>> {} Accuracy (%): {:.4f}'.format(name, acc))\n print('>>> {} Error (%): {:.4f}\\n'.format(name, err))\n value_summary({'{}/accuracy'.format(name): acc,\n '{}/error'.format(name): err}, summary_writer, global_step)\n # Write scores\n if global_step:\n score_dir = pjoin(os.path.dirname(c.log_path), 'run_{:02d}___infer_{}'.format(c.run, name))\n if not os.path.exists(score_dir):\n os.makedirs(score_dir)\n with open(pjoin(score_dir, 'sparsity_values.csv'), 'a') as f:\n f.write('Global step,Total sparsity\\r\\n')\n with open(pjoin(score_dir, 'metric_scores.csv'), 'a') as f:\n f.write('{:d},{:.4f},{:.4f}\\r\\n'.format(global_step, acc, err))\n with open(pjoin(score_dir, 'sparsity_values.csv'), 'a') as f:\n f.write('{:d},{:.7f}\\r\\n'.format(global_step, sps))\n return acc","title":""},{"docid":"f043348b531e6811086bb1042931fb70","score":"0.5815661","text":"def run_crnet_eccv(model, cv_split=1, epoch=30):\n criterion = CRLoss()\n\n optimizer_ft = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)\n\n exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=20, gamma=0.1)\n\n print('start loading ECCV HotOrNot dataset...')\n train_dataset = HotOrNotDataset(cv_split=cv_split, train=True, transform=transforms.Compose([\n transforms.Resize(227),\n transforms.RandomCrop(224),\n transforms.RandomRotation(30),\n transforms.ColorJitter(),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.5, 0.5, 0.5],\n std=[1, 1, 1])\n ]))\n\n test_dataset = HotOrNotDataset(cv_split=cv_split, train=False, transform=transforms.Compose([\n transforms.Resize(227),\n transforms.RandomCrop(224),\n transforms.RandomRotation(30),\n transforms.ColorJitter(),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.5, 0.5, 0.5],\n std=[1, 1, 1])\n ]))\n\n train_dataloader = DataLoader(train_dataset, batch_size=cfg['batch_size'],\n shuffle=True, num_workers=50, drop_last=True)\n test_dataloader = DataLoader(test_dataset, batch_size=cfg['batch_size'],\n shuffle=False, num_workers=50, drop_last=False)\n\n print('finish loading ECCV HotOrNot dataset...')\n\n train_model(model=model, train_dataloader=train_dataloader, test_dataloader=test_dataloader,\n criterion=criterion, optimizer=optimizer_ft, scheduler=exp_lr_scheduler, num_epochs=epoch,\n inference=False)","title":""},{"docid":"298ad825cb83c1132e1b29aca84321e2","score":"0.580828","text":"def build_model():\n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer=tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(estimator=LinearSVC(max_iter=10000)))\n ])\n parameters = {\n #'clf__estimator__n_estimators': [50, 100],\n #'clf__estimator__min_samples_split': [2, 5]\n 'clf__estimator__C': [1, 10],\n 'clf__estimator__max_iter': [1000, 100000]\n }\n\n cv = GridSearchCV(pipeline, param_grid=parameters)\n return cv","title":""},{"docid":"b61f39a099d1d052c261104817126e97","score":"0.57949626","text":"def evaluate(model_path, images_dir, output_dir):\n\n device = torch.device('cpu')\n # build mobilenetv3 ssd and cast to cpu\n cfg.merge_from_file('configs/mobilenet_v3_ssd320_voc0712.yaml') # load config file for mobilenetv3 SSD\n cfg.freeze()\n model = build_detection_model(cfg)\n model = model.to(device)\n\n # load model weights\n weights = torch.load(model_path, map_location=device)['model']\n model.load_state_dict(weights)\n\n # prepare model for inference\n model.eval()\n # inference preprocessing\n # resize, subtract mean, make channel first dim and make Tensor from np array\n data_preprocess = build_transforms(cfg, is_train=False)\n image_paths = glob.glob(images_dir + '/*.jpg')\n\n # predict\n for i, image_path in enumerate(image_paths):\n # load and preprocess image\n image = np.array(Image.open(image_path).convert(\"RGB\"))\n image_tensor = data_preprocess(image)[0] # use only the returned image (not boxes/labels)\n image_tensor = image_tensor.unsqueeze(0) # add batch dim\n image_name = os.path.basename(image_path)\n height, width = image.shape[:2]\n\n # predict\n result = model(image_tensor)[0]\n result = result.resize((width, height)).numpy()\n boxes, labels, scores = result['boxes'], result['labels'], result['scores'] # unpack results\n\n # filter results\n indices = scores > 0.5\n boxes = boxes[indices]\n labels = labels[indices]\n scores = scores[indices]\n\n # draw and save boxes\n # VOC dataset was used for training and hence its class names are used\n drawn_image = draw_boxes(image, boxes, labels, scores, VOCDataset.class_names).astype(np.uint8)\n Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))","title":""},{"docid":"d724c2fc7981461b626f93bb3db010d0","score":"0.5789101","text":"def model_evaluate(df: pd.DataFrame, features: list, target: str, metrics: dict, model, cv_rounds: int = 5):\n df = df.copy()\n scores = cross_validate(estimator=model.model,\n X=df[features],\n y=df[target],\n scoring=metrics,\n cv=cv_rounds,\n return_train_score=True)\n\n preds_proba = cross_val_predict(estimator=model.model,\n X=df[features],\n y=df[target],\n cv=cv_rounds,\n method=\"predict_proba\")[:, 1]\n preds = cross_val_predict(estimator=model.model,\n X=df[features],\n y=df[target],\n cv=cv_rounds,\n method=\"predict\")\n return scores, preds_proba, preds","title":""},{"docid":"43dc07fafd41acd71fbab9980f625fcb","score":"0.5788027","text":"def build_model():\n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer = tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\n ])\n \n parameters = {'vect__min_df': [1, 5],\n 'tfidf__use_idf':[True, False],\n 'clf__estimator__n_estimators':[10, 20]}\n\n cv = GridSearchCV(pipeline, param_grid = parameters,verbose = 3)\n return cv","title":""},{"docid":"800c2a370aa08e3962c8e53cc6ae4f60","score":"0.5787464","text":"def evaluate_model(model, testx, testy):\n\n score = model.evaluate(testx, testy, batch_size=32)\n print('Test Accuracy: ', score[1])\n print_baseline(testy)","title":""},{"docid":"ef73d5ac9c563f57ce066e243d9fc21f","score":"0.57821023","text":"def evaluate_model(\n estimator,\n fold: Optional[Union[int, Any]] = None,\n fit_kwargs: Optional[dict] = None,\n groups: Optional[Union[str, Any]] = None,\n use_train_data: bool = False,\n):\n\n return pycaret.internal.tabular.evaluate_model(\n estimator=estimator,\n fold=fold,\n fit_kwargs=fit_kwargs,\n groups=groups,\n use_train_data=use_train_data,\n )","title":""},{"docid":"2534c76c5583e5d297d7536364e3bcb9","score":"0.5775548","text":"def evaluate_split(self, **kwargs):","title":""},{"docid":"e61ed93c2a880981f52590faeca878d6","score":"0.57704806","text":"def _eval(args, model, features):\n data_loader = ner_data_loader(features, batch_size=args.eval_batch_size, task='test', auto=True)\n eval_size = len(data_loader)\n args.logger.info(\"***** Running evaluation on {} number of test data *****\".format(eval_size))\n args.logger.info(\" Instantaneous batch size per GPU = {}\".format(args.eval_batch_size))\n args.logger.info(\"******************************\")\n\n # not data for evaluation\n if eval_size < 1:\n return [], [], .0\n\n # prepare processing results for each batch\n y_trues, y_preds = [], []\n y_pred, y_true = [], []\n prev_gd = 0\n\n # prediction\n model.eval()\n eval_loss = .0\n for batch in tqdm(data_loader, desc='evaluation', disable=False if args.progress_bar else True):\n original_tkid = batch[0].numpy()\n original_mask = batch[1].numpy()\n original_labels = batch[3].numpy()\n guards = batch[4].numpy()\n\n batch = tuple(b.to(args.device) for b in batch)\n eval_inputs = batch_to_model_inputs(batch, args.model_type)\n\n with torch.no_grad():\n raw_logits, _, loss = model(**eval_inputs)\n # get softmax output of the raw logits (keep dimensions)\n if not args.use_crf:\n raw_logits = torch.argmax(F.log_softmax(raw_logits, dim=-1), dim=-1)\n raw_logits = raw_logits.detach().cpu().numpy()\n # update evaluate loss\n eval_loss += loss.item()\n\n assert guards.shape == original_tkid.shape == original_mask.shape == original_labels.shape == raw_logits.shape, \\\n \"\"\"\n expect same dimension for all the inputs and outputs but get\n input_tokens: {}\n mask: {}\n label: {}\n logits: {}\n \"\"\".format(original_tkid.shape, original_mask.shape, original_labels.shape, raw_logits.shape)\n\n # tk=token, mk=mask, lb=label, lgt=logits\n for mks, lbs, lgts, gds in zip(original_mask, original_labels, raw_logits, guards):\n connect_sent_flag = False\n for mk, lb, lgt, gd in zip(mks, lbs, lgts, gds):\n if mk == 0:\n # after hit first mask, we can stop for the current sentence since all rest will be pad\n if args.model_type == \"xlnet\":\n continue\n else:\n break\n if gd == 0 or prev_gd == gd:\n continue\n if gd == NEXT_GUARD:\n connect_sent_flag = True\n break\n if prev_gd != gd:\n y_true.append(args.idx2label[lb])\n y_pred.append(args.idx2label[lgt])\n prev_gd = gd\n if connect_sent_flag:\n continue\n y_trues.append(y_true)\n y_preds.append(y_pred)\n y_pred, y_true = [], []\n prev_gd = 0\n\n return y_trues, y_preds, round(eval_loss / eval_size, 4)","title":""},{"docid":"de472e3e174a36516c5873b4f464030b","score":"0.57676053","text":"def evaluate(data_loader, model, paradigm, task, sents):\n device = torch.device(f'cuda:{args.n_gpu}')\n model.model.to(device)\n \n model.model.eval()\n outputs, targets = [], []\n for batch in tqdm(data_loader):\n # need to push the data to device\n outs = model.model.generate(input_ids=batch['source_ids'].to(device), \n attention_mask=batch['source_mask'].to(device), \n max_length=128)\n\n dec = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]\n target = [tokenizer.decode(ids, skip_special_tokens=True) for ids in batch[\"target_ids\"]]\n\n outputs.extend(dec)\n targets.extend(target)\n\n raw_scores, fixed_scores, all_labels, all_preds, all_preds_fixed = compute_scores(outputs, targets, sents, paradigm, task)\n results = {'raw_scores': raw_scores, 'fixed_scores': fixed_scores, 'labels': all_labels,\n 'preds': all_preds, 'preds_fixed': all_preds_fixed}\n # pickle.dump(results, open(f\"{args.output_dir}/results-{args.task}-{args.dataset}-{args.paradigm}.pickle\", 'wb'))\n\n return raw_scores, fixed_scores","title":""},{"docid":"931411a900e92b97ac5868871a577ce6","score":"0.5763745","text":"def build_model():\n pipeline = Pipeline([\n \n ('vect', CountVectorizer(tokenizer=tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\n\n ])\n \n \n parameters = {\n 'vect__max_features':(None, 5000),\n 'clf__estimator__n_estimators': [5, 10, 20]\n }\n\n cv = GridSearchCV(estimator=pipeline, param_grid=parameters, n_jobs=-1,verbose=2)\n \n return cv","title":""},{"docid":"f15cc68b812280bb6e7c9f4f8ad09913","score":"0.57635653","text":"def run_model(params, mode):\n assert mode in ['train', 'eval', 'train_and_eval'], mode\n dataset_fn_fn = fast_imagenet_input.make_dataset\n\n if mode in ['train', 'train_and_eval']:\n dataset_mode = 'train' if params['use_held_out_test_set'] else 'l2l_train'\n dataset_fn, _ = dataset_fn_fn(\n params['dataset_dir'],\n dataset_mode,\n training=True,\n use_bfloat16=params['use_bfloat16'])\n estimator = _make_estimator(params, is_training=True)\n estimator.train(dataset_fn, max_steps=params['max_global_step'])\n if mode in ['eval', 'train_and_eval']:\n dataset_mode = 'test' if params['use_held_out_test_set'] else 'l2l_valid'\n dataset_fn, dataset_size = dataset_fn_fn(\n params['dataset_dir'],\n dataset_mode,\n training=False,\n use_bfloat16=params['use_bfloat16'],\n final_batch_mode=fast_imagenet_input.FinalBatchMode.PAD)\n\n estimator = _make_estimator(params, is_training=False)\n for checkpoint_path in tf.train.checkpoints_iterator(\n params['checkpoint_dir']):\n eval_metrics = estimator.evaluate(\n dataset_fn,\n steps=dataset_size // params['eval_batch_size'],\n checkpoint_path=checkpoint_path)\n tf.logging.info('eval metrics = %s', eval_metrics)\n if eval_metrics['global_step'] >= params['max_global_step']:\n return eval_metrics","title":""},{"docid":"078d2cb66a455b4f6c832ba1720ab237","score":"0.57605547","text":"def optimise(self, params):\n\n self.Model_sklearn.initialize_params(self.y, params)\n\n # logger.info(self.Model_sklearn.p)\n\n start = time.perf_counter()\n\n if self.x_val is None:\n # cross-validation\n fold_id = np.ones((len(self.y),)) * -1\n oof_val = np.zeros((self.y.shape[0], self.y.shape[1]))\n if \"time_series\" not in self.Model_sklearn.objective:\n # Cross-validation split in self.nfolds but train only on self.nfolds_train chosen randomly\n rd.seed(self.Model_sklearn.seed)\n fold_to_train = rd.sample([i for i in range(self.nfolds)], k=max(min(self.nfolds_train, self.nfolds), 1))\n\n if self.cv_strategy == \"StratifiedKFold\":\n skf = StratifiedKFold(n_splits=self.nfolds, random_state=self.Model_sklearn.seed, shuffle=True)\n folds = skf.split(self.y, self.y)\n else:\n kf = KFold(n_splits=self.nfolds, random_state=self.Model_sklearn.seed, shuffle=True)\n folds = kf.split(self.y)\n else:\n folds = [\n ([i for i in range(self.Model_sklearn.size_train)], [i for i in range(self.Model_sklearn.size_train, fold_id.shape[0])])]\n fold_to_train = [0]\n else:\n # validation\n fold_id = np.ones((len(self.y_val),)) * -1\n oof_val = np.zeros((self.y_val.shape[0], self.y_val.shape[1]))\n folds = [('all', [i for i in range(oof_val.shape[0])])]\n fold_to_train = [0]\n\n for n, (tr, te) in enumerate(folds):\n if n not in fold_to_train:\n continue\n\n if tr == 'all':\n # validation\n if isinstance(self.x, pd.DataFrame):\n x_tr, x_val = self.x.values, self.x_val.values\n else:\n x_tr, x_val = self.x, self.x_val\n if isinstance(self.y, pd.DataFrame):\n y_tr, y_val = self.y.values, self.y_val.values\n else:\n y_tr, y_val = self.y, self.y_val\n else:\n # cross-validation\n if isinstance(self.x, pd.DataFrame):\n x_tr, x_val = self.x.values[tr], self.x.values[te]\n else:\n x_tr, x_val = self.x[tr], self.x[te]\n if isinstance(self.y, pd.DataFrame):\n y_tr, y_val = self.y.values[tr], self.y.values[te]\n else:\n y_tr, y_val = self.y[tr], self.y[te]\n\n model = self.Model_sklearn.model()\n model.fit(x_tr, y_tr)\n\n if self.Model_sklearn.shape_y == 1:\n oof_val[te, :] = model.predict(x_val).reshape(-1, 1)\n else:\n oof_val[te, :] = model.predict(x_val)\n fold_id[te] = n\n del model\n\n metrics = []\n for i in range(self.Model_sklearn.shape_y):\n if self.x_val is None:\n # cross_validation\n if isinstance(self.y, pd.DataFrame):\n y_true = self.y.iloc[:, [i]].copy()\n else:\n y_true = self.y[:, i]\n else:\n # validation\n if isinstance(self.y, pd.DataFrame):\n y_true = self.y_val.iloc[:, i].copy()\n else:\n y_true = self.y_val[:, i]\n # subset, only use data where fold_id >= 0 :\n if isinstance(y_true, pd.DataFrame):\n y_true_sample = y_true.values[np.where(fold_id >= 0)[0]]\n else:\n y_true_sample = y_true[np.where(fold_id >= 0)[0]]\n prediction_oof_val = oof_val[:, i][np.where(fold_id >= 0)[0]]\n if 'regression' in self.Model_sklearn.objective:\n if 'explained_variance' == self.scoring:\n metrics.append(-explained_variance_score(y_true_sample, prediction_oof_val))\n elif 'r2' == self.scoring:\n metrics.append(-r2_score(y_true_sample, prediction_oof_val))\n else:\n metrics.append(mean_squared_error(y_true_sample, prediction_oof_val))\n else:\n if 'f1' in self.scoring:\n if 'binary' in self.Model_sklearn.objective:\n metrics.append(-f1_score(y_true_sample, prediction_oof_val))\n else:\n metrics.append(\n -f1_score(y_true_sample, prediction_oof_val, average=self.Model_sklearn.average_scoring))\n elif 'recall' in self.scoring:\n if 'binary' in self.Model_sklearn.objective:\n metrics.append(-recall_score(y_true_sample, prediction_oof_val))\n else:\n metrics.append(-recall_score(y_true_sample, prediction_oof_val,\n average=self.Model_sklearn.average_scoring))\n elif 'precision' in self.scoring:\n if 'binary' in self.Model_sklearn.objective:\n metrics.append(-precision_score(y_true_sample, prediction_oof_val))\n else:\n metrics.append(-precision_score(y_true_sample, prediction_oof_val,\n average=self.Model_sklearn.average_scoring))\n elif 'roc' in self.scoring or 'auc' in self.scoring:\n if 'binary' in self.Model_sklearn.objective:\n metrics.append(-roc_auc_score(y_true_sample, prediction_oof_val))\n else:\n metrics.append(-roc_auc_score(y_true_sample, prediction_oof_val,\n average=self.Model_sklearn.average_scoring))\n else:\n metrics.append(-accuracy_score(y_true_sample, prediction_oof_val))\n\n score = -np.mean(metrics)\n logger.info('oof_val score {} Metric {}'.format(self.scoring, score))\n\n # store hyperparameters optimization in a Dataframe self.df_all_results:\n self.df_all_results['mean_fit_time'].append(time.perf_counter() - start)\n self.df_all_results['params'].append(params)\n self.df_all_results['mean_test_score'].append(score)\n self.df_all_results['std_test_score'].append(0) # just 0\n\n return np.mean(metrics)","title":""},{"docid":"6a9d06b232bf7f34923d426255fa4d69","score":"0.57535625","text":"def evaluate_model(self, model, test_X, test_y):\n acc = model.evaluate(test_X, test_y, verbose=0)[1]\n print('Accuracy on testing data: ', acc)","title":""},{"docid":"0c95d3b3e1f4a22f0069419ead271cc7","score":"0.5748173","text":"def run_cross_validation(als, train):\n logger.info('Training ALS model with k-fold cross-validation..........')\n\n param_grid = (ParamGridBuilder()\n .addGrid(als.rank, [5, 10])\n .addGrid(als.maxIter, [10, 25, 50])\n .addGrid(als.regParam, [0.1, 0.01])\n .build())\n\n cv = CrossValidator(estimator=als,\n estimatorParamMaps=param_grid,\n evaluator=RegressionEvaluator(labelCol='count'),\n numFolds=5,\n parallelism=10,\n seed=42)\n\n # NOTE: Cut dataframe lineage before cross-validation to avoid local StackOverflow error\n spark.sparkContext.setCheckpointDir(\"/tmp\")\n\n cv_model = cv.fit(train)\n best_model = cv_model.bestModel\n logger.info('Average cross-validation RMSE by submodel: {}'.format(cv_model.avgMetrics))\n logger.info('Lowest cross-validation submodel RMSE: {}'.format(round(min(cv_model.avgMetrics), 3)))\n return best_model","title":""},{"docid":"aa6abc0bdaea847b0a32c6d30986ea84","score":"0.57477003","text":"def _fit_cv(self, examples, labels, groups=None, selection_settings=None):\n selection_settings = selection_settings or self.config.param_selection\n cv_iterator = self._get_cv_iterator(selection_settings)\n\n if selection_settings is None:\n return self._fit(examples, labels, self.config.params), self.config.params\n\n cv_type = selection_settings[\"type\"]\n num_splits = cv_iterator.get_n_splits(examples, labels, groups)\n logger.info(\n \"Selecting hyperparameters using %s cross-validation with %s split%s\",\n cv_type,\n num_splits,\n \"\" if num_splits == 1 else \"s\",\n )\n\n scoring = self._get_cv_scorer(selection_settings)\n n_jobs = selection_settings.get(\"n_jobs\", -1)\n\n param_grid = self._convert_params(selection_settings[\"grid\"], labels)\n model_class = self._get_model_constructor()\n estimator, param_grid = self._get_cv_estimator_and_params(\n model_class, param_grid\n )\n # set GridSearchCV's return_train_score attribute to False improves cross-validation\n # runtime perf as it doesn't have to compute training scores and which we don't consume\n grid_cv = GridSearchCV(\n estimator=estimator,\n scoring=scoring,\n param_grid=param_grid,\n cv=cv_iterator,\n n_jobs=n_jobs,\n return_train_score=False,\n )\n model = grid_cv.fit(examples, labels, groups)\n\n for idx, params in enumerate(model.cv_results_[\"params\"]):\n logger.debug(\"Candidate parameters: %s\", params)\n std_err = (\n 2.0\n * model.cv_results_[\"std_test_score\"][idx]\n / math.sqrt(model.n_splits_)\n )\n if scoring == Model.LIKELIHOOD_SCORING:\n msg = \"Candidate average log likelihood: {:.4} ± {:.4}\"\n else:\n msg = \"Candidate average accuracy: {:.2%} ± {:.2%}\"\n # pylint: disable=logging-format-interpolation\n logger.debug(msg.format(model.cv_results_[\"mean_test_score\"][idx], std_err))\n\n if scoring == Model.LIKELIHOOD_SCORING:\n msg = \"Best log likelihood: {:.4}, params: {}\"\n self.cv_loss_ = -model.best_score_\n else:\n msg = \"Best accuracy: {:.2%}, params: {}\"\n self.cv_loss_ = 1 - model.best_score_\n\n best_params = self._process_cv_best_params(model.best_params_)\n # pylint: disable=logging-format-interpolation\n logger.info(msg.format(model.best_score_, best_params))\n\n return model.best_estimator_, model.best_params_","title":""},{"docid":"326ea1e6bd1e503a6e87bd994356f83e","score":"0.5743013","text":"def build_model():\n \n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer=tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(KNeighborsClassifier()))\n ])\n\n \n parameters = {\n #'vect__max_df': (0.75, 1.0),\n 'tfidf__use_idf': (True, False),\n 'clf__estimator__n_neighbors': [3, 5]\n }\n \n\n cv = GridSearchCV(pipeline, parameters, cv = 3, n_jobs = -1)\n\n \n return cv","title":""},{"docid":"9d4108d52cd5d39bda6b7f71b9a9e8ff","score":"0.573901","text":"def knn_cv(X_train, y_train, cv=5, verbose=False):\n pipe = imbPipeline(steps=[\n ('sample', SMOTE()), \n ('scaler', MinMaxScaler()), \n ('knn', KNeighborsClassifier())\n ])\n params = [{'knn__n_neighbors': range(2,25), 'knn__p': [1, 2]}]\n\n model = GridSearchCV(pipe, params, cv=cv, n_jobs=-1, scoring=f2scorer, verbose=verbose)\n model.fit(X_train, y_train)\n return model","title":""},{"docid":"214e606b3c498b195dff125c2a6effa4","score":"0.57385755","text":"def test(self):\r\n # self.clf = LogisticRegression(penalty='l2', solver='newton-cg', multi_class='multinomial', C=10, max_iter=300, fit_intercept=True)\r\n self.clf = LogisticRegression()\r\n self.clf.set_params(**self.best_parameter)\r\n print(\"*** Test Result for Logistic Regression ***\")\r\n ModelEvaluation.evaluateModelWithCV(self.clf, self.dataset_x, self.dataset_y, cv=10)","title":""},{"docid":"9602e4a850176d88fb8ae8e81e4c6677","score":"0.5738553","text":"def build_model():\n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer=tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\n ])\n \n params = {'clf__estimator__n_estimators': [50, 100],\n 'clf__estimator__min_samples_split': [2, 4],\n 'clf__estimator__criterion': ['entropy', 'gini']\n }\n \n cv = GridSearchCV(pipeline, param_grid=params)\n \n return cv","title":""},{"docid":"a27404cfc73845866ddce5de3b2d7e1c","score":"0.57378215","text":"def evaluate_classifier(self):\n pass","title":""},{"docid":"e13d078adef53c2346e47c5148f0b685","score":"0.57359105","text":"def use_trained_model(project_dir, pickled_model, evaluate_performance, input_features = None, \\\n internal_features_to_drop = None, input_data = None, splits = None, \\\n add_new_splits = False, perf_by_sample = False):\n # first make sure features were supplied.\n if (input_features is None) and (internal_features_to_drop is None):\n raise ValueError('Features must be supplied, either through input_features argument \\\n (recommended) or by dropping from list of internal features.')\n # see if path to input data is supplied. if not, load from relative directory. \n if input_data is None:\n print('Using default path to engineered input data.')\n df = load_filter_engineered_df(project_dir)\n else:\n df = load_filter_engineered_df(project_dir, input_data)\n # use new splits created in cross-validation fold?\n # if so, use the data_splits in project_dir to define the splits\n print(f'input df has {df.shape[0]} variants.')\n if add_new_splits:\n df = load_and_merge_new_splits(engineered_df = df, fold_dir = project_dir)\n \n classifier = pickle.load(open(pickled_model, 'rb'))\n\n if input_features is not None: \n # check if any features are missing from df.\n for input_feature in input_features:\n if input_feature not in df.columns:\n raise ValueError(f'input feature {input_feature} is missing from dataframe.')\n features = input_features \n else:\n # drop from list of all internal features\n features = get_internal_features( use_purecn_purity = ('purity' in df.columns), internal_features_to_drop = internal_features_to_drop)\n features = list(features)\n print('finished selecting features.')\n print(f'df.columns: {df.columns}')\n print(f'features: {features}')\n print(f'Number of input variants to classify (before eval): {df.shape[0]}')\n if evaluate_performance:\n performance_df = evaluate_classifier(df, classifier, features, splits, perf_by_sample)\n performance_df.to_csv(make_filename(project_dir, pickled_model, \\\n '_full_performance_across_groups.csv'))\n single_samples = performance_df[performance_df['is_single_sample']]\n if single_samples.shape[0] > 0: # if there are some single samples\n print('Performance averaged across single samples.')\n # It wouldn't make sense to average over these two categorical columns\n mean_by_sample = single_samples.drop(columns = ['subset','is_single_sample']).mean()\n print(mean_by_sample)\n mean_by_sample.to_csv(make_filename(project_dir, pickled_model, \\\n '_average_performance_stats_by_sample.csv'))\n \n X_full = df[features]\n #print(f'df.shape: {df.shape}')\n X_full_values = X_full.fillna(0).values\n #print(f'X_full_values.shape: {X_full_values.shape}')\n\n # make binary predictions and probabilities for saving.\n y_pred = classifier.predict(X_full_values)\n y_proba = classifier.predict_proba(X_full_values)\n #print(f'y_pred.shape: {y_pred.shape}')\n # df already has features plus truth values; add y_pred prediction and class probabilities.\n df['tabnet_pred'] = y_pred\n df['tabnet_proba_0'] = y_proba[:,0]\n df['tabnet_proba_1'] = y_proba[:,1]\n\n # uncomment if you need to generate the full matrix including unused features.\n output_predictions_path = make_filename(project_dir, pickled_model, '_predictions_all_features.csv')\n print(output_predictions_path)\n df.to_csv(output_predictions_path)\n # drop superfluous unused features from dataframe.\n #if input_features is not None:\n # no good way to do this. skipping!\n # df_dropped_unused_features = df[input_features]\n #else:\n # intersect_features_to_drop = set(df.columns).intersection(set(internal_features_to_drop))\n # df_dropped_unused_features = df.drop(columns = list(intersect_features_to_drop))\n #df_dropped_unused_features.reset_index(drop = True).to_csv(make_filename(project_dir, pickled_model, '_predictions.csv'))","title":""},{"docid":"3636ad9abfa742b023fb9641061f8a97","score":"0.57272565","text":"def build_model():\n pipeline = Pipeline([\n ('vect', CountVectorizer(tokenizer=tokenize)),\n ('tfidf', TfidfTransformer()),\n ('clf', MultiOutputClassifier(estimator=RandomForestClassifier()))\n ])\n print(pipeline.get_params())\n parameters = {\n 'clf__estimator__n_estimators': [10, 15, 20],\n 'vect__min_df': [1, 2]\n }\n cv = GridSearchCV(pipeline, param_grid=parameters)\n return cv","title":""},{"docid":"8875475424f1ceffb6c8b644fc650226","score":"0.57260394","text":"def evaluate_classifier(clf,features, labels, num_iter, test_size, params):\n\n features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=test_size, random_state=42)\n\n\n precision_values = []\n recall_values = []\n accuracy_values = []\n\n for i in xrange(0, num_iter):\n #print params\n clf = GridSearchCV(clf, params)\n clf.fit(features_train, labels_train)\n\n clf = clf.best_estimator_\n #test_classifier(clf, dataset, feature_list)\n pred = clf.predict(features_test)\n precision_values.append(precision_score(labels_test, pred))\n recall_values.append(recall_score(labels_test, pred))\n accuracy_values.append(accuracy_score(labels_test, pred))\n \n print '_______________________________________________________________'\n print clf\n \n print 'Recall score: ', mean(recall_values)\n print 'Precision score: ', mean(precision_values)\n print 'Accuracy score: ' , mean(accuracy_values)","title":""},{"docid":"3f8fa6b107c18826fe40f6f211ccdf67","score":"0.57215756","text":"def timeseries_cv(model, data, n_splits=5):\n X_train, X_test, y_train, y_test = ts_train_test_split(data)\n \n tscv = TimeSeriesSplit(n_splits=n_splits)\n scores = np.sqrt(-cross_val_score(estimator=model, X=X_train.values, y=y_train.values, cv=tscv, scoring='neg_mean_squared_error', n_jobs=-1))\n \n return scores","title":""},{"docid":"65fa2da24a25f001cc1820d7ae0213df","score":"0.5721298","text":"def testClassifier():\n # load problem\n print(\"Loading testing data...\")\n y, x = svm_read_problem(\"./libsvm_test_problem\")\n problem = svm_problem(y, x)\n\n # load pretrained model\n model = svm_load_model(\"./trained-model-rbf\")\n\n # test model on training data\n print(\"Testing model...\")\n pLabel, pAcc, pVal = svm_predict(y, x, model)","title":""},{"docid":"c1625a65b8b15e73501a605e3f726d5b","score":"0.57195264","text":"def cv_score(clf,\n X,\n y,\n sample_weight=None,\n scoring='neg_log_loss',\n n_splits=3,\n t1=None,\n cv_gen=None,\n pct_embargo=0.,\n purging=True,\n return_combs=False,\n ret=None,\n num_threads=1,\n **kwargs):\n if cv_gen is None:\n if t1 is not None:\n cv_gen = PurgedKFold(\n n_splits=n_splits,\n t1=t1,\n pct_embargo=pct_embargo,\n purging=purging,\n num_threads=num_threads)\n else:\n cv_gen = KFold(n_splits=n_splits)\n elif cv_gen == 'cp':\n cv_gen = CPKFold(\n n_splits=n_splits,\n t1=t1,\n pct_embargo=pct_embargo,\n purging=purging,\n num_threads=num_threads)\n scores = []\n for train, test in cv_gen.split(X=X):\n train_params = dict()\n test_params = dict()\n # Sample weight is an optional parameter\n if sample_weight is not None:\n train_params['sample_weight'] = sample_weight.iloc[train].values\n test_params['sample_weight'] = sample_weight.iloc[test].values\n test_params.update(kwargs)\n clf_fit = clf.fit(\n X=X.iloc[train, :].values, y=y.iloc[train].values, **train_params)\n if hasattr(clf_fit, 'classes_'):\n test_params['labels'] = clf_fit.classes_\n if ret is not None:\n test_params['ret'] = ret.iloc[test]\n # Scoring\n score_ = evaluate(clf_fit, X.iloc[test, :].values, y.iloc[test].values,\n scoring, **test_params)\n scores.append(score_)\n if scoring not in ['roc', 'precision_recall']:\n scores = np.array(scores)\n if return_combs:\n return scores, cv_gen.get_test_combs()\n else:\n return scores","title":""},{"docid":"a6c22cdbaba02f0f2138bd38141ca2cb","score":"0.5714878","text":"def testOverall():\n # load problem\n print(\"Loading testing data...\")\n y, x = svm_read_problem(\"./libsvm_all_problem\")\n problem = svm_problem(y, x)\n\n # load pretrained model\n model = svm_load_model(\"./trained-model-rbf\")\n\n # test model on training data\n print(\"Testing model...\")\n pLabel, pAcc, pVal = svm_predict(y, x, model)","title":""},{"docid":"8977ad810ac31c19ff17fb7777b5396f","score":"0.5714392","text":"def evaluate_model(self, model):\n\n biases, stereotypes = self.gender_neutrality_test(model)\n preds = self.gender_identification_test(model)\n ppl = self.PTB_test(model)\n\n self._log_neutrality(biases, stereotypes)\n self._log_identification(preds)\n self._log_PTB(ppl)\n\n return self.score_data","title":""},{"docid":"0a6c3d5dd720194c83cc2af0bf025cec","score":"0.5713862","text":"def score_model(model, X_train, y_train, X_test, y_test):\n model.fit(X_train, y_train)\n y_pred = model.predict(X_test)\n\n if is_classifier(model):\n score = f1_score(y_test, y_pred)\n else:\n score = r2_score(y_test, y_pred)\n return model, score","title":""},{"docid":"a227d4b9a32c42d496574d81eb31b11f","score":"0.57075506","text":"def evaluate_model(model, meta_data, test_data_folder):\n print(\"[Info] Cargando test dataset\")\n X, Y = load_test_dataset(meta_data, test_data_folder)\n print(f\"[Info] Imagenes cargadas {len(X)}\")\n\n loss, acc = model.evaluate(X, Y)\n print(f\"[Info] loss: {loss}\\taccuracy: {acc}\")","title":""},{"docid":"b1d50040a146ead260b28347e3513874","score":"0.57074225","text":"def test_trained_model(X_test,y_test,y_train_labels,y_test_labels,lamda_list,model_num):","title":""},{"docid":"f91393142be69e6580726bacc560ec15","score":"0.5698971","text":"def run_xval_evaluation(task, arch='segnet_proper', pretrained=None,\n hyperparams={}, test_keys=None, fit=True):\n # import os\n # os.environ['GLOG_minloglevel'] = '3'\n\n from clab import harness\n # from clab import models\n\n xval_base = abspath(task.exptdir('xval'))\n\n xval_results = []\n for idx, xval_split in enumerate(task.xval_splits(test_keys=test_keys)):\n print(ub.color_text('XVAL iteration {}'.format(idx), 'blue'))\n\n (train, test) = xval_split\n xval_dpath = ub.ensuredir((xval_base, 'split_{:0=2}'.format(idx)))\n\n harn = harness.Harness(workdir=xval_dpath, arch=arch, task=task)\n harn.task = task\n harn.set_inputs(train, test)\n\n harn.init_pretrained_fpath = pretrained\n harn.init_pretrained_fpath = None\n harn.params.update(hyperparams)\n\n harn.test.prepare_images()\n harn.train.prepare_images()\n\n harn.gpu_num = gpu_util.find_unused_gpu(min_memory=6000)\n print('harn.gpu_num = {!r}'.format(harn.gpu_num))\n if harn.gpu_num is not None:\n avail_mb = gpu_util.gpu_info()[harn.gpu_num]['mem_avail']\n # Estimate how much we can fit in memory\n # TODO: estimate this from the model arch instead.\n # (90 is a mgic num corresponding to segnet_proper)\n harn.train_batch_size = int(\n (avail_mb * 90) // np.prod(task.input_shape))\n harn.train_batch_size = int(harn.train_batch_size)\n if harn.train_batch_size == 0:\n raise MemoryError(\n 'not enough GPU memory to train the model')\n else:\n # not sure what the best to do on CPU is. Probably nothing.\n harn.train_batch_size = 4\n\n harn.prepare_solver()\n\n # Check if we can resume a previous training state\n print(ub.color_text('Checking for previous snapshot states', 'blue'))\n previous_states = harn.snapshot_states()\n print('Found {} previous_states'.format(len(previous_states)))\n\n if fit:\n if len(previous_states) == 0:\n print(ub.color_text('Starting a fresh training session', 'blue'))\n harn.fit()\n else:\n from clab.backend import iface_caffe as iface\n solver_info = iface.parse_solver_info(harn.solver_fpath)\n prev_state = previous_states[-1]\n prev_iter = iface.snapshot_iterno(prev_state)\n if prev_iter < solver_info['max_iter']:\n # continue from the previous iteration\n print(ub.color_text(\n 'Resume training from iter {}'.format(prev_iter), 'blue'))\n harn.fit(prevstate_fpath=prev_state)\n else:\n print(ub.color_text(\n 'Already finished training this model', 'blue'))\n\n for _ in harn.deploy_trained_for_testing():\n # hack to evaulate while deploying\n harn.evaulate_all()\n xval_results.append(list(harn._test_results_fpaths()))\n return xval_results","title":""},{"docid":"61203862b24fdd9277d5c8fbdf8652ca","score":"0.56965387","text":"def sl_model_evaluation(classifier, eval_df):\n\n untampered_df = eval_df[eval_df['tamper'] == 1]\n attacks_df = eval_df[eval_df['tamper'] == -1]\n\n y_pred_test = classifier.predict(np.asarray(untampered_df[FEATURES]))\n y_pred_outliers = classifier.predict(np.asarray(attacks_df[FEATURES]))\n\n # Format the output of the classifier\n y_pred_test[y_pred_test == 0] = -1\n y_pred_outliers[y_pred_outliers == 0] = -1\n\n n_accurate_test = y_pred_test[y_pred_test == 1].size\n n_accurate_outliers = y_pred_outliers[y_pred_outliers == -1].size\n\n\n f_beta = fbeta_score(np.concatenate([np.ones(y_pred_test.shape[0]),\n -1 * np.ones(y_pred_outliers.shape[0])]),\n np.concatenate([y_pred_test, y_pred_outliers]),\n beta=20,\n pos_label=1)\n\n tnr = n_accurate_outliers / attacks_df.shape[0]\n tpr_test = n_accurate_test / untampered_df.shape[0]\n\n return f_beta, tnr, tpr_test","title":""},{"docid":"bcae29934c124893a8bc0ac7eab03b32","score":"0.56955665","text":"def evaluation(x, y, cut_size, w, h, fimg, model):\n fimg = cv2.imread(fimg.filename)\n image = fimg[y:y+cut_size, x:x+cut_size]\n # cv2.imshow(\"cropped\", image)\n\n results = model.get_predictions(image=image, plot=False)\n\n if len(results) == 0:\n return None\n\n c = results.cpu().numpy()\n\n if(x != 0 and y != 0 and x+cut_size != w and y+cut_size != h):\n i = 0\n while i < c.shape[0]:\n if(c[i, 0] < settings.BOARDCACHE or c[i, 1] < settings.BOARDCACHE or\n c[i, 2] < settings.BOARDCACHE or c[i, 3] < settings.BOARDCACHE):\n c = np.delete(c, i, axis=0)\n i -= 1\n i += 1\n i = 0\n\n while i < c.shape[0]:\n c[i, 0] += x\n c[i, 1] += y\n c[i, 2] += x\n c[i, 3] += y\n i += 1\n\n return c","title":""},{"docid":"dfa0dab4fe08c8bb9d6e128d28a311d1","score":"0.568342","text":"def score_model(\n self, target=\"estimated_demand\", true_demand=\"estimated_demand\", cv=5\n ):\n\n # Get the product history and drop the date and any possible demand columns\n records = self.history\n X = records.drop(\n columns=[\"date\", \"estimated_demand\", \"measured_demand\", \"demand\"],\n errors=\"ignore\",\n )\n\n # Get the specified column as the target data for training\n y = records[[target]]\n\n # Get the specified column for evaluation of production predictions\n demand = records[[true_demand]]\n\n # set up lists for recording values for each fold of the cross validation\n score = {\n \"val_loss\": [],\n \"days\": [],\n \"demand\": [],\n \"produced\": [],\n \"revenue\": [],\n \"cost\": [],\n \"sales\": [],\n \"profit\": [],\n \"waste\": [],\n }\n\n # split is done in groups, but could be switched to random samples\n # splitter = ShuffleSplit(n_splits=cv, test_size=1/cv)\n splitter = KFold(n_splits=cv, shuffle=False)\n for train_indices, test_indices in splitter.split(X):\n X_train, X_test = X.iloc[train_indices], X.iloc[test_indices]\n y_train, y_test = y.iloc[train_indices], y.iloc[test_indices]\n y_true = demand.iloc[test_indices].to_numpy()\n score[\"demand\"].append(y_true)\n\n # only look for fit_history if epochs is present, i.e. if model is a deep learning model.\n if hasattr(self.model, \"epochs\"):\n self.model.fit(\n X_train, y_train, validation_data=(X_test, y_test), reset_model=True\n )\n score[\"val_loss\"].append(self.model.fit_history[\"val_loss\"])\n else:\n self.model.fit(X_train, y_train)\n\n # Get productions on test set.\n production = self.model.predict(X_test)\n\n # Get total produced over all days.\n produced = np.sum(production)\n score[\"produced\"].append(produced)\n\n # Find actual sales\n sales = np.sum(np.min(np.hstack([y_true, production]), axis=1))\n score[\"sales\"].append(sales)\n revenue = sales * self.unit_sale_price\n score[\"revenue\"].append(revenue)\n cost = np.sum(production) / self.batch_size * self.batch_cost\n score[\"cost\"].append(cost)\n score[\"days\"].append(X_test.shape[0])\n profit = revenue - cost\n score[\"profit\"].append(profit)\n waste = produced - sales\n score[\"waste\"].append(waste)\n\n total_days = np.sum(score[\"days\"])\n score[\"revenue_avg\"] = np.sum(score[\"revenue\"]) / total_days\n score[\"profit_avg\"] = np.sum(score[\"profit\"]) / total_days\n score[\"profit_margin\"] = (\n np.sum(score[\"profit\"]) * 100.0 / np.sum(score[\"revenue\"])\n )\n score[\"waste_pct\"] = np.sum(score[\"waste\"]) * 100 / np.sum(score[\"produced\"])\n score[\"waste_avg\"] = np.sum(score[\"waste\"]) / total_days\n return score","title":""},{"docid":"808eec7baabc23944bec6c2b73fe8fe4","score":"0.567953","text":"def cv_train(modelname,config,kfold,lstep,n_samples,feature_weight,\n\t\t\t pkl_filename=\"data/adni.pkl\",json_filename=\"conf/dataConfig.json\"):\n\n\t\"\"\"load data and data config\n\tThe data file *pkl contains three parts: {'demo','dync','max_len'}\n\t\t'demo' : a list of dataframes storing patients' demographics information\n\t\t'dync' : a list of dataframes storing patients' dynamic information, including continous features and diganosis\n\t\t'max_len' : int, the maximum lengths of patient sequence\n\t\"\"\"\n\tadni = _pickle.load(open(pkl_filename,\"rb\"))\n\tdataConfig = json.load(open(json_filename))\n\tmax_len = adni[\"max_len\"]\n\t\n\t\"\"\"\n\trecord val_loss change / trends;\n\tthe smallest loss value for each fold is given by the best model\n\t\"\"\"\n\tloss_curve = {}\n\n\t# build model graph\n\tif modelname == \"rnn\":\n\t\tmodel = rnn(len(dataConfig[\"demo_vars\"]),len(dataConfig[\"input_x_vars\"]),len(dataConfig[\"input_y_vars\"]),\n\t\t\t\t\t\t\t max_len,config[\"batch_size\"],config[\"n_h\"],config[\"n_z\"],lstep,feature_weight)\n\telif modelname == \"stocast\":\n\t\tmodel = stocast(len(dataConfig[\"demo_vars\"]),len(dataConfig[\"input_x_vars\"]),len(dataConfig[\"input_y_vars\"]),\n\t\t\t\t\t\tmax_len,config[\"batch_size\"],config[\"n_h\"],config[\"n_z\"],lstep,feature_weight)\n\telif modelname == \"storn\":\n\t\tmodel = storn(len(dataConfig[\"demo_vars\"]),len(dataConfig[\"input_x_vars\"]),len(dataConfig[\"input_y_vars\"]),\n\t\t\t\t\t\tmax_len,config[\"batch_size\"],config[\"n_h\"],config[\"n_z\"],lstep,feature_weight)\n\telif modelname == \"retain\":\n\t\tmodel = retain(len(dataConfig[\"demo_vars\"]),len(dataConfig[\"input_x_vars\"]),len(dataConfig[\"input_y_vars\"]),\n\t\t\t\t\t\tmax_len,config[\"batch_size\"],config[\"n_h\"],config[\"n_z\"],lstep,feature_weight)\n\telif modelname == \"tlstm\":\n\t\tmodel = tlstm(len(dataConfig[\"demo_vars\"]),len(dataConfig[\"input_x_vars\"]),len(dataConfig[\"input_y_vars\"]),\n\t\t\t\t\t\tmax_len,config[\"batch_size\"],config[\"n_h\"],config[\"n_z\"],lstep,feature_weight)\n\n\n\n\t# saving ...\n\tdirname = \"save/{} {}\".format(modelname,time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time())))\n\tif not os.path.exists(dirname):\n\t\tos.makedirs(dirname)\n\n\twith tf.Session() as sess:\n\t\tsummary_writer = tf.summary.FileWriter('logs/' + datetime.now().isoformat().replace(':', '-'), sess.graph)\n\t\tmerged = tf.summary.merge_all()\n\t\tsaver = tf.train.Saver(tf.global_variables(),max_to_keep=1)\n\n\t\tstart = time.time()\n\t\ttotaltime = 0\n\t\tfor k in range(kfold):\n\t\t\t\n\t\t\t\"\"\" if k = 0, random initialization params \"\"\"\n\t\t\t\"\"\" else, inherit previous best model's params \"\"\"\n\t\t\tif k == 0:\n\t\t\t\ttf.global_variables_initializer().run()\n\t\t\telse:\n\t\t\t\tckpt = tf.train.get_checkpoint_state(dirname)\n\t\t\t\tsaver.restore(sess, ckpt.model_checkpoint_path)\n\n\t\t\t# split into trainining, validation, testing data\n\t\t\ttrain_ds,valid_ds = split_data(k,kfold,adni,dataConfig,max_len)\n\n\t\t\t# train\n\t\t\tminVlloss = 1e10\n\t\t\tloss_curve[k] = []\n\t\t\tn_batchs = int(train_ds.num_examples / config[\"batch_size\"])\n\n\t\t\t# each epoch\n\t\t\tno_improvement = 0 # number of no improvements\n\t\t\te = 0\n\t\t\twhile e < config[\"num_epochs\"]:\n\t\t\t\tsess.run(tf.assign(model.lr, config[\"learning_rate\"] * (config[\"decay_rate\"] ** e)))\n\n\t\t\t\tfor b in range(n_batchs):\n\t\t\t\t\n\t\t\t\t\twrap = train_ds.next_batch(config[\"batch_size\"])\n\t\t\t\t\t\n\t\t\t\t\tfeed = {model.input_demo: wrap['input_demo'],\n\t\t\t\t\t\t\tmodel.input_x: wrap['input_x'],\n\t\t\t\t\t\t\tmodel.input_y: wrap['input_y'],\n\t\t\t\t\t\t\tmodel.input_dt: wrap['input_dt'],\n\t\t\t\t\t\t\tmodel.seqlens: wrap['seqlens'],\n\t\t\t\t\t\t\tmodel.mask: wrap['mask']}\n\t\t\t\t\n\t\t\t\t\t_,loss,summary = sess.run([model.train_op, model.loss, merged],feed)\n\t\t\t\t\tsummary_writer.add_summary(summary, e * n_batchs + b)\n\n\t\t\t\t# validation\n\t\t\t\tvloss = val_loss(sess,model,valid_ds,config[\"batch_size\"])\n\t\t\t\tloss_curve[k].append(vloss)\n\t\t\t\t\n\t\t\t\tprint(\" |- FOLD:%d, EPOCH:%d, VLOSS:%.4f\" % (k,e,vloss))\n\n\t\t\t\tif minVlloss > vloss:\n\t\t\t\t\tminVlloss = vloss\n\t\t\t\t\tcheckpoint_path = os.path.join(dirname, \"best_model_k={}_e={}.ckpt\".format(k,e))\n\t\t\t\t\tsaver.save(sess, checkpoint_path, global_step=e * n_batchs + k * config[\"num_epochs\"] * n_batchs)\n\t\t\t\t\tprint(\" |- Best model saved to {}\".format(checkpoint_path))\n\t\t\t\t\tno_improvement = 0\n\t\t\t\telse:\n\t\t\t\t\tno_improvement += 1\n\t\t\t\t\n\t\t\t\t# if the number of improvement reaches 10, stop running\n\t\t\t\tif no_improvement < 10:\n\t\t\t\t\te += 1\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\n\t\t\tend = time.time()\n\t\t\tprint(\"|- %2d fold costs %.4f seconds.\\n\" % (k,end-start))\n\t\t\ttotaltime += end-start\n\t\t\tstart = time.time()\n\t\tprint(\"Total train time is %.4f seconds.\" % totaltime)\n\n\t\t# testing\n\t\tprint(\"Starting testing\")\n\t\tckpt = tf.train.get_checkpoint_state(dirname)\n\t\tif ckpt:\n\t\t\tsaver.restore(sess, ckpt.model_checkpoint_path)\n\t\t\tprint(\"Loading model: \",ckpt.model_checkpoint_path)\n\t\t\n\t\ttest_ds = DataSet(dataConfig,adni[\"demo\"],adni[\"dync\"],max_len)\n\t\ttest_res = test(sess,model,modelname,test_ds,\n\t\t\tconfig[\"batch_size\"],max_len,dataConfig[\"input_y_vars\"],lstep,n_samples=n_samples)\n\t\t\n\t\tprint(\"Saving test results...\")\n\t\t\"\"\"The results are saved in the following format.\n\t\tres = pickle.load(open(filename,'rb'))\n\t\t\tres : a list of dicts, with each dict() stores the prediction results corresponding to a specific patient\n\t\t\tres[i] : the dict for patient i, {'curr_labels','target_labels','pred_pi','target_features','pred_mu'}\n\t\t\t\t'curr_labels' : a list of labels\n\t\t\t\t'target_labels' : a list of target labels\n\t\t\t\t'pred_pi' : a list of predictions, the length is timesteps\n\t\t\t\t\t- pred_pi[t] is a 1d array for deterministic methods, or a 2d array for stocast with size (n_samples, 3)\n\t\t\t\t'target_features' : list, the length is timesteps\n\t\t\t\t\t- target_features[t] : a 1d array\n\t\t\t\t'pred_mu' : list, the length is timesteps\n\t\t\t\t\t- pred_mu[t] : a 1d array for deterministic methods, or a 2d array for stocast\n\t\t\"\"\"\n\n\t\tdirname = \"result_fw={}/{}\".format(feature_weight,modelname)\n\t\tif not os.path.exists(dirname):\n\t\t\tos.makedirs(dirname)\n\t\t_pickle.dump(test_res,open(os.path.join(dirname,\"lstep{}_nsamples{}_result.pkl\".format(lstep,n_samples)),\"wb\"))\n\t\t_pickle.dump(loss_curve,open(os.path.join(dirname,\"lstep{}_nsamples{}_losses.pkl\".format(lstep,n_samples)),\"wb\"))","title":""},{"docid":"b09cd531a8c96a4c83a59cd50bcd6a32","score":"0.56758386","text":"def test(self):\n self.model.eval()\n self.predictions = []\n self.targets = []\n for cluster in self.clustering_machine.clusters:\n prediction, target = self.do_prediction(cluster)\n self.predictions.append(prediction.cpu().detach().numpy())\n self.targets.append(target.cpu().detach().numpy())\n self.targets = np.concatenate(self.targets)\n self.predictions = np.concatenate(self.predictions).argmax(1)\n score = f1_score(self.targets, self.predictions, average=\"micro\")\n print(\"\\nF-1 score: {:.4f}\".format(score))","title":""}],"string":"[\n {\n \"docid\": \"35ea1019c0b7a7f5741d8cba7b8458c8\",\n \"score\": \"0.68407726\",\n \"text\": \"def evaluate_model(model_name, model, x, y):\\n print('%s:' % model_name)\\n model.fit(x, y.values.ravel())\\n print('CV f1_micro (not reusing data): %s' % np.mean(cross_val_score(model,\\n x, y.values.ravel(), cv=5, scoring='f1_micro')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3b00ea569d3b4775dd801f447a796b\",\n \"score\": \"0.6775238\",\n \"text\": \"def evaluate_model(dataset, split):\\n data, ne_list, model = load_data_and_initialize_model(dataset, split_list=[split])\\n \\n saver = tf.train.Saver()\\n saver.restore(model.sess, \\\"./tmp.model\\\")\\n ner_hat_list = predict_dataset(model, data[split][\\\"tree_pyramid_list\\\"], ne_list)\\n score = evaluate_prediction(data[split][\\\"ner_list\\\"], ner_hat_list)\\n print \\\"[%s]\\\" % split + \\\" precision=%.1f%% recall=%.1f%% f1=%.3f%%\\\" % score\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958b869ac03bccb5df39cede8da933d2\",\n \"score\": \"0.66875386\",\n \"text\": \"def fit(self, X, y=None, cv=5, eval_split=0.8):\\n for model in self.models:\\n kfold = KFoldCV(model, k=cv, eval_split=eval_split)\\n hparams = {} # TODO: nerds/issues/24 (passing hyperparams).\\n # Right now it will fall back to the default hyperparameters.\\n score = kfold.cross_validate(X, hparams)\\n self.confidence_scores.append(score)\\n return super().fit(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fab813d78a7f929649809af9c9a0bfa\",\n \"score\": \"0.66832787\",\n \"text\": \"def doCV(modelInit, training_size, validation_size, maxK=100, saveModel = False):\\n if training_size + validation_size > 100:\\n print('validation_size + training_size should be smaller than 101')\\n return\\n if training_size > 100:\\n print('TRAINING_SIZE should be less than 100') \\n return\\n if training_size < 1:\\n print('VALIDATION_SIZE should be bigger than 1') \\n return \\n print (\\\"Run Cross Validation on a training set of size \\\"+str(training_size)+\\\" with a validation set size of \\\"+str(validation_size))\\n \\n \\\"\\\"\\\" --- RUN CROSS VALIDATION --- \\\"\\\"\\\"\\n total_size = training_size + validation_size\\n valset_start_id = 1\\n all_training_ids = list(range(1, total_size+1))\\n score = 0\\n run = 1\\n while valset_start_id < total_size and run <= maxK:\\n print(\\\"-----------------------------------------------\\\")\\n print(\\\" run: \\\"+str(run))\\n print(\\\"-----------------------------------------------\\\")\\n model = modelInit(run)\\n #define validation and training set for each run\\n valset_end_id = valset_start_id+validation_size\\n val_set = range(valset_start_id, valset_end_id)\\n training_set = []\\n if valset_start_id > 1:\\n training_set += all_training_ids[:valset_start_id-1]\\n if valset_end_id < total_size:\\n training_set += all_training_ids[valset_end_id:]\\n #run\\n train_classifier(model, training_set, saveModel) # (model isn't stored in tmp; only locally)\\n new_score = validate(model, val_set, valset_start_id==1) # calculates F1 Score of the valset using the trained model\\n print(\\\"score of run \\\"+str(run)+\\\": \\\"+str(new_score))\\n #average\\n score = score + ( new_score - score) / run\\n run += 1\\n valset_start_id = valset_end_id\\n \\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5ceba4df38bdf51f82b30840e1036c8\",\n \"score\": \"0.6546712\",\n \"text\": \"def evaluate(self, splits=[\\\"train\\\", \\\"val\\\"], constant_preprocessor=None):\\n if self.model:\\n for split in splits:\\n self.__evaluate_and_log_to_mlflow(split=split, balance_method=None, constant_preprocessor=constant_preprocessor)\\n if split == \\\"val\\\":\\n self.__evaluate_and_log_to_mlflow(split=split, balance_method=\\\"translate\\\", constant_preprocessor=constant_preprocessor)\\n elif split == \\\"train\\\" and self.data.balance_method:\\n self.__evaluate_and_log_to_mlflow(split=split, balance_method=self.data.balance_method, constant_preprocessor=constant_preprocessor)\\n else:\\n logger.info(f\\\"No model available! Please run `train()` first.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e36fea9dbaf61d295d936c3a86c152\",\n \"score\": \"0.6545675\",\n \"text\": \"def compute_evaluation(config, cv_split_filename,\\n train_fraction=1.0, mmap_mode='r'):\\n # All module imports should be executed in the worker namespace to make\\n # possible to run an an engine node.\\n from time import time, sleep\\n from sklearn.externals import joblib\\n\\n time_start = time()\\n\\n pre_processing = config['pre_processing']\\n classifier = config['classifier']\\n\\n X_train, y_train, X_test, y_test = joblib.load(\\n cv_split_filename, mmap_mode=mmap_mode)\\n\\n # Slice a subset of the training set for plotting learning curves\\n n_samples_train = int(train_fraction * X_train.shape[0])\\n X_train = X_train[:n_samples_train]\\n y_train = y_train[:n_samples_train]\\n\\n pre_processing.fit(X_train)\\n X_train_pp = pre_processing.transform(X_train)\\n\\n # Fit model and measure training time\\n tick = time()\\n classifier.fit(X_train_pp, y_train)\\n train_time = time() - tick\\n\\n # Compute score on training set\\n train_score = classifier.score(X_train_pp, y_train)\\n\\n # Compute score on test set\\n X_test_pp = pre_processing.transform(X_test)\\n test_score = classifier.score(X_test_pp, y_test)\\n\\n sleep(3)\\n\\n time_end = time()\\n\\n # Wrap evaluation results in a simple tuple datastructure\\n return {\\n 'loss': 1 - test_score,\\n 'loss_': {\\n 'duration': time_end - time_start,\\n 'erate': 1 - test_score, #XXX should be validation error\\n },\\n 'test_score': test_score,\\n 'train_score': train_score,\\n 'train_time': train_time,\\n 'train_fraction': train_fraction,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a54377fd5853c98058b72cddef34f435\",\n \"score\": \"0.6501031\",\n \"text\": \"def evaluate(self,fit = True,split = True,cross_validation = False,**kwargs):\\n\\n # Evaluate models using train test split\\n if split:\\n\\n # If already prepared a split, we will be using it\\n if hasattr(self.dataset,\\\"splits\\\"):\\n X_train,X_test,y_train,y_test = self.dataset.splits\\n\\n # Otherwise prepare the split using function arguments\\n else:\\n X_train,X_test,y_train,y_test = self.dataset.train_test_split(inplace = False,**kwargs)\\n \\n # Fit the models\\n if fit:\\n self.fit(X = X_train,y = y_train)\\n\\n # Predict the probability\\n pred = self.predict(X = X_test)\\n pred[\\\"true\\\"] = y_test.values\\n pred.insert(0,\\\"index\\\",y_test.index)\\n\\n # Compute metrics\\n metrics,confusion_matrices = self.compute_metrics(pred)\\n\\n return pred,metrics,confusion_matrices\\n\\n else:\\n raise ProblemsError(\\\"Cross validation is not implemented right now\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f4cba2dbef2956ec9fb4cc225949e5\",\n \"score\": \"0.6428906\",\n \"text\": \"def evaluate_cv(self, clf, embedding, n_splits):\\n embedding = embedding[self.label_ind, :]\\n results = defaultdict(list)\\n for i in range(10):\\n rskf = StratifiedKFold(n_splits=n_splits, shuffle=True)\\n for train_idx, test_idx in rskf.split(embedding, self.labels):\\n x_train, x_test, y_train, y_test, w_train = self._get_split(embedding, test_idx, train_idx)\\n pred, probs = self.get_predictions(\\n clf,\\n x_train,\\n x_test,\\n y_train,\\n y_test,\\n sample_weights=w_train\\n )\\n self._assemble_results(y_test, i, pred, probs[:, 1], results)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7e1758546e6b7e217be2aed71d04a1\",\n \"score\": \"0.6404894\",\n \"text\": \"def evaluate_model(cv, X_test, y_test, category_names):\\n y_pred = cv.predict(X_test)\\n for i in range(35):\\n print(category_names[i])\\n print(classification_report(y_test[:,i], y_pred[:,i]))\\n print('\\\\nBest Parameters:', cv.best_params_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bed545c4cac25bc21967d632302bb31\",\n \"score\": \"0.63968045\",\n \"text\": \"def evalrank(model_path, data_path=None, split='dev', fold5=False):\\n # load model and options\\n checkpoint = torch.load(model_path)\\n opt = checkpoint['opt']\\n\\n if data_path is not None:\\n opt.data_path = data_path\\n opt.vocab_path = \\\"./vocab/\\\"\\n # load vocabulary\\t \\n vocab = pickle.load(open(os.path.join(\\n opt.vocab_path, 'vocab.pkl'), 'rb'))\\n \\n opt.vocab_size = len(vocab)\\n\\n # construct model\\n model = VSE(opt)\\n \\n # load model state\\n model.load_state_dict(checkpoint['model'])\\n print(opt)\\t\\n\\t\\n ####### input video files\\n path= os.path.join(opt.data_path, opt.data_name)+\\\"/Caption/charades_\\\"+ str(split) + \\\".csv\\\"\\n df=pandas.read_csv(open(path,'rb'))\\n #columns=df.columns\\n inds=df['video']\\n desc=df['description']\\n\\n print('Loading dataset')\\n data_loader = get_test_loader(split, opt.data_name, vocab, opt.crop_size,\\n opt.batch_size, opt.workers, opt)\\n\\n print('Computing results...')\\n img_embs, cap_embs, attn_index, lengths_img = encode_data(model, data_loader)\\n\\n print(img_embs.shape)\\n print(cap_embs.shape)\\n print('Images: %d, Captions: %d' %\\n (img_embs.shape[0], cap_embs.shape[0]))\\n\\n\\t# retrieve moments\\n r13, r15, r17 = t2i(img_embs, cap_embs, df, attn_index, lengths_img, measure=opt.measure, return_ranks=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64d67b52c3c655815c9bb4aec01a76f\",\n \"score\": \"0.63791084\",\n \"text\": \"def test_model(model, X_test, y_test, n_splits):\\n n_splits_x_test = n_splits\\n number = 1\\n # val es un trozo que nunca se repite\\n for (_, one_split) in KFold(n_splits=n_splits_x_test).split(X_test):\\n y_pred = model.predict(X_test[one_split])\\n print(\\\"-----------------------------\\\")\\n print(\\\"Mean squared error test\\\", number, \\\": \\\", mean_squared_error(y_test[one_split], y_pred))\\n number += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467a2f1347d8a29bc0dddaba6749e583\",\n \"score\": \"0.62942666\",\n \"text\": \"def eval_models_CV(X, y, models, label_col_name='label', prediction_col_name='prediction', value_col_name='value',\\n n_splits=None, verbose=True, window_size_for_metrics=3):\\n\\n if X is None:\\n raise TypeError\\n if y is None:\\n raise TypeError\\n\\n if n_splits is None:\\n n_splits = int(len(X.index.levels[0]) / 2)\\n print(\\\"Running {} splits\\\".format(n_splits))\\n\\n if n_splits > len(X.index.levels[0]):\\n n_splits = int(len(X.index.levels[0]) / 2)\\n print(\\\"Warning: n_splits cannot be larger than the number of dates. Reducing n_splits to {}\\\".format(n_splits))\\n\\n per_model_res = {}\\n\\n datetimeindex = X.index.levels[0]\\n categories = X.index.levels[1]\\n for model in models:\\n counter = 0\\n metrics = _initialize_metrics(categories)\\n\\n tscv = TimeSeriesSplit(n_splits=n_splits)\\n if verbose:\\n print(\\\"Model: {}\\\".format(str(model)))\\n all_iterations_res = []\\n for train, test in tscv.split(datetimeindex):\\n if verbose:\\n print(\\\"Iteration: %s, Train size: %s, Test size: %s, Data size: %s \\\" % (\\n counter, len(train), len(test), len(datetimeindex)))\\n\\n train_samples = _prep_set(X, datetimeindex[train])\\n train_labels = _prep_set(y, datetimeindex[train])\\n\\n test_samples = _prep_set(X, datetimeindex[test])\\n test_labels = _prep_set(y, datetimeindex[test])\\n\\n if (test_labels is not None) and (len(test_labels) > 0):\\n # run the model\\n model.fit(X=train_samples, y=train_labels)\\n prediction = model.predict(X=test_samples)\\n\\n # evaluate results, aggregate metrics\\n metrics = get_evaluation_metrics(test_samples, prediction, test_labels, metrics,\\n value_col_name=value_col_name, label_col_name=label_col_name,\\n prediction_col_name=prediction_col_name,\\n window_size_for_metrics=window_size_for_metrics)\\n\\n counter += 1\\n\\n final_metrics = get_final_metrics(metrics)\\n if verbose:\\n print(final_metrics)\\n per_model_res[str(model.__name__)] = final_metrics\\n return per_model_res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ed21c0681e1d3532dcd65d9c427556\",\n \"score\": \"0.6263902\",\n \"text\": \"def cv_deep_learning_pred( X, y, nb_classes = 3, mode = \\\"CNNC_Name\\\", \\n\\t\\t\\t\\t\\t\\t\\t l = None, param_d = None, graph = True, n_splits = 5):\\n\\t\\n\\tKF = model_selection.KFold(n_splits, shuffle=True)\\n\\tkf = KF.split(X)\\n\\n\\tdcnn_score_l = []\\n\\tc_wb_l = []\\n\\ty_cv = np.copy( y)\\n\\tfor tr, te in kf:\\n\\t\\tX_train, y_train = X[tr,:], y[tr]\\n\\t\\tX_test, y_test = X[te,:], y[te]\\n\\t\\t\\n\\t\\tif mode == \\\"CNNC_Name\\\":\\n\\t\\t\\tmodel = kkeras.CNNC_Name( param_d[\\\"n_cv_flt\\\"], param_d[\\\"n_cv_ln\\\"], param_d[\\\"cv_activation\\\"],\\n\\t\\t\\t\\t\\tl=l)\\n\\t\\t\\tn_flt = param_d[\\\"n_cv_flt\\\"]\\n\\t\\telif mode == \\\"CNNC_Name_Border\\\":\\n\\t\\t\\tmodel = kkeras.CNNC_Name_Border( param_d[\\\"n_cv_flt\\\"], param_d[\\\"n_cv_ln\\\"], param_d[\\\"cv_activation\\\"],\\n\\t\\t\\t\\t\\tl=l, border_mode = 'valid')\\n\\t\\t\\tn_flt = param_d[\\\"n_cv_flt\\\"] \\n\\t\\telif mode == \\\"MLPC_Name\\\":\\n\\t\\t\\tmodel = kkeras.MLPC_Name(l=l)\\n\\t\\t\\tn_flt = l[1]\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"The given mode is not supported: mode={}\\\".format(mode))\\n\\t\\t\\t\\n\\t\\tmodel.fit( X_train, y_train, X_test, y_test, nb_classes, batch_size=6, nb_epoch = 5)\\n\\t\\ttr_score = model.score( X_train, y_train)\\n\\t\\t#dcnn_score_l.append(dcnn_score)\\n\\t\\tprint( \\\"Training Accuracy:\\\", tr_score)\\n\\n\\t\\tdcnn_score = model.score( X_test, y_test)\\n\\t\\tdcnn_score_l.append(dcnn_score)\\n\\t\\tprint( \\\"Testing Accuracy:\\\", dcnn_score)\\n\\t\\t\\n\\t\\tc_w, c_b = model.get_c_wb()\\n\\t\\tprint( \\\"c_w.shape=\\\", c_w.shape)\\n\\t\\tc_w = c_w.reshape(-1, n_flt)\\n\\t\\tc_wb_l.append( (c_w, c_b))\\n\\n\\t\\ty_cv[ te] = model.predict( X_test)\\n\\n\\tprint( dcnn_score_l)\\n\\tprint( \\\"Mean:{0}, Std:{1}\\\".format( np.mean( dcnn_score_l), np.std( dcnn_score_l)))\\n\\n\\tif graph:\\n\\t\\t# One of weight vectors are drawn.\\n\\t\\tc_w = c_wb_l[0][0] # 0 = c_w, 1 = c_b\\n\\t\\tfor ll in range(n_flt):\\n\\t\\t\\t#plt.figure()\\n\\t\\t\\tplt.plot( c_w[:,ll], label=\\\"Filter #{}\\\".format(ll))\\n\\t\\tplt.legend() \\n\\t\\n\\treturn dcnn_score_l, c_wb_l, y_cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc2febfba7f9fe66e990068a9da48f5\",\n \"score\": \"0.6254168\",\n \"text\": \"def compute_score_crossvalidation(self, model, n_splits):\\n\\n if n_splits is not None and isinstance(n_splits, int):\\n # we create a copy because we don't want to \\\"modify\\\" an already trained model\\n copy_model = cp.deepcopy(model)\\n copy_model.estimator = cp.deepcopy(self.base_estimator)\\n # copy_model.estimator.reset()\\n score = 0\\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=0)\\n for train_idx, test_idx in kf.split(X=self.X_chunk, y=self.y_chunk):\\n X_train, y_train = self.X_chunk[train_idx], self.y_chunk[train_idx]\\n X_test, y_test = self.X_chunk[test_idx], self.y_chunk[test_idx]\\n copy_model.estimator = self.train_model(\\n model=copy_model.estimator,\\n X=X_train,\\n y=y_train,\\n classes=copy_model.seen_labels,\\n sample_weight=None)\\n score += self.compute_score(model=copy_model, X=X_test, y=y_test) / self.n_splits\\n else:\\n # compute the score on the entire data chunk\\n score = self.compute_score(X=self.X_chunk, y=self.y_chunk, model=model)\\n\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474e07db18db453049714b335960899\",\n \"score\": \"0.6225613\",\n \"text\": \"def CV_learning(paras,n_splits,scoring='roc_auc'):\\n kf = StratifiedKFold(n_splits=n_splits, shuffle=True)\\n scores = []\\n pred_proba_group = []\\n try:\\n scorer = check_scoring(paras.model, scoring=scoring)\\n except:pass\\n for train, test in kf.split(paras.X,paras.y):\\n X_train = paras.X.iloc[train,:]\\n y_train = paras.y.iloc[train]\\n X_test = paras.X.iloc[test,:]\\n y_test = paras.y.iloc[test]\\n paras.model.fit(X_train,y_train)\\n pred_proba_tmp = DataFrame(cal_pred_prob(paras.model,X_test),index = X_test.index)\\n pred_proba_group.append(pred_proba_tmp)\\n try:\\n scores.append(scorer(paras.model,X_test,y_test))\\n except:\\n if scoring == 'aic':\\n scores.append(Criterion(y_test,pred_proba_tmp.iloc[:,1],len(X_test.columns)).AIC())\\n elif scoring == 'bic':\\n scores.append(Criterion(y_test,pred_proba_tmp.iloc[:,1],len(X_test.columns)).BIC())\\n else:\\n raise ValueError('%r is not a valid scoring value.' \\n 'Use sorted(sklearn.metrics.SCORERS.keys()' \\n 'to get valid options.' %(scoring))\\n pred_proba_all = concat(pred_proba_group).sort_index()\\n return(scores,pred_proba_group,pred_proba_all)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00c26c2ff72430e2d36022d70aa39fd\",\n \"score\": \"0.62111855\",\n \"text\": \"def results_classification(\\n model_class,\\n model_name,\\n run_id,\\n ensemble_folds,\\n test_set,\\n data_params,\\n robust,\\n device,\\n eval_type=\\\"checkpoint\\\",\\n):\\n\\n print(\\n \\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\n\\\"\\n \\\"------------Evaluate model on Test Set------------\\\\n\\\"\\n \\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\n\\\"\\n )\\n\\n test_generator = DataLoader(test_set, **data_params)\\n\\n y_pre_logits = []\\n y_logits = []\\n if robust:\\n y_pre_ale = []\\n\\n acc = np.zeros((ensemble_folds))\\n roc_auc = np.zeros((ensemble_folds))\\n precision = np.zeros((ensemble_folds))\\n recall = np.zeros((ensemble_folds))\\n fscore = np.zeros((ensemble_folds))\\n\\n for j in range(ensemble_folds):\\n\\n if ensemble_folds == 1:\\n resume = f\\\"models/{model_name}/{eval_type}-r{run_id}.pth.tar\\\"\\n print(\\\"Evaluating Model\\\")\\n else:\\n resume = f\\\"models/{model_name}/{eval_type}-r{j}.pth.tar\\\"\\n print(\\\"Evaluating Model {}/{}\\\".format(j + 1, ensemble_folds))\\n\\n assert os.path.isfile(resume), f\\\"no checkpoint found at '{resume}'\\\"\\n checkpoint = torch.load(resume, map_location=device)\\n assert (\\n checkpoint[\\\"model_params\\\"][\\\"robust\\\"] == robust\\n ), f\\\"robustness of checkpoint '{resume}' is not {robust}\\\"\\n\\n model = model_class(**checkpoint[\\\"model_params\\\"], device=device,)\\n model.to(device)\\n model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n\\n with torch.no_grad():\\n idx, comp, y_test, output = model.predict(generator=test_generator)\\n\\n if model.robust:\\n mean, log_std = output.chunk(2, dim=1)\\n logits = sampled_softmax(mean, log_std, samples=10).data.cpu().numpy()\\n pre_logits = mean.data.cpu().numpy()\\n pre_logits_std = torch.exp(log_std).data.cpu().numpy()\\n y_pre_ale.append(pre_logits_std)\\n else:\\n pre_logits = output.data.cpu().numpy()\\n\\n logits = softmax(pre_logits, axis=1)\\n\\n y_pre_logits.append(pre_logits)\\n y_logits.append(logits)\\n\\n y_test_ohe = np.zeros_like(pre_logits)\\n y_test_ohe[np.arange(y_test.size), y_test] = 1\\n\\n acc[j] = accuracy_score(y_test, np.argmax(logits, axis=1))\\n roc_auc[j] = roc_auc_score(y_test_ohe, logits)\\n precision[j], recall[j], fscore[j] = precision_recall_fscore_support(\\n y_test, np.argmax(logits, axis=1), average=\\\"weighted\\\"\\n )[:3]\\n\\n if ensemble_folds == 1:\\n print(\\\"\\\\nModel Performance Metrics:\\\")\\n print(\\\"Accuracy : {:.4f} \\\".format(acc[0]))\\n print(\\\"ROC-AUC : {:.4f}\\\".format(roc_auc[0]))\\n print(\\\"Weighted Precision : {:.4f}\\\".format(precision[0]))\\n print(\\\"Weighted Recall : {:.4f}\\\".format(recall[0]))\\n print(\\\"Weighted F-score : {:.4f}\\\".format(fscore[0]))\\n else:\\n acc_avg = np.mean(acc)\\n acc_std = np.std(acc)\\n\\n roc_auc_avg = np.mean(roc_auc)\\n roc_auc_std = np.std(roc_auc)\\n\\n precision_avg = np.mean(precision)\\n precision_std = np.std(precision)\\n\\n recall_avg = np.mean(recall)\\n recall_std = np.std(recall)\\n\\n fscore_avg = np.mean(fscore)\\n fscore_std = np.std(fscore)\\n\\n print(\\\"\\\\nModel Performance Metrics:\\\")\\n print(f\\\"Accuracy : {acc_avg:.4f} +/- {acc_std:.4f}\\\")\\n print(f\\\"ROC-AUC : {roc_auc_avg:.4f} +/- {roc_auc_std:.4f}\\\")\\n print(f\\\"Weighted Precision : {precision_avg:.4f} +/- {precision_std:.4f}\\\")\\n print(f\\\"Weighted Recall : {recall_avg:.4f} +/- {recall_std:.4f}\\\")\\n print(f\\\"Weighted F-score : {fscore_avg:.4f} +/- {fscore_std:.4f}\\\")\\n\\n # calculate metrics and errors with associated errors for ensembles\\n ens_logits = np.mean(y_logits, axis=0)\\n\\n y_test_ohe = np.zeros_like(ens_logits)\\n y_test_ohe[np.arange(y_test.size), y_test] = 1\\n\\n ens_acc = accuracy_score(y_test, np.argmax(ens_logits, axis=1))\\n ens_roc_auc = roc_auc_score(y_test_ohe, ens_logits)\\n ens_precision, ens_recall, ens_fscore = precision_recall_fscore_support(\\n y_test, np.argmax(ens_logits, axis=1), average=\\\"weighted\\\"\\n )[:3]\\n\\n print(\\\"\\\\nEnsemble Performance Metrics:\\\")\\n print(f\\\"Accuracy : {ens_acc:.4f} \\\")\\n print(f\\\"ROC-AUC : {ens_roc_auc:.4f}\\\")\\n print(f\\\"Weighted Precision : {ens_precision:.4f}\\\")\\n print(f\\\"Weighted Recall : {ens_recall:.4f}\\\")\\n print(f\\\"Weighted F-score : {ens_fscore:.4f}\\\")\\n\\n # NOTE we save pre_logits rather than logits due to fact that with the\\n # hetroskedastic setup we want to be able to sample from the gaussian\\n # distributed pre_logits we parameterise.