not
# write your credentials to your disk. Using oauth all... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api_authentication():\r\n apikey = request.args.get('api_key', None)\r\n from flask import _request_ctx_stack\r\n if 'Authorization' in request.headers:\r\n apikey = request.headers.get('Authorization')\r\n if apikey:\r\n user = db.session.query(model.user.User... | [
"0.72514385",
"0.7194521",
"0.66663784",
"0.66555786",
"0.6583456",
"0.6552307",
"0.6540641",
"0.64537",
"0.6420162",
"0.64155054",
"0.6405242",
"0.63488394",
"0.6289332",
"0.62696224",
"0.62566435",
"0.62319964",
"0.6192098",
"0.6162103",
"0.61557543",
"0.61531574",
"0.61462... | 0.65322834 | 7 |
Demonstrate some api features. | def demonstrate():
api = authenticate()
# Demonstrate upload feature.
# Create a list of one or more file paths of the mp3s you would like
# to upload
filepaths = []
filepaths.append('./song1.mp3')
# Upload an mp3 to your library. upload() returns a tuple of information
# about the s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api():\n\treturn \"The API call\"",
"def api(self) -> str:",
"def testApi(self):",
"def hello_world():\n \n apis = []\n apis.append({\n 'method' : 'POST', \n 'url' : '/api/analysis',\n 'params': [{\n 'attribute' : 'text',\n ... | [
"0.7289153",
"0.7226013",
"0.69252145",
"0.66564894",
"0.662737",
"0.66099566",
"0.6588083",
"0.65751857",
"0.655524",
"0.65286803",
"0.64784944",
"0.64357257",
"0.6433924",
"0.64230484",
"0.6387545",
"0.6387545",
"0.6387545",
"0.6387545",
"0.6387545",
"0.6349674",
"0.6277104... | 0.57035404 | 99 |
Ensures that a websafe key is valid and of the desired kind. | def _raiseIfWebsafeKeyNotValid(websafeKey, kind):
# Check that websafeKey is not None
if not websafeKey:
raise endpoints.BadRequestException(
"Websafe key not provided for '%s'" % kind)
# Try to decode the websafe key into a real key
try:
key = ndb.Key(urlsafe=websafeKey)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isValidKey(key):\n return True",
"def validate_key(key):\r\n try:\r\n secret.Secret(key)\r\n except secret.Secret.InvalidSecret as e:\r\n raise KeyIsInvalid(e.message)",
"def _check_key(self, key):\n raise NotImplementedError",
"def test_valid_key(self):\n f = lws.valid... | [
"0.66728324",
"0.6390312",
"0.6318158",
"0.62363094",
"0.6178258",
"0.61090034",
"0.59969664",
"0.59606546",
"0.59150714",
"0.5909401",
"0.5852684",
"0.5826533",
"0.5803395",
"0.5795646",
"0.5785924",
"0.5733037",
"0.5733037",
"0.57124954",
"0.57062685",
"0.56519485",
"0.5633... | 0.8084664 | 0 |
Attempts to retrieve entity, performing verification in the process. | def _getEntityByWebsafeKey(websafeKey, kind):
# Ensure that the websafe key is valid
key = _raiseIfWebsafeKeyNotValid(websafeKey, kind)
# Get the entity
entity = key.get()
if not entity:
raise endpoints.NotFoundException(
"No '%s' entity found using websafe key: %s" %
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_entity(self):\n if not self.entity and await self.get_input_entity():\n try:\n self._entity =\\\n await self._client.get_entity(self._input_entity)\n except ValueError:\n pass\n\n return self._entity",
"async def t... | [
"0.66966546",
"0.64643776",
"0.6297204",
"0.5874635",
"0.58368284",
"0.580103",
"0.5799007",
"0.5783325",
"0.57693875",
"0.5749898",
"0.5693132",
"0.5627393",
"0.5605186",
"0.5590932",
"0.5526674",
"0.54998803",
"0.5461038",
"0.5455379",
"0.54105365",
"0.5380263",
"0.5332381"... | 0.57059026 | 10 |
Create Announcement & assign to memcache; used by memcache cron job & putAnnouncement(). | def _cacheAnnouncement():
confs = Conference.query(ndb.AND(