\\n core = {\\\"id\\\": idx, \\\"composition\\\": comp, \\\"target\\\": y_test}\\n\\n results = {}\\n for n_ens, y_pre_logit in enumerate(y_pre_logits):\\n pred_dict = {\\n f\\\"class-{lab}-pred_{n_ens}\\\": val for lab, val in enumerate(y_pre_logit.T)\\n }\\n results.update(pred_dict)\\n if model.robust:\\n ale_dict = {\\n f\\\"class-{lab}-ale_{n_ens}\\\": val\\n for lab, val in enumerate(y_pre_ale[n_ens].T)\\n }\\n results.update(ale_dict)\\n\\n df = pd.DataFrame({**core, **results})\\n\\n if ensemble_folds == 1:\\n df.to_csv(\\n index=False,\\n path_or_buf=(f\\\"results/test_results_{model_name}_r-{run_id}.csv\\\"),\\n )\\n else:\\n df.to_csv(\\n index=False, path_or_buf=(f\\\"results/ensemble_results_{model_name}.csv\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63c8a0cfd8d7af83bbe0f0bda598ad6\",\n \"score\": \"0.619019\",\n \"text\": \"def test_train_split(clf, split):\\n\\n X_train, X_test, y_train, y_test, train_idx, test_idx = split\\n\\n clf.fit(X_train, y_train)\\n\\n probs = clf.predict_proba(X_test)\\n train_probs = clf.predict_proba(X_train)\\n\\n score = fmax(probs, y_test)\\n train_score = fmax(train_probs, y_train)\\n\\n return score, train_score, clf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689f9115883883d0a943417edb4a2c53\",\n \"score\": \"0.6190071\",\n \"text\": \"def compute_CV(self,X_train,Y_train,CVfolds=None,scoring='neg_mean_squared_error'):\\n ##- Select the number of folds for leave-one-out CV\\n if CVfolds is None:\\n CVfolds = X_train.shape[0]\\n\\n ##- Perform CV for both regressor performance evaluation and hyperparameter tuning\\n estimator = self.pipe\\n param_grid = get_params_dict(self.model)\\n model_grid = GridSearchCV(estimator,param_grid,scoring=scoring,cv=CVfolds,return_train_score=False)\\n model_grid.fit(X_train,Y_train)\\n\\n return model_grid.best_score_, model_grid.best_estimator_, model_grid.best_params_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104e0b1a30de1e5e8b35d33f0f554554\",\n \"score\": \"0.618767\",\n \"text\": \"def cross_validate_submodels(self, X, Y, cv=None, n_jobs=1):\\n d_submodel_results = defaultdict(OrderedDict)\\n for submodel in self.submodels:\\n # Get node data\\n attributes = self.get_submodel_field(submodel=submodel, field=\\\"attributes\\\")\\n clf = clone(self.get_submodel_field(submodel=submodel, field=\\\"clf\\\"))\\n\\n if cv is None:\\n assert self.get_submodel_field(submodel=submodel, field=\\\"cv\\\") is not None, \\\"`cv` must be provided in `add_submodel` if `cv is None` in this method\\\"\\n _cv = self.get_submodel_field(submodel=submodel, field=\\\"cv\\\")\\n else:\\n _cv = cv\\n index = self.get_submodel_field(submodel=submodel, field=\\\"index\\\")\\n self._check_cv(_cv, index)\\n\\n # If a custom cv is provided then there must be an index provided as well\\n condition_1 = index is not None\\n condition_2 = _cv is not None\\n if all([condition_1, condition_2]):\\n y_query = Y[submodel][index]\\n X_query = X.loc[index,attributes]\\n # If a integer cv is provided\\n else:\\n y_query = Y[submodel].dropna()\\n X_query = X.loc[y_query.index,attributes]\\n\\n # Stats\\n scores = model_selection.cross_val_score(clf, X=X_query.values, y=y_query.values, scoring=\\\"accuracy\\\", cv=_cv, n_jobs=n_jobs)\\n accuracy = np.mean(scores)\\n sem = stats.sem(scores)\\n # Update\\n self.set_submodel_field(submodel=submodel, field=\\\"accuracy\\\", obj=accuracy)\\n self.set_submodel_field(submodel=submodel, field=\\\"sem\\\", obj=sem)\\n self.submodel_performance[submodel] = accuracy\\n d_submodel_results[submodel][\\\"accuracy\\\"] = accuracy\\n d_submodel_results[submodel][\\\"sem\\\"] = sem\\n d_submodel_results[submodel][\\\"scores\\\"] = scores\\n\\n # Scores\\n df_performance = pd.DataFrame(d_submodel_results).T.loc[:,[\\\"accuracy\\\", \\\"sem\\\", \\\"scores\\\"]]\\n df_performance.index.name = \\\"submodel\\\"\\n self.add_weights_from_accuracy(df_performance[\\\"accuracy\\\"])\\n self._cross_validation_complete = True\\n return df_performance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fce7cb4d70249ff28204648e3ee2f3\",\n \"score\": \"0.6184491\",\n \"text\": \"def evaluate_models_with_kfold_validation(seed, models, X_train, Y_train, scoring, num_splits):\\n results = list()\\n names = list()\\n mean = int()\\n for name, model in models:\\n # rskf = model_selection.RepeatedStratifiedKFold(n_splits=num_kfold_splits, n_repeats=3, random_state=seed)\\n skf = KFold(n_splits=num_splits, random_state=seed)\\n cv_results = cross_val_score(model, X_train, Y_train, cv=skf, scoring=scoring)\\n results.append(cv_results)\\n names.append(name)\\n print(\\\"Algorithm {0} - Mean: {1:.3f}, Std.deviation: {2:.3f}\\\".format(name, cv_results.mean(), cv_results.std()))\\n if cv_results.mean() >= mean:\\n mean = cv_results.mean()\\n best_algorithm = name\\n return best_algorithm, results, names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf38a99d3b412fedbd6e0600ba402cd7\",\n \"score\": \"0.61775506\",\n \"text\": \"def evaluate_model(model, X_test, Y_test, category_names):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29fb188edf5bc71501a4e907cbf4311f\",\n \"score\": \"0.61558455\",\n \"text\": \"def train_model(X_s, Xb_s, X, Xb, model_type, hyper1,\\n hyper2, hyper3, n_cv=3, recall_thresh=0.8):\\n # iterate over hypers, cv\\n scores = []\\n hyp1_best = 0\\n hyp2_best = 0\\n hyp3_best = 0\\n score_best = 0\\n recall_best = 0\\n bckgnd_best = 0\\n hypers = list(itertools.product(*[hyper1, hyper2, hyper3]))\\n for h1, h2, h3 in hypers:\\n sc, rc, bg = [], [], []\\n for i in range(n_cv):\\n clf = initialize_model(model_type, h1, h2, h3)\\n X_train, X_test = random_train_test_split(X_s)\\n Xb_train, Xb_test = random_train_test_split(Xb_s)\\n if model_type == 'BiasedSVM':\\n # Pseudo-Outliers, see Baldeck et al. (2015)\\n PO, _ = random_train_test_split(np.concatenate((X_train,\\n Xb_train)))\\n X_ = np.concatenate((X_train, PO))\\n y_ = np.concatenate((np.ones(len(X_train)),\\n -1 * np.ones(len(PO))))\\n clf.fit(X_, y_)\\n else:\\n clf.fit(X_train)\\n y_pred_test = clf.predict(X_test)\\n y_pred_bkgnd = clf.predict(Xb_train)\\n sc_, rc_, bg_ = focal_score(y_pred_test, y_pred_bkgnd, h1, h2, h3)\\n sc.append(sc_)\\n rc.append(rc_)\\n bg.append(bg_)\\n meansc = np.mean(sc)\\n meanrc = np.mean(rc)\\n meanbg = np.mean(bg)\\n\\n if (meansc > score_best) and (meanrc > recall_thresh):\\n hyp1_best = h1\\n hyp2_best = h2\\n hyp3_best = h3\\n recall_best = meanrc\\n bckgnd_best = meanbg\\n score_best = meansc\\n scores.append([h1, h2, h3, meansc, meanrc, meanbg])\\n\\n # train best model\\n clf_best = initialize_model(model_type, hyp1_best, hyp2_best, hyp3_best)\\n if model_type == 'BiasedSVM':\\n PO, _ = random_train_test_split(np.concatenate((X_s, Xb_s)))\\n X_ = np.concatenate((X_s, PO))\\n y_ = np.concatenate((np.ones(len(X_s)), -1 * np.ones(len(PO))))\\n clf_best.fit(X_, y_)\\n else:\\n clf_best.fit(X_s)\\n\\n # write positive/negative classes to file\\n y_X = clf_best.predict(X_s)\\n y_Xb = clf_best.predict(Xb_s)\\n X_pos = np.concatenate((X[y_X == 1], Xb[y_Xb == 1])) # unscaled\\n X_neg = np.concatenate((X[y_X == -1], Xb[y_Xb == -1])) # unscaled\\n np.save('models/X_pos_unscaled_%s.npy' % model_type, X_pos)\\n np.save('models/X_neg_unscaled_%s.npy' % model_type, X_neg)\\n\\n # write/save stuff\\n clf_name = 'models/%s.pkl' % model_type\\n joblib.dump(clf_best, clf_name)\\n best = [clf_best, hyp1_best, hyp2_best, hyp3_best, score_best]\\n print(('best: hyp1=%f, hyp2=%f, hyp3=%f, recall=%f, bckgnd=%f, score=%f')\\n % (hyp1_best, hyp2_best, hyp3_best, recall_best, bckgnd_best, score_best))\\n return scores, best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b84b31c3822f200d08282adfcd6bd7c3\",\n \"score\": \"0.61540526\",\n \"text\": \"def evaluate(self, model):\\n model.eval()\\n cuda = torch.cuda.is_available()\\n Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\\n ids = []\\n data_dict = []\\n dataiterator = iter(self.dataloader)\\n while True: # all the data in val2017\\n try:\\n img, _, info_img, id_ = next(dataiterator) # load a batch\\n except StopIteration:\\n break\\n info_img = [float(info) for info in info_img]\\n id_ = int(id_)\\n ids.append(id_)\\n with torch.no_grad():\\n img = Variable(img.type(Tensor))\\n _, outputs = model(img)\\n outputs = outputs.unsqueeze(0)\\n outputs = postprocess(outputs, 80, self.confthre, self.nmsthre)\\n if outputs[0] is None:\\n continue\\n outputs = outputs[0].cpu().data\\n\\n for output in outputs:\\n x1 = float(output[0])\\n y1 = float(output[1])\\n x2 = float(output[2])\\n y2 = float(output[3])\\n label = self.dataset.class_ids[int(output[6])]\\n box = yolobox2label((y1, x1, y2, x2), info_img)\\n bbox = [box[1], box[0], box[3] - box[1], box[2] - box[0]]\\n score = float(\\n output[4].data.item() * output[5].data.item()\\n ) # object score * class score\\n A = {\\n \\\"image_id\\\": id_,\\n \\\"category_id\\\": label,\\n \\\"bbox\\\": bbox,\\n \\\"score\\\": score,\\n \\\"segmentation\\\": [],\\n } # COCO json format\\n data_dict.append(A)\\n\\n annType = [\\\"segm\\\", \\\"bbox\\\", \\\"keypoints\\\"]\\n\\n # Evaluate the Dt (detection) json comparing with the ground truth\\n if len(data_dict) > 0:\\n cocoGt = self.dataset.coco\\n # workaround: temporarily write data to json file because pycocotools can't process dict in py36.\\n _, tmp = tempfile.mkstemp()\\n json.dump(data_dict, open(tmp, \\\"w\\\"))\\n cocoDt = cocoGt.loadRes(tmp)\\n cocoEval = COCOeval(self.dataset.coco, cocoDt, annType[1])\\n cocoEval.params.imgIds = ids\\n cocoEval.evaluate()\\n cocoEval.accumulate()\\n cocoEval.summarize()\\n return cocoEval.stats[0], cocoEval.stats[1]\\n else:\\n return 0, 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368bd9be167c97eca8ec1c4b7ae3b4a4\",\n \"score\": \"0.61147386\",\n \"text\": \"def evaluate_model():\\n model = get_model()\\n X_train, X_test, y_train, y_test = get_data()\\n model.fit(X_train, y_train)\\n score = model.score(X_test, y_test)\\n print(\\\"Accuracy test set: %0.4f\\\" % score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde19c2cef44b6c6841d4b78c4fd068c\",\n \"score\": \"0.6097888\",\n \"text\": \"def kfold_cv(model, X, y, catg_names, n_splits=3):\\n\\n\\n # create splits\\n kf = KFold(n_splits=n_splits, shuffle=True, random_state=11)\\n\\n kfold = 0\\n for train_idx, test_idx in kf.split(X):\\n print('\\\\n')\\n print('-'*75)\\n print('\\\\nEvaluating Fold:', kfold)\\n\\n # define train and test subsets\\n X_train, y_train = X.loc[train_idx], y.loc[train_idx]\\n X_test, y_test = X.loc[test_idx], y.loc[test_idx]\\n # train and evaluate\\n model.fit(X_train.ravel(), y_train)\\n evaluate_model(model, X_test.ravel(), y_test.values, catg_names)\\n\\n del X_train, X_test, y_train, y_test\\n gc.collect()\\n\\n kfold += 1\\n\\n print('-'*75)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3a53247d276083da48393e2d49af94\",\n \"score\": \"0.6085852\",\n \"text\": \"def get_cv_and_modells(data, features, model, CV, scaled, folder, model_type): \\n\\n\\t# [1] Check Inputs:\\n\\t# [1-1] Data contains \\\"Respond\\\", \\\"sid\\\" & all names passed in 'features'\\n\\tfor _feat in features:\\n\\t\\tif _feat not in data.columns.values:\\n\\t\\t\\traise ValueError(_feat + \\\"is not a columname in data\\\")\\n\\t\\t\\t\\n\\tif \\\"Response\\\" not in data.columns.values:\\n\\t\\traise ValueError(\\\"data needs a 'Repsonse' Column\\\") \\n\\t\\t\\n\\tif \\\"sid\\\" not in data.columns.values:\\n\\t\\traise ValueError(\\\"data needs a 'sid' Column\\\") \\n\\t\\n\\t# [1-2] Check whether the model has the methods needed:\\n\\tif not hasattr(model, 'fit'): \\n\\t\\traise ValueError(\\\"model has no 'fit'-method\\\")\\n\\t\\t\\n\\tif not hasattr(model, 'predict'): \\n\\t\\traise ValueError(\\\"model has no 'predict'-method\\\")\\n\\t\\t\\n\\tif not hasattr(model, 'get_params'): \\n\\t\\traise ValueError(\\\"model has no 'get_params'-method\\\")\\n\\t\\n\\t# [1-3] CV should be of length two at least\\n\\tif len(CV) < 2: raise ValueError(\\\"CV has length < 2\\\")\\n\\t\\n\\t# [1-4] Check whether the folder to save results is existent\\n\\t# if it exists we count number of summaries [.csv-files] and assign \\n\\t# the number\\n\\tif not os.path.isdir(\\\"models/Multivariate Approach/\\\" + str(folder)):\\n\\t\\traise ValueError(str(folder) + \\\"is not existent in 'models/Multivariate Approach/'\\\")\\n\\telse:\\n\\t\\tnumb = 0\\n\\t\\tfor _files_ in os.listdir(\\\"models/Multivariate Approach/\\\" + str(folder)):\\n\\t\\t\\tif \\\".csv\\\" in _files_:\\n\\t\\t\\t\\tnumb = numb + 1\\t\\n\\t\\n\\t# [2] Start doing the CV:\\n\\t# [2-1] lists to save the results of the k-fold CV for all performance measures:\\n\\tF1 = []\\n\\tAccuracy = []\\n\\tPrecision = []\\n\\tConf_Matrix = []\\t\\n\\t\\n\\t# [2-2] Loop over the different Test/Train Splits\\n\\tprint(\\\"Start CV: \\\\n\\\")\\n\\t\\n\\t# Initalize a DF to save the predictions needed for stacking!\\n\\tres = pd.DataFrame()\\n\\tfor i in range(len(CV)):\\n\\t\\n\\t\\t# Print the current Process:\\n\\t\\tprint(\\\"CV number: \\\" + str(i + 1) + \\\" / \\\" + str(len(CV)))\\n\\t\\t\\n\\t\\t# Extract Test_Set based on the current CV:\\n\\t\\tcurrent_test = data.loc[data[\\\"sid\\\"].isin(CV[i]), features]\\n\\t\\tcurrent_test_response = data.loc[data[\\\"sid\\\"].isin(CV[i]), \\\"Response\\\"] \\n\\t\\tcurrent_test_index = data.loc[data[\\\"sid\\\"].isin(CV[i]), \\\"sid\\\"]\\n\\t\\t\\n\\t\\t# Extract SIDs we use for training & select correponding training points!\\n\\t\\ttrain_sids = []\\n\\t\\tfor j in range(len(CV)):\\n\\t\\t\\tif j != i:\\n\\t\\t\\t\\ttrain_sids = train_sids + CV[j]\\n\\t\\t\\t\\t\\n\\t\\tcurrent_train = data.loc[data[\\\"sid\\\"].isin(train_sids), features]\\n\\t\\tcurrent_train_response = data.loc[data[\\\"sid\\\"].isin(train_sids), \\\"Response\\\"] \\n\\t\\t\\n\\t\\t# Feature Scaling (only if \\\"scaled\\\" = True)\\n\\t\\tif scaled:\\n\\t\\t\\tscaler = StandardScaler()\\n\\t\\t\\t\\n\\t\\t\\tscaler.fit(current_test)\\n\\t\\t\\tcurrent_test = scaler.transform(current_test)\\n\\t\\t\\t\\n\\t\\t\\tscaler.fit(current_train)\\n\\t\\t\\tcurrent_train = scaler.transform(current_train)\\n\\t\\t\\t\\n\\t\\t# Fit the Model and get predcitions of the testset\\n\\t\\tmodel.fit(current_train, current_train_response)\\n\\t\\tpredictions = model.predict(current_test)\\n\\t\\t\\n\\t\\t# Add Scores to the corresponding lists:\\n\\t\\tF1.append(sklearn.metrics.f1_score(current_test_response, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t predictions,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t average=\\\"weighted\\\"))\\n\\t\\t\\n\\t\\tAccuracy.append(sklearn.metrics.accuracy_score(current_test_response, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t predictions))\\n\\t\\t\\n\\t\\tPrecision.append(sklearn.metrics.recall_score(current_test_response, \\n\\t\\t\\t\\t\\t\\t\\t predictions,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t average = \\\"weighted\\\"))\\n\\t\\t\\n\\t\\tConf_Matrix.append(sklearn.metrics.confusion_matrix(current_test_response, \\n\\t\\t\\t\\t\\t\\t\\t\\t predictions))\\n\\t\\t\\n\\t\\t\\n\\t\\t# If predicting probabilities works, we add them as metafeatures to\\n\\t\\t# our metafile, else we will use only the predicted class & save it!\\n\\t\\t# Add also the fold of the current TestSIDs\\n\\t\\tif hasattr(model, 'predict_proba'):\\n\\t\\t\\tpredictions_prob = model.predict_proba(current_test)\\n\\t\\t\\t\\n\\t\\t\\t# join the predicitons w/ the current fold number\\n\\t\\t\\tcolstoadd = np.append(predictions_prob,\\n\\t\\t\\t\\t\\t\\t np.repeat((i+1), len(predictions_prob)).reshape(len(predictions_prob), 1), 1)\\n\\t\\t\\t \\n\\t\\t\\tcolnames = [\\\"M_prob_\\\" + str(_i) for _i in range(12)]\\n\\t\\t\\tcolnames.append(\\\"fold\\\")\\n\\t\\t\\t\\n\\t\\t\\tres = pd.concat([res, pd.DataFrame(colstoadd,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t columns = colnames, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t index = current_test_index)])\\n\\t\\n\\t\\telse:\\n\\t\\t\\tcolnames = [\\\"M_1\\\", \\\"fold\\\"]\\n\\t\\t\\t\\n\\t\\t\\t# join the predicitons w/ the current fold number\\n\\t\\t\\tcolstoadd = np.column_stack((predictions, \\n\\t\\t\\t\\t\\t\\t np.repeat((i+1), len(predictions)).reshape(len(predictions), 1)))\\n\\t\\t\\t\\n\\t\\t\\tres = pd.concat([res, pd.DataFrame(colstoadd,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t columns = colnames,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t index = current_test_index)])\\n\\t\\t\\n\\tres.to_csv(\\\"models/Multivariate Approach/\\\" + str(folder) + \\\"/CV_Predicitons\\\" + str(numb) + \\\".csv\\\")\\n\\t\\t\\n\\t\\n\\tprint(\\\"CV done\\\\n\\\")\\n\\t# [3] Save the Results:\\n\\t# [3-1] Extract ParameterSettings\\n\\t_params = json.dumps(model.get_params())\\n\\t\\n\\t# [3-2] Create BasicShape for the Result .csv\\n\\tdict_to_pd = {'model_type' : model_type, 'parameters' : _params,\\n\\t\\t\\t 'features' : \\\" - \\\".join(features), \\\"Number\\\": numb}\\n\\t\\n\\t# [3-3] Add CV-Scores to the Dict:\\n\\tfor index in range(len(F1)):\\n\\t\\tdict_to_pd[\\\"F1_\\\" + str(index + 1)] = F1[index]\\n\\t\\tdict_to_pd[\\\"Acc_\\\" + str(index + 1)] = Accuracy[index]\\n\\t\\tdict_to_pd[\\\"Precision_\\\" + str(index + 1)] = Precision[index]\\n\\t\\tdict_to_pd[\\\"Conf_\\\" + str(index + 1)] = Conf_Matrix[index]\\n\\t\\t\\n\\t# [3-4] Add mean of the scores [except for Confmatrix]\\n\\tdict_to_pd[\\\"F1_mean\\\"] = np.mean(F1)\\n\\tdict_to_pd[\\\"Acc_mean\\\"] = np.mean(Accuracy)\\n\\tdict_to_pd[\\\"Prec_mean\\\"] = np.mean(Precision)\\n\\t\\n\\t# [3-5] Transform it to pandas, order the columns and save it: \\n\\tpd_res = pd.DataFrame([dict_to_pd])\\n\\t\\t\\t\\n\\tpd_res.to_csv(\\\"models/Multivariate Approach/\\\" + str(folder) + \\\"/Summary\\\" + str(numb) + \\\".csv\\\")\\n\\t\\n\\t\\\"\\\"\\\"\\n\\tCommented out, as it takes a lot of memory which leads to errors in the VM....\\n\\t# [4] Train the final model [on all train data] and save it:\\n\\tprint(\\\"train the final model\\\")\\n\\tmodel.fit(data.loc[:, features], data[\\\"Response\\\"])\\n\\t\\n\\tfinal_model_name = \\\"models/Multivariate Approach/\\\"+ folder + \\\"/Final_Mod\\\" + str(numb) + \\\".pickle\\\"\\n\\tpickle.dump(model, open(final_model_name, 'wb'))\\n\\t\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5395a1726f3406f39679858232fbdd80\",\n \"score\": \"0.6078891\",\n \"text\": \"def evaluate(self, splits=['train', 'valid', 'test'], best=True, final=True):\\n if not self.args.save:\\n logging.info('No model saved! Cannot give final status.')\\n return\\n\\n # Evaluate final model (at end of training)\\n if final:\\n logging.info('Getting predictions for model in last checkpoint.')\\n\\n # Load checkpoint model to make predictions\\n checkpoint = torch.load(self.args.checkfile)\\n self.model.load_state_dict(checkpoint['model_state'])\\n\\n # Loop over splits, predict, and output/log predictions\\n for split in splits:\\n predict, targets = self.predict(split)\\n self.log_predict(predict, targets, split, description='Final')\\n\\n # Evaluate best model as determined by validation error\\n if best:\\n logging.info('Getting predictions for best model.')\\n\\n # Load best model to make predictions\\n checkpoint = torch.load(self.args.bestfile)\\n self.model.load_state_dict(checkpoint['model_state'])\\n\\n # Loop over splits, predict, and output/log predictions\\n for split in splits:\\n predict, targets = self.predict(split)\\n self.log_predict(predict, targets, split, description='Best')\\n\\n logging.info('Inference phase complete!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b9e2edcb04c4761b6e6eff5d553f913\",\n \"score\": \"0.6069931\",\n \"text\": \"def _eval_model(model: Model, X, y) -> None:\\n logger.info(f\\\"Step - Evaluating {model.name} model BEGIN\\\")\\n cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=RANDOM_STATE)\\n cross_val_score(model.model, X, ravel_y(y), scoring='f1_macro', cv=cv, verbose=10, n_jobs=-1)\\n logger.info(f\\\"Step - Evaluating {model.name} model {timer.time_stage(f'{model.name} Evaluation')} END\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4e46a6d131e30826c65948659e558ef\",\n \"score\": \"0.60392827\",\n \"text\": \"def eval_model(self, model):\\n # FIXME ugly condition branches\\n if self.model_class == \\\"PoincareNIPS\\\":\\n alphas_to_validate = [1000, 100, 30, 10, 3, 1, 0.3, 0.1, 0]\\n elif self.model_class == \\\"HypCones\\\":\\n # FIXME Invalid use of variable: K is passed instead of alpha.\\n alphas_to_validate = [model.K]\\n else:\\n # FIXME Meaningless value is passed if alpha is not needed.\\n alphas_to_validate = [-1e12]\\n \\n input_data = self.input()[\\\"data\\\"]\\n\\n # Validation\\n eval_result_classif, best_alpha, _, best_test_f1, best_valid_f1 = evaluations.eval_classification(\\n logger=self.logger,\\n task=self.task_type,\\n valid_pos_path=input_data[\\\"valid_pos\\\"].path,\\n valid_neg_path=input_data[\\\"valid_neg\\\"].path,\\n test_pos_path=input_data[\\\"test_pos\\\"].path,\\n test_neg_path=input_data[\\\"test_neg\\\"].path,\\n vocab=model.kv.vocab,\\n score_fn=model.kv.is_a_scores_from_indices,\\n alphas_to_validate=alphas_to_validate, # 0 means only distance\\n )\\n\\n print(eval_result_classif)\\n result_str = evaluations.pretty_print_eval_map(eval_result_classif)\\n self.logger.info('BEST classification ALPHA = %.3f' % best_alpha)\\n self.logger.info(result_str)\\n return float(best_test_f1), float(best_valid_f1), result_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7075338bf20af8a39e3b8b40769f9fc7\",\n \"score\": \"0.6034869\",\n \"text\": \"def model_train_evaluation(train_test_2c, train_test_3c, classifier_method):\\r\\n if classifier_method == 'knn':\\r\\n # Selects and fits the k-nearest neighbors classifier\\r\\n from sklearn.neighbors import KNeighborsClassifier\\r\\n classifer_2c = KNeighborsClassifier(n_neighbors=5)\\r\\n classifer_2c.fit(train_test_2c[0], train_test_2c[2])\\r\\n\\r\\n classifer_3c = KNeighborsClassifier(n_neighbors=5)\\r\\n classifer_3c.fit(train_test_3c[0], train_test_3c[2])\\r\\n\\r\\n elif classifier_method == 'svm':\\r\\n # Selects and fits the SVM neighbors classifier (must be feature scaled)\\r\\n from sklearn.preprocessing import StandardScaler\\r\\n sc_2c = StandardScaler()\\r\\n train_test_2c[0] = sc_2c.fit_transform(train_test_2c[0]) # 2c train\\r\\n train_test_2c[1] = sc_2c.transform(train_test_2c[1]) # 2c test\\r\\n\\r\\n sc_3c = StandardScaler()\\r\\n train_test_3c[0] = sc_3c.fit_transform(train_test_3c[0]) # 3c train\\r\\n train_test_3c[1] = sc_3c.transform(train_test_3c[1]) # 3c test\\r\\n\\r\\n from sklearn.svm import SVC\\r\\n classifer_2c = SVC(random_state=42)\\r\\n classifer_2c.fit(train_test_2c[0], train_test_2c[2])\\r\\n\\r\\n classifer_3c = SVC(random_state=42)\\r\\n classifer_3c.fit(train_test_3c[0], train_test_3c[2])\\r\\n\\r\\n elif classifier_method == 'nb':\\r\\n # Selects and fits the naive Bayes neighbors classifier\\r\\n from sklearn.naive_bayes import GaussianNB\\r\\n classifer_2c = GaussianNB()\\r\\n classifer_2c.fit(train_test_2c[0], train_test_2c[2])\\r\\n\\r\\n classifer_3c = GaussianNB()\\r\\n classifer_3c.fit(train_test_3c[0], train_test_3c[2])\\r\\n\\r\\n\\r\\n elif classifier_method == 'dt':\\r\\n # Selects and fits the decesion tree classifier\\r\\n from sklearn. tree import DecisionTreeClassifier\\r\\n classifer_2c = DecisionTreeClassifier(criterion='entropy', random_state=42)\\r\\n classifer_2c.fit(train_test_2c[0], train_test_2c[2])\\r\\n\\r\\n classifer_3c = DecisionTreeClassifier(criterion='entropy', random_state=42)\\r\\n classifer_3c.fit(train_test_3c[0], train_test_3c[2]) \\r\\n\\r\\n elif classifier_method == 'rf':\\r\\n # Selects and fits the random forest classifier\\r\\n from sklearn.ensemble import RandomForestClassifier\\r\\n classifer_2c = RandomForestClassifier(criterion='entropy', random_state=42)\\r\\n classifer_2c.fit(train_test_2c[0], train_test_2c[2])\\r\\n\\r\\n classifer_3c = RandomForestClassifier(criterion='entropy', random_state=42)\\r\\n classifer_3c.fit(train_test_3c[0], train_test_3c[2])\\r\\n\\r\\n # Predict test data\\r\\n y_pred_2c = classifer_2c.predict(train_test_2c[1])\\r\\n y_pred_3c = classifer_3c.predict(train_test_3c[1])\\r\\n\\r\\n # Calculate confusion matrix and normalized accuracy\\r\\n from sklearn import metrics\\r\\n cm_2c = metrics.confusion_matrix(train_test_2c[3], y_pred_2c)\\r\\n acc_2c = metrics.accuracy_score(train_test_2c[3], y_pred_2c, normalize=True)\\r\\n\\r\\n cm_3c = metrics.confusion_matrix(train_test_3c[3], y_pred_3c)\\r\\n acc_3c = metrics.accuracy_score(train_test_3c[3], y_pred_3c, normalize=True)\\r\\n\\r\\n # Perform k-fold cross validation for improved accuracy score\\r\\n from sklearn.model_selection import cross_val_score\\r\\n accuracies_2c = cross_val_score(classifer_2c, train_test_2c[0], train_test_2c[2], cv=10, n_jobs=-1)\\r\\n kfold_acc_2c = accuracies_2c.mean()\\r\\n kfold_std_2c = accuracies_2c.std()\\r\\n\\r\\n accuracies_3c = cross_val_score(classifer_3c, train_test_3c[0], train_test_3c[2], cv=10, n_jobs=-1)\\r\\n kfold_acc_3c = accuracies_3c.mean()\\r\\n kfold_std_3c = accuracies_3c.std()\\r\\n\\r\\n evaluation_results = [cm_2c, acc_2c, cm_3c, acc_3c, kfold_acc_2c, kfold_std_2c, kfold_acc_3c, kfold_std_3c]\\r\\n\\r\\n return evaluation_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c006e2f47be46f18ead3ff0f89fdf5\",\n \"score\": \"0.6032966\",\n \"text\": \"def my_cross_validate(X, Y,\\n mltype,\\n model_name='lgb_reg',\\n cv=5, groups=None,\\n lr_curve_ticks=5, data_sizes_frac=None,\\n args=None, fit_params=None, init_params=None,\\n n_jobs=1, random_state=None, logger=None, outdir='.'):\\n X = pd.DataFrame(X).values\\n Y = pd.DataFrame(Y).values\\n\\n # TODO: didn't test!\\n if isinstance(cv, int) and groups is None:\\n cv_folds = cv\\n cv = KFold(n_splits=cv_folds, shuffle=False, random_state=random_state)\\n if isinstance(cv, int) and groups is not None:\\n cv_folds = cv\\n cv = GroupKFold(n_splits=cv_folds)\\n else:\\n cv_folds = cv.get_n_splits()\\n\\n if is_string_dtype(groups):\\n group_encoder = LabelEncoder()\\n groups = group_encoder.fit_transform(groups)\\n \\n # ... Now start a nested loop of train size and cv folds ...\\n tr_scores_all = [] # list dicts\\n vl_scores_all = [] # list dicts\\n\\n if mltype == 'cls':\\n if Y.ndim > 1 and Y.shape[1] > 1:\\n splitter = cv.split(X, np.argmax(Y, axis=1), groups=groups)\\n else:\\n splitter = cv.split(X, Y, groups=groups)\\n elif mltype == 'reg':\\n splitter = cv.split(X, Y, groups=groups)\\n\\n # Placeholder to save the best model\\n best_model = None\\n best_score = -np.Inf\\n\\n # Start CV iters\\n for fold_id, (tr_idx, vl_idx) in enumerate(splitter):\\n if logger is not None:\\n logger.info(f'Fold {fold_id+1}/{cv_folds}')\\n\\n # Samples from this dataset are sampled for training\\n xtr = X[tr_idx, :]\\n ytr = np.squeeze(Y[tr_idx, :])\\n\\n # A fixed set of validation samples for the current CV split\\n xvl = X[vl_idx, :]\\n yvl = np.squeeze(Y[vl_idx, :]) \\n\\n # # Confirm that group splits are correct ...\\n # tr_grps_unq = set(groups[tr_idx])\\n # vl_grps_unq = set(groups[vl_idx])\\n # print('Total group (e.g., cell) intersections btw tr and vl: ', len(tr_grps_unq.intersection(vl_grps_unq)))\\n # print('A few intersections : ', list(tr_grps_unq.intersection(vl_grps_unq))[:3])\\n\\n # Get the estimator\\n estimator = ml_models.get_model(model_name=model_name, init_params=init_params)\\n\\n if 'nn' in model_name:\\n from keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard\\n\\n # Create output dir\\n out_nn_model = outdir / ('cv'+str(fold_id+1))\\n os.makedirs(out_nn_model, exist_ok=False)\\n \\n # Callbacks (custom)\\n clr_triangular = CyclicLR(base_lr=0.0001, max_lr=0.001, mode='triangular')\\n \\n # Keras callbacks\\n checkpointer = ModelCheckpoint(str(out_nn_model / 'autosave.model.h5'), verbose=0, save_weights_only=False, save_best_only=True)\\n csv_logger = CSVLogger(out_nn_model / 'training.log')\\n reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.75, patience=20, verbose=1, mode='auto',\\n min_delta=0.0001, cooldown=3, min_lr=0.000000001)\\n early_stop = EarlyStopping(monitor='val_loss', patience=60, verbose=1, mode='auto')\\n \\n # Callbacks list\\n callback_list = [checkpointer, csv_logger, early_stop, reduce_lr, # keras callbacks\\n clr_triangular] # custom callbacks\\n\\n # Fit params\\n fit_params['validation_data'] = (xvl, yvl)\\n fit_params['callbacks'] = callback_list\\n\\n # Train model\\n history = estimator.model.fit(xtr, ytr, **fit_params)\\n\\n # Calc preds and scores TODO: dump preds\\n # ... training set\\n y_preds, y_true = utils.calc_preds(estimator=estimator.model, xdata=xtr, ydata=ytr, mltype=mltype)\\n tr_scores = utils.calc_scores(y_true=y_true, y_preds=y_preds, mltype=mltype)\\n # ... val set\\n y_preds, y_true = utils.calc_preds(estimator=estimator.model, xdata=xvl, ydata=yvl, mltype=mltype)\\n vl_scores = utils.calc_scores(y_true=y_true, y_preds=y_preds, mltype=mltype)\\n\\n # Save the best model\\n if mltype == 'cls':\\n vl_scores['f1_score'] > best_score\\n best_score = vl_scores['f1_score']\\n best_model = estimator\\n elif mltype == 'reg':\\n vl_scores['r2'] > best_score\\n best_score = vl_scores['r2']\\n best_model = estimator\\n\\n # Plot training curves\\n if 'nn' in model_name:\\n ml_models.plot_prfrm_metrics(history=history, title=f'cv fold: {fold_id+1}',\\n skip_epochs=1, add_lr=True, outdir=out_nn_model)\\n\\n # Add info\\n tr_scores['tr_set'] = True\\n vl_scores['tr_set'] = False\\n tr_scores['fold'] = 'f'+str(fold_id)\\n vl_scores['fold'] = 'f'+str(fold_id)\\n\\n # Aggregate scores\\n tr_scores_all.append(tr_scores)\\n vl_scores_all.append(vl_scores)\\n\\n # Delete the estimator/model\\n del estimator, history\\n\\n # Comet log fold scores\\n # https://medium.com/comet-ml/building-reliable-machine-learning-models-with-cross-validation-20b2c3e32f3e\\n# if (args is not None) and ('comet' in args):\\n# experiment = args['comet']\\n# experiment.log_metric('Fold {}'.format(fold_id), vl_scores['r2'])\\n \\n tr_df = scores_to_df(tr_scores_all)\\n vl_df = scores_to_df(vl_scores_all)\\n scores_all_df = pd.concat([tr_df, vl_df], axis=0)\\n\\n\\n # Comet log fold scores\\n# if (args is not None) and ('comet' in args):\\n# experiment = args['comet']\\n# experiment.log_metric('Best score', best_score)\\n\\n return scores_all_df, best_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c47d4bc252584e236e92931d25145b9\",\n \"score\": \"0.6029732\",\n \"text\": \"def run_models(self, X, y, param_ridge):\\n\\n ##################################\\n ## OLS CV\\n ##################################\\n #ols = linear_model.LinearRegression(fit_intercept=True,\\n # normalize=False,\\n # copy_X=True)\\n #ols_cv_score = cross_validation.cross_val_score(\\n # ols, X, y,\\n # cv=self.cv, scoring=self.scoring,\\n # n_jobs=self.n_jobs)\\n \\\"\\\"\\\"\\n self.ols_cv_score.shape = (cv,)\\n \\\"\\\"\\\"\\n\\n ##################################\\n ## PLS CV\\n ##################################\\n tuned_parameters = [{'n_components': range(1, 5)}]\\n pls = PLSRegression()\\n pls_cv = GridSearchCV(pls, tuned_parameters,\\n cv=self.cv, scoring=self.scoring,\\n n_jobs=self.n_jobs,\\n refit=self.refit, iid=self.iid)\\n pls_cv.fit(X, y)\\n\\n\\n ##################################\\n ## Ridge CV\\n ##################################\\n tuned_parameters = [{'alpha': param_ridge}]\\n ridge = linear_model.Ridge(alpha = 1)\\n ridge_cv = GridSearchCV(ridge, tuned_parameters,\\n cv=self.cv, scoring=self.scoring,\\n n_jobs=self.n_jobs,\\n refit=self.refit, iid=self.iid)\\n ridge_cv.fit(X, y)\\n\\n return (pls_cv, ridge_cv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13d98ccb51bdef868d14237dc00a5e7\",\n \"score\": \"0.6027703\",\n \"text\": \"def run_cv_pred(X, y, clf, n_folds, name, results):\\n # Construct a kfolds object\\n kf = KFold(n_splits=n_folds)\\n splits = kf.split(X, y)\\n y_pred = y.copy()\\n\\n # Iterate through folds\\n for idx, (train_index, test_index) in enumerate(splits):\\n X_train, X_test = X[train_index], X[test_index]\\n y_train = y[train_index]\\n # Initialize a classifier with key word arguments\\n clf.fit(X_train, y_train)\\n try: # Gradient boosted trees do not accept sparse matrices in the predict function currently\\n probs = clf.predict_proba(X_test)\\n except TypeError:\\n probs = clf.predict_proba(X_test.todense())\\n macro, micro = mle.evaluate(probs, y[test_index])\\n print 'macro F1, micro F1', macro, micro\\n results[0].loc[name, idx] = macro\\n results[1].loc[name, idx] = micro\\n # y_pred[test_index] = preds\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04360f748e0a67f6aeddf26c0aeb4c56\",\n \"score\": \"0.5967814\",\n \"text\": \"def perform_CV(self, CV = 5, query ='*'):\\n if not self.trained_models.keys():\\n raise AttributeError(\\\"Models not trained. First call trading_train()\\\")\\n\\n for i, (coin, _ ) in enumerate(self.coins_ratios):\\n sql_features = \\\"\\\"\\\" SELECT {}\\n from crypto_main.features_1h_5m\\n where symbol = '{}'\\n and entry_time between '{}' and '{}'\\n order by entry_time asc;\\n \\\"\\\"\\\".format(query, self.coin_dict[coin][0], self.timeperiod_2, self.date_test)\\n test_data = pd.read_sql(sql=sql_features, con=con_2)\\n test_features = (test_data.drop(labels=['entry_time', 'symbol'], axis=1)).fillna(0)\\n min_time = test_data['entry_time'].min() - datetime.timedelta(hours=2)\\n max_time = test_data['entry_time'].max() + datetime.timedelta(hours=2)\\n\\n sql_price = \\\"\\\"\\\" SELECT reporting_date, price_eur, price_usd, price_btc\\n FROM crypto_main.currency_hist_per_5_minute\\n where currency_id = '{}'\\n and reporting_date between '{}' and '{}'\\n order by reporting_date asc;\\n \\\"\\\"\\\".format(self.coin_dict[coin][1], min_time, max_time)\\n price_df = pd.read_sql(sql=sql_price, con=con_2)\\n time_df = test_data['entry_time'] + self.lag\\n price_df_temp = price_df.iloc[price_df['reporting_date'].searchsorted(time_df)]\\n price_df_temp['change'] = price_df['price_eur'].diff().fillna(1)\\n price_df_temp['change_bool'] = np.sign(price_df_temp['change'])\\n\\n cv_df = pd.DataFrame(index=range(CV * len(self.models)))\\n entries = []\\n for model in self.models:\\n model_name = model\\n accuracies = cross_val_score(self.models[model], test_features, price_df_temp['change_bool'], scoring='accuracy', cv=CV)\\n for fold_idx, accuracy in enumerate(accuracies):\\n entries.append((model_name, fold_idx, accuracy))\\n cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])\\n\\n fig_1, ax_1 = plt.subplots(figsize=(10,10))\\n sns.boxplot(x='model_name', y='accuracy', data=cv_df)\\n sns.stripplot(x='model_name', y='accuracy', data=cv_df,\\n size=8, jitter=True, edgecolor=\\\"gray\\\", linewidth=2)\\n plt.title('Boxplot of CV scores of {} with a time delay of {} min'.format(coin, int((self.lag.seconds)/60)))\\n return fig_1\\n #plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9299571f2a15f83b95fe35120094822\",\n \"score\": \"0.59625655\",\n \"text\": \"def launch_for_splits(self, model, parameter_grid, cv_split_filenames,\\r\\n pre_warm=True, collect_files_on_reset=False):\\r\\n\\r\\n # Abort any existing processing and erase previous state\\r\\n self.reset()\\r\\n self.parameter_grid = parameter_grid\\r\\n\\r\\n # Mark the files for garbage collection\\r\\n if collect_files_on_reset:\\r\\n self._temp_files.extend(cv_split_filenames)\\r\\n\\r\\n # Warm the OS disk cache on each host with sequential reads instead\\r\\n # of having concurrent evaluation tasks compete for the the same host\\r\\n # disk resources later.\\r\\n if pre_warm:\\r\\n warm_mmap_on_cv_splits(self.lb_view.client, cv_split_filenames)\\r\\n\\r\\n # Randomize the grid order\\r\\n random_state = check_random_state(self.random_state)\\r\\n self.all_parameters = list(ParameterGrid(parameter_grid))\\r\\n random_state.shuffle(self.all_parameters)\\r\\n\\r\\n for params in self.all_parameters:\\r\\n task_group = []\\r\\n\\r\\n for cv_split_filename in cv_split_filenames:\\r\\n task = self.lb_view.apply(\\r\\n compute_evaluation,\\r\\n model, cv_split_filename, params=params)\\r\\n task_group.append(task)\\r\\n\\r\\n self.task_groups.append(task_group)\\r\\n\\r\\n # Make it possible to chain method calls\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dca8a5fffc7f1297e8d80fdd576366c\",\n \"score\": \"0.5959495\",\n \"text\": \"def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),\\n obj=None, feval=None, fpreproc=None, show_stdv=True, seed=0):\\n results = []\\n cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)\\n for i in range(num_boost_round):\\n for f in cvfolds:\\n f.update(i, obj)\\n res = aggcv([f.eval(i, feval) for f in cvfolds], show_stdv)\\n sys.stderr.write(res + '\\\\n')\\n results.append(res)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beec1f3f4df9b9c90821338c2be18f2e\",\n \"score\": \"0.59589815\",\n \"text\": \"def evaluate_nested_cv(self, clf: BaseEstimator, embedding, n_splits):\\n roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True, needs_proba=True)\\n grid_search = GridSearchCV(\\n clf,\\n NESTED_CV_PARAMETERS,\\n scoring=roc_auc_scorer,\\n cv=n_splits,\\n n_jobs=-1\\n )\\n embedding = embedding[self.label_ind, :]\\n best_params = Counter()\\n results = defaultdict(list)\\n for i in range(10):\\n rskf = StratifiedKFold(n_splits=n_splits, shuffle=True)\\n for train_idx, test_idx in rskf.split(embedding, self.labels):\\n x_train, x_test, y_train, y_test, w_train = self._get_split(embedding, test_idx, train_idx)\\n grid_search.fit(x_train, y_train, sample_weight=w_train)\\n clf.C = grid_search.best_params_['C']\\n clf.class_weight = grid_search.best_params_['class_weight']\\n\\n cw_idx = NESTED_CV_PARAMETERS['class_weight'].index(clf.class_weight)\\n best_params[('C', clf.C)] += 1\\n best_params[('cw', cw_idx)] += 1\\n best_params[(clf.C, cw_idx)] += 1\\n\\n pred, probs = self.get_predictions(\\n clf,\\n x_train,\\n x_test,\\n y_train,\\n y_test,\\n sample_weights=w_train\\n )\\n self._assemble_results(y_test, i, pred, probs[:, 1], results)\\n logger.debug('Best parameters for nested cross validation are:')\\n logger.debug(best_params)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ac64c7f5e50492affc43ea46e2e257\",\n \"score\": \"0.5952746\",\n \"text\": \"def nested_cv(estimator, X, y, groups=None, param_distributions=None, n_iter=20, internal_n_folds=5,\\n internal_total_folds=None, external_n_folds=5, external_total_folds=None, n_jobs=-1, scoring='f1'):\\n # convert groups and labels\\n if isinstance(y, pd.Series):\\n y = y.values\\n if isinstance(groups, pd.Series):\\n groups = groups.values\\n\\n # select defaults\\n if groups is None:\\n groups = np.arange(y.shape[0])\\n if internal_total_folds is None:\\n internal_total_folds = internal_n_folds\\n if external_total_folds is None:\\n external_total_folds = external_n_folds\\n\\n # get the external splits\\n splits = generate_grouped_splits(X, y, groups, total_folds=external_total_folds,\\n n_folds=external_n_folds)\\n\\n # list in which to store all cv results\\n all_cv_results = []\\n\\n # get the scorer class for the metrics\\n scorer = get_scorer(scoring)\\n scores = np.zeros(external_n_folds, dtype='float32')\\n\\n for run_nb, split in zip(range(external_n_folds), splits):\\n logger.info('Model selection on fold number {}...'.format(run_nb))\\n\\n # split the dataset\\n if isinstance(X, pd.DataFrame):\\n X_train, X_test = X.iloc[split[0]], X.iloc[split[1]]\\n else:\\n X_train, X_test = X[split[0]], X[split[1]]\\n y_train, y_test = y[split[0]], y[split[1]]\\n groups_train, groups_test = groups[split[0]], groups[split[1]]\\n\\n # do the internal loop, pass the corresponding groups\\n best_params, cv_results = search_params(estimator, X_train, y_train, groups=groups_train,\\n param_distributions=param_distributions,\\n n_iter=n_iter, n_folds=internal_n_folds,\\n total_folds=internal_total_folds, n_jobs=n_jobs,\\n scoring=scoring)\\n\\n # refit the the best estimator with all the data\\n logger.info('Refitting estimator with best params...')\\n best_est = estimator\\n best_est.set_params(**best_params) # set as kwargs!\\n best_est.fit(X_train, y_train)\\n\\n # add the score to the list of all scores\\n scores[run_nb] = scorer(best_est, X_test, y_test)\\n\\n # log the result\\n logger.info('SCORE FOR BEST ESTIMATOR ON FOLD NUMBER {} = {}'.format(run_nb, scores[run_nb]))\\n\\n # add the cross validation dataframe to the list\\n cv_results['run_nb'] = run_nb\\n all_cv_results.append(cv_results)\\n\\n return scores, pd.concat(all_cv_results, ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cbeec6b9d3725a5bac21d864d64c1b8\",\n \"score\": \"0.5947677\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\\n ])\\n max_features = ['auto', 'sqrt']\\n max_depth = [int(x) for x in np.linspace(10, 100, num = 4)] \\n max_depth.append(None)\\n min_samples_split = [2, 5, 10]\\n min_samples_leaf = [1, 2, 4]\\n bootstrap = [True, False]\\n parameters = { 'clf__estimator__max_features': max_features,\\n 'clf__estimator__max_depth': max_depth,\\n 'clf__estimator__min_samples_split': min_samples_split,\\n 'clf__estimator__min_samples_leaf': min_samples_leaf,\\n 'clf__estimator__bootstrap': bootstrap}\\n\\n\\n cv = GridSearchCV(pipeline, param_grid=parameters ,cv = 3)\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9a938c37d6dd18d43396529f90ad93\",\n \"score\": \"0.5946431\",\n \"text\": \"def launch_cv(self):\\n self.launch_op(op='cv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d523bb90410c3c00a7354708adfedde\",\n \"score\": \"0.5938454\",\n \"text\": \"def evaluate(args, model, new_model_dir, features, epoch, global_step, best_score):\\n y_true, y_pred, eval_loss = _eval(args, model, features)\\n args.eval_tool.eval_mem(y_true, y_pred)\\n # # # debug\\n # args.logger.debug(args.eval_tool.get_counts())\\n # args.logger.debug(args.eval_tool.get_performance())\\n eval_metrix = args.eval_tool.get_performance()\\n score_lvl, score_method, _ = args.model_selection_scoring.split(\\\"-\\\")\\n cur_score = eval_metrix['overall'][score_lvl][score_method]\\n args.eval_tool.reset()\\n\\n # select model based on best score\\n # if best_score < cur_score:\\n if cur_score - best_score > 1e-5:\\n args.logger.info('''\\n Global step: {}; \\n Epoch: {}; \\n previous best score: {:.4f}; \\n new best score: {:.4f}; \\n full evaluation metrix: {}\\n '''.format(global_step, epoch + 1, best_score, cur_score, eval_metrix))\\n best_score = cur_score\\n save_model(args, model, new_model_dir, global_step, latest=args.max_num_checkpoints)\\n\\n # save model transformer core\\n if args.save_model_core:\\n save_only_transformer_core(args, model)\\n\\n return best_score, eval_loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4686505d1b757670369c740514004ad\",\n \"score\": \"0.59308714\",\n \"text\": \"def results_regression(\\n model_class,\\n model_name,\\n run_id,\\n ensemble_folds,\\n test_set,\\n data_params,\\n robust,\\n device,\\n eval_type=\\\"checkpoint\\\",\\n):\\n\\n print(\\n \\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\n\\\"\\n \\\"------------Evaluate model on Test Set------------\\\\n\\\"\\n \\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\n\\\"\\n )\\n\\n test_generator = DataLoader(test_set, **data_params)\\n\\n y_ensemble = np.zeros((ensemble_folds, len(test_set)))\\n if robust:\\n y_ale = np.zeros((ensemble_folds, len(test_set)))\\n\\n for j in range(ensemble_folds):\\n\\n if ensemble_folds == 1:\\n resume = f\\\"models/{model_name}/{eval_type}-r{run_id}.pth.tar\\\"\\n print(\\\"Evaluating Model\\\")\\n else:\\n resume = f\\\"models/{model_name}/{eval_type}-r{j}.pth.tar\\\"\\n print(\\\"Evaluating Model {}/{}\\\".format(j + 1, ensemble_folds))\\n\\n assert os.path.isfile(resume), f\\\"no checkpoint found at '{resume}'\\\"\\n checkpoint = torch.load(resume, map_location=device)\\n checkpoint[\\\"model_params\\\"][\\\"robust\\\"]\\n assert (\\n checkpoint[\\\"model_params\\\"][\\\"robust\\\"] == robust\\n ), f\\\"robustness of checkpoint '{resume}' is not {robust}\\\"\\n\\n model = model_class(**checkpoint[\\\"model_params\\\"], device=device,)\\n model.to(device)\\n model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n\\n normalizer = Normalizer()\\n normalizer.load_state_dict(checkpoint[\\\"normalizer\\\"])\\n\\n with torch.no_grad():\\n idx, comp, y_test, output = model.predict(generator=test_generator,)\\n\\n if robust:\\n mean, log_std = output.chunk(2, dim=1)\\n pred = normalizer.denorm(mean.data.cpu())\\n ale_std = torch.exp(log_std).data.cpu() * normalizer.std\\n y_ale[j, :] = ale_std.view(-1).numpy()\\n else:\\n pred = normalizer.denorm(output.data)\\n\\n y_ensemble[j, :] = pred.view(-1).numpy()\\n\\n res = y_ensemble - y_test\\n mae = np.mean(np.abs(res), axis=1)\\n mse = np.mean(np.square(res), axis=1)\\n rmse = np.sqrt(mse)\\n r2 = r2_score(\\n np.repeat(y_test[:, np.newaxis], ensemble_folds, axis=1),\\n y_ensemble.T,\\n multioutput=\\\"raw_values\\\",\\n )\\n\\n if ensemble_folds == 1:\\n print(\\\"\\\\nModel Performance Metrics:\\\")\\n print(\\\"R2 Score: {:.4f} \\\".format(r2[0]))\\n print(\\\"MAE: {:.4f}\\\".format(mae[0]))\\n print(\\\"RMSE: {:.4f}\\\".format(rmse[0]))\\n else:\\n r2_avg = np.mean(r2)\\n r2_std = np.std(r2)\\n\\n mae_avg = np.mean(mae)\\n mae_std = np.std(mae)\\n\\n rmse_avg = np.mean(rmse)\\n rmse_std = np.std(rmse)\\n\\n print(\\\"\\\\nModel Performance Metrics:\\\")\\n print(f\\\"R2 Score: {r2_avg:.4f} +/- {r2_std:.4f}\\\")\\n print(f\\\"MAE: {mae_avg:.4f} +/- {mae_std:.4f}\\\")\\n print(f\\\"RMSE: {rmse_avg:.4f} +/- {rmse_std:.4f}\\\")\\n\\n # calculate metrics and errors with associated errors for ensembles\\n y_ens = np.mean(y_ensemble, axis=0)\\n\\n mae_ens = np.abs(y_test - y_ens).mean()\\n mse_ens = np.square(y_test - y_ens).mean()\\n rmse_ens = np.sqrt(mse_ens)\\n\\n r2_ens = r2_score(y_test, y_ens)\\n\\n print(\\\"\\\\nEnsemble Performance Metrics:\\\")\\n print(f\\\"R2 Score : {r2_ens:.4f} \\\")\\n print(f\\\"MAE : {mae_ens:.4f}\\\")\\n print(f\\\"RMSE : {rmse_ens:.4f}\\\")\\n\\n core = {\\\"id\\\": idx, \\\"composition\\\": comp, \\\"target\\\": y_test}\\n results = {f\\\"pred_{n_ens}\\\": val for (n_ens, val) in enumerate(y_ensemble)}\\n if model.robust:\\n ale = {f\\\"ale_{n_ens}\\\": val for (n_ens, val) in enumerate(y_ale)}\\n results.update(ale)\\n\\n df = pd.DataFrame({**core, **results})\\n\\n if ensemble_folds == 1:\\n df.to_csv(\\n index=False,\\n path_or_buf=(f\\\"results/test_results_{model_name}_r-{run_id}.csv\\\"),\\n )\\n else:\\n df.to_csv(\\n index=False, path_or_buf=(f\\\"results/ensemble_results_{model_name}.csv\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc33c5ad8e54b6f17048747ff1d66767\",\n \"score\": \"0.59296167\",\n \"text\": \"def _inner_loop(self, input_x, input_y):\\n inner = KFold(n_splits=self.n_splits_inner)\\n params = ParameterGrid(self.hparams)\\n auc_grid = np.zeros([self.n_splits_inner, len(params)]) # holds all the performance metrics for each k and each fold\\n for split_idx, (t, v) in enumerate(inner.split(input_x)):\\n print(f\\\"Inner fold {split_idx+1} of {self.n_splits_inner}\\\")\\n for param_idx, param in enumerate(params):\\n this_model = self.model_class(param)\\n x_train, y_train = input_x.iloc[t], input_y.iloc[t]\\n x_valid, y_valid = input_x.iloc[v], input_y.iloc[v]\\n print(f\\\"Fitting model with params: {param}\\\")\\n this_model.fit(x_train, y_train)\\n valid_prob = this_model.predict_proba(x_valid)\\n valid_auc = roc_auc_score(y_true=y_valid, y_score=valid_prob[:,1])\\n auc_grid[split_idx, param_idx] = valid_auc\\n # which hparam combination has best average performance across all splits?\\n # select these params, train on all the data and return a trained model\\n mean_auc = np.mean(auc_grid, axis=0)\\n best_idx = np.argmax(mean_auc)\\n best_auc = mean_auc[best_idx]\\n best_params = params[best_idx]\\n print(f\\\"Best params: {best_params}, training final model\\\")\\n this_model = self.model_class(best_params)\\n this_model.fit(input_x, input_y)\\n return this_model, best_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d13daf70b3cd2957a7987f2207694fc\",\n \"score\": \"0.5925097\",\n \"text\": \"def eval_child_model(session, model, dataset, mode):\\n if mode == 'val':\\n loader = dataset.val_loader\\n elif mode == 'test':\\n loader = dataset.test_loader\\n else:\\n raise ValueError('Not valid eval mode')\\n tf.logging.info('model.batch_size is {}'.format(model.batch_size))\\n\\n correct = 0\\n count = 0\\n cost_epoch = []\\n for i, batch in enumerate(loader):\\n images, labels = batch\\n images = images.numpy()\\n labels = labels.numpy()\\n labels = np.eye(dataset.num_classes)[labels]\\n preds, loss = session.run(\\n [model.predictions, model.cost],\\n feed_dict={\\n model.images: images,\\n model.labels: labels,\\n })\\n cost_epoch.append(loss)\\n correct += np.sum(\\n np.equal(np.argmax(labels, 1), np.argmax(preds, 1)))\\n count += len(preds)\\n return correct / count, np.mean(np.array(cost_epoch))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"590368d43dfcdda8bfbab8ced2298c4e\",\n \"score\": \"0.5922307\",\n \"text\": \"def model_data(data, test=False):\\n features = data.drop(['balance'], axis=1)\\n labels = data['balance'].to_frame()\\n X_work, X_test, y_work, y_test = train_test_split(features, labels)\\n # we don't touch the test set until the end.\\n\\n def grid_search(estimator, grid, X, y):\\n gs = GridSearchCV(estimator, cv=5, n_jobs=-1, param_grid=grid)\\n gs.fit(X, y)\\n print(gs.best_params_)\\n return gs.best_estimator_\\n\\n def support_vector(X, y):\\n svc = SVC()\\n grid = {\\n 'kernel': ['linear', 'poly', 'rbf', 'sigmoid']\\n }\\n return grid_search(svc, grid, X, y)\\n\\n def random_forest(X, y):\\n rfc = RandomForestClassifier(n_jobs=-1)\\n grid = {\\n 'n_estimators': np.arange(5, 15),\\n 'criterion': ['gini', 'entropy']\\n }\\n return grid_search(rfc, grid, X, y)\\n\\n def knn(X, y):\\n knc = KNeighborsClassifier()\\n grid = {\\n 'n_neighbors': np.arange(1, 10)\\n }\\n return grid_search(knc, grid, X, y)\\n\\n def perceptron(X, y):\\n mlp = MLPClassifier()\\n grid = {\\n 'activation': ['identity', 'logistic', 'tanh', 'relu']\\n }\\n return grid_search(mlp, grid, X, y)\\n\\n def vote(X, y):\\n estimators = [\\n ('random_forest', random_forest(X, y)),\\n ('knn', knn(X, y)),\\n ('perceptron', perceptron(X, y))\\n ]\\n vc = VotingClassifier(estimators, n_jobs=-1)\\n vc.fit(X, y)\\n return vc\\n\\n if not test:\\n avg_train, avg_validate = 0, 0\\n skf = StratifiedKFold(n_splits=5)\\n for train_idx, test_idx in skf.split(X_work, y_work):\\n X_train, X_test, y_train, y_test = \\\\\\n X_work.iloc[train_idx], X_work.iloc[test_idx], y_work.iloc[train_idx], y_work.iloc[test_idx]\\n model = vote(X_train, y_train)\\n avg_train += accuracy_score(y_train, model.predict(X_train))\\n avg_validate += accuracy_score(y_test, model.predict(X_test))\\n print('train: {}'.format(avg_train/5))\\n print('validate: {}'.format(avg_validate/5))\\n else:\\n def precision_recall(y, scores):\\n \\\"\\\"\\\"\\n Plots precision recall curve. Not used in article.\\n\\n :param y: true y values\\n :param scores: decision boundary scores\\n :return:\\n \\\"\\\"\\\"\\n for i in range(0, 3):\\n y_class = y['balance'].apply(lambda x: 1 if x == i else 0) # binarized for class\\n scores_class = scores[:, i].flatten()\\n precision, recall, _ = precision_recall_curve(y_class, scores_class)\\n average_precision = average_precision_score(y_class, scores_class)\\n print('average precision: {}'.format(average_precision))\\n\\n plt.step(recall, precision, color='b', alpha=0.2, where='post')\\n plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\\n plt.xlabel('Recall')\\n plt.ylabel('Precision')\\n plt.ylim([0.0, 1.05])\\n plt.xlim([0.0, 1.0])\\n plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\\n average_precision))\\n plt.show()\\n\\n model = vote(X_work, y_work['balance'])\\n # train_scores = model.decision_function(X_work)\\n # precision_recall(y_work, train_scores)\\n\\n print('train: {}'.format(model.score(X_work, y_work)))\\n print('test: {}'.format(model.score(X_test, y_test)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ce24ba381ad8109d1d0a3821cfb184\",\n \"score\": \"0.5898583\",\n \"text\": \"def run_model_evaluation(self, X_train, y_train, X_test, y_test):\\n self.y_test = y_test\\n logger.info(\\\"Stage - Model Analysis BEGIN\\\")\\n for classifier in (self.knn_model, self.rf_model):\\n classifier.fit(X_train, y_train)\\n self._eval_model(classifier, X=X_test, y=y_test)\\n classifier.predict(X_test=X_test)\\n logger.info(\\\"Stage - Model Analysis END\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cf177174e4bbd61964bc2b4b8660b1\",\n \"score\": \"0.5897472\",\n \"text\": \"def evaluate_trained_classifier(model=None, reader=sick_dev_reader):\\n mod, vectorizer, feature_selector, features = model\\n feats, labels = featurizer(reader=reader, features_funcs = features)\\n feat_matrix = vectorizer.transform(feats)\\n if feature_selector:\\n feat_matrix = feature_selector.transform(feat_matrix)\\n predictions = mod.predict(feat_matrix)\\n print metrics.classification_report(labels, predictions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3e2eb4a254832585279c7829c953d3\",\n \"score\": \"0.587122\",\n \"text\": \"def eval_model(model, val_dataset, name='validation', dataloader=custom_dataloader, verbose=True):\\n if verbose:\\n print(f\\\"Evaluation on {name}\\\")\\n model.eval()\\n val_loader = dataloader(dataset=val_dataset, batch_size=10, shuffle=False, num_workers=4)\\n predictions, labels = [], []\\n for _, sample_batched in enumerate(val_loader):\\n val_features, val_labels = sample_batched\\n labels.append(val_labels)\\n if baseCONFIG.cuda:\\n val_features = val_features.to(baseCONFIG.device)\\n val_pred = model.predict(val_features).detach()\\n predictions.append(val_pred)\\n predictions, labels = torch.cat(predictions).cpu().numpy(), torch.cat(labels).cpu().numpy()\\n auc_score = roc_auc_score(y_true=labels, y_score=predictions)\\n if verbose:\\n print(f'{name} AUC: {auc_score}')\\n return auc_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f9fab5a7326de41eb9e3a8138ebdd5\",\n \"score\": \"0.5861194\",\n \"text\": \"def evaluate_model(self, X_test, y_test):\\n return cross_val_score(self.pipeline,\\n X_test, y_test,\\n scoring=\\\"accuracy\\\",\\n n_jobs=-1) \\\\\\n .mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea80c5e9273b091eadeecae2ee8d1e07\",\n \"score\": \"0.58587676\",\n \"text\": \"def rf_cv(X_train, y_train, cv=5, verbose=False):\\n rf = RandomForestClassifier()\\n params = [{\\n 'rf__n_estimators': range(50, 450, 50),\\n 'rf__max_depth': [5, 10], \\n 'rf__min_samples_split': [2, 10, 20],\\n 'rf__max_features': ['sqrt', 8, 10],\\n 'rf__criterion': ['gini'], \\n }]\\n pipe = imbPipeline(steps=[('sample', SMOTE()), ('rf', rf)])\\n model = GridSearchCV(pipe, params, cv=cv, n_jobs=-1, scoring=f2scorer, verbose=verbose)\\n model.fit(X_train, y_train)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916097f34adc8577797a6c2d14940541\",\n \"score\": \"0.58488816\",\n \"text\": \"def run(self, model, verbose=False):\\n print(\\\"Run!\\\")\\n\\n f1_I = []\\n f1_H = []\\n f1_M = []\\n\\n i = 0\\n\\n kf = KFold(n_splits=10, shuffle=False)\\n for train_index, test_index in kf.split(self.X_train):\\n i += 1\\n print(\\\"Progress: \\\" + str(i) + \\\" out of \\\" + str(10))\\n\\n X_train = self.X_train[train_index]\\n y_I_train = self.y_I[train_index]\\n y_H_train = self.y_H[train_index]\\n y_M_train = self.y_M[train_index]\\n\\n X_cv = self.X_train[test_index]\\n y_I_cv = self.y_I[test_index]\\n y_H_cv = self.y_H[test_index]\\n y_M_cv = self.y_M[test_index]\\n\\n if verbose:\\n print(\\\"Printing all shapes:\\\", X_train.shape, y_I_train.shape, y_H_train.shape, y_M_train.shape)\\n print(\\\"CV\\\", X_cv.shape, y_I_cv.shape, y_H_cv.shape, y_M_cv.shape )\\n\\n assert(test_index.shape[0] > 0)\\n\\n # Go through each array!\\n # I first!\\n model.fit(X_train, y_I_train)\\n y_pred = model.predict(X_cv)\\n assert(y_pred.shape[0] == test_index.shape[0])\\n f1_value = f1_score(y_pred, y_I_cv)\\n f1_I.append(f1_value)\\n\\n # H second!\\n model.fit(X_train, y_H_train)\\n y_pred = model.predict(X_cv)\\n assert(y_pred.shape[0] == test_index.shape[0])\\n f1_value = f1_score(y_pred, y_H_cv)\\n f1_H.append(f1_value)\\n\\n # M third!\\n model.fit(X_train, y_M_train)\\n y_pred = model.predict(X_cv)\\n assert(y_pred.shape[0] == test_index.shape[0])\\n f1_value = f1_score(y_pred, y_M_cv)\\n f1_M.append(f1_value)\\n\\n if verbose:\\n print(\\\"Predicted average f1 scores are: \\\")\\n print(\\\"F1 score for I: \\\", np.mean(f1_I))\\n print(\\\"F1 score for M: \\\", np.mean(f1_M))\\n print(\\\"F1 score for H: \\\", np.mean(f1_H))\\n\\n return np.mean(f1_H), np.mean(f1_I), np.mean(f1_M)\\n\\n # # classifier with ensemble\\n # # ATTENTION: unbalanced data\\n # # adaboost for each of the problem\\n # # reweighting (later)\\n # # can play with K\\n # clf.fit(X_M_train, y_M_train)\\n # score = f1(X_M_validation, y_M_validation)\\n # print(\\\"Score \\\" + score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3676a284de371aeb4ca48002933e2f60\",\n \"score\": \"0.5847166\",\n \"text\": \"def fit(self, X, y):\\r\\n\\r\\n print(f\\\"\\\\nDataset '{self.dataset_name}'\\\")\\r\\n print('{} <-- Running this model now'.format(type(self.model).__name__))\\r\\n\\r\\n self.X = X\\r\\n self.y = y\\r\\n self.y_type = type_of_target(y)\\r\\n\\r\\n if isinstance(self.outer_cv, numbers.Number) and isinstance(self.inner_cv, numbers.Number):\\r\\n outer_cv = StratifiedKFold(n_splits=self.outer_cv, shuffle=True, random_state=self.random_state)\\r\\n inner_cv = StratifiedKFold(n_splits=self.inner_cv, shuffle=True, random_state=self.random_state)\\r\\n else:\\r\\n outer_cv = self.outer_cv\\r\\n inner_cv = self.inner_cv\\r\\n\\r\\n outer_scores = []\\r\\n best_inner_params_list = []\\r\\n\\r\\n # Split X and y into K-partitions to Outer CV\\r\\n for (i, (train_index, test_index)) in enumerate(outer_cv.split(X, y)):\\r\\n print('\\\\n{}/{} <-- Current outer fold'.format(i+1, self.outer_cv))\\r\\n X_train_outer, X_test_outer = X.iloc[train_index], X.iloc[test_index]\\r\\n y_train_outer, y_test_outer = y.iloc[train_index], y.iloc[test_index]\\r\\n\\r\\n if self.y_type in 'binary':\\r\\n auc_scorer = self.scorer if self.scorer is not None else make_scorer(roc_auc_score)\\r\\n else:\\r\\n auc_scorer = self.scorer if self.scorer is not None else make_scorer(roc_auc_score,\\r\\n average=self.multiclass_average,\\r\\n needs_proba=self.predict_proba,\\r\\n multi_class=self.multiclass_method)\\r\\n\\r\\n if self.randomized_search:\\r\\n randomized_search_inner_model = clone(self.model)\\r\\n search_model = randomized_search_inner_model\\r\\n\\r\\n if self.wrap_model_using_ovr:\\r\\n one_vs_rest_randomized_inner_classifier = OneVsRestClassifier(randomized_search_inner_model)\\r\\n search_model = one_vs_rest_randomized_inner_classifier\\r\\n\\r\\n randomized_search_cv = RandomizedSearchCV(search_model,\\r\\n param_distributions=self.params_grid,\\r\\n scoring=auc_scorer,\\r\\n cv=inner_cv,\\r\\n n_iter=self.search_iter,\\r\\n verbose=self.verbose,\\r\\n n_jobs=self.n_jobs,\\r\\n random_state=self.random_state)\\r\\n randomized_search_cv.fit(X_train_outer, y_train_outer)\\r\\n\\r\\n best_inner_params = randomized_search_cv.best_params_\\r\\n else:\\r\\n def objective(params, cv=inner_cv, X_inner=X_train_outer, y_inner=y_train_outer, scorer=auc_scorer):\\r\\n bayes_search_inner_model = clone(self.model)\\r\\n bayes_search_inner_model.set_params(**params)\\r\\n bayes_search_inner_model = clone(bayes_search_inner_model)\\r\\n search_model = bayes_search_inner_model\\r\\n\\r\\n if self.wrap_model_using_ovr and self.y_type not in 'binary':\\r\\n search_model = OneVsRestClassifier(bayes_search_inner_model)\\r\\n\\r\\n cv_search = cross_validate(search_model, X_inner, y_inner, scoring=scorer, cv=cv,\\r\\n n_jobs=self.n_jobs, verbose=self.verbose)\\r\\n cv_test_score = cv_search['test_score'].mean()\\r\\n\\r\\n # should minimize\\r\\n loss = 1 - cv_test_score\\r\\n\\r\\n if self.verbose > 1:\\r\\n print(\\\"Bayesian search iteration params: {}\\\".format(params))\\r\\n print(\\\"Bayesian search iteration loss: {}\\\".format(loss))\\r\\n print()\\r\\n\\r\\n return loss\\r\\n\\r\\n trials = Trials()\\r\\n best_inner_params = fmin(fn=objective, space=self.params_grid, max_evals=self.search_iter,\\r\\n rstate=np.random.RandomState(self.random_state),\\r\\n algo=tpe.suggest, trials=trials, verbose=self.verbose)\\r\\n\\r\\n if not self.randomized_search:\\r\\n best_inner_params = space_eval(self.params_grid, best_inner_params)\\r\\n\\r\\n best_inner_params_list.append(best_inner_params)\\r\\n\\r\\n # Fit the best hyper-parameters from one of the self.search_iter inner loops\\r\\n cloned_model = clone(self.model)\\r\\n outer_model = cloned_model\\r\\n\\r\\n if self.y_type not in 'binary':\\r\\n if not self.randomized_search:\\r\\n cloned_model.set_params(**best_inner_params)\\r\\n cloned_model = clone(cloned_model)\\r\\n one_vs_rest_outer_classifier = OneVsRestClassifier(cloned_model)\\r\\n else:\\r\\n one_vs_rest_outer_classifier = OneVsRestClassifier(cloned_model)\\r\\n one_vs_rest_outer_classifier.set_params(**best_inner_params)\\r\\n\\r\\n outer_model = one_vs_rest_outer_classifier\\r\\n else:\\r\\n outer_model.set_params(**best_inner_params)\\r\\n outer_model = clone(outer_model)\\r\\n\\r\\n train_start = time.time()\\r\\n outer_model.fit(X_train_outer, y_train_outer)\\r\\n train_stop = time.time()\\r\\n\\r\\n training_time_sec = train_stop - train_start\\r\\n\\r\\n inference_time_sec = measure_sec_of_n_sample_predictions(outer_model, X_train_outer)\\r\\n\\r\\n # Get score and prediction\\r\\n accuracy, tpr, fpr, precision, recall, roc_auc, pr_auc = \\\\\\r\\n self._predict_and_score_outer(X_test_outer, y_test_outer, outer_model)\\r\\n\\r\\n assert tpr == recall\\r\\n\\r\\n outer_scores.append([accuracy, tpr, fpr, precision, roc_auc, pr_auc, training_time_sec, inference_time_sec])\\r\\n\\r\\n print('Inner fold best parameters: {}'.format(best_inner_params_list[i]))\\r\\n print('Results for outer fold:')\\r\\n print('Accuracy {}, TPR {}, FPR {}, Precision {}, AUC {}, PR-Curve {}, Training Time {}, Inference Time {}'.format(*outer_scores[i]))\\r\\n\\r\\n print()\\r\\n print(\\\"-\\\" * 100)\\r\\n\\r\\n self.outer_scores = outer_scores\\r\\n self.best_inner_params_list = best_inner_params_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91139be39e1664ed0f2072be1cf4f1e\",\n \"score\": \"0.5845858\",\n \"text\": \"def cv_deep_learning_pred_loss( X, y, nb_classes = 3, mode = \\\"CNNC_Name\\\", \\n\\t\\t\\t\\t\\t\\t\\t l = None, param_d = None, mp = 1,\\n\\t\\t\\t\\t\\t\\t\\t graph = True, n_splits = 5):\\n\\t\\n\\tKF = model_selection.KFold(n_splits, shuffle=True)\\n\\tkf = KF.split(X)\\n\\n\\tdcnn_score_l, dcnn_loss_l = [], []\\n\\tc_wb_l = []\\n\\ty_cv = np.copy( y)\\n\\tfor tr, te in kf:\\n\\t\\tX_train, y_train = X[tr,:], y[tr]\\n\\t\\tX_test, y_test = X[te,:], y[te]\\n\\t\\t\\n\\t\\tif mode == \\\"CNNC_Name\\\":\\n\\t\\t\\tmodel = kkeras.CNNC_Name( param_d[\\\"n_cv_flt\\\"], param_d[\\\"n_cv_ln\\\"],\\n\\t\\t\\t\\tparam_d[\\\"cv_activation\\\"], l=l, mp=mp)\\n\\t\\t\\tn_flt = param_d[\\\"n_cv_flt\\\"]\\n\\t\\telif mode == \\\"CNNC_Name_Border\\\":\\n\\t\\t\\tmodel = kkeras.CNNC_Name_Border( param_d[\\\"n_cv_flt\\\"], param_d[\\\"n_cv_ln\\\"], \\n\\t\\t\\t\\tparam_d[\\\"cv_activation\\\"], l=l, border_mode = 'valid')\\n\\t\\t\\tn_flt = param_d[\\\"n_cv_flt\\\"] \\n\\t\\telif mode == \\\"MLPC_Name\\\":\\n\\t\\t\\tmodel = kkeras.MLPC_Name(l=l)\\n\\t\\t\\tn_flt = l[1]\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"The given mode is not supported: mode={}\\\".format(mode))\\n\\t\\t\\t\\n\\t\\tmodel.fit( X_train, y_train, X_test, y_test, nb_classes, batch_size=6, nb_epoch = 5)\\n\\t\\ttr_score_lossacc = model.evaluate( X_train, y_train)\\n\\t\\ttr_score = tr_score_lossacc[1]\\n\\t\\ttr_loss = tr_score_lossacc[0]\\n\\t\\tprint( \\\"Training Accuracy:\\\", tr_score)\\n\\t\\tprint( \\\"Training Loss:\\\", tr_loss)\\n\\n\\t\\tdcnn_score_lossacc = model.evaluate( X_test, y_test)\\n\\t\\tdcnn_score_l.append(dcnn_score_lossacc[1])\\n\\t\\tdcnn_loss_l.append(dcnn_score_lossacc[0])\\t\\n\\t\\tprint( \\\"Testing Accuracy:\\\", dcnn_score_lossacc[1])\\n\\t\\tprint( \\\"Testing Loss:\\\", dcnn_score_lossacc[0])\\n\\t\\t\\n\\t\\tc_w, c_b = model.get_c_wb()\\n\\t\\tprint( \\\"c_w.shape=\\\", c_w.shape)\\n\\t\\tc_w = c_w.reshape(-1, n_flt)\\n\\t\\tc_wb_l.append( (c_w, c_b))\\n\\n\\t\\ty_cv[ te] = model.predict( X_test)\\n\\n\\tprint( dcnn_score_l)\\n\\tprint( \\\"[Accuracy] Mean:{0}, Std:{1}\\\".format( np.mean( dcnn_score_l), np.std( dcnn_score_l)))\\n\\tprint( dcnn_loss_l)\\n\\tprint( \\\"[Loss] Mean:{0}, Std:{1}\\\".format( np.mean( dcnn_loss_l), np.std( dcnn_loss_l)))\\n\\n\\tif graph:\\n\\t\\t# One of weight vectors are drawn.\\n\\t\\tc_w = c_wb_l[0][0] # 0 = c_w, 1 = c_b\\n\\t\\tfor ll in range(n_flt):\\n\\t\\t\\t#plt.figure()\\n\\t\\t\\tplt.plot( c_w[:,ll], label=\\\"Filter #{}\\\".format(ll))\\n\\t\\tplt.legend() \\n\\t\\n\\treturn dcnn_score_l, c_wb_l, y_cv, dcnn_loss_l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09439864b27dac64ebe0482da4299ca\",\n \"score\": \"0.584459\",\n \"text\": \"def build_model(): \\n #define ML pipeline\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer = tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\\n ])\\n\\n parameters = {\\n 'clf__estimator__n_estimators': [50, 100, 150],\\n 'clf__estimator__criterion': [\\\"gini\\\", \\\"entropy\\\"]\\n }\\n\\n cv = GridSearchCV(pipeline, param_grid=parameters)\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94268faaa24bba527c9457725071e781\",\n \"score\": \"0.5844449\",\n \"text\": \"def run_model_selection_for_ml_model():\\n\\n # Load data\\n tweets = load_tweets(sample=True, frac=1)\\n tweets = apply_preprocessing(tweets)\\n\\n train_tweets = tweets[['tweet']]\\n labels = tweets[['polarity']]\\n\\n models = ['lg', 'svm', 'nb', 'rf']\\n scores = list()\\n for model in models:\\n scores.append(run_tfidf_ml_model(tweets=train_tweets, labels=labels, model=model))\\n\\n print(pd.DataFrame(scores))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d4e94f620f7009cf8d4f9622c1f683\",\n \"score\": \"0.58399606\",\n \"text\": \"def evaluate4svm(labels,\\n feats,\\n params={'c': 1, 'kernal': 'gauss'},\\n Nsplit=2):\\n c = params.get('c')\\n if params.get('kernal' == 'gauss'):\\n kernal = GaussianKernel()\\n kernal.set_width(80)\\n elif params.get('kernal' == 'sigmoid'):\\n kernal = SigmoidKernel()\\n else:\\n kernal = LinearKernel()\\n\\n split = CrossValidationSplitting(labels, Nsplit)\\n split.build_subsets()\\n\\n accuracy = np.zeros(Nsplit)\\n time_test = np.zeros(accuracy.shape)\\n for i in range(Nsplit):\\n idx_train = split.generate_subset_inverse(i)\\n idx_test = split.generate_subset_indices(i)\\n\\n feats.add_subset(idx_train)\\n labels.add_subset(idx_train)\\n svm = GMNPSVM(c, kernal, labels)\\n _ = svm.train(feats)\\n out = svm.apply(feats_test)\\n evaluator = MulticlassAccuracy()\\n accuracy[i] = evaluator.evaluate(out, labels_test)\\n\\n feats.remove_subset()\\n labels.remove_subset()\\n feats.add_subset(idx_test)\\n labels.add_subset(idx_test)\\n\\n t_start = time.clock()\\n time_test[i] = (time.clock() - t_start) / labels.get_num_labels()\\n feats.remove_subset()\\n labels.remove_subset()\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc8b6a64cdc3b3ff3f950597b14fae4\",\n \"score\": \"0.5835834\",\n \"text\": \"def evaluate(self):\\n if not self.kfold:\\n classes = {\\\"A\\\":\\\"Walking\\\",\\\"B\\\":\\\"Jogging\\\",\\\"C\\\":\\\"Stairs\\\",\\\"D\\\":\\\"Sitting\\\",\\n \\\"E\\\":\\\"Standing\\\",\\\"F\\\":\\\"Typing\\\",\\\"G\\\":\\\"Brushing teeth\\\",\\\"H\\\":\\\"Eating soup\\\",\\n \\\"I\\\":\\\"Eating chips\\\",\\\"J\\\":\\\"Eating pasta\\\",\\\"K\\\":\\\"Drinking from a cup\\\",\\n \\\"L\\\":\\\"Eating sandwich\\\",\\\"M\\\":\\\"Kicking (soccer ball)\\\",\\\"O\\\":\\\"Playing catch (with tennis ball)\\\",\\n \\\"P\\\":\\\"Dribbling (basketball)\\\", \\\"Q\\\":\\\"Writing\\\",\\\"R\\\":\\\"Clapping\\\",\\\"S\\\":\\\"Folding clothes\\\"}\\n print(\\\"Model prediction accuracies based on phone acceleration data:\\\")\\n print(\\\"=\\\"*61)\\n for i in self.encoder.classes_: # iterate through classes (string letters)\\n j = self.encoder.transform([i])[0] # use indexer because .transform returns a list instead of an integer ex: [1] instead of 1\\n idx = self.Y_test[j] # create reference array to only choose samples from the i'th (aka j'th) class\\n X1 = self.X_test[idx==1]\\n Y1 = self.Y_test[idx==1]\\n print(classes[i], \\\"test score:\\\",\\\"-\\\"*(40-len(classes[i])),str(self.estimator.score(X1, Y1)))\\n else:\\n print(\\\"Model evaluation is not supported for kfold-trained data. Try using .train(kfold=False) first.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcce0a0fa3e00ca7fbefb58abcf08b7\",\n \"score\": \"0.5829663\",\n \"text\": \"def build_model():\\n \\n pipeline = Pipeline([(\\\"vect\\\", CountVectorizer(tokenizer = tokenize)),\\n (\\\"tfidf\\\", TfidfTransformer()),\\n (\\\"clf\\\", RandomForestClassifier())\\n ])\\n \\n parameters = {'clf__min_samples_leaf': [1,2,5],\\n 'clf__n_estimators': [50, 100, 200]\\n }\\n\\n cv = GridSearchCV(pipeline, param_grid = parameters, n_jobs = -1)\\n \\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e59ed6aff860d09a48a1e7f7e289288b\",\n \"score\": \"0.5822049\",\n \"text\": \"def model_evaluation_main(\\n hyperparameters,\\n data_dict,\\n global_settings,\\n objective='auc',\\n weight='totalWeight'\\n):\\n model = create_model(\\n hyperparameters, data_dict, global_settings['nthread'], objective,\\n weight\\n )\\n score, train, test = evaluate_model(\\n data_dict, global_settings, model)\\n return score, train, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4394577214fe1fe699977c58c974625a\",\n \"score\": \"0.5821532\",\n \"text\": \"def build_model():\\n\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('mcfl', MultiOutputClassifier(estimator=SVC()))\\n ])\\n\\n parameters = {\\n 'mcfl__estimator__kernel' : ['linear', 'rbf'],\\n }\\n\\n cv = GridSearchCV(pipeline, param_grid=parameters, verbose=10, cv=2, \\\\\\n n_jobs=-1, scoring='f1_micro')\\n\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd0eb78b8d978c36f82fe35ff6c1728\",\n \"score\": \"0.58190554\",\n \"text\": \"def evaluate(backbone, ckpt_path, test_folder):\\n\\n test_set = ImageFolder(test_folder, transform=TEST_TRANSFORM)\\n\\n test_loader = DataLoader(test_set,\\n batch_size=32,\\n shuffle=False,\\n num_workers=12,\\n pin_memory=True)\\n\\n model = XRayClassification.load_from_checkpoint(\\n ckpt_path, map_location=lambda storage, loc: storage, model=backbone)\\n\\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n model.to(device)\\n\\n trainer = pl.Trainer(gpus=1, checkpoint_callback=False, logger=False)\\n\\n result_dict = trainer.test(model, test_loader, verbose=False)[0]\\n\\n for k, v in result_dict.items():\\n print(k)\\n print(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729ff2338e793403ee4f4f264f74ee72\",\n \"score\": \"0.5816539\",\n \"text\": \"def _eval_classification(session, c, m, sps_op, summary_writer, global_step):\\n name = m.name\\n batch_size = c.batch_size_eval\\n split_size = c.split_sizes[name]\\n assert split_size % batch_size == 0\\n num_batches = int(split_size / batch_size)\\n print('\\\\nEvaluating model...\\\\n')\\n\\n preds = np.zeros(shape=[split_size])\\n labels = np.zeros(shape=[split_size])\\n if sps_op is None:\\n sps = 0.\\n else:\\n sps = session.run(sps_op)\\n for step in tqdm(range(num_batches), desc='evaluation', ncols=100):\\n logits, lbl = session.run([m.dec_logits, m.batch_ops[1]])\\n labels[step * batch_size: (step + 1) * batch_size] = np.squeeze(lbl)\\n preds[step * batch_size: (step + 1) * batch_size] = np.argmax(np.squeeze(logits), axis=1)\\n\\n # noinspection PyUnresolvedReferences\\n acc = np.mean((preds == labels).astype(np.float32)) * 100.\\n err = (100. - acc)\\n print('>>> {} Accuracy (%): {:.4f}'.format(name, acc))\\n print('>>> {} Error (%): {:.4f}\\\\n'.format(name, err))\\n value_summary({'{}/accuracy'.format(name): acc,\\n '{}/error'.format(name): err}, summary_writer, global_step)\\n # Write scores\\n if global_step:\\n score_dir = pjoin(os.path.dirname(c.log_path), 'run_{:02d}___infer_{}'.format(c.run, name))\\n if not os.path.exists(score_dir):\\n os.makedirs(score_dir)\\n with open(pjoin(score_dir, 'sparsity_values.csv'), 'a') as f:\\n f.write('Global step,Total sparsity\\\\r\\\\n')\\n with open(pjoin(score_dir, 'metric_scores.csv'), 'a') as f:\\n f.write('{:d},{:.4f},{:.4f}\\\\r\\\\n'.format(global_step, acc, err))\\n with open(pjoin(score_dir, 'sparsity_values.csv'), 'a') as f:\\n f.write('{:d},{:.7f}\\\\r\\\\n'.format(global_step, sps))\\n return acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f043348b531e6811086bb1042931fb70\",\n \"score\": \"0.5815661\",\n \"text\": \"def run_crnet_eccv(model, cv_split=1, epoch=30):\\n criterion = CRLoss()\\n\\n optimizer_ft = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)\\n\\n exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=20, gamma=0.1)\\n\\n print('start loading ECCV HotOrNot dataset...')\\n train_dataset = HotOrNotDataset(cv_split=cv_split, train=True, transform=transforms.Compose([\\n transforms.Resize(227),\\n transforms.RandomCrop(224),\\n transforms.RandomRotation(30),\\n transforms.ColorJitter(),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.5, 0.5, 0.5],\\n std=[1, 1, 1])\\n ]))\\n\\n test_dataset = HotOrNotDataset(cv_split=cv_split, train=False, transform=transforms.Compose([\\n transforms.Resize(227),\\n transforms.RandomCrop(224),\\n transforms.RandomRotation(30),\\n transforms.ColorJitter(),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.5, 0.5, 0.5],\\n std=[1, 1, 1])\\n ]))\\n\\n train_dataloader = DataLoader(train_dataset, batch_size=cfg['batch_size'],\\n shuffle=True, num_workers=50, drop_last=True)\\n test_dataloader = DataLoader(test_dataset, batch_size=cfg['batch_size'],\\n shuffle=False, num_workers=50, drop_last=False)\\n\\n print('finish loading ECCV HotOrNot dataset...')\\n\\n train_model(model=model, train_dataloader=train_dataloader, test_dataloader=test_dataloader,\\n criterion=criterion, optimizer=optimizer_ft, scheduler=exp_lr_scheduler, num_epochs=epoch,\\n inference=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298ad825cb83c1132e1b29aca84321e2\",\n \"score\": \"0.580828\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(estimator=LinearSVC(max_iter=10000)))\\n ])\\n parameters = {\\n #'clf__estimator__n_estimators': [50, 100],\\n #'clf__estimator__min_samples_split': [2, 5]\\n 'clf__estimator__C': [1, 10],\\n 'clf__estimator__max_iter': [1000, 100000]\\n }\\n\\n cv = GridSearchCV(pipeline, param_grid=parameters)\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61f39a099d1d052c261104817126e97\",\n \"score\": \"0.57949626\",\n \"text\": \"def evaluate(model_path, images_dir, output_dir):\\n\\n device = torch.device('cpu')\\n # build mobilenetv3 ssd and cast to cpu\\n cfg.merge_from_file('configs/mobilenet_v3_ssd320_voc0712.yaml') # load config file for mobilenetv3 SSD\\n cfg.freeze()\\n model = build_detection_model(cfg)\\n model = model.to(device)\\n\\n # load model weights\\n weights = torch.load(model_path, map_location=device)['model']\\n model.load_state_dict(weights)\\n\\n # prepare model for inference\\n model.eval()\\n # inference preprocessing\\n # resize, subtract mean, make channel first dim and make Tensor from np array\\n data_preprocess = build_transforms(cfg, is_train=False)\\n image_paths = glob.glob(images_dir + '/*.jpg')\\n\\n # predict\\n for i, image_path in enumerate(image_paths):\\n # load and preprocess image\\n image = np.array(Image.open(image_path).convert(\\\"RGB\\\"))\\n image_tensor = data_preprocess(image)[0] # use only the returned image (not boxes/labels)\\n image_tensor = image_tensor.unsqueeze(0) # add batch dim\\n image_name = os.path.basename(image_path)\\n height, width = image.shape[:2]\\n\\n # predict\\n result = model(image_tensor)[0]\\n result = result.resize((width, height)).numpy()\\n boxes, labels, scores = result['boxes'], result['labels'], result['scores'] # unpack results\\n\\n # filter results\\n indices = scores > 0.5\\n boxes = boxes[indices]\\n labels = labels[indices]\\n scores = scores[indices]\\n\\n # draw and save boxes\\n # VOC dataset was used for training and hence its class names are used\\n drawn_image = draw_boxes(image, boxes, labels, scores, VOCDataset.class_names).astype(np.uint8)\\n Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d724c2fc7981461b626f93bb3db010d0\",\n \"score\": \"0.5789101\",\n \"text\": \"def model_evaluate(df: pd.DataFrame, features: list, target: str, metrics: dict, model, cv_rounds: int = 5):\\n df = df.copy()\\n scores = cross_validate(estimator=model.model,\\n X=df[features],\\n y=df[target],\\n scoring=metrics,\\n cv=cv_rounds,\\n return_train_score=True)\\n\\n preds_proba = cross_val_predict(estimator=model.model,\\n X=df[features],\\n y=df[target],\\n cv=cv_rounds,\\n method=\\\"predict_proba\\\")[:, 1]\\n preds = cross_val_predict(estimator=model.model,\\n X=df[features],\\n y=df[target],\\n cv=cv_rounds,\\n method=\\\"predict\\\")\\n return scores, preds_proba, preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43dc07fafd41acd71fbab9980f625fcb\",\n \"score\": \"0.5788027\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer = tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\\n ])\\n \\n parameters = {'vect__min_df': [1, 5],\\n 'tfidf__use_idf':[True, False],\\n 'clf__estimator__n_estimators':[10, 20]}\\n\\n cv = GridSearchCV(pipeline, param_grid = parameters,verbose = 3)\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800c2a370aa08e3962c8e53cc6ae4f60\",\n \"score\": \"0.5787464\",\n \"text\": \"def evaluate_model(model, testx, testy):\\n\\n score = model.evaluate(testx, testy, batch_size=32)\\n print('Test Accuracy: ', score[1])\\n print_baseline(testy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef73d5ac9c563f57ce066e243d9fc21f\",\n \"score\": \"0.57821023\",\n \"text\": \"def evaluate_model(\\n estimator,\\n fold: Optional[Union[int, Any]] = None,\\n fit_kwargs: Optional[dict] = None,\\n groups: Optional[Union[str, Any]] = None,\\n use_train_data: bool = False,\\n):\\n\\n return pycaret.internal.tabular.evaluate_model(\\n estimator=estimator,\\n fold=fold,\\n fit_kwargs=fit_kwargs,\\n groups=groups,\\n use_train_data=use_train_data,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2534c76c5583e5d297d7536364e3bcb9\",\n \"score\": \"0.5775548\",\n \"text\": \"def evaluate_split(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61ed93c2a880981f52590faeca878d6\",\n \"score\": \"0.57704806\",\n \"text\": \"def _eval(args, model, features):\\n data_loader = ner_data_loader(features, batch_size=args.eval_batch_size, task='test', auto=True)\\n eval_size = len(data_loader)\\n args.logger.info(\\\"***** Running evaluation on {} number of test data *****\\\".format(eval_size))\\n args.logger.info(\\\" Instantaneous batch size per GPU = {}\\\".format(args.eval_batch_size))\\n args.logger.info(\\\"******************************\\\")\\n\\n # not data for evaluation\\n if eval_size < 1:\\n return [], [], .0\\n\\n # prepare processing results for each batch\\n y_trues, y_preds = [], []\\n y_pred, y_true = [], []\\n prev_gd = 0\\n\\n # prediction\\n model.eval()\\n eval_loss = .0\\n for batch in tqdm(data_loader, desc='evaluation', disable=False if args.progress_bar else True):\\n original_tkid = batch[0].numpy()\\n original_mask = batch[1].numpy()\\n original_labels = batch[3].numpy()\\n guards = batch[4].numpy()\\n\\n batch = tuple(b.to(args.device) for b in batch)\\n eval_inputs = batch_to_model_inputs(batch, args.model_type)\\n\\n with torch.no_grad():\\n raw_logits, _, loss = model(**eval_inputs)\\n # get softmax output of the raw logits (keep dimensions)\\n if not args.use_crf:\\n raw_logits = torch.argmax(F.log_softmax(raw_logits, dim=-1), dim=-1)\\n raw_logits = raw_logits.detach().cpu().numpy()\\n # update evaluate loss\\n eval_loss += loss.item()\\n\\n assert guards.shape == original_tkid.shape == original_mask.shape == original_labels.shape == raw_logits.shape, \\\\\\n \\\"\\\"\\\"\\n expect same dimension for all the inputs and outputs but get\\n input_tokens: {}\\n mask: {}\\n label: {}\\n logits: {}\\n \\\"\\\"\\\".format(original_tkid.shape, original_mask.shape, original_labels.shape, raw_logits.shape)\\n\\n # tk=token, mk=mask, lb=label, lgt=logits\\n for mks, lbs, lgts, gds in zip(original_mask, original_labels, raw_logits, guards):\\n connect_sent_flag = False\\n for mk, lb, lgt, gd in zip(mks, lbs, lgts, gds):\\n if mk == 0:\\n # after hit first mask, we can stop for the current sentence since all rest will be pad\\n if args.model_type == \\\"xlnet\\\":\\n continue\\n else:\\n break\\n if gd == 0 or prev_gd == gd:\\n continue\\n if gd == NEXT_GUARD:\\n connect_sent_flag = True\\n break\\n if prev_gd != gd:\\n y_true.append(args.idx2label[lb])\\n y_pred.append(args.idx2label[lgt])\\n prev_gd = gd\\n if connect_sent_flag:\\n continue\\n y_trues.append(y_true)\\n y_preds.append(y_pred)\\n y_pred, y_true = [], []\\n prev_gd = 0\\n\\n return y_trues, y_preds, round(eval_loss / eval_size, 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de472e3e174a36516c5873b4f464030b\",\n \"score\": \"0.57676053\",\n \"text\": \"def evaluate(data_loader, model, paradigm, task, sents):\\n device = torch.device(f'cuda:{args.n_gpu}')\\n model.model.to(device)\\n \\n model.model.eval()\\n outputs, targets = [], []\\n for batch in tqdm(data_loader):\\n # need to push the data to device\\n outs = model.model.generate(input_ids=batch['source_ids'].to(device), \\n attention_mask=batch['source_mask'].to(device), \\n max_length=128)\\n\\n dec = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]\\n target = [tokenizer.decode(ids, skip_special_tokens=True) for ids in batch[\\\"target_ids\\\"]]\\n\\n outputs.extend(dec)\\n targets.extend(target)\\n\\n raw_scores, fixed_scores, all_labels, all_preds, all_preds_fixed = compute_scores(outputs, targets, sents, paradigm, task)\\n results = {'raw_scores': raw_scores, 'fixed_scores': fixed_scores, 'labels': all_labels,\\n 'preds': all_preds, 'preds_fixed': all_preds_fixed}\\n # pickle.dump(results, open(f\\\"{args.output_dir}/results-{args.task}-{args.dataset}-{args.paradigm}.pickle\\\", 'wb'))\\n\\n return raw_scores, fixed_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"931411a900e92b97ac5868871a577ce6\",\n \"score\": \"0.5763745\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n \\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\\n\\n ])\\n \\n \\n parameters = {\\n 'vect__max_features':(None, 5000),\\n 'clf__estimator__n_estimators': [5, 10, 20]\\n }\\n\\n cv = GridSearchCV(estimator=pipeline, param_grid=parameters, n_jobs=-1,verbose=2)\\n \\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15cc68b812280bb6e7c9f4f8ad09913\",\n \"score\": \"0.57635653\",\n \"text\": \"def run_model(params, mode):\\n assert mode in ['train', 'eval', 'train_and_eval'], mode\\n dataset_fn_fn = fast_imagenet_input.make_dataset\\n\\n if mode in ['train', 'train_and_eval']:\\n dataset_mode = 'train' if params['use_held_out_test_set'] else 'l2l_train'\\n dataset_fn, _ = dataset_fn_fn(\\n params['dataset_dir'],\\n dataset_mode,\\n training=True,\\n use_bfloat16=params['use_bfloat16'])\\n estimator = _make_estimator(params, is_training=True)\\n estimator.train(dataset_fn, max_steps=params['max_global_step'])\\n if mode in ['eval', 'train_and_eval']:\\n dataset_mode = 'test' if params['use_held_out_test_set'] else 'l2l_valid'\\n dataset_fn, dataset_size = dataset_fn_fn(\\n params['dataset_dir'],\\n dataset_mode,\\n training=False,\\n use_bfloat16=params['use_bfloat16'],\\n final_batch_mode=fast_imagenet_input.FinalBatchMode.PAD)\\n\\n estimator = _make_estimator(params, is_training=False)\\n for checkpoint_path in tf.train.checkpoints_iterator(\\n params['checkpoint_dir']):\\n eval_metrics = estimator.evaluate(\\n dataset_fn,\\n steps=dataset_size // params['eval_batch_size'],\\n checkpoint_path=checkpoint_path)\\n tf.logging.info('eval metrics = %s', eval_metrics)\\n if eval_metrics['global_step'] >= params['max_global_step']:\\n return eval_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078d2cb66a455b4f6c832ba1720ab237\",\n \"score\": \"0.57605547\",\n \"text\": \"def optimise(self, params):\\n\\n self.Model_sklearn.initialize_params(self.y, params)\\n\\n # logger.info(self.Model_sklearn.p)\\n\\n start = time.perf_counter()\\n\\n if self.x_val is None:\\n # cross-validation\\n fold_id = np.ones((len(self.y),)) * -1\\n oof_val = np.zeros((self.y.shape[0], self.y.shape[1]))\\n if \\\"time_series\\\" not in self.Model_sklearn.objective:\\n # Cross-validation split in self.nfolds but train only on self.nfolds_train chosen randomly\\n rd.seed(self.Model_sklearn.seed)\\n fold_to_train = rd.sample([i for i in range(self.nfolds)], k=max(min(self.nfolds_train, self.nfolds), 1))\\n\\n if self.cv_strategy == \\\"StratifiedKFold\\\":\\n skf = StratifiedKFold(n_splits=self.nfolds, random_state=self.Model_sklearn.seed, shuffle=True)\\n folds = skf.split(self.y, self.y)\\n else:\\n kf = KFold(n_splits=self.nfolds, random_state=self.Model_sklearn.seed, shuffle=True)\\n folds = kf.split(self.y)\\n else:\\n folds = [\\n ([i for i in range(self.Model_sklearn.size_train)], [i for i in range(self.Model_sklearn.size_train, fold_id.shape[0])])]\\n fold_to_train = [0]\\n else:\\n # validation\\n fold_id = np.ones((len(self.y_val),)) * -1\\n oof_val = np.zeros((self.y_val.shape[0], self.y_val.shape[1]))\\n folds = [('all', [i for i in range(oof_val.shape[0])])]\\n fold_to_train = [0]\\n\\n for n, (tr, te) in enumerate(folds):\\n if n not in fold_to_train:\\n continue\\n\\n if tr == 'all':\\n # validation\\n if isinstance(self.x, pd.DataFrame):\\n x_tr, x_val = self.x.values, self.x_val.values\\n else:\\n x_tr, x_val = self.x, self.x_val\\n if isinstance(self.y, pd.DataFrame):\\n y_tr, y_val = self.y.values, self.y_val.values\\n else:\\n y_tr, y_val = self.y, self.y_val\\n else:\\n # cross-validation\\n if isinstance(self.x, pd.DataFrame):\\n x_tr, x_val = self.x.values[tr], self.x.values[te]\\n else:\\n x_tr, x_val = self.x[tr], self.x[te]\\n if isinstance(self.y, pd.DataFrame):\\n y_tr, y_val = self.y.values[tr], self.y.values[te]\\n else:\\n y_tr, y_val = self.y[tr], self.y[te]\\n\\n model = self.Model_sklearn.model()\\n model.fit(x_tr, y_tr)\\n\\n if self.Model_sklearn.shape_y == 1:\\n oof_val[te, :] = model.predict(x_val).reshape(-1, 1)\\n else:\\n oof_val[te, :] = model.predict(x_val)\\n fold_id[te] = n\\n del model\\n\\n metrics = []\\n for i in range(self.Model_sklearn.shape_y):\\n if self.x_val is None:\\n # cross_validation\\n if isinstance(self.y, pd.DataFrame):\\n y_true = self.y.iloc[:, [i]].copy()\\n else:\\n y_true = self.y[:, i]\\n else:\\n # validation\\n if isinstance(self.y, pd.DataFrame):\\n y_true = self.y_val.iloc[:, i].copy()\\n else:\\n y_true = self.y_val[:, i]\\n # subset, only use data where fold_id >= 0 :\\n if isinstance(y_true, pd.DataFrame):\\n y_true_sample = y_true.values[np.where(fold_id >= 0)[0]]\\n else:\\n y_true_sample = y_true[np.where(fold_id >= 0)[0]]\\n prediction_oof_val = oof_val[:, i][np.where(fold_id >= 0)[0]]\\n if 'regression' in self.Model_sklearn.objective:\\n if 'explained_variance' == self.scoring:\\n metrics.append(-explained_variance_score(y_true_sample, prediction_oof_val))\\n elif 'r2' == self.scoring:\\n metrics.append(-r2_score(y_true_sample, prediction_oof_val))\\n else:\\n metrics.append(mean_squared_error(y_true_sample, prediction_oof_val))\\n else:\\n if 'f1' in self.scoring:\\n if 'binary' in self.Model_sklearn.objective:\\n metrics.append(-f1_score(y_true_sample, prediction_oof_val))\\n else:\\n metrics.append(\\n -f1_score(y_true_sample, prediction_oof_val, average=self.Model_sklearn.average_scoring))\\n elif 'recall' in self.scoring:\\n if 'binary' in self.Model_sklearn.objective:\\n metrics.append(-recall_score(y_true_sample, prediction_oof_val))\\n else:\\n metrics.append(-recall_score(y_true_sample, prediction_oof_val,\\n average=self.Model_sklearn.average_scoring))\\n elif 'precision' in self.scoring:\\n if 'binary' in self.Model_sklearn.objective:\\n metrics.append(-precision_score(y_true_sample, prediction_oof_val))\\n else:\\n metrics.append(-precision_score(y_true_sample, prediction_oof_val,\\n average=self.Model_sklearn.average_scoring))\\n elif 'roc' in self.scoring or 'auc' in self.scoring:\\n if 'binary' in self.Model_sklearn.objective:\\n metrics.append(-roc_auc_score(y_true_sample, prediction_oof_val))\\n else:\\n metrics.append(-roc_auc_score(y_true_sample, prediction_oof_val,\\n average=self.Model_sklearn.average_scoring))\\n else:\\n metrics.append(-accuracy_score(y_true_sample, prediction_oof_val))\\n\\n score = -np.mean(metrics)\\n logger.info('oof_val score {} Metric {}'.format(self.scoring, score))\\n\\n # store hyperparameters optimization in a Dataframe self.df_all_results:\\n self.df_all_results['mean_fit_time'].append(time.perf_counter() - start)\\n self.df_all_results['params'].append(params)\\n self.df_all_results['mean_test_score'].append(score)\\n self.df_all_results['std_test_score'].append(0) # just 0\\n\\n return np.mean(metrics)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9d06b232bf7f34923d426255fa4d69\",\n \"score\": \"0.57535625\",\n \"text\": \"def evaluate_model(self, model, test_X, test_y):\\n acc = model.evaluate(test_X, test_y, verbose=0)[1]\\n print('Accuracy on testing data: ', acc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c95d3b3e1f4a22f0069419ead271cc7\",\n \"score\": \"0.5748173\",\n \"text\": \"def run_cross_validation(als, train):\\n logger.info('Training ALS model with k-fold cross-validation..........')\\n\\n param_grid = (ParamGridBuilder()\\n .addGrid(als.rank, [5, 10])\\n .addGrid(als.maxIter, [10, 25, 50])\\n .addGrid(als.regParam, [0.1, 0.01])\\n .build())\\n\\n cv = CrossValidator(estimator=als,\\n estimatorParamMaps=param_grid,\\n evaluator=RegressionEvaluator(labelCol='count'),\\n numFolds=5,\\n parallelism=10,\\n seed=42)\\n\\n # NOTE: Cut dataframe lineage before cross-validation to avoid local StackOverflow error\\n spark.sparkContext.setCheckpointDir(\\\"/tmp\\\")\\n\\n cv_model = cv.fit(train)\\n best_model = cv_model.bestModel\\n logger.info('Average cross-validation RMSE by submodel: {}'.format(cv_model.avgMetrics))\\n logger.info('Lowest cross-validation submodel RMSE: {}'.format(round(min(cv_model.avgMetrics), 3)))\\n return best_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa6abc0bdaea847b0a32c6d30986ea84\",\n \"score\": \"0.57477003\",\n \"text\": \"def _fit_cv(self, examples, labels, groups=None, selection_settings=None):\\n selection_settings = selection_settings or self.config.param_selection\\n cv_iterator = self._get_cv_iterator(selection_settings)\\n\\n if selection_settings is None:\\n return self._fit(examples, labels, self.config.params), self.config.params\\n\\n cv_type = selection_settings[\\\"type\\\"]\\n num_splits = cv_iterator.get_n_splits(examples, labels, groups)\\n logger.info(\\n \\\"Selecting hyperparameters using %s cross-validation with %s split%s\\\",\\n cv_type,\\n num_splits,\\n \\\"\\\" if num_splits == 1 else \\\"s\\\",\\n )\\n\\n scoring = self._get_cv_scorer(selection_settings)\\n n_jobs = selection_settings.get(\\\"n_jobs\\\", -1)\\n\\n param_grid = self._convert_params(selection_settings[\\\"grid\\\"], labels)\\n model_class = self._get_model_constructor()\\n estimator, param_grid = self._get_cv_estimator_and_params(\\n model_class, param_grid\\n )\\n # set GridSearchCV's return_train_score attribute to False improves cross-validation\\n # runtime perf as it doesn't have to compute training scores and which we don't consume\\n grid_cv = GridSearchCV(\\n estimator=estimator,\\n scoring=scoring,\\n param_grid=param_grid,\\n cv=cv_iterator,\\n n_jobs=n_jobs,\\n return_train_score=False,\\n )\\n model = grid_cv.fit(examples, labels, groups)\\n\\n for idx, params in enumerate(model.cv_results_[\\\"params\\\"]):\\n logger.debug(\\\"Candidate parameters: %s\\\", params)\\n std_err = (\\n 2.0\\n * model.cv_results_[\\\"std_test_score\\\"][idx]\\n / math.sqrt(model.n_splits_)\\n )\\n if scoring == Model.LIKELIHOOD_SCORING:\\n msg = \\\"Candidate average log likelihood: {:.4} ± {:.4}\\\"\\n else:\\n msg = \\\"Candidate average accuracy: {:.2%} ± {:.2%}\\\"\\n # pylint: disable=logging-format-interpolation\\n logger.debug(msg.format(model.cv_results_[\\\"mean_test_score\\\"][idx], std_err))\\n\\n if scoring == Model.LIKELIHOOD_SCORING:\\n msg = \\\"Best log likelihood: {:.4}, params: {}\\\"\\n self.cv_loss_ = -model.best_score_\\n else:\\n msg = \\\"Best accuracy: {:.2%}, params: {}\\\"\\n self.cv_loss_ = 1 - model.best_score_\\n\\n best_params = self._process_cv_best_params(model.best_params_)\\n # pylint: disable=logging-format-interpolation\\n logger.info(msg.format(model.best_score_, best_params))\\n\\n return model.best_estimator_, model.best_params_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326ea1e6bd1e503a6e87bd994356f83e\",\n \"score\": \"0.5743013\",\n \"text\": \"def build_model():\\n \\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(KNeighborsClassifier()))\\n ])\\n\\n \\n parameters = {\\n #'vect__max_df': (0.75, 1.0),\\n 'tfidf__use_idf': (True, False),\\n 'clf__estimator__n_neighbors': [3, 5]\\n }\\n \\n\\n cv = GridSearchCV(pipeline, parameters, cv = 3, n_jobs = -1)\\n\\n \\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d4108d52cd5d39bda6b7f71b9a9e8ff\",\n \"score\": \"0.573901\",\n \"text\": \"def knn_cv(X_train, y_train, cv=5, verbose=False):\\n pipe = imbPipeline(steps=[\\n ('sample', SMOTE()), \\n ('scaler', MinMaxScaler()), \\n ('knn', KNeighborsClassifier())\\n ])\\n params = [{'knn__n_neighbors': range(2,25), 'knn__p': [1, 2]}]\\n\\n model = GridSearchCV(pipe, params, cv=cv, n_jobs=-1, scoring=f2scorer, verbose=verbose)\\n model.fit(X_train, y_train)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214e606b3c498b195dff125c2a6effa4\",\n \"score\": \"0.57385755\",\n \"text\": \"def test(self):\\r\\n # self.clf = LogisticRegression(penalty='l2', solver='newton-cg', multi_class='multinomial', C=10, max_iter=300, fit_intercept=True)\\r\\n self.clf = LogisticRegression()\\r\\n self.clf.set_params(**self.best_parameter)\\r\\n print(\\\"*** Test Result for Logistic Regression ***\\\")\\r\\n ModelEvaluation.evaluateModelWithCV(self.clf, self.dataset_x, self.dataset_y, cv=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9602e4a850176d88fb8ae8e81e4c6677\",\n \"score\": \"0.5738553\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(RandomForestClassifier()))\\n ])\\n \\n params = {'clf__estimator__n_estimators': [50, 100],\\n 'clf__estimator__min_samples_split': [2, 4],\\n 'clf__estimator__criterion': ['entropy', 'gini']\\n }\\n \\n cv = GridSearchCV(pipeline, param_grid=params)\\n \\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27404cfc73845866ddce5de3b2d7e1c\",\n \"score\": \"0.57378215\",\n \"text\": \"def evaluate_classifier(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13d078adef53c2346e47c5148f0b685\",\n \"score\": \"0.57359105\",\n \"text\": \"def use_trained_model(project_dir, pickled_model, evaluate_performance, input_features = None, \\\\\\n internal_features_to_drop = None, input_data = None, splits = None, \\\\\\n add_new_splits = False, perf_by_sample = False):\\n # first make sure features were supplied.\\n if (input_features is None) and (internal_features_to_drop is None):\\n raise ValueError('Features must be supplied, either through input_features argument \\\\\\n (recommended) or by dropping from list of internal features.')\\n # see if path to input data is supplied. if not, load from relative directory. \\n if input_data is None:\\n print('Using default path to engineered input data.')\\n df = load_filter_engineered_df(project_dir)\\n else:\\n df = load_filter_engineered_df(project_dir, input_data)\\n # use new splits created in cross-validation fold?\\n # if so, use the data_splits in project_dir to define the splits\\n print(f'input df has {df.shape[0]} variants.')\\n if add_new_splits:\\n df = load_and_merge_new_splits(engineered_df = df, fold_dir = project_dir)\\n \\n classifier = pickle.load(open(pickled_model, 'rb'))\\n\\n if input_features is not None: \\n # check if any features are missing from df.\\n for input_feature in input_features:\\n if input_feature not in df.columns:\\n raise ValueError(f'input feature {input_feature} is missing from dataframe.')\\n features = input_features \\n else:\\n # drop from list of all internal features\\n features = get_internal_features( use_purecn_purity = ('purity' in df.columns), internal_features_to_drop = internal_features_to_drop)\\n features = list(features)\\n print('finished selecting features.')\\n print(f'df.columns: {df.columns}')\\n print(f'features: {features}')\\n print(f'Number of input variants to classify (before eval): {df.shape[0]}')\\n if evaluate_performance:\\n performance_df = evaluate_classifier(df, classifier, features, splits, perf_by_sample)\\n performance_df.to_csv(make_filename(project_dir, pickled_model, \\\\\\n '_full_performance_across_groups.csv'))\\n single_samples = performance_df[performance_df['is_single_sample']]\\n if single_samples.shape[0] > 0: # if there are some single samples\\n print('Performance averaged across single samples.')\\n # It wouldn't make sense to average over these two categorical columns\\n mean_by_sample = single_samples.drop(columns = ['subset','is_single_sample']).mean()\\n print(mean_by_sample)\\n mean_by_sample.to_csv(make_filename(project_dir, pickled_model, \\\\\\n '_average_performance_stats_by_sample.csv'))\\n \\n X_full = df[features]\\n #print(f'df.shape: {df.shape}')\\n X_full_values = X_full.fillna(0).values\\n #print(f'X_full_values.shape: {X_full_values.shape}')\\n\\n # make binary predictions and probabilities for saving.\\n y_pred = classifier.predict(X_full_values)\\n y_proba = classifier.predict_proba(X_full_values)\\n #print(f'y_pred.shape: {y_pred.shape}')\\n # df already has features plus truth values; add y_pred prediction and class probabilities.\\n df['tabnet_pred'] = y_pred\\n df['tabnet_proba_0'] = y_proba[:,0]\\n df['tabnet_proba_1'] = y_proba[:,1]\\n\\n # uncomment if you need to generate the full matrix including unused features.\\n output_predictions_path = make_filename(project_dir, pickled_model, '_predictions_all_features.csv')\\n print(output_predictions_path)\\n df.to_csv(output_predictions_path)\\n # drop superfluous unused features from dataframe.\\n #if input_features is not None:\\n # no good way to do this. skipping!\\n # df_dropped_unused_features = df[input_features]\\n #else:\\n # intersect_features_to_drop = set(df.columns).intersection(set(internal_features_to_drop))\\n # df_dropped_unused_features = df.drop(columns = list(intersect_features_to_drop))\\n #df_dropped_unused_features.reset_index(drop = True).to_csv(make_filename(project_dir, pickled_model, '_predictions.csv'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3636ad9abfa742b023fb9641061f8a97\",\n \"score\": \"0.57272565\",\n \"text\": \"def build_model():\\n pipeline = Pipeline([\\n ('vect', CountVectorizer(tokenizer=tokenize)),\\n ('tfidf', TfidfTransformer()),\\n ('clf', MultiOutputClassifier(estimator=RandomForestClassifier()))\\n ])\\n print(pipeline.get_params())\\n parameters = {\\n 'clf__estimator__n_estimators': [10, 15, 20],\\n 'vect__min_df': [1, 2]\\n }\\n cv = GridSearchCV(pipeline, param_grid=parameters)\\n return cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8875475424f1ceffb6c8b644fc650226\",\n \"score\": \"0.57260394\",\n \"text\": \"def evaluate_classifier(clf,features, labels, num_iter, test_size, params):\\n\\n features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=test_size, random_state=42)\\n\\n\\n precision_values = []\\n recall_values = []\\n accuracy_values = []\\n\\n for i in xrange(0, num_iter):\\n #print params\\n clf = GridSearchCV(clf, params)\\n clf.fit(features_train, labels_train)\\n\\n clf = clf.best_estimator_\\n #test_classifier(clf, dataset, feature_list)\\n pred = clf.predict(features_test)\\n precision_values.append(precision_score(labels_test, pred))\\n recall_values.append(recall_score(labels_test, pred))\\n accuracy_values.append(accuracy_score(labels_test, pred))\\n \\n print '_______________________________________________________________'\\n print clf\\n \\n print 'Recall score: ', mean(recall_values)\\n print 'Precision score: ', mean(precision_values)\\n print 'Accuracy score: ' , mean(accuracy_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8fa6b107c18826fe40f6f211ccdf67\",\n \"score\": \"0.57215756\",\n \"text\": \"def timeseries_cv(model, data, n_splits=5):\\n X_train, X_test, y_train, y_test = ts_train_test_split(data)\\n \\n tscv = TimeSeriesSplit(n_splits=n_splits)\\n scores = np.sqrt(-cross_val_score(estimator=model, X=X_train.values, y=y_train.values, cv=tscv, scoring='neg_mean_squared_error', n_jobs=-1))\\n \\n return scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65fa2da24a25f001cc1820d7ae0213df\",\n \"score\": \"0.5721298\",\n \"text\": \"def testClassifier():\\n # load problem\\n print(\\\"Loading testing data...\\\")\\n y, x = svm_read_problem(\\\"./libsvm_test_problem\\\")\\n problem = svm_problem(y, x)\\n\\n # load pretrained model\\n model = svm_load_model(\\\"./trained-model-rbf\\\")\\n\\n # test model on training data\\n print(\\\"Testing model...\\\")\\n pLabel, pAcc, pVal = svm_predict(y, x, model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1625a65b8b15e73501a605e3f726d5b\",\n \"score\": \"0.57195264\",\n \"text\": \"def cv_score(clf,\\n X,\\n y,\\n sample_weight=None,\\n scoring='neg_log_loss',\\n n_splits=3,\\n t1=None,\\n cv_gen=None,\\n pct_embargo=0.,\\n purging=True,\\n return_combs=False,\\n ret=None,\\n num_threads=1,\\n **kwargs):\\n if cv_gen is None:\\n if t1 is not None:\\n cv_gen = PurgedKFold(\\n n_splits=n_splits,\\n t1=t1,\\n pct_embargo=pct_embargo,\\n purging=purging,\\n num_threads=num_threads)\\n else:\\n cv_gen = KFold(n_splits=n_splits)\\n elif cv_gen == 'cp':\\n cv_gen = CPKFold(\\n n_splits=n_splits,\\n t1=t1,\\n pct_embargo=pct_embargo,\\n purging=purging,\\n num_threads=num_threads)\\n scores = []\\n for train, test in cv_gen.split(X=X):\\n train_params = dict()\\n test_params = dict()\\n # Sample weight is an optional parameter\\n if sample_weight is not None:\\n train_params['sample_weight'] = sample_weight.iloc[train].values\\n test_params['sample_weight'] = sample_weight.iloc[test].values\\n test_params.update(kwargs)\\n clf_fit = clf.fit(\\n X=X.iloc[train, :].values, y=y.iloc[train].values, **train_params)\\n if hasattr(clf_fit, 'classes_'):\\n test_params['labels'] = clf_fit.classes_\\n if ret is not None:\\n test_params['ret'] = ret.iloc[test]\\n # Scoring\\n score_ = evaluate(clf_fit, X.iloc[test, :].values, y.iloc[test].values,\\n scoring, **test_params)\\n scores.append(score_)\\n if scoring not in ['roc', 'precision_recall']:\\n scores = np.array(scores)\\n if return_combs:\\n return scores, cv_gen.get_test_combs()\\n else:\\n return scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6c22cdbaba02f0f2138bd38141ca2cb\",\n \"score\": \"0.5714878\",\n \"text\": \"def testOverall():\\n # load problem\\n print(\\\"Loading testing data...\\\")\\n y, x = svm_read_problem(\\\"./libsvm_all_problem\\\")\\n problem = svm_problem(y, x)\\n\\n # load pretrained model\\n model = svm_load_model(\\\"./trained-model-rbf\\\")\\n\\n # test model on training data\\n print(\\\"Testing model...\\\")\\n pLabel, pAcc, pVal = svm_predict(y, x, model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8977ad810ac31c19ff17fb7777b5396f\",\n \"score\": \"0.5714392\",\n \"text\": \"def evaluate_model(self, model):\\n\\n biases, stereotypes = self.gender_neutrality_test(model)\\n preds = self.gender_identification_test(model)\\n ppl = self.PTB_test(model)\\n\\n self._log_neutrality(biases, stereotypes)\\n self._log_identification(preds)\\n self._log_PTB(ppl)\\n\\n return self.score_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6c3d5dd720194c83cc2af0bf025cec\",\n \"score\": \"0.5713862\",\n \"text\": \"def score_model(model, X_train, y_train, X_test, y_test):\\n model.fit(X_train, y_train)\\n y_pred = model.predict(X_test)\\n\\n if is_classifier(model):\\n score = f1_score(y_test, y_pred)\\n else:\\n score = r2_score(y_test, y_pred)\\n return model, score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a227d4b9a32c42d496574d81eb31b11f\",\n \"score\": \"0.57075506\",\n \"text\": \"def evaluate_model(model, meta_data, test_data_folder):\\n print(\\\"[Info] Cargando test dataset\\\")\\n X, Y = load_test_dataset(meta_data, test_data_folder)\\n print(f\\\"[Info] Imagenes cargadas {len(X)}\\\")\\n\\n loss, acc = model.evaluate(X, Y)\\n print(f\\\"[Info] loss: {loss}\\\\taccuracy: {acc}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d50040a146ead260b28347e3513874\",\n \"score\": \"0.57074225\",\n \"text\": \"def test_trained_model(X_test,y_test,y_train_labels,y_test_labels,lamda_list,model_num):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91393142be69e6580726bacc560ec15\",\n \"score\": \"0.5698971\",\n \"text\": \"def run_xval_evaluation(task, arch='segnet_proper', pretrained=None,\\n hyperparams={}, test_keys=None, fit=True):\\n # import os\\n # os.environ['GLOG_minloglevel'] = '3'\\n\\n from clab import harness\\n # from clab import models\\n\\n xval_base = abspath(task.exptdir('xval'))\\n\\n xval_results = []\\n for idx, xval_split in enumerate(task.xval_splits(test_keys=test_keys)):\\n print(ub.color_text('XVAL iteration {}'.format(idx), 'blue'))\\n\\n (train, test) = xval_split\\n xval_dpath = ub.ensuredir((xval_base, 'split_{:0=2}'.format(idx)))\\n\\n harn = harness.Harness(workdir=xval_dpath, arch=arch, task=task)\\n harn.task = task\\n harn.set_inputs(train, test)\\n\\n harn.init_pretrained_fpath = pretrained\\n harn.init_pretrained_fpath = None\\n harn.params.update(hyperparams)\\n\\n harn.test.prepare_images()\\n harn.train.prepare_images()\\n\\n harn.gpu_num = gpu_util.find_unused_gpu(min_memory=6000)\\n print('harn.gpu_num = {!r}'.format(harn.gpu_num))\\n if harn.gpu_num is not None:\\n avail_mb = gpu_util.gpu_info()[harn.gpu_num]['mem_avail']\\n # Estimate how much we can fit in memory\\n # TODO: estimate this from the model arch instead.\\n # (90 is a mgic num corresponding to segnet_proper)\\n harn.train_batch_size = int(\\n (avail_mb * 90) // np.prod(task.input_shape))\\n harn.train_batch_size = int(harn.train_batch_size)\\n if harn.train_batch_size == 0:\\n raise MemoryError(\\n 'not enough GPU memory to train the model')\\n else:\\n # not sure what the best to do on CPU is. Probably nothing.\\n harn.train_batch_size = 4\\n\\n harn.prepare_solver()\\n\\n # Check if we can resume a previous training state\\n print(ub.color_text('Checking for previous snapshot states', 'blue'))\\n previous_states = harn.snapshot_states()\\n print('Found {} previous_states'.format(len(previous_states)))\\n\\n if fit:\\n if len(previous_states) == 0:\\n print(ub.color_text('Starting a fresh training session', 'blue'))\\n harn.fit()\\n else:\\n from clab.backend import iface_caffe as iface\\n solver_info = iface.parse_solver_info(harn.solver_fpath)\\n prev_state = previous_states[-1]\\n prev_iter = iface.snapshot_iterno(prev_state)\\n if prev_iter < solver_info['max_iter']:\\n # continue from the previous iteration\\n print(ub.color_text(\\n 'Resume training from iter {}'.format(prev_iter), 'blue'))\\n harn.fit(prevstate_fpath=prev_state)\\n else:\\n print(ub.color_text(\\n 'Already finished training this model', 'blue'))\\n\\n for _ in harn.deploy_trained_for_testing():\\n # hack to evaulate while deploying\\n harn.evaulate_all()\\n xval_results.append(list(harn._test_results_fpaths()))\\n return xval_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61203862b24fdd9277d5c8fbdf8652ca\",\n \"score\": \"0.56965387\",\n \"text\": \"def sl_model_evaluation(classifier, eval_df):\\n\\n untampered_df = eval_df[eval_df['tamper'] == 1]\\n attacks_df = eval_df[eval_df['tamper'] == -1]\\n\\n y_pred_test = classifier.predict(np.asarray(untampered_df[FEATURES]))\\n y_pred_outliers = classifier.predict(np.asarray(attacks_df[FEATURES]))\\n\\n # Format the output of the classifier\\n y_pred_test[y_pred_test == 0] = -1\\n y_pred_outliers[y_pred_outliers == 0] = -1\\n\\n n_accurate_test = y_pred_test[y_pred_test == 1].size\\n n_accurate_outliers = y_pred_outliers[y_pred_outliers == -1].size\\n\\n\\n f_beta = fbeta_score(np.concatenate([np.ones(y_pred_test.shape[0]),\\n -1 * np.ones(y_pred_outliers.shape[0])]),\\n np.concatenate([y_pred_test, y_pred_outliers]),\\n beta=20,\\n pos_label=1)\\n\\n tnr = n_accurate_outliers / attacks_df.shape[0]\\n tpr_test = n_accurate_test / untampered_df.shape[0]\\n\\n return f_beta, tnr, tpr_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcae29934c124893a8bc0ac7eab03b32\",\n \"score\": \"0.56955665\",\n \"text\": \"def evaluation(x, y, cut_size, w, h, fimg, model):\\n fimg = cv2.imread(fimg.filename)\\n image = fimg[y:y+cut_size, x:x+cut_size]\\n # cv2.imshow(\\\"cropped\\\", image)\\n\\n results = model.get_predictions(image=image, plot=False)\\n\\n if len(results) == 0:\\n return None\\n\\n c = results.cpu().numpy()\\n\\n if(x != 0 and y != 0 and x+cut_size != w and y+cut_size != h):\\n i = 0\\n while i < c.shape[0]:\\n if(c[i, 0] < settings.BOARDCACHE or c[i, 1] < settings.BOARDCACHE or\\n c[i, 2] < settings.BOARDCACHE or c[i, 3] < settings.BOARDCACHE):\\n c = np.delete(c, i, axis=0)\\n i -= 1\\n i += 1\\n i = 0\\n\\n while i < c.shape[0]:\\n c[i, 0] += x\\n c[i, 1] += y\\n c[i, 2] += x\\n c[i, 3] += y\\n i += 1\\n\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa0dab4fe08c8bb9d6e128d28a311d1\",\n \"score\": \"0.568342\",\n \"text\": \"def score_model(\\n self, target=\\\"estimated_demand\\\", true_demand=\\\"estimated_demand\\\", cv=5\\n ):\\n\\n # Get the product history and drop the date and any possible demand columns\\n records = self.history\\n X = records.drop(\\n columns=[\\\"date\\\", \\\"estimated_demand\\\", \\\"measured_demand\\\", \\\"demand\\\"],\\n errors=\\\"ignore\\\",\\n )\\n\\n # Get the specified column as the target data for training\\n y = records[[target]]\\n\\n # Get the specified column for evaluation of production predictions\\n demand = records[[true_demand]]\\n\\n # set up lists for recording values for each fold of the cross validation\\n score = {\\n \\\"val_loss\\\": [],\\n \\\"days\\\": [],\\n \\\"demand\\\": [],\\n \\\"produced\\\": [],\\n \\\"revenue\\\": [],\\n \\\"cost\\\": [],\\n \\\"sales\\\": [],\\n \\\"profit\\\": [],\\n \\\"waste\\\": [],\\n }\\n\\n # split is done in groups, but could be switched to random samples\\n # splitter = ShuffleSplit(n_splits=cv, test_size=1/cv)\\n splitter = KFold(n_splits=cv, shuffle=False)\\n for train_indices, test_indices in splitter.split(X):\\n X_train, X_test = X.iloc[train_indices], X.iloc[test_indices]\\n y_train, y_test = y.iloc[train_indices], y.iloc[test_indices]\\n y_true = demand.iloc[test_indices].to_numpy()\\n score[\\\"demand\\\"].append(y_true)\\n\\n # only look for fit_history if epochs is present, i.e. if model is a deep learning model.\\n if hasattr(self.model, \\\"epochs\\\"):\\n self.model.fit(\\n X_train, y_train, validation_data=(X_test, y_test), reset_model=True\\n )\\n score[\\\"val_loss\\\"].append(self.model.fit_history[\\\"val_loss\\\"])\\n else:\\n self.model.fit(X_train, y_train)\\n\\n # Get productions on test set.\\n production = self.model.predict(X_test)\\n\\n # Get total produced over all days.\\n produced = np.sum(production)\\n score[\\\"produced\\\"].append(produced)\\n\\n # Find actual sales\\n sales = np.sum(np.min(np.hstack([y_true, production]), axis=1))\\n score[\\\"sales\\\"].append(sales)\\n revenue = sales * self.unit_sale_price\\n score[\\\"revenue\\\"].append(revenue)\\n cost = np.sum(production) / self.batch_size * self.batch_cost\\n score[\\\"cost\\\"].append(cost)\\n score[\\\"days\\\"].append(X_test.shape[0])\\n profit = revenue - cost\\n score[\\\"profit\\\"].append(profit)\\n waste = produced - sales\\n score[\\\"waste\\\"].append(waste)\\n\\n total_days = np.sum(score[\\\"days\\\"])\\n score[\\\"revenue_avg\\\"] = np.sum(score[\\\"revenue\\\"]) / total_days\\n score[\\\"profit_avg\\\"] = np.sum(score[\\\"profit\\\"]) / total_days\\n score[\\\"profit_margin\\\"] = (\\n np.sum(score[\\\"profit\\\"]) * 100.0 / np.sum(score[\\\"revenue\\\"])\\n )\\n score[\\\"waste_pct\\\"] = np.sum(score[\\\"waste\\\"]) * 100 / np.sum(score[\\\"produced\\\"])\\n score[\\\"waste_avg\\\"] = np.sum(score[\\\"waste\\\"]) / total_days\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808eec7baabc23944bec6c2b73fe8fe4\",\n \"score\": \"0.567953\",\n \"text\": \"def cv_train(modelname,config,kfold,lstep,n_samples,feature_weight,\\n\\t\\t\\t pkl_filename=\\\"data/adni.pkl\\\",json_filename=\\\"conf/dataConfig.json\\\"):\\n\\n\\t\\\"\\\"\\\"load data and data config\\n\\tThe data file *pkl contains three parts: {'demo','dync','max_len'}\\n\\t\\t'demo' : a list of dataframes storing patients' demographics information\\n\\t\\t'dync' : a list of dataframes storing patients' dynamic information, including continous features and diganosis\\n\\t\\t'max_len' : int, the maximum lengths of patient sequence\\n\\t\\\"\\\"\\\"\\n\\tadni = _pickle.load(open(pkl_filename,\\\"rb\\\"))\\n\\tdataConfig = json.load(open(json_filename))\\n\\tmax_len = adni[\\\"max_len\\\"]\\n\\t\\n\\t\\\"\\\"\\\"\\n\\trecord val_loss change / trends;\\n\\tthe smallest loss value for each fold is given by the best model\\n\\t\\\"\\\"\\\"\\n\\tloss_curve = {}\\n\\n\\t# build model graph\\n\\tif modelname == \\\"rnn\\\":\\n\\t\\tmodel = rnn(len(dataConfig[\\\"demo_vars\\\"]),len(dataConfig[\\\"input_x_vars\\\"]),len(dataConfig[\\\"input_y_vars\\\"]),\\n\\t\\t\\t\\t\\t\\t\\t max_len,config[\\\"batch_size\\\"],config[\\\"n_h\\\"],config[\\\"n_z\\\"],lstep,feature_weight)\\n\\telif modelname == \\\"stocast\\\":\\n\\t\\tmodel = stocast(len(dataConfig[\\\"demo_vars\\\"]),len(dataConfig[\\\"input_x_vars\\\"]),len(dataConfig[\\\"input_y_vars\\\"]),\\n\\t\\t\\t\\t\\t\\tmax_len,config[\\\"batch_size\\\"],config[\\\"n_h\\\"],config[\\\"n_z\\\"],lstep,feature_weight)\\n\\telif modelname == \\\"storn\\\":\\n\\t\\tmodel = storn(len(dataConfig[\\\"demo_vars\\\"]),len(dataConfig[\\\"input_x_vars\\\"]),len(dataConfig[\\\"input_y_vars\\\"]),\\n\\t\\t\\t\\t\\t\\tmax_len,config[\\\"batch_size\\\"],config[\\\"n_h\\\"],config[\\\"n_z\\\"],lstep,feature_weight)\\n\\telif modelname == \\\"retain\\\":\\n\\t\\tmodel = retain(len(dataConfig[\\\"demo_vars\\\"]),len(dataConfig[\\\"input_x_vars\\\"]),len(dataConfig[\\\"input_y_vars\\\"]),\\n\\t\\t\\t\\t\\t\\tmax_len,config[\\\"batch_size\\\"],config[\\\"n_h\\\"],config[\\\"n_z\\\"],lstep,feature_weight)\\n\\telif modelname == \\\"tlstm\\\":\\n\\t\\tmodel = tlstm(len(dataConfig[\\\"demo_vars\\\"]),len(dataConfig[\\\"input_x_vars\\\"]),len(dataConfig[\\\"input_y_vars\\\"]),\\n\\t\\t\\t\\t\\t\\tmax_len,config[\\\"batch_size\\\"],config[\\\"n_h\\\"],config[\\\"n_z\\\"],lstep,feature_weight)\\n\\n\\n\\n\\t# saving ...\\n\\tdirname = \\\"save/{} {}\\\".format(modelname,time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(time.time())))\\n\\tif not os.path.exists(dirname):\\n\\t\\tos.makedirs(dirname)\\n\\n\\twith tf.Session() as sess:\\n\\t\\tsummary_writer = tf.summary.FileWriter('logs/' + datetime.now().isoformat().replace(':', '-'), sess.graph)\\n\\t\\tmerged = tf.summary.merge_all()\\n\\t\\tsaver = tf.train.Saver(tf.global_variables(),max_to_keep=1)\\n\\n\\t\\tstart = time.time()\\n\\t\\ttotaltime = 0\\n\\t\\tfor k in range(kfold):\\n\\t\\t\\t\\n\\t\\t\\t\\\"\\\"\\\" if k = 0, random initialization params \\\"\\\"\\\"\\n\\t\\t\\t\\\"\\\"\\\" else, inherit previous best model's params \\\"\\\"\\\"\\n\\t\\t\\tif k == 0:\\n\\t\\t\\t\\ttf.global_variables_initializer().run()\\n\\t\\t\\telse:\\n\\t\\t\\t\\tckpt = tf.train.get_checkpoint_state(dirname)\\n\\t\\t\\t\\tsaver.restore(sess, ckpt.model_checkpoint_path)\\n\\n\\t\\t\\t# split into trainining, validation, testing data\\n\\t\\t\\ttrain_ds,valid_ds = split_data(k,kfold,adni,dataConfig,max_len)\\n\\n\\t\\t\\t# train\\n\\t\\t\\tminVlloss = 1e10\\n\\t\\t\\tloss_curve[k] = []\\n\\t\\t\\tn_batchs = int(train_ds.num_examples / config[\\\"batch_size\\\"])\\n\\n\\t\\t\\t# each epoch\\n\\t\\t\\tno_improvement = 0 # number of no improvements\\n\\t\\t\\te = 0\\n\\t\\t\\twhile e < config[\\\"num_epochs\\\"]:\\n\\t\\t\\t\\tsess.run(tf.assign(model.lr, config[\\\"learning_rate\\\"] * (config[\\\"decay_rate\\\"] ** e)))\\n\\n\\t\\t\\t\\tfor b in range(n_batchs):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\twrap = train_ds.next_batch(config[\\\"batch_size\\\"])\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tfeed = {model.input_demo: wrap['input_demo'],\\n\\t\\t\\t\\t\\t\\t\\tmodel.input_x: wrap['input_x'],\\n\\t\\t\\t\\t\\t\\t\\tmodel.input_y: wrap['input_y'],\\n\\t\\t\\t\\t\\t\\t\\tmodel.input_dt: wrap['input_dt'],\\n\\t\\t\\t\\t\\t\\t\\tmodel.seqlens: wrap['seqlens'],\\n\\t\\t\\t\\t\\t\\t\\tmodel.mask: wrap['mask']}\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t_,loss,summary = sess.run([model.train_op, model.loss, merged],feed)\\n\\t\\t\\t\\t\\tsummary_writer.add_summary(summary, e * n_batchs + b)\\n\\n\\t\\t\\t\\t# validation\\n\\t\\t\\t\\tvloss = val_loss(sess,model,valid_ds,config[\\\"batch_size\\\"])\\n\\t\\t\\t\\tloss_curve[k].append(vloss)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tprint(\\\" |- FOLD:%d, EPOCH:%d, VLOSS:%.4f\\\" % (k,e,vloss))\\n\\n\\t\\t\\t\\tif minVlloss > vloss:\\n\\t\\t\\t\\t\\tminVlloss = vloss\\n\\t\\t\\t\\t\\tcheckpoint_path = os.path.join(dirname, \\\"best_model_k={}_e={}.ckpt\\\".format(k,e))\\n\\t\\t\\t\\t\\tsaver.save(sess, checkpoint_path, global_step=e * n_batchs + k * config[\\\"num_epochs\\\"] * n_batchs)\\n\\t\\t\\t\\t\\tprint(\\\" |- Best model saved to {}\\\".format(checkpoint_path))\\n\\t\\t\\t\\t\\tno_improvement = 0\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tno_improvement += 1\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# if the number of improvement reaches 10, stop running\\n\\t\\t\\t\\tif no_improvement < 10:\\n\\t\\t\\t\\t\\te += 1\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\tend = time.time()\\n\\t\\t\\tprint(\\\"|- %2d fold costs %.4f seconds.\\\\n\\\" % (k,end-start))\\n\\t\\t\\ttotaltime += end-start\\n\\t\\t\\tstart = time.time()\\n\\t\\tprint(\\\"Total train time is %.4f seconds.\\\" % totaltime)\\n\\n\\t\\t# testing\\n\\t\\tprint(\\\"Starting testing\\\")\\n\\t\\tckpt = tf.train.get_checkpoint_state(dirname)\\n\\t\\tif ckpt:\\n\\t\\t\\tsaver.restore(sess, ckpt.model_checkpoint_path)\\n\\t\\t\\tprint(\\\"Loading model: \\\",ckpt.model_checkpoint_path)\\n\\t\\t\\n\\t\\ttest_ds = DataSet(dataConfig,adni[\\\"demo\\\"],adni[\\\"dync\\\"],max_len)\\n\\t\\ttest_res = test(sess,model,modelname,test_ds,\\n\\t\\t\\tconfig[\\\"batch_size\\\"],max_len,dataConfig[\\\"input_y_vars\\\"],lstep,n_samples=n_samples)\\n\\t\\t\\n\\t\\tprint(\\\"Saving test results...\\\")\\n\\t\\t\\\"\\\"\\\"The results are saved in the following format.\\n\\t\\tres = pickle.load(open(filename,'rb'))\\n\\t\\t\\tres : a list of dicts, with each dict() stores the prediction results corresponding to a specific patient\\n\\t\\t\\tres[i] : the dict for patient i, {'curr_labels','target_labels','pred_pi','target_features','pred_mu'}\\n\\t\\t\\t\\t'curr_labels' : a list of labels\\n\\t\\t\\t\\t'target_labels' : a list of target labels\\n\\t\\t\\t\\t'pred_pi' : a list of predictions, the length is timesteps\\n\\t\\t\\t\\t\\t- pred_pi[t] is a 1d array for deterministic methods, or a 2d array for stocast with size (n_samples, 3)\\n\\t\\t\\t\\t'target_features' : list, the length is timesteps\\n\\t\\t\\t\\t\\t- target_features[t] : a 1d array\\n\\t\\t\\t\\t'pred_mu' : list, the length is timesteps\\n\\t\\t\\t\\t\\t- pred_mu[t] : a 1d array for deterministic methods, or a 2d array for stocast\\n\\t\\t\\\"\\\"\\\"\\n\\n\\t\\tdirname = \\\"result_fw={}/{}\\\".format(feature_weight,modelname)\\n\\t\\tif not os.path.exists(dirname):\\n\\t\\t\\tos.makedirs(dirname)\\n\\t\\t_pickle.dump(test_res,open(os.path.join(dirname,\\\"lstep{}_nsamples{}_result.pkl\\\".format(lstep,n_samples)),\\\"wb\\\"))\\n\\t\\t_pickle.dump(loss_curve,open(os.path.join(dirname,\\\"lstep{}_nsamples{}_losses.pkl\\\".format(lstep,n_samples)),\\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09cd531a8c96a4c83a59cd50bcd6a32\",\n \"score\": \"0.56758386\",\n \"text\": \"def test(self):\\n self.model.eval()\\n self.predictions = []\\n self.targets = []\\n for cluster in self.clustering_machine.clusters:\\n prediction, target = self.do_prediction(cluster)\\n self.predictions.append(prediction.cpu().detach().numpy())\\n self.targets.append(target.cpu().detach().numpy())\\n self.targets = np.concatenate(self.targets)\\n self.predictions = np.concatenate(self.predictions).argmax(1)\\n score = f1_score(self.targets, self.predictions, average=\\\"micro\\\")\\n print(\\\"\\\\nF-1 score: {:.4f}\\\".format(score))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993983,"cells":{"query_id":{"kind":"string","value":"1015f0d475a10360dd0e41ccacad1fc6"},"query":{"kind":"string","value":"Create a site configuration from a Volt config file."},"positive_passages":{"kind":"list like","value":[{"docid":"2cca93d684692315c46caed80f111696","score":"0.0","text":"def from_file_name(\n cls,\n invoc_dir: Path,\n project_dir: Path,\n config_file_name: str,\n **kwargs: Any,\n ) -> Self:\n config_path = project_dir / config_file_name\n with config_path.open() as src:\n user_conf = cast(Dict[str, Any], tomlkit.load(src))\n\n return cls(\n invoc_dir=invoc_dir,\n project_dir=project_dir,\n user_conf=user_conf,\n config_path=config_path,\n **kwargs,\n )","title":""}],"string":"[\n {\n \"docid\": \"2cca93d684692315c46caed80f111696\",\n \"score\": \"0.0\",\n \"text\": \"def from_file_name(\\n cls,\\n invoc_dir: Path,\\n project_dir: Path,\\n config_file_name: str,\\n **kwargs: Any,\\n ) -> Self:\\n config_path = project_dir / config_file_name\\n with config_path.open() as src:\\n user_conf = cast(Dict[str, Any], tomlkit.load(src))\\n\\n return cls(\\n invoc_dir=invoc_dir,\\n project_dir=project_dir,\\n user_conf=user_conf,\\n config_path=config_path,\\n **kwargs,\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8c9a1a385e5301c630e9e1751cbf39e6","score":"0.713112","text":"def load_from_file(self, config_file):\n with open(config_file, 'r') as f:\n parser = ConfigParser()\n parser.readfp(f)\n if parser.has_option('site', 'outdir'):\n self.outdir = os.path.expanduser(parser.get('site', 'outdir'))","title":""},{"docid":"420991adb29a7dbd7c29181d763ab85b","score":"0.6845928","text":"def load_site_config(site_name = \"cmc\"):\n (the_site_name, the_site_host, the_site_port) = \\\n get_site_specific_info(DB_PATH, site_name)\n yml = yaml.load(open(os.path.join(EXC_PATH, \"config\", \"site.yaml\")))\n\n #name is first field in table\n the_site = yml[ the_site_name ]\n the_site['host'] = the_site_host\n the_site['port'] = the_site_port\n return the_site","title":""},{"docid":"fa3d5fb6f03c76e141ee9d2efeedf78c","score":"0.6822562","text":"def load_from_file(self, config_file):\n with open(config_file, 'r') as f:\n self.parser.readfp(f)\n if self.parser.has_option('site', 'domain'):\n self._domain = self.parser.get('site', 'domain')\n\n if self.parser.has_option('site', 'outdir'):\n outdir = os.path.expanduser(self.parser.get('site', 'outdir'))\n if not os.path.isabs(outdir):\n path = os.path.dirname(config_file)\n outdir = os.path.abspath(os.sep.join([path, outdir]))\n self.outdir = outdir\n\n if self.parser.has_section('site'):\n self._find_extensions(self.parser)","title":""},{"docid":"7989c2ff1abb8120c9697cb1095d6013","score":"0.62964994","text":"def parseConfig(file):\n \n sites = []\n # read all lines\n for line in open(file, 'r'):\n line = line.strip()\n if not re.match(r'^[^#]', line):\n continue\n \n args = [arg for arg in line.split(';')]\n sites.append(Site(args[0], args[1], args[2:]))\n\n return sites","title":""},{"docid":"f69f397605260786404e366bb5832e5e","score":"0.6267531","text":"def up_site_config():\n\n apache_root = '/etc/apache2'\n apache_available = os.path.join(apache_root, 'sites-available')\n apache_enabled = os.path.join(apache_root, 'sites-enabled')\n app_conf = os.path.join(apache_available, '%s.conf' % env.server_name)\n\n fabtools.files.upload_template(\n 'apache_host.conf.tpl',\n app_conf,\n context=env,\n template_dir=os.path.join(env.lib_path, 'templates'),\n use_jinja=True,\n use_sudo=True,\n user='root',\n chown=True,\n mode='644',\n )\n\n fabric.api.execute(apache_enable_site, env.server_name)","title":""},{"docid":"2a2a160f05eca66dc262a86949c16a6d","score":"0.6153214","text":"def configure(name):\n\n CONFIG = {}\n module = __import__('cfgs.%s' % name)\n CONFIG.update(getattr(module, name).CONFIG)\n\n from string import Template\n\n print '\\tCreate src/settings.py'\n with open('./tpls/settings.template', 'r') as src:\n with open('./src/settings.py', 'w') as dst:\n template = Template(src.read())\n dst.write(template.substitute(CONFIG))\n dst.close()\n src.close()\n\n print '\\tCreate fabfile/hosts.py'\n with open('./tpls/hosts.template', 'r') as src:\n with open('./fabfile/hosts.py', 'w') as dst:\n CONFIG.update({\n 'DB_USER': CONFIG['DATABASES']['default']['USER'],\n 'DB_PASSWORD': CONFIG['DATABASES']['default']['PASSWORD'],\n })\n template = Template(src.read())\n dst.write(template.substitute(CONFIG))\n dst.close()\n src.close()","title":""},{"docid":"702e324a431ed7f34107a381f6f1bf0f","score":"0.61366117","text":"def init_config(config_file):\r\n import ConfigParser\r\n config = ConfigParser.ConfigParser()\r\n config.read(config_file)\r\n return config","title":""},{"docid":"e63d3abfa889399a6fce42be4d5c205a","score":"0.59605837","text":"def genconf(quiet: bool, domain: str) -> None:\n explain_step(f\"HTTPS configuration file for website {domain}:\", quiet=quiet)\n with path(package=\"webserver\", resource=\"config\") as folder:\n click.echo(f\"#=== Generated with webserver v{__version__} ===#\")\n run_command(\n f'cat {folder}/443-template.conf {folder}/examples/* | sed \"s/{{{{SERVER_NAME}}}}/{domain}/g\"'\n )\n click.echo(\"}\")","title":""},{"docid":"b365dfb20fe291b19fb9676f4f244d5d","score":"0.5919497","text":"def new_from_template(filename):\n import opus\n opus_dir = opus.__path__[0]\n configfile = os.path.join(opus_dir, \"lib\", \"conf\", \"default_settings.json\")\n config = OpusConfig(configfile)\n config._filename = filename\n config[\"SECRET_KEY\"] = ''.join([random.choice('abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)') for _ in range(50)])\n return config","title":""},{"docid":"ff13c4e6975a74a54a68007bfb88cdbe","score":"0.58986014","text":"def from_file(self, config_file):\n with open(str(config_file), 'r') as f:\n self.config = yaml.load(f)\n self.set_env_vars()","title":""},{"docid":"b073a2b4404fd3963b85fffc9c818d20","score":"0.58558446","text":"def createConfig(self):\n config = configparser.ConfigParser()\n config.add_section(\"DEVICE\")\n config.set(\"DEVICE\", \"port\", \"com7\")\n config.set(\"DEVICE\", \"baudrate\", '9600')\n config.set(\"DEVICE\", \"timeout\", '1')\n \n config.add_section(\"DB\")\n config.set(\"DB\", \"db\", \"checkStaff.db\")\n\n config.add_section(\"MACHINE\")\n config.set(\"MACHINE\", \"stanok\", '77')\n\n config.add_section(\"CONNECT_1C\")\n config.set(\"CONNECT_1C\", \"url_http_service\" , 'http://1c-www-server/erp/hs/staffstatis/import/')\n config.set(\"CONNECT_1C\", \"user\" , '1cv8')\n config.set(\"CONNECT_1C\", \"password\" , '')\n\n\n with open(self.path, \"w\") as config_file:\n config.write(config_file)","title":""},{"docid":"c0ed624af3378322aa6c646abc4341ce","score":"0.58347595","text":"def up_site_config():\n nginx_root = '/etc/nginx'\n nginx_available = os.path.join(nginx_root, 'sites-available')\n nginx_enabled = os.path.join(nginx_root, 'sites-enabled')\n app_conf = os.path.join(nginx_available, '%s.conf' % env.server_name)\n\n fabtools.files.upload_template(\n 'nginx.conf.tpl',\n app_conf,\n context=env,\n template_dir=os.path.join(env.lib_path, 'templates'),\n use_jinja=True,\n use_sudo=True,\n user='root',\n chown=True,\n mode='644',\n )\n\n if not fabtools.files.is_link('%s/%s.conf' % (nginx_enabled, env.server_name)):\n with fabric.api.cd(nginx_enabled):\n fabric.api.sudo('ln -s %s .' % app_conf)","title":""},{"docid":"e81a1fb0236cc3af2cd32ac881254e3a","score":"0.58207387","text":"def build_config(self):\n self.tempest_configure()\n # find template\n template_path = os.path.join(os.path.dirname(\n os.path.abspath(__file__)), os.pardir,\n \"data/files/tempest_neutron.conf\")\n\n # open template and add values\n with open(template_path) as f:\n template = Template(f.read()).substitute(\n self.tempest_config)\n\n # save config\n with open(self.path, 'w') as w:\n logger.info(\"Writing tempest config: {0}\".format(self.path))\n logger.debug(template)\n w.write(template)","title":""},{"docid":"3860d55c92b946340c3895ef345e4e3b","score":"0.5811174","text":"def conf(request):\n path = request.config.getoption(\"vars\")\n return BaseConfig(path)","title":""},{"docid":"a7d6b55acff773a6decd34b81351158f","score":"0.57835644","text":"def create_conf():\n with open('config.py', 'w') as fd:\n fd.write(\"BUFF_SIZE = 4096\\n\")\n fd.write(\"PATH_FILES = '/tmp/'\\n\")\n fd.write(\"SRV_ADDR = '127.0.0.1'\\n\")\n fd.write(\"SRV_PORT = 5100\\n\")\n fd.write(\"LOG_FILE = '/tmp/logfile.log'\\n\")\n fd.write(\"PID_FILE = '/tmp/atc_daemon.pid'\\n\")","title":""},{"docid":"1074ef701e6f0b30e29681267104623d","score":"0.57806355","text":"def conf():\n text = \"\"\"\\\nThis is a simple prolog.\n\n[TEST]\n# This is a test section.