Conference.seatsAvailable <= 5,
Conference.seatsAvailable > 0)
).fetch(projection=[Conference.name])
if confs:
# If there are conferences close to being sold out,
# format announcement and... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAnnouncement(self, request):\n announcement = memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\"\n return StringMessage(data=announcement)",
"def cache_announcement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5, Conference.seatsAvailable > 0\n ... | [
"0.6307641",
"0.624139",
"0.61396885",
"0.5956356",
"0.5670804",
"0.5596737",
"0.5565057",
"0.5479931",
"0.5426172",
"0.5361342",
"0.5328143",
"0.5242326",
"0.51843905",
"0.5149838",
"0.50455034",
"0.5040873",
"0.50037575",
"0.4943809",
"0.49090248",
"0.4906064",
"0.4883745",... | 0.6339458 | 0 |
Register or unregister user for selected conference. | def _conferenceRegistration(self, request, reg=True):
retval = None
# Get user profile
prof = self._getProfileFromUser()
# Check if conference given in the websafeConferenceKey exists
wsck = request.websafeConferenceKey
conf = _getEntityByWebsafeKey(wsck, 'Conference')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conference_registration(self, request, reg=True):\n prof = self.profile_service.get_profile_from_user() # get user Profile\n\n # check if conf exists given websafeConfKey\n # get conference; check that it exists\n wsck = request.websafeConferenceKey\n conf = ndb.Key(urlsafe=... | [
"0.6628083",
"0.64813596",
"0.6294456",
"0.6073893",
"0.58963567",
"0.5738126",
"0.55700624",
"0.55217904",
"0.54235077",
"0.541907",
"0.5362311",
"0.53576905",
"0.5338589",
"0.52861226",
"0.5157034",
"0.51566017",
"0.5153176",
"0.5135656",
"0.5130247",
"0.5127976",
"0.510271... | 0.67306554 | 0 |
Copy relevant fields from Conference to ConferenceForm. | def _copyConferenceToForm(self, conf, displayName):
cf = ConferenceForm()
for field in cf.all_fields():
if hasattr(conf, field.name):
# Convert Date to date string; just copy others
if field.name.endswith('Date'):
setattr(cf, field.name, st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _copyConferenceToForm(self, conf, displayName):\n cf = ConferenceForm()\n for field in cf.all_fields():\n if hasattr(conf, field.name):\n # convert Date to date string; just copy others\n if field.name.endswith('Date'):\n setattr(cf, field.name, str(getattr(con... | [
"0.7727503",
"0.6019656",
"0.5861441",
"0.57970494",
"0.57781494",
"0.57305723",
"0.5715702",
"0.56819946",
"0.5602228",
"0.5507136",
"0.5495653",
"0.5426627",
"0.5412202",
"0.53416955",
"0.5293119",
"0.5292693",
"0.5292534",
"0.5275663",
"0.52391994",
"0.52208537",
"0.521184... | 0.77495456 | 0 |
Create or update a conference, returning ConferenceForm/request. | def _createConferenceObject(self, request):
# Preload necessary data items
user = endpoints.get_current_user()
if not user:
raise endpoints.UnauthorizedException('Authorization required')
user_id = user.email()
if not request.name:
raise endpoints.BadReque... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _createConferenceObject(self, request):\n # preload necessary data items\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException('Authorization required')\n user_id = getUserId(user)\n\n if not request.name:\n raise endpoints.BadRequestException... | [
"0.7081421",
"0.7047886",
"0.6980478",
"0.6792568",
"0.67714655",
"0.6611755",
"0.6580865",
"0.61293185",
"0.60343635",
"0.59881353",
"0.5875531",
"0.57053417",
"0.5569745",
"0.5504398",
"0.5384232",
"0.53397477",
"0.5316019",
"0.52989537",
"0.51942",
"0.5168115",
"0.5145852"... | 0.68450546 | 3 |