\n\nvalue1 = 53\nvalue2 = 54\n\"\"\"\n conf = seamm_installer.Configuration()\n conf.from_string(text)\n return conf","title":""},{"docid":"2c6cfd40680b15ce2f07974d535aafe4","score":"0.57622224","text":"def load_backblaze_config(config):\n fd = open(\"%s/.backblaze.conf\" % os.environ['HOME'])\n for line in fd.readlines():\n words = line.split()\n config[words[0]] = words[1]\n fd.close()\n return config","title":""},{"docid":"794c497dd1553638b1098eaf809108e9","score":"0.57605445","text":"def setup():\n try:\n config = ConfigParser.ConfigParser()\n config.readfp(open(CONFIG_FILE))\n lib.create.setApiVersion(config.get('general', 'api_version'))\n except:\n lib.logger.critical('Could not open config file \"%s\"' % (CONFIG_FILE,))\n sys.exit(-1)","title":""},{"docid":"4a229dfe18233d6683e8c0d4977a8ada","score":"0.57544714","text":"def parse_config_file(config_file):","title":""},{"docid":"376da956e4207567b4cf765bce627e14","score":"0.57271695","text":"def write_config(self, config_file: Union[str, Path] = None):\n config_file = config_file or self.config_file\n config_file = Path(config_file)\n parser: ConfigParser = ConfigParser(allow_no_value=True)\n\n # populate the config file with the template\n for section in self.template.keys():\n parser[section] = self.template[section] # type: ignore\n\n try:\n if not config_file.parent.exists():\n config_file.parent.mkdir(parents=True)\n with config_file.open(\"w\") as configfile:\n parser.write(configfile)\n except PermissionError as error:\n raise error from SystemError()\n\n print(f\"Configuration file created at {config_file}\")","title":""},{"docid":"5d4df81c0759e74c5bdaffc54e34d365","score":"0.5713698","text":"def createConfigFile(self):\n config = ConfigParser.RawConfigParser()\n config.add_section('db')\n config.set('db', 'wanteddb', 'postgresql')\n config.add_section('postgresql')\n config.set('postgresql', 'database', 'sshkey')\n config.set('postgresql', 'user', os.getenv('USER'))\n config.set('postgresql', 'password', 'EDITTHIS')\n config.set('postgresql', 'host', 'localhost')\n config.set('postgresql', 'port', '5432')\n config.add_section('client')\n config.set('client', 'database', 'sshkey')\n config.set('client', 'user', os.getenv('USER'))\n config.set('client', 'password', 'EDITTHIS')\n config.set('client', 'host', 'localhost')\n try:\n configfile = open(os.path.expanduser(pathToConfig), 'wb')\n config.write(configfile)\n return 0\n except:\n print(\"Cannot write the defaultconfig\")\n return 1","title":""},{"docid":"e387dea2087a6d11a613652fd0d57fc7","score":"0.5701189","text":"def load(file):\n\n config_raw = ruamel.yaml.safe_load(file)\n\n config = dacite.from_dict(data=config_raw, data_class=VideoPipelineConfig)\n return config","title":""},{"docid":"6c5ab178a5f4a9f053705f7fcda306ab","score":"0.5695858","text":"def create_config(path):\n cfgfile = open(path, 'w')\n config = configparser.ConfigParser()\n config.add_section(\"Settings\")\n config.set(\"Settings\", \"parser\", \"ReutersParser\")\n config.set(\"Settings\", \"client\", \"PostgresClient\")\n config.set(\"Settings\", \"schema\", \"\")\n config.write(cfgfile)\n cfgfile.close()","title":""},{"docid":"d1117f8e2dcb801fba14ca7992e8eef3","score":"0.56880933","text":"def load_config(configfile='~/labpi.conf'):\n # Create a default configuration\n config = configparser.ConfigParser()\n config.read_dict({\n 'server': {\n 'username': '',\n 'password': '',\n 'base_url': 'http://example.com/container/{id}'\n },\n 'pi': {\n 'lcd_panel': 'dummy',\n }\n })\n # Load the configuration from disk\n config.read(os.path.expanduser(configfile))\n return config","title":""},{"docid":"37b0948425daae3e517485ea76f29389","score":"0.56838214","text":"def load_configuration(self):\r\n s = os.path.sep\r\n \r\n default = dict(\r\n python = 'python',\r\n web2py = os.path.join(s.join(__file__.split(s)[:-3]), 'web2py.py'),\r\n http_enabled = True,\r\n http_ip = '0.0.0.0',\r\n http_port = 8000,\r\n https_enabled = True,\r\n https_ip = '0.0.0.0',\r\n https_port = 8001,\r\n https_key = '',\r\n https_cert = '',\r\n password = '<recycle>',\r\n )\r\n \r\n config = default\r\n if self.config_file:\r\n try:\r\n f = open(self.config_file, 'r')\r\n lines = f.readlines()\r\n f.close()\r\n \r\n for line in lines:\r\n fields = line.split('=', 1)\r\n if len(fields) == 2:\r\n key, value = fields\r\n key = key.strip()\r\n value = value.strip()\r\n config[key] = value\r\n except:\r\n pass\r\n \r\n web2py_path = os.path.dirname(config['web2py'])\r\n os.chdir(web2py_path)\r\n \r\n args = [config['python'], config['web2py']]\r\n interfaces = []\r\n ports = []\r\n\r\n if config['http_enabled']:\r\n ip = config['http_ip']\r\n port = config['http_port']\r\n interfaces.append('%s:%s' % (ip, port))\r\n ports.append(port)\r\n if config['https_enabled']:\r\n ip = config['https_ip']\r\n port = config['https_port']\r\n key = config['https_key']\r\n cert = config['https_cert']\r\n if key != '' and cert != '':\r\n interfaces.append('%s:%s:%s:%s' % (ip, port, cert, key))\r\n ports.append(ports)\r\n if len(interfaces) == 0:\r\n sys.exit('Configuration error. Must have settings for http and/or https')\r\n \r\n password = config['password']\r\n if not password == '<recycle>':\r\n from gluon import main\r\n for port in ports:\r\n main.save_password(password, port)\r\n \r\n password = '<recycle>'\r\n \r\n args.append('-a \"%s\"' % password)\r\n \r\n interfaces = ';'.join(interfaces)\r\n args.append('--interfaces=%s' % interfaces)\r\n \r\n if 'log_filename' in config.key():\r\n log_filename = config['log_filename']\r\n args.append('--log_filename=%s' % log_filename)\r\n \r\n return (args, config)","title":""},{"docid":"6ba5738033345c6a0e3a189a9ea81a15","score":"0.56726974","text":"def load_configuration(file_name='config'):\n config = Configuration()\n parser = configparser.ConfigParser()\n parser.read(file_name)\n default_section = parser['DEFAULT']\n config.token = environ.get('BOT_TOKEN', default_section['token'])\n return config","title":""},{"docid":"80f6a41a88c581e6fb69bdcda1c69329","score":"0.5672508","text":"def loadConfig(self, path):\n\n parsedConfig = None\n\n try:\n # Try to open a the project config file\n tomlConfig = open(path+\"/config.toml\", \"r\")\n parsedConfig = toml.loads(tomlConfig.read())\n\n except Exception:\n raise Exception(\"Could not load config file\")\n\n print(\"TOML '\" + parsedConfig[\"BlogGen\"][\"WebsiteTitle\"] + \"' project config loaded\")\n\n return parsedConfig","title":""},{"docid":"787f90dcd2768b2686466c8e61ee77ef","score":"0.5670884","text":"def make_config():\n # Remove the current config from SONG_TEMP_DIR\n config_path = os.path.join(DEFAULTS.CONFIG_PATH, 'config')\n\n # Check if the ytmdl folder is present in config\n if not os.path.isdir(DEFAULTS.CONFIG_PATH):\n # Make the ytmdl folder\n os.makedirs(DEFAULTS.CONFIG_PATH)\n elif os.path.isfile(config_path):\n os.remove(config_path)\n\n # Now write the config test to config file\n with open(config_path, 'w') as write_config:\n write_config.write(config_text)","title":""},{"docid":"eab098fd42ce2d5e84be1a3c2e406f6a","score":"0.5654302","text":"def create_config_file(self):\n self.config_fail = True\n if not os.path.exists(self.path):\n os.makedirs(self.path)\n try:\n if os.path.exists(self.settings_path):\n os.remove(self.settings_path)\n except OSError:\n pass\n with open((self.settings_path), 'w') as config_file:\n config_file.write(\"\")\n config = ConfigParser.RawConfigParser()\n config.add_section('Settings')\n config.set('Settings', 'default master address', '192.168.1.1')\n config.set('Settings', 'default device number', '0')\n config.set('Settings', 'default connection type', 'TCP')\n config.set('Settings', 'default enable dhcp', True)\n config.set('Settings', 'number of threads', 20)\n config.set('Settings', 'telnet client executable', ('putty.exe'))\n config.set('Settings', 'telnet timeout in seconds', '4')\n config.set('Settings',\n 'display notification of successful connections', True)\n config.set('Settings', 'DHCP sniffing enabled', True)\n config.set('Settings', 'filter incoming DHCP for AMX only', False)\n config.set('Settings', 'subnet filter enabled', False)\n config.set('Settings', 'subnet filter', '')\n config.set('Settings', 'play sounds', True)\n config.set('Settings', 'check for updates', True)\n config.add_section('Config')\n config.set('Config', 'debug', False)\n config.set('Config', 'columns_config', self.columns_default)\n config.set('Config', 'DXLink TX Models', self.dxtx_models_default)\n config.set('Config', 'DXLink RX Models', self.dxrx_models_default)\n config.set(\n 'Config', 'DXLink Fibre TX Models', self.dxftx_models_default)\n config.set(\n 'Config', 'DXLink Fibre RX Models', self.dxfrx_models_default)\n with open((self.settings_path), 'w') as configfile:\n config.write(configfile)","title":""},{"docid":"839144b0a29d189d6064767591da60b0","score":"0.5634978","text":"def build_config(config_file, args):\n config = Configuration()\n if os.path.exists(config_file):\n config.load_from_file(config_file)\n\n config.load_from_arguments(args)\n return config","title":""},{"docid":"839144b0a29d189d6064767591da60b0","score":"0.5634978","text":"def build_config(config_file, args):\n config = Configuration()\n if os.path.exists(config_file):\n config.load_from_file(config_file)\n\n config.load_from_arguments(args)\n return config","title":""},{"docid":"d660f0960ebc9f48bcbd3ac05668d7d7","score":"0.56208354","text":"def create_config_from_file(filepath: str) -> Config:\n assert os.path.exists(filepath), \"No config file found at {}\".format(filepath)\n _logger.debug(\"Loading config file from {}\".format(filepath))\n with open(filepath) as file_:\n raw_config = yaml.safe_load(file_)\n return _create_config(raw_config, is_template=False)","title":""},{"docid":"3d8ce46d1e9cd02c15d5f1d2eee3d8d2","score":"0.5609841","text":"def make_settings():\n Config.make()","title":""},{"docid":"2e8e6c5cba9496f29e796976a4cac642","score":"0.56074923","text":"def scout_config(request, config_file):\n print(\"\")\n in_handle = get_file_handle(config_file)\n data = yaml.safe_load(in_handle)\n return data","title":""},{"docid":"ea3632f9ce8c39e4f031078bccc2912b","score":"0.55826586","text":"def crud_config(path):\n if not os.path.exists(path):\n create_config(path)\n\n config = configparser.ConfigParser()\n config.read(path)\n\n # Read from config file\n parser = config.get(\"Settings\", \"parser\")\n client = config.get(\"Settings\", \"client\")\n schema = config.get(\"Settings\", \"schema\")\n\n Setting = namedtuple('Settings', ['parser', 'client', 'schema'])\n\n return Setting(parser, client, schema)","title":""},{"docid":"8babcdf72ef46f8a586ada2b30bcc25b","score":"0.55735433","text":"def load_settings():\r\n config_parser = configparser.RawConfigParser()\r\n config_file_path = 'final/settings.txt'\r\n config_parser.read(config_file_path)\r\n\r\n browser = config_parser.get('your-config', 'BROWSER')\r\n browser_path = config_parser.get('your-config', 'BROWSER_PATH')\r\n name = config_parser.get('your-config', 'NAME')\r\n page = config_parser.get('your-config', 'PAGE')\r\n output = config_parser.get('your-config', 'Output')\r\n\r\n settings = {\r\n 'browser': browser,\r\n 'browser_path': browser_path,\r\n 'name': name,\r\n 'page': page,\r\n 'output' : output\r\n }\r\n return settings","title":""},{"docid":"1209092a435836f0cff60bc141b34733","score":"0.5567676","text":"def make_config(self, fileo):\n config = ConfigParser.SafeConfigParser() #new class instance\n for data_type, configuration in self.data_types.iteritems():\n config.add_section(data_type)\n for option, value in configuration.iteritems():\n config.set(data_type, option, value)\n config.write(fileo)","title":""},{"docid":"1525cf8b57ae0a6d98b8fc7aca71ebb8","score":"0.5549005","text":"def parseConfigFile(cfg):","title":""},{"docid":"1525cf8b57ae0a6d98b8fc7aca71ebb8","score":"0.5549005","text":"def parseConfigFile(cfg):","title":""},{"docid":"1525cf8b57ae0a6d98b8fc7aca71ebb8","score":"0.5549005","text":"def parseConfigFile(cfg):","title":""},{"docid":"ee6af1b742b912d76dec588c5a3ce21b","score":"0.5546118","text":"def make_config():\n tmpdir = tempfile.mkdtemp()\n global db_dir\n db_dir = tmpdir\n cwd = os.getcwd()\n os.chdir(tmpdir)\n with open('haas.cfg', 'w') as f:\n config = '\\n'.join([\n '[general]',\n '[devel]',\n 'dry_run=True',\n '[auth]',\n 'require_authentication = True',\n\n '[headnode]',\n 'base_imgs = base-headnode, img1, img2, img3, img4',\n '[database]',\n 'uri = sqlite:///%s/haas.db' % tmpdir,\n '[extensions]',\n 'haas.ext.auth.database =',\n 'haas.ext.switches.mock =',\n 'haas.ext.switches.nexus =',\n 'haas.ext.switches.dell =',\n 'haas.ext.switches.brocade =',\n 'haas.ext.obm.mock =',\n 'haas.ext.obm.ipmi =',\n 'haas.ext.network_allocators.vlan_pool =',\n '[haas.ext.network_allocators.vlan_pool]',\n 'vlans = 1001-1040',\n\n ])\n f.write(config)\n return (tmpdir, cwd)","title":""},{"docid":"793705206172008876610eb222772894","score":"0.5538213","text":"def create_config_file_from_upstream(self) -> None:\n\n PyFunceble.helpers.File(self.path_to_default_config).copy(self.path_to_config)","title":""},{"docid":"8618279c876f3b3b5dcdd1d95450dbf0","score":"0.55377173","text":"def test_new_site(sitename):\n print(test_new_site.__doc__ + '...', end=' ')\n mld = rhfn.new_conf(sitename)\n assert_equal(mld, '')\n mld, sett = rhfn.read_conf(sitename)\n assert_equal(mld, '')\n assert_equal(sett, {})\n mld = rhfn.new_conf('test')\n assert_equal(mld, 'site_name_taken')\n sett = {x: y for x, y in rhfn.DFLT_CONF.items()}\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\n mld, sett = rhfn.read_conf(sitename)\n\n sett['hig'] = 'Too high'\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\n assert_equal(mld, 'Config: invalid value for hig')\n sett['hig'] = 32\n sett['lang'] = 'xx'\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\n assert_equal(mld, 'Config: invalid value for lang')\n sett['lang'] = 'en'\n sett['url'] = 'http://www.example.com'\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\n assert_equal(mld, '')\n rhfn.init_css(sitename)\n mld, conf = rhfn.read_conf(sitename)\n for item in BASIC_CSS:\n test = WEBROOT / sitename / item\n assert 'url + ' + item in conf['css']\n assert test.exists()\n print('ok')","title":""},{"docid":"1bede075b08609bb99dd6e933a34e9ab","score":"0.5533709","text":"def config():\n conf = ConfigParser()\n # Set up defaults\n conf.add_section('io')\n conf.set('io', 'compression', 'zlib')\n\n conf.read(os.path.expanduser('~/.deepdish.conf'))\n return conf","title":""},{"docid":"593265363c82e6879182611816cc7b0f","score":"0.55328953","text":"def create_config_file(file_path):\n dir_path = file_path.parent\n if not dir_path.is_dir(): # pragma: no cover\n print(f'Creating application etc/ directory \"{str(dir_path)}\".')\n dir_path.mkdir(mode=0o775, parents=True)\n if not file_path.exists():\n with file_path.open(mode=\"w\") as config_fh:\n print(f'Creating instance config file at \"{str(file_path)}\".')\n print(CONFIG_FILE_HEADER, file=config_fh)\n #\n # Set SECRET_KEY to a random string.\n #\n write_kv_to_config_file(\n file_path, \"SECRET_KEY\", generate_random_password(), str, \"\"\n )","title":""},{"docid":"77f35293734d264849a4bd8064d05519","score":"0.55297446","text":"def _init_cfg_file(force=False):\n from shutil import rmtree, copyfile\n home = Path.home()\n dir_ = home / PACKAGE_CONF_DIR\n cfg_file = dir_ / 'conf.yml'\n\n dataset_dir = dir_ / 'dataset'\n cached_dir = dir_ / 'cached'\n\n if force:\n rmtree(dir_)\n\n if not dir_.is_dir():\n # create config root dir\n dir_.mkdir()\n\n # create other dirs\n dataset_dir.mkdir()\n cached_dir.mkdir()\n\n if not cfg_file.exists() or force:\n # copy default conf.yml to ~/.XenonPy\n copyfile(str(Path(__file__).parent / 'conf.yml'), cfg_file)","title":""},{"docid":"579b7e4197bfa33717de9efbe81278d6","score":"0.5527012","text":"def set_configs(self):\n for config_item in ['tag_creds', 'encrypt_tag' ]:\n define(config_item)\n # Get configuration values from configuration filescript_name = path.basename(__file__)\n script_path = path.abspath(path.realpath(__file__))\n script_dir = path.split(script_path)[0]\n config_path = path.abspath(path.join(script_dir, 'model/EmsgEncrypt.conf'))\n options.parse_config_file(config_path)","title":""},{"docid":"23526978594edf608881b5425f5e7cae","score":"0.55216306","text":"def config_test():\n cfg = ConfigParser.ConfigParser()\n cfg.read('TestConfig.cfg')\n return cfg","title":""},{"docid":"cf39c72c9e5e05037e0f6220ee6f8bf7","score":"0.551976","text":"def load_config(): # type: ignore","title":""},{"docid":"30faa66a01a398f9048673e6fda03a01","score":"0.55030286","text":"def load_config():\n parser = argparse.ArgumentParser(description=\"pyWegaRadio\")\n parser.add_argument(\"-c\", \"--config\", help=\"filename of the application config (YAML)\", required=True)\n args = parser.parse_args()\n with open(args.config) as application_config:\n cfg = yaml.load(application_config)\n\n # setting defaults\n cfg['mpd_host'] = cfg['mpd_client'] if 'mpd_host' in cfg else 'localhost'\n cfg['mpd_port'] = cfg['mpd_port'] if 'mpd_port' in cfg else 6600\n\n return cfg","title":""},{"docid":"00822e430d6ee5cdcff3c1a9098317b1","score":"0.5496693","text":"def cli(config_file):\n if config_file is not None:\n config.update_from_file(config_file)","title":""},{"docid":"f10a61fe63d5616d5cc284d2c5156940","score":"0.5495602","text":"def set_config(self):\n\n self.parser['CONNECTION'] = strings.get_config_connection()\n self.parser['DATABASE'] = strings.get_config_db()\n self.parser['METRICS'] = strings.get_config_metrics()\n\n with open(strings.get_config_path(), 'w') as file:\n self.parser.write(file)","title":""},{"docid":"165d7adacf17eef282f7078e7d340d93","score":"0.5495298","text":"def load_from_file(cls, config_path):\n # type: (str) -> Config\n if not os.path.isfile(config_path):\n LOGGER.error(\"Runway config file was not found (looking for \"\n \"%s)\",\n config_path)\n sys.exit(1)\n with open(config_path) as data_file:\n config_file = yaml.safe_load(data_file)\n result = Config(config_file.pop('deployments'),\n config_file.pop('tests', []),\n config_file.pop('ignore_git_branch',\n config_file.pop(\n 'ignore-git-branch',\n False)),\n config_file.pop('variables', {}))\n\n if config_file:\n LOGGER.warning(\n 'Invalid keys found in runway file have been ignored: %s',\n ', '.join(config_file.keys())\n )\n return result","title":""},{"docid":"bf970f814bd9190d3a94f92067f7dc5a","score":"0.54845643","text":"def load_file(pth):\n fil = open(pth, 'r')\n return Configuration(fil.read())","title":""},{"docid":"38625a74b57d297ae8ab7e8926a10f58","score":"0.54842806","text":"def from_configfile(cls, configfile):\n import yaml\n with open(configfile) as fh:\n config = yaml.safe_load(fh)\n\n return cls.from_config(config)","title":""},{"docid":"9ad70cf40a191b6a24d34756707bc09c","score":"0.5480038","text":"def config(self, settings):\n for k, v in settings.iteritems():\n self.sites[k] = v[0]\n self.sites_config[v[0]] = {'site_name': k}\n for _k, _v in v[1].iteritems():\n self.sites_config[v[0]][_k.upper()] = _v\n \n self._configed = True","title":""},{"docid":"0033170d05a3bfe1a686e5d78299af53","score":"0.547585","text":"def parse_config_file(config_file):\n config = configparser.ConfigParser()\n config.read(config_file)\n\n settings = {}\n settings[\"world\"] = config.get(\"RuneScape\", \"world\", fallback=\"www\")\n settings[\"ram\"] = config.get(\"Java\", \"ram\", fallback=\"-Xmx1024m\")\n settings[\"stacksize\"] = config.get(\"Java\", \"stacksize\", fallback=\"-Xss1m\")\n return settings","title":""},{"docid":"05210fe3fce6ea8f512bd9cb10396f0f","score":"0.54738665","text":"def _make_cfg_file(self, **kwargs):\n cfg_file = self._cfg_dir / \"config.yaml\"\n self.parameters.update(**kwargs)\n with cfg_file.open(mode=\"w\") as file:\n yaml.dump({k: v for k, v in self.parameters}, file)\n\n return cfg_file","title":""},{"docid":"dcd10b6a6012a3305d175ec3f2dd26e4","score":"0.54719794","text":"def generate_config(options):\n # template name -> file mode\n config_file_templates = {}\n\n config = get_config(options.host)\n target_dir = path('target')\n\n for parent_files_dir in filter(lambda p: p.exists(),\n [target_dir.joinpath(p, 'files') for p in config['parents']]):\n for config_file in parent_files_dir.walkfiles():\n template_name = parent_files_dir.relpathto(config_file)\n if template_name not in config_file_templates:\n config_file_templates[template_name] = config_file.lstat()\n\n # Load Jinja Environment\n template_env = Environment(loader=ConfigdichLoader(target_dir))\n\n build_dir = path('config').joinpath(options.host)\n # Clean out the build dir\n build_dir.rmtree()\n build_dir.makedirs_p()\n\n for config_file_name, template_file_stat in config_file_templates.items():\n rendered_config_file_path = build_dir.joinpath(config_file_name)\n # Create the directory that the config file will be rendered to in if needed\n rendered_config_file_path.dirname().makedirs_p()\n # Render the template to the file\n t = template_env.get_template(options.host + \"/\" + str(config_file_name))\n rendered_config_file_path.write_text(t.render(config))\n rendered_config_file_path.chmod(template_file_stat.st_mode)","title":""},{"docid":"88d1a4f2d28cfc378269bff766331c1d","score":"0.546764","text":"def generic_config_parser(config_path, local):\n config = ConfigParser.ConfigParser()\n if local:\n filename = config_path + \"/local_datastore_operator.conf\"\n else:\n filename = config_path + \"/collector_operator.conf\"\n config.read(filename)\n return config","title":""},{"docid":"4dd78c123605aef6b83f97ed0eb623cb","score":"0.54675925","text":"def new_config_file(self):\n config_source = os.path.join(os.path.dirname(configs.__file__), 'clitest.yaml')\n out_config = os.path.join(self.test_dir, 'config.yaml')\n\n with open(config_source, 'r') as f_in:\n with open(out_config, 'w') as f_out:\n s = f_in.read()\n f_out.write(s.format(\n root=self.test_dir,\n source=os.path.dirname(bundles.__file__)\n ))\n return out_config","title":""},{"docid":"4d98b746daa3accf08cffdc55619f55d","score":"0.545733","text":"def create_blank_conf(self):\n\n self.config['data_file'] = \"\"\n self.config.comments['data_file'] = wrap(\n \"File containing the input data. The columns are 'id' (name of the\"\n \" object), 'redshift' (if 0 the distance is assumed to be 10 pc), \"\n \"the filter names for the fluxes, and the filter names with the \"\n \"'_err' suffix for the uncertainties. The fluxes and the \"\n \"uncertainties must be in mJy. This file is optional to generate \"\n \"the configuration file, in particular for the savefluxes module.\")\n self.spec['data_file'] = \"string\"\n\n self.config['parameters_file'] = \"\"\n self.config.comments['parameters_file'] = [\"\"] + wrap(\n \"Optional file containing the list of physical parameters. Each \"\n \"column must be in the form module_name.parameter_name, with each \"\n \"line behind a different model. The columns must be in the order \"\n \"the modules will be called. The redshift column must be the last \"\n \"one. Finally, if this parameters is not left empty, cigale will \"\n \"not interpret the configuration parameters given in pcigale.ini. \"\n \"They will be given only for information.\")\n self.spec['parameters_file'] = \"string()\"\n\n self.config['sed_modules'] = []\n self.config.comments['sed_modules'] = ([\"\"] +\n [\"Order of the modules use for SED creation. Available modules:\"] +\n [\"SFH: sfh2exp, sfhdelayed, sfhfromfile, sfhperiodic\"] +\n [\"SSP: bc03, m2005\"] +\n [\"Nebular emission: nebular\"] +\n [\"Dust attenuation: dustatt_calzleit, dustatt_powerlaw, \"\n \"dustatt_2powerlaws\"] +\n [\"Dust emission: casey2012, dale2014, dl2007, dl2014\"] +\n [\"AGN: dale2014, fritz2006\"] +\n [\"Radio: radio\"] +\n [\"Redshift: redshifting (mandatory!)\"])\n self.spec['sed_modules'] = \"cigale_string_list()\"\n\n self.config['analysis_method'] = \"\"\n self.config.comments['analysis_method'] = [\"\"] + wrap(\n \"Method used for statistical analysis. Available methods: \"\n \"pdf_analysis, savefluxes.\")\n self.spec['analysis_method'] = \"string()\"\n\n self.config['cores'] = \"\"\n self.config.comments['cores'] = [\"\"] + wrap(\n \"Number of CPU cores available. This computer has {} cores.\"\n .format(mp.cpu_count()))\n self.spec['cores'] = \"integer(min=1)\"\n\n self.config.write()\n self.spec.write()","title":""},{"docid":"5721b6cc0302e7d6a826212398bc72d1","score":"0.54488325","text":"def update_configfile(cls):\n # New database path\n cls.database_path = os.path.join(cls.tmpdir, 'test.db')\n cls.database_url = 'sqlite:///%s' % cls.database_path\n\n # Update config with database url\n config['common']['DATABASE_URL'] = cls.database_url\n\n # New config file\n cls.config_path = os.path.join(cls.tmpdir, 'test.conf')\n with open(cls.config_path, 'w') as configfile:\n config.write(configfile)","title":""},{"docid":"99c370f0fa2a07a8de8cbf9c9dad67a6","score":"0.54463136","text":"def setconfig(jamla_path, secret_key, template_folder, static_folder, \\\n uploaded_images_dest, db_full_path, success_redirect_url, \\\n thankyou_url, mail_server, mail_port, \\\n mail_default_sender, mail_username, mail_password, mail_use_tls,\\\n email_login_from, gocardless_client_id, \\\n gocardless_client_secret, template_base_dir):\n newConfig = ''\n with open('./instance/config.py', 'r') as fh:\n for line in fh:\n frame = inspect.currentframe()\n options = inspect.getargvalues(frame).args\n for option in options:\n if option.swapcase() in line and frame.f_locals[option] is not None :\n newValue = ''.join([option.swapcase(), '=\"', str(frame.f_locals[option]), '\"'])\n expr = r\"^\" + option.swapcase() + \".*\"\n line = re.sub(expr, newValue, line)\n logging.info(\"Writing: %s\", newValue)\n newConfig = ''.join([newConfig, line])\n # Writeout new config file\n with open('./instance/config.py', 'w') as fh:\n fh.write(newConfig)","title":""},{"docid":"ac0ba2d625b3994fe049062a44d035c3","score":"0.54410285","text":"def loadConfig(self):\n with open(self.configfile) as f:\n config = yaml.load(f)\n self.binddn = config['binddn']\n self.bindPW = config['bindPW']\n self.serverURL = config['serverURL']\n self.baseDN = config['baseDN']\n self.usersBase = config['usersBase']\n self.employeeFilter = config['employeeFilter']\n f.close","title":""},{"docid":"7cd7d863bba7fa1b24f40eb2ba21c4a1","score":"0.5431443","text":"def create_default_config(path, conf):\n if platform.system() == 'Windows':\n base_path = os.path.join(os.path.expanduser('~'), 'seprcph')\n else:\n base_path = os.path.join(os.path.expanduser('~'), '.config', 'seprcph')\n\n try:\n os.makedirs(os.path.dirname(base_path))\n except OSError as err:\n if err.errno != errno.EEXIST:\n raise\n\n conf.add_section('general')\n conf.set('general', 'screen_height', '480')\n conf.set('general', 'screen_width', '640')\n conf.set('general', 'fullscreen', 'false')\n conf.set('general', 'data_dir', os.path.join(base_path, 'data'))\n conf.set('general', 'image_dir', os.path.join(base_path, 'assets', 'images'))\n conf.set('general', 'sound_dir', os.path.join(base_path, 'assets', 'sounds'))\n\n conf.add_section('logging')\n conf.set('logging', 'format', '%(asctime)s - %(levelname)s - %(funcName)s '\n '- %(message)s')\n conf.set('logging', 'date_format', '%d/%m/%Y %I:%M:%S %p')\n conf.set('logging', 'level', 'DEBUG')\n conf.set('logging', 'file', os.path.join(base_path, 'log.txt'))\n\n conf.add_section('graphics')\n conf.set('graphics', 'fps', '60')\n conf.set('graphics', 'draw_fps', 'false')\n\n conf.add_section('gameplay')\n conf.set('gameplay', 'turn_limit', '30')\n\n with open(path, 'w') as conf_file:\n conf.write(conf_file)","title":""},{"docid":"6d294e401d799371e32810413008056d","score":"0.54309314","text":"def create_config(self):\n # Generate install-config from template\n _templating = Templating()\n ocp_install_template = (\n f\"install-config-{self.deployment_platform}-\"\n f\"{self.deployment_type}.yaml.j2\"\n )\n ocp_install_template_path = os.path.join(\n \"ocp-deployment\", ocp_install_template\n )\n install_config_str = _templating.render_template(\n ocp_install_template_path, config.ENV_DATA\n )\n install_config_obj = yaml.safe_load(install_config_str)\n install_config_obj[\"pullSecret\"] = self.get_pull_secret()\n install_config_obj[\"sshKey\"] = self.get_ssh_key()\n install_config_obj[\"platform\"][\"vsphere\"][\"apiVIP\"] = config.ENV_DATA[\n \"vips\"\n ][0]\n install_config_obj[\"platform\"][\"vsphere\"][\"ingressVIP\"] = config.ENV_DATA[\n \"vips\"\n ][1]\n install_config_obj[\"metadata\"][\"name\"] = config.ENV_DATA.get(\"cluster_name\")\n install_config_obj[\"baseDomain\"] = config.ENV_DATA.get(\"base_domain\")\n install_config_str = yaml.safe_dump(install_config_obj)\n install_config = os.path.join(self.cluster_path, \"install-config.yaml\")\n\n with open(install_config, \"w\") as f:\n f.write(install_config_str)","title":""},{"docid":"c450143b54e5a138152443e1ed40ebc5","score":"0.54287505","text":"def read_cfg():\n\timport copy\n\tglobal global_config\n\tconfig_parse = ConfigParser.ConfigParser()\n\tconfig_parse.optionxform = str\n\tconfig_parse.read(config.file_cfg)\n\tglobal_config = copy.deepcopy(default_config)\n\tfor section in config_parse.sections():\n\t\tfor (name, value) in config_parse.items(section):\n\t\t\tglobal_config[section][name] = value","title":""},{"docid":"1ddc0ec266171954c8fe4a84d2b2d1b4","score":"0.5426491","text":"def load_settings():\n config_parser = configparser.RawConfigParser()\n config_file_path = 'settings.txt'\n config_parser.read(config_file_path)\n\n browser = config_parser.get('config', 'BROWSER')\n browser_path = config_parser.get('config', 'BROWSER_PATH')\n name = config_parser.get('config', 'NAME')\n page = config_parser.get('config', 'PAGE')\n csv_location = config_parser.get('config', OUTPUT_FILE_LOCATION)\n\n settings = {\n 'browser': browser,\n 'browser_path': browser_path,\n 'name': name,\n 'page': page,\n 'csv_location': csv_location\n }\n return settings","title":""},{"docid":"001fb7ee6bc27d12a98acc8c4b33096f","score":"0.54166555","text":"def __create_default_config_file(self) -> None:\n with open(self.__config_filename, 'w') as configfile:\n configfile.write(open(cemu.const.TEMPLATE_CONFIG, \"r\").read())\n return","title":""},{"docid":"553fa8e1f68f634cdd3a863f2e6d1cf1","score":"0.5408216","text":"def readSiteConfig(self, siteId, configFilePath):\n d = self.readConfig(configFilePath)\n if siteId in d:\n return d[siteId]\n else:\n return {}","title":""},{"docid":"dd6e9a760ddcdd192a7168242ccf26a3","score":"0.5402126","text":"def __init__(self, file: str) -> None:\r\n self.load_config_from_file(file)","title":""},{"docid":"ae07b5963093d1693d846a18ece8dea4","score":"0.53898674","text":"def get_clusto_config(filename=None):\r\n\r\n filesearchpath = ['/etc/clusto/clusto.conf']\r\n\r\n \r\n filename = filename or os.environ.get('CLUSTOCONFIG')\r\n\r\n if not filename:\r\n filename = filesearchpath[0]\r\n\r\n if filename:\r\n if not os.path.exists(os.path.realpath(filename)):\r\n raise CmdLineError(\"Config file %s doesn't exist.\" % filename)\r\n \r\n config = SafeConfigParser()\r\n config.read([filename])\r\n\r\n if not config.has_section('clusto'):\r\n config.add_section('clusto')\r\n\r\n if 'CLUSTODSN' in os.environ:\r\n config.set('clusto', 'dsn', os.environ['CLUSTODSN'])\r\n\r\n if not config.has_option('clusto', 'dsn'):\r\n raise CmdLineError(\"No database given for clusto data.\")\r\n\r\n return config","title":""},{"docid":"07044e5c9c37b198525f23dbe6087509","score":"0.5381551","text":"def generate_config(device):\n hostname = device[\"hostname\"]\n config_data = yaml.safe_load(open(f\"host_vars/{hostname}.yaml\"))\n env = Environment(\n loader=FileSystemLoader(\"./templates\"), trim_blocks=True, lstrip_blocks=True\n )\n template = env.get_template(\"ospf.j2\")\n configuration = template.render(config_data)\n return configuration","title":""},{"docid":"3dfbad3961df131242c601342467008c","score":"0.5376425","text":"def create_config(self):\n # Generate install-config from template\n _templating = Templating()\n ocp_install_template = (\n f\"install-config-{self.deployment_platform}-\"\n f\"{self.deployment_type}.yaml.j2\"\n )\n ocp_install_template_path = os.path.join(\n \"ocp-deployment\", ocp_install_template\n )\n install_config_str = _templating.render_template(\n ocp_install_template_path, config.ENV_DATA\n )\n\n # Parse the rendered YAML so that we can manipulate the object directly\n install_config_obj = yaml.safe_load(install_config_str)\n if version.get_semantic_ocp_version_from_config() >= version.VERSION_4_10:\n install_config_obj[\"platform\"][\"vsphere\"][\"network\"] = config.ENV_DATA[\n \"vm_network\"\n ]\n install_config_obj[\"pullSecret\"] = self.get_pull_secret()\n install_config_obj[\"sshKey\"] = self.get_ssh_key()\n install_config_str = yaml.safe_dump(install_config_obj)\n install_config = os.path.join(self.cluster_path, \"install-config.yaml\")\n with open(install_config, \"w\") as f:\n f.write(install_config_str)","title":""},{"docid":"34d1bafcf14d487d6167a077cbe5754e","score":"0.5368864","text":"def setup(args):\n cfg = get_cfg()\n add_custom_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.freeze()\n default_setup(cfg, args)\n return cfg","title":""},{"docid":"3533fe0269fed1b36b7b28d3a13fe2c1","score":"0.5353794","text":"def generate_supervisor_conf():\n\n tmp_file = \"/tmp/supervisor.conf\"\n target_file = os.path.join(env.config.ApiServer.document_root, \"local\", \"conf\")\n\n run(\"mkdir -p %s\" % target_file)\n render_to_file(\"supervisor.conf\", tmp_file)\n\n put(tmp_file, target_file)\n print \"supervisor config file generated at %s\" % target_file","title":""},{"docid":"5714d34e47639c754d788a129d45f06f","score":"0.53531885","text":"def read_config_file(self):\n parser = SafeConfigParser()\n try:\n parser.read(self.filename)\n except IOError as error:\n logging.info(\"Cannot read file: {}.\".format(error))\n sys.exit(0)\n\n options_name = [\n 'host',\n 'user',\n 'database',\n 'password'\n ]\n\n for section_name in parser:\n self.configuration[section_name] = {}\n if section_name != 'DEFAULT':\n for option in options_name:\n try:\n value = parser.get(section_name, option)\n self.configuration[section_name][option] = value\n except ConfigParser.NoOptionError, err:\n logging.info(str(err))\n sys.exit(0)","title":""},{"docid":"c42dc0a539aae8a4a3f3e9101892a948","score":"0.53474313","text":"def get_user_configuration(config_file, default_config_file=None,\n bld_dir=os.getcwd(), v=1):\n # Get the configuration\n verbose = abs(v) > 0\n\n logging.info('Looking for user configuration')\n parser = ConfigParser(allow_no_value=True)\n parser.optionxform = lambda option: option # retain case sensitivity\n\n default_config_file = get_default_configuration_file(bld_dir,\n default_config_file=default_config_file)\n if verbose:\n logging.info('Loading default configuration file: '\n '{green:%s}' % default_config_file)\n parser.read(default_config_file)\n\n if not config_file:\n if os.getenv('TRILINOS_CFG') is not None:\n config_file = os.environ['TRILINOS_CFG']\n\n if config_file:\n if not os.path.isfile(config_file):\n # Look for config file in known locations\n for d in (bld_dir, ETC):\n f = os.path.join(d, config_file)\n if os.path.isfile(f):\n config_file = f\n break\n else:\n # No config file is explicitly given, give precedence to ./Trilinos.cfg\n if os.path.isfile('Trilinos.cfg'):\n config_file = 'Trilinos.cfg'\n\n if config_file:\n if not os.path.isfile(config_file):\n raise Exception('Configuration file {0!r} does '\n 'not exist'.format(config_file))\n if verbose:\n s1 = config_file\n logging.info('Loading user configuration file: {green:%s}' % config_file)\n parser.read(config_file)\n\n if not os.path.isfile(os.path.join(bld_dir, 'Default.cfg')):\n with open(os.path.join(bld_dir, 'Default.cfg'), 'w') as fh:\n fh.write(open(default_config_file).read())\n\n return parser","title":""},{"docid":"078a41fa37f83bc8607ba8bdd0a1ff7c","score":"0.53471076","text":"def read_config(cls, file_like):\n\n with open(file_like) as f:\n config = yaml.safe_load(f) \n return cls(**config)","title":""},{"docid":"91534fc50088bd95f9aa1b9400c1b5e1","score":"0.53469664","text":"def setup(ctx):\n __, template_config = configuration.load_config(load_template=True)\n __, config = configuration.load_config()\n if config is None:\n config = template_config\n\n click.echo(ui.style_info(\"Setting configuration fields:\"))\n for category_name, category in config.items():\n for field, value in category.items():\n new_value = click.prompt(ui.style_prompt(field), default=value)\n if category_name == \"path\":\n new_value = os.path.abspath(new_value)\n click.echo(ui.style_info_path(\"Absolute path is\", new_value))\n config[category_name][field] = new_value\n\n click.echo(ui.style_info(\"Checking configuration:\"))\n for category_name, category in template_config.items():\n if category_name not in config:\n click.echo(\n ui.style_error(f\"Category {category_name} not in config, creating\")\n )\n config[category_name] = {}\n for field, value in category.items():\n if field not in config[category_name]:\n click.echo(ui.style_error(f\"Field {field} not in config, creating\"))\n new_value = click.prompt(ui.style_prompt(field), default=value)\n if category_name == \"path\":\n new_value = os.path.abspath(new_value)\n click.echo(ui.style_info_path(\"Absolute path is\", new_value))\n config[category_name][field] = new_value\n\n click.echo(\n ui.style_info_path(\"Saved configuration at\", configuration.write_config(config))\n )\n click.echo(\n ui.style_info_path(\n \"Copied review template to\",\n configuration.copy_template_review(\n os.path.abspath(config[\"path\"][\"reviews_directory\"])\n ),\n )\n )\n click.echo(\n ui.style_info_path(\n \"Copied HTML/CSS export templates to\",\n configuration.copy_template_html(config[\"path\"][\"export_directory\"]),\n )\n )\n return config","title":""},{"docid":"7429b17fd8520aca87366541858b90d4","score":"0.53251046","text":"def open_config(self):\r\n config = configparser.ConfigParser()\r\n\r\n cf = os.path.join(\r\n os.path.dirname(os.path.realpath(__file__)),\r\n \"api.cfg\"\r\n )\r\n if not config.read([cf]):\r\n print(\"No login configuration file found!\")\r\n\r\n self.email = config.get(self.account, 'email')\r\n self.password = config.get(self.account, 'password')","title":""},{"docid":"7cfb96c2fd95286d5dac2499570ee504","score":"0.532114","text":"def readconfig():\n config = ConfigParser.ConfigParser()\n config.read(configfile())\n configdict = {}\n\n configdict['server_port'] = config.get('server', 'server_port', 8212)\n configdict['verbose'] = config.getint('server', 'verbose')\n db_url = config.get('server', 'db_url')\n if db_url == 'tests/test.db':\n configdict['db_url'] = 'sqlite:///%s/tests/test.db' % \\\n os.environ['QB_ROOT']\n else:\n configdict['db_url'] = db_url\n# configdict['db_alias'] = config.get('server', 'db_alias')\n configdict['map_file'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'map_file', ''))\n configdict['alias_mapfile'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'alias_mapfile', ''))\n configdict['split_file'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'split_file', ''))\n configdict['logconfig'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'logconfig', ''))\n configdict['doc_dir'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'doc_dir', ''))\n configdict['left_join'] = os.path.join(os.environ['QB_ROOT'], \\\n config.get('server', 'left_join', ''))\n\n configdict['environment'] = config.get('server', 'environment', 'production')\n configdict['profiler'] = int(config.get('server', 'profiler', 0))\n configdict['threadpool'] = int(config.get('server', 'thread_pool', 30))\n configdict['socket_queue_size'] = int(config.get('server', 'socket_queue_size', 15))\n configdict['expires'] = int(config.get('server', 'expires', 300))\n configdict['log_screen'] = bool(config.get('server', 'log_screen', True))\n configdict['access_log_file'] = config.get('server', 'access_log_file', \\\n '/tmp/access_log.log')\n configdict['error_log_file'] = config.get('server', 'error_log_file', \\\n '/tmp/error_log.log')\n configdict['access_log_level'] = int(config.get('server', 'access_log_level', 0))\n configdict['error_log_level'] = int(config.get('server', 'error_log_level', 0))\n configdict['algo'] = config.get('server', 'algo', 'MIS')\n\n return configdict","title":""},{"docid":"8c385c7e40f4f5477e0155db3d443ec5","score":"0.53203917","text":"def gen_supervisor_conf(conf_file='local/supervisord.conf.tmp'):\n # Back up first\n _backup_file(conf_file)\n\n env.run('python manage.py supervisor getconfig > %s' % conf_file)\n print green(\"Wrote supervisor conf file to %s\" % conf_file)","title":""},{"docid":"dcababd44abb64d5ff2669218c654d5f","score":"0.5319802","text":"def get_config(self):\n\n config = ConfigParser.ConfigParser()\n if os.path.isfile(self.args.config):\n myconf = self.args.config\n else: myconf = 'config/db.conf'\n config.read(myconf)\n\n self.db['host'] = config.get('siemstress', 'server')\n self.db['user'] = config.get('siemstress', 'user')\n self.db['password'] = config.get('siemstress', 'password')\n self.db['database'] = config.get('siemstress', 'database')\n sectionfile = config.get('siemstress', 'sectionfile')\n\n if not sectionfile.startswith('/'):\n sectionfile = '/'.join(os.path.abspath(myconf).split('/')[:-1]) + \\\n '/' + sectionfile\n\n config.read(sectionfile)\n \n self.table = config.get(self.args.section, 'table')\n self.helpers = config.get(self.args.section, 'helpers')\n try:\n self.parsername = config.get(self.args.section, 'parser')\n except Exception:\n # To Do: narrow down exception\n self.parsername = 'syslogbsd'\n\n\n if self.parsername == 'syslogbsd':\n self.parser = logdissect.parsers.syslogbsd.ParseModule()\n elif self.parsername == 'syslogiso':\n self.parser = logdissect.parsers.syslogiso.ParseModule()\n elif self.parsername == 'nohost':\n self.parser = logdissect.parsers.nohost.ParseModule()\n elif self.parsername == 'tcpdump':\n self.parser = logdissect.parsers.tcpdump.ParseModule()","title":""},{"docid":"3c9d03322b3e96d8419c8cd02ad61dc2","score":"0.5319087","text":"def parse_config(self):\n file_name = '{}/dyson_pure_link.yaml'.format(os.path.dirname(os.path.abspath(__file__)))\n\n if os.path.isfile(file_name):\n self.config = yaml.safe_load(open(file_name))\n\n return self.config","title":""},{"docid":"57edd0435b833444b5fa7780273976fe","score":"0.5318298","text":"def load_config(config_file: str) -> dict:\n return toml.loads(open(config_file).read())","title":""},{"docid":"dfc48fbd682a18e8dd29aed57d2a5388","score":"0.53126377","text":"def __init_config(cfg):\n config_path = os.getenv(\"BB_CONFIG_FILE\", None)\n if not config_path:\n config_path = find_config()\n\n if config_path:\n cfg.load(config_path)\n cfg[\"config_file\"] = os.path.abspath(config_path)\n logging.debug(\"Configuration loaded from {0}\".format(os.path.abspath(\n config_path)))\n cfg.init_from_env()","title":""},{"docid":"d97aacbb7303a87c5c1de0636f6cc027","score":"0.5306472","text":"def setup_config(command, filename, section, vars):\r\n confuri = \"config:\" + filename\r\n if \":\" in section:\r\n confuri += \"#\" + section.rsplit(\":\", 1)[-1]\r\n conf = appconfig(confuri)\r\n\r\n load_environment(conf.global_conf, conf.local_conf)","title":""},{"docid":"04d6d6db430486bd019522c3373026ca","score":"0.53013945","text":"def config(self, sscloud_config='/etc/sscloud/config.yml'):\n\n\t\t# Get settings from sscloud configuration\n\t\twith open(sscloud_config, 'r') as f:\n\t\t\tsscloud = yaml.load(f)\n\n\t\tsettings = dict()\n\t\t# Set master\n\t\tif not self.master:\n\t\t\tself.master = sscloud['openstack']['controller']\n\n\t\tif self.host == self.master:\n\t\t\tsettings['config'] = sscloud_config\n\t\t\tlogger.debug('Generate master configuration for saltstack')\n\t\t\tself.genconfig('/etc/salt/master', '/opt/sscloud/templates/salt/master', settings)\n\t\telse:\n\t\t\tlogger.debug('This host(%s) is not master(%s)', self.host, self.master)\n\n\t\t# Set minion\n\t\t# Get keystone settings\n\t\ttry:\n\t\t\tsettings['token'] = sscloud['keystone']['token']\n\t\texcept:\n\t\t\tsettings['token'] = sscloud['openstack']['admin']['token']\n\n\t\ttry:\n\t\t\tsettings['endpoint'] = sscloud['keystone']['endpoint']['admin']\n\t\texcept:\n\t\t\tsettings['endpoint'] = 'http://%s:35357/v2.0' % sscloud['openstack']['controller']\n\n\t\tsettings['master'] = self.master\n\t\tsettings['minion'] = self.host\n\t\tlogger.debug('Generate minion configuration for saltstack')\n\t\tself.genconfig('/etc/salt/minion', '/opt/sscloud/templates/salt/minion', settings)\n\t\tlogger.debug('Generate keystone configuration for saltstack minion')\n\t\tself.genconfig('/etc/salt/minion.d/keystone.conf', '/opt/sscloud/templates/salt/minion.d/keystone.conf', settings)\n\n\t\treturn True","title":""},{"docid":"bc6942ac12dab4a58a96e0320096f959","score":"0.5301158","text":"def parse_config(self, config_file):\n config = configparser.ConfigParser()\n config.read(config_file)\n\n return config","title":""},{"docid":"638fb05a33d33136dd507c224e4d593c","score":"0.52998525","text":"def build_config(args):\n args.subject = str(args.subject)\n args.exclude_words = [\"sp\", \"{lg}\", \"{ns}\"]\n create_directory_paths(args)\n\n write_config(vars(args))\n\n return args","title":""},{"docid":"90d53a7eca52b662995310e634af8094","score":"0.5298649","text":"def get_config(self) -> None:\n self.config = {}\n self.config_file = self.config_file if self.config_file else self.DEFAULT_CONFIG\n\n if os.path.exists(self.config_file):\n cfg = open(self.config_file).readlines()\n else:\n raise Exception(\"Cannot open configuration file: \" + self.config_file + \"\\n\" + \"Use sudo, perhaps?\")\n\n for line in cfg:\n try:\n key, value = line.replace(\" \", \"\").strip().split(\"=\", 1)\n if key.startswith(\"db_\"):\n # Handling odd change in Spacewalk 1.7 where \"db_name\" could be URI\n self.config[key] = value.split(\"/\")[-1] if (key == \"db_name\") and value.startswith(\"//\") else value\n except Exception:\n # Suppress error as config not loaded or broken, so is defaulted\n pass","title":""},{"docid":"a035446fa072636a660653158ad17e11","score":"0.529783","text":"def cfg() -> dict:\n return load_config_data(\"./l5kit/tests/artefacts/config.yaml\")","title":""},{"docid":"1681609687408f97ce3c104439c010cd","score":"0.5296773","text":"def from_toml(cls, toml_file: str):\n assert os.path.exists(toml_file), f\"TOML file '{toml_file}' doesn't exist!\"\n config = attempt_parse_toml(toml_file)\n base_dir = pathlib.Path(toml_file).parent\n\n # load in global config\n if \"global_config\" in config:\n gconfig_filepath = join_if_not_abs(base_dir, config['global_config'])\n config[\"global_config\"] = str(pathlib.Path(gconfig_filepath).resolve())\n assert os.path.exists(gconfig_filepath), f\"TOML file '{gconfig_filepath}' doesn't exist!\"\n global_config = attempt_parse_toml(gconfig_filepath)\n config = {**global_config, **config} # favor the local config\n\n # check that all config keys are valid\n for k in config.keys():\n assert k in SystemConfig.get_valid_fields(), f\"config field '{k}' not a valid config field!\"\n\n # make directories\n dpath = join_if_not_abs(base_dir, config['codec_dir'])\n config[\"codec_dir\"] = str(pathlib.Path(dpath).resolve())\n outcome = mkdir_if_not_exist(dpath)\n if outcome:\n csaf_logger.info(f\"created codec directory {dpath} because it did not exist\")\n dpath = join_if_not_abs(base_dir, config['output_dir'])\n config[\"output_dir\"] = str(pathlib.Path(dpath).resolve())\n outcome = mkdir_if_not_exist(dpath)\n\n # setup logging\n log_filepath = join_if_not_abs(config[\"output_dir\"], config[\"log_file\"], exist=False)\n config[\"log_file\"] = str(pathlib.Path(log_filepath).resolve())\n\n # log is permissive -- will use filepath specified\n if os.path.exists(log_filepath):\n print(f\"WARNING! log will output to file that already exists: '{log_filepath}'\")\n open(log_filepath, 'w').close()\n if \"log_level\" in config:\n level = config[\"log_level\"]\n llevel = {\"critical\" : logging.CRITICAL, \"error\" : logging.ERROR,\n \"warning\" : logging.WARNING, \"info\" : logging.INFO,\n \"debug\" : logging.DEBUG, \"notset\" : logging.NOTSET}\n if level in llevel:\n log_level = llevel[level]\n else:\n AssertionError(f\"log level '{level}' found in config file {toml_file} is not valid!\")\n else:\n log_level = logging.INFO\n formatter = logging.Formatter('%(asctime)s: (%(levelname)s) %(message)s', datefmt='%I:%M:%S %p')\n # reflect user specified log level as logger level\n csaf_logger.setLevel(log_level)\n # setup file logging -- accept log level\n fh = logging.FileHandler(log_filepath)\n fh.setFormatter(formatter)\n fh.setLevel(log_level)\n # setup console logging -- set to info log level\n sh = logging.StreamHandler()\n sh.setFormatter(formatter)\n sh.setLevel(logging.INFO)\n # add the two handlers to csaf logging\n csaf_logger.addHandler(fh)\n csaf_logger.addHandler(sh)\n # print config paths\n csaf_logger.info(f\"setting up CSAF System from TOML file '{toml_file}'\")\n if outcome:\n csaf_logger.info(f\"created output directory {dpath} because it did not exist\")\n csaf_logger.info(f\"Output Dir: {config['output_dir']}\")\n csaf_logger.info(f\"Codec Dir: {config['codec_dir']}\")\n csaf_logger.info(f\"Log Level: {config['log_level']}\")\n\n # load component level config file into this config dict\n for dname, dconfig in config[\"components\"].items():\n # make process absolute path\n process_path = pathlib.Path(join_if_not_abs(base_dir, dconfig[\"process\"], project_dir=\"components\"))\n assert os.path.exists(process_path), f\"process path '{process_path}' for component '{dname}' must exist!\"\n config[\"components\"][dname][\"process\"] = str(process_path.resolve())\n\n # load in config file per component\n if 'config' in dconfig:\n dcconfig_path = join_if_not_abs(base_dir, dconfig['config'])\n else:\n dcconfig_path = pathlib.Path(join_if_not_abs(base_dir, dconfig['process'])).with_suffix('.toml')\n assert os.path.exists(dcconfig_path), f\"config file '{dcconfig_path}' for component '{dname}' must exist\"\n dcconfig = attempt_parse_toml(dcconfig_path)\n config[\"components\"][dname]['config'] = dcconfig\n dbase_dir = pathlib.Path(dcconfig_path).parent.resolve()\n\n # load special field inputs\n if 'inputs' in dcconfig:\n assert 'msgs' in dcconfig['inputs'], f\"inputs field in {dcconfig_path} needs to have field msgs\"\n msg_paths = [join_if_not_abs(dbase_dir, m, project_dir=\"msg\") for m in dcconfig['inputs']['msgs']]\n config[\"components\"][dname][\"config\"][\"inputs\"]['msgs'] = [CsafMsg.from_msg_file(msg_path) for msg_path in msg_paths]\n\n # make all path to msg files absolute\n if 'topics' in dcconfig:\n for tname, tconf in dcconfig['topics'].items():\n if 'msg' in tconf:\n msg_path = join_if_not_abs(dbase_dir, tconf[\"msg\"], project_dir=\"msg\")\n assert os.path.exists(msg_path), f\"message file '{msg_path}' in topic '{tname}' for \" \\\n f\"component '{dname}' must exist!\"\n config[\"components\"][dname][\"config\"][\"topics\"][tname]['msg'] = CsafMsg.from_msg_file(msg_path)\n config[\"components\"][dname][\"config\"][\"topics\"][tname]['serializer'] = generate_serializer(msg_path, config[\"codec_dir\"])\n\n return cls(config)","title":""},{"docid":"2c8e830b00474ce078cfa48aeb157cc1","score":"0.52914757","text":"def apply_config(self, *args):\n self.generate_configfile(self.CONFIG)","title":""},{"docid":"616b6dd94afe6cf5a3afea2b5a21a906","score":"0.5288262","text":"def __recreate_configuration__(self,config_file='/etc/haproxy/haproxy.cfg', template_name='/etc/haproxy/haproxy.cfg.template',config_dir='/etc/haproxy'):\n print 'recreating ' + config_file+' from ' + template_name + ' in ' + config_dir\n #first back up the file\n fu = focaplo.files.file.FileUtils()\n fu.backup(config_file)\n \n #search the ACL files in the configuration directory\n acl=fu.concat(config_dir, '*.acl')\n print acl\n aclbackend=fu.concat(config_dir, '*.aclbackend')\n print acl\n backend=fu.concat(config_dir, '*.backend')\n print backend\n #read template\n configtemplate=Template(open(template_name).read())\n \n #write new to file\n conf = open(config_file, 'w')\n conf.write(configtemplate.substitute(aclplaceholder=acl,aclbackendplaceholder=aclbackend,backendplaceholder=backend))\n conf.close()\n print 'done'","title":""},{"docid":"bdd260dc55783f0a42fa06dbc48d60db","score":"0.5287948","text":"def create_config(self, config_file):\n default = {\n 'Hare_birth': 0.08,\n 'Hare_predation': 0.04,\n 'Hare_diffusion': 0.2,\n 'Puma_birth': 0.02,\n 'Puma_mortality': 0.06,\n 'Puma_diffusion': 0.2,\n 'Time_step': 0.4,\n 'Steps': 100,\n 'Output_interval': 8\n }\n\n try:\n with open(config_file, 'w') as outfile:\n json.dump(default, outfile, sort_keys=True, indent=4*' ')\n print(\"New config file created:\\n%s\\n\" % config_file)\n except:\n print(\"Something went wrong...\")\n raise","title":""},{"docid":"ca5a5c69e8bc4c1b9e021f26dbdbc6f7","score":"0.52835","text":"def _setup_framework_configuration_file(self):\n\n # Check configuration file and sections\n sections = [CFS_GENERAL, CFS_DYNAMIC_PARAMS]\n if common.CONF_FILE_LOCATION:\n Validation.file_exist(common.CONF_FILE_LOCATION)\n common.CONF_FILE = \\\n utils.ConfigurationFile(sections, common.CONF_FILE_LOCATION)\n\n # Load dynamic parameters within common\n for common_attrib in common.CONF_FILE.get_variable_list(CFS_DYNAMIC_PARAMS):\n setattr(\n common, common_attrib.upper(),\n common.CONF_FILE.get_variable(\n CFS_DYNAMIC_PARAMS, common_attrib))","title":""},{"docid":"54798e75080debbf7675728973dbbb9e","score":"0.52803916","text":"def set_config(config_file, config_yaml):\n global creds\n with open(config_file, 'w') as f:\n try:\n creds = config_yaml['config'][os.environ['SNAP_NAME']]\n except (KeyError, TypeError):\n default = {'config': {\n os.environ['SNAP_NAME']: creds } }\n print(\"You need to give a yaml file like: {}\".format(default))\n save_shell_source(f, creds)","title":""},{"docid":"73633f5e88cc8c5b524d81d2642b4c06","score":"0.5280276","text":"def _config_from_file(configfile):\n\n conf = {}\n\n # set from config if possible\n if configfile:\n with open(configfile, 'r') as fp:\n config_yaml = yaml.load(fp)\n\n conf = config_yaml\n\n # in the config yaml, 'years' is a map of years to styles; in the config\n # dict used in this module, 'year_styles' is that map and 'years' is\n # simply a list of the years to graph\n conf['year_styles'] = conf.pop('years', {})\n conf['years'] = list(conf['year_styles'].keys())\n\n return conf","title":""}],"string":"[\n {\n \"docid\": \"8c9a1a385e5301c630e9e1751cbf39e6\",\n \"score\": \"0.713112\",\n \"text\": \"def load_from_file(self, config_file):\\n with open(config_file, 'r') as f:\\n parser = ConfigParser()\\n parser.readfp(f)\\n if parser.has_option('site', 'outdir'):\\n self.outdir = os.path.expanduser(parser.get('site', 'outdir'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420991adb29a7dbd7c29181d763ab85b\",\n \"score\": \"0.6845928\",\n \"text\": \"def load_site_config(site_name = \\\"cmc\\\"):\\n (the_site_name, the_site_host, the_site_port) = \\\\\\n get_site_specific_info(DB_PATH, site_name)\\n yml = yaml.load(open(os.path.join(EXC_PATH, \\\"config\\\", \\\"site.yaml\\\")))\\n\\n #name is first field in table\\n the_site = yml[ the_site_name ]\\n the_site['host'] = the_site_host\\n the_site['port'] = the_site_port\\n return the_site\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3d5fb6f03c76e141ee9d2efeedf78c\",\n \"score\": \"0.6822562\",\n \"text\": \"def load_from_file(self, config_file):\\n with open(config_file, 'r') as f:\\n self.parser.readfp(f)\\n if self.parser.has_option('site', 'domain'):\\n self._domain = self.parser.get('site', 'domain')\\n\\n if self.parser.has_option('site', 'outdir'):\\n outdir = os.path.expanduser(self.parser.get('site', 'outdir'))\\n if not os.path.isabs(outdir):\\n path = os.path.dirname(config_file)\\n outdir = os.path.abspath(os.sep.join([path, outdir]))\\n self.outdir = outdir\\n\\n if self.parser.has_section('site'):\\n self._find_extensions(self.parser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7989c2ff1abb8120c9697cb1095d6013\",\n \"score\": \"0.62964994\",\n \"text\": \"def parseConfig(file):\\n \\n sites = []\\n # read all lines\\n for line in open(file, 'r'):\\n line = line.strip()\\n if not re.match(r'^[^#]', line):\\n continue\\n \\n args = [arg for arg in line.split(';')]\\n sites.append(Site(args[0], args[1], args[2:]))\\n\\n return sites\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69f397605260786404e366bb5832e5e\",\n \"score\": \"0.6267531\",\n \"text\": \"def up_site_config():\\n\\n apache_root = '/etc/apache2'\\n apache_available = os.path.join(apache_root, 'sites-available')\\n apache_enabled = os.path.join(apache_root, 'sites-enabled')\\n app_conf = os.path.join(apache_available, '%s.conf' % env.server_name)\\n\\n fabtools.files.upload_template(\\n 'apache_host.conf.tpl',\\n app_conf,\\n context=env,\\n template_dir=os.path.join(env.lib_path, 'templates'),\\n use_jinja=True,\\n use_sudo=True,\\n user='root',\\n chown=True,\\n mode='644',\\n )\\n\\n fabric.api.execute(apache_enable_site, env.server_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2a160f05eca66dc262a86949c16a6d\",\n \"score\": \"0.6153214\",\n \"text\": \"def configure(name):\\n\\n CONFIG = {}\\n module = __import__('cfgs.%s' % name)\\n CONFIG.update(getattr(module, name).CONFIG)\\n\\n from string import Template\\n\\n print '\\\\tCreate src/settings.py'\\n with open('./tpls/settings.template', 'r') as src:\\n with open('./src/settings.py', 'w') as dst:\\n template = Template(src.read())\\n dst.write(template.substitute(CONFIG))\\n dst.close()\\n src.close()\\n\\n print '\\\\tCreate fabfile/hosts.py'\\n with open('./tpls/hosts.template', 'r') as src:\\n with open('./fabfile/hosts.py', 'w') as dst:\\n CONFIG.update({\\n 'DB_USER': CONFIG['DATABASES']['default']['USER'],\\n 'DB_PASSWORD': CONFIG['DATABASES']['default']['PASSWORD'],\\n })\\n template = Template(src.read())\\n dst.write(template.substitute(CONFIG))\\n dst.close()\\n src.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702e324a431ed7f34107a381f6f1bf0f\",\n \"score\": \"0.61366117\",\n \"text\": \"def init_config(config_file):\\r\\n import ConfigParser\\r\\n config = ConfigParser.ConfigParser()\\r\\n config.read(config_file)\\r\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63d3abfa889399a6fce42be4d5c205a\",\n \"score\": \"0.59605837\",\n \"text\": \"def genconf(quiet: bool, domain: str) -> None:\\n explain_step(f\\\"HTTPS configuration file for website {domain}:\\\", quiet=quiet)\\n with path(package=\\\"webserver\\\", resource=\\\"config\\\") as folder:\\n click.echo(f\\\"#=== Generated with webserver v{__version__} ===#\\\")\\n run_command(\\n f'cat {folder}/443-template.conf {folder}/examples/* | sed \\\"s/{{{{SERVER_NAME}}}}/{domain}/g\\\"'\\n )\\n click.echo(\\\"}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b365dfb20fe291b19fb9676f4f244d5d\",\n \"score\": \"0.5919497\",\n \"text\": \"def new_from_template(filename):\\n import opus\\n opus_dir = opus.__path__[0]\\n configfile = os.path.join(opus_dir, \\\"lib\\\", \\\"conf\\\", \\\"default_settings.json\\\")\\n config = OpusConfig(configfile)\\n config._filename = filename\\n config[\\\"SECRET_KEY\\\"] = ''.join([random.choice('abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)') for _ in range(50)])\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff13c4e6975a74a54a68007bfb88cdbe\",\n \"score\": \"0.58986014\",\n \"text\": \"def from_file(self, config_file):\\n with open(str(config_file), 'r') as f:\\n self.config = yaml.load(f)\\n self.set_env_vars()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b073a2b4404fd3963b85fffc9c818d20\",\n \"score\": \"0.58558446\",\n \"text\": \"def createConfig(self):\\n config = configparser.ConfigParser()\\n config.add_section(\\\"DEVICE\\\")\\n config.set(\\\"DEVICE\\\", \\\"port\\\", \\\"com7\\\")\\n config.set(\\\"DEVICE\\\", \\\"baudrate\\\", '9600')\\n config.set(\\\"DEVICE\\\", \\\"timeout\\\", '1')\\n \\n config.add_section(\\\"DB\\\")\\n config.set(\\\"DB\\\", \\\"db\\\", \\\"checkStaff.db\\\")\\n\\n config.add_section(\\\"MACHINE\\\")\\n config.set(\\\"MACHINE\\\", \\\"stanok\\\", '77')\\n\\n config.add_section(\\\"CONNECT_1C\\\")\\n config.set(\\\"CONNECT_1C\\\", \\\"url_http_service\\\" , 'http://1c-www-server/erp/hs/staffstatis/import/')\\n config.set(\\\"CONNECT_1C\\\", \\\"user\\\" , '1cv8')\\n config.set(\\\"CONNECT_1C\\\", \\\"password\\\" , '')\\n\\n\\n with open(self.path, \\\"w\\\") as config_file:\\n config.write(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ed624af3378322aa6c646abc4341ce\",\n \"score\": \"0.58347595\",\n \"text\": \"def up_site_config():\\n nginx_root = '/etc/nginx'\\n nginx_available = os.path.join(nginx_root, 'sites-available')\\n nginx_enabled = os.path.join(nginx_root, 'sites-enabled')\\n app_conf = os.path.join(nginx_available, '%s.conf' % env.server_name)\\n\\n fabtools.files.upload_template(\\n 'nginx.conf.tpl',\\n app_conf,\\n context=env,\\n template_dir=os.path.join(env.lib_path, 'templates'),\\n use_jinja=True,\\n use_sudo=True,\\n user='root',\\n chown=True,\\n mode='644',\\n )\\n\\n if not fabtools.files.is_link('%s/%s.conf' % (nginx_enabled, env.server_name)):\\n with fabric.api.cd(nginx_enabled):\\n fabric.api.sudo('ln -s %s .' % app_conf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81a1fb0236cc3af2cd32ac881254e3a\",\n \"score\": \"0.58207387\",\n \"text\": \"def build_config(self):\\n self.tempest_configure()\\n # find template\\n template_path = os.path.join(os.path.dirname(\\n os.path.abspath(__file__)), os.pardir,\\n \\\"data/files/tempest_neutron.conf\\\")\\n\\n # open template and add values\\n with open(template_path) as f:\\n template = Template(f.read()).substitute(\\n self.tempest_config)\\n\\n # save config\\n with open(self.path, 'w') as w:\\n logger.info(\\\"Writing tempest config: {0}\\\".format(self.path))\\n logger.debug(template)\\n w.write(template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3860d55c92b946340c3895ef345e4e3b\",\n \"score\": \"0.5811174\",\n \"text\": \"def conf(request):\\n path = request.config.getoption(\\\"vars\\\")\\n return BaseConfig(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d6b55acff773a6decd34b81351158f\",\n \"score\": \"0.57835644\",\n \"text\": \"def create_conf():\\n with open('config.py', 'w') as fd:\\n fd.write(\\\"BUFF_SIZE = 4096\\\\n\\\")\\n fd.write(\\\"PATH_FILES = '/tmp/'\\\\n\\\")\\n fd.write(\\\"SRV_ADDR = '127.0.0.1'\\\\n\\\")\\n fd.write(\\\"SRV_PORT = 5100\\\\n\\\")\\n fd.write(\\\"LOG_FILE = '/tmp/logfile.log'\\\\n\\\")\\n fd.write(\\\"PID_FILE = '/tmp/atc_daemon.pid'\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1074ef701e6f0b30e29681267104623d\",\n \"score\": \"0.57806355\",\n \"text\": \"def conf():\\n text = \\\"\\\"\\\"\\\\\\nThis is a simple prolog.\\n\\n[TEST]\\n# This is a test section.\\n\\nvalue1 = 53\\nvalue2 = 54\\n\\\"\\\"\\\"\\n conf = seamm_installer.Configuration()\\n conf.from_string(text)\\n return conf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c6cfd40680b15ce2f07974d535aafe4\",\n \"score\": \"0.57622224\",\n \"text\": \"def load_backblaze_config(config):\\n fd = open(\\\"%s/.backblaze.conf\\\" % os.environ['HOME'])\\n for line in fd.readlines():\\n words = line.split()\\n config[words[0]] = words[1]\\n fd.close()\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"794c497dd1553638b1098eaf809108e9\",\n \"score\": \"0.57605445\",\n \"text\": \"def setup():\\n try:\\n config = ConfigParser.ConfigParser()\\n config.readfp(open(CONFIG_FILE))\\n lib.create.setApiVersion(config.get('general', 'api_version'))\\n except:\\n lib.logger.critical('Could not open config file \\\"%s\\\"' % (CONFIG_FILE,))\\n sys.exit(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a229dfe18233d6683e8c0d4977a8ada\",\n \"score\": \"0.57544714\",\n \"text\": \"def parse_config_file(config_file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376da956e4207567b4cf765bce627e14\",\n \"score\": \"0.57271695\",\n \"text\": \"def write_config(self, config_file: Union[str, Path] = None):\\n config_file = config_file or self.config_file\\n config_file = Path(config_file)\\n parser: ConfigParser = ConfigParser(allow_no_value=True)\\n\\n # populate the config file with the template\\n for section in self.template.keys():\\n parser[section] = self.template[section] # type: ignore\\n\\n try:\\n if not config_file.parent.exists():\\n config_file.parent.mkdir(parents=True)\\n with config_file.open(\\\"w\\\") as configfile:\\n parser.write(configfile)\\n except PermissionError as error:\\n raise error from SystemError()\\n\\n print(f\\\"Configuration file created at {config_file}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4df81c0759e74c5bdaffc54e34d365\",\n \"score\": \"0.5713698\",\n \"text\": \"def createConfigFile(self):\\n config = ConfigParser.RawConfigParser()\\n config.add_section('db')\\n config.set('db', 'wanteddb', 'postgresql')\\n config.add_section('postgresql')\\n config.set('postgresql', 'database', 'sshkey')\\n config.set('postgresql', 'user', os.getenv('USER'))\\n config.set('postgresql', 'password', 'EDITTHIS')\\n config.set('postgresql', 'host', 'localhost')\\n config.set('postgresql', 'port', '5432')\\n config.add_section('client')\\n config.set('client', 'database', 'sshkey')\\n config.set('client', 'user', os.getenv('USER'))\\n config.set('client', 'password', 'EDITTHIS')\\n config.set('client', 'host', 'localhost')\\n try:\\n configfile = open(os.path.expanduser(pathToConfig), 'wb')\\n config.write(configfile)\\n return 0\\n except:\\n print(\\\"Cannot write the defaultconfig\\\")\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e387dea2087a6d11a613652fd0d57fc7\",\n \"score\": \"0.5701189\",\n \"text\": \"def load(file):\\n\\n config_raw = ruamel.yaml.safe_load(file)\\n\\n config = dacite.from_dict(data=config_raw, data_class=VideoPipelineConfig)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5ab178a5f4a9f053705f7fcda306ab\",\n \"score\": \"0.5695858\",\n \"text\": \"def create_config(path):\\n cfgfile = open(path, 'w')\\n config = configparser.ConfigParser()\\n config.add_section(\\\"Settings\\\")\\n config.set(\\\"Settings\\\", \\\"parser\\\", \\\"ReutersParser\\\")\\n config.set(\\\"Settings\\\", \\\"client\\\", \\\"PostgresClient\\\")\\n config.set(\\\"Settings\\\", \\\"schema\\\", \\\"\\\")\\n config.write(cfgfile)\\n cfgfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1117f8e2dcb801fba14ca7992e8eef3\",\n \"score\": \"0.56880933\",\n \"text\": \"def load_config(configfile='~/labpi.conf'):\\n # Create a default configuration\\n config = configparser.ConfigParser()\\n config.read_dict({\\n 'server': {\\n 'username': '',\\n 'password': '',\\n 'base_url': 'http://example.com/container/{id}'\\n },\\n 'pi': {\\n 'lcd_panel': 'dummy',\\n }\\n })\\n # Load the configuration from disk\\n config.read(os.path.expanduser(configfile))\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b0948425daae3e517485ea76f29389\",\n \"score\": \"0.56838214\",\n \"text\": \"def load_configuration(self):\\r\\n s = os.path.sep\\r\\n \\r\\n default = dict(\\r\\n python = 'python',\\r\\n web2py = os.path.join(s.join(__file__.split(s)[:-3]), 'web2py.py'),\\r\\n http_enabled = True,\\r\\n http_ip = '0.0.0.0',\\r\\n http_port = 8000,\\r\\n https_enabled = True,\\r\\n https_ip = '0.0.0.0',\\r\\n https_port = 8001,\\r\\n https_key = '',\\r\\n https_cert = '',\\r\\n password = '<recycle>',\\r\\n )\\r\\n \\r\\n config = default\\r\\n if self.config_file:\\r\\n try:\\r\\n f = open(self.config_file, 'r')\\r\\n lines = f.readlines()\\r\\n f.close()\\r\\n \\r\\n for line in lines:\\r\\n fields = line.split('=', 1)\\r\\n if len(fields) == 2:\\r\\n key, value = fields\\r\\n key = key.strip()\\r\\n value = value.strip()\\r\\n config[key] = value\\r\\n except:\\r\\n pass\\r\\n \\r\\n web2py_path = os.path.dirname(config['web2py'])\\r\\n os.chdir(web2py_path)\\r\\n \\r\\n args = [config['python'], config['web2py']]\\r\\n interfaces = []\\r\\n ports = []\\r\\n\\r\\n if config['http_enabled']:\\r\\n ip = config['http_ip']\\r\\n port = config['http_port']\\r\\n interfaces.append('%s:%s' % (ip, port))\\r\\n ports.append(port)\\r\\n if config['https_enabled']:\\r\\n ip = config['https_ip']\\r\\n port = config['https_port']\\r\\n key = config['https_key']\\r\\n cert = config['https_cert']\\r\\n if key != '' and cert != '':\\r\\n interfaces.append('%s:%s:%s:%s' % (ip, port, cert, key))\\r\\n ports.append(ports)\\r\\n if len(interfaces) == 0:\\r\\n sys.exit('Configuration error. Must have settings for http and/or https')\\r\\n \\r\\n password = config['password']\\r\\n if not password == '<recycle>':\\r\\n from gluon import main\\r\\n for port in ports:\\r\\n main.save_password(password, port)\\r\\n \\r\\n password = '<recycle>'\\r\\n \\r\\n args.append('-a \\\"%s\\\"' % password)\\r\\n \\r\\n interfaces = ';'.join(interfaces)\\r\\n args.append('--interfaces=%s' % interfaces)\\r\\n \\r\\n if 'log_filename' in config.key():\\r\\n log_filename = config['log_filename']\\r\\n args.append('--log_filename=%s' % log_filename)\\r\\n \\r\\n return (args, config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba5738033345c6a0e3a189a9ea81a15\",\n \"score\": \"0.56726974\",\n \"text\": \"def load_configuration(file_name='config'):\\n config = Configuration()\\n parser = configparser.ConfigParser()\\n parser.read(file_name)\\n default_section = parser['DEFAULT']\\n config.token = environ.get('BOT_TOKEN', default_section['token'])\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f6a41a88c581e6fb69bdcda1c69329\",\n \"score\": \"0.5672508\",\n \"text\": \"def loadConfig(self, path):\\n\\n parsedConfig = None\\n\\n try:\\n # Try to open a the project config file\\n tomlConfig = open(path+\\\"/config.toml\\\", \\\"r\\\")\\n parsedConfig = toml.loads(tomlConfig.read())\\n\\n except Exception:\\n raise Exception(\\\"Could not load config file\\\")\\n\\n print(\\\"TOML '\\\" + parsedConfig[\\\"BlogGen\\\"][\\\"WebsiteTitle\\\"] + \\\"' project config loaded\\\")\\n\\n return parsedConfig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787f90dcd2768b2686466c8e61ee77ef\",\n \"score\": \"0.5670884\",\n \"text\": \"def make_config():\\n # Remove the current config from SONG_TEMP_DIR\\n config_path = os.path.join(DEFAULTS.CONFIG_PATH, 'config')\\n\\n # Check if the ytmdl folder is present in config\\n if not os.path.isdir(DEFAULTS.CONFIG_PATH):\\n # Make the ytmdl folder\\n os.makedirs(DEFAULTS.CONFIG_PATH)\\n elif os.path.isfile(config_path):\\n os.remove(config_path)\\n\\n # Now write the config test to config file\\n with open(config_path, 'w') as write_config:\\n write_config.write(config_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab098fd42ce2d5e84be1a3c2e406f6a\",\n \"score\": \"0.5654302\",\n \"text\": \"def create_config_file(self):\\n self.config_fail = True\\n if not os.path.exists(self.path):\\n os.makedirs(self.path)\\n try:\\n if os.path.exists(self.settings_path):\\n os.remove(self.settings_path)\\n except OSError:\\n pass\\n with open((self.settings_path), 'w') as config_file:\\n config_file.write(\\\"\\\")\\n config = ConfigParser.RawConfigParser()\\n config.add_section('Settings')\\n config.set('Settings', 'default master address', '192.168.1.1')\\n config.set('Settings', 'default device number', '0')\\n config.set('Settings', 'default connection type', 'TCP')\\n config.set('Settings', 'default enable dhcp', True)\\n config.set('Settings', 'number of threads', 20)\\n config.set('Settings', 'telnet client executable', ('putty.exe'))\\n config.set('Settings', 'telnet timeout in seconds', '4')\\n config.set('Settings',\\n 'display notification of successful connections', True)\\n config.set('Settings', 'DHCP sniffing enabled', True)\\n config.set('Settings', 'filter incoming DHCP for AMX only', False)\\n config.set('Settings', 'subnet filter enabled', False)\\n config.set('Settings', 'subnet filter', '')\\n config.set('Settings', 'play sounds', True)\\n config.set('Settings', 'check for updates', True)\\n config.add_section('Config')\\n config.set('Config', 'debug', False)\\n config.set('Config', 'columns_config', self.columns_default)\\n config.set('Config', 'DXLink TX Models', self.dxtx_models_default)\\n config.set('Config', 'DXLink RX Models', self.dxrx_models_default)\\n config.set(\\n 'Config', 'DXLink Fibre TX Models', self.dxftx_models_default)\\n config.set(\\n 'Config', 'DXLink Fibre RX Models', self.dxfrx_models_default)\\n with open((self.settings_path), 'w') as configfile:\\n config.write(configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839144b0a29d189d6064767591da60b0\",\n \"score\": \"0.5634978\",\n \"text\": \"def build_config(config_file, args):\\n config = Configuration()\\n if os.path.exists(config_file):\\n config.load_from_file(config_file)\\n\\n config.load_from_arguments(args)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839144b0a29d189d6064767591da60b0\",\n \"score\": \"0.5634978\",\n \"text\": \"def build_config(config_file, args):\\n config = Configuration()\\n if os.path.exists(config_file):\\n config.load_from_file(config_file)\\n\\n config.load_from_arguments(args)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d660f0960ebc9f48bcbd3ac05668d7d7\",\n \"score\": \"0.56208354\",\n \"text\": \"def create_config_from_file(filepath: str) -> Config:\\n assert os.path.exists(filepath), \\\"No config file found at {}\\\".format(filepath)\\n _logger.debug(\\\"Loading config file from {}\\\".format(filepath))\\n with open(filepath) as file_:\\n raw_config = yaml.safe_load(file_)\\n return _create_config(raw_config, is_template=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8ce46d1e9cd02c15d5f1d2eee3d8d2\",\n \"score\": \"0.5609841\",\n \"text\": \"def make_settings():\\n Config.make()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8e6c5cba9496f29e796976a4cac642\",\n \"score\": \"0.56074923\",\n \"text\": \"def scout_config(request, config_file):\\n print(\\\"\\\")\\n in_handle = get_file_handle(config_file)\\n data = yaml.safe_load(in_handle)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3632f9ce8c39e4f031078bccc2912b\",\n \"score\": \"0.55826586\",\n \"text\": \"def crud_config(path):\\n if not os.path.exists(path):\\n create_config(path)\\n\\n config = configparser.ConfigParser()\\n config.read(path)\\n\\n # Read from config file\\n parser = config.get(\\\"Settings\\\", \\\"parser\\\")\\n client = config.get(\\\"Settings\\\", \\\"client\\\")\\n schema = config.get(\\\"Settings\\\", \\\"schema\\\")\\n\\n Setting = namedtuple('Settings', ['parser', 'client', 'schema'])\\n\\n return Setting(parser, client, schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8babcdf72ef46f8a586ada2b30bcc25b\",\n \"score\": \"0.55735433\",\n \"text\": \"def load_settings():\\r\\n config_parser = configparser.RawConfigParser()\\r\\n config_file_path = 'final/settings.txt'\\r\\n config_parser.read(config_file_path)\\r\\n\\r\\n browser = config_parser.get('your-config', 'BROWSER')\\r\\n browser_path = config_parser.get('your-config', 'BROWSER_PATH')\\r\\n name = config_parser.get('your-config', 'NAME')\\r\\n page = config_parser.get('your-config', 'PAGE')\\r\\n output = config_parser.get('your-config', 'Output')\\r\\n\\r\\n settings = {\\r\\n 'browser': browser,\\r\\n 'browser_path': browser_path,\\r\\n 'name': name,\\r\\n 'page': page,\\r\\n 'output' : output\\r\\n }\\r\\n return settings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1209092a435836f0cff60bc141b34733\",\n \"score\": \"0.5567676\",\n \"text\": \"def make_config(self, fileo):\\n config = ConfigParser.SafeConfigParser() #new class instance\\n for data_type, configuration in self.data_types.iteritems():\\n config.add_section(data_type)\\n for option, value in configuration.iteritems():\\n config.set(data_type, option, value)\\n config.write(fileo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1525cf8b57ae0a6d98b8fc7aca71ebb8\",\n \"score\": \"0.5549005\",\n \"text\": \"def parseConfigFile(cfg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1525cf8b57ae0a6d98b8fc7aca71ebb8\",\n \"score\": \"0.5549005\",\n \"text\": \"def parseConfigFile(cfg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1525cf8b57ae0a6d98b8fc7aca71ebb8\",\n \"score\": \"0.5549005\",\n \"text\": \"def parseConfigFile(cfg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee6af1b742b912d76dec588c5a3ce21b\",\n \"score\": \"0.5546118\",\n \"text\": \"def make_config():\\n tmpdir = tempfile.mkdtemp()\\n global db_dir\\n db_dir = tmpdir\\n cwd = os.getcwd()\\n os.chdir(tmpdir)\\n with open('haas.cfg', 'w') as f:\\n config = '\\\\n'.join([\\n '[general]',\\n '[devel]',\\n 'dry_run=True',\\n '[auth]',\\n 'require_authentication = True',\\n\\n '[headnode]',\\n 'base_imgs = base-headnode, img1, img2, img3, img4',\\n '[database]',\\n 'uri = sqlite:///%s/haas.db' % tmpdir,\\n '[extensions]',\\n 'haas.ext.auth.database =',\\n 'haas.ext.switches.mock =',\\n 'haas.ext.switches.nexus =',\\n 'haas.ext.switches.dell =',\\n 'haas.ext.switches.brocade =',\\n 'haas.ext.obm.mock =',\\n 'haas.ext.obm.ipmi =',\\n 'haas.ext.network_allocators.vlan_pool =',\\n '[haas.ext.network_allocators.vlan_pool]',\\n 'vlans = 1001-1040',\\n\\n ])\\n f.write(config)\\n return (tmpdir, cwd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793705206172008876610eb222772894\",\n \"score\": \"0.5538213\",\n \"text\": \"def create_config_file_from_upstream(self) -> None:\\n\\n PyFunceble.helpers.File(self.path_to_default_config).copy(self.path_to_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8618279c876f3b3b5dcdd1d95450dbf0\",\n \"score\": \"0.55377173\",\n \"text\": \"def test_new_site(sitename):\\n print(test_new_site.__doc__ + '...', end=' ')\\n mld = rhfn.new_conf(sitename)\\n assert_equal(mld, '')\\n mld, sett = rhfn.read_conf(sitename)\\n assert_equal(mld, '')\\n assert_equal(sett, {})\\n mld = rhfn.new_conf('test')\\n assert_equal(mld, 'site_name_taken')\\n sett = {x: y for x, y in rhfn.DFLT_CONF.items()}\\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\\n mld, sett = rhfn.read_conf(sitename)\\n\\n sett['hig'] = 'Too high'\\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\\n assert_equal(mld, 'Config: invalid value for hig')\\n sett['hig'] = 32\\n sett['lang'] = 'xx'\\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\\n assert_equal(mld, 'Config: invalid value for lang')\\n sett['lang'] = 'en'\\n sett['url'] = 'http://www.example.com'\\n mld = rhfn.save_conf(sitename, rhfn.conf2text(sett))\\n assert_equal(mld, '')\\n rhfn.init_css(sitename)\\n mld, conf = rhfn.read_conf(sitename)\\n for item in BASIC_CSS:\\n test = WEBROOT / sitename / item\\n assert 'url + ' + item in conf['css']\\n assert test.exists()\\n print('ok')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bede075b08609bb99dd6e933a34e9ab\",\n \"score\": \"0.5533709\",\n \"text\": \"def config():\\n conf = ConfigParser()\\n # Set up defaults\\n conf.add_section('io')\\n conf.set('io', 'compression', 'zlib')\\n\\n conf.read(os.path.expanduser('~/.deepdish.conf'))\\n return conf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593265363c82e6879182611816cc7b0f\",\n \"score\": \"0.55328953\",\n \"text\": \"def create_config_file(file_path):\\n dir_path = file_path.parent\\n if not dir_path.is_dir(): # pragma: no cover\\n print(f'Creating application etc/ directory \\\"{str(dir_path)}\\\".')\\n dir_path.mkdir(mode=0o775, parents=True)\\n if not file_path.exists():\\n with file_path.open(mode=\\\"w\\\") as config_fh:\\n print(f'Creating instance config file at \\\"{str(file_path)}\\\".')\\n print(CONFIG_FILE_HEADER, file=config_fh)\\n #\\n # Set SECRET_KEY to a random string.\\n #\\n write_kv_to_config_file(\\n file_path, \\\"SECRET_KEY\\\", generate_random_password(), str, \\\"\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f35293734d264849a4bd8064d05519\",\n \"score\": \"0.55297446\",\n \"text\": \"def _init_cfg_file(force=False):\\n from shutil import rmtree, copyfile\\n home = Path.home()\\n dir_ = home / PACKAGE_CONF_DIR\\n cfg_file = dir_ / 'conf.yml'\\n\\n dataset_dir = dir_ / 'dataset'\\n cached_dir = dir_ / 'cached'\\n\\n if force:\\n rmtree(dir_)\\n\\n if not dir_.is_dir():\\n # create config root dir\\n dir_.mkdir()\\n\\n # create other dirs\\n dataset_dir.mkdir()\\n cached_dir.mkdir()\\n\\n if not cfg_file.exists() or force:\\n # copy default conf.yml to ~/.XenonPy\\n copyfile(str(Path(__file__).parent / 'conf.yml'), cfg_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579b7e4197bfa33717de9efbe81278d6\",\n \"score\": \"0.5527012\",\n \"text\": \"def set_configs(self):\\n for config_item in ['tag_creds', 'encrypt_tag' ]:\\n define(config_item)\\n # Get configuration values from configuration filescript_name = path.basename(__file__)\\n script_path = path.abspath(path.realpath(__file__))\\n script_dir = path.split(script_path)[0]\\n config_path = path.abspath(path.join(script_dir, 'model/EmsgEncrypt.conf'))\\n options.parse_config_file(config_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23526978594edf608881b5425f5e7cae\",\n \"score\": \"0.55216306\",\n \"text\": \"def config_test():\\n cfg = ConfigParser.ConfigParser()\\n cfg.read('TestConfig.cfg')\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf39c72c9e5e05037e0f6220ee6f8bf7\",\n \"score\": \"0.551976\",\n \"text\": \"def load_config(): # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30faa66a01a398f9048673e6fda03a01\",\n \"score\": \"0.55030286\",\n \"text\": \"def load_config():\\n parser = argparse.ArgumentParser(description=\\\"pyWegaRadio\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--config\\\", help=\\\"filename of the application config (YAML)\\\", required=True)\\n args = parser.parse_args()\\n with open(args.config) as application_config:\\n cfg = yaml.load(application_config)\\n\\n # setting defaults\\n cfg['mpd_host'] = cfg['mpd_client'] if 'mpd_host' in cfg else 'localhost'\\n cfg['mpd_port'] = cfg['mpd_port'] if 'mpd_port' in cfg else 6600\\n\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00822e430d6ee5cdcff3c1a9098317b1\",\n \"score\": \"0.5496693\",\n \"text\": \"def cli(config_file):\\n if config_file is not None:\\n config.update_from_file(config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10a61fe63d5616d5cc284d2c5156940\",\n \"score\": \"0.5495602\",\n \"text\": \"def set_config(self):\\n\\n self.parser['CONNECTION'] = strings.get_config_connection()\\n self.parser['DATABASE'] = strings.get_config_db()\\n self.parser['METRICS'] = strings.get_config_metrics()\\n\\n with open(strings.get_config_path(), 'w') as file:\\n self.parser.write(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"165d7adacf17eef282f7078e7d340d93\",\n \"score\": \"0.5495298\",\n \"text\": \"def load_from_file(cls, config_path):\\n # type: (str) -> Config\\n if not os.path.isfile(config_path):\\n LOGGER.error(\\\"Runway config file was not found (looking for \\\"\\n \\\"%s)\\\",\\n config_path)\\n sys.exit(1)\\n with open(config_path) as data_file:\\n config_file = yaml.safe_load(data_file)\\n result = Config(config_file.pop('deployments'),\\n config_file.pop('tests', []),\\n config_file.pop('ignore_git_branch',\\n config_file.pop(\\n 'ignore-git-branch',\\n False)),\\n config_file.pop('variables', {}))\\n\\n if config_file:\\n LOGGER.warning(\\n 'Invalid keys found in runway file have been ignored: %s',\\n ', '.join(config_file.keys())\\n )\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf970f814bd9190d3a94f92067f7dc5a\",\n \"score\": \"0.54845643\",\n \"text\": \"def load_file(pth):\\n fil = open(pth, 'r')\\n return Configuration(fil.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38625a74b57d297ae8ab7e8926a10f58\",\n \"score\": \"0.54842806\",\n \"text\": \"def from_configfile(cls, configfile):\\n import yaml\\n with open(configfile) as fh:\\n config = yaml.safe_load(fh)\\n\\n return cls.from_config(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad70cf40a191b6a24d34756707bc09c\",\n \"score\": \"0.5480038\",\n \"text\": \"def config(self, settings):\\n for k, v in settings.iteritems():\\n self.sites[k] = v[0]\\n self.sites_config[v[0]] = {'site_name': k}\\n for _k, _v in v[1].iteritems():\\n self.sites_config[v[0]][_k.upper()] = _v\\n \\n self._configed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0033170d05a3bfe1a686e5d78299af53\",\n \"score\": \"0.547585\",\n \"text\": \"def parse_config_file(config_file):\\n config = configparser.ConfigParser()\\n config.read(config_file)\\n\\n settings = {}\\n settings[\\\"world\\\"] = config.get(\\\"RuneScape\\\", \\\"world\\\", fallback=\\\"www\\\")\\n settings[\\\"ram\\\"] = config.get(\\\"Java\\\", \\\"ram\\\", fallback=\\\"-Xmx1024m\\\")\\n settings[\\\"stacksize\\\"] = config.get(\\\"Java\\\", \\\"stacksize\\\", fallback=\\\"-Xss1m\\\")\\n return settings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05210fe3fce6ea8f512bd9cb10396f0f\",\n \"score\": \"0.54738665\",\n \"text\": \"def _make_cfg_file(self, **kwargs):\\n cfg_file = self._cfg_dir / \\\"config.yaml\\\"\\n self.parameters.update(**kwargs)\\n with cfg_file.open(mode=\\\"w\\\") as file:\\n yaml.dump({k: v for k, v in self.parameters}, file)\\n\\n return cfg_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd10b6a6012a3305d175ec3f2dd26e4\",\n \"score\": \"0.54719794\",\n \"text\": \"def generate_config(options):\\n # template name -> file mode\\n config_file_templates = {}\\n\\n config = get_config(options.host)\\n target_dir = path('target')\\n\\n for parent_files_dir in filter(lambda p: p.exists(),\\n [target_dir.joinpath(p, 'files') for p in config['parents']]):\\n for config_file in parent_files_dir.walkfiles():\\n template_name = parent_files_dir.relpathto(config_file)\\n if template_name not in config_file_templates:\\n config_file_templates[template_name] = config_file.lstat()\\n\\n # Load Jinja Environment\\n template_env = Environment(loader=ConfigdichLoader(target_dir))\\n\\n build_dir = path('config').joinpath(options.host)\\n # Clean out the build dir\\n build_dir.rmtree()\\n build_dir.makedirs_p()\\n\\n for config_file_name, template_file_stat in config_file_templates.items():\\n rendered_config_file_path = build_dir.joinpath(config_file_name)\\n # Create the directory that the config file will be rendered to in if needed\\n rendered_config_file_path.dirname().makedirs_p()\\n # Render the template to the file\\n t = template_env.get_template(options.host + \\\"/\\\" + str(config_file_name))\\n rendered_config_file_path.write_text(t.render(config))\\n rendered_config_file_path.chmod(template_file_stat.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d1a4f2d28cfc378269bff766331c1d\",\n \"score\": \"0.546764\",\n \"text\": \"def generic_config_parser(config_path, local):\\n config = ConfigParser.ConfigParser()\\n if local:\\n filename = config_path + \\\"/local_datastore_operator.conf\\\"\\n else:\\n filename = config_path + \\\"/collector_operator.conf\\\"\\n config.read(filename)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd78c123605aef6b83f97ed0eb623cb\",\n \"score\": \"0.54675925\",\n \"text\": \"def new_config_file(self):\\n config_source = os.path.join(os.path.dirname(configs.__file__), 'clitest.yaml')\\n out_config = os.path.join(self.test_dir, 'config.yaml')\\n\\n with open(config_source, 'r') as f_in:\\n with open(out_config, 'w') as f_out:\\n s = f_in.read()\\n f_out.write(s.format(\\n root=self.test_dir,\\n source=os.path.dirname(bundles.__file__)\\n ))\\n return out_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d98b746daa3accf08cffdc55619f55d\",\n \"score\": \"0.545733\",\n \"text\": \"def create_blank_conf(self):\\n\\n self.config['data_file'] = \\\"\\\"\\n self.config.comments['data_file'] = wrap(\\n \\\"File containing the input data. The columns are 'id' (name of the\\\"\\n \\\" object), 'redshift' (if 0 the distance is assumed to be 10 pc), \\\"\\n \\\"the filter names for the fluxes, and the filter names with the \\\"\\n \\\"'_err' suffix for the uncertainties. The fluxes and the \\\"\\n \\\"uncertainties must be in mJy. This file is optional to generate \\\"\\n \\\"the configuration file, in particular for the savefluxes module.\\\")\\n self.spec['data_file'] = \\\"string\\\"\\n\\n self.config['parameters_file'] = \\\"\\\"\\n self.config.comments['parameters_file'] = [\\\"\\\"] + wrap(\\n \\\"Optional file containing the list of physical parameters. Each \\\"\\n \\\"column must be in the form module_name.parameter_name, with each \\\"\\n \\\"line behind a different model. The columns must be in the order \\\"\\n \\\"the modules will be called. The redshift column must be the last \\\"\\n \\\"one. Finally, if this parameters is not left empty, cigale will \\\"\\n \\\"not interpret the configuration parameters given in pcigale.ini. \\\"\\n \\\"They will be given only for information.\\\")\\n self.spec['parameters_file'] = \\\"string()\\\"\\n\\n self.config['sed_modules'] = []\\n self.config.comments['sed_modules'] = ([\\\"\\\"] +\\n [\\\"Order of the modules use for SED creation. Available modules:\\\"] +\\n [\\\"SFH: sfh2exp, sfhdelayed, sfhfromfile, sfhperiodic\\\"] +\\n [\\\"SSP: bc03, m2005\\\"] +\\n [\\\"Nebular emission: nebular\\\"] +\\n [\\\"Dust attenuation: dustatt_calzleit, dustatt_powerlaw, \\\"\\n \\\"dustatt_2powerlaws\\\"] +\\n [\\\"Dust emission: casey2012, dale2014, dl2007, dl2014\\\"] +\\n [\\\"AGN: dale2014, fritz2006\\\"] +\\n [\\\"Radio: radio\\\"] +\\n [\\\"Redshift: redshifting (mandatory!)\\\"])\\n self.spec['sed_modules'] = \\\"cigale_string_list()\\\"\\n\\n self.config['analysis_method'] = \\\"\\\"\\n self.config.comments['analysis_method'] = [\\\"\\\"] + wrap(\\n \\\"Method used for statistical analysis. Available methods: \\\"\\n \\\"pdf_analysis, savefluxes.\\\")\\n self.spec['analysis_method'] = \\\"string()\\\"\\n\\n self.config['cores'] = \\\"\\\"\\n self.config.comments['cores'] = [\\\"\\\"] + wrap(\\n \\\"Number of CPU cores available. This computer has {} cores.\\\"\\n .format(mp.cpu_count()))\\n self.spec['cores'] = \\\"integer(min=1)\\\"\\n\\n self.config.write()\\n self.spec.write()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5721b6cc0302e7d6a826212398bc72d1\",\n \"score\": \"0.54488325\",\n \"text\": \"def update_configfile(cls):\\n # New database path\\n cls.database_path = os.path.join(cls.tmpdir, 'test.db')\\n cls.database_url = 'sqlite:///%s' % cls.database_path\\n\\n # Update config with database url\\n config['common']['DATABASE_URL'] = cls.database_url\\n\\n # New config file\\n cls.config_path = os.path.join(cls.tmpdir, 'test.conf')\\n with open(cls.config_path, 'w') as configfile:\\n config.write(configfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c370f0fa2a07a8de8cbf9c9dad67a6\",\n \"score\": \"0.54463136\",\n \"text\": \"def setconfig(jamla_path, secret_key, template_folder, static_folder, \\\\\\n uploaded_images_dest, db_full_path, success_redirect_url, \\\\\\n thankyou_url, mail_server, mail_port, \\\\\\n mail_default_sender, mail_username, mail_password, mail_use_tls,\\\\\\n email_login_from, gocardless_client_id, \\\\\\n gocardless_client_secret, template_base_dir):\\n newConfig = ''\\n with open('./instance/config.py', 'r') as fh:\\n for line in fh:\\n frame = inspect.currentframe()\\n options = inspect.getargvalues(frame).args\\n for option in options:\\n if option.swapcase() in line and frame.f_locals[option] is not None :\\n newValue = ''.join([option.swapcase(), '=\\\"', str(frame.f_locals[option]), '\\\"'])\\n expr = r\\\"^\\\" + option.swapcase() + \\\".*\\\"\\n line = re.sub(expr, newValue, line)\\n logging.info(\\\"Writing: %s\\\", newValue)\\n newConfig = ''.join([newConfig, line])\\n # Writeout new config file\\n with open('./instance/config.py', 'w') as fh:\\n fh.write(newConfig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac0ba2d625b3994fe049062a44d035c3\",\n \"score\": \"0.54410285\",\n \"text\": \"def loadConfig(self):\\n with open(self.configfile) as f:\\n config = yaml.load(f)\\n self.binddn = config['binddn']\\n self.bindPW = config['bindPW']\\n self.serverURL = config['serverURL']\\n self.baseDN = config['baseDN']\\n self.usersBase = config['usersBase']\\n self.employeeFilter = config['employeeFilter']\\n f.close\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd7d863bba7fa1b24f40eb2ba21c4a1\",\n \"score\": \"0.5431443\",\n \"text\": \"def create_default_config(path, conf):\\n if platform.system() == 'Windows':\\n base_path = os.path.join(os.path.expanduser('~'), 'seprcph')\\n else:\\n base_path = os.path.join(os.path.expanduser('~'), '.config', 'seprcph')\\n\\n try:\\n os.makedirs(os.path.dirname(base_path))\\n except OSError as err:\\n if err.errno != errno.EEXIST:\\n raise\\n\\n conf.add_section('general')\\n conf.set('general', 'screen_height', '480')\\n conf.set('general', 'screen_width', '640')\\n conf.set('general', 'fullscreen', 'false')\\n conf.set('general', 'data_dir', os.path.join(base_path, 'data'))\\n conf.set('general', 'image_dir', os.path.join(base_path, 'assets', 'images'))\\n conf.set('general', 'sound_dir', os.path.join(base_path, 'assets', 'sounds'))\\n\\n conf.add_section('logging')\\n conf.set('logging', 'format', '%(asctime)s - %(levelname)s - %(funcName)s '\\n '- %(message)s')\\n conf.set('logging', 'date_format', '%d/%m/%Y %I:%M:%S %p')\\n conf.set('logging', 'level', 'DEBUG')\\n conf.set('logging', 'file', os.path.join(base_path, 'log.txt'))\\n\\n conf.add_section('graphics')\\n conf.set('graphics', 'fps', '60')\\n conf.set('graphics', 'draw_fps', 'false')\\n\\n conf.add_section('gameplay')\\n conf.set('gameplay', 'turn_limit', '30')\\n\\n with open(path, 'w') as conf_file:\\n conf.write(conf_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d294e401d799371e32810413008056d\",\n \"score\": \"0.54309314\",\n \"text\": \"def create_config(self):\\n # Generate install-config from template\\n _templating = Templating()\\n ocp_install_template = (\\n f\\\"install-config-{self.deployment_platform}-\\\"\\n f\\\"{self.deployment_type}.yaml.j2\\\"\\n )\\n ocp_install_template_path = os.path.join(\\n \\\"ocp-deployment\\\", ocp_install_template\\n )\\n install_config_str = _templating.render_template(\\n ocp_install_template_path, config.ENV_DATA\\n )\\n install_config_obj = yaml.safe_load(install_config_str)\\n install_config_obj[\\\"pullSecret\\\"] = self.get_pull_secret()\\n install_config_obj[\\\"sshKey\\\"] = self.get_ssh_key()\\n install_config_obj[\\\"platform\\\"][\\\"vsphere\\\"][\\\"apiVIP\\\"] = config.ENV_DATA[\\n \\\"vips\\\"\\n ][0]\\n install_config_obj[\\\"platform\\\"][\\\"vsphere\\\"][\\\"ingressVIP\\\"] = config.ENV_DATA[\\n \\\"vips\\\"\\n ][1]\\n install_config_obj[\\\"metadata\\\"][\\\"name\\\"] = config.ENV_DATA.get(\\\"cluster_name\\\")\\n install_config_obj[\\\"baseDomain\\\"] = config.ENV_DATA.get(\\\"base_domain\\\")\\n install_config_str = yaml.safe_dump(install_config_obj)\\n install_config = os.path.join(self.cluster_path, \\\"install-config.yaml\\\")\\n\\n with open(install_config, \\\"w\\\") as f:\\n f.write(install_config_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c450143b54e5a138152443e1ed40ebc5\",\n \"score\": \"0.54287505\",\n \"text\": \"def read_cfg():\\n\\timport copy\\n\\tglobal global_config\\n\\tconfig_parse = ConfigParser.ConfigParser()\\n\\tconfig_parse.optionxform = str\\n\\tconfig_parse.read(config.file_cfg)\\n\\tglobal_config = copy.deepcopy(default_config)\\n\\tfor section in config_parse.sections():\\n\\t\\tfor (name, value) in config_parse.items(section):\\n\\t\\t\\tglobal_config[section][name] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ddc0ec266171954c8fe4a84d2b2d1b4\",\n \"score\": \"0.5426491\",\n \"text\": \"def load_settings():\\n config_parser = configparser.RawConfigParser()\\n config_file_path = 'settings.txt'\\n config_parser.read(config_file_path)\\n\\n browser = config_parser.get('config', 'BROWSER')\\n browser_path = config_parser.get('config', 'BROWSER_PATH')\\n name = config_parser.get('config', 'NAME')\\n page = config_parser.get('config', 'PAGE')\\n csv_location = config_parser.get('config', OUTPUT_FILE_LOCATION)\\n\\n settings = {\\n 'browser': browser,\\n 'browser_path': browser_path,\\n 'name': name,\\n 'page': page,\\n 'csv_location': csv_location\\n }\\n return settings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001fb7ee6bc27d12a98acc8c4b33096f\",\n \"score\": \"0.54166555\",\n \"text\": \"def __create_default_config_file(self) -> None:\\n with open(self.__config_filename, 'w') as configfile:\\n configfile.write(open(cemu.const.TEMPLATE_CONFIG, \\\"r\\\").read())\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"553fa8e1f68f634cdd3a863f2e6d1cf1\",\n \"score\": \"0.5408216\",\n \"text\": \"def readSiteConfig(self, siteId, configFilePath):\\n d = self.readConfig(configFilePath)\\n if siteId in d:\\n return d[siteId]\\n else:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6e9a760ddcdd192a7168242ccf26a3\",\n \"score\": \"0.5402126\",\n \"text\": \"def __init__(self, file: str) -> None:\\r\\n self.load_config_from_file(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae07b5963093d1693d846a18ece8dea4\",\n \"score\": \"0.53898674\",\n \"text\": \"def get_clusto_config(filename=None):\\r\\n\\r\\n filesearchpath = ['/etc/clusto/clusto.conf']\\r\\n\\r\\n \\r\\n filename = filename or os.environ.get('CLUSTOCONFIG')\\r\\n\\r\\n if not filename:\\r\\n filename = filesearchpath[0]\\r\\n\\r\\n if filename:\\r\\n if not os.path.exists(os.path.realpath(filename)):\\r\\n raise CmdLineError(\\\"Config file %s doesn't exist.\\\" % filename)\\r\\n \\r\\n config = SafeConfigParser()\\r\\n config.read([filename])\\r\\n\\r\\n if not config.has_section('clusto'):\\r\\n config.add_section('clusto')\\r\\n\\r\\n if 'CLUSTODSN' in os.environ:\\r\\n config.set('clusto', 'dsn', os.environ['CLUSTODSN'])\\r\\n\\r\\n if not config.has_option('clusto', 'dsn'):\\r\\n raise CmdLineError(\\\"No database given for clusto data.\\\")\\r\\n\\r\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07044e5c9c37b198525f23dbe6087509\",\n \"score\": \"0.5381551\",\n \"text\": \"def generate_config(device):\\n hostname = device[\\\"hostname\\\"]\\n config_data = yaml.safe_load(open(f\\\"host_vars/{hostname}.yaml\\\"))\\n env = Environment(\\n loader=FileSystemLoader(\\\"./templates\\\"), trim_blocks=True, lstrip_blocks=True\\n )\\n template = env.get_template(\\\"ospf.j2\\\")\\n configuration = template.render(config_data)\\n return configuration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfbad3961df131242c601342467008c\",\n \"score\": \"0.5376425\",\n \"text\": \"def create_config(self):\\n # Generate install-config from template\\n _templating = Templating()\\n ocp_install_template = (\\n f\\\"install-config-{self.deployment_platform}-\\\"\\n f\\\"{self.deployment_type}.yaml.j2\\\"\\n )\\n ocp_install_template_path = os.path.join(\\n \\\"ocp-deployment\\\", ocp_install_template\\n )\\n install_config_str = _templating.render_template(\\n ocp_install_template_path, config.ENV_DATA\\n )\\n\\n # Parse the rendered YAML so that we can manipulate the object directly\\n install_config_obj = yaml.safe_load(install_config_str)\\n if version.get_semantic_ocp_version_from_config() >= version.VERSION_4_10:\\n install_config_obj[\\\"platform\\\"][\\\"vsphere\\\"][\\\"network\\\"] = config.ENV_DATA[\\n \\\"vm_network\\\"\\n ]\\n install_config_obj[\\\"pullSecret\\\"] = self.get_pull_secret()\\n install_config_obj[\\\"sshKey\\\"] = self.get_ssh_key()\\n install_config_str = yaml.safe_dump(install_config_obj)\\n install_config = os.path.join(self.cluster_path, \\\"install-config.yaml\\\")\\n with open(install_config, \\\"w\\\") as f:\\n f.write(install_config_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d1bafcf14d487d6167a077cbe5754e\",\n \"score\": \"0.5368864\",\n \"text\": \"def setup(args):\\n cfg = get_cfg()\\n add_custom_config(cfg)\\n cfg.merge_from_file(args.config_file)\\n cfg.freeze()\\n default_setup(cfg, args)\\n return cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3533fe0269fed1b36b7b28d3a13fe2c1\",\n \"score\": \"0.5353794\",\n \"text\": \"def generate_supervisor_conf():\\n\\n tmp_file = \\\"/tmp/supervisor.conf\\\"\\n target_file = os.path.join(env.config.ApiServer.document_root, \\\"local\\\", \\\"conf\\\")\\n\\n run(\\\"mkdir -p %s\\\" % target_file)\\n render_to_file(\\\"supervisor.conf\\\", tmp_file)\\n\\n put(tmp_file, target_file)\\n print \\\"supervisor config file generated at %s\\\" % target_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5714d34e47639c754d788a129d45f06f\",\n \"score\": \"0.53531885\",\n \"text\": \"def read_config_file(self):\\n parser = SafeConfigParser()\\n try:\\n parser.read(self.filename)\\n except IOError as error:\\n logging.info(\\\"Cannot read file: {}.\\\".format(error))\\n sys.exit(0)\\n\\n options_name = [\\n 'host',\\n 'user',\\n 'database',\\n 'password'\\n ]\\n\\n for section_name in parser:\\n self.configuration[section_name] = {}\\n if section_name != 'DEFAULT':\\n for option in options_name:\\n try:\\n value = parser.get(section_name, option)\\n self.configuration[section_name][option] = value\\n except ConfigParser.NoOptionError, err:\\n logging.info(str(err))\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c42dc0a539aae8a4a3f3e9101892a948\",\n \"score\": \"0.53474313\",\n \"text\": \"def get_user_configuration(config_file, default_config_file=None,\\n bld_dir=os.getcwd(), v=1):\\n # Get the configuration\\n verbose = abs(v) > 0\\n\\n logging.info('Looking for user configuration')\\n parser = ConfigParser(allow_no_value=True)\\n parser.optionxform = lambda option: option # retain case sensitivity\\n\\n default_config_file = get_default_configuration_file(bld_dir,\\n default_config_file=default_config_file)\\n if verbose:\\n logging.info('Loading default configuration file: '\\n '{green:%s}' % default_config_file)\\n parser.read(default_config_file)\\n\\n if not config_file:\\n if os.getenv('TRILINOS_CFG') is not None:\\n config_file = os.environ['TRILINOS_CFG']\\n\\n if config_file:\\n if not os.path.isfile(config_file):\\n # Look for config file in known locations\\n for d in (bld_dir, ETC):\\n f = os.path.join(d, config_file)\\n if os.path.isfile(f):\\n config_file = f\\n break\\n else:\\n # No config file is explicitly given, give precedence to ./Trilinos.cfg\\n if os.path.isfile('Trilinos.cfg'):\\n config_file = 'Trilinos.cfg'\\n\\n if config_file:\\n if not os.path.isfile(config_file):\\n raise Exception('Configuration file {0!r} does '\\n 'not exist'.format(config_file))\\n if verbose:\\n s1 = config_file\\n logging.info('Loading user configuration file: {green:%s}' % config_file)\\n parser.read(config_file)\\n\\n if not os.path.isfile(os.path.join(bld_dir, 'Default.cfg')):\\n with open(os.path.join(bld_dir, 'Default.cfg'), 'w') as fh:\\n fh.write(open(default_config_file).read())\\n\\n return parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078a41fa37f83bc8607ba8bdd0a1ff7c\",\n \"score\": \"0.53471076\",\n \"text\": \"def read_config(cls, file_like):\\n\\n with open(file_like) as f:\\n config = yaml.safe_load(f) \\n return cls(**config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91534fc50088bd95f9aa1b9400c1b5e1\",\n \"score\": \"0.53469664\",\n \"text\": \"def setup(ctx):\\n __, template_config = configuration.load_config(load_template=True)\\n __, config = configuration.load_config()\\n if config is None:\\n config = template_config\\n\\n click.echo(ui.style_info(\\\"Setting configuration fields:\\\"))\\n for category_name, category in config.items():\\n for field, value in category.items():\\n new_value = click.prompt(ui.style_prompt(field), default=value)\\n if category_name == \\\"path\\\":\\n new_value = os.path.abspath(new_value)\\n click.echo(ui.style_info_path(\\\"Absolute path is\\\", new_value))\\n config[category_name][field] = new_value\\n\\n click.echo(ui.style_info(\\\"Checking configuration:\\\"))\\n for category_name, category in template_config.items():\\n if category_name not in config:\\n click.echo(\\n ui.style_error(f\\\"Category {category_name} not in config, creating\\\")\\n )\\n config[category_name] = {}\\n for field, value in category.items():\\n if field not in config[category_name]:\\n click.echo(ui.style_error(f\\\"Field {field} not in config, creating\\\"))\\n new_value = click.prompt(ui.style_prompt(field), default=value)\\n if category_name == \\\"path\\\":\\n new_value = os.path.abspath(new_value)\\n click.echo(ui.style_info_path(\\\"Absolute path is\\\", new_value))\\n config[category_name][field] = new_value\\n\\n click.echo(\\n ui.style_info_path(\\\"Saved configuration at\\\", configuration.write_config(config))\\n )\\n click.echo(\\n ui.style_info_path(\\n \\\"Copied review template to\\\",\\n configuration.copy_template_review(\\n os.path.abspath(config[\\\"path\\\"][\\\"reviews_directory\\\"])\\n ),\\n )\\n )\\n click.echo(\\n ui.style_info_path(\\n \\\"Copied HTML/CSS export templates to\\\",\\n configuration.copy_template_html(config[\\\"path\\\"][\\\"export_directory\\\"]),\\n )\\n )\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7429b17fd8520aca87366541858b90d4\",\n \"score\": \"0.53251046\",\n \"text\": \"def open_config(self):\\r\\n config = configparser.ConfigParser()\\r\\n\\r\\n cf = os.path.join(\\r\\n os.path.dirname(os.path.realpath(__file__)),\\r\\n \\\"api.cfg\\\"\\r\\n )\\r\\n if not config.read([cf]):\\r\\n print(\\\"No login configuration file found!\\\")\\r\\n\\r\\n self.email = config.get(self.account, 'email')\\r\\n self.password = config.get(self.account, 'password')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfb96c2fd95286d5dac2499570ee504\",\n \"score\": \"0.532114\",\n \"text\": \"def readconfig():\\n config = ConfigParser.ConfigParser()\\n config.read(configfile())\\n configdict = {}\\n\\n configdict['server_port'] = config.get('server', 'server_port', 8212)\\n configdict['verbose'] = config.getint('server', 'verbose')\\n db_url = config.get('server', 'db_url')\\n if db_url == 'tests/test.db':\\n configdict['db_url'] = 'sqlite:///%s/tests/test.db' % \\\\\\n os.environ['QB_ROOT']\\n else:\\n configdict['db_url'] = db_url\\n# configdict['db_alias'] = config.get('server', 'db_alias')\\n configdict['map_file'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'map_file', ''))\\n configdict['alias_mapfile'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'alias_mapfile', ''))\\n configdict['split_file'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'split_file', ''))\\n configdict['logconfig'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'logconfig', ''))\\n configdict['doc_dir'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'doc_dir', ''))\\n configdict['left_join'] = os.path.join(os.environ['QB_ROOT'], \\\\\\n config.get('server', 'left_join', ''))\\n\\n configdict['environment'] = config.get('server', 'environment', 'production')\\n configdict['profiler'] = int(config.get('server', 'profiler', 0))\\n configdict['threadpool'] = int(config.get('server', 'thread_pool', 30))\\n configdict['socket_queue_size'] = int(config.get('server', 'socket_queue_size', 15))\\n configdict['expires'] = int(config.get('server', 'expires', 300))\\n configdict['log_screen'] = bool(config.get('server', 'log_screen', True))\\n configdict['access_log_file'] = config.get('server', 'access_log_file', \\\\\\n '/tmp/access_log.log')\\n configdict['error_log_file'] = config.get('server', 'error_log_file', \\\\\\n '/tmp/error_log.log')\\n configdict['access_log_level'] = int(config.get('server', 'access_log_level', 0))\\n configdict['error_log_level'] = int(config.get('server', 'error_log_level', 0))\\n configdict['algo'] = config.get('server', 'algo', 'MIS')\\n\\n return configdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c385c7e40f4f5477e0155db3d443ec5\",\n \"score\": \"0.53203917\",\n \"text\": \"def gen_supervisor_conf(conf_file='local/supervisord.conf.tmp'):\\n # Back up first\\n _backup_file(conf_file)\\n\\n env.run('python manage.py supervisor getconfig > %s' % conf_file)\\n print green(\\\"Wrote supervisor conf file to %s\\\" % conf_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcababd44abb64d5ff2669218c654d5f\",\n \"score\": \"0.5319802\",\n \"text\": \"def get_config(self):\\n\\n config = ConfigParser.ConfigParser()\\n if os.path.isfile(self.args.config):\\n myconf = self.args.config\\n else: myconf = 'config/db.conf'\\n config.read(myconf)\\n\\n self.db['host'] = config.get('siemstress', 'server')\\n self.db['user'] = config.get('siemstress', 'user')\\n self.db['password'] = config.get('siemstress', 'password')\\n self.db['database'] = config.get('siemstress', 'database')\\n sectionfile = config.get('siemstress', 'sectionfile')\\n\\n if not sectionfile.startswith('/'):\\n sectionfile = '/'.join(os.path.abspath(myconf).split('/')[:-1]) + \\\\\\n '/' + sectionfile\\n\\n config.read(sectionfile)\\n \\n self.table = config.get(self.args.section, 'table')\\n self.helpers = config.get(self.args.section, 'helpers')\\n try:\\n self.parsername = config.get(self.args.section, 'parser')\\n except Exception:\\n # To Do: narrow down exception\\n self.parsername = 'syslogbsd'\\n\\n\\n if self.parsername == 'syslogbsd':\\n self.parser = logdissect.parsers.syslogbsd.ParseModule()\\n elif self.parsername == 'syslogiso':\\n self.parser = logdissect.parsers.syslogiso.ParseModule()\\n elif self.parsername == 'nohost':\\n self.parser = logdissect.parsers.nohost.ParseModule()\\n elif self.parsername == 'tcpdump':\\n self.parser = logdissect.parsers.tcpdump.ParseModule()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9d03322b3e96d8419c8cd02ad61dc2\",\n \"score\": \"0.5319087\",\n \"text\": \"def parse_config(self):\\n file_name = '{}/dyson_pure_link.yaml'.format(os.path.dirname(os.path.abspath(__file__)))\\n\\n if os.path.isfile(file_name):\\n self.config = yaml.safe_load(open(file_name))\\n\\n return self.config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57edd0435b833444b5fa7780273976fe\",\n \"score\": \"0.5318298\",\n \"text\": \"def load_config(config_file: str) -> dict:\\n return toml.loads(open(config_file).read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc48fbd682a18e8dd29aed57d2a5388\",\n \"score\": \"0.53126377\",\n \"text\": \"def __init_config(cfg):\\n config_path = os.getenv(\\\"BB_CONFIG_FILE\\\", None)\\n if not config_path:\\n config_path = find_config()\\n\\n if config_path:\\n cfg.load(config_path)\\n cfg[\\\"config_file\\\"] = os.path.abspath(config_path)\\n logging.debug(\\\"Configuration loaded from {0}\\\".format(os.path.abspath(\\n config_path)))\\n cfg.init_from_env()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97aacbb7303a87c5c1de0636f6cc027\",\n \"score\": \"0.5306472\",\n \"text\": \"def setup_config(command, filename, section, vars):\\r\\n confuri = \\\"config:\\\" + filename\\r\\n if \\\":\\\" in section:\\r\\n confuri += \\\"#\\\" + section.rsplit(\\\":\\\", 1)[-1]\\r\\n conf = appconfig(confuri)\\r\\n\\r\\n load_environment(conf.global_conf, conf.local_conf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d6d6db430486bd019522c3373026ca\",\n \"score\": \"0.53013945\",\n \"text\": \"def config(self, sscloud_config='/etc/sscloud/config.yml'):\\n\\n\\t\\t# Get settings from sscloud configuration\\n\\t\\twith open(sscloud_config, 'r') as f:\\n\\t\\t\\tsscloud = yaml.load(f)\\n\\n\\t\\tsettings = dict()\\n\\t\\t# Set master\\n\\t\\tif not self.master:\\n\\t\\t\\tself.master = sscloud['openstack']['controller']\\n\\n\\t\\tif self.host == self.master:\\n\\t\\t\\tsettings['config'] = sscloud_config\\n\\t\\t\\tlogger.debug('Generate master configuration for saltstack')\\n\\t\\t\\tself.genconfig('/etc/salt/master', '/opt/sscloud/templates/salt/master', settings)\\n\\t\\telse:\\n\\t\\t\\tlogger.debug('This host(%s) is not master(%s)', self.host, self.master)\\n\\n\\t\\t# Set minion\\n\\t\\t# Get keystone settings\\n\\t\\ttry:\\n\\t\\t\\tsettings['token'] = sscloud['keystone']['token']\\n\\t\\texcept:\\n\\t\\t\\tsettings['token'] = sscloud['openstack']['admin']['token']\\n\\n\\t\\ttry:\\n\\t\\t\\tsettings['endpoint'] = sscloud['keystone']['endpoint']['admin']\\n\\t\\texcept:\\n\\t\\t\\tsettings['endpoint'] = 'http://%s:35357/v2.0' % sscloud['openstack']['controller']\\n\\n\\t\\tsettings['master'] = self.master\\n\\t\\tsettings['minion'] = self.host\\n\\t\\tlogger.debug('Generate minion configuration for saltstack')\\n\\t\\tself.genconfig('/etc/salt/minion', '/opt/sscloud/templates/salt/minion', settings)\\n\\t\\tlogger.debug('Generate keystone configuration for saltstack minion')\\n\\t\\tself.genconfig('/etc/salt/minion.d/keystone.conf', '/opt/sscloud/templates/salt/minion.d/keystone.conf', settings)\\n\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc6942ac12dab4a58a96e0320096f959\",\n \"score\": \"0.5301158\",\n \"text\": \"def parse_config(self, config_file):\\n config = configparser.ConfigParser()\\n config.read(config_file)\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638fb05a33d33136dd507c224e4d593c\",\n \"score\": \"0.52998525\",\n \"text\": \"def build_config(args):\\n args.subject = str(args.subject)\\n args.exclude_words = [\\\"sp\\\", \\\"{lg}\\\", \\\"{ns}\\\"]\\n create_directory_paths(args)\\n\\n write_config(vars(args))\\n\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d53a7eca52b662995310e634af8094\",\n \"score\": \"0.5298649\",\n \"text\": \"def get_config(self) -> None:\\n self.config = {}\\n self.config_file = self.config_file if self.config_file else self.DEFAULT_CONFIG\\n\\n if os.path.exists(self.config_file):\\n cfg = open(self.config_file).readlines()\\n else:\\n raise Exception(\\\"Cannot open configuration file: \\\" + self.config_file + \\\"\\\\n\\\" + \\\"Use sudo, perhaps?\\\")\\n\\n for line in cfg:\\n try:\\n key, value = line.replace(\\\" \\\", \\\"\\\").strip().split(\\\"=\\\", 1)\\n if key.startswith(\\\"db_\\\"):\\n # Handling odd change in Spacewalk 1.7 where \\\"db_name\\\" could be URI\\n self.config[key] = value.split(\\\"/\\\")[-1] if (key == \\\"db_name\\\") and value.startswith(\\\"//\\\") else value\\n except Exception:\\n # Suppress error as config not loaded or broken, so is defaulted\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a035446fa072636a660653158ad17e11\",\n \"score\": \"0.529783\",\n \"text\": \"def cfg() -> dict:\\n return load_config_data(\\\"./l5kit/tests/artefacts/config.yaml\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1681609687408f97ce3c104439c010cd\",\n \"score\": \"0.5296773\",\n \"text\": \"def from_toml(cls, toml_file: str):\\n assert os.path.exists(toml_file), f\\\"TOML file '{toml_file}' doesn't exist!\\\"\\n config = attempt_parse_toml(toml_file)\\n base_dir = pathlib.Path(toml_file).parent\\n\\n # load in global config\\n if \\\"global_config\\\" in config:\\n gconfig_filepath = join_if_not_abs(base_dir, config['global_config'])\\n config[\\\"global_config\\\"] = str(pathlib.Path(gconfig_filepath).resolve())\\n assert os.path.exists(gconfig_filepath), f\\\"TOML file '{gconfig_filepath}' doesn't exist!\\\"\\n global_config = attempt_parse_toml(gconfig_filepath)\\n config = {**global_config, **config} # favor the local config\\n\\n # check that all config keys are valid\\n for k in config.keys():\\n assert k in SystemConfig.get_valid_fields(), f\\\"config field '{k}' not a valid config field!\\\"\\n\\n # make directories\\n dpath = join_if_not_abs(base_dir, config['codec_dir'])\\n config[\\\"codec_dir\\\"] = str(pathlib.Path(dpath).resolve())\\n outcome = mkdir_if_not_exist(dpath)\\n if outcome:\\n csaf_logger.info(f\\\"created codec directory {dpath} because it did not exist\\\")\\n dpath = join_if_not_abs(base_dir, config['output_dir'])\\n config[\\\"output_dir\\\"] = str(pathlib.Path(dpath).resolve())\\n outcome = mkdir_if_not_exist(dpath)\\n\\n # setup logging\\n log_filepath = join_if_not_abs(config[\\\"output_dir\\\"], config[\\\"log_file\\\"], exist=False)\\n config[\\\"log_file\\\"] = str(pathlib.Path(log_filepath).resolve())\\n\\n # log is permissive -- will use filepath specified\\n if os.path.exists(log_filepath):\\n print(f\\\"WARNING! log will output to file that already exists: '{log_filepath}'\\\")\\n open(log_filepath, 'w').close()\\n if \\\"log_level\\\" in config:\\n level = config[\\\"log_level\\\"]\\n llevel = {\\\"critical\\\" : logging.CRITICAL, \\\"error\\\" : logging.ERROR,\\n \\\"warning\\\" : logging.WARNING, \\\"info\\\" : logging.INFO,\\n \\\"debug\\\" : logging.DEBUG, \\\"notset\\\" : logging.NOTSET}\\n if level in llevel:\\n log_level = llevel[level]\\n else:\\n AssertionError(f\\\"log level '{level}' found in config file {toml_file} is not valid!\\\")\\n else:\\n log_level = logging.INFO\\n formatter = logging.Formatter('%(asctime)s: (%(levelname)s) %(message)s', datefmt='%I:%M:%S %p')\\n # reflect user specified log level as logger level\\n csaf_logger.setLevel(log_level)\\n # setup file logging -- accept log level\\n fh = logging.FileHandler(log_filepath)\\n fh.setFormatter(formatter)\\n fh.setLevel(log_level)\\n # setup console logging -- set to info log level\\n sh = logging.StreamHandler()\\n sh.setFormatter(formatter)\\n sh.setLevel(logging.INFO)\\n # add the two handlers to csaf logging\\n csaf_logger.addHandler(fh)\\n csaf_logger.addHandler(sh)\\n # print config paths\\n csaf_logger.info(f\\\"setting up CSAF System from TOML file '{toml_file}'\\\")\\n if outcome:\\n csaf_logger.info(f\\\"created output directory {dpath} because it did not exist\\\")\\n csaf_logger.info(f\\\"Output Dir: {config['output_dir']}\\\")\\n csaf_logger.info(f\\\"Codec Dir: {config['codec_dir']}\\\")\\n csaf_logger.info(f\\\"Log Level: {config['log_level']}\\\")\\n\\n # load component level config file into this config dict\\n for dname, dconfig in config[\\\"components\\\"].items():\\n # make process absolute path\\n process_path = pathlib.Path(join_if_not_abs(base_dir, dconfig[\\\"process\\\"], project_dir=\\\"components\\\"))\\n assert os.path.exists(process_path), f\\\"process path '{process_path}' for component '{dname}' must exist!\\\"\\n config[\\\"components\\\"][dname][\\\"process\\\"] = str(process_path.resolve())\\n\\n # load in config file per component\\n if 'config' in dconfig:\\n dcconfig_path = join_if_not_abs(base_dir, dconfig['config'])\\n else:\\n dcconfig_path = pathlib.Path(join_if_not_abs(base_dir, dconfig['process'])).with_suffix('.toml')\\n assert os.path.exists(dcconfig_path), f\\\"config file '{dcconfig_path}' for component '{dname}' must exist\\\"\\n dcconfig = attempt_parse_toml(dcconfig_path)\\n config[\\\"components\\\"][dname]['config'] = dcconfig\\n dbase_dir = pathlib.Path(dcconfig_path).parent.resolve()\\n\\n # load special field inputs\\n if 'inputs' in dcconfig:\\n assert 'msgs' in dcconfig['inputs'], f\\\"inputs field in {dcconfig_path} needs to have field msgs\\\"\\n msg_paths = [join_if_not_abs(dbase_dir, m, project_dir=\\\"msg\\\") for m in dcconfig['inputs']['msgs']]\\n config[\\\"components\\\"][dname][\\\"config\\\"][\\\"inputs\\\"]['msgs'] = [CsafMsg.from_msg_file(msg_path) for msg_path in msg_paths]\\n\\n # make all path to msg files absolute\\n if 'topics' in dcconfig:\\n for tname, tconf in dcconfig['topics'].items():\\n if 'msg' in tconf:\\n msg_path = join_if_not_abs(dbase_dir, tconf[\\\"msg\\\"], project_dir=\\\"msg\\\")\\n assert os.path.exists(msg_path), f\\\"message file '{msg_path}' in topic '{tname}' for \\\" \\\\\\n f\\\"component '{dname}' must exist!\\\"\\n config[\\\"components\\\"][dname][\\\"config\\\"][\\\"topics\\\"][tname]['msg'] = CsafMsg.from_msg_file(msg_path)\\n config[\\\"components\\\"][dname][\\\"config\\\"][\\\"topics\\\"][tname]['serializer'] = generate_serializer(msg_path, config[\\\"codec_dir\\\"])\\n\\n return cls(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8e830b00474ce078cfa48aeb157cc1\",\n \"score\": \"0.52914757\",\n \"text\": \"def apply_config(self, *args):\\n self.generate_configfile(self.CONFIG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616b6dd94afe6cf5a3afea2b5a21a906\",\n \"score\": \"0.5288262\",\n \"text\": \"def __recreate_configuration__(self,config_file='/etc/haproxy/haproxy.cfg', template_name='/etc/haproxy/haproxy.cfg.template',config_dir='/etc/haproxy'):\\n print 'recreating ' + config_file+' from ' + template_name + ' in ' + config_dir\\n #first back up the file\\n fu = focaplo.files.file.FileUtils()\\n fu.backup(config_file)\\n \\n #search the ACL files in the configuration directory\\n acl=fu.concat(config_dir, '*.acl')\\n print acl\\n aclbackend=fu.concat(config_dir, '*.aclbackend')\\n print acl\\n backend=fu.concat(config_dir, '*.backend')\\n print backend\\n #read template\\n configtemplate=Template(open(template_name).read())\\n \\n #write new to file\\n conf = open(config_file, 'w')\\n conf.write(configtemplate.substitute(aclplaceholder=acl,aclbackendplaceholder=aclbackend,backendplaceholder=backend))\\n conf.close()\\n print 'done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd260dc55783f0a42fa06dbc48d60db\",\n \"score\": \"0.5287948\",\n \"text\": \"def create_config(self, config_file):\\n default = {\\n 'Hare_birth': 0.08,\\n 'Hare_predation': 0.04,\\n 'Hare_diffusion': 0.2,\\n 'Puma_birth': 0.02,\\n 'Puma_mortality': 0.06,\\n 'Puma_diffusion': 0.2,\\n 'Time_step': 0.4,\\n 'Steps': 100,\\n 'Output_interval': 8\\n }\\n\\n try:\\n with open(config_file, 'w') as outfile:\\n json.dump(default, outfile, sort_keys=True, indent=4*' ')\\n print(\\\"New config file created:\\\\n%s\\\\n\\\" % config_file)\\n except:\\n print(\\\"Something went wrong...\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca5a5c69e8bc4c1b9e021f26dbdbc6f7\",\n \"score\": \"0.52835\",\n \"text\": \"def _setup_framework_configuration_file(self):\\n\\n # Check configuration file and sections\\n sections = [CFS_GENERAL, CFS_DYNAMIC_PARAMS]\\n if common.CONF_FILE_LOCATION:\\n Validation.file_exist(common.CONF_FILE_LOCATION)\\n common.CONF_FILE = \\\\\\n utils.ConfigurationFile(sections, common.CONF_FILE_LOCATION)\\n\\n # Load dynamic parameters within common\\n for common_attrib in common.CONF_FILE.get_variable_list(CFS_DYNAMIC_PARAMS):\\n setattr(\\n common, common_attrib.upper(),\\n common.CONF_FILE.get_variable(\\n CFS_DYNAMIC_PARAMS, common_attrib))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54798e75080debbf7675728973dbbb9e\",\n \"score\": \"0.52803916\",\n \"text\": \"def set_config(config_file, config_yaml):\\n global creds\\n with open(config_file, 'w') as f:\\n try:\\n creds = config_yaml['config'][os.environ['SNAP_NAME']]\\n except (KeyError, TypeError):\\n default = {'config': {\\n os.environ['SNAP_NAME']: creds } }\\n print(\\\"You need to give a yaml file like: {}\\\".format(default))\\n save_shell_source(f, creds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73633f5e88cc8c5b524d81d2642b4c06\",\n \"score\": \"0.5280276\",\n \"text\": \"def _config_from_file(configfile):\\n\\n conf = {}\\n\\n # set from config if possible\\n if configfile:\\n with open(configfile, 'r') as fp:\\n config_yaml = yaml.load(fp)\\n\\n conf = config_yaml\\n\\n # in the config yaml, 'years' is a map of years to styles; in the config\\n # dict used in this module, 'year_styles' is that map and 'years' is\\n # simply a list of the years to graph\\n conf['year_styles'] = conf.pop('years', {})\\n conf['years'] = list(conf['year_styles'].keys())\\n\\n return conf\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993984,"cells":{"query_id":{"kind":"string","value":"ac797dc1950168e935fd4e6788e034a4"},"query":{"kind":"string","value":"Check that a TableDumpV2 JSON document from mabo is correctly abstracted."},"positive_passages":{"kind":"list like","value":[{"docid":"30c2a70372eb4dab434bc2014251637c","score":"0.5781272","text":"def test_TableDumpV2(self):\n\n # Get the abstract BGP message\n abstracted = MaboTableDumpV2Document(\"collector\", json_TableDumpV2)\n\n # Check each element\n elements = abstracted.elements()\n assert len(elements) == 5\n\n element1 = elements.pop(0)\n assert element1 == TableDumpV2Element(asn=5678, as_path=\"1234 5678\", peer_as=1234, peer_ip=\"1.2.3.4\")\n\n element2 = elements.pop(0)\n assert element2 == TableDumpV2Element(asn=910112, as_path=\"5678 910112\", peer_as=5678, peer_ip=\"5.6.7.8\")\n\n element3 = elements.pop(0)\n assert element3 == TableDumpV2Element(asn=13141516, as_path=\"89101112 13141516\", peer_as=89101112, peer_ip=\"8.9.10.11\")\n\n element4 = elements.pop(0)\n assert element4 == TableDumpV2Element(asn=123, as_path=\"89101112 {123,456}\", peer_as=89101112, peer_ip=\"8.9.10.11\")\n\n element5 = elements.pop(0)\n assert element5 == TableDumpV2Element(asn=456, as_path=\"89101112 {123,456}\", peer_as=89101112, peer_ip=\"8.9.10.11\")\n\n # There is no withdraws\n assert abstracted.withdraws() == []\n\n # Check announces\n assert list(abstracted.announces()) == [ InternalMessage(timestamp=2807, collector=\"collector\",\n peer_as=1234, peer_ip=\"1.2.3.4\",\n prefix=\"10.11.12.0/24\", asn=5678, as_path=\"1234 5678\"),\n InternalMessage(timestamp=2807, collector=\"collector\",\n peer_as=5678, peer_ip=\"5.6.7.8\",\n prefix=\"10.11.12.0/24\", asn=910112, as_path=\"5678 910112\"),\n InternalMessage(timestamp=2807, collector=\"collector\",\n peer_as=89101112, peer_ip=\"8.9.10.11\",\n prefix=\"10.11.12.0/24\", asn=13141516, as_path=\"89101112 13141516\"),\n InternalMessage(timestamp=2807, collector=\"collector\",\n peer_as=89101112, peer_ip=\"8.9.10.11\",\n prefix=\"10.11.12.0/24\", asn=123, as_path=\"89101112 {123,456}\"),\n InternalMessage(timestamp=2807, collector=\"collector\",\n peer_as=89101112, peer_ip=\"8.9.10.11\",\n prefix=\"10.11.12.0/24\", asn=456, as_path=\"89101112 {123,456}\") ]","title":""}],"string":"[\n {\n \"docid\": \"30c2a70372eb4dab434bc2014251637c\",\n \"score\": \"0.5781272\",\n \"text\": \"def test_TableDumpV2(self):\\n\\n # Get the abstract BGP message\\n abstracted = MaboTableDumpV2Document(\\\"collector\\\", json_TableDumpV2)\\n\\n # Check each element\\n elements = abstracted.elements()\\n assert len(elements) == 5\\n\\n element1 = elements.pop(0)\\n assert element1 == TableDumpV2Element(asn=5678, as_path=\\\"1234 5678\\\", peer_as=1234, peer_ip=\\\"1.2.3.4\\\")\\n\\n element2 = elements.pop(0)\\n assert element2 == TableDumpV2Element(asn=910112, as_path=\\\"5678 910112\\\", peer_as=5678, peer_ip=\\\"5.6.7.8\\\")\\n\\n element3 = elements.pop(0)\\n assert element3 == TableDumpV2Element(asn=13141516, as_path=\\\"89101112 13141516\\\", peer_as=89101112, peer_ip=\\\"8.9.10.11\\\")\\n\\n element4 = elements.pop(0)\\n assert element4 == TableDumpV2Element(asn=123, as_path=\\\"89101112 {123,456}\\\", peer_as=89101112, peer_ip=\\\"8.9.10.11\\\")\\n\\n element5 = elements.pop(0)\\n assert element5 == TableDumpV2Element(asn=456, as_path=\\\"89101112 {123,456}\\\", peer_as=89101112, peer_ip=\\\"8.9.10.11\\\")\\n\\n # There is no withdraws\\n assert abstracted.withdraws() == []\\n\\n # Check announces\\n assert list(abstracted.announces()) == [ InternalMessage(timestamp=2807, collector=\\\"collector\\\",\\n peer_as=1234, peer_ip=\\\"1.2.3.4\\\",\\n prefix=\\\"10.11.12.0/24\\\", asn=5678, as_path=\\\"1234 5678\\\"),\\n InternalMessage(timestamp=2807, collector=\\\"collector\\\",\\n peer_as=5678, peer_ip=\\\"5.6.7.8\\\",\\n prefix=\\\"10.11.12.0/24\\\", asn=910112, as_path=\\\"5678 910112\\\"),\\n InternalMessage(timestamp=2807, collector=\\\"collector\\\",\\n peer_as=89101112, peer_ip=\\\"8.9.10.11\\\",\\n prefix=\\\"10.11.12.0/24\\\", asn=13141516, as_path=\\\"89101112 13141516\\\"),\\n InternalMessage(timestamp=2807, collector=\\\"collector\\\",\\n peer_as=89101112, peer_ip=\\\"8.9.10.11\\\",\\n prefix=\\\"10.11.12.0/24\\\", asn=123, as_path=\\\"89101112 {123,456}\\\"),\\n InternalMessage(timestamp=2807, collector=\\\"collector\\\",\\n peer_as=89101112, peer_ip=\\\"8.9.10.11\\\",\\n prefix=\\\"10.11.12.0/24\\\", asn=456, as_path=\\\"89101112 {123,456}\\\") ]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c8ac963d2472571f1d37e326483a25e1","score":"0.62551606","text":"def test_parse_biom_table(self):\r\n # This is a TSV as a list of lines\r\n t = parse_biom_table(self.classic_otu_table1_no_tax)\r\n\r\n # Test TSV as a list of lines\r\n t_tsv_str = t.to_tsv()\r\n t_tsv_lines = t_tsv_str.splitlines()\r\n t_tsv = parse_biom_table(t_tsv_lines)\r\n self.assertEqual(t, t_tsv)\r\n # Test TSV as a file-like object\r\n t_tsv_stringio = StringIO(t_tsv_str)\r\n t_tsv = parse_biom_table(t_tsv_stringio)\r\n self.assertEqual(t, t_tsv)\r\n\r\n # Test JSON as a list of lines\r\n t_json_str = t.to_json('asd')\r\n t_json_lines = t_json_str.splitlines()\r\n t_json = parse_biom_table(t_json_lines)\r\n self.assertEqual(t, t_json)\r\n # Test JSON as a file-like object\r\n t_json_str = t.to_json('asd')\r\n t_json_stringio = StringIO(t_json_str)\r\n t_json = parse_biom_table(t_json_stringio)\r\n self.assertEqual(t, t_json)","title":""},{"docid":"e9b4da21e1953372fb1da67c7ff99f27","score":"0.6164362","text":"def test_parse_biom_json(self):\r\n # light test. this code is used thoroughly within the other\r\n # parse_biom_table methods\r\n tab1_fh = json.load(StringIO(self.biom_minimal_sparse))\r\n tab = Table.from_json(tab1_fh)\r\n npt.assert_equal(tab.ids(), ('Sample1', 'Sample2', 'Sample3',\r\n 'Sample4', 'Sample5', 'Sample6'))\r\n npt.assert_equal(tab.ids(axis='observation'),\r\n ('GG_OTU_1', 'GG_OTU_2', 'GG_OTU_3',\r\n 'GG_OTU_4', 'GG_OTU_5'))\r\n self.assertEqual(tab.metadata(), None)\r\n self.assertEqual(tab.metadata(axis='observation'), None)\r\n\r\n tab = parse_biom_table(StringIO(self.biom_minimal_sparse))\r\n npt.assert_equal(tab.ids(), ('Sample1', 'Sample2', 'Sample3',\r\n 'Sample4', 'Sample5', 'Sample6'))\r\n npt.assert_equal(tab.ids(axis='observation'),\r\n ('GG_OTU_1', 'GG_OTU_2', 'GG_OTU_3',\r\n 'GG_OTU_4', 'GG_OTU_5'))\r\n self.assertEqual(tab.metadata(), None)\r\n self.assertEqual(tab.metadata(axis='observation'), None)\r\n\r\n tablestring = u'''{\r\n \"id\":null,\r\n \"format\": \"Biological Observation Matrix 0.9.1-dev\",\r\n \"format_url\": \"http://biom-format.org\",\r\n \"type\": \"OTU table\",\r\n \"generated_by\": \"QIIME revision XYZ\",\r\n \"date\": \"2011-12-19T19:00:00\",\r\n \"rows\":[\r\n {\"id\":\"GG_OTU_1\", \"metadata\":null},\r\n {\"id\":\"GG_OTU_2\", \"metadata\":null},\r\n {\"id\":\"GG_OTU_3\", \"metadata\":null},\r\n {\"id\":\"GG_OTU_4\", \"metadata\":null},\r\n {\"id\":\"GG_OTU_5\", \"metadata\":null}\r\n ],\r\n \"columns\": [\r\n {\"id\":\"Sample1\", \"metadata\":null},\r\n {\"id\":\"Sample2\", \"metadata\":null},\r\n {\"id\":\"Sample3\", \"metadata\":null},\r\n {\"id\":\"Sample4\", \"metadata\":null},\r\n {\"id\":\"Sample5\", \"metadata\":null},\r\n {\"id\":\"Sample6\", \"metadata\":null}\r\n ],\r\n \"matrix_type\": \"dense\",\r\n \"matrix_element_type\": \"int\",\r\n \"shape\": [5,6],\r\n \"data\": [[0,0,1,0,0,0],\r\n [5,1,0,2,3,1],\r\n [0,0,1,4,2,0],\r\n [2,1,1,0,0,1],\r\n [0,1,1,0,0,0]]\r\n }'''\r\n tbs_fh = json.load(StringIO(tablestring))\r\n tab1 = Table.from_json(tbs_fh)\r\n tab2 = parse_biom_table(tablestring)\r\n self.assertEqual(tab1, tab2)","title":""},{"docid":"92f1cb036c17c6d02896f699d090e14c","score":"0.5828839","text":"def test_schema_fail(self):\n options = {\n 'schema': {\n 'unit_key_01': 'integer',\n 'unit_key_02': 'string'\n }\n }\n\n data = {'hey': 1}\n record_data = json.dumps(data)\n\n # get parsed data\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), False)\n\n expected_result = [\n data\n ]\n assert_equal(parser.invalid_parses, expected_result)","title":""},{"docid":"497bbea88f665654ebec5d75b63b7058","score":"0.5712711","text":"def test_valid_json_file(self, file_url):\n obj = BMICalc(file_url)\n try :\n obj.define_logic_map()\n obj.read_inputfile()\n except Exception as exp:\n assert False\n else :\n assert True","title":""},{"docid":"e32d065ca3b20aa2a2b82183cccca7d1","score":"0.56740147","text":"def handle_tables(self,subjson):\n noerr = True\n for descr in subjson:\n t = Table.parse_json(descr)\n if t is None: \n noerr = False\n continue\n if t.database not in self.databases: \n log.warning(\"Database info for table % is not known.Please specify before\")\n noerr = False\n continue\n t.database = self.databases[t.database]\n self.tables[t.name] = t\n\n return noerr","title":""},{"docid":"48bc6396b70986b9ed84c201b7e31bff","score":"0.55641615","text":"def checkjson(self):\n raise Exception(\"This method needs to be overridden\")","title":""},{"docid":"598b53d02b82b35280cdd20a08bba6c3","score":"0.5550788","text":"def test_parse_biom_table_with_hdf5(self):\r\n # We will round-trip the HDF5 file to several different formats, and\r\n # make sure we can recover the same table using parse_biom_table\r\n cwd = os.getcwd()\r\n if '/' in __file__[1:]:\r\n os.chdir(__file__.rsplit('/', 1)[0])\r\n\r\n t = parse_biom_table(h5py.File('test_data/test.biom'))\r\n\r\n # These things are not round-trippable using the general-purpose\r\n # parse_biom_table function\r\n t._sample_metadata = None\r\n t._observation_metadata = None\r\n t.type = None\r\n\r\n # Test TSV as a list of lines\r\n t_tsv_str = t.to_tsv()\r\n t_tsv_lines = t_tsv_str.splitlines()\r\n t_tsv = parse_biom_table(t_tsv_lines)\r\n self.assertEqual(t, t_tsv)\r\n # Test TSV as a file-like object\r\n t_tsv_stringio = StringIO(t_tsv_str)\r\n t_tsv = parse_biom_table(t_tsv_stringio)\r\n self.assertEqual(t, t_tsv)\r\n\r\n # Test JSON as a list of lines\r\n t_json_str = t.to_json('asd')\r\n t_json_lines = t_json_str.splitlines()\r\n t_json = parse_biom_table(t_json_lines)\r\n self.assertEqual(t, t_json)\r\n # Test JSON as a file-like object\r\n t_json_str = t.to_json('asd')\r\n t_json_stringio = StringIO(t_json_str)\r\n t_json = parse_biom_table(t_json_stringio)\r\n self.assertEqual(t, t_json)","title":""},{"docid":"30967c66a1c4bba2d798772096c0ca29","score":"0.55146545","text":"def validateJSON( jsonData ):","title":""},{"docid":"a5c5a3f1ed95ee923036c6b696f97a22","score":"0.5512445","text":"def test_parse_record_not_dict_mismatch(self):\n options = {\n 'schema': {\n 'key': 'string'\n },\n 'parser': 'json'\n }\n record_data = \"[{\\\"key\\\": \\\"value\\\"}]\"\n\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), False)","title":""},{"docid":"60832dab25d6b0758616bc3712f74d03","score":"0.5506996","text":"def is_valid_json(self):\n try:\n json_object = json.loads(self._file)\n except (ValueError, TypeError) as e:\n raise","title":""},{"docid":"86877acc3019242c33689bb5046f21a8","score":"0.55048716","text":"def test_nontable_file(self):\n self.setNonTableData()\n self.assertError()","title":""},{"docid":"2e8e92887e1a7f747c9c510c67f1b718","score":"0.5503046","text":"def test_from_json_obj_wrong_type():\n for t, encoding in WRONG_TYPE_ENCODINGS:\n try:\n from_json_obj(encoding, t)\n assert False, \"Should not be decoding %s as %s.\"%(str(encoding), str(t))\n except TypeError:\n assert True","title":""},{"docid":"f459534417ebff548387ecef220b20e8","score":"0.54715943","text":"def validate_schema(bdb, table, mml_json):\n bad_cols = []\n for col, typ in mml_json['columns'].items():\n # If the column is already ignored there's nothing to check\n if typ['stattype'] == 'IGNORE':\n continue\n one_col_json = copy.deepcopy(mml_json)\n one_col_json['columns'] = {col: typ}\n # Create a temp generator\n gen_name = uuid.uuid4().hex\n try:\n bdb.execute(to_mml(one_col_json, table, gen_name))\n bdb.execute('INITIALIZE 1 MODEL FOR %s'\n % (bql_quote_name(gen_name),))\n bdb.execute('ANALYZE %s FOR 1 ITERATION WAIT'\n % (bql_quote_name(gen_name),))\n except AssertionError:\n bad_cols.append(col)\n finally:\n # Drop our temp generator\n bdb.execute('DROP GENERATOR %s' % bql_quote_name(gen_name))\n modified_schema = copy.deepcopy(mml_json)\n # TODO(asilvers): Should we also return a summary of the modifications?\n for col in bad_cols:\n modified_schema['columns'][col]['guessed'] = (\n modified_schema['columns'][col]['stattype'])\n modified_schema['columns'][col]['stattype'] = 'IGNORE'\n modified_schema['columns'][col]['reason'] = 'Caused ANALYZE to error'\n jsonschema.validate(modified_schema, MML_SCHEMA)\n return modified_schema","title":""},{"docid":"2c361a5298b91e447fddf78f500ce073","score":"0.54509556","text":"def copy_t2_schema_data() -> tuple[JSON, BytesInstance]:\n\n test_2_schema = {\n \"title\": \"SOME-RECORD-2\",\n \"$anchor\": \"SOME-RECORD-2\",\n \"cobol\": \"01 SOME-RECORD-2\",\n \"type\": \"object\",\n \"properties\": {\n \"FILLER-1\": {\n \"title\": \"FILLER\",\n \"$anchor\": \"FILLER-1\",\n \"cobol\": \"05 FILLER PIC X(5)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"REPEAT\": {\n \"title\": \"REPEAT\",\n \"$anchor\": \"REPEAT\",\n \"cobol\": \"05 REPEAT OCCURS 4 PIC XXX\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"REPEAT\": {\n \"title\": \"REPEAT\",\n \"$anchor\": \"REPEAT\",\n \"cobol\": \"05 REPEAT OCCURS 4 PIC XXX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n \"maxItems\": 4\n }\n }\n }\n\n assert SchemaValidator.check_schema(test_2_schema) is None\n test_2_data = BytesInstance('12345ABCDEFGHIJKL'.encode(\"CP037\"))\n return test_2_schema, test_2_data","title":""},{"docid":"a2c62613813eb665838e3f06503dbbae","score":"0.5434971","text":"def setUp(self):\n self.biom_text = \"\"\"{\n \"id\":null,\n \"format\": \"Biological Observation Matrix 0.9.1-dev\",\n \"format_url\": \"http://biom-format.org/documentation/format_versions/biom-1.0.html\",\n \"type\": \"OTU table\",\n \"generated_by\": \"QIIME revision 1.4.0-dev\",\n \"date\": \"2011-12-19T19:00:00\",\n \"rows\":[\n {\"id\":\"GG_OTU_1\", \"metadata\":{\"taxonomy\":[\"k__Bacteria\", \"p__Proteobacteria\", \"c__Gammaproteobacteria\", \"o__Enterobacteriales\", \"f__Enterobacteriaceae\", \"g__Escherichia\", \"s__\"]}},\n {\"id\":\"GG_OTU_2\", \"metadata\":{\"taxonomy\":[\"k__Bacteria\", \"p__Cyanobacteria\", \"c__Nostocophycideae\", \"o__Nostocales\", \"f__Nostocaceae\", \"g__Dolichospermum\", \"s__\"]}},\n {\"id\":\"GG_OTU_3\", \"metadata\":{\"taxonomy\":[\"k__Archaea\", \"p__Euryarchaeota\", \"c__Methanomicrobia\", \"o__Methanosarcinales\", \"f__Methanosarcinaceae\", \"g__Methanosarcina\", \"s__\"]}},\n {\"id\":\"GG_OTU_4\", \"metadata\":{\"taxonomy\":[\"k__Bacteria\", \"p__Firmicutes\", \"c__Clostridia\", \"o__Halanaerobiales\", \"f__Halanaerobiaceae\", \"g__Halanaerobium\", \"s__Halanaerobiumsaccharolyticum\"]}},\n {\"id\":\"GG_OTU_5\", \"metadata\":{\"taxonomy\":[\"k__Bacteria\", \"p__Proteobacteria\", \"c__Gammaproteobacteria\", \"o__Enterobacteriales\", \"f__Enterobacteriaceae\", \"g__Escherichia\", \"s__\"]}}\n ],\n \"columns\":[\n {\"id\":\"Sample1\", \"metadata\":{\n \"BarcodeSequence\":\"CGCTTATCGAGA\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"gut\",\n \"Description\":\"human gut\"}},\n {\"id\":\"Sample2\", \"metadata\":{\n \"BarcodeSequence\":\"CATACCAGTAGC\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"gut\",\n \"Description\":\"human gut\"}},\n {\"id\":\"Sample3\", \"metadata\":{\n \"BarcodeSequence\":\"CTCTCTACCTGT\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"gut\",\n \"Description\":\"human gut\"}},\n {\"id\":\"Sample4\", \"metadata\":{\n \"BarcodeSequence\":\"CTCTCGGCCTGT\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"skin\",\n \"Description\":\"human skin\"}},\n {\"id\":\"Sample5\", \"metadata\":{\n \"BarcodeSequence\":\"CTCTCTACCAAT\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"skin\",\n \"Description\":\"human skin\"}},\n {\"id\":\"Sample6\", \"metadata\":{\n \"BarcodeSequence\":\"CTAACTACCAAT\",\n \"LinkerPrimerSequence\":\"CATGCTGCCTCCCGTAGGAGT\",\n \"BODY_SITE\":\"skin\",\n \"Description\":\"human skin\"}}\n ],\n \"matrix_type\": \"sparse\",\n \"matrix_element_type\": \"int\",\n \"shape\": [5, 6],\n \"data\":[[0,2,1],\n [1,0,5],\n [1,1,1],\n [1,3,2],\n [1,4,3],\n [1,5,1],\n [2,2,1],\n [2,3,4],\n [2,5,2],\n [3,0,2],\n [3,1,1],\n [3,2,1],\n [3,5,1],\n [4,1,1],\n [4,2,1]\n ]\n }\"\"\"\n\n self.coreOTUdata = '''# Core OTUs across 75 % of samples.