Parse, check validity and format user supplied filters. | def _formatFilters(self, filters):
formatted_filters = []
inequality_field = None
for f in filters:
filtr = {
field.name: getattr(f, field.name) for field in f.all_fields()
}
try:
filtr["field"] = FIELDS[filtr["field"]]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_filters(filters: Optional[List[str]]) -> List[Dict[str, Any]]:\n filters_to_send = []\n for filter_ in filters or []:\n split_filter = filter_.split('=')\n if len(split_filter) != 2 or not split_filter[0] or not split_filter[1]:\n raise DemistoException('Filters should be ... | [
"0.6897771",
"0.6654421",
"0.6647934",
"0.6647418",
"0.6594005",
"0.65434283",
"0.65231794",
"0.64544934",
"0.6430273",
"0.6366135",
"0.62607723",
"0.6208928",
"0.6146251",
"0.6066933",
"0.6063317",
"0.6019137",
"0.60165054",
"0.59925497",
"0.5987568",
"0.59434175",
"0.594083... | 0.65210766 | 7 |
Retrieve all conferences matching one or more given topics. | def _getConferencesByTopicSearch(self, request):
# Generate list of filters from the topic arguments
filters = [Conference.topics == topic for topic in request.topics]
if not filters:
raise endpoints.BadRequestException(
'At least one topic must be specified'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getConferencesByTopicSearch(self, request):\n conferences = self._getConferencesByTopicSearch(request)\n # Need to fetch organiser displayName from profiles\n # Get all keys and use get_multi for speed\n organisers = [\n (ndb.Key(Profile, conf.organizerUserId)) for conf i... | [
"0.6711613",
"0.6230136",
"0.61121726",
"0.60039115",
"0.5985098",
"0.5940796",
"0.58975244",
"0.58861005",
"0.5882982",
"0.5880553",
"0.5836876",
"0.58033025",
"0.57397395",
"0.5726651",
"0.5705699",
"0.5686112",
"0.56527764",
"0.56140727",
"0.54820526",
"0.5472352",
"0.5471... | 0.790961 | 0 |
Return formatted query from the submitted filters. | def _getQuery(self, request):
q = Conference.query()
inequality_filter, filters = self._formatFilters(request.filters)
# If exists, sort on inequality filter first
if not inequality_filter:
q = q.order(Conference.name)
else:
q = q.order(ndb.GenericProperty... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_query(self):\n columns = ','.join(['\"{}\"'.format(x) for x in self.columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self.table)\n filter_params = []\n if self.filters:\n filter_sql, filter_params = filter_postgis(self.filters)\n query += ' WHERE... | [
"0.67433846",
"0.67309666",
"0.66847456",
"0.6650939",
"0.6648182",
"0.64935666",
"0.6440689",
"0.63192457",
"0.6306208",
"0.628635",
"0.62689066",
"0.62631875",
"0.6200151",
"0.6186324",
"0.6155119",
"0.61444587",
"0.61128426",
"0.60982704",
"0.6098094",
"0.6062554",
"0.6021... | 0.62215513 | 12 |
Return Announcement from memcache. | def getAnnouncement(self, request):
announcement = memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or ""
return StringMessage(data=announcement) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_announcement(self, request):\n return StringMessage(\n data=memcache.get(MEMCACHE_ANNOUNCEMENTS_KEY) or \"\")",
"def _cacheAnnouncement():\n confs = Conference.query(ndb.AND(\n Conference.seatsAvailable <= 5,\n Conference.seatsAvailable > 0)\n ).fetch... | [
"0.74955446",
"0.7363284",
"0.705641",
"0.58456796",
"0.5752198",
"0.56402856",
"0.56222874",
"0.5593154",
"0.5495106",
"0.5467809",
"0.54591787",
"0.54124725",
"0.5330662",
"0.53072345",
"0.5287989",
"0.527153",
"0.52640253",
"0.523533",
"0.52221245",
"0.52221245",
"0.521772... | 0.78246075 | 0 |
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