\n064EK048\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__HOT.064']\n200CZ006\t[u'k__Bacteria', u'p__Fusobacteria', u'c__Fusobacteria', u'o__Fusobacteriales', u'f__Fusobacteriaceae', u'g__Fusobacterium', u's__nucleatum']\n161Vparv\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Veillonella', u's__parvula']\n111_3523\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Peptostreptococcaceae_[XIII]', u'g__Parvimonas', u's__micra']\n644_4671\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__intermedius']\n758_3928\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__sanguinis']\n524Vatyp\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Veillonella', u's__atypica']\n151_K168\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Selenomonas', u's__sputigena']\n530_5256\t[u'k__Bacteria', u'p__Actinobacteria', u'c__Actinobacteria', u'o__Actinomycetales', u'f__Propionibacteriaceae', u'g__Propionibacterium', u's__acnes']\n612_6662\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Gammaproteobacteria', u'o__Pseudomonadales', u'f__Pseudomonadaceae', u'g__Pseudomonas', u's__fluorescens']\n539_6962\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Peptostreptococcaceae_[XI]', u'g__Filifactor', u's__alocis']\n073-T1E5\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__australis']\n398_7051\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__mitis']\n622_3931\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__gordonii']\n826_8612\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Gammaproteobacteria', u'o__Pasteurellales', u'f__Pasteurellaceae', u'g__Terrahaemophilus', u's__aromaticivorans']\n623_4320\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Epsilonproteobacteria', u'o__Campylobacterales', u'f__Campylobacteraceae', u'g__Campylobacter', u's__gracilis']\n205FL002\t[u'k__Bacteria', u'p__Fusobacteria', u'c__Fusobacteria', u'o__Fusobacteriales', u'f__Fusobacteriaceae', u'g__Fusobacterium', u's__HOT.205']'''\n\n self.biom = json.loads(self.biom_text)\n self.row = self.biom['rows']","title":""},{"docid":"f0ee69fc9871c44fc84de18f3abcf636","score":"0.5420853","text":"def test_valid_json(self):\n self.assertTrue(json.loads(self.ratings.get_json()))","title":""},{"docid":"f0ee69fc9871c44fc84de18f3abcf636","score":"0.5420853","text":"def test_valid_json(self):\n self.assertTrue(json.loads(self.ratings.get_json()))","title":""},{"docid":"3999dcecbe6aec5d912dc15cb0917c48","score":"0.538307","text":"def check_valid_report(context):\n response = context.response.json()\n if context.history:\n assert isinstance(response['objects'], list)\n else:\n assert isinstance(response, dict)","title":""},{"docid":"c41c96316dce2d78a9eb824c8ac0f1ce","score":"0.53647953","text":"def test_instance_resize_prep_end_valid_json(self):\n expected_obj = self.resize_prep_end_obj\n\n actual_json = json.dumps(self.instance_resize_prep_end_dict)\n actual_obj = InstanceResizePrepEnd.deserialize(actual_json, 'json')\n\n self.assertEqual(expected_obj, actual_obj)\n self.assertFalse(actual_obj.is_empty())","title":""},{"docid":"942f27d43d60da04f9e6f5e885bad519","score":"0.53493994","text":"def test_json(self, tmpdir):\n _ = self.__class__ # just to get rid of codacy warning, I know, it's stupid\n config_from_yaml = Menu.safe_read_yaml(yaml_path())\n p = tmpdir.mkdir(\"pytabbytest\").join(\"temp.json\")\n p.write(json.dumps(config_from_yaml))\n config_from_json = Menu.read_json(str(p))\n if not config_from_yaml == config_from_json:\n raise AssertionError","title":""},{"docid":"d979a568404fd4b97971851b61c888a4","score":"0.53352386","text":"def test_check_json_health(self):\n # check health json\n with open(self.test_file_name, 'w') as fp:\n json.dump({'tasks': {}, 'users': {}}, fp)\n\n with open(self.test_file_name) as fp:\n self.assertEqual(\n interactive.check_json_health(fp),\n {'tasks': {}, 'users': {}}\n )\n\n # check corrupted json\n with open(self.test_file_name, 'w') as fp:\n json.dump({'tasks': {}}, fp)\n\n with open(self.test_file_name) as fp:\n self.assertIsNone(interactive.check_json_health(fp))","title":""},{"docid":"4bae938a6b2a9be5595c7461afbbc7f7","score":"0.53255975","text":"def test_exports(self):\n # Get rid of weird formatting included in class attribute\n self.maxDiff = 5_000\n self.assertEquals(\n self._meta.to_json().replace('\\n','').replace(' ','') ,\n self._META_MODEL_JSON.replace('\\n','').replace(' ','') \n )\n # Test Risk Table\n param_dict = json.loads(self._META_MODEL_JSON)\n del param_dict['creation_date']\n del param_dict['model_uuid']\n del param_dict['name']\n del param_dict['type']\n self.assertEquals(\n self._meta.export_params(),\n param_dict\n )","title":""},{"docid":"c31f08393039889c3412f02550034d72","score":"0.5313659","text":"def _jsonYayForTabs(self, content):\n\n\t\tfor line in content.split('\\n'):\n\t\t\tpattern = re.compile(r'\\t*(?P<spaces> *)')\n\t\t\tmatches = pattern.match(line)\n\t\t\tif matches.groupdict()['spaces']:\n\t\t\t\tself.fail('Spaces are the devil!')","title":""},{"docid":"d03be5d7152ea3dbbacb164a2b03f912","score":"0.53131914","text":"def test8(self):\n # arrange\n model = IrisModel()\n\n # act\n json_schema = model.output_schema.json_schema(\"https://example.com/my-schema.json\")\n\n # assert\n print(json_schema)\n self.assertTrue(type(json_schema) is dict)","title":""},{"docid":"4623c594c47984ba45bff2a5c85ecf75","score":"0.5312796","text":"def test_WbTabularData_error_on_wrong_page_type(self):\n non_data_page = Page(self.commons, 'File:Foo.jpg')\n non_map_page = Page(self.commons, 'Data:Lyngby Hovedgade.map')\n regex = (r\"^Page must be in 'Data:' namespace and end in '\\.tab' \"\n r'for tabular-data\\.$')\n with self.assertRaisesRegex(ValueError, regex):\n pywikibot.WbTabularData(non_data_page, self.get_repo())\n with self.assertRaisesRegex(ValueError, regex):\n pywikibot.WbTabularData(non_map_page, self.get_repo())","title":""},{"docid":"3bb2e80bf2b0d19d05d8692003d482d1","score":"0.5296964","text":"def test_read_json(self):\n self.assertEqual(len(cleanUtils._read_json('abbreviated_cantons.json')), 47)","title":""},{"docid":"72c6a575077f279d21857b0d07e055bd","score":"0.52787226","text":"def test_to_dict(self):\n my_model_dict = self.bm_obj.to_dict()\n actual = 1\n try:\n serialized = json.dumps(my_model_dict)\n except:\n actual = 0\n self.assertTrue(1 == actual)","title":""},{"docid":"2b64075114bf834af6426bb023eb597f","score":"0.52676225","text":"def test_schema_valid(self) -> None:\n instance = TNSObjectSearchResult.from_dict(FAKE_TNS_OBJECT_DATA)\n assert isinstance(instance, TNSObjectSearchResult)\n assert instance._data == FAKE_TNS_OBJECT_DATA\n assert instance.object_type_name == FAKE_TNS_TYPE_NAME\n assert instance.redshift == FAKE_TNS_REDSHIFT","title":""},{"docid":"c57325136b5ae13ff61c2fcc45f4ca8e","score":"0.5267283","text":"def test_character_from_tibiadata_unrelated_json(self):\n with self.assertRaises(InvalidContent):\n Character.from_tibiadata(self._load_resource(tests.tests_guild.FILE_GUILD_TIBIADATA))","title":""},{"docid":"84a52c5dbdcdf5c288fb98ff443336c9","score":"0.5260765","text":"def test_character_from_tibiadata_invalid_json(self):\n with self.assertRaises(InvalidContent):\n Character.from_tibiadata(\"<html><b>Not a json string</b></html>\")","title":""},{"docid":"1dc3b5ab52aec0434f2a986a51fea297","score":"0.52593917","text":"def test_WbTabularData_error_on_non_page(self):\n regex = r'^Page .+? must be a pywikibot\\.Page object not a'\n with self.assertRaisesRegex(ValueError, regex):\n pywikibot.WbTabularData('A string', self.get_repo())","title":""},{"docid":"f5269412829a13c8e2ce20c7037eaae3","score":"0.5254655","text":"def copy_t1_schema_data() -> tuple[JSON, BytesInstance]:\n test_1_schema = {\n \"title\": \"SOME-RECORD-1\",\n \"$anchor\": \"SOME-RECORD-1\",\n \"cobol\": \"01 SOME-RECORD-1\",\n \"type\": \"object\",\n \"properties\": {\n \"SOME-COLUMN\": {\n \"title\": \"SOME-COLUMN\",\n \"$anchor\": \"SOME-COLUMN\",\n \"cobol\": \"05 SOME-COLUMN PIC X(5)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n assert SchemaValidator.check_schema(test_1_schema) is None\n test_1_data = BytesInstance('12345'.encode(\"CP037\"))\n return test_1_schema, test_1_data","title":""},{"docid":"a92c767374fad04fe043f1882159f28c","score":"0.5254229","text":"def contains_valid_json(obj):\n if obj is None:\n return False\n try:\n try:\n # json_object = json.loads(obj.read())\n json.load(obj)\n except AttributeError:\n # obj is not a file\n json.loads(obj)\n except ValueError:\n return False\n return True","title":""},{"docid":"57476200bca642877417c888734d0b1f","score":"0.52422327","text":"def test_create_from_json_fails():\n global invalid_json\n i = se.instrument.Instrument(invalid_json)\n assert type(i) != se.instrument.Instrument","title":""},{"docid":"ffcd39acd6cd65a935697f0b87ca811e","score":"0.5238401","text":"def copy_t3_schema_data() -> tuple[JSON, BytesInstance]:\n test_3_schema = {\n \"title\": \"SOME-RECORD-3\",\n \"$anchor\": \"SOME-RECORD-3\",\n \"cobol\": \"01 SOME-RECORD-3\",\n \"type\": \"object\",\n \"properties\": {\n \"FILLER-1\": {\n \"title\": \"FILLER\",\n \"$anchor\": \"FILLER-1\",\n \"cobol\": \"05 FILLER PIC X(5)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"REPEAT\": {\n \"title\": \"REPEAT\",\n \"$anchor\": \"REPEAT\",\n \"cobol\": \"05 REPEAT OCCURS 5\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"ITEM\": {\n \"title\": \"ITEM\",\n \"$anchor\": \"ITEM\",\n \"cobol\": \"10 ITEM OCCURS 4 PIC XX\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"ITEM\": {\n \"title\": \"ITEM\",\n \"$anchor\": \"ITEM\",\n \"cobol\": \"10 ITEM OCCURS 4 PIC XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n \"maxItems\": 4\n }\n }\n },\n \"maxItems\": 5\n }\n }\n }\n\n assert SchemaValidator.check_schema(test_3_schema) is None\n test_3_data = BytesInstance('12345ABCDEFGHabcdefghIJKLMNOPijklmnopQRSTUVWZ'.encode(\"CP037\"))\n return test_3_schema, test_3_data","title":""},{"docid":"18fac9829092d64cb98f5964cc0d1f5f","score":"0.52317256","text":"def test_metacritic_pages_parsed(self, m):\n set_mock_data(m)\n data = json.loads(get_json_data())\n # print('total:', len(data))\n # print(data)\n self.assertTrue(type(data) == list)\n self.assertEqual(len(data), 2037)\n for entry in data:\n self.assertTrue('title' in entry)\n self.assertTrue(type(entry['title']) == str)\n self.assertTrue('score' in entry)\n self.assertTrue(type(entry['score']) == str)","title":""},{"docid":"5e8e3f656dfb20f00b379a017b61e5ac","score":"0.52272767","text":"def test_output_type_data(bamfile,bedfile):\n\n transposonmapper(bamfile)\n\n chrom_names_dict, chrom_start_line_dict, chrom_end_line_dict = chromosome_name_bedfile(bed_file=bedfile)\n\n assert isinstance(chrom_names_dict, dict), \"Expected dict type\"\n assert isinstance(chrom_start_line_dict, dict), \"Expected dict type\"\n assert isinstance(chrom_end_line_dict, dict), \"Expected dict type\"","title":""},{"docid":"8d84942d26ae68b5bd89b614fcd24c49","score":"0.52089804","text":"def test_WCAG_PDF_06(self):\n\n if len(self.awamHandler.tableStructDict) == 0:\n self.logger.info('No tables found in Document')\n # No tables ? test not applicable\n return 2\n\n results = self.initResult()\n self.logger.debug('No of tables =>',len(self.awamHandler.tableStructDict))\n\n # Loop through each and see if it is marked invalid\n #if any([x.invalid for x in self.awamHandler.tableStructDict.values()]):\n # # Failed\n # print 'Invalid table structure found in Document' \n # return 0\n for tbl in self.awamHandler.tableStructDict.values():\n pg = tbl.getPage()\n if tbl.invalid:\n self.updateResult(results[0], pg, tbl)\n else:\n self.updateResult(results[1], pg, tbl)\n \n self.logger.info('wcag.pdf.06 - Test passed')\n return results","title":""},{"docid":"6f0b0fadda47cc4d98e953292c644aa5","score":"0.5208406","text":"def testReadJsonFile(self):\n try:\n rObj = self.__ioU.deserialize(self.__pathJsonTestFile, fmt=\"json\")\n logger.debug(\"Object length %d\", len(rObj))\n self.assertGreaterEqual(len(rObj), 1)\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","title":""},{"docid":"dbc6c01ab5319b7fd48be8d110245774","score":"0.5198041","text":"def test_from_json_obj_basetypes():\n for val, t, encoding in BASETYPES_ENCODINGS:\n assert from_json_obj(encoding, t) == val","title":""},{"docid":"aa482574c348dad4e816be593de27bd1","score":"0.51910233","text":"def test_web_json(self):\n data = jsonreader.get_web_elements()\n assert type(data) == list\n assert len(data) > 0\n for entry in data:\n assert type(entry) == dict\n assert entry.get('phase') is not None\n assert type(entry.get('phase')) == str\n assert type(entry.get('route')) == str\n assert type(entry.get('html')) == list\n assert type(entry.get('xpath')) == str","title":""},{"docid":"bb29b33fe7d9b3a7ec27cd18a8802106","score":"0.51827407","text":"def test_plant_serializer_with_valida_data(new_plant):\n result = plant_schema.load(new_plant)\n assert result[\"name\"] == \"Monstera\"\n assert result[\"latin\"] == \"Monstera Adans.\"\n assert result[\"difficulty\"] == 5","title":""},{"docid":"fd05aa4fd90fa8da075c40b6f368dc01","score":"0.5177884","text":"def _verify_json_content(self):\n self.assertTrue(all(\n json_item['auth'] == 'Bearer fake token'\n for json_item in OnTaskSharedState.json_outbox))","title":""},{"docid":"8933534c4ede0291330d9ce38076bd02","score":"0.51730776","text":"def test_from_json_obj_typeerrors():\n class NonTypechekableT:\n name: str\n val: int\n def __init__(self, name, val):\n self.name = name\n self.val = val\n x = NonTypechekableT(\"x\", 0)\n try:\n from_json_obj(x, NonTypechekableT)\n assert False\n except TypeError:\n assert True","title":""},{"docid":"c7d60fa7fdd21c675d502350a760bca4","score":"0.516112","text":"def validate_table(self, model):\n expected = bloop.tables.expected_description(model)\n status = \"BLOOP_NOT_ACTIVE\"\n while status == \"BLOOP_NOT_ACTIVE\":\n description = self.describe_table(model)\n status = bloop.tables.simple_status(description)\n try:\n actual = bloop.tables.sanitized_description(description)\n except KeyError:\n raise bloop.exceptions.TableMismatch(model, expected, description)\n if bloop.util.ordered(actual) != bloop.util.ordered(expected):\n raise bloop.exceptions.TableMismatch(model, expected, actual)","title":""},{"docid":"7d2e4cfe8e0a91bc5af8f6350a1cae52","score":"0.5155278","text":"def test_parse_record_copy(self):\n options = {\n 'schema': {\n 'key': 'string'\n }\n }\n record_data = {\n 'key': 'value'\n }\n\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), True)\n assert_equal(id(parser.parsed_records[0]) == id(record_data), False)","title":""},{"docid":"c60f67023a0ac6b5729a7a041388c6e5","score":"0.51532674","text":"def test_ingest_json_file(self):\n ingested_json = py_challenge.ingest_json_file('data/split_ac.fastq.data.json')\n self.assertNotEqual(ingested_json, None)","title":""},{"docid":"8f3023bcdc70aba8807fee85ca67a229","score":"0.5147551","text":"def is_json_serializable(document: dict):\n assert isinstance(document, dict), \"Check only 1 item!\"\n test_json = user_data_dir(\"test_vectorai\", \"test.json\")\n\n with open(test_json, \"w\"):\n json.dump(test_json)\n\n with open(test_json) as f:\n check_json = json.load(f)\n os.remove(test_json)\n \n assert check_json == document, (\n \"This will not upload correctly. Please ensure all items \"\n + \"in the dictionary are lists/floats/ints/strings.\"\n )\n print(\"Checked! Feel free to upload!\")","title":""},{"docid":"8741e9d5d25bd6c5caaef26533663986","score":"0.5140689","text":"def copy_11_schema_data() -> tuple[JSON, BytesInstance]:\n copy_11_schema = {\n \"title\": \"MAIN-AREA\",\n \"$anchor\": \"MAIN-AREA\",\n \"cobol\": \"01 MAIN-AREA\",\n \"type\": \"object\",\n \"properties\": {\n \"REC-1\": {\n \"title\": \"REC-1\",\n \"$anchor\": \"REC-1\",\n \"cobol\": \"03 REC-1\",\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-1\": {\n \"title\": \"FIELD-1\",\n \"$anchor\": \"FIELD-1\",\n \"cobol\": \"05 FIELD-1 PIC 9\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n },\n \"FIELD-3\": {\n \"title\": \"FIELD-3\",\n \"$anchor\": \"FIELD-3\",\n \"cobol\": \"05 FIELD-3 PIC 9\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n },\n \"FIELD-2\": {\n \"title\": \"FIELD-2\",\n \"$anchor\": \"FIELD-2\",\n \"cobol\": \"05 FIELD-2 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-1 PIC X(05)\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-2\": {\n \"cobol\": \"05 FIELD-2 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-1 PIC X(05)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n \"maxItemsDependsOn\": {\n \"$ref\": \"#FIELD-1\"\n }\n }\n }\n },\n \"REC-2\": {\n \"title\": \"REC-2\",\n \"$anchor\": \"REC-2\",\n \"cobol\": \"03 REC-2\",\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-4\": {\n \"title\": \"FIELD-4\",\n \"$anchor\": \"FIELD-4\",\n \"cobol\": \"05 FIELD-4 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-3 PIC X(05)\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-4\": {\n \"cobol\": \"05 FIELD-4 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-3 PIC X(05)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n \"maxItemsDependsOn\": {\n \"$ref\": \"#FIELD-3\"\n }\n }\n }\n }\n }\n }\n assert SchemaValidator.check_schema(copy_11_schema) is None\n\n copy_11_data = BytesInstance(\"321111122222333334444455555\".encode(\"CP037\"))\n return copy_11_schema, copy_11_data","title":""},{"docid":"866948a1ded7ca6789fe57e56be47cd1","score":"0.51384306","text":"def basic_json_check(datadir, filename, json_data, samehost=False):\n header = json.loads(str(json_data, 'UTF-8'))\n if not isinstance(header, dict):\n raise BoardLibError(\"%s contains JSON which does not generate a dictionary.\"\n % (filename))\n required_keys = {'schema', 'hostname', 'absdatadir', 'sequence_number',\n 'most_recent', 'most_recent_hash', 'last_process_most_recent',\n 'last_process_most_recent_hash', 'payload'}\n for header_key in list(header):\n if header_key not in required_keys:\n raise BoardLibError(\"%s is an unexpected key in the JSON data from %s.\"\n % (header_key, filename))\n for required_key in list(required_keys):\n if not required_key in header:\n raise BoardLibError(\"%s is not in JSON data in file %s\"\n % (required_key, filename))\n if header['schema'] not in schema_dictionary:\n raise BoardLibError(\"Schema %s from file %s does not appear in the \"\n \"schema dictionary.\" % (header['schema'], filename))\n if not isinstance(header['sequence_number'], int):\n raise BoardLibError(\"Filename %s holds a sequence number which is not \"\n \"an integer.\" % (filename))\n if header['sequence_number'] < 0:\n raise BoardLibError(\"Sequence_number %d from file %s is negative\"\n % (header['sequence_number'], filename))\n # <now>.<hostname>.<inode>\n unique_file_match = re.match('^\\d{10}\\.(\\S+)\\.\\d+$', filename)\n if unique_file_match:\n file_hostname = unique_file_match.group(1)\n if file_hostname != header['hostname']:\n raise BoardLibError(\"Filename %s does not match hostname in JSON: %s\"\n % (filename, header['hostname']))\n else:\n raise BoardLibError(\"Filename %s has the wrong structure.\" % (filename))\n if header['most_recent'] == None and header['sequence_number'] != 0:\n raise BoardLibError(\"Filename %s has no most_recent predecessor but \"\n \"sequence number is non-zero: %d\"\n % (filename, header['sequence_number']))\n if header['last_process_most_recent'] and header['most_recent']:\n raise BoardLibError(\"Filename %s has last_process_most_recent (%s) \"\n \"and most_recent (%s) defined.\"\n % (filename, header['last_process_most_recent'], header['most_recent']))\n if samehost:\n hostname = socket.gethostname()\n if header['hostname'] != hostname:\n raise BoardLibError(\"Hostname %s from file %s is not the same \"\n \"as our host: %s\"\n % (header['hostname'], filename, hostname))\n if datadir != header['absdatadir']:\n raise BoardLibError(\"Absdatadir %s from file %s is not the same \"\n \"as the datadir we were given: %s\"\n % (header['absdatadir'], filename, datadir))\n schema_check_func = schema_dictionary[header['schema']]\n if not schema_check_func(header['payload']):\n raise BoardLibError(\"Filename %s failed its schema check.\" % (filename))\n if (header['most_recent'] and not header['most_recent_hash']) or \\\n (not header['most_recent'] and header['most_recent_hash']):\n raise BoardLibError(\"In file %s most_recent and most_recent_hash are \"\n \"not both defined.\" % (filename))\n if (header['last_process_most_recent'] and not header['last_process_most_recent_hash']) \\\n or (not header['last_process_most_recent'] and header['last_process_most_recent_hash']):\n raise BoardLibError(\"In file %s last_process_most_recent and \"\n \"last_process_most_recent_hash are not both defined.\"\n % (filename))\n if header['most_recent_hash'] and \\\n not re.match('^[0-9a-f]{64}$', header['most_recent_hash']):\n raise BoardLibError(\"In file %s the most_recent_hash does not look like \"\n \"a valid hash: %s\" % (filename, header['most_recent_hash']))\n return None","title":""},{"docid":"d66a637d2d313e23ef53b867a036b066","score":"0.5135247","text":"def test_read_json(self):\n # Ensure metamodel fails\n self.assertRaises(\n FairException, \n FairMetaModel.read_json, \n self._MODEL_JSON\n )","title":""},{"docid":"8a0013128f44bfb6d9f2a86b78c0b072","score":"0.51279455","text":"def test_storm_objects_to_cells(self):\n\n this_storm_object_table = storms_to_winds._storm_objects_to_cells(\n STORM_OBJECT_TABLE_NO_CELL_INFO)\n self.assertTrue(this_storm_object_table.equals(\n STORM_OBJECT_TABLE_WITH_CELL_INFO))","title":""},{"docid":"ad536e8c84b877b75ebaf8cd7dfbf8cc","score":"0.51265985","text":"def check_schema(self, data):\n module = \"check_schema\"\n self.mu_log.log(self.mu_log.DEBUG, \"expected meta_version is >\" + self.metaschema_version + \"<\", module)\n self.json_data = data\n\n try:\n self.meta_type = data[\"meta\"]\n self.meta_version = data[\"meta_version\"]\n self.mu_log.log(self.mu_log.DEBUG\n , \"schema is >\" + self.meta_type + \"<, version >\"\n + self.meta_version + \"<\", module)\n except KeyError as e:\n self.mu_log.log(self.mu_log.DEBUG,\n \"Key error. meta and meta_version must be in JSON file. That is not the case with \"\n + self.json_file, module)\n return messages.message[\"meta_error\"]\n except jsonschema.exceptions.SchemaError as e:\n self.mu_log.log(self.mu_log.FATAL, \"Schema error: \" + e.message, module)\n return messages.message[\"json_schema_error\"]\n except jsonschema.exceptions.ValidationError as e:\n self.mu_log.log(self.mu_log.FATAL, \"Validation error: \" + e.message, module)\n return messages.message[\"json_validation_error\"]\n except json.decoder.JSONDecodeError as e:\n self.mu_log.log(self.mu_log.FATAL, \"Error parsing JSON:\" + e.msg, module)\n return messages.message[\"json_parse_error\"]\n\n if self.meta_version == generic_settings.GenericSettings().meta_version:\n self.mu_log.log(self.mu_log.INFO, \"file meta version matches expected schema version\", module)\n schema_directory = generic_settings.GenericSettings().schema_directory\n self.schema_file = schema_directory + self.meta_type + \".json\"\n with open(self.schema_file) as f:\n schema = json.load(f)\n try:\n jsonschema.validate(data, schema)\n self.mu_log.log(self.mu_log.INFO, \"JSON file validated successfully against schema\", module)\n except jsonschema.exceptions.SchemaError as e:\n self.mu_log.log(self.mu_log.FATAL, \"A schema error occurred during validation\", module)\n return messages.message[\"jsonschema_validation_error\"]\n except jsonschema.exceptions.ValidationError as e:\n self.mu_log.log(self.mu_log.ERROR, \"A validation error occurred\", module)\n return messages.message[\"jsonschema_validation_error\"]\n else:\n self.mu_log.log(self.mu_log.DEBUG, \"File meta version does not match expected schema version\", module)\n return messages.message[\"incorrect_meta_version\"]\n\n return messages.message[\"ok\"]","title":""},{"docid":"b7f29ceec2661014054d4c79ce05701d","score":"0.51249635","text":"def copy_9_schema_data() -> tuple[JSON, BytesInstance]:\n copy_9_schema_1 = {\n \"title\": \"DETAIL-LINE\",\n \"$anchor\": \"DETAIL-LINE\",\n \"cobol\": \"01 DETAIL-LINE\",\n \"type\": \"object\",\n \"properties\": {\n \"QUESTION\": {\n \"title\": \"QUESTION\",\n \"$anchor\": \"QUESTION\",\n \"cobol\": \"05 QUESTION PIC ZZ\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"PRINT-YES\": {\n \"title\": \"PRINT-YES\",\n \"$anchor\": \"PRINT-YES\",\n \"cobol\": \"05 PRINT-YES PIC ZZ\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"PRINT-NO\": {\n \"title\": \"PRINT-NO\",\n \"$anchor\": \"PRINT-NO\",\n \"cobol\": \"05 PRINT-NO PIC ZZ\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"NOT-SURE\": {\n \"title\": \"NOT-SURE\",\n \"$anchor\": \"NOT-SURE\",\n \"cobol\": \"05 NOT-SURE PIC ZZ\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n\n copy_9_schema_2 = {\n \"title\": \"SUMMARY-LINE\",\n \"$anchor\": \"SUMMARY-LINE\",\n \"cobol\": \"01 SUMMARY-LINE REDEFINES DETAIL-LINE\",\n \"type\": \"object\",\n \"properties\": {\n \"COUNT\": {\n \"title\": \"COUNT\",\n \"$anchor\": \"COUNT\",\n \"cobol\": \"05 COUNT PIC ZZ\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"FILLER-1\": {\n \"title\": \"FILLER\",\n \"$anchor\": \"FILLER-1\",\n \"cobol\": \"05 FILLER PIC XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"FILLER-2\": {\n \"title\": \"FILLER\",\n \"$anchor\": \"FILLER-2\",\n \"cobol\": \"05 FILLER PIC XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"FILLER-3\": {\n \"title\": \"FILLER\",\n \"$anchor\": \"FILLER-3\",\n \"cobol\": \"05 FILLER PIC XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n\n assert SchemaValidator.check_schema(copy_9_schema_1) is None\n assert SchemaValidator.check_schema(copy_9_schema_2) is None\n\n copy_9_data = BytesInstance(\"01020304\".encode(\"CP037\"))\n return [copy_9_schema_1, copy_9_schema_2], copy_9_data","title":""},{"docid":"ee5511d6983bad620cf86e4d156165a7","score":"0.5094555","text":"def test_AttType(self):\n bModel_dict = self.bModel.to_dict()\n self.assertEqual(self.bModel.__class__.__name__, \"BaseModel\")\n self.assertIsInstance(bModel_dict['created_at'], str)\n self.assertIsInstance(bModel_dict['updated_at'], str)","title":""},{"docid":"b138a83698378d9b10a30abfae4640f0","score":"0.5094188","text":"def test_get_json(json_bytes_data: TypingJsonBytesData):\n assert json_bytes_data[\"json_bytes\"].get_json()","title":""},{"docid":"eb973c24d0d105f3173e581edef7481c","score":"0.50940955","text":"def test_schema_maker_5(capsys):\n schema_json_5 = {\n \"title\": \"REDEFINES-RECORD\",\n \"$anchor\": \"REDEFINES-RECORD\",\n \"cobol\": \"01 REDEFINES-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"REDEFINES-A\": {\n \"oneOf\": [\n {\n \"title\": \"A\",\n \"$anchor\": \"REDEFINES-A.A\",\n \"type\": \"object\",\n \"properties\": {\n \"A\": {\n \"title\": \"A\",\n \"$anchor\": \"A\",\n \"cobol\": \"05 A PICTURE X(6)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n {\n \"title\": \"B\",\n \"$anchor\": \"REDEFINES-A.B\",\n \"type\": \"object\",\n \"properties\": {\n \"B\": {\n \"title\": \"B\",\n \"$anchor\": \"B\",\n \"cobol\": \"05 B REDEFINES A\",\n \"type\": \"object\",\n \"properties\": {\n \"B-1\": {\n \"title\": \"B-1\",\n \"$anchor\": \"B-1\",\n \"cobol\": \"10 B-1 PICTURE X(2)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"B-2\": {\n \"title\": \"B-2\",\n \"$anchor\": \"B-2\",\n \"cobol\": \"10 B-2 PICTURE 9(4)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n }\n }\n ],\n \"$anchor\": \"REDEFINES-A\"\n },\n \"A\": {\n \"title\": \"A\",\n \"$anchor\": \"A\",\n \"cobol\": \"05 A PICTURE X(6)\",\n \"$ref\": \"#REDEFINES-A.A\"\n },\n \"B\": {\n \"title\": \"B\",\n \"$anchor\": \"B\",\n \"cobol\": \"05 B REDEFINES A\",\n \"$ref\": \"#REDEFINES-A.B\"\n },\n \"C\": {\n \"title\": \"C\",\n \"$anchor\": \"C\",\n \"cobol\": \"05 C PICTURE 99V99\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n }\n }\n }\n SchemaValidator.check_schema(schema_json_5)\n s = SchemaMaker.from_json(schema_json_5)\n s.print()\n out, err = capsys.readouterr()\n assert out.splitlines() == [\n \"ObjectSchema({'title': 'REDEFINES-RECORD', '$anchor': 'REDEFINES-RECORD', 'cobol': '01 REDEFINES-RECORD', 'type': 'object'})\",\n \" OneOfSchema({'$anchor': 'REDEFINES-A'})\",\n \" ObjectSchema({'title': 'A', '$anchor': 'REDEFINES-A.A', 'type': 'object'})\",\n \" AtomicSchema({'title': 'A', '$anchor': 'A', 'cobol': '05 A PICTURE X(6)', 'type': 'string', 'contentEncoding': 'cp037'})\",\n \" ObjectSchema({'title': 'B', '$anchor': 'REDEFINES-A.B', 'type': 'object'})\",\n \" ObjectSchema({'title': 'B', '$anchor': 'B', 'cobol': '05 B REDEFINES A', 'type': 'object'})\",\n \" AtomicSchema({'title': 'B-1', '$anchor': 'B-1', 'cobol': '10 B-1 PICTURE X(2)', 'type': 'string', 'contentEncoding': 'cp037'})\",\n \" AtomicSchema({'title': 'B-2', '$anchor': 'B-2', 'cobol': '10 B-2 PICTURE 9(4)', 'type': 'string', 'contentEncoding': 'cp037'})\",\n \" RefToSchema({'title': 'A', '$anchor': 'A', 'cobol': '05 A PICTURE X(6)', '$ref': '#REDEFINES-A.A'})\",\n \" RefToSchema({'title': 'B', '$anchor': 'B', 'cobol': '05 B REDEFINES A', '$ref': '#REDEFINES-A.B'})\",\n \" AtomicSchema({'title': 'C', '$anchor': 'C', 'cobol': '05 C PICTURE 99V99', 'type': 'string', 'contentEncoding': 'cp037', 'conversion': 'decimal'})\",\n ]","title":""},{"docid":"8f422cc8f50a8bfc2d85be5cc54ecd66","score":"0.50846183","text":"def test_good_load(self):\n simple_db_path = Path(__file__).parent / \"test_dbs\" / \"simple_db.json\"\n try:\n db = type(self).load_db(simple_db_path)\n except jsonschema.exceptions.ValidationError:\n pytest.fail(\"Unexpected exception jsonschema.exceptions.ValidationError\")\n\n assert config.NAME_KIT_CAT_NVGS in db\n assert \"CUP_NVG_PVS14\" in db[config.NAME_KIT_CAT_NVGS]\n assert \"description\" in db[config.NAME_KIT_CAT_NVGS][\"CUP_NVG_PVS14\"]","title":""},{"docid":"efa13f8d8711daa8c79e7ba454825de7","score":"0.5083122","text":"def test_unparseable(self) -> None:\n self.assertThat(\n matches_json(Always()).match(\"not json\"),\n Not(Is(None)),\n )","title":""},{"docid":"151b0b1dafc42f4eb0dfb75e45ced4ef","score":"0.5079233","text":"def test_instance_resize_prep_start_valid_json(self):\n expected_obj = self.resize_prep_start_obj\n\n actual_json = json.dumps(self.base_resize_prep_dict)\n actual_obj = InstanceResizePrepStart.deserialize(actual_json, 'json')\n\n self.assertEqual(expected_obj, actual_obj)\n self.assertFalse(actual_obj.is_empty())","title":""},{"docid":"6a09d724cfab442fe5750fcf68c02919","score":"0.5068002","text":"def test_desc_serializer_with_valida_data(new_desc):\n result = desc_schema.load(new_desc)\n assert result[\"content\"] == \"Description content\"\n assert result[\"source\"] == \"wikipedia\"","title":""},{"docid":"eb8f6b0829f4cc09a8fa7c2c98e271fe","score":"0.5064395","text":"def test_structures_endpoint_data(self):\n # mpf_23 contains no relationships, which shouldn't break anything\n assert \"data\" in self.json_response\n assert len(self.json_response[\"data\"]) == 2\n assert \"included\" in self.json_response\n assert len(self.json_response[\"included\"]) == 1","title":""},{"docid":"a000a3b8985acc8f4324cd7d6b8cc4ee","score":"0.5061462","text":"def validate(self, json_data):\n pass","title":""},{"docid":"341d2535a5d05f8bfb55457ceae1064d","score":"0.50600654","text":"def _verify(self):\r\n if not self._verified:\r\n raise AssertionError(\r\n 'jsonpickle requires at least one of the following:\\n'\r\n ' cjson, json (new in python2.6), simplejson, demjson'\r\n )","title":""},{"docid":"d4a5743355063c75f99bfe03be69236b","score":"0.5053852","text":"def test_unserializable(self):\n try:\n _json_handler(1)\n success = False\n except TypeError:\n # An int can not be serialized, so, this error is the\n # success.\n success = True\n self.assertTrue(success)","title":""},{"docid":"54f99c34d01f028776265de88907d477","score":"0.505098","text":"def copy_6_schema_data() -> tuple[JSON, BytesInstance]:\n copy_6_schema = {\n \"title\": \"REDEFINES-RECORD\",\n \"$anchor\": \"REDEFINES-RECORD\",\n \"cobol\": \"01 REDEFINES-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"REDEFINES-NAME-2\": {\n \"oneOf\": [\n {\n \"title\": \"NAME-2\",\n \"$anchor\": \"NAME-2\",\n \"cobol\": \"05 NAME-2\",\n \"type\": \"object\",\n \"properties\": {\n \"SALARY\": {\n \"title\": \"SALARY\",\n \"$anchor\": \"SALARY\",\n \"cobol\": \"10 SALARY PICTURE XXX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"SO-SEC-NO\": {\n \"title\": \"SO-SEC-NO\",\n \"$anchor\": \"SO-SEC-NO\",\n \"cobol\": \"10 SO-SEC-NO PICTURE X(9)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"MONTH\": {\n \"title\": \"MONTH\",\n \"$anchor\": \"MONTH\",\n \"cobol\": \"10 MONTH PICTURE XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n },\n \"anchor\": \"REDEFINES-NAME-2.NAME-2\"\n },\n {\n \"title\": \"NAME-1\",\n \"$anchor\": \"NAME-1\",\n \"cobol\": \"05 NAME-1 REDEFINES NAME-2\",\n \"type\": \"object\",\n \"properties\": {\n \"WAGE\": {\n \"title\": \"WAGE\",\n \"$anchor\": \"WAGE\",\n \"cobol\": \"10 WAGE PICTURE 999V999\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n },\n \"EMP-NO\": {\n \"title\": \"EMP-NO\",\n \"$anchor\": \"EMP-NO\",\n \"cobol\": \"10 EMP-NO PICTURE X(6)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"YEAR\": {\n \"title\": \"YEAR\",\n \"$anchor\": \"YEAR\",\n \"cobol\": \"10 YEAR PICTURE XX\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n },\n \"anchor\": \"REDEFINES-NAME-2.NAME-1\"\n }\n ],\n \"$anchor\": \"REDEFINES-NAME-2\"\n },\n \"NAME-2\": {\n \"title\": \"NAME-2\",\n \"cobol\": \"05 NAME-2\",\n \"$ref\": \"#NAME-2\"\n },\n \"NAME-1\": {\n \"title\": \"NAME-1\",\n \"cobol\": \"05 NAME-1 REDEFINES NAME-2\",\n \"$ref\": \"#NAME-1\"\n }\n }\n }\n\n assert SchemaValidator.check_schema(copy_6_schema) is None\n\n copy_6_data = BytesInstance(\"ABC123456789DE\".encode(\"CP037\"))\n return copy_6_schema, copy_6_data","title":""},{"docid":"6a3d63b33a98d4e1331386600b1392b6","score":"0.5046336","text":"def assertJSONEqual(self, a, b):\n self.assertDictEqual(json.loads(a),json.loads(b))","title":""},{"docid":"b93760539e3f788f4b1ea1503adeb92e","score":"0.5043583","text":"def copy_13_schema_data() -> tuple[JSON, BytesInstance]:\n copy_13_schema_1 = {\n \"title\": \"GENERIC-RECORD\",\n \"$anchor\": \"GENERIC-RECORD\",\n \"cobol\": \"01 GENERIC-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"HEADER\": {\n \"title\": \"HEADER\",\n \"$anchor\": \"HEADER\",\n \"cobol\": \"05 HEADER PIC X(3)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"GENERIC-FIELD\": {\n \"title\": \"GENERIC-FIELD\",\n \"$anchor\": \"GENERIC-FIELD\",\n \"cobol\": \"05 GENERIC-FIELD PIC X(17)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n\n copy_13_schema_2 = {\n \"title\": \"ABC-SPECIFIC-RECORD\",\n \"$anchor\": \"ABC-SPECIFIC-RECORD\",\n \"cobol\": \"01 ABC-SPECIFIC-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"ITEM-1\": {\n \"title\": \"ITEM-1\",\n \"$anchor\": \"ITEM-1\",\n \"cobol\": \"05 ITEM-1 PIC X(10)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"ITEM-2\": {\n \"title\": \"ITEM-2\",\n \"$anchor\": \"ITEM-2\",\n \"cobol\": \"05 ITEM-2 PIC X(7)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n\n copy_13_schema_3 = {\n \"title\": \"DEF-ANOTHER-RECORD\",\n \"$anchor\": \"DEF-ANOTHER-RECORD\",\n \"cobol\": \"01 DEF-ANOTHER-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"ITEM-3\": {\n \"title\": \"ITEM-3\",\n \"$anchor\": \"ITEM-3\",\n \"cobol\": \"05 ITEM-3 PIC X(7)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"ITEM-4\": {\n \"title\": \"ITEM-4\",\n \"$anchor\": \"ITEM-4\",\n \"cobol\": \"05 ITEM-4 PIC X(10)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n\n assert SchemaValidator.check_schema(copy_13_schema_1) is None\n assert SchemaValidator.check_schema(copy_13_schema_2) is None\n assert SchemaValidator.check_schema(copy_13_schema_3) is None\n\n copy_13_data_1 = BytesInstance(\"ABC0123456789TUVWXYZDEFG\".encode(\"CP037\"))\n copy_13_data_2 = BytesInstance(\"DEF0123456QRSTUVWXYZ\".encode(\"CP037\"))\n return [copy_13_schema_1, copy_13_schema_2, copy_13_schema_3], [copy_13_data_1, copy_13_data_2]","title":""},{"docid":"32e3d24bef4244246cae522215fdf97a","score":"0.50357985","text":"def validate_against_schema(self):\n try:\n jsonschema.validate(self.json_data, self.schema)\n return True\n except jsonschema.ValidationError as err:\n self.error_log = \"JSON does not comply with schema: %s\" % err\n return False","title":""},{"docid":"39789fcb3866e1b2f7771bff1466b7ee","score":"0.50239426","text":"def is_valid_json(self):\n content = (\n self.body\n if self.patches is None\n else \"\".join(str(patch) for patch in self.patches)\n )\n try:\n json.loads(content)\n return True\n except ValueError:\n return False","title":""},{"docid":"8e000357c7668d2b9e13743d19053214","score":"0.50196415","text":"def validate_json(file):\n max_file_size = current_app.config.get(\n 'PREVIEWER_MAX_FILE_SIZE_BYTES', 1 * 1024 * 1024)\n if file.size > max_file_size:\n return False\n\n with file.open() as fp:\n try:\n json.loads(fp.read().decode('utf-8'))\n return True\n except Exception:\n return False","title":""},{"docid":"165215b6020a256eb3d14bc76eca4ef7","score":"0.50186133","text":"def test_biom_to_classic2(self):\r\n self.cmd(table=self.biom_table1,\r\n output_filepath=self.output_filepath, to_tsv=True,\r\n header_key='taxonomy', output_metadata_id='foo')\r\n obs = load_table(self.output_filepath)\r\n self.assertTrue('foo' in obs.metadata(axis='observation')[0])","title":""},{"docid":"fa21080d60ce1dacd27e9ee02ca7c85a","score":"0.50157845","text":"def test_WbTabularData_WbRepresentation_methods(self):\n q = pywikibot.WbTabularData(self.page)\n self._test_hashable(q)","title":""},{"docid":"594585df14cf4d7eeb4c793a49135183","score":"0.500506","text":"def copy_5_schema_data() -> tuple[JSON, BytesInstance]:\n copy_5_schema = {\n \"title\": \"REDEFINES-RECORD\",\n \"$anchor\": \"REDEFINES-RECORD\",\n \"cobol\": \"01 REDEFINES-RECORD\",\n \"type\": \"object\",\n \"properties\": {\n \"REDEFINES-A\": {\n \"oneOf\": [\n {\n \"title\": \"A\",\n \"$anchor\": \"A\",\n \"cobol\": \"05 A PICTURE X(6)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n {\n \"title\": \"B\",\n \"$anchor\": \"B\",\n \"cobol\": \"05 B REDEFINES A\",\n \"type\": \"object\",\n \"properties\": {\n \"B-1\": {\n \"title\": \"B-1\",\n \"$anchor\": \"B-1\",\n \"cobol\": \"10 B-1 PICTURE X(2)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n },\n \"B-2\": {\n \"title\": \"B-2\",\n \"$anchor\": \"B-2\",\n \"cobol\": \"10 B-2 PICTURE 9(4)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n }\n ],\n \"$anchor\": \"REDEFINES-A\"\n },\n \"A\": {\n \"title\": \"A\",\n \"cobol\": \"05 A PICTURE X(6)\",\n \"$ref\": \"#A\"\n },\n \"B\": {\n \"title\": \"B\",\n \"cobol\": \"05 B REDEFINES A\",\n \"$ref\": \"#B\"\n },\n \"C\": {\n \"title\": \"C\",\n \"$anchor\": \"C\",\n \"cobol\": \"05 C PICTURE 99V99\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n }\n }\n }\n\n assert SchemaValidator.check_schema(copy_5_schema) is None\n\n copy_5_data = BytesInstance(\"AB12345678\".encode(\"CP037\"))\n return copy_5_schema, copy_5_data","title":""},{"docid":"78e0cf27b6eed67aeccd8e593a907f45","score":"0.5003732","text":"def assertJsonSuccess(data):\n obj = json.loads(data)\n if 'status' in obj and obj['status'] == \"error\":\n print(\"Error: JSON object returns an error. \" + str(obj))\n return False\n else:\n return True","title":""},{"docid":"a5cb9ff24ab9edf030dd1075b5274913","score":"0.49955502","text":"def test_deserialise_python_builtins(self):\n data = dict(a=123, b=\"text\")\n jdata = json.dumps(data)\n got = deserialise_object(jdata)\n self.assertEqual(got, data)","title":""},{"docid":"080671c8645dd31b3cec0ce799180193","score":"0.4991183","text":"def assert_serialization_works(self, snapshot):\n roundtrip = replication.proto_to_auth_db_snapshot(\n replication.auth_db_snapshot_to_proto(snapshot))\n self.assertEqual(snapshot_to_dict(snapshot), snapshot_to_dict(roundtrip))","title":""},{"docid":"eb8f8813928bae6aaa874237a3dcf84e","score":"0.49896422","text":"def test_export_game(self):\n connect_str = \"sqlite:///:memory:\"\n engine = create_engine(connect_str)\n self.load_fixture(engine, \"tv-themes-v4.sql\")\n DatabaseConnection.bind(self.options.database, create_tables=False,\n engine=engine, debug=False)\n with models.db.session_scope() as dbs:\n for song in models.Song.all(dbs):\n song.check_for_uuid()\n result = models.export_game_to_object(\"20-04-24-2\", dbs)\n result = utils.flatten(result)\n validate_json(JsonSchema.GAME_TRACKS_V4, result)\n if self.EXPECTED_OUTPUT is not None:\n destination = self.EXPECTED_OUTPUT / 'exported-game-v4.json'\n with open(destination, 'wt', encoding='utf-8') as dst:\n json.dump(result, dst, indent=2, sort_keys=True)\n json_filename = fixture_filename(\"exported-game-v4.json\")\n with json_filename.open('rt', encoding='utf-8') as src:\n expected = json.load(src)\n for table in expected.keys():\n # print(f'Check {table}')\n self.assertModelListEqual(result[table], expected[table], table)","title":""},{"docid":"3b53185849857f551bf98d7b8e56647f","score":"0.49890664","text":"def test_basic_json(self):\n options = {\n 'schema': {\n 'name': 'string',\n 'age': 'integer'\n }\n }\n\n data = {\n 'name': 'john',\n 'age': 30\n }\n\n record_data = json.dumps(data)\n\n # get parsed data\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), True)\n\n expected_result = [\n data\n ]\n\n assert_equal(parser.parsed_records, expected_result)","title":""},{"docid":"e4a409a14caa243e9599662c2b99aa89","score":"0.49819562","text":"def test_WbTabularData_error_on_non_exitant_page(self):\n page = Page(self.commons, 'Non-existant page... really')\n regex = r'^Page \\[\\[.+?\\]\\] must exist\\.$'\n with self.assertRaisesRegex(ValueError, regex):\n pywikibot.WbTabularData(page, self.get_repo())","title":""},{"docid":"9525818e6b1615a0c3de9ddf67e11a45","score":"0.49761963","text":"def CheckRequiredFormat(self, json_dict):\n operation_name = json_dict.get('operationName') or None\n properties = json_dict.get('properties') or None\n timestamp = json_dict.get('timeStamp') or None\n\n if (None in (operation_name, properties, timestamp) or\n operation_name != 'ApplicationGatewayAccess'):\n return False\n\n try:\n date_time = dfdatetime_time_elements.TimeElementsInMicroseconds()\n date_time.CopyFromStringISO8601(timestamp)\n except ValueError:\n return False\n\n return True","title":""},{"docid":"92bedd89eed7f664c31911a40e43b624","score":"0.4964713","text":"def test_json_teams_format(self):\n miner.copy_miner_file()\n json_team = miner.get_json_teams(1)[0]\n self.assertEqual(len(json_team), 6)\n pokemon_dict = json_team[0]\n self.assertEqual(set(['ability', 'evs', 'item', 'ivs', 'level', 'moves', 'name', 'shiny',\n 'species']),\n set(pokemon_dict.keys()))\n self.assertTrue(set(['atk', 'def', 'hp', 'spa', 'spd', 'spe']) ==\n set(pokemon_dict['evs'].keys()) ==\n set(pokemon_dict['ivs'].keys()))\n\n self.assertEqual(len(pokemon_dict['moves']), 1 if pokemon_dict['name'] == 'Ditto' else 4)","title":""},{"docid":"0e4cb6df699dc193a385e91f94e4945c","score":"0.4964026","text":"def test_json(self):\n processor = ProcessToRenderInfo(\n HtmlDocumentManager('test_json'),\n ProcessedEntityManager())\n processor.max_uncollapsable_json_lines = 20\n processor.max_uncollapsable_entity_rows = 20\n\n # Numeric literals wont be treated as json.\n for n in [-1, 0, 1, 3.14]:\n info = processor.process_json_html_if_possible(n)\n self.assertEquals('{0}'.format(n), info.detail_html)\n self.assertEquals(None, info.summary_html)\n\n # None of these strings are well-defined JSON documents\n # so should just be strings.\n for s in ['test', 'a phrase', 'True']:\n info = processor.process_json_html_if_possible(s)\n self.assertEquals('\"{0}\"'.format(s), info.detail_html)\n self.assertEquals(None, info.summary_html)\n\n # Boolean values wont be considered JSON.\n for b in [True, False]:\n info = processor.process_json_html_if_possible(b)\n self.assertEquals('{0}'.format(str(b)), info.detail_html)\n self.assertEquals(None, info.summary_html)\n\n # Dictionaries and JSON dictionary strings normalize to JSON.\n for d in [{'A': 'a', 'B': True}, '{\"A\":\"a\", \"B\":true}']:\n info = processor.process_json_html_if_possible(d)\n self.assertEquals('<pre>{{\\n \"A\": \"a\",\\n'\n ' \"B\": true\\n'\n '}}</pre>'.format(), info.detail_html)\n self.assertEquals(None, info.summary_html)\n self.assertEquals(None, info.summary_html)\n\n # Lists and JSON lists strings normalize to JSON.\n for l in [[123, 'abc', True, {'A': 'a', 'B': 'b'}],\n '[123, \"abc\", true, {\"A\":\"a\", \"B\":\"b\"}]']:\n info = processor.process_json_html_if_possible(l)\n self.assertEquals('<pre>[\\n 123,\\n \"abc\",\\n true,\\n'\n ' {{\\n'\n ' \"A\": \"a\",\\n'\n ' \"B\": \"b\"\\n'\n ' }}\\n'\n ']</pre>'.format(), info.detail_html)\n self.assertEquals(None, info.summary_html)","title":""},{"docid":"2fafd82345d9bbcd2a55dd4ea7b3b48b","score":"0.49624068","text":"def test___bytes__(json_bytes_data: TypingJsonBytesData):\n assert bytes(json_bytes_data[\"json_bytes\"]) == json_bytes_data[\"bytes\"]","title":""},{"docid":"1e31b0227bb6a91a545725159ce2031b","score":"0.49622917","text":"def test_Place_is_dict(self):\n\n st = FileStorage()\n st_dict = st.all()\n self.assertEqual(dict, type(st_dict))","title":""},{"docid":"9957b5a603c6abe649505980c3dd938f","score":"0.4946569","text":"def test_dump_all_formats(format_key, expected):\n data = [\n {\n \"color\":\"#ffffff\", \"name\": \"white\"\n },\n {\n \"color\":\"#000000\", \"name\": \"black\"\n },\n ]\n output = build_dump(format_key, data)\n\n assert output[\"content\"] == expected","title":""},{"docid":"e0348ec7650d86fb15112724c1624867","score":"0.4946224","text":"def testReadWriteJsonFile(self):\n try:\n rObj = self.__ioU.deserialize(self.__pathJsonTestFile, fmt=\"json\")\n logger.debug(\"Object length %d\", len(rObj))\n self.assertGreaterEqual(len(rObj), 1)\n ok = self.__ioU.serialize(self.__pathSaveJsonTestFile, rObj, fmt=\"json\")\n self.assertTrue(ok)\n\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","title":""},{"docid":"304497723bd10badc7eddfed67864cce","score":"0.4939063","text":"def testfromjson(self):\n dicty = {\"id\": 5, \"class\": \"string\", \"list\": [], \"set\": {}}\n self.assertEqual([dicty], Base.from_json_string(json.dumps([dicty])))","title":""},{"docid":"d30994a523aefdc93cd4d6c73be16cb2","score":"0.49368024","text":"def export_test(self, schema_version):\n connect_str = \"sqlite:///:memory:\"\n engine = create_engine(connect_str) # , echo=True)\n self.load_fixture(engine, f\"tv-themes-v{schema_version}.sql\")\n\n DatabaseConnection.bind(self.options.database, create_tables=False,\n engine=engine, debug=False)\n output = io.StringIO()\n # pylint: disable=no-value-for-parameter\n with models.db.session_scope() as dbs:\n models.export_database_to_file(output, self.options, Progress(), dbs, None)\n output.seek(0)\n actual_json = json.load(output)\n if self.EXPECTED_OUTPUT is not None:\n destination = self.EXPECTED_OUTPUT / f\"exported-tv-themes-v{schema_version}.json\"\n with open(destination, 'wt', encoding='utf-8') as dbg:\n dbg.write(output.getvalue())\n validate_json(JsonSchema.DATABASE, actual_json)\n json_filename = fixture_filename(f\"exported-tv-themes-v{schema_version}.json\")\n with json_filename.open('rt', encoding='utf-8') as src:\n expected_json = json.load(src)\n self.assertEqual(expected_json['Users'][0]['username'], 'admin')\n expected_json['Users'][0]['last_login'] = None\n # self.maxDiff = None\n for table in expected_json.keys():\n # print(f'Check {table}')\n if isinstance(actual_json[table], dict):\n self.assertDictEqual(actual_json[table], expected_json[table], table)\n else:\n self.assertModelListEqual(actual_json[table], expected_json[table], table)","title":""},{"docid":"353d25f2abc322e940c9e521e926ef78","score":"0.4936759","text":"def testDumpData(self):\r\n self.assertCommandSucceeds(\"dumpdata\")","title":""},{"docid":"8d898c45ae770a76f03a979835ca91e7","score":"0.49342582","text":"def checkSchema(self):\n\n\t\traise NotImplementedError()","title":""},{"docid":"d32acb33bcb6cf97eeb846abc3a97704","score":"0.49338663","text":"def testfromjson(self):\n dicty = {\"id\": 5, \"class\": \"string\", \"list\": [], \"set\": {}}\n self.assertEqual([dicty, dicty],\n Base.from_json_string(json.dumps([dicty, dicty])))","title":""},{"docid":"6b723a3847dac70b847b465041ec67c0","score":"0.4933373","text":"def is_tsv(to_test):\n if isinstance(to_test, bytes):\n as_str = to_test.decode('utf-8')\n as_list = as_str.splitlines()\n else:\n as_str = str(to_test)\n as_list = as_str.split('\\n')\n if len(as_list) > 1:\n if len(as_str.split('\\t')) > 1:\n return True\n return False","title":""},{"docid":"4ab063f47e00dc7e2801dd8db146b7f4","score":"0.49322158","text":"def copy_10_schema_data() -> tuple[JSON, BytesInstance]:\n copy_10_schema = {\n \"title\": \"MAIN-AREA\",\n \"$anchor\": \"MAIN-AREA\",\n \"cobol\": \"01 MAIN-AREA\",\n \"type\": \"object\",\n \"properties\": {\n \"REC-1\": {\n \"title\": \"REC-1\",\n \"$anchor\": \"REC-1\",\n \"cobol\": \"03 REC-1\",\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-1\": {\n \"title\": \"FIELD-1\",\n \"$anchor\": \"FIELD-1\",\n \"cobol\": \"05 FIELD-1 PIC 9\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\",\n \"conversion\": \"decimal\"\n },\n \"FIELD-2\": {\n \"title\": \"FIELD-2\",\n \"$anchor\": \"FIELD-2\",\n \"cobol\": \"05 FIELD-2 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-1 PIC X(05)\",\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"FIELD-2\": {\n \"cobol\": \"05 FIELD-2 OCCURS 1 TO 5 TIMES\\n DEPENDING ON FIELD-1 PIC X(05)\",\n \"type\": \"string\",\n \"contentEncoding\": \"cp037\"\n }\n }\n },\n \"maxItemsDependsOn\": {\n \"$ref\": \"#FIELD-1\"\n }\n }\n }\n }\n }\n }\n\n assert SchemaValidator.check_schema(copy_10_schema) is None\n\n copy_10_data = BytesInstance(\"3111112222233333\".encode(\"CP037\"))\n return copy_10_schema, copy_10_data","title":""},{"docid":"e000f97f33621b58909e0dfb19bd6281","score":"0.4932113","text":"def test_dont_mix_schema_and_object_signature(self):\n url = \"pg://localhost:5432/tuttle_test_db/test_schema\"\n res = PostgreSQLResource(url)\n assert not res.exists(), \"{} should not exist because no table, view nor any object with that name \" \\\n \"exists\".format(url)","title":""},{"docid":"4d838408b6780ed3bead70cd3212caf8","score":"0.4931609","text":"def test_table_fits_io_astropy(table):\n # Check Table -> BinTableHDU\n hdu = fits.BinTableHDU(table)\n assert hdu.header[\"TTYPE2\"] == \"b\"\n assert hdu.header[\"TFORM2\"] == \"K\"\n assert hdu.header[\"TUNIT2\"] == \"m\"\n\n # Check BinTableHDU -> Table\n table2 = Table.read(hdu)\n assert isinstance(table2.meta, dict)\n assert table2.meta == {\"VERSION\": 42}\n assert table2[\"b\"].unit == \"m\"\n # Note: description doesn't come back in older versions of Astropy\n # that we still support, so we're not asserting on that here for now.","title":""},{"docid":"e317c705efb14fd26dca1d8394a5e2db","score":"0.49231386","text":"def test_metadata(list_json_output):\n\n listing = list_json_output('valid-json', metadata=True)\n for c in listing:\n if 'meta' in c:\n assert c['meta'] == True\n assert c['title'] == 'NPC Listing'\n assert 'created' in c","title":""},{"docid":"6144d395c5711ac22a4c29dcd2685984","score":"0.4922923","text":"def assert_valid_json(data: str, schema_id: str) -> None: # noqa: E501 (line too long)\n assert_valid(json.loads(data), schema_id)","title":""},{"docid":"5608356d400fe8445df78527c6d36ea7","score":"0.4917935","text":"def test_invalid_json_path(self):\n options = {\n 'schema': {\n 'name': 'string',\n 'result': 'string'\n },\n 'configuration': {\n 'json_path': 'Records[*]'\n }\n }\n\n record_data = {'name': 'test', 'result': 'test'}\n\n # get parsed data\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), False)\n\n expected_result = [\n record_data\n ]\n assert_equal(parser.invalid_parses, expected_result)","title":""},{"docid":"1c69f818cb6b1e5c3514060050b6d385","score":"0.49155313","text":"def schema_check(table1, table2):\n\n # compares the first row of each table to ensure the titles and number of columns match\n if table1[0] != table2[0]:\n raise MismatchedAttributesException","title":""}],"string":"[\n {\n \"docid\": \"c8ac963d2472571f1d37e326483a25e1\",\n \"score\": \"0.62551606\",\n \"text\": \"def test_parse_biom_table(self):\\r\\n # This is a TSV as a list of lines\\r\\n t = parse_biom_table(self.classic_otu_table1_no_tax)\\r\\n\\r\\n # Test TSV as a list of lines\\r\\n t_tsv_str = t.to_tsv()\\r\\n t_tsv_lines = t_tsv_str.splitlines()\\r\\n t_tsv = parse_biom_table(t_tsv_lines)\\r\\n self.assertEqual(t, t_tsv)\\r\\n # Test TSV as a file-like object\\r\\n t_tsv_stringio = StringIO(t_tsv_str)\\r\\n t_tsv = parse_biom_table(t_tsv_stringio)\\r\\n self.assertEqual(t, t_tsv)\\r\\n\\r\\n # Test JSON as a list of lines\\r\\n t_json_str = t.to_json('asd')\\r\\n t_json_lines = t_json_str.splitlines()\\r\\n t_json = parse_biom_table(t_json_lines)\\r\\n self.assertEqual(t, t_json)\\r\\n # Test JSON as a file-like object\\r\\n t_json_str = t.to_json('asd')\\r\\n t_json_stringio = StringIO(t_json_str)\\r\\n t_json = parse_biom_table(t_json_stringio)\\r\\n self.assertEqual(t, t_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b4da21e1953372fb1da67c7ff99f27\",\n \"score\": \"0.6164362\",\n \"text\": \"def test_parse_biom_json(self):\\r\\n # light test. this code is used thoroughly within the other\\r\\n # parse_biom_table methods\\r\\n tab1_fh = json.load(StringIO(self.biom_minimal_sparse))\\r\\n tab = Table.from_json(tab1_fh)\\r\\n npt.assert_equal(tab.ids(), ('Sample1', 'Sample2', 'Sample3',\\r\\n 'Sample4', 'Sample5', 'Sample6'))\\r\\n npt.assert_equal(tab.ids(axis='observation'),\\r\\n ('GG_OTU_1', 'GG_OTU_2', 'GG_OTU_3',\\r\\n 'GG_OTU_4', 'GG_OTU_5'))\\r\\n self.assertEqual(tab.metadata(), None)\\r\\n self.assertEqual(tab.metadata(axis='observation'), None)\\r\\n\\r\\n tab = parse_biom_table(StringIO(self.biom_minimal_sparse))\\r\\n npt.assert_equal(tab.ids(), ('Sample1', 'Sample2', 'Sample3',\\r\\n 'Sample4', 'Sample5', 'Sample6'))\\r\\n npt.assert_equal(tab.ids(axis='observation'),\\r\\n ('GG_OTU_1', 'GG_OTU_2', 'GG_OTU_3',\\r\\n 'GG_OTU_4', 'GG_OTU_5'))\\r\\n self.assertEqual(tab.metadata(), None)\\r\\n self.assertEqual(tab.metadata(axis='observation'), None)\\r\\n\\r\\n tablestring = u'''{\\r\\n \\\"id\\\":null,\\r\\n \\\"format\\\": \\\"Biological Observation Matrix 0.9.1-dev\\\",\\r\\n \\\"format_url\\\": \\\"http://biom-format.org\\\",\\r\\n \\\"type\\\": \\\"OTU table\\\",\\r\\n \\\"generated_by\\\": \\\"QIIME revision XYZ\\\",\\r\\n \\\"date\\\": \\\"2011-12-19T19:00:00\\\",\\r\\n \\\"rows\\\":[\\r\\n {\\\"id\\\":\\\"GG_OTU_1\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"GG_OTU_2\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"GG_OTU_3\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"GG_OTU_4\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"GG_OTU_5\\\", \\\"metadata\\\":null}\\r\\n ],\\r\\n \\\"columns\\\": [\\r\\n {\\\"id\\\":\\\"Sample1\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"Sample2\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"Sample3\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"Sample4\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"Sample5\\\", \\\"metadata\\\":null},\\r\\n {\\\"id\\\":\\\"Sample6\\\", \\\"metadata\\\":null}\\r\\n ],\\r\\n \\\"matrix_type\\\": \\\"dense\\\",\\r\\n \\\"matrix_element_type\\\": \\\"int\\\",\\r\\n \\\"shape\\\": [5,6],\\r\\n \\\"data\\\": [[0,0,1,0,0,0],\\r\\n [5,1,0,2,3,1],\\r\\n [0,0,1,4,2,0],\\r\\n [2,1,1,0,0,1],\\r\\n [0,1,1,0,0,0]]\\r\\n }'''\\r\\n tbs_fh = json.load(StringIO(tablestring))\\r\\n tab1 = Table.from_json(tbs_fh)\\r\\n tab2 = parse_biom_table(tablestring)\\r\\n self.assertEqual(tab1, tab2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f1cb036c17c6d02896f699d090e14c\",\n \"score\": \"0.5828839\",\n \"text\": \"def test_schema_fail(self):\\n options = {\\n 'schema': {\\n 'unit_key_01': 'integer',\\n 'unit_key_02': 'string'\\n }\\n }\\n\\n data = {'hey': 1}\\n record_data = json.dumps(data)\\n\\n # get parsed data\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), False)\\n\\n expected_result = [\\n data\\n ]\\n assert_equal(parser.invalid_parses, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497bbea88f665654ebec5d75b63b7058\",\n \"score\": \"0.5712711\",\n \"text\": \"def test_valid_json_file(self, file_url):\\n obj = BMICalc(file_url)\\n try :\\n obj.define_logic_map()\\n obj.read_inputfile()\\n except Exception as exp:\\n assert False\\n else :\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32d065ca3b20aa2a2b82183cccca7d1\",\n \"score\": \"0.56740147\",\n \"text\": \"def handle_tables(self,subjson):\\n noerr = True\\n for descr in subjson:\\n t = Table.parse_json(descr)\\n if t is None: \\n noerr = False\\n continue\\n if t.database not in self.databases: \\n log.warning(\\\"Database info for table % is not known.Please specify before\\\")\\n noerr = False\\n continue\\n t.database = self.databases[t.database]\\n self.tables[t.name] = t\\n\\n return noerr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48bc6396b70986b9ed84c201b7e31bff\",\n \"score\": \"0.55641615\",\n \"text\": \"def checkjson(self):\\n raise Exception(\\\"This method needs to be overridden\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598b53d02b82b35280cdd20a08bba6c3\",\n \"score\": \"0.5550788\",\n \"text\": \"def test_parse_biom_table_with_hdf5(self):\\r\\n # We will round-trip the HDF5 file to several different formats, and\\r\\n # make sure we can recover the same table using parse_biom_table\\r\\n cwd = os.getcwd()\\r\\n if '/' in __file__[1:]:\\r\\n os.chdir(__file__.rsplit('/', 1)[0])\\r\\n\\r\\n t = parse_biom_table(h5py.File('test_data/test.biom'))\\r\\n\\r\\n # These things are not round-trippable using the general-purpose\\r\\n # parse_biom_table function\\r\\n t._sample_metadata = None\\r\\n t._observation_metadata = None\\r\\n t.type = None\\r\\n\\r\\n # Test TSV as a list of lines\\r\\n t_tsv_str = t.to_tsv()\\r\\n t_tsv_lines = t_tsv_str.splitlines()\\r\\n t_tsv = parse_biom_table(t_tsv_lines)\\r\\n self.assertEqual(t, t_tsv)\\r\\n # Test TSV as a file-like object\\r\\n t_tsv_stringio = StringIO(t_tsv_str)\\r\\n t_tsv = parse_biom_table(t_tsv_stringio)\\r\\n self.assertEqual(t, t_tsv)\\r\\n\\r\\n # Test JSON as a list of lines\\r\\n t_json_str = t.to_json('asd')\\r\\n t_json_lines = t_json_str.splitlines()\\r\\n t_json = parse_biom_table(t_json_lines)\\r\\n self.assertEqual(t, t_json)\\r\\n # Test JSON as a file-like object\\r\\n t_json_str = t.to_json('asd')\\r\\n t_json_stringio = StringIO(t_json_str)\\r\\n t_json = parse_biom_table(t_json_stringio)\\r\\n self.assertEqual(t, t_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30967c66a1c4bba2d798772096c0ca29\",\n \"score\": \"0.55146545\",\n \"text\": \"def validateJSON( jsonData ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c5a3f1ed95ee923036c6b696f97a22\",\n \"score\": \"0.5512445\",\n \"text\": \"def test_parse_record_not_dict_mismatch(self):\\n options = {\\n 'schema': {\\n 'key': 'string'\\n },\\n 'parser': 'json'\\n }\\n record_data = \\\"[{\\\\\\\"key\\\\\\\": \\\\\\\"value\\\\\\\"}]\\\"\\n\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60832dab25d6b0758616bc3712f74d03\",\n \"score\": \"0.5506996\",\n \"text\": \"def is_valid_json(self):\\n try:\\n json_object = json.loads(self._file)\\n except (ValueError, TypeError) as e:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86877acc3019242c33689bb5046f21a8\",\n \"score\": \"0.55048716\",\n \"text\": \"def test_nontable_file(self):\\n self.setNonTableData()\\n self.assertError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8e92887e1a7f747c9c510c67f1b718\",\n \"score\": \"0.5503046\",\n \"text\": \"def test_from_json_obj_wrong_type():\\n for t, encoding in WRONG_TYPE_ENCODINGS:\\n try:\\n from_json_obj(encoding, t)\\n assert False, \\\"Should not be decoding %s as %s.\\\"%(str(encoding), str(t))\\n except TypeError:\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f459534417ebff548387ecef220b20e8\",\n \"score\": \"0.54715943\",\n \"text\": \"def validate_schema(bdb, table, mml_json):\\n bad_cols = []\\n for col, typ in mml_json['columns'].items():\\n # If the column is already ignored there's nothing to check\\n if typ['stattype'] == 'IGNORE':\\n continue\\n one_col_json = copy.deepcopy(mml_json)\\n one_col_json['columns'] = {col: typ}\\n # Create a temp generator\\n gen_name = uuid.uuid4().hex\\n try:\\n bdb.execute(to_mml(one_col_json, table, gen_name))\\n bdb.execute('INITIALIZE 1 MODEL FOR %s'\\n % (bql_quote_name(gen_name),))\\n bdb.execute('ANALYZE %s FOR 1 ITERATION WAIT'\\n % (bql_quote_name(gen_name),))\\n except AssertionError:\\n bad_cols.append(col)\\n finally:\\n # Drop our temp generator\\n bdb.execute('DROP GENERATOR %s' % bql_quote_name(gen_name))\\n modified_schema = copy.deepcopy(mml_json)\\n # TODO(asilvers): Should we also return a summary of the modifications?\\n for col in bad_cols:\\n modified_schema['columns'][col]['guessed'] = (\\n modified_schema['columns'][col]['stattype'])\\n modified_schema['columns'][col]['stattype'] = 'IGNORE'\\n modified_schema['columns'][col]['reason'] = 'Caused ANALYZE to error'\\n jsonschema.validate(modified_schema, MML_SCHEMA)\\n return modified_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c361a5298b91e447fddf78f500ce073\",\n \"score\": \"0.54509556\",\n \"text\": \"def copy_t2_schema_data() -> tuple[JSON, BytesInstance]:\\n\\n test_2_schema = {\\n \\\"title\\\": \\\"SOME-RECORD-2\\\",\\n \\\"$anchor\\\": \\\"SOME-RECORD-2\\\",\\n \\\"cobol\\\": \\\"01 SOME-RECORD-2\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FILLER-1\\\": {\\n \\\"title\\\": \\\"FILLER\\\",\\n \\\"$anchor\\\": \\\"FILLER-1\\\",\\n \\\"cobol\\\": \\\"05 FILLER PIC X(5)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"REPEAT\\\": {\\n \\\"title\\\": \\\"REPEAT\\\",\\n \\\"$anchor\\\": \\\"REPEAT\\\",\\n \\\"cobol\\\": \\\"05 REPEAT OCCURS 4 PIC XXX\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REPEAT\\\": {\\n \\\"title\\\": \\\"REPEAT\\\",\\n \\\"$anchor\\\": \\\"REPEAT\\\",\\n \\\"cobol\\\": \\\"05 REPEAT OCCURS 4 PIC XXX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n \\\"maxItems\\\": 4\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(test_2_schema) is None\\n test_2_data = BytesInstance('12345ABCDEFGHIJKL'.encode(\\\"CP037\\\"))\\n return test_2_schema, test_2_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c62613813eb665838e3f06503dbbae\",\n \"score\": \"0.5434971\",\n \"text\": \"def setUp(self):\\n self.biom_text = \\\"\\\"\\\"{\\n \\\"id\\\":null,\\n \\\"format\\\": \\\"Biological Observation Matrix 0.9.1-dev\\\",\\n \\\"format_url\\\": \\\"http://biom-format.org/documentation/format_versions/biom-1.0.html\\\",\\n \\\"type\\\": \\\"OTU table\\\",\\n \\\"generated_by\\\": \\\"QIIME revision 1.4.0-dev\\\",\\n \\\"date\\\": \\\"2011-12-19T19:00:00\\\",\\n \\\"rows\\\":[\\n {\\\"id\\\":\\\"GG_OTU_1\\\", \\\"metadata\\\":{\\\"taxonomy\\\":[\\\"k__Bacteria\\\", \\\"p__Proteobacteria\\\", \\\"c__Gammaproteobacteria\\\", \\\"o__Enterobacteriales\\\", \\\"f__Enterobacteriaceae\\\", \\\"g__Escherichia\\\", \\\"s__\\\"]}},\\n {\\\"id\\\":\\\"GG_OTU_2\\\", \\\"metadata\\\":{\\\"taxonomy\\\":[\\\"k__Bacteria\\\", \\\"p__Cyanobacteria\\\", \\\"c__Nostocophycideae\\\", \\\"o__Nostocales\\\", \\\"f__Nostocaceae\\\", \\\"g__Dolichospermum\\\", \\\"s__\\\"]}},\\n {\\\"id\\\":\\\"GG_OTU_3\\\", \\\"metadata\\\":{\\\"taxonomy\\\":[\\\"k__Archaea\\\", \\\"p__Euryarchaeota\\\", \\\"c__Methanomicrobia\\\", \\\"o__Methanosarcinales\\\", \\\"f__Methanosarcinaceae\\\", \\\"g__Methanosarcina\\\", \\\"s__\\\"]}},\\n {\\\"id\\\":\\\"GG_OTU_4\\\", \\\"metadata\\\":{\\\"taxonomy\\\":[\\\"k__Bacteria\\\", \\\"p__Firmicutes\\\", \\\"c__Clostridia\\\", \\\"o__Halanaerobiales\\\", \\\"f__Halanaerobiaceae\\\", \\\"g__Halanaerobium\\\", \\\"s__Halanaerobiumsaccharolyticum\\\"]}},\\n {\\\"id\\\":\\\"GG_OTU_5\\\", \\\"metadata\\\":{\\\"taxonomy\\\":[\\\"k__Bacteria\\\", \\\"p__Proteobacteria\\\", \\\"c__Gammaproteobacteria\\\", \\\"o__Enterobacteriales\\\", \\\"f__Enterobacteriaceae\\\", \\\"g__Escherichia\\\", \\\"s__\\\"]}}\\n ],\\n \\\"columns\\\":[\\n {\\\"id\\\":\\\"Sample1\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CGCTTATCGAGA\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"gut\\\",\\n \\\"Description\\\":\\\"human gut\\\"}},\\n {\\\"id\\\":\\\"Sample2\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CATACCAGTAGC\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"gut\\\",\\n \\\"Description\\\":\\\"human gut\\\"}},\\n {\\\"id\\\":\\\"Sample3\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CTCTCTACCTGT\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"gut\\\",\\n \\\"Description\\\":\\\"human gut\\\"}},\\n {\\\"id\\\":\\\"Sample4\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CTCTCGGCCTGT\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"skin\\\",\\n \\\"Description\\\":\\\"human skin\\\"}},\\n {\\\"id\\\":\\\"Sample5\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CTCTCTACCAAT\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"skin\\\",\\n \\\"Description\\\":\\\"human skin\\\"}},\\n {\\\"id\\\":\\\"Sample6\\\", \\\"metadata\\\":{\\n \\\"BarcodeSequence\\\":\\\"CTAACTACCAAT\\\",\\n \\\"LinkerPrimerSequence\\\":\\\"CATGCTGCCTCCCGTAGGAGT\\\",\\n \\\"BODY_SITE\\\":\\\"skin\\\",\\n \\\"Description\\\":\\\"human skin\\\"}}\\n ],\\n \\\"matrix_type\\\": \\\"sparse\\\",\\n \\\"matrix_element_type\\\": \\\"int\\\",\\n \\\"shape\\\": [5, 6],\\n \\\"data\\\":[[0,2,1],\\n [1,0,5],\\n [1,1,1],\\n [1,3,2],\\n [1,4,3],\\n [1,5,1],\\n [2,2,1],\\n [2,3,4],\\n [2,5,2],\\n [3,0,2],\\n [3,1,1],\\n [3,2,1],\\n [3,5,1],\\n [4,1,1],\\n [4,2,1]\\n ]\\n }\\\"\\\"\\\"\\n\\n self.coreOTUdata = '''# Core OTUs across 75 % of samples.\\n064EK048\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__HOT.064']\\n200CZ006\\t[u'k__Bacteria', u'p__Fusobacteria', u'c__Fusobacteria', u'o__Fusobacteriales', u'f__Fusobacteriaceae', u'g__Fusobacterium', u's__nucleatum']\\n161Vparv\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Veillonella', u's__parvula']\\n111_3523\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Peptostreptococcaceae_[XIII]', u'g__Parvimonas', u's__micra']\\n644_4671\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__intermedius']\\n758_3928\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__sanguinis']\\n524Vatyp\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Veillonella', u's__atypica']\\n151_K168\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Veillonellaceae', u'g__Selenomonas', u's__sputigena']\\n530_5256\\t[u'k__Bacteria', u'p__Actinobacteria', u'c__Actinobacteria', u'o__Actinomycetales', u'f__Propionibacteriaceae', u'g__Propionibacterium', u's__acnes']\\n612_6662\\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Gammaproteobacteria', u'o__Pseudomonadales', u'f__Pseudomonadaceae', u'g__Pseudomonas', u's__fluorescens']\\n539_6962\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Clostridia', u'o__Clostridiales', u'f__Peptostreptococcaceae_[XI]', u'g__Filifactor', u's__alocis']\\n073-T1E5\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__australis']\\n398_7051\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__mitis']\\n622_3931\\t[u'k__Bacteria', u'p__Firmicutes', u'c__Bacilli', u'o__Lactobacillales', u'f__Streptococcaceae', u'g__Streptococcus', u's__gordonii']\\n826_8612\\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Gammaproteobacteria', u'o__Pasteurellales', u'f__Pasteurellaceae', u'g__Terrahaemophilus', u's__aromaticivorans']\\n623_4320\\t[u'k__Bacteria', u'p__Proteobacteria', u'c__Epsilonproteobacteria', u'o__Campylobacterales', u'f__Campylobacteraceae', u'g__Campylobacter', u's__gracilis']\\n205FL002\\t[u'k__Bacteria', u'p__Fusobacteria', u'c__Fusobacteria', u'o__Fusobacteriales', u'f__Fusobacteriaceae', u'g__Fusobacterium', u's__HOT.205']'''\\n\\n self.biom = json.loads(self.biom_text)\\n self.row = self.biom['rows']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ee69fc9871c44fc84de18f3abcf636\",\n \"score\": \"0.5420853\",\n \"text\": \"def test_valid_json(self):\\n self.assertTrue(json.loads(self.ratings.get_json()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ee69fc9871c44fc84de18f3abcf636\",\n \"score\": \"0.5420853\",\n \"text\": \"def test_valid_json(self):\\n self.assertTrue(json.loads(self.ratings.get_json()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3999dcecbe6aec5d912dc15cb0917c48\",\n \"score\": \"0.538307\",\n \"text\": \"def check_valid_report(context):\\n response = context.response.json()\\n if context.history:\\n assert isinstance(response['objects'], list)\\n else:\\n assert isinstance(response, dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41c96316dce2d78a9eb824c8ac0f1ce\",\n \"score\": \"0.53647953\",\n \"text\": \"def test_instance_resize_prep_end_valid_json(self):\\n expected_obj = self.resize_prep_end_obj\\n\\n actual_json = json.dumps(self.instance_resize_prep_end_dict)\\n actual_obj = InstanceResizePrepEnd.deserialize(actual_json, 'json')\\n\\n self.assertEqual(expected_obj, actual_obj)\\n self.assertFalse(actual_obj.is_empty())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"942f27d43d60da04f9e6f5e885bad519\",\n \"score\": \"0.53493994\",\n \"text\": \"def test_json(self, tmpdir):\\n _ = self.__class__ # just to get rid of codacy warning, I know, it's stupid\\n config_from_yaml = Menu.safe_read_yaml(yaml_path())\\n p = tmpdir.mkdir(\\\"pytabbytest\\\").join(\\\"temp.json\\\")\\n p.write(json.dumps(config_from_yaml))\\n config_from_json = Menu.read_json(str(p))\\n if not config_from_yaml == config_from_json:\\n raise AssertionError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d979a568404fd4b97971851b61c888a4\",\n \"score\": \"0.53352386\",\n \"text\": \"def test_check_json_health(self):\\n # check health json\\n with open(self.test_file_name, 'w') as fp:\\n json.dump({'tasks': {}, 'users': {}}, fp)\\n\\n with open(self.test_file_name) as fp:\\n self.assertEqual(\\n interactive.check_json_health(fp),\\n {'tasks': {}, 'users': {}}\\n )\\n\\n # check corrupted json\\n with open(self.test_file_name, 'w') as fp:\\n json.dump({'tasks': {}}, fp)\\n\\n with open(self.test_file_name) as fp:\\n self.assertIsNone(interactive.check_json_health(fp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bae938a6b2a9be5595c7461afbbc7f7\",\n \"score\": \"0.53255975\",\n \"text\": \"def test_exports(self):\\n # Get rid of weird formatting included in class attribute\\n self.maxDiff = 5_000\\n self.assertEquals(\\n self._meta.to_json().replace('\\\\n','').replace(' ','') ,\\n self._META_MODEL_JSON.replace('\\\\n','').replace(' ','') \\n )\\n # Test Risk Table\\n param_dict = json.loads(self._META_MODEL_JSON)\\n del param_dict['creation_date']\\n del param_dict['model_uuid']\\n del param_dict['name']\\n del param_dict['type']\\n self.assertEquals(\\n self._meta.export_params(),\\n param_dict\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31f08393039889c3412f02550034d72\",\n \"score\": \"0.5313659\",\n \"text\": \"def _jsonYayForTabs(self, content):\\n\\n\\t\\tfor line in content.split('\\\\n'):\\n\\t\\t\\tpattern = re.compile(r'\\\\t*(?P<spaces> *)')\\n\\t\\t\\tmatches = pattern.match(line)\\n\\t\\t\\tif matches.groupdict()['spaces']:\\n\\t\\t\\t\\tself.fail('Spaces are the devil!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03be5d7152ea3dbbacb164a2b03f912\",\n \"score\": \"0.53131914\",\n \"text\": \"def test8(self):\\n # arrange\\n model = IrisModel()\\n\\n # act\\n json_schema = model.output_schema.json_schema(\\\"https://example.com/my-schema.json\\\")\\n\\n # assert\\n print(json_schema)\\n self.assertTrue(type(json_schema) is dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4623c594c47984ba45bff2a5c85ecf75\",\n \"score\": \"0.5312796\",\n \"text\": \"def test_WbTabularData_error_on_wrong_page_type(self):\\n non_data_page = Page(self.commons, 'File:Foo.jpg')\\n non_map_page = Page(self.commons, 'Data:Lyngby Hovedgade.map')\\n regex = (r\\\"^Page must be in 'Data:' namespace and end in '\\\\.tab' \\\"\\n r'for tabular-data\\\\.$')\\n with self.assertRaisesRegex(ValueError, regex):\\n pywikibot.WbTabularData(non_data_page, self.get_repo())\\n with self.assertRaisesRegex(ValueError, regex):\\n pywikibot.WbTabularData(non_map_page, self.get_repo())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb2e80bf2b0d19d05d8692003d482d1\",\n \"score\": \"0.5296964\",\n \"text\": \"def test_read_json(self):\\n self.assertEqual(len(cleanUtils._read_json('abbreviated_cantons.json')), 47)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c6a575077f279d21857b0d07e055bd\",\n \"score\": \"0.52787226\",\n \"text\": \"def test_to_dict(self):\\n my_model_dict = self.bm_obj.to_dict()\\n actual = 1\\n try:\\n serialized = json.dumps(my_model_dict)\\n except:\\n actual = 0\\n self.assertTrue(1 == actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b64075114bf834af6426bb023eb597f\",\n \"score\": \"0.52676225\",\n \"text\": \"def test_schema_valid(self) -> None:\\n instance = TNSObjectSearchResult.from_dict(FAKE_TNS_OBJECT_DATA)\\n assert isinstance(instance, TNSObjectSearchResult)\\n assert instance._data == FAKE_TNS_OBJECT_DATA\\n assert instance.object_type_name == FAKE_TNS_TYPE_NAME\\n assert instance.redshift == FAKE_TNS_REDSHIFT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57325136b5ae13ff61c2fcc45f4ca8e\",\n \"score\": \"0.5267283\",\n \"text\": \"def test_character_from_tibiadata_unrelated_json(self):\\n with self.assertRaises(InvalidContent):\\n Character.from_tibiadata(self._load_resource(tests.tests_guild.FILE_GUILD_TIBIADATA))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84a52c5dbdcdf5c288fb98ff443336c9\",\n \"score\": \"0.5260765\",\n \"text\": \"def test_character_from_tibiadata_invalid_json(self):\\n with self.assertRaises(InvalidContent):\\n Character.from_tibiadata(\\\"<html><b>Not a json string</b></html>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3b5ab52aec0434f2a986a51fea297\",\n \"score\": \"0.52593917\",\n \"text\": \"def test_WbTabularData_error_on_non_page(self):\\n regex = r'^Page .+? must be a pywikibot\\\\.Page object not a'\\n with self.assertRaisesRegex(ValueError, regex):\\n pywikibot.WbTabularData('A string', self.get_repo())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5269412829a13c8e2ce20c7037eaae3\",\n \"score\": \"0.5254655\",\n \"text\": \"def copy_t1_schema_data() -> tuple[JSON, BytesInstance]:\\n test_1_schema = {\\n \\\"title\\\": \\\"SOME-RECORD-1\\\",\\n \\\"$anchor\\\": \\\"SOME-RECORD-1\\\",\\n \\\"cobol\\\": \\\"01 SOME-RECORD-1\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"SOME-COLUMN\\\": {\\n \\\"title\\\": \\\"SOME-COLUMN\\\",\\n \\\"$anchor\\\": \\\"SOME-COLUMN\\\",\\n \\\"cobol\\\": \\\"05 SOME-COLUMN PIC X(5)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n assert SchemaValidator.check_schema(test_1_schema) is None\\n test_1_data = BytesInstance('12345'.encode(\\\"CP037\\\"))\\n return test_1_schema, test_1_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92c767374fad04fe043f1882159f28c\",\n \"score\": \"0.5254229\",\n \"text\": \"def contains_valid_json(obj):\\n if obj is None:\\n return False\\n try:\\n try:\\n # json_object = json.loads(obj.read())\\n json.load(obj)\\n except AttributeError:\\n # obj is not a file\\n json.loads(obj)\\n except ValueError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57476200bca642877417c888734d0b1f\",\n \"score\": \"0.52422327\",\n \"text\": \"def test_create_from_json_fails():\\n global invalid_json\\n i = se.instrument.Instrument(invalid_json)\\n assert type(i) != se.instrument.Instrument\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcd39acd6cd65a935697f0b87ca811e\",\n \"score\": \"0.5238401\",\n \"text\": \"def copy_t3_schema_data() -> tuple[JSON, BytesInstance]:\\n test_3_schema = {\\n \\\"title\\\": \\\"SOME-RECORD-3\\\",\\n \\\"$anchor\\\": \\\"SOME-RECORD-3\\\",\\n \\\"cobol\\\": \\\"01 SOME-RECORD-3\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FILLER-1\\\": {\\n \\\"title\\\": \\\"FILLER\\\",\\n \\\"$anchor\\\": \\\"FILLER-1\\\",\\n \\\"cobol\\\": \\\"05 FILLER PIC X(5)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"REPEAT\\\": {\\n \\\"title\\\": \\\"REPEAT\\\",\\n \\\"$anchor\\\": \\\"REPEAT\\\",\\n \\\"cobol\\\": \\\"05 REPEAT OCCURS 5\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"ITEM\\\": {\\n \\\"title\\\": \\\"ITEM\\\",\\n \\\"$anchor\\\": \\\"ITEM\\\",\\n \\\"cobol\\\": \\\"10 ITEM OCCURS 4 PIC XX\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"ITEM\\\": {\\n \\\"title\\\": \\\"ITEM\\\",\\n \\\"$anchor\\\": \\\"ITEM\\\",\\n \\\"cobol\\\": \\\"10 ITEM OCCURS 4 PIC XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n \\\"maxItems\\\": 4\\n }\\n }\\n },\\n \\\"maxItems\\\": 5\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(test_3_schema) is None\\n test_3_data = BytesInstance('12345ABCDEFGHabcdefghIJKLMNOPijklmnopQRSTUVWZ'.encode(\\\"CP037\\\"))\\n return test_3_schema, test_3_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18fac9829092d64cb98f5964cc0d1f5f\",\n \"score\": \"0.52317256\",\n \"text\": \"def test_metacritic_pages_parsed(self, m):\\n set_mock_data(m)\\n data = json.loads(get_json_data())\\n # print('total:', len(data))\\n # print(data)\\n self.assertTrue(type(data) == list)\\n self.assertEqual(len(data), 2037)\\n for entry in data:\\n self.assertTrue('title' in entry)\\n self.assertTrue(type(entry['title']) == str)\\n self.assertTrue('score' in entry)\\n self.assertTrue(type(entry['score']) == str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8e3f656dfb20f00b379a017b61e5ac\",\n \"score\": \"0.52272767\",\n \"text\": \"def test_output_type_data(bamfile,bedfile):\\n\\n transposonmapper(bamfile)\\n\\n chrom_names_dict, chrom_start_line_dict, chrom_end_line_dict = chromosome_name_bedfile(bed_file=bedfile)\\n\\n assert isinstance(chrom_names_dict, dict), \\\"Expected dict type\\\"\\n assert isinstance(chrom_start_line_dict, dict), \\\"Expected dict type\\\"\\n assert isinstance(chrom_end_line_dict, dict), \\\"Expected dict type\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d84942d26ae68b5bd89b614fcd24c49\",\n \"score\": \"0.52089804\",\n \"text\": \"def test_WCAG_PDF_06(self):\\n\\n if len(self.awamHandler.tableStructDict) == 0:\\n self.logger.info('No tables found in Document')\\n # No tables ? test not applicable\\n return 2\\n\\n results = self.initResult()\\n self.logger.debug('No of tables =>',len(self.awamHandler.tableStructDict))\\n\\n # Loop through each and see if it is marked invalid\\n #if any([x.invalid for x in self.awamHandler.tableStructDict.values()]):\\n # # Failed\\n # print 'Invalid table structure found in Document' \\n # return 0\\n for tbl in self.awamHandler.tableStructDict.values():\\n pg = tbl.getPage()\\n if tbl.invalid:\\n self.updateResult(results[0], pg, tbl)\\n else:\\n self.updateResult(results[1], pg, tbl)\\n \\n self.logger.info('wcag.pdf.06 - Test passed')\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f0b0fadda47cc4d98e953292c644aa5\",\n \"score\": \"0.5208406\",\n \"text\": \"def testReadJsonFile(self):\\n try:\\n rObj = self.__ioU.deserialize(self.__pathJsonTestFile, fmt=\\\"json\\\")\\n logger.debug(\\\"Object length %d\\\", len(rObj))\\n self.assertGreaterEqual(len(rObj), 1)\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc6c01ab5319b7fd48be8d110245774\",\n \"score\": \"0.5198041\",\n \"text\": \"def test_from_json_obj_basetypes():\\n for val, t, encoding in BASETYPES_ENCODINGS:\\n assert from_json_obj(encoding, t) == val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa482574c348dad4e816be593de27bd1\",\n \"score\": \"0.51910233\",\n \"text\": \"def test_web_json(self):\\n data = jsonreader.get_web_elements()\\n assert type(data) == list\\n assert len(data) > 0\\n for entry in data:\\n assert type(entry) == dict\\n assert entry.get('phase') is not None\\n assert type(entry.get('phase')) == str\\n assert type(entry.get('route')) == str\\n assert type(entry.get('html')) == list\\n assert type(entry.get('xpath')) == str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb29b33fe7d9b3a7ec27cd18a8802106\",\n \"score\": \"0.51827407\",\n \"text\": \"def test_plant_serializer_with_valida_data(new_plant):\\n result = plant_schema.load(new_plant)\\n assert result[\\\"name\\\"] == \\\"Monstera\\\"\\n assert result[\\\"latin\\\"] == \\\"Monstera Adans.\\\"\\n assert result[\\\"difficulty\\\"] == 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd05aa4fd90fa8da075c40b6f368dc01\",\n \"score\": \"0.5177884\",\n \"text\": \"def _verify_json_content(self):\\n self.assertTrue(all(\\n json_item['auth'] == 'Bearer fake token'\\n for json_item in OnTaskSharedState.json_outbox))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8933534c4ede0291330d9ce38076bd02\",\n \"score\": \"0.51730776\",\n \"text\": \"def test_from_json_obj_typeerrors():\\n class NonTypechekableT:\\n name: str\\n val: int\\n def __init__(self, name, val):\\n self.name = name\\n self.val = val\\n x = NonTypechekableT(\\\"x\\\", 0)\\n try:\\n from_json_obj(x, NonTypechekableT)\\n assert False\\n except TypeError:\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d60fa7fdd21c675d502350a760bca4\",\n \"score\": \"0.516112\",\n \"text\": \"def validate_table(self, model):\\n expected = bloop.tables.expected_description(model)\\n status = \\\"BLOOP_NOT_ACTIVE\\\"\\n while status == \\\"BLOOP_NOT_ACTIVE\\\":\\n description = self.describe_table(model)\\n status = bloop.tables.simple_status(description)\\n try:\\n actual = bloop.tables.sanitized_description(description)\\n except KeyError:\\n raise bloop.exceptions.TableMismatch(model, expected, description)\\n if bloop.util.ordered(actual) != bloop.util.ordered(expected):\\n raise bloop.exceptions.TableMismatch(model, expected, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2e4cfe8e0a91bc5af8f6350a1cae52\",\n \"score\": \"0.5155278\",\n \"text\": \"def test_parse_record_copy(self):\\n options = {\\n 'schema': {\\n 'key': 'string'\\n }\\n }\\n record_data = {\\n 'key': 'value'\\n }\\n\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), True)\\n assert_equal(id(parser.parsed_records[0]) == id(record_data), False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60f67023a0ac6b5729a7a041388c6e5\",\n \"score\": \"0.51532674\",\n \"text\": \"def test_ingest_json_file(self):\\n ingested_json = py_challenge.ingest_json_file('data/split_ac.fastq.data.json')\\n self.assertNotEqual(ingested_json, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3023bcdc70aba8807fee85ca67a229\",\n \"score\": \"0.5147551\",\n \"text\": \"def is_json_serializable(document: dict):\\n assert isinstance(document, dict), \\\"Check only 1 item!\\\"\\n test_json = user_data_dir(\\\"test_vectorai\\\", \\\"test.json\\\")\\n\\n with open(test_json, \\\"w\\\"):\\n json.dump(test_json)\\n\\n with open(test_json) as f:\\n check_json = json.load(f)\\n os.remove(test_json)\\n \\n assert check_json == document, (\\n \\\"This will not upload correctly. Please ensure all items \\\"\\n + \\\"in the dictionary are lists/floats/ints/strings.\\\"\\n )\\n print(\\\"Checked! Feel free to upload!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8741e9d5d25bd6c5caaef26533663986\",\n \"score\": \"0.5140689\",\n \"text\": \"def copy_11_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_11_schema = {\\n \\\"title\\\": \\\"MAIN-AREA\\\",\\n \\\"$anchor\\\": \\\"MAIN-AREA\\\",\\n \\\"cobol\\\": \\\"01 MAIN-AREA\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REC-1\\\": {\\n \\\"title\\\": \\\"REC-1\\\",\\n \\\"$anchor\\\": \\\"REC-1\\\",\\n \\\"cobol\\\": \\\"03 REC-1\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-1\\\": {\\n \\\"title\\\": \\\"FIELD-1\\\",\\n \\\"$anchor\\\": \\\"FIELD-1\\\",\\n \\\"cobol\\\": \\\"05 FIELD-1 PIC 9\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n },\\n \\\"FIELD-3\\\": {\\n \\\"title\\\": \\\"FIELD-3\\\",\\n \\\"$anchor\\\": \\\"FIELD-3\\\",\\n \\\"cobol\\\": \\\"05 FIELD-3 PIC 9\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n },\\n \\\"FIELD-2\\\": {\\n \\\"title\\\": \\\"FIELD-2\\\",\\n \\\"$anchor\\\": \\\"FIELD-2\\\",\\n \\\"cobol\\\": \\\"05 FIELD-2 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-1 PIC X(05)\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-2\\\": {\\n \\\"cobol\\\": \\\"05 FIELD-2 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-1 PIC X(05)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n \\\"maxItemsDependsOn\\\": {\\n \\\"$ref\\\": \\\"#FIELD-1\\\"\\n }\\n }\\n }\\n },\\n \\\"REC-2\\\": {\\n \\\"title\\\": \\\"REC-2\\\",\\n \\\"$anchor\\\": \\\"REC-2\\\",\\n \\\"cobol\\\": \\\"03 REC-2\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-4\\\": {\\n \\\"title\\\": \\\"FIELD-4\\\",\\n \\\"$anchor\\\": \\\"FIELD-4\\\",\\n \\\"cobol\\\": \\\"05 FIELD-4 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-3 PIC X(05)\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-4\\\": {\\n \\\"cobol\\\": \\\"05 FIELD-4 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-3 PIC X(05)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n \\\"maxItemsDependsOn\\\": {\\n \\\"$ref\\\": \\\"#FIELD-3\\\"\\n }\\n }\\n }\\n }\\n }\\n }\\n assert SchemaValidator.check_schema(copy_11_schema) is None\\n\\n copy_11_data = BytesInstance(\\\"321111122222333334444455555\\\".encode(\\\"CP037\\\"))\\n return copy_11_schema, copy_11_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866948a1ded7ca6789fe57e56be47cd1\",\n \"score\": \"0.51384306\",\n \"text\": \"def basic_json_check(datadir, filename, json_data, samehost=False):\\n header = json.loads(str(json_data, 'UTF-8'))\\n if not isinstance(header, dict):\\n raise BoardLibError(\\\"%s contains JSON which does not generate a dictionary.\\\"\\n % (filename))\\n required_keys = {'schema', 'hostname', 'absdatadir', 'sequence_number',\\n 'most_recent', 'most_recent_hash', 'last_process_most_recent',\\n 'last_process_most_recent_hash', 'payload'}\\n for header_key in list(header):\\n if header_key not in required_keys:\\n raise BoardLibError(\\\"%s is an unexpected key in the JSON data from %s.\\\"\\n % (header_key, filename))\\n for required_key in list(required_keys):\\n if not required_key in header:\\n raise BoardLibError(\\\"%s is not in JSON data in file %s\\\"\\n % (required_key, filename))\\n if header['schema'] not in schema_dictionary:\\n raise BoardLibError(\\\"Schema %s from file %s does not appear in the \\\"\\n \\\"schema dictionary.\\\" % (header['schema'], filename))\\n if not isinstance(header['sequence_number'], int):\\n raise BoardLibError(\\\"Filename %s holds a sequence number which is not \\\"\\n \\\"an integer.\\\" % (filename))\\n if header['sequence_number'] < 0:\\n raise BoardLibError(\\\"Sequence_number %d from file %s is negative\\\"\\n % (header['sequence_number'], filename))\\n # <now>.<hostname>.<inode>\\n unique_file_match = re.match('^\\\\d{10}\\\\.(\\\\S+)\\\\.\\\\d+$', filename)\\n if unique_file_match:\\n file_hostname = unique_file_match.group(1)\\n if file_hostname != header['hostname']:\\n raise BoardLibError(\\\"Filename %s does not match hostname in JSON: %s\\\"\\n % (filename, header['hostname']))\\n else:\\n raise BoardLibError(\\\"Filename %s has the wrong structure.\\\" % (filename))\\n if header['most_recent'] == None and header['sequence_number'] != 0:\\n raise BoardLibError(\\\"Filename %s has no most_recent predecessor but \\\"\\n \\\"sequence number is non-zero: %d\\\"\\n % (filename, header['sequence_number']))\\n if header['last_process_most_recent'] and header['most_recent']:\\n raise BoardLibError(\\\"Filename %s has last_process_most_recent (%s) \\\"\\n \\\"and most_recent (%s) defined.\\\"\\n % (filename, header['last_process_most_recent'], header['most_recent']))\\n if samehost:\\n hostname = socket.gethostname()\\n if header['hostname'] != hostname:\\n raise BoardLibError(\\\"Hostname %s from file %s is not the same \\\"\\n \\\"as our host: %s\\\"\\n % (header['hostname'], filename, hostname))\\n if datadir != header['absdatadir']:\\n raise BoardLibError(\\\"Absdatadir %s from file %s is not the same \\\"\\n \\\"as the datadir we were given: %s\\\"\\n % (header['absdatadir'], filename, datadir))\\n schema_check_func = schema_dictionary[header['schema']]\\n if not schema_check_func(header['payload']):\\n raise BoardLibError(\\\"Filename %s failed its schema check.\\\" % (filename))\\n if (header['most_recent'] and not header['most_recent_hash']) or \\\\\\n (not header['most_recent'] and header['most_recent_hash']):\\n raise BoardLibError(\\\"In file %s most_recent and most_recent_hash are \\\"\\n \\\"not both defined.\\\" % (filename))\\n if (header['last_process_most_recent'] and not header['last_process_most_recent_hash']) \\\\\\n or (not header['last_process_most_recent'] and header['last_process_most_recent_hash']):\\n raise BoardLibError(\\\"In file %s last_process_most_recent and \\\"\\n \\\"last_process_most_recent_hash are not both defined.\\\"\\n % (filename))\\n if header['most_recent_hash'] and \\\\\\n not re.match('^[0-9a-f]{64}$', header['most_recent_hash']):\\n raise BoardLibError(\\\"In file %s the most_recent_hash does not look like \\\"\\n \\\"a valid hash: %s\\\" % (filename, header['most_recent_hash']))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66a637d2d313e23ef53b867a036b066\",\n \"score\": \"0.5135247\",\n \"text\": \"def test_read_json(self):\\n # Ensure metamodel fails\\n self.assertRaises(\\n FairException, \\n FairMetaModel.read_json, \\n self._MODEL_JSON\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0013128f44bfb6d9f2a86b78c0b072\",\n \"score\": \"0.51279455\",\n \"text\": \"def test_storm_objects_to_cells(self):\\n\\n this_storm_object_table = storms_to_winds._storm_objects_to_cells(\\n STORM_OBJECT_TABLE_NO_CELL_INFO)\\n self.assertTrue(this_storm_object_table.equals(\\n STORM_OBJECT_TABLE_WITH_CELL_INFO))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad536e8c84b877b75ebaf8cd7dfbf8cc\",\n \"score\": \"0.51265985\",\n \"text\": \"def check_schema(self, data):\\n module = \\\"check_schema\\\"\\n self.mu_log.log(self.mu_log.DEBUG, \\\"expected meta_version is >\\\" + self.metaschema_version + \\\"<\\\", module)\\n self.json_data = data\\n\\n try:\\n self.meta_type = data[\\\"meta\\\"]\\n self.meta_version = data[\\\"meta_version\\\"]\\n self.mu_log.log(self.mu_log.DEBUG\\n , \\\"schema is >\\\" + self.meta_type + \\\"<, version >\\\"\\n + self.meta_version + \\\"<\\\", module)\\n except KeyError as e:\\n self.mu_log.log(self.mu_log.DEBUG,\\n \\\"Key error. meta and meta_version must be in JSON file. That is not the case with \\\"\\n + self.json_file, module)\\n return messages.message[\\\"meta_error\\\"]\\n except jsonschema.exceptions.SchemaError as e:\\n self.mu_log.log(self.mu_log.FATAL, \\\"Schema error: \\\" + e.message, module)\\n return messages.message[\\\"json_schema_error\\\"]\\n except jsonschema.exceptions.ValidationError as e:\\n self.mu_log.log(self.mu_log.FATAL, \\\"Validation error: \\\" + e.message, module)\\n return messages.message[\\\"json_validation_error\\\"]\\n except json.decoder.JSONDecodeError as e:\\n self.mu_log.log(self.mu_log.FATAL, \\\"Error parsing JSON:\\\" + e.msg, module)\\n return messages.message[\\\"json_parse_error\\\"]\\n\\n if self.meta_version == generic_settings.GenericSettings().meta_version:\\n self.mu_log.log(self.mu_log.INFO, \\\"file meta version matches expected schema version\\\", module)\\n schema_directory = generic_settings.GenericSettings().schema_directory\\n self.schema_file = schema_directory + self.meta_type + \\\".json\\\"\\n with open(self.schema_file) as f:\\n schema = json.load(f)\\n try:\\n jsonschema.validate(data, schema)\\n self.mu_log.log(self.mu_log.INFO, \\\"JSON file validated successfully against schema\\\", module)\\n except jsonschema.exceptions.SchemaError as e:\\n self.mu_log.log(self.mu_log.FATAL, \\\"A schema error occurred during validation\\\", module)\\n return messages.message[\\\"jsonschema_validation_error\\\"]\\n except jsonschema.exceptions.ValidationError as e:\\n self.mu_log.log(self.mu_log.ERROR, \\\"A validation error occurred\\\", module)\\n return messages.message[\\\"jsonschema_validation_error\\\"]\\n else:\\n self.mu_log.log(self.mu_log.DEBUG, \\\"File meta version does not match expected schema version\\\", module)\\n return messages.message[\\\"incorrect_meta_version\\\"]\\n\\n return messages.message[\\\"ok\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f29ceec2661014054d4c79ce05701d\",\n \"score\": \"0.51249635\",\n \"text\": \"def copy_9_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_9_schema_1 = {\\n \\\"title\\\": \\\"DETAIL-LINE\\\",\\n \\\"$anchor\\\": \\\"DETAIL-LINE\\\",\\n \\\"cobol\\\": \\\"01 DETAIL-LINE\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"QUESTION\\\": {\\n \\\"title\\\": \\\"QUESTION\\\",\\n \\\"$anchor\\\": \\\"QUESTION\\\",\\n \\\"cobol\\\": \\\"05 QUESTION PIC ZZ\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"PRINT-YES\\\": {\\n \\\"title\\\": \\\"PRINT-YES\\\",\\n \\\"$anchor\\\": \\\"PRINT-YES\\\",\\n \\\"cobol\\\": \\\"05 PRINT-YES PIC ZZ\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"PRINT-NO\\\": {\\n \\\"title\\\": \\\"PRINT-NO\\\",\\n \\\"$anchor\\\": \\\"PRINT-NO\\\",\\n \\\"cobol\\\": \\\"05 PRINT-NO PIC ZZ\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"NOT-SURE\\\": {\\n \\\"title\\\": \\\"NOT-SURE\\\",\\n \\\"$anchor\\\": \\\"NOT-SURE\\\",\\n \\\"cobol\\\": \\\"05 NOT-SURE PIC ZZ\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n\\n copy_9_schema_2 = {\\n \\\"title\\\": \\\"SUMMARY-LINE\\\",\\n \\\"$anchor\\\": \\\"SUMMARY-LINE\\\",\\n \\\"cobol\\\": \\\"01 SUMMARY-LINE REDEFINES DETAIL-LINE\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"COUNT\\\": {\\n \\\"title\\\": \\\"COUNT\\\",\\n \\\"$anchor\\\": \\\"COUNT\\\",\\n \\\"cobol\\\": \\\"05 COUNT PIC ZZ\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"FILLER-1\\\": {\\n \\\"title\\\": \\\"FILLER\\\",\\n \\\"$anchor\\\": \\\"FILLER-1\\\",\\n \\\"cobol\\\": \\\"05 FILLER PIC XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"FILLER-2\\\": {\\n \\\"title\\\": \\\"FILLER\\\",\\n \\\"$anchor\\\": \\\"FILLER-2\\\",\\n \\\"cobol\\\": \\\"05 FILLER PIC XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"FILLER-3\\\": {\\n \\\"title\\\": \\\"FILLER\\\",\\n \\\"$anchor\\\": \\\"FILLER-3\\\",\\n \\\"cobol\\\": \\\"05 FILLER PIC XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(copy_9_schema_1) is None\\n assert SchemaValidator.check_schema(copy_9_schema_2) is None\\n\\n copy_9_data = BytesInstance(\\\"01020304\\\".encode(\\\"CP037\\\"))\\n return [copy_9_schema_1, copy_9_schema_2], copy_9_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee5511d6983bad620cf86e4d156165a7\",\n \"score\": \"0.5094555\",\n \"text\": \"def test_AttType(self):\\n bModel_dict = self.bModel.to_dict()\\n self.assertEqual(self.bModel.__class__.__name__, \\\"BaseModel\\\")\\n self.assertIsInstance(bModel_dict['created_at'], str)\\n self.assertIsInstance(bModel_dict['updated_at'], str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b138a83698378d9b10a30abfae4640f0\",\n \"score\": \"0.5094188\",\n \"text\": \"def test_get_json(json_bytes_data: TypingJsonBytesData):\\n assert json_bytes_data[\\\"json_bytes\\\"].get_json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb973c24d0d105f3173e581edef7481c\",\n \"score\": \"0.50940955\",\n \"text\": \"def test_schema_maker_5(capsys):\\n schema_json_5 = {\\n \\\"title\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"$anchor\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"cobol\\\": \\\"01 REDEFINES-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REDEFINES-A\\\": {\\n \\\"oneOf\\\": [\\n {\\n \\\"title\\\": \\\"A\\\",\\n \\\"$anchor\\\": \\\"REDEFINES-A.A\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"A\\\": {\\n \\\"title\\\": \\\"A\\\",\\n \\\"$anchor\\\": \\\"A\\\",\\n \\\"cobol\\\": \\\"05 A PICTURE X(6)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n {\\n \\\"title\\\": \\\"B\\\",\\n \\\"$anchor\\\": \\\"REDEFINES-A.B\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"B\\\": {\\n \\\"title\\\": \\\"B\\\",\\n \\\"$anchor\\\": \\\"B\\\",\\n \\\"cobol\\\": \\\"05 B REDEFINES A\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"B-1\\\": {\\n \\\"title\\\": \\\"B-1\\\",\\n \\\"$anchor\\\": \\\"B-1\\\",\\n \\\"cobol\\\": \\\"10 B-1 PICTURE X(2)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"B-2\\\": {\\n \\\"title\\\": \\\"B-2\\\",\\n \\\"$anchor\\\": \\\"B-2\\\",\\n \\\"cobol\\\": \\\"10 B-2 PICTURE 9(4)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n }\\n }\\n ],\\n \\\"$anchor\\\": \\\"REDEFINES-A\\\"\\n },\\n \\\"A\\\": {\\n \\\"title\\\": \\\"A\\\",\\n \\\"$anchor\\\": \\\"A\\\",\\n \\\"cobol\\\": \\\"05 A PICTURE X(6)\\\",\\n \\\"$ref\\\": \\\"#REDEFINES-A.A\\\"\\n },\\n \\\"B\\\": {\\n \\\"title\\\": \\\"B\\\",\\n \\\"$anchor\\\": \\\"B\\\",\\n \\\"cobol\\\": \\\"05 B REDEFINES A\\\",\\n \\\"$ref\\\": \\\"#REDEFINES-A.B\\\"\\n },\\n \\\"C\\\": {\\n \\\"title\\\": \\\"C\\\",\\n \\\"$anchor\\\": \\\"C\\\",\\n \\\"cobol\\\": \\\"05 C PICTURE 99V99\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n }\\n }\\n }\\n SchemaValidator.check_schema(schema_json_5)\\n s = SchemaMaker.from_json(schema_json_5)\\n s.print()\\n out, err = capsys.readouterr()\\n assert out.splitlines() == [\\n \\\"ObjectSchema({'title': 'REDEFINES-RECORD', '$anchor': 'REDEFINES-RECORD', 'cobol': '01 REDEFINES-RECORD', 'type': 'object'})\\\",\\n \\\" OneOfSchema({'$anchor': 'REDEFINES-A'})\\\",\\n \\\" ObjectSchema({'title': 'A', '$anchor': 'REDEFINES-A.A', 'type': 'object'})\\\",\\n \\\" AtomicSchema({'title': 'A', '$anchor': 'A', 'cobol': '05 A PICTURE X(6)', 'type': 'string', 'contentEncoding': 'cp037'})\\\",\\n \\\" ObjectSchema({'title': 'B', '$anchor': 'REDEFINES-A.B', 'type': 'object'})\\\",\\n \\\" ObjectSchema({'title': 'B', '$anchor': 'B', 'cobol': '05 B REDEFINES A', 'type': 'object'})\\\",\\n \\\" AtomicSchema({'title': 'B-1', '$anchor': 'B-1', 'cobol': '10 B-1 PICTURE X(2)', 'type': 'string', 'contentEncoding': 'cp037'})\\\",\\n \\\" AtomicSchema({'title': 'B-2', '$anchor': 'B-2', 'cobol': '10 B-2 PICTURE 9(4)', 'type': 'string', 'contentEncoding': 'cp037'})\\\",\\n \\\" RefToSchema({'title': 'A', '$anchor': 'A', 'cobol': '05 A PICTURE X(6)', '$ref': '#REDEFINES-A.A'})\\\",\\n \\\" RefToSchema({'title': 'B', '$anchor': 'B', 'cobol': '05 B REDEFINES A', '$ref': '#REDEFINES-A.B'})\\\",\\n \\\" AtomicSchema({'title': 'C', '$anchor': 'C', 'cobol': '05 C PICTURE 99V99', 'type': 'string', 'contentEncoding': 'cp037', 'conversion': 'decimal'})\\\",\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f422cc8f50a8bfc2d85be5cc54ecd66\",\n \"score\": \"0.50846183\",\n \"text\": \"def test_good_load(self):\\n simple_db_path = Path(__file__).parent / \\\"test_dbs\\\" / \\\"simple_db.json\\\"\\n try:\\n db = type(self).load_db(simple_db_path)\\n except jsonschema.exceptions.ValidationError:\\n pytest.fail(\\\"Unexpected exception jsonschema.exceptions.ValidationError\\\")\\n\\n assert config.NAME_KIT_CAT_NVGS in db\\n assert \\\"CUP_NVG_PVS14\\\" in db[config.NAME_KIT_CAT_NVGS]\\n assert \\\"description\\\" in db[config.NAME_KIT_CAT_NVGS][\\\"CUP_NVG_PVS14\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa13f8d8711daa8c79e7ba454825de7\",\n \"score\": \"0.5083122\",\n \"text\": \"def test_unparseable(self) -> None:\\n self.assertThat(\\n matches_json(Always()).match(\\\"not json\\\"),\\n Not(Is(None)),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151b0b1dafc42f4eb0dfb75e45ced4ef\",\n \"score\": \"0.5079233\",\n \"text\": \"def test_instance_resize_prep_start_valid_json(self):\\n expected_obj = self.resize_prep_start_obj\\n\\n actual_json = json.dumps(self.base_resize_prep_dict)\\n actual_obj = InstanceResizePrepStart.deserialize(actual_json, 'json')\\n\\n self.assertEqual(expected_obj, actual_obj)\\n self.assertFalse(actual_obj.is_empty())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a09d724cfab442fe5750fcf68c02919\",\n \"score\": \"0.5068002\",\n \"text\": \"def test_desc_serializer_with_valida_data(new_desc):\\n result = desc_schema.load(new_desc)\\n assert result[\\\"content\\\"] == \\\"Description content\\\"\\n assert result[\\\"source\\\"] == \\\"wikipedia\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8f6b0829f4cc09a8fa7c2c98e271fe\",\n \"score\": \"0.5064395\",\n \"text\": \"def test_structures_endpoint_data(self):\\n # mpf_23 contains no relationships, which shouldn't break anything\\n assert \\\"data\\\" in self.json_response\\n assert len(self.json_response[\\\"data\\\"]) == 2\\n assert \\\"included\\\" in self.json_response\\n assert len(self.json_response[\\\"included\\\"]) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a000a3b8985acc8f4324cd7d6b8cc4ee\",\n \"score\": \"0.5061462\",\n \"text\": \"def validate(self, json_data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"341d2535a5d05f8bfb55457ceae1064d\",\n \"score\": \"0.50600654\",\n \"text\": \"def _verify(self):\\r\\n if not self._verified:\\r\\n raise AssertionError(\\r\\n 'jsonpickle requires at least one of the following:\\\\n'\\r\\n ' cjson, json (new in python2.6), simplejson, demjson'\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a5743355063c75f99bfe03be69236b\",\n \"score\": \"0.5053852\",\n \"text\": \"def test_unserializable(self):\\n try:\\n _json_handler(1)\\n success = False\\n except TypeError:\\n # An int can not be serialized, so, this error is the\\n # success.\\n success = True\\n self.assertTrue(success)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f99c34d01f028776265de88907d477\",\n \"score\": \"0.505098\",\n \"text\": \"def copy_6_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_6_schema = {\\n \\\"title\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"$anchor\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"cobol\\\": \\\"01 REDEFINES-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REDEFINES-NAME-2\\\": {\\n \\\"oneOf\\\": [\\n {\\n \\\"title\\\": \\\"NAME-2\\\",\\n \\\"$anchor\\\": \\\"NAME-2\\\",\\n \\\"cobol\\\": \\\"05 NAME-2\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"SALARY\\\": {\\n \\\"title\\\": \\\"SALARY\\\",\\n \\\"$anchor\\\": \\\"SALARY\\\",\\n \\\"cobol\\\": \\\"10 SALARY PICTURE XXX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"SO-SEC-NO\\\": {\\n \\\"title\\\": \\\"SO-SEC-NO\\\",\\n \\\"$anchor\\\": \\\"SO-SEC-NO\\\",\\n \\\"cobol\\\": \\\"10 SO-SEC-NO PICTURE X(9)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"MONTH\\\": {\\n \\\"title\\\": \\\"MONTH\\\",\\n \\\"$anchor\\\": \\\"MONTH\\\",\\n \\\"cobol\\\": \\\"10 MONTH PICTURE XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n },\\n \\\"anchor\\\": \\\"REDEFINES-NAME-2.NAME-2\\\"\\n },\\n {\\n \\\"title\\\": \\\"NAME-1\\\",\\n \\\"$anchor\\\": \\\"NAME-1\\\",\\n \\\"cobol\\\": \\\"05 NAME-1 REDEFINES NAME-2\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"WAGE\\\": {\\n \\\"title\\\": \\\"WAGE\\\",\\n \\\"$anchor\\\": \\\"WAGE\\\",\\n \\\"cobol\\\": \\\"10 WAGE PICTURE 999V999\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n },\\n \\\"EMP-NO\\\": {\\n \\\"title\\\": \\\"EMP-NO\\\",\\n \\\"$anchor\\\": \\\"EMP-NO\\\",\\n \\\"cobol\\\": \\\"10 EMP-NO PICTURE X(6)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"YEAR\\\": {\\n \\\"title\\\": \\\"YEAR\\\",\\n \\\"$anchor\\\": \\\"YEAR\\\",\\n \\\"cobol\\\": \\\"10 YEAR PICTURE XX\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n },\\n \\\"anchor\\\": \\\"REDEFINES-NAME-2.NAME-1\\\"\\n }\\n ],\\n \\\"$anchor\\\": \\\"REDEFINES-NAME-2\\\"\\n },\\n \\\"NAME-2\\\": {\\n \\\"title\\\": \\\"NAME-2\\\",\\n \\\"cobol\\\": \\\"05 NAME-2\\\",\\n \\\"$ref\\\": \\\"#NAME-2\\\"\\n },\\n \\\"NAME-1\\\": {\\n \\\"title\\\": \\\"NAME-1\\\",\\n \\\"cobol\\\": \\\"05 NAME-1 REDEFINES NAME-2\\\",\\n \\\"$ref\\\": \\\"#NAME-1\\\"\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(copy_6_schema) is None\\n\\n copy_6_data = BytesInstance(\\\"ABC123456789DE\\\".encode(\\\"CP037\\\"))\\n return copy_6_schema, copy_6_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3d63b33a98d4e1331386600b1392b6\",\n \"score\": \"0.5046336\",\n \"text\": \"def assertJSONEqual(self, a, b):\\n self.assertDictEqual(json.loads(a),json.loads(b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93760539e3f788f4b1ea1503adeb92e\",\n \"score\": \"0.5043583\",\n \"text\": \"def copy_13_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_13_schema_1 = {\\n \\\"title\\\": \\\"GENERIC-RECORD\\\",\\n \\\"$anchor\\\": \\\"GENERIC-RECORD\\\",\\n \\\"cobol\\\": \\\"01 GENERIC-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"HEADER\\\": {\\n \\\"title\\\": \\\"HEADER\\\",\\n \\\"$anchor\\\": \\\"HEADER\\\",\\n \\\"cobol\\\": \\\"05 HEADER PIC X(3)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"GENERIC-FIELD\\\": {\\n \\\"title\\\": \\\"GENERIC-FIELD\\\",\\n \\\"$anchor\\\": \\\"GENERIC-FIELD\\\",\\n \\\"cobol\\\": \\\"05 GENERIC-FIELD PIC X(17)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n\\n copy_13_schema_2 = {\\n \\\"title\\\": \\\"ABC-SPECIFIC-RECORD\\\",\\n \\\"$anchor\\\": \\\"ABC-SPECIFIC-RECORD\\\",\\n \\\"cobol\\\": \\\"01 ABC-SPECIFIC-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"ITEM-1\\\": {\\n \\\"title\\\": \\\"ITEM-1\\\",\\n \\\"$anchor\\\": \\\"ITEM-1\\\",\\n \\\"cobol\\\": \\\"05 ITEM-1 PIC X(10)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"ITEM-2\\\": {\\n \\\"title\\\": \\\"ITEM-2\\\",\\n \\\"$anchor\\\": \\\"ITEM-2\\\",\\n \\\"cobol\\\": \\\"05 ITEM-2 PIC X(7)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n\\n copy_13_schema_3 = {\\n \\\"title\\\": \\\"DEF-ANOTHER-RECORD\\\",\\n \\\"$anchor\\\": \\\"DEF-ANOTHER-RECORD\\\",\\n \\\"cobol\\\": \\\"01 DEF-ANOTHER-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"ITEM-3\\\": {\\n \\\"title\\\": \\\"ITEM-3\\\",\\n \\\"$anchor\\\": \\\"ITEM-3\\\",\\n \\\"cobol\\\": \\\"05 ITEM-3 PIC X(7)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"ITEM-4\\\": {\\n \\\"title\\\": \\\"ITEM-4\\\",\\n \\\"$anchor\\\": \\\"ITEM-4\\\",\\n \\\"cobol\\\": \\\"05 ITEM-4 PIC X(10)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(copy_13_schema_1) is None\\n assert SchemaValidator.check_schema(copy_13_schema_2) is None\\n assert SchemaValidator.check_schema(copy_13_schema_3) is None\\n\\n copy_13_data_1 = BytesInstance(\\\"ABC0123456789TUVWXYZDEFG\\\".encode(\\\"CP037\\\"))\\n copy_13_data_2 = BytesInstance(\\\"DEF0123456QRSTUVWXYZ\\\".encode(\\\"CP037\\\"))\\n return [copy_13_schema_1, copy_13_schema_2, copy_13_schema_3], [copy_13_data_1, copy_13_data_2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e3d24bef4244246cae522215fdf97a\",\n \"score\": \"0.50357985\",\n \"text\": \"def validate_against_schema(self):\\n try:\\n jsonschema.validate(self.json_data, self.schema)\\n return True\\n except jsonschema.ValidationError as err:\\n self.error_log = \\\"JSON does not comply with schema: %s\\\" % err\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39789fcb3866e1b2f7771bff1466b7ee\",\n \"score\": \"0.50239426\",\n \"text\": \"def is_valid_json(self):\\n content = (\\n self.body\\n if self.patches is None\\n else \\\"\\\".join(str(patch) for patch in self.patches)\\n )\\n try:\\n json.loads(content)\\n return True\\n except ValueError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e000357c7668d2b9e13743d19053214\",\n \"score\": \"0.50196415\",\n \"text\": \"def validate_json(file):\\n max_file_size = current_app.config.get(\\n 'PREVIEWER_MAX_FILE_SIZE_BYTES', 1 * 1024 * 1024)\\n if file.size > max_file_size:\\n return False\\n\\n with file.open() as fp:\\n try:\\n json.loads(fp.read().decode('utf-8'))\\n return True\\n except Exception:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"165215b6020a256eb3d14bc76eca4ef7\",\n \"score\": \"0.50186133\",\n \"text\": \"def test_biom_to_classic2(self):\\r\\n self.cmd(table=self.biom_table1,\\r\\n output_filepath=self.output_filepath, to_tsv=True,\\r\\n header_key='taxonomy', output_metadata_id='foo')\\r\\n obs = load_table(self.output_filepath)\\r\\n self.assertTrue('foo' in obs.metadata(axis='observation')[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa21080d60ce1dacd27e9ee02ca7c85a\",\n \"score\": \"0.50157845\",\n \"text\": \"def test_WbTabularData_WbRepresentation_methods(self):\\n q = pywikibot.WbTabularData(self.page)\\n self._test_hashable(q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594585df14cf4d7eeb4c793a49135183\",\n \"score\": \"0.500506\",\n \"text\": \"def copy_5_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_5_schema = {\\n \\\"title\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"$anchor\\\": \\\"REDEFINES-RECORD\\\",\\n \\\"cobol\\\": \\\"01 REDEFINES-RECORD\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REDEFINES-A\\\": {\\n \\\"oneOf\\\": [\\n {\\n \\\"title\\\": \\\"A\\\",\\n \\\"$anchor\\\": \\\"A\\\",\\n \\\"cobol\\\": \\\"05 A PICTURE X(6)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n {\\n \\\"title\\\": \\\"B\\\",\\n \\\"$anchor\\\": \\\"B\\\",\\n \\\"cobol\\\": \\\"05 B REDEFINES A\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"B-1\\\": {\\n \\\"title\\\": \\\"B-1\\\",\\n \\\"$anchor\\\": \\\"B-1\\\",\\n \\\"cobol\\\": \\\"10 B-1 PICTURE X(2)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n },\\n \\\"B-2\\\": {\\n \\\"title\\\": \\\"B-2\\\",\\n \\\"$anchor\\\": \\\"B-2\\\",\\n \\\"cobol\\\": \\\"10 B-2 PICTURE 9(4)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n }\\n ],\\n \\\"$anchor\\\": \\\"REDEFINES-A\\\"\\n },\\n \\\"A\\\": {\\n \\\"title\\\": \\\"A\\\",\\n \\\"cobol\\\": \\\"05 A PICTURE X(6)\\\",\\n \\\"$ref\\\": \\\"#A\\\"\\n },\\n \\\"B\\\": {\\n \\\"title\\\": \\\"B\\\",\\n \\\"cobol\\\": \\\"05 B REDEFINES A\\\",\\n \\\"$ref\\\": \\\"#B\\\"\\n },\\n \\\"C\\\": {\\n \\\"title\\\": \\\"C\\\",\\n \\\"$anchor\\\": \\\"C\\\",\\n \\\"cobol\\\": \\\"05 C PICTURE 99V99\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(copy_5_schema) is None\\n\\n copy_5_data = BytesInstance(\\\"AB12345678\\\".encode(\\\"CP037\\\"))\\n return copy_5_schema, copy_5_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e0cf27b6eed67aeccd8e593a907f45\",\n \"score\": \"0.5003732\",\n \"text\": \"def assertJsonSuccess(data):\\n obj = json.loads(data)\\n if 'status' in obj and obj['status'] == \\\"error\\\":\\n print(\\\"Error: JSON object returns an error. \\\" + str(obj))\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5cb9ff24ab9edf030dd1075b5274913\",\n \"score\": \"0.49955502\",\n \"text\": \"def test_deserialise_python_builtins(self):\\n data = dict(a=123, b=\\\"text\\\")\\n jdata = json.dumps(data)\\n got = deserialise_object(jdata)\\n self.assertEqual(got, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080671c8645dd31b3cec0ce799180193\",\n \"score\": \"0.4991183\",\n \"text\": \"def assert_serialization_works(self, snapshot):\\n roundtrip = replication.proto_to_auth_db_snapshot(\\n replication.auth_db_snapshot_to_proto(snapshot))\\n self.assertEqual(snapshot_to_dict(snapshot), snapshot_to_dict(roundtrip))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8f8813928bae6aaa874237a3dcf84e\",\n \"score\": \"0.49896422\",\n \"text\": \"def test_export_game(self):\\n connect_str = \\\"sqlite:///:memory:\\\"\\n engine = create_engine(connect_str)\\n self.load_fixture(engine, \\\"tv-themes-v4.sql\\\")\\n DatabaseConnection.bind(self.options.database, create_tables=False,\\n engine=engine, debug=False)\\n with models.db.session_scope() as dbs:\\n for song in models.Song.all(dbs):\\n song.check_for_uuid()\\n result = models.export_game_to_object(\\\"20-04-24-2\\\", dbs)\\n result = utils.flatten(result)\\n validate_json(JsonSchema.GAME_TRACKS_V4, result)\\n if self.EXPECTED_OUTPUT is not None:\\n destination = self.EXPECTED_OUTPUT / 'exported-game-v4.json'\\n with open(destination, 'wt', encoding='utf-8') as dst:\\n json.dump(result, dst, indent=2, sort_keys=True)\\n json_filename = fixture_filename(\\\"exported-game-v4.json\\\")\\n with json_filename.open('rt', encoding='utf-8') as src:\\n expected = json.load(src)\\n for table in expected.keys():\\n # print(f'Check {table}')\\n self.assertModelListEqual(result[table], expected[table], table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b53185849857f551bf98d7b8e56647f\",\n \"score\": \"0.49890664\",\n \"text\": \"def test_basic_json(self):\\n options = {\\n 'schema': {\\n 'name': 'string',\\n 'age': 'integer'\\n }\\n }\\n\\n data = {\\n 'name': 'john',\\n 'age': 30\\n }\\n\\n record_data = json.dumps(data)\\n\\n # get parsed data\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), True)\\n\\n expected_result = [\\n data\\n ]\\n\\n assert_equal(parser.parsed_records, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a409a14caa243e9599662c2b99aa89\",\n \"score\": \"0.49819562\",\n \"text\": \"def test_WbTabularData_error_on_non_exitant_page(self):\\n page = Page(self.commons, 'Non-existant page... really')\\n regex = r'^Page \\\\[\\\\[.+?\\\\]\\\\] must exist\\\\.$'\\n with self.assertRaisesRegex(ValueError, regex):\\n pywikibot.WbTabularData(page, self.get_repo())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9525818e6b1615a0c3de9ddf67e11a45\",\n \"score\": \"0.49761963\",\n \"text\": \"def CheckRequiredFormat(self, json_dict):\\n operation_name = json_dict.get('operationName') or None\\n properties = json_dict.get('properties') or None\\n timestamp = json_dict.get('timeStamp') or None\\n\\n if (None in (operation_name, properties, timestamp) or\\n operation_name != 'ApplicationGatewayAccess'):\\n return False\\n\\n try:\\n date_time = dfdatetime_time_elements.TimeElementsInMicroseconds()\\n date_time.CopyFromStringISO8601(timestamp)\\n except ValueError:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bedd89eed7f664c31911a40e43b624\",\n \"score\": \"0.4964713\",\n \"text\": \"def test_json_teams_format(self):\\n miner.copy_miner_file()\\n json_team = miner.get_json_teams(1)[0]\\n self.assertEqual(len(json_team), 6)\\n pokemon_dict = json_team[0]\\n self.assertEqual(set(['ability', 'evs', 'item', 'ivs', 'level', 'moves', 'name', 'shiny',\\n 'species']),\\n set(pokemon_dict.keys()))\\n self.assertTrue(set(['atk', 'def', 'hp', 'spa', 'spd', 'spe']) ==\\n set(pokemon_dict['evs'].keys()) ==\\n set(pokemon_dict['ivs'].keys()))\\n\\n self.assertEqual(len(pokemon_dict['moves']), 1 if pokemon_dict['name'] == 'Ditto' else 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4cb6df699dc193a385e91f94e4945c\",\n \"score\": \"0.4964026\",\n \"text\": \"def test_json(self):\\n processor = ProcessToRenderInfo(\\n HtmlDocumentManager('test_json'),\\n ProcessedEntityManager())\\n processor.max_uncollapsable_json_lines = 20\\n processor.max_uncollapsable_entity_rows = 20\\n\\n # Numeric literals wont be treated as json.\\n for n in [-1, 0, 1, 3.14]:\\n info = processor.process_json_html_if_possible(n)\\n self.assertEquals('{0}'.format(n), info.detail_html)\\n self.assertEquals(None, info.summary_html)\\n\\n # None of these strings are well-defined JSON documents\\n # so should just be strings.\\n for s in ['test', 'a phrase', 'True']:\\n info = processor.process_json_html_if_possible(s)\\n self.assertEquals('\\\"{0}\\\"'.format(s), info.detail_html)\\n self.assertEquals(None, info.summary_html)\\n\\n # Boolean values wont be considered JSON.\\n for b in [True, False]:\\n info = processor.process_json_html_if_possible(b)\\n self.assertEquals('{0}'.format(str(b)), info.detail_html)\\n self.assertEquals(None, info.summary_html)\\n\\n # Dictionaries and JSON dictionary strings normalize to JSON.\\n for d in [{'A': 'a', 'B': True}, '{\\\"A\\\":\\\"a\\\", \\\"B\\\":true}']:\\n info = processor.process_json_html_if_possible(d)\\n self.assertEquals('<pre>{{\\\\n \\\"A\\\": \\\"a\\\",\\\\n'\\n ' \\\"B\\\": true\\\\n'\\n '}}</pre>'.format(), info.detail_html)\\n self.assertEquals(None, info.summary_html)\\n self.assertEquals(None, info.summary_html)\\n\\n # Lists and JSON lists strings normalize to JSON.\\n for l in [[123, 'abc', True, {'A': 'a', 'B': 'b'}],\\n '[123, \\\"abc\\\", true, {\\\"A\\\":\\\"a\\\", \\\"B\\\":\\\"b\\\"}]']:\\n info = processor.process_json_html_if_possible(l)\\n self.assertEquals('<pre>[\\\\n 123,\\\\n \\\"abc\\\",\\\\n true,\\\\n'\\n ' {{\\\\n'\\n ' \\\"A\\\": \\\"a\\\",\\\\n'\\n ' \\\"B\\\": \\\"b\\\"\\\\n'\\n ' }}\\\\n'\\n ']</pre>'.format(), info.detail_html)\\n self.assertEquals(None, info.summary_html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fafd82345d9bbcd2a55dd4ea7b3b48b\",\n \"score\": \"0.49624068\",\n \"text\": \"def test___bytes__(json_bytes_data: TypingJsonBytesData):\\n assert bytes(json_bytes_data[\\\"json_bytes\\\"]) == json_bytes_data[\\\"bytes\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e31b0227bb6a91a545725159ce2031b\",\n \"score\": \"0.49622917\",\n \"text\": \"def test_Place_is_dict(self):\\n\\n st = FileStorage()\\n st_dict = st.all()\\n self.assertEqual(dict, type(st_dict))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9957b5a603c6abe649505980c3dd938f\",\n \"score\": \"0.4946569\",\n \"text\": \"def test_dump_all_formats(format_key, expected):\\n data = [\\n {\\n \\\"color\\\":\\\"#ffffff\\\", \\\"name\\\": \\\"white\\\"\\n },\\n {\\n \\\"color\\\":\\\"#000000\\\", \\\"name\\\": \\\"black\\\"\\n },\\n ]\\n output = build_dump(format_key, data)\\n\\n assert output[\\\"content\\\"] == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0348ec7650d86fb15112724c1624867\",\n \"score\": \"0.4946224\",\n \"text\": \"def testReadWriteJsonFile(self):\\n try:\\n rObj = self.__ioU.deserialize(self.__pathJsonTestFile, fmt=\\\"json\\\")\\n logger.debug(\\\"Object length %d\\\", len(rObj))\\n self.assertGreaterEqual(len(rObj), 1)\\n ok = self.__ioU.serialize(self.__pathSaveJsonTestFile, rObj, fmt=\\\"json\\\")\\n self.assertTrue(ok)\\n\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304497723bd10badc7eddfed67864cce\",\n \"score\": \"0.4939063\",\n \"text\": \"def testfromjson(self):\\n dicty = {\\\"id\\\": 5, \\\"class\\\": \\\"string\\\", \\\"list\\\": [], \\\"set\\\": {}}\\n self.assertEqual([dicty], Base.from_json_string(json.dumps([dicty])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30994a523aefdc93cd4d6c73be16cb2\",\n \"score\": \"0.49368024\",\n \"text\": \"def export_test(self, schema_version):\\n connect_str = \\\"sqlite:///:memory:\\\"\\n engine = create_engine(connect_str) # , echo=True)\\n self.load_fixture(engine, f\\\"tv-themes-v{schema_version}.sql\\\")\\n\\n DatabaseConnection.bind(self.options.database, create_tables=False,\\n engine=engine, debug=False)\\n output = io.StringIO()\\n # pylint: disable=no-value-for-parameter\\n with models.db.session_scope() as dbs:\\n models.export_database_to_file(output, self.options, Progress(), dbs, None)\\n output.seek(0)\\n actual_json = json.load(output)\\n if self.EXPECTED_OUTPUT is not None:\\n destination = self.EXPECTED_OUTPUT / f\\\"exported-tv-themes-v{schema_version}.json\\\"\\n with open(destination, 'wt', encoding='utf-8') as dbg:\\n dbg.write(output.getvalue())\\n validate_json(JsonSchema.DATABASE, actual_json)\\n json_filename = fixture_filename(f\\\"exported-tv-themes-v{schema_version}.json\\\")\\n with json_filename.open('rt', encoding='utf-8') as src:\\n expected_json = json.load(src)\\n self.assertEqual(expected_json['Users'][0]['username'], 'admin')\\n expected_json['Users'][0]['last_login'] = None\\n # self.maxDiff = None\\n for table in expected_json.keys():\\n # print(f'Check {table}')\\n if isinstance(actual_json[table], dict):\\n self.assertDictEqual(actual_json[table], expected_json[table], table)\\n else:\\n self.assertModelListEqual(actual_json[table], expected_json[table], table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"353d25f2abc322e940c9e521e926ef78\",\n \"score\": \"0.4936759\",\n \"text\": \"def testDumpData(self):\\r\\n self.assertCommandSucceeds(\\\"dumpdata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d898c45ae770a76f03a979835ca91e7\",\n \"score\": \"0.49342582\",\n \"text\": \"def checkSchema(self):\\n\\n\\t\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32acb33bcb6cf97eeb846abc3a97704\",\n \"score\": \"0.49338663\",\n \"text\": \"def testfromjson(self):\\n dicty = {\\\"id\\\": 5, \\\"class\\\": \\\"string\\\", \\\"list\\\": [], \\\"set\\\": {}}\\n self.assertEqual([dicty, dicty],\\n Base.from_json_string(json.dumps([dicty, dicty])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b723a3847dac70b847b465041ec67c0\",\n \"score\": \"0.4933373\",\n \"text\": \"def is_tsv(to_test):\\n if isinstance(to_test, bytes):\\n as_str = to_test.decode('utf-8')\\n as_list = as_str.splitlines()\\n else:\\n as_str = str(to_test)\\n as_list = as_str.split('\\\\n')\\n if len(as_list) > 1:\\n if len(as_str.split('\\\\t')) > 1:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab063f47e00dc7e2801dd8db146b7f4\",\n \"score\": \"0.49322158\",\n \"text\": \"def copy_10_schema_data() -> tuple[JSON, BytesInstance]:\\n copy_10_schema = {\\n \\\"title\\\": \\\"MAIN-AREA\\\",\\n \\\"$anchor\\\": \\\"MAIN-AREA\\\",\\n \\\"cobol\\\": \\\"01 MAIN-AREA\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"REC-1\\\": {\\n \\\"title\\\": \\\"REC-1\\\",\\n \\\"$anchor\\\": \\\"REC-1\\\",\\n \\\"cobol\\\": \\\"03 REC-1\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-1\\\": {\\n \\\"title\\\": \\\"FIELD-1\\\",\\n \\\"$anchor\\\": \\\"FIELD-1\\\",\\n \\\"cobol\\\": \\\"05 FIELD-1 PIC 9\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\",\\n \\\"conversion\\\": \\\"decimal\\\"\\n },\\n \\\"FIELD-2\\\": {\\n \\\"title\\\": \\\"FIELD-2\\\",\\n \\\"$anchor\\\": \\\"FIELD-2\\\",\\n \\\"cobol\\\": \\\"05 FIELD-2 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-1 PIC X(05)\\\",\\n \\\"type\\\": \\\"array\\\",\\n \\\"items\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"FIELD-2\\\": {\\n \\\"cobol\\\": \\\"05 FIELD-2 OCCURS 1 TO 5 TIMES\\\\n DEPENDING ON FIELD-1 PIC X(05)\\\",\\n \\\"type\\\": \\\"string\\\",\\n \\\"contentEncoding\\\": \\\"cp037\\\"\\n }\\n }\\n },\\n \\\"maxItemsDependsOn\\\": {\\n \\\"$ref\\\": \\\"#FIELD-1\\\"\\n }\\n }\\n }\\n }\\n }\\n }\\n\\n assert SchemaValidator.check_schema(copy_10_schema) is None\\n\\n copy_10_data = BytesInstance(\\\"3111112222233333\\\".encode(\\\"CP037\\\"))\\n return copy_10_schema, copy_10_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e000f97f33621b58909e0dfb19bd6281\",\n \"score\": \"0.4932113\",\n \"text\": \"def test_dont_mix_schema_and_object_signature(self):\\n url = \\\"pg://localhost:5432/tuttle_test_db/test_schema\\\"\\n res = PostgreSQLResource(url)\\n assert not res.exists(), \\\"{} should not exist because no table, view nor any object with that name \\\" \\\\\\n \\\"exists\\\".format(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d838408b6780ed3bead70cd3212caf8\",\n \"score\": \"0.4931609\",\n \"text\": \"def test_table_fits_io_astropy(table):\\n # Check Table -> BinTableHDU\\n hdu = fits.BinTableHDU(table)\\n assert hdu.header[\\\"TTYPE2\\\"] == \\\"b\\\"\\n assert hdu.header[\\\"TFORM2\\\"] == \\\"K\\\"\\n assert hdu.header[\\\"TUNIT2\\\"] == \\\"m\\\"\\n\\n # Check BinTableHDU -> Table\\n table2 = Table.read(hdu)\\n assert isinstance(table2.meta, dict)\\n assert table2.meta == {\\\"VERSION\\\": 42}\\n assert table2[\\\"b\\\"].unit == \\\"m\\\"\\n # Note: description doesn't come back in older versions of Astropy\\n # that we still support, so we're not asserting on that here for now.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e317c705efb14fd26dca1d8394a5e2db\",\n \"score\": \"0.49231386\",\n \"text\": \"def test_metadata(list_json_output):\\n\\n listing = list_json_output('valid-json', metadata=True)\\n for c in listing:\\n if 'meta' in c:\\n assert c['meta'] == True\\n assert c['title'] == 'NPC Listing'\\n assert 'created' in c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6144d395c5711ac22a4c29dcd2685984\",\n \"score\": \"0.4922923\",\n \"text\": \"def assert_valid_json(data: str, schema_id: str) -> None: # noqa: E501 (line too long)\\n assert_valid(json.loads(data), schema_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5608356d400fe8445df78527c6d36ea7\",\n \"score\": \"0.4917935\",\n \"text\": \"def test_invalid_json_path(self):\\n options = {\\n 'schema': {\\n 'name': 'string',\\n 'result': 'string'\\n },\\n 'configuration': {\\n 'json_path': 'Records[*]'\\n }\\n }\\n\\n record_data = {'name': 'test', 'result': 'test'}\\n\\n # get parsed data\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), False)\\n\\n expected_result = [\\n record_data\\n ]\\n assert_equal(parser.invalid_parses, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c69f818cb6b1e5c3514060050b6d385\",\n \"score\": \"0.49155313\",\n \"text\": \"def schema_check(table1, table2):\\n\\n # compares the first row of each table to ensure the titles and number of columns match\\n if table1[0] != table2[0]:\\n raise MismatchedAttributesException\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993985,"cells":{"query_id":{"kind":"string","value":"890b2bfc4b102965155938e2ca874bb1"},"query":{"kind":"string","value":"Sets the residence of this Criminal."},"positive_passages":{"kind":"list like","value":[{"docid":"ef8fd305f836631f8748820b1c0b4118","score":"0.8034765","text":"def residence(self, residence):\n\n self._residence = residence","title":""}],"string":"[\n {\n \"docid\": \"ef8fd305f836631f8748820b1c0b4118\",\n \"score\": \"0.8034765\",\n \"text\": \"def residence(self, residence):\\n\\n self._residence = residence\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"43fd1c6070ff4412d49071a8e6ed45bb","score":"0.6250316","text":"def residence(self):\n return self._residence","title":""},{"docid":"44706b55c15938a2eec0f688e775491e","score":"0.57946104","text":"def set_evidence(self, evidence):\n self.evidence = evidence\n\n # we do not have to sample evidence nodes\n self.nodes_minus_e = [\n node for node in self.nodes if node not in evidence\n ]\n\n # parents of the evidence nodes\n self.evidence_parents = []\n for e in self.evidence:\n for node in self.graph[e]:\n self.evidence_parents.append(node)\n\n if self.rep == \"CPT\":\n self._set_icpt()\n elif self.rep == \"Noisy-OR\":\n self._set_causality_strength()\n else:\n raise ValueError(\"Unknown option.\")","title":""},{"docid":"0a9d8ee1b30bbf42b69efac30fcfd66f","score":"0.55060416","text":"def discipline(self, discipline):\n\n self._discipline = discipline","title":""},{"docid":"0a9d8ee1b30bbf42b69efac30fcfd66f","score":"0.55060416","text":"def discipline(self, discipline):\n\n self._discipline = discipline","title":""},{"docid":"df0a9dc452cee5c857362d2781f3d6d6","score":"0.5466117","text":"def SetEvidence(self, evidence):\n self._evidence = evidence","title":""},{"docid":"817fbcd65ddd359ee08081e848bc7ba5","score":"0.5239293","text":"def r_ide(self, r_ide):\n\n self._r_ide = r_ide","title":""},{"docid":"5f6cb7e436229e774bcfd5e47dc99bf1","score":"0.50677645","text":"def set_corrector(self, x=-2):\n self.corrector = x","title":""},{"docid":"01f3cda2060a06e5109c599b4f997d5f","score":"0.5040256","text":"def SetConstraintRHS(self, r):\n return _constraints.ConstrainedSolver_SetConstraintRHS(self, r)","title":""},{"docid":"b836ff4738c8ebb176f855a109d8b586","score":"0.49993312","text":"def resiliency_details(self, resiliency_details):\n\n self._resiliency_details = resiliency_details","title":""},{"docid":"018f1920b2a9ffc3039b94f5a0dd031e","score":"0.49891087","text":"def Re(self, Re):\n raise ValueError('Cannot directly set Reynolds number as an '\n 'attribute')","title":""},{"docid":"b487e89701ef192b2f5f75d6e10b5d2e","score":"0.49070406","text":"def set_rc(self):\n self.set_gene_is_rc_from_descr()\n if self.descr[-1] == '-':\n self.rc = True\n elif self.descr[-1] == '+':\n self.rc = False\n else:\n raise ValueError(\"State could not be detected.\")","title":""},{"docid":"84ef5a325b09ff7f97a8ad4241c65ddf","score":"0.48808113","text":"def crr(self, crr):\n\n self._crr = crr","title":""},{"docid":"56a3f5237f05746d6e4f508bde465b8e","score":"0.48648283","text":"def referent(self, referent):\n\n self._referent = referent","title":""},{"docid":"c6d14f152dcdbeb31bf9724c6af04a10","score":"0.48481292","text":"def set_resistivity(cell, resistivity, group=\"all\"):\n # type: (Cell, str, str) -> None\n cell.biophysical_properties.intracellular_properties.resistivities = [Resistivity(value=resistivity, segment_groups=group)]","title":""},{"docid":"35e77bcb9c306b7a95f99ec12bc95ee7","score":"0.48346528","text":"def setDisc(self, novoDisc):\r\n self.__disc = novoDisc","title":""},{"docid":"6bb4b2ec94eb9165d7e88c2a20238e43","score":"0.48121318","text":"def restated_question(self, restated_question):\n\n self._restated_question = restated_question","title":""},{"docid":"48432a819ca216189d62ab1b4e77be85","score":"0.47964615","text":"def setRes(self, res):\n self.sRes = (int(res[0]),int(res[1]))","title":""},{"docid":"2237219a9340321b562a486396ca8cf0","score":"0.47943404","text":"def car_rental(self, car_rental):\n\n self._car_rental = car_rental","title":""},{"docid":"37e65a2ccbac6f7c4ef57c59ecfedca9","score":"0.4760247","text":"def nvr(self, nvr):\n\n self._nvr = nvr","title":""},{"docid":"b3ca68c4da0b65365c598e2cc54bd116","score":"0.47552612","text":"def setActualRAWRC(self, actualRAWRC: int):\n\t\tself._actualRAWRC = actualRAWRC","title":""},{"docid":"fbdbf7500c526743da36dac2aaf2b540","score":"0.47549906","text":"def set_resistance(self, resistance):\n # Validate the desired value\n if ~(0 <= resistance <= self.max_resistance):\n return\n # Round to the nearest wipe (multiple of 100 ohms)\n wipe_equivalent = int(round(resistance, -2)) / 100\n # Set the digipot\n self.set_wipe(wipe_equivalent)","title":""},{"docid":"fdc1503a21e4fd0bb3754a96bf37f5c3","score":"0.4748327","text":"def _set_is_querier(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name=\"is-querier\", rest_name=\"is-querier\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mc-hms-operational', defining_module='brocade-mc-hms-operational', yang_type='uint8', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"is_querier must be of a type compatible with uint8\"\"\",\n 'defined-type': \"uint8\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name=\"is-querier\", rest_name=\"is-querier\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mc-hms-operational', defining_module='brocade-mc-hms-operational', yang_type='uint8', is_config=False)\"\"\",\n })\n\n self.__is_querier = t\n if hasattr(self, '_set'):\n self._set()","title":""},{"docid":"e3371754d785b0cd33705d6f9fedb035","score":"0.4726097","text":"def kind_of_criminal(self, kind_of_criminal):\n\n self._kind_of_criminal = kind_of_criminal","title":""},{"docid":"f8a42738215a4233e7db1622eed91c98","score":"0.47248423","text":"def tax_reciever_id(self, tax_reciever_id):\n\n self._tax_reciever_id = tax_reciever_id","title":""},{"docid":"205863e72e9e7c113164f24616234b99","score":"0.47195417","text":"def set_ricci_scalar(self):\n\n self.ricci_scalar = self.compute_ricci_scalar()\n if(self.suppress_printing == False):\n print(\"\")\n print(\"\")\n print(\"Ricci curvature scalar\")\n print(\"======================\")\n self.print_ricci_scalar()","title":""},{"docid":"d8a9a357d9a42ec44a85214c73f6ec69","score":"0.47082615","text":"def set_constraint(self, constraint):\r\n self.constraint = constraint","title":""},{"docid":"b8ccb876058dbacae02047a2ce1d007c","score":"0.47032556","text":"def init_res(self, rail):\n err = 0\n if rail['rsense'][0] != rail['rsense'][1]:\n gpio = next((item for item in self.board_mapping_gpio_i2c if item['name'] == rail['rsense_ctl']), None)\n init_value = rail['rsense'][2]\n self.setgpio(gpio, init_value * 0xFF)\n while (self.pca6416_get_output(gpio) / gpio['pca6416'][2]) != init_value:\n err += 1\n self.setgpio(gpio, init_value * 0xFF)\n if err == 5:\n print('Failed to init resistance switch ' + gpio['name'] + ' to high level.')\n break\n CURR_RSENSE[rail['name']] = rail['rsense'][0]","title":""},{"docid":"c950259e3243ff18699407db03ac7df2","score":"0.46935955","text":"def rid_id(self, rid_id):\n\n self._rid_id = rid_id","title":""},{"docid":"24feface2a8d4e66967028b12dc80a2e","score":"0.46875778","text":"def set_corrector(self, x=-2):\n self.table.corrector = x","title":""},{"docid":"24feface2a8d4e66967028b12dc80a2e","score":"0.46875778","text":"def set_corrector(self, x=-2):\n self.table.corrector = x","title":""},{"docid":"eb00cd8530ee7bfafef5014ac22306ab","score":"0.46737856","text":"def set_was_influenced_by(self, resource):\n self.add(PROV.wasInfluencedBy, resource)\n\n if using_inverse_properties():\n resource.add(PROV.influenced, self)","title":""},{"docid":"69104fde41608c5287b32c7a5686fb06","score":"0.46691978","text":"def crv(self, crv: \"str\"):\n self._attrs[\"crv\"] = crv","title":""},{"docid":"6567d781e43bdbf71017797ff42b9bb3","score":"0.46456328","text":"def set_dominance(self, dominance):\n self.left_dominant = bool(dominance)\n\n self.dom_center_line = self.Ldom_center_line if self.left_dominant else self.Rdom_center_line\n self.sub_center_line = self.Lsub_center_line if self.left_dominant else self.Rsub_center_line\n self.dom_inner_bound = self.Ldom_inner_bound if self.left_dominant else self.Rdom_inner_bound\n self.sub_inner_bound = self.Lsub_inner_bound if self.left_dominant else self.Rsub_inner_bound\n\n self.min_angle_line = LineString([self.dom_center_line.coords[0], self.get_pos_intersection(self.sub_inner_bound, self.visual_space.boundary).coords[0]])\n self.min_angle = math.degrees(self.get_angle_between(\n self.min_angle_line,\n self.dom_center_line\n ))\n self.sub_min_angle = math.degrees(self.get_angle_between(\n self.sub_inner_bound,\n self.sub_center_line\n ))","title":""},{"docid":"ade5fb6eabe0acceb053434e08ea63a8","score":"0.4634538","text":"def set_was_influenced_by(self, resource):\n resource = Resource.ensure_type(resource)\n self.add(PROV.wasInfluencedBy, resource)\n\n if using_inverse_properties():\n resource.add(PROV.influenced, self)","title":""},{"docid":"6142a514c75f6760b3d0db95fe3c9d44","score":"0.46173596","text":"def declareDiscretizationSegmentConVariables(self, pyM):\n setattr(pyM, 'discretizationSegmentCon_' + self.abbrvName,\n pyomo.Var(getattr(pyM, 'discretizationSegmentVarSet_' + self.abbrvName), pyM.timeSet, domain=pyomo.NonNegativeReals))","title":""},{"docid":"6663b7c2e6dde92b5eaa5812cfb99326","score":"0.45879376","text":"def SetDetectionProperties(self, det_roi):\n pass","title":""},{"docid":"92b12d4b9fadbacc381e783aba5b9968","score":"0.4580052","text":"def set_rho(self, rho):\n rho = max(0.0, float(rho))\n self.rho = rho","title":""},{"docid":"7142857b7c1d3b4ce606de7810139400","score":"0.45655277","text":"def reign(self, reign):\n\n self._reign = reign","title":""},{"docid":"8fdac93e8ed5ee6137672d36f73867d0","score":"0.45590976","text":"def set_relax(self, relaxamounts, timeind, dt):\n rp = self.relax_parameters\n if rp is None:\n return\n relaxamount_s, relaxamount_v = relaxamounts\n hs.relaxation.relax_helper_vhd(rp, relaxamount_s, relaxamount_v, self, timeind, dt)","title":""},{"docid":"80ae77a4c5fde38f7dd04b3ec92efc86","score":"0.455399","text":"def set_cvv(self, cvv):\n self.cvv = cvv","title":""},{"docid":"7bca202ca0f5f99975825a465965d48c","score":"0.4533174","text":"def _setResource(self, r_type, x, y, amount):\n cell = self.get_cell(x, y)\n cell.resource = Cell.Resource(r_type, amount)","title":""},{"docid":"2ac9a4be728632b81e30a5f46e5a18ac","score":"0.4527307","text":"def set_constraint(self, prop):\n query = \"CREATE \" + cypher.constraint_query(\n 'n', self._node_label, prop)\n result = self.execute(query)\n return result","title":""},{"docid":"33d4897eb34a094f6989ce0fa891abaf","score":"0.45270556","text":"def SetDetectionProperties(self, det_roi):\n self.send_SetDetectionProperties(det_roi)\n self.recv_SetDetectionProperties()","title":""},{"docid":"bb6bcc037779d3ac0e8102e9751731c5","score":"0.451965","text":"def inclination(self, inclination):\n\n self._inclination = inclination","title":""},{"docid":"d588c6b91ae50c31bec2f27d1dece31b","score":"0.45049813","text":"def set_race(self, race_id):\n api.set_race(self.profile['id'], race_id)\n self.active_race = race_id","title":""},{"docid":"d83c4637fe198e6fc2d8690b346547fd","score":"0.4496941","text":"def conta_corrente(self, conta_corrente):\n self._conta_corrente = conta_corrente","title":""},{"docid":"e8e47936c0bee9f7ed69234c0f7d8c8b","score":"0.44900003","text":"def setConstraint(constraint):","title":""},{"docid":"fa9107c4d734237a93235c58b38a5ef4","score":"0.4484793","text":"def influenced(self, influenced):\n\n self._influenced = influenced","title":""},{"docid":"b7fd81a362e0df9454d3b4d4935f7ee7","score":"0.44749215","text":"def remarks(self, remarks):\n\n self._remarks = remarks","title":""},{"docid":"2142d9e3bef9edd45ce9268517ef4f7e","score":"0.44628522","text":"def setRebalance(self,rebalance):\n self.rebalance = rebalance","title":""},{"docid":"f5a937735161698b0ab263eac92b382b","score":"0.445133","text":"def circumcised(self, circumcised):\n\n self._circumcised = circumcised","title":""},{"docid":"d22fa3211de2b13f99328034d3acc4b4","score":"0.44486108","text":"def set_was_influenced_by(self, resource):\n self.add(PROV.wasInfluencedBy, resource)","title":""},{"docid":"d22fa3211de2b13f99328034d3acc4b4","score":"0.44486108","text":"def set_was_influenced_by(self, resource):\n self.add(PROV.wasInfluencedBy, resource)","title":""},{"docid":"ff976c4663193371e8046529c6b6000f","score":"0.44467577","text":"def set_reifier (self, reifier):\n if reifier is None:\n reified = None\n else:\n if self.get_topic_map() != reifier.topic_map:\n raise ModelConstraintException(\n self, 'The reifier is not from the same topic map')\n reified = reifier.get_reified()\n if reified is None:\n self.reifier = reifier\n self.save()\n elif reified == self:\n pass\n else:\n raise ModelConstraintException(\n self, 'The reifier already reifies another construct')","title":""},{"docid":"ad076556a30107f03ec3777425001a6a","score":"0.4414624","text":"def Set(self, *args):\n return _TDataXtd.TDataXtd_Constraint_Set(self, *args)","title":""},{"docid":"b54ec26ed02b21df9d4d80602cd20cfd","score":"0.43996385","text":"def risk(self) -> int:\n return self.__risk","title":""},{"docid":"ab7d1a0f1c067be8822918cc13a9e6dd","score":"0.4393859","text":"def cvv(self, cvv):\n\n self._cvv = cvv","title":""},{"docid":"f7307787ebea6799bbda6d95e65221c3","score":"0.4374353","text":"def set_resolution(self, c, serial_number, resolution):\n ps = self._get_interface(serial_number)\n ps.setResolution(resolution)","title":""},{"docid":"b575372d7809da6bbe60a62303548d58","score":"0.43694827","text":"def SetPred(self, pred):\n self.pred = pred","title":""},{"docid":"ca6270cf9dbc184213b72628fe06c6fe","score":"0.43674427","text":"def confidence(self, confidence):\n\n self._confidence = confidence","title":""},{"docid":"ef372a4560dc88c010020bd74ce3aaad","score":"0.43640766","text":"def resident_id_number(self):\n return self.__resident_id_number","title":""},{"docid":"a5fdd62a233b4f88c275d7d81eac4588","score":"0.43603277","text":"def sexual_orientation(self, sexual_orientation):\n\n self._sexual_orientation = sexual_orientation","title":""},{"docid":"bbf4f9ab6602808c7f24ec7eb7e2657b","score":"0.43597004","text":"def resource_subtype(self, resource_subtype):\n\n self._resource_subtype = resource_subtype","title":""},{"docid":"322b08903cc6d4fabfe5e8dc56d094a5","score":"0.43554628","text":"def setNoeud(self, A):\n self.racine = A","title":""},{"docid":"97f8aa196c692aa2a7c012464093e792","score":"0.43508938","text":"def set_roidb(self, roidb):\n self._roidb = roidb\n self._shuffle_roidb_inds()","title":""},{"docid":"fc8894decee577335a95a0f80a01817a","score":"0.43449247","text":"def rset(self):\n return self._shortcmd('RSET')","title":""},{"docid":"45f6f6c6d8450b8c61f9be7b25ff2d2c","score":"0.434268","text":"def set(self, val, scope=None):\r\n self._expreval.set(self._transform(val), scope, force=True)","title":""},{"docid":"fec4ed640373cd8599f5abbdc901856f","score":"0.43419963","text":"def _set_icpt(self):\n # setting icpt for parents of evidence nodes\n # to uniform distribution (according to original paper on AIS-BN)\n for e in self.evidence:\n\n if self.proposal.is_root_node(e):\n continue\n\n for parent in self.graph[e]:\n if parent is not None:\n if isinstance(self.proposal.cpt[parent], list):\n # prior node\n n = len(self.proposal.cpt[parent])\n self.proposal.cpt[parent] = [1.0 / n] * n\n else:\n for p in self.proposal.cpt[parent]:\n n = len(self.proposal.cpt[parent][p])\n self.proposal.cpt[parent][p] = [1.0 / n] * n","title":""},{"docid":"4f35c40d1077009578a895bcf9eb7de5","score":"0.43412566","text":"def set_evidences(self, request=None):\n\t\tif bbn.set_evidences(request) == True:\n\t\t\treturn self.GOOD;\n\t\telse:\n\t\t\treturn self.FAILED\n\t\t#DONE","title":""},{"docid":"6afa61c2b7f962d7aed38d8b10cbbb90","score":"0.4330183","text":"def sport_discipline(self, sport_discipline):\n\n self._sport_discipline = sport_discipline","title":""},{"docid":"6117272461c4ade0786ad9c5b4727eac","score":"0.4329829","text":"def set_cvid(self, cvid):\n\n\t\tif cvid is not None and not isinstance(cvid, str):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: cvid EXPECTED TYPE: str', None, None)\n\t\t\n\t\tself.__cvid = cvid\n\t\tself.__key_modified['cvid'] = 1","title":""},{"docid":"cb9519513086ccf3f5e746aa9a9c486a","score":"0.43294677","text":"def data_residency(self) -> pulumi.Output[Optional['outputs.DataResidencyResponse']]:\n return pulumi.get(self, \"data_residency\")","title":""},{"docid":"4e46e47c8999bf8d469c0b47da916071","score":"0.43231082","text":"def citizen_id(self, citizen_id):\n\n self._citizen_id = citizen_id","title":""},{"docid":"470b2ae63d3b465d90fba21212f0f49f","score":"0.43186593","text":"def set_rflag(self, rflag):\n with self.lock:\n self.rflag = rflag\n if self.closed:\n self.rflag.set()","title":""},{"docid":"7aa0d362a4aa00aaa9ee3f98cd1c4b2e","score":"0.43171245","text":"def insurance_area(self, insurance_area):\n\n self._insurance_area = insurance_area","title":""},{"docid":"537a186344c8a599ff8a26f14d9d07e1","score":"0.4311873","text":"def __init__(self,discipline):\n self.__discipline=discipline","title":""},{"docid":"434b556cc187c887c339d736a24bd4ca","score":"0.43091756","text":"def ordinal(self, ordinal):\n\n self._ordinal = ordinal","title":""},{"docid":"b9c15f7443261c29bfd8afd65aba3287","score":"0.43015742","text":"def valor_renda(self, valor_renda):\n self._valor_renda = valor_renda","title":""},{"docid":"aa30e873984f310d570dceafed266c8f","score":"0.42999122","text":"def set(self, var1, var2):\n self.cap.set(var1, var2)","title":""},{"docid":"954c7711b75128de38850988ab5eef15","score":"0.42975128","text":"def affair(self, affair):\n\n self._affair = affair","title":""},{"docid":"0fada127a726ca62de71400dbaf14aef","score":"0.42973864","text":"def changeIris(self, *args):\n self.cvIrisLoc = self.guideName+\"_IrisLoc\"\n cmds.setAttr(self.moduleGrp+\".iris\", cmds.checkBox(self.irisCB, query=True, value=True))\n cmds.setAttr(self.cvIrisLoc+\".visibility\", cmds.checkBox(self.irisCB, query=True, value=True))","title":""},{"docid":"c806c6477ef0b511b11984e9ba760e55","score":"0.42914957","text":"def set_old_resnum(self, old_resnum, icode):\n self.set_extra_info('old_resnum', old_resnum)\n self.set_extra_info('old_icode', icode)","title":""},{"docid":"0c4498d4e058f8ceb546435af1f3f8a8","score":"0.42868125","text":"def orcid_id(self, orcid_id):\n\n self._orcid_id = orcid_id","title":""},{"docid":"4d0c7d72ab3a603d47d21b1351278cfc","score":"0.4282853","text":"def set_referencia(self,referencia):\n self.referencia = referencia","title":""},{"docid":"300e17488eb924110e200f547683d213","score":"0.42686978","text":"def propensity_carn(self, value):\n self._propensity_carn = value","title":""},{"docid":"c206bfa059b55c58716e65aaadb16b3b","score":"0.42506772","text":"def assessment_id(self, assessment_id):\n\n self._assessment_id = assessment_id","title":""},{"docid":"c36d2f4dfaf1bbe497d9bf48b08030ac","score":"0.42431724","text":"def SetIRCut(self, is_ir_on):\n self.send_SetIRCut(is_ir_on)\n self.recv_SetIRCut()","title":""},{"docid":"3dc10e3cf137fe1cc2217145044919dc","score":"0.4242249","text":"def rub(self, rub):\n if rub is None:\n raise ValueError(\"Invalid value for `rub`, must not be `None`\") # noqa: E501\n\n self._rub = rub","title":""},{"docid":"57fbb07fc09e7e8eb4ab45e6d72adfc3","score":"0.42418826","text":"def risk_limit_base(self, risk_limit_base):\n\n self._risk_limit_base = risk_limit_base","title":""},{"docid":"db3bdb3b6d420522cb8640d1bd8dc50d","score":"0.42300165","text":"def set_score_land(self, objective, constraints):\n self.set_err_obj_distances(objective, constraints)\n\n score = self.obj_distance/(parameters.RATIONAL_EPSILON+self.err_distance)\n self.score = score\n\n self.set_ancestor_pass()","title":""},{"docid":"76627690347c4be880189a86de8d2d8f","score":"0.42248377","text":"def rs(self, rs):\n self._rs = rs","title":""},{"docid":"17ec03029cb391d17f2704790e4e73ab","score":"0.42221814","text":"def set_roi(self):\n # create the MAV_CMD_DO_SET_ROI command\n location = self.drone.location.global_relative_frame\n msg = self.drone.message_factory.command_long_encode(\n 0, 0, # target system, target component\n mavutil.mavlink.MAV_CMD_DO_SET_ROI, #command\n 0, #confirmation\n 0, 0, 0, 0, #params 1-4\n location.lat,\n location.lon,\n location.alt\n )\n # send command to vehicle\n self.drone.send_mavlink(msg)","title":""},{"docid":"0ec55320612085aeb2c7430fc401cd95","score":"0.4219721","text":"def insurance_type(self, insurance_type):\n\n self._insurance_type = insurance_type","title":""},{"docid":"887ef7da9152c129c05b7b9f6d3c2c58","score":"0.42166197","text":"def set_retorno(self, retorno):\n\n self.retornos = retorno","title":""},{"docid":"2fa581359e84560ec93a122c41376606","score":"0.42163342","text":"def rstudio(self, rstudio):\n\n self._rstudio = rstudio","title":""},{"docid":"475bd3d9c3112418693f61f10518479b","score":"0.4211241","text":"def encounter_id(self, encounter_id):\n\n self._encounter_id = encounter_id","title":""},{"docid":"6373a0d905a722c666910f6db2624b32","score":"0.4208462","text":"def recurrence(self, recurrence):\n if recurrence is None:\n raise ValueError(\"Invalid value for `recurrence`, must not be `None`\") # noqa: E501\n\n self._recurrence = recurrence","title":""},{"docid":"8049e861f49bc1d3f64d551245be517d","score":"0.42081845","text":"def resolution(self, resolution: ExtendedDisputeallOf1propertiesResolution):\n\n self._resolution = resolution","title":""},{"docid":"f9a65d04d7bebaa934f8269480a02952","score":"0.42042467","text":"def data_residency(self) -> Optional[pulumi.Input['DataResidencyArgs']]:\n return pulumi.get(self, \"data_residency\")","title":""},{"docid":"ed058986eae13e1ff71bc03902e83399","score":"0.4194669","text":"def set_voltage_rise_rate(self, rise_rate):\n self.voltage_rise_rate.put(rise_rate)","title":""}],"string":"[\n {\n \"docid\": \"43fd1c6070ff4412d49071a8e6ed45bb\",\n \"score\": \"0.6250316\",\n \"text\": \"def residence(self):\\n return self._residence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44706b55c15938a2eec0f688e775491e\",\n \"score\": \"0.57946104\",\n \"text\": \"def set_evidence(self, evidence):\\n self.evidence = evidence\\n\\n # we do not have to sample evidence nodes\\n self.nodes_minus_e = [\\n node for node in self.nodes if node not in evidence\\n ]\\n\\n # parents of the evidence nodes\\n self.evidence_parents = []\\n for e in self.evidence:\\n for node in self.graph[e]:\\n self.evidence_parents.append(node)\\n\\n if self.rep == \\\"CPT\\\":\\n self._set_icpt()\\n elif self.rep == \\\"Noisy-OR\\\":\\n self._set_causality_strength()\\n else:\\n raise ValueError(\\\"Unknown option.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9d8ee1b30bbf42b69efac30fcfd66f\",\n \"score\": \"0.55060416\",\n \"text\": \"def discipline(self, discipline):\\n\\n self._discipline = discipline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9d8ee1b30bbf42b69efac30fcfd66f\",\n \"score\": \"0.55060416\",\n \"text\": \"def discipline(self, discipline):\\n\\n self._discipline = discipline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0a9dc452cee5c857362d2781f3d6d6\",\n \"score\": \"0.5466117\",\n \"text\": \"def SetEvidence(self, evidence):\\n self._evidence = evidence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817fbcd65ddd359ee08081e848bc7ba5\",\n \"score\": \"0.5239293\",\n \"text\": \"def r_ide(self, r_ide):\\n\\n self._r_ide = r_ide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6cb7e436229e774bcfd5e47dc99bf1\",\n \"score\": \"0.50677645\",\n \"text\": \"def set_corrector(self, x=-2):\\n self.corrector = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f3cda2060a06e5109c599b4f997d5f\",\n \"score\": \"0.5040256\",\n \"text\": \"def SetConstraintRHS(self, r):\\n return _constraints.ConstrainedSolver_SetConstraintRHS(self, r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b836ff4738c8ebb176f855a109d8b586\",\n \"score\": \"0.49993312\",\n \"text\": \"def resiliency_details(self, resiliency_details):\\n\\n self._resiliency_details = resiliency_details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018f1920b2a9ffc3039b94f5a0dd031e\",\n \"score\": \"0.49891087\",\n \"text\": \"def Re(self, Re):\\n raise ValueError('Cannot directly set Reynolds number as an '\\n 'attribute')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b487e89701ef192b2f5f75d6e10b5d2e\",\n \"score\": \"0.49070406\",\n \"text\": \"def set_rc(self):\\n self.set_gene_is_rc_from_descr()\\n if self.descr[-1] == '-':\\n self.rc = True\\n elif self.descr[-1] == '+':\\n self.rc = False\\n else:\\n raise ValueError(\\\"State could not be detected.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ef5a325b09ff7f97a8ad4241c65ddf\",\n \"score\": \"0.48808113\",\n \"text\": \"def crr(self, crr):\\n\\n self._crr = crr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56a3f5237f05746d6e4f508bde465b8e\",\n \"score\": \"0.48648283\",\n \"text\": \"def referent(self, referent):\\n\\n self._referent = referent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d14f152dcdbeb31bf9724c6af04a10\",\n \"score\": \"0.48481292\",\n \"text\": \"def set_resistivity(cell, resistivity, group=\\\"all\\\"):\\n # type: (Cell, str, str) -> None\\n cell.biophysical_properties.intracellular_properties.resistivities = [Resistivity(value=resistivity, segment_groups=group)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e77bcb9c306b7a95f99ec12bc95ee7\",\n \"score\": \"0.48346528\",\n \"text\": \"def setDisc(self, novoDisc):\\r\\n self.__disc = novoDisc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb4b2ec94eb9165d7e88c2a20238e43\",\n \"score\": \"0.48121318\",\n \"text\": \"def restated_question(self, restated_question):\\n\\n self._restated_question = restated_question\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48432a819ca216189d62ab1b4e77be85\",\n \"score\": \"0.47964615\",\n \"text\": \"def setRes(self, res):\\n self.sRes = (int(res[0]),int(res[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2237219a9340321b562a486396ca8cf0\",\n \"score\": \"0.47943404\",\n \"text\": \"def car_rental(self, car_rental):\\n\\n self._car_rental = car_rental\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e65a2ccbac6f7c4ef57c59ecfedca9\",\n \"score\": \"0.4760247\",\n \"text\": \"def nvr(self, nvr):\\n\\n self._nvr = nvr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ca68c4da0b65365c598e2cc54bd116\",\n \"score\": \"0.47552612\",\n \"text\": \"def setActualRAWRC(self, actualRAWRC: int):\\n\\t\\tself._actualRAWRC = actualRAWRC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdbf7500c526743da36dac2aaf2b540\",\n \"score\": \"0.47549906\",\n \"text\": \"def set_resistance(self, resistance):\\n # Validate the desired value\\n if ~(0 <= resistance <= self.max_resistance):\\n return\\n # Round to the nearest wipe (multiple of 100 ohms)\\n wipe_equivalent = int(round(resistance, -2)) / 100\\n # Set the digipot\\n self.set_wipe(wipe_equivalent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc1503a21e4fd0bb3754a96bf37f5c3\",\n \"score\": \"0.4748327\",\n \"text\": \"def _set_is_querier(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name=\\\"is-querier\\\", rest_name=\\\"is-querier\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mc-hms-operational', defining_module='brocade-mc-hms-operational', yang_type='uint8', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"is_querier must be of a type compatible with uint8\\\"\\\"\\\",\\n 'defined-type': \\\"uint8\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..255']}, int_size=8), is_leaf=True, yang_name=\\\"is-querier\\\", rest_name=\\\"is-querier\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mc-hms-operational', defining_module='brocade-mc-hms-operational', yang_type='uint8', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__is_querier = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3371754d785b0cd33705d6f9fedb035\",\n \"score\": \"0.4726097\",\n \"text\": \"def kind_of_criminal(self, kind_of_criminal):\\n\\n self._kind_of_criminal = kind_of_criminal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8a42738215a4233e7db1622eed91c98\",\n \"score\": \"0.47248423\",\n \"text\": \"def tax_reciever_id(self, tax_reciever_id):\\n\\n self._tax_reciever_id = tax_reciever_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205863e72e9e7c113164f24616234b99\",\n \"score\": \"0.47195417\",\n \"text\": \"def set_ricci_scalar(self):\\n\\n self.ricci_scalar = self.compute_ricci_scalar()\\n if(self.suppress_printing == False):\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"Ricci curvature scalar\\\")\\n print(\\\"======================\\\")\\n self.print_ricci_scalar()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a9a357d9a42ec44a85214c73f6ec69\",\n \"score\": \"0.47082615\",\n \"text\": \"def set_constraint(self, constraint):\\r\\n self.constraint = constraint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ccb876058dbacae02047a2ce1d007c\",\n \"score\": \"0.47032556\",\n \"text\": \"def init_res(self, rail):\\n err = 0\\n if rail['rsense'][0] != rail['rsense'][1]:\\n gpio = next((item for item in self.board_mapping_gpio_i2c if item['name'] == rail['rsense_ctl']), None)\\n init_value = rail['rsense'][2]\\n self.setgpio(gpio, init_value * 0xFF)\\n while (self.pca6416_get_output(gpio) / gpio['pca6416'][2]) != init_value:\\n err += 1\\n self.setgpio(gpio, init_value * 0xFF)\\n if err == 5:\\n print('Failed to init resistance switch ' + gpio['name'] + ' to high level.')\\n break\\n CURR_RSENSE[rail['name']] = rail['rsense'][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c950259e3243ff18699407db03ac7df2\",\n \"score\": \"0.46935955\",\n \"text\": \"def rid_id(self, rid_id):\\n\\n self._rid_id = rid_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24feface2a8d4e66967028b12dc80a2e\",\n \"score\": \"0.46875778\",\n \"text\": \"def set_corrector(self, x=-2):\\n self.table.corrector = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24feface2a8d4e66967028b12dc80a2e\",\n \"score\": \"0.46875778\",\n \"text\": \"def set_corrector(self, x=-2):\\n self.table.corrector = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb00cd8530ee7bfafef5014ac22306ab\",\n \"score\": \"0.46737856\",\n \"text\": \"def set_was_influenced_by(self, resource):\\n self.add(PROV.wasInfluencedBy, resource)\\n\\n if using_inverse_properties():\\n resource.add(PROV.influenced, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69104fde41608c5287b32c7a5686fb06\",\n \"score\": \"0.46691978\",\n \"text\": \"def crv(self, crv: \\\"str\\\"):\\n self._attrs[\\\"crv\\\"] = crv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6567d781e43bdbf71017797ff42b9bb3\",\n \"score\": \"0.46456328\",\n \"text\": \"def set_dominance(self, dominance):\\n self.left_dominant = bool(dominance)\\n\\n self.dom_center_line = self.Ldom_center_line if self.left_dominant else self.Rdom_center_line\\n self.sub_center_line = self.Lsub_center_line if self.left_dominant else self.Rsub_center_line\\n self.dom_inner_bound = self.Ldom_inner_bound if self.left_dominant else self.Rdom_inner_bound\\n self.sub_inner_bound = self.Lsub_inner_bound if self.left_dominant else self.Rsub_inner_bound\\n\\n self.min_angle_line = LineString([self.dom_center_line.coords[0], self.get_pos_intersection(self.sub_inner_bound, self.visual_space.boundary).coords[0]])\\n self.min_angle = math.degrees(self.get_angle_between(\\n self.min_angle_line,\\n self.dom_center_line\\n ))\\n self.sub_min_angle = math.degrees(self.get_angle_between(\\n self.sub_inner_bound,\\n self.sub_center_line\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade5fb6eabe0acceb053434e08ea63a8\",\n \"score\": \"0.4634538\",\n \"text\": \"def set_was_influenced_by(self, resource):\\n resource = Resource.ensure_type(resource)\\n self.add(PROV.wasInfluencedBy, resource)\\n\\n if using_inverse_properties():\\n resource.add(PROV.influenced, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6142a514c75f6760b3d0db95fe3c9d44\",\n \"score\": \"0.46173596\",\n \"text\": \"def declareDiscretizationSegmentConVariables(self, pyM):\\n setattr(pyM, 'discretizationSegmentCon_' + self.abbrvName,\\n pyomo.Var(getattr(pyM, 'discretizationSegmentVarSet_' + self.abbrvName), pyM.timeSet, domain=pyomo.NonNegativeReals))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6663b7c2e6dde92b5eaa5812cfb99326\",\n \"score\": \"0.45879376\",\n \"text\": \"def SetDetectionProperties(self, det_roi):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b12d4b9fadbacc381e783aba5b9968\",\n \"score\": \"0.4580052\",\n \"text\": \"def set_rho(self, rho):\\n rho = max(0.0, float(rho))\\n self.rho = rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7142857b7c1d3b4ce606de7810139400\",\n \"score\": \"0.45655277\",\n \"text\": \"def reign(self, reign):\\n\\n self._reign = reign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdac93e8ed5ee6137672d36f73867d0\",\n \"score\": \"0.45590976\",\n \"text\": \"def set_relax(self, relaxamounts, timeind, dt):\\n rp = self.relax_parameters\\n if rp is None:\\n return\\n relaxamount_s, relaxamount_v = relaxamounts\\n hs.relaxation.relax_helper_vhd(rp, relaxamount_s, relaxamount_v, self, timeind, dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ae77a4c5fde38f7dd04b3ec92efc86\",\n \"score\": \"0.455399\",\n \"text\": \"def set_cvv(self, cvv):\\n self.cvv = cvv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bca202ca0f5f99975825a465965d48c\",\n \"score\": \"0.4533174\",\n \"text\": \"def _setResource(self, r_type, x, y, amount):\\n cell = self.get_cell(x, y)\\n cell.resource = Cell.Resource(r_type, amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ac9a4be728632b81e30a5f46e5a18ac\",\n \"score\": \"0.4527307\",\n \"text\": \"def set_constraint(self, prop):\\n query = \\\"CREATE \\\" + cypher.constraint_query(\\n 'n', self._node_label, prop)\\n result = self.execute(query)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d4897eb34a094f6989ce0fa891abaf\",\n \"score\": \"0.45270556\",\n \"text\": \"def SetDetectionProperties(self, det_roi):\\n self.send_SetDetectionProperties(det_roi)\\n self.recv_SetDetectionProperties()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6bcc037779d3ac0e8102e9751731c5\",\n \"score\": \"0.451965\",\n \"text\": \"def inclination(self, inclination):\\n\\n self._inclination = inclination\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d588c6b91ae50c31bec2f27d1dece31b\",\n \"score\": \"0.45049813\",\n \"text\": \"def set_race(self, race_id):\\n api.set_race(self.profile['id'], race_id)\\n self.active_race = race_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83c4637fe198e6fc2d8690b346547fd\",\n \"score\": \"0.4496941\",\n \"text\": \"def conta_corrente(self, conta_corrente):\\n self._conta_corrente = conta_corrente\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e47936c0bee9f7ed69234c0f7d8c8b\",\n \"score\": \"0.44900003\",\n \"text\": \"def setConstraint(constraint):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9107c4d734237a93235c58b38a5ef4\",\n \"score\": \"0.4484793\",\n \"text\": \"def influenced(self, influenced):\\n\\n self._influenced = influenced\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7fd81a362e0df9454d3b4d4935f7ee7\",\n \"score\": \"0.44749215\",\n \"text\": \"def remarks(self, remarks):\\n\\n self._remarks = remarks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2142d9e3bef9edd45ce9268517ef4f7e\",\n \"score\": \"0.44628522\",\n \"text\": \"def setRebalance(self,rebalance):\\n self.rebalance = rebalance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a937735161698b0ab263eac92b382b\",\n \"score\": \"0.445133\",\n \"text\": \"def circumcised(self, circumcised):\\n\\n self._circumcised = circumcised\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22fa3211de2b13f99328034d3acc4b4\",\n \"score\": \"0.44486108\",\n \"text\": \"def set_was_influenced_by(self, resource):\\n self.add(PROV.wasInfluencedBy, resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22fa3211de2b13f99328034d3acc4b4\",\n \"score\": \"0.44486108\",\n \"text\": \"def set_was_influenced_by(self, resource):\\n self.add(PROV.wasInfluencedBy, resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff976c4663193371e8046529c6b6000f\",\n \"score\": \"0.44467577\",\n \"text\": \"def set_reifier (self, reifier):\\n if reifier is None:\\n reified = None\\n else:\\n if self.get_topic_map() != reifier.topic_map:\\n raise ModelConstraintException(\\n self, 'The reifier is not from the same topic map')\\n reified = reifier.get_reified()\\n if reified is None:\\n self.reifier = reifier\\n self.save()\\n elif reified == self:\\n pass\\n else:\\n raise ModelConstraintException(\\n self, 'The reifier already reifies another construct')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad076556a30107f03ec3777425001a6a\",\n \"score\": \"0.4414624\",\n \"text\": \"def Set(self, *args):\\n return _TDataXtd.TDataXtd_Constraint_Set(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54ec26ed02b21df9d4d80602cd20cfd\",\n \"score\": \"0.43996385\",\n \"text\": \"def risk(self) -> int:\\n return self.__risk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab7d1a0f1c067be8822918cc13a9e6dd\",\n \"score\": \"0.4393859\",\n \"text\": \"def cvv(self, cvv):\\n\\n self._cvv = cvv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7307787ebea6799bbda6d95e65221c3\",\n \"score\": \"0.4374353\",\n \"text\": \"def set_resolution(self, c, serial_number, resolution):\\n ps = self._get_interface(serial_number)\\n ps.setResolution(resolution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b575372d7809da6bbe60a62303548d58\",\n \"score\": \"0.43694827\",\n \"text\": \"def SetPred(self, pred):\\n self.pred = pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6270cf9dbc184213b72628fe06c6fe\",\n \"score\": \"0.43674427\",\n \"text\": \"def confidence(self, confidence):\\n\\n self._confidence = confidence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef372a4560dc88c010020bd74ce3aaad\",\n \"score\": \"0.43640766\",\n \"text\": \"def resident_id_number(self):\\n return self.__resident_id_number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fdd62a233b4f88c275d7d81eac4588\",\n \"score\": \"0.43603277\",\n \"text\": \"def sexual_orientation(self, sexual_orientation):\\n\\n self._sexual_orientation = sexual_orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf4f9ab6602808c7f24ec7eb7e2657b\",\n \"score\": \"0.43597004\",\n \"text\": \"def resource_subtype(self, resource_subtype):\\n\\n self._resource_subtype = resource_subtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322b08903cc6d4fabfe5e8dc56d094a5\",\n \"score\": \"0.43554628\",\n \"text\": \"def setNoeud(self, A):\\n self.racine = A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f8aa196c692aa2a7c012464093e792\",\n \"score\": \"0.43508938\",\n \"text\": \"def set_roidb(self, roidb):\\n self._roidb = roidb\\n self._shuffle_roidb_inds()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8894decee577335a95a0f80a01817a\",\n \"score\": \"0.43449247\",\n \"text\": \"def rset(self):\\n return self._shortcmd('RSET')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f6f6c6d8450b8c61f9be7b25ff2d2c\",\n \"score\": \"0.434268\",\n \"text\": \"def set(self, val, scope=None):\\r\\n self._expreval.set(self._transform(val), scope, force=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec4ed640373cd8599f5abbdc901856f\",\n \"score\": \"0.43419963\",\n \"text\": \"def _set_icpt(self):\\n # setting icpt for parents of evidence nodes\\n # to uniform distribution (according to original paper on AIS-BN)\\n for e in self.evidence:\\n\\n if self.proposal.is_root_node(e):\\n continue\\n\\n for parent in self.graph[e]:\\n if parent is not None:\\n if isinstance(self.proposal.cpt[parent], list):\\n # prior node\\n n = len(self.proposal.cpt[parent])\\n self.proposal.cpt[parent] = [1.0 / n] * n\\n else:\\n for p in self.proposal.cpt[parent]:\\n n = len(self.proposal.cpt[parent][p])\\n self.proposal.cpt[parent][p] = [1.0 / n] * n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f35c40d1077009578a895bcf9eb7de5\",\n \"score\": \"0.43412566\",\n \"text\": \"def set_evidences(self, request=None):\\n\\t\\tif bbn.set_evidences(request) == True:\\n\\t\\t\\treturn self.GOOD;\\n\\t\\telse:\\n\\t\\t\\treturn self.FAILED\\n\\t\\t#DONE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afa61c2b7f962d7aed38d8b10cbbb90\",\n \"score\": \"0.4330183\",\n \"text\": \"def sport_discipline(self, sport_discipline):\\n\\n self._sport_discipline = sport_discipline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6117272461c4ade0786ad9c5b4727eac\",\n \"score\": \"0.4329829\",\n \"text\": \"def set_cvid(self, cvid):\\n\\n\\t\\tif cvid is not None and not isinstance(cvid, str):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: cvid EXPECTED TYPE: str', None, None)\\n\\t\\t\\n\\t\\tself.__cvid = cvid\\n\\t\\tself.__key_modified['cvid'] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9519513086ccf3f5e746aa9a9c486a\",\n \"score\": \"0.43294677\",\n \"text\": \"def data_residency(self) -> pulumi.Output[Optional['outputs.DataResidencyResponse']]:\\n return pulumi.get(self, \\\"data_residency\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e46e47c8999bf8d469c0b47da916071\",\n \"score\": \"0.43231082\",\n \"text\": \"def citizen_id(self, citizen_id):\\n\\n self._citizen_id = citizen_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470b2ae63d3b465d90fba21212f0f49f\",\n \"score\": \"0.43186593\",\n \"text\": \"def set_rflag(self, rflag):\\n with self.lock:\\n self.rflag = rflag\\n if self.closed:\\n self.rflag.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa0d362a4aa00aaa9ee3f98cd1c4b2e\",\n \"score\": \"0.43171245\",\n \"text\": \"def insurance_area(self, insurance_area):\\n\\n self._insurance_area = insurance_area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"537a186344c8a599ff8a26f14d9d07e1\",\n \"score\": \"0.4311873\",\n \"text\": \"def __init__(self,discipline):\\n self.__discipline=discipline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434b556cc187c887c339d736a24bd4ca\",\n \"score\": \"0.43091756\",\n \"text\": \"def ordinal(self, ordinal):\\n\\n self._ordinal = ordinal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c15f7443261c29bfd8afd65aba3287\",\n \"score\": \"0.43015742\",\n \"text\": \"def valor_renda(self, valor_renda):\\n self._valor_renda = valor_renda\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa30e873984f310d570dceafed266c8f\",\n \"score\": \"0.42999122\",\n \"text\": \"def set(self, var1, var2):\\n self.cap.set(var1, var2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954c7711b75128de38850988ab5eef15\",\n \"score\": \"0.42975128\",\n \"text\": \"def affair(self, affair):\\n\\n self._affair = affair\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fada127a726ca62de71400dbaf14aef\",\n \"score\": \"0.42973864\",\n \"text\": \"def changeIris(self, *args):\\n self.cvIrisLoc = self.guideName+\\\"_IrisLoc\\\"\\n cmds.setAttr(self.moduleGrp+\\\".iris\\\", cmds.checkBox(self.irisCB, query=True, value=True))\\n cmds.setAttr(self.cvIrisLoc+\\\".visibility\\\", cmds.checkBox(self.irisCB, query=True, value=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c806c6477ef0b511b11984e9ba760e55\",\n \"score\": \"0.42914957\",\n \"text\": \"def set_old_resnum(self, old_resnum, icode):\\n self.set_extra_info('old_resnum', old_resnum)\\n self.set_extra_info('old_icode', icode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4498d4e058f8ceb546435af1f3f8a8\",\n \"score\": \"0.42868125\",\n \"text\": \"def orcid_id(self, orcid_id):\\n\\n self._orcid_id = orcid_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0c7d72ab3a603d47d21b1351278cfc\",\n \"score\": \"0.4282853\",\n \"text\": \"def set_referencia(self,referencia):\\n self.referencia = referencia\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300e17488eb924110e200f547683d213\",\n \"score\": \"0.42686978\",\n \"text\": \"def propensity_carn(self, value):\\n self._propensity_carn = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c206bfa059b55c58716e65aaadb16b3b\",\n \"score\": \"0.42506772\",\n \"text\": \"def assessment_id(self, assessment_id):\\n\\n self._assessment_id = assessment_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c36d2f4dfaf1bbe497d9bf48b08030ac\",\n \"score\": \"0.42431724\",\n \"text\": \"def SetIRCut(self, is_ir_on):\\n self.send_SetIRCut(is_ir_on)\\n self.recv_SetIRCut()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc10e3cf137fe1cc2217145044919dc\",\n \"score\": \"0.4242249\",\n \"text\": \"def rub(self, rub):\\n if rub is None:\\n raise ValueError(\\\"Invalid value for `rub`, must not be `None`\\\") # noqa: E501\\n\\n self._rub = rub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57fbb07fc09e7e8eb4ab45e6d72adfc3\",\n \"score\": \"0.42418826\",\n \"text\": \"def risk_limit_base(self, risk_limit_base):\\n\\n self._risk_limit_base = risk_limit_base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3bdb3b6d420522cb8640d1bd8dc50d\",\n \"score\": \"0.42300165\",\n \"text\": \"def set_score_land(self, objective, constraints):\\n self.set_err_obj_distances(objective, constraints)\\n\\n score = self.obj_distance/(parameters.RATIONAL_EPSILON+self.err_distance)\\n self.score = score\\n\\n self.set_ancestor_pass()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76627690347c4be880189a86de8d2d8f\",\n \"score\": \"0.42248377\",\n \"text\": \"def rs(self, rs):\\n self._rs = rs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ec03029cb391d17f2704790e4e73ab\",\n \"score\": \"0.42221814\",\n \"text\": \"def set_roi(self):\\n # create the MAV_CMD_DO_SET_ROI command\\n location = self.drone.location.global_relative_frame\\n msg = self.drone.message_factory.command_long_encode(\\n 0, 0, # target system, target component\\n mavutil.mavlink.MAV_CMD_DO_SET_ROI, #command\\n 0, #confirmation\\n 0, 0, 0, 0, #params 1-4\\n location.lat,\\n location.lon,\\n location.alt\\n )\\n # send command to vehicle\\n self.drone.send_mavlink(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec55320612085aeb2c7430fc401cd95\",\n \"score\": \"0.4219721\",\n \"text\": \"def insurance_type(self, insurance_type):\\n\\n self._insurance_type = insurance_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"887ef7da9152c129c05b7b9f6d3c2c58\",\n \"score\": \"0.42166197\",\n \"text\": \"def set_retorno(self, retorno):\\n\\n self.retornos = retorno\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa581359e84560ec93a122c41376606\",\n \"score\": \"0.42163342\",\n \"text\": \"def rstudio(self, rstudio):\\n\\n self._rstudio = rstudio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"475bd3d9c3112418693f61f10518479b\",\n \"score\": \"0.4211241\",\n \"text\": \"def encounter_id(self, encounter_id):\\n\\n self._encounter_id = encounter_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6373a0d905a722c666910f6db2624b32\",\n \"score\": \"0.4208462\",\n \"text\": \"def recurrence(self, recurrence):\\n if recurrence is None:\\n raise ValueError(\\\"Invalid value for `recurrence`, must not be `None`\\\") # noqa: E501\\n\\n self._recurrence = recurrence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8049e861f49bc1d3f64d551245be517d\",\n \"score\": \"0.42081845\",\n \"text\": \"def resolution(self, resolution: ExtendedDisputeallOf1propertiesResolution):\\n\\n self._resolution = resolution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a65d04d7bebaa934f8269480a02952\",\n \"score\": \"0.42042467\",\n \"text\": \"def data_residency(self) -> Optional[pulumi.Input['DataResidencyArgs']]:\\n return pulumi.get(self, \\\"data_residency\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed058986eae13e1ff71bc03902e83399\",\n \"score\": \"0.4194669\",\n \"text\": \"def set_voltage_rise_rate(self, rise_rate):\\n self.voltage_rise_rate.put(rise_rate)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993986,"cells":{"query_id":{"kind":"string","value":"4be294a3daaf1f9dd3c0be1dd7890a7c"},"query":{"kind":"string","value":"Test the result preserves input dimension order when the coordinate to integrate is not the first dimension (eg there's a leading realization coordinate)"},"positive_passages":{"kind":"list like","value":[{"docid":"98dbb8c89a9297a91af2488f6b5d6a1d","score":"0.5471508","text":"def test_dimension_preservation(self):\n cube = set_up_variable_cube(280 * np.ones((3, 3, 3), dtype=np.float32))\n cube = add_coordinate(\n cube, np.array([5.0, 10.0, 20.0]), \"height\", coord_units=\"m\"\n )\n cube.transpose([1, 0, 2, 3])\n expected_coord_order = [coord.name() for coord in cube.coords(dim_coords=True)]\n result = self.plugin.process(cube)\n self.assertEqual(result.coord_dims(\"height\"), (1,))\n result_coord_order = [coord.name() for coord in result.coords(dim_coords=True)]\n self.assertListEqual(result_coord_order, expected_coord_order)","title":""}],"string":"[\n {\n \"docid\": \"98dbb8c89a9297a91af2488f6b5d6a1d\",\n \"score\": \"0.5471508\",\n \"text\": \"def test_dimension_preservation(self):\\n cube = set_up_variable_cube(280 * np.ones((3, 3, 3), dtype=np.float32))\\n cube = add_coordinate(\\n cube, np.array([5.0, 10.0, 20.0]), \\\"height\\\", coord_units=\\\"m\\\"\\n )\\n cube.transpose([1, 0, 2, 3])\\n expected_coord_order = [coord.name() for coord in cube.coords(dim_coords=True)]\\n result = self.plugin.process(cube)\\n self.assertEqual(result.coord_dims(\\\"height\\\"), (1,))\\n result_coord_order = [coord.name() for coord in result.coords(dim_coords=True)]\\n self.assertListEqual(result_coord_order, expected_coord_order)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f41ba7a1922d8fd415d35d28ac8c8643","score":"0.5874654","text":"def test_integrate_column_coordinates(self):\n x = np.linspace(0, 1, 5)\n t = math.integrate_column(np.arange(5), x)\n\n assert(np.isclose(t, 2))","title":""},{"docid":"2b120bfe091aa6100e43fe2a71643861","score":"0.58647597","text":"def test_integrate_column(self):\n t = math.integrate_column(np.arange(5))\n\n assert(np.isclose(t, 8))","title":""},{"docid":"b2403f079c00970dfe91f9d97fee6694","score":"0.58519876","text":"def test_integration_element(two_element_geometries):\n for geom in two_element_geometries:\n np.testing.assert_almost_equal(geom.integration_element, 1)","title":""},{"docid":"9fa448548de0c599fc017651fe04c643","score":"0.56866986","text":"def ol_integrate_local(\n arr: np.ndarray,\n ) -> Callable[[np.ndarray], NumberOrArray]:\n if arr.ndim == num_axes:\n # `arr` is a scalar field\n grid_shape = self.shape\n\n def impl(arr: np.ndarray) -> Number:\n \"\"\"integrate a scalar field\"\"\"\n assert arr.shape == grid_shape\n total = 0\n for i in range(arr.size):\n total += get_cell_volume(i) * arr.flat[i]\n return total\n\n else:\n # `arr` is a tensorial field with rank >= 1\n tensor_shape = (self.dim,) * (arr.ndim - num_axes)\n data_shape = tensor_shape + self.shape\n\n def impl(arr: np.ndarray) -> np.ndarray: # type: ignore\n \"\"\"integrate a tensorial field\"\"\"\n assert arr.shape == data_shape\n total = np.zeros(tensor_shape)\n for idx in np.ndindex(*tensor_shape):\n arr_comp = arr[idx]\n for i in range(arr_comp.size):\n total[idx] += get_cell_volume(i) * arr_comp.flat[i]\n return total\n\n return impl","title":""},{"docid":"dda6697dad35f662b41ce1b9205d87d4","score":"0.5632582","text":"def test_basic(self):\n coord_name = \"height\"\n result = str(Integration(coord_name))\n msg = (\n \"<Integration: coord_name_to_integrate: height, \"\n \"start_point: None, end_point: None, \"\n \"positive_integration: False>\"\n )\n self.assertEqual(result, msg)","title":""},{"docid":"a25a133c3619f7bbf3d9826435cbeffc","score":"0.55956846","text":"def test_partial_trace_sys_int_dim_int_2():\n test_input_mat = np.arange(1, 17).reshape(4, 4)\n\n expected_res = 34\n\n res = partial_trace(test_input_mat, [1], [1, 4])\n\n bool_mat = np.isclose(expected_res, res)\n np.testing.assert_equal(np.all(bool_mat), True)","title":""},{"docid":"cef3f46e8d656e419521c30f4ab90ba2","score":"0.5576368","text":"def test_partial_trace_sys_int_dim_int():\n test_input_mat = np.arange(1, 17).reshape(4, 4)\n\n expected_res = np.array([[7, 11], [23, 27]])\n\n res = partial_trace(test_input_mat, [1])\n\n bool_mat = np.isclose(expected_res, res)\n np.testing.assert_equal(np.all(bool_mat), True)","title":""},{"docid":"ed7aaac0d41d38f29f9be73a63ca8dce","score":"0.5549346","text":"def _check_exact_dimension(*args):\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the function's number of dimensions is equal to `-1` (n-dimensional)\n if obj.dims == -1:\n if x.shape[0] == 0:\n raise e.SizeError(f'{obj.name} input should be n-dimensional')\n\n return f(obj, x)\n\n # If the input dimensions is different from function's allowed dimensions\n if x.shape[0] != obj.dims:\n raise e.SizeError(f'{obj.name} input should be {obj.dims}-dimensional')\n\n return f(obj, x)","title":""},{"docid":"6e2f200e63ac4e2d44e7bad28ac50cb5","score":"0.5507682","text":"def integrate_global(arr: np.ndarray) -> NumberOrArray:\n integral = integrate_local(arr)\n return mpi_allreduce(integral) # type: ignore","title":""},{"docid":"a9117b82545ed0f06fdb6110e6016a02","score":"0.54777163","text":"def _check_dimensions(x, y, solved_shapes):\n # print(f\"{x}x{y}\")\n if (x, y) in solved_shapes.keys():\n return solved_shapes[(x, y)]\n elif (y, x) in solved_shapes.keys():\n return solved_shapes[(y, x)]\n elif x == 1:\n solved_shapes[(x, y)] = y + 1\n return y + 1\n elif y == 1:\n solved_shapes[(x, y)] = x + 1\n return x + 1\n else:\n solved_shapes[(x, y)] = _check_dimensions(x - 1, y, solved_shapes) + _check_dimensions(x, y - 1, solved_shapes)\n return solved_shapes[(x, y)]","title":""},{"docid":"0a9bab8e9760f4b44481de0c7f2c202b","score":"0.54430807","text":"def test_first_integration(fits_input, fits_output):\n\n assert np.all(fits_input['SCI'].data[0] == fits_output['SCI'].data[0])","title":""},{"docid":"e91b778437f292faf37a4da4bdc40f11","score":"0.5376729","text":"def integrate(self,x1,x2):\n return 1","title":""},{"docid":"8681ede88cc398aa5ec1703293f0ebdf","score":"0.5371367","text":"def unit(x_hat):\n temp = scipy.squeeze(x_hat)\n if temp.shape[0] != 3:\n raise ValueError('input is not in a coordinate system, first dimension must be 3')\n return temp","title":""},{"docid":"d15d37b135e4fa3524e96b7140e78255","score":"0.534553","text":"def check_exact_dimension(f):\n\n def _check_exact_dimension(*args):\n \"\"\"Wraps the dimension checking in order to provide additional logic.\n\n Returns:\n The wrapped function output.\n\n \"\"\"\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the function's number of dimensions is equal to `-1` (n-dimensional)\n if obj.dims == -1:\n if x.shape[0] == 0:\n raise e.SizeError(f'{obj.name} input should be n-dimensional')\n\n return f(obj, x)\n\n # If the input dimensions is different from function's allowed dimensions\n if x.shape[0] != obj.dims:\n raise e.SizeError(f'{obj.name} input should be {obj.dims}-dimensional')\n\n return f(obj, x)\n\n return _check_exact_dimension","title":""},{"docid":"38285fa940de30a24c454ad1c29dcdab","score":"0.53400886","text":"def IS_integrate(f, g, h, sampler, n):\n raise NotImplementedError(\"Problem 2 Incomplete\")","title":""},{"docid":"35ff273b0c290200176e87db6457d7a5","score":"0.5323855","text":"def _check_x_dimension(x):\n x = np.atleast_1d(x)\n if len(x.shape) > 2 or (len(x.shape) == 2 and x.shape[1] != 1):\n raise ValueError('Wrong dimension in x.')\n return x.reshape((-1, ))","title":""},{"docid":"b7a2a16c92652ca135f3cb11db89cd92","score":"0.5306183","text":"def integrate_local(arr: np.ndarray) -> NumberOrArray:\n amounts = arr * self.cell_volumes\n return amounts.sum(axis=tuple(range(-num_axes, 0, 1))) # type: ignore","title":""},{"docid":"ba66271f35da3b9791cade5a6da6cc03","score":"0.5305297","text":"def _check_less_equal_dimension(*args):\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the input dimensions is different from function's allowed dimensions\n if x.shape[0] > obj.dims:\n raise e.SizeError(f'{obj.name} input should be less or equal to {obj.dims}-dimensional')\n\n return f(obj, x)","title":""},{"docid":"6172d2fccfdc339980975a36fda0e29f","score":"0.5293063","text":"def _check_x_dimension(x):\n x = np.atleast_1d(x)\n if len(x.shape) > 2 or (len(x.shape) == 2 and x.shape[1] != 1):\n raise ValueError('Wrong dimension in x.')\n return x.reshape((-1,))","title":""},{"docid":"a4ae356fd1f6cf6d7dc439d62b3b45b1","score":"0.5279457","text":"def requires_2d_input(self):","title":""},{"docid":"1853098cd20e6c1fd093914daa97c81e","score":"0.5267114","text":"def test_integraldomain_is_in_domain():\n dom_params = Namespace(min_max=[(0, 5), (-3.2, 1)])\n pts_01 = np.array([[0, 1], [0.0, 1.0], [3, -3], [4, -2]])\n pts_02 = np.array(\n [[-1, 0], [6, 0], [2, -4], [2, 2], [2.2, 0], [2, 0.3], [2.2, 0.3]]\n )\n domain = IntegralDomain(params=dom_params)\n in_dom_list_01 = [domain.is_in_domain(pt) for pt in pts_01]\n in_dom_list_02 = [domain.is_in_domain(pt) for pt in pts_02]\n assert all(b is True for b in in_dom_list_01)\n assert all(b is False for b in in_dom_list_02)","title":""},{"docid":"f34e631f41c84b7961081b1e0c587872","score":"0.52346665","text":"def test_partial_trace_sys_int():\n test_input_mat = np.arange(1, 17).reshape(4, 4)\n\n expected_res = np.array([[12, 14], [20, 22]])\n\n res = partial_trace(test_input_mat, 0)\n\n bool_mat = np.isclose(expected_res, res)\n np.testing.assert_equal(np.all(bool_mat), True)","title":""},{"docid":"04f910e715999a68bbdd30eaeadef36a","score":"0.52221394","text":"def test_reordering(self):\n result = self.plugin._slice_over_coordinate(self.cube, \"realization\")\n self.assertEqual(len(result), 3)\n for i in range(3):\n dim_coord_names = [\n coord.name() for coord in result[i].coords(dim_coords=True)]\n self.assertEqual(dim_coord_names[0], \"realization\")","title":""},{"docid":"b14329e0b5cbf164863c9cfb52f49cf9","score":"0.51861185","text":"def is_in_bound(trial, param):\n return param.value in self.new_dimension","title":""},{"docid":"7d5cfc3d95510ba8212c4a5787cf2711","score":"0.51828134","text":"def _check_x_dimension(x, d=None):\n x = np.array(x)\n if len(x.shape) != 2:\n raise ValueError('Wrong dimension in x.')\n if (d is not None) and (x.shape[1] != d):\n raise ValueError('Wrong dimension in x.')\n return x","title":""},{"docid":"8cd2742a208a212bc5190a416d284ced","score":"0.5182152","text":"def is_1d(self):\n if self.shape is None:\n return False\n return all([dim == 1 for dim in self.shape[1:]])","title":""},{"docid":"e870edc231c4f5cb4fad1b7f50cd4276","score":"0.51817715","text":"def check_dimension(self, init_x):\n if self.f == extended_rosenbrock_function and init_x.size % 2 != 0:\n raise ValueError(\"Invalid input dimension.\")\n elif self.f == extended_powell_singular_function and init_x.size % 4 != 0:\n raise ValueError(\"Invalide input dimension\")","title":""},{"docid":"e870edc231c4f5cb4fad1b7f50cd4276","score":"0.51817715","text":"def check_dimension(self, init_x):\n if self.f == extended_rosenbrock_function and init_x.size % 2 != 0:\n raise ValueError(\"Invalid input dimension.\")\n elif self.f == extended_powell_singular_function and init_x.size % 4 != 0:\n raise ValueError(\"Invalide input dimension\")","title":""},{"docid":"892edcb4a0236c94c4616e9f9348c173","score":"0.51809335","text":"def check_x_dimension(x):\n array = np.atleast_1d(x)\n if len(array.shape) > 2 or (len(array.shape) == 2 and array.shape[1] != 1):\n raise ValueError(\"Wrong dimension in x.\")\n return array.reshape((-1,))","title":""},{"docid":"b895e965073c3393eef1f9844efb7c16","score":"0.5174387","text":"def single_elem_2_array_p(one_arg):\r\n elem, funct = one_arg\r\n intermed = integrate_elem(elem, funct)\r\n return intermed","title":""},{"docid":"eb88e689bdf7606e8041ee04894648c7","score":"0.5173186","text":"def make_integrator(self) -> Callable[[np.ndarray], NumberOrArray]:\n num_axes = self.num_axes\n # cell volume varies with position\n get_cell_volume = self.make_cell_volume_compiled(flat_index=True)\n\n def integrate_local(arr: np.ndarray) -> NumberOrArray:\n \"\"\"integrates data over a grid using numpy\"\"\"\n amounts = arr * self.cell_volumes\n return amounts.sum(axis=tuple(range(-num_axes, 0, 1))) # type: ignore\n\n @overload(integrate_local)\n def ol_integrate_local(\n arr: np.ndarray,\n ) -> Callable[[np.ndarray], NumberOrArray]:\n \"\"\"integrates data over a grid using numba\"\"\"\n if arr.ndim == num_axes:\n # `arr` is a scalar field\n grid_shape = self.shape\n\n def impl(arr: np.ndarray) -> Number:\n \"\"\"integrate a scalar field\"\"\"\n assert arr.shape == grid_shape\n total = 0\n for i in range(arr.size):\n total += get_cell_volume(i) * arr.flat[i]\n return total\n\n else:\n # `arr` is a tensorial field with rank >= 1\n tensor_shape = (self.dim,) * (arr.ndim - num_axes)\n data_shape = tensor_shape + self.shape\n\n def impl(arr: np.ndarray) -> np.ndarray: # type: ignore\n \"\"\"integrate a tensorial field\"\"\"\n assert arr.shape == data_shape\n total = np.zeros(tensor_shape)\n for idx in np.ndindex(*tensor_shape):\n arr_comp = arr[idx]\n for i in range(arr_comp.size):\n total[idx] += get_cell_volume(i) * arr_comp.flat[i]\n return total\n\n return impl\n\n # deal with MPI multiprocessing\n if self._mesh is None or len(self._mesh) == 1:\n # standard case of a single integral\n @jit\n def integrate_global(arr: np.ndarray) -> NumberOrArray:\n \"\"\"integrate data\n\n Args:\n arr (:class:`~numpy.ndarray`): discretized data on grid\n \"\"\"\n return integrate_local(arr)\n\n else:\n # we are in a parallel run, so we need to gather the sub-integrals from all\n # subgrids in the grid mesh\n from ..tools.mpi import mpi_allreduce\n\n @jit\n def integrate_global(arr: np.ndarray) -> NumberOrArray:\n \"\"\"integrate data over MPI parallelized grid\n\n Args:\n arr (:class:`~numpy.ndarray`): discretized data on grid\n \"\"\"\n integral = integrate_local(arr)\n return mpi_allreduce(integral) # type: ignore\n\n return integrate_global # type: ignore","title":""},{"docid":"4117968108c12c6eed95cae185aea070","score":"0.516667","text":"def integrate(self):\n raise MethodImplementationError(self, 'integrate')","title":""},{"docid":"35a79cfed83cf9095e9642d723d10238","score":"0.51280475","text":"def test_separate_coords_error(arr, expected):\n with expected:\n assert EnsembleKalmanFilter.separate_coords(arr)","title":""},{"docid":"9346e0d8e4b6079a708ff5051a7be3f2","score":"0.51262695","text":"def check_less_equal_dimension(f):\n\n def _check_less_equal_dimension(*args):\n \"\"\"Wraps the dimension checking in order to provide additional logic.\n\n Returns:\n The wrapped function output.\n\n \"\"\"\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the input dimensions is different from function's allowed dimensions\n if x.shape[0] > obj.dims:\n raise e.SizeError(f'{obj.name} input should be less or equal to {obj.dims}-dimensional')\n\n return f(obj, x)\n\n return _check_less_equal_dimension","title":""},{"docid":"0d62813e5bcc1f28a7488f1809afdfe9","score":"0.51135397","text":"def isIntegral():","title":""},{"docid":"0d62813e5bcc1f28a7488f1809afdfe9","score":"0.51135397","text":"def isIntegral():","title":""},{"docid":"c49d47f6cd107736ff7dfda6e69e4c05","score":"0.51011837","text":"def integrate(\n self: T,\n *,\n domain: Optional[DomainRange] = None,\n ) -> NDArrayFloat:\n pass","title":""},{"docid":"17ff7d7cfd0ad4650d15eb51306fae52","score":"0.50880814","text":"def integrate_over_grid(X, Y, Z):\n int_x = np.trapz(Z, X, axis=1)\n return np.trapz(int_x, Y[:,0])","title":""},{"docid":"e33403327e00884f2f90154ddac99ded","score":"0.5079505","text":"def test_integrated_variance_fails_with_out_of_domain_test_points(model):\n space = ParameterSpace([ContinuousParameter('x1', 0, 1), ContinuousParameter('x2', 0, 1)])\n\n x_monte_carlo = np.array([[0.5, 20.], [0.2, 0.3]])\n\n with pytest.raises(ValueError):\n IntegratedVarianceReduction(model, space, x_monte_carlo)","title":""},{"docid":"e96be989be73e25c708518b43472c341","score":"0.5076574","text":"def test_dim_coords(dat, dat_inst, variable, dat_name, failed):\n for dim in dat.dims:\n if (dim not in dat_inst.dims) or (dim == \"Time\"):\n continue\n c = dat[dim].values\n cr = dat_inst[dim].values\n if (len(c) != len(cr)) or (c != cr).any():\n print(\n \"Coordinates for dimension {} in data {} of variable {}\"\n \" differs between postprocessed output and WRF output:\"\n \"\\n {} vs. {}\".format(dim, dat_name, variable, c, cr)\n )\n f = \"FAIL\"\n else:\n f = \"pass\"\n f0 = failed.loc[variable, \"dim_coords\"]\n if (f0 == \"\") or (f0 == \"pass\"):\n failed.loc[variable, \"dim_coords\"] = f","title":""},{"docid":"d7046a09a8002349f403a18e53f522c6","score":"0.5072907","text":"def nd_shape_checking(x, y, mvaxis, traxis):\n assert x.ndim == y.ndim\n dims = np.delete(np.arange(x.ndim), -2)\n assert all([x.shape[k] == y.shape[k] for k in dims])","title":""},{"docid":"9c3c587fc151c58b2e4df2c2d597cba9","score":"0.5067622","text":"def test_loc_deph_lindblad_op(dims, site_j, expected):\n\n assert np.all(lind.loc_deph_lindblad_op(dims, site_j) == expected)","title":""},{"docid":"87849ac836869c0e4217f7eb9dc6e7ba","score":"0.5053569","text":"def test_partial_trace_sys():\n test_input_mat = np.arange(1, 17).reshape(4, 4)\n\n expected_res = np.array([[12, 14], [20, 22]])\n\n res = partial_trace(test_input_mat, [0])\n\n bool_mat = np.isclose(expected_res, res)\n np.testing.assert_equal(np.all(bool_mat), True)","title":""},{"docid":"ccd2731d76a5c21abfb2f386dc9c92aa","score":"0.5021951","text":"def clearance(self, x):\n x = scipy.asarray(x)\n if (x < 0).any():\n return False\n if any(i >= j for i, j in zip(x, self.space.shape)):\n return False\n return self.space[tuple(x)]","title":""},{"docid":"eafae0acc3d1459d26cc87fa69ff8b8b","score":"0.5013441","text":"def test_GridDataInterpolator_1DGrid():\n from pyirf.interpolation import GridDataInterpolator\n\n grid_points = np.array([[0], [1]])\n target_point = np.array([[0.5]])\n\n dummy_data1 = np.array([[[0, 1], [1, 1]], [[0, 2], [2, 3]], [[0, 3], [3, 5]]])\n dummy_data2 = np.array([[[0, 2], [2, 2]], [[0, 4], [4, 6]], [[0, 6], [6, 10]]])\n\n dummy_data = np.array([dummy_data1, dummy_data2])\n\n interpolator = GridDataInterpolator(\n grid_points=grid_points, params=dummy_data, method=\"linear\"\n )\n interpolant = interpolator(target_point)\n\n dummy_data_target = 1.5 * dummy_data1\n\n assert np.allclose(interpolant, dummy_data_target)\n assert interpolant.shape == (1, *dummy_data.shape[1:])","title":""},{"docid":"b6b5fdb3c56f8368080e493e3c06f7e9","score":"0.4996801","text":"def test_multidimensional(self):\r\n # Get some higher-dimensional test data\r\n c1 = istk.realistic_4d()\r\n # Chop down to small size, and mask some data\r\n c1 = c1[:3, :4, :16, :12]\r\n c1.data[:, 2, :, :] = np.ma.masked\r\n c1.data[1, 1, 3:9, 4:7] = np.ma.masked\r\n # Give it a slightly more challenging indexing order: tzyx --> xzty\r\n c1.transpose((3, 1, 0, 2))\r\n\r\n # Construct a (coarser) target grid of about the same extent\r\n c1_cs = c1.coord(axis='x').coord_system\r\n xlims = _minmax(c1.coord(axis='x').contiguous_bounds())\r\n ylims = _minmax(c1.coord(axis='y').contiguous_bounds())\r\n # Reduce the dimensions slightly to avoid NaNs in regridded orography\r\n delta = 0.05\r\n # || NOTE: this is *not* a small amount. Think there is a bug.\r\n # || NOTE: See https://github.com/SciTools/iris/issues/458\r\n xlims = np.interp([delta, 1.0 - delta], [0, 1], xlims)\r\n ylims = np.interp([delta, 1.0 - delta], [0, 1], ylims)\r\n pole_latlon = (c1_cs.grid_north_pole_latitude,\r\n c1_cs.grid_north_pole_longitude)\r\n c2 = _make_test_cube((7, 8), xlims, ylims, pole_latlon=pole_latlon)\r\n\r\n # regrid onto new grid\r\n c1_to_c2 = regrid_conservative_via_esmpy(c1, c2)\r\n\r\n # check that all the original coords exist in the new cube\r\n # NOTE: this also effectively confirms we haven't lost the orography\r\n def list_coord_names(cube):\r\n return sorted([coord.name() for coord in cube.coords()])\r\n\r\n self.assertEqual(list_coord_names(c1_to_c2), list_coord_names(c1))\r\n\r\n # check that each xy 'slice' has same values as if done on its own.\r\n for i_p, i_t in np.ndindex(c1.shape[1:3]):\r\n c1_slice = c1[:, i_p, i_t]\r\n c2_slice = regrid_conservative_via_esmpy(c1_slice, c2)\r\n subcube = c1_to_c2[:, i_p, i_t]\r\n self.assertEqual(subcube, c2_slice)\r\n\r\n # check all other metadata\r\n self.assertEqual(c1_to_c2.metadata, c1.metadata)","title":""},{"docid":"d7d15166959b545e79f8ca37f0f4784a","score":"0.4994784","text":"def test_shape_of_inflate_array(shape: IntTuple, grid: IntTuple) -> None:\n grid = grid[: len(shape)]\n\n inflated_array = nf.inflate_array(np.empty(shape), grid)\n\n assert inflated_array.ndim == len(shape)\n for i in range(len(grid)):\n assert inflated_array.shape[i] == grid[i] * shape[i]\n for i in range(len(grid), inflated_array.ndim):\n assert inflated_array.shape[i] == shape[i]","title":""},{"docid":"b6ccfe2464dfd151dc7447d6ad022ba9","score":"0.4989598","text":"def isneginf(x=None, y=None):\n return ndarray()","title":""},{"docid":"ed399df240d492086b495d7462e728bd","score":"0.49836963","text":"def integration_1d():\n I = integration_1d_sym(x, x0, x1)\n\n def P0(f):\n p = I[0](f(x))\n return sy.lambdify((x0,), p)\n\n def P1(f):\n p = I[1](f(x))\n return sy.lambdify((x0, x1), p)\n\n return P0, P1","title":""},{"docid":"a7b1a82005f746cbfddf171b05a37fe0","score":"0.49753493","text":"def test_x_wrong_ndims():\n z = np.zeros((0, 2))\n x = [[np.array([0., 0.]), np.array([[1., 0.]]), np.array([[0., 1.]]), np.array([[1., 1.]])],\n [z, z, z, z], [z], []]\n assert_failure(x=x)","title":""},{"docid":"ff4a335aad407847b4038eede45c3c6a","score":"0.49692056","text":"def first_tensor_invariant(A): \n return A[0,0] + A[1,1] + A[2,2]","title":""},{"docid":"d7354adf5aac0eb2b3794b8c34791a1e","score":"0.4966854","text":"def _check_dimension(params):\n input_col = params['input_col']\n output_col = params['output_col']\n\n interaction_only = params['interaction_only']\n degree = params['degree']\n\n from sklearn.preprocessing import PolynomialFeatures\n poly = PolynomialFeatures(degree=degree, interaction_only=interaction_only)\n tmp = [[0 for _ in range(len(input_col))]]\n dim = poly.fit_transform(tmp).shape[1]\n\n params['output_col'] = ['col{}{}'.format(i, output_col) for i in range(dim)]\n return params","title":""},{"docid":"7f6841e153d617aa50bcb5d13c4cb8f4","score":"0.49655724","text":"def test_check_data_array_dimensions_match_unknown_dimension():\n time_size, depth_size = 4, 3\n lon_size, lat_size = 10, 15\n\n dataset = xarray.Dataset(\n data_vars={\n 'botz': ([\"lat\", \"lon\"], 50 + 25 * numpy.random.random_sample((lat_size, lon_size))),\n 'eta': (\n [\"time\", \"lat\", \"lon\"],\n numpy.random.random_sample((time_size, lat_size, lon_size)),\n ),\n },\n coords={\n 'time': ([\"time\"], pandas.date_range(\"2021-12-21\", periods=time_size)),\n 'lon': ([\"lon\"], numpy.arange(lon_size) + 0.5),\n 'lat': ([\"lat\"], numpy.arange(lat_size) + 0.5),\n }\n )\n\n # Slicing the temperature to get a subset of the lat/lon grid will cause\n # the dimensions to differ\n temp = xarray.DataArray(\n data=15 + 3 * numpy.random.random_sample((time_size, depth_size, lat_size, lon_size)),\n dims=['time', 'depth', 'lat', 'lon'],\n )\n\n with pytest.raises(ValueError):\n utils.check_data_array_dimensions_match(dataset, temp)\n\n # If you remove the extra dimension, this should be fine\n surface_temp = temp.isel({'depth': 0}, drop=True)\n utils.check_data_array_dimensions_match(dataset, surface_temp)","title":""},{"docid":"2bed6c0451a6a726bb30ad1aa92fe0c4","score":"0.49650878","text":"def is_single_unit(data):\n try:\n if isinstance(data[0][0], list) or isinstance(data[0][0], np.ndarray):\n warnings.warn(\"spike times input has too many layers!\")\n if max(np.array(data).shape[:-1]) > 1:\n # singletons = True\n return False\n data = np.squeeze(data)\n except (IndexError, TypeError):\n pass\n try:\n if isinstance(data[1], list) or isinstance(data[1], np.ndarray):\n return False\n except (IndexError, TypeError):\n pass\n return True","title":""},{"docid":"32b78db03be1c39ffe970d2456c9497c","score":"0.4958135","text":"def _check_exact_dimension_and_auxiliary_matrix(*args):\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the input dimensions differs from function's allowed dimensions\n if x.shape[0] not in [2, 10, 30, 50]:\n raise e.SizeError(f'{obj.name} input should be 2-, 10-, 30- or 50-dimensional')\n\n if x.shape[0] == 2:\n setattr(obj, 'M', obj.M2)\n\n elif x.shape[0] == 10:\n setattr(obj, 'M', obj.M10)\n\n elif x.shape[0] == 30:\n setattr(obj, 'M', obj.M30)\n\n elif x.shape[0] == 50:\n setattr(obj, 'M', obj.M50)\n\n return f(obj, x)","title":""},{"docid":"52ea77bb84f211310b7a6e2db87d88ec","score":"0.49564505","text":"def test_partial_trace():\n test_input_mat = np.arange(1, 17).reshape(4, 4)\n\n expected_res = np.array([[7, 11], [23, 27]])\n\n res = partial_trace(test_input_mat)\n\n bool_mat = np.isclose(expected_res, res)\n np.testing.assert_equal(np.all(bool_mat), True)","title":""},{"docid":"59eebe69be0d9507ef3c05dae0b81843","score":"0.49482214","text":"def test_do_interpolate_func(my_data):\n loss = do_interpolate(my_data, scipy=False, func=func)\n assert 0 < loss < np.inf","title":""},{"docid":"44ad4527f255f97e0c4c7681ce973830","score":"0.4942044","text":"def test_addition_centered_grid(self):\n shape = [32, 27]\n for boundary in [CLOSED, PERIODIC, OPEN]:\n domain = Domain(shape, boundaries=(boundary, boundary))\n centered_grid = CenteredGrid.sample(1, domain)\n result_array = (centered_grid + centered_grid).data\n np.testing.assert_array_equal(result_array, 2)","title":""},{"docid":"d49a5818feafbc1ae551a9cea90fecdf","score":"0.49379927","text":"def integrate(func, x_init, x_final, slices=100, mode=\"simpson\"):\n\n h = (x_final - x_init)/float(slices)\n\n integral = 0.0\n if mode == \"midpoint\":\n for i in range(slices):\n x0 = x_init + i*h\n x1 = x0 + i*h\n xmid = (x0 + x1)/2.0\n integral += func(xmid)*h\n\n if mode == \"trapezoid\":\n for i in range(slices):\n x0 = x_init + i*h\n x1 = x0 + h\n y0 = func(x0)\n y1 = func(x1)\n integral += (y0 + y1)/2.0 * h\n if mode == \"simpson\":\n #enforce even number of slices out of laziness\n for i in range(0, slices, 2): \n x0 = x_init + i*h\n x1 = x0 + h\n x2 = x1 + h\n y0 = func(x0)\n y1 = func(x1)\n y2 = func(x2)\n integral += (y0 + 4*y1 + y2) * (h/3)\n\n return integral","title":""},{"docid":"a4d15c3a4042e57b988f47d04303f463","score":"0.49265644","text":"def test_dimensionality_output(self):\n\n detrending = data_prep.Detrending(alpha = 0.001,\n variance = 0.001,\n noise = 0.001)\n\n matrix = detrending.cov_matrix_SE(self.test_x, self.test_x)\n prior_trace = detrending.generate_prior_SE_trace(self.test_x, 1)\n\n self.assertEqual(np.shape(matrix), tuple((len(self.test_x), len(self.test_x))))\n self.assertEqual(np.shape(prior_trace), tuple((len(self.test_x), 2))) # (number_of_observations, number_of_traces + 1)\n # self.assertEqual(prior_trace[:,0], self.test_x)\n\n optim = detrending.optimise_SE_trace(0.001, 0.001, 0.001, self.test_x, self.test_y)\n\n self.assertTrue(len(optim) == 3) # three parameters optimised\n\n posterior_trace = detrending.fit_SE(self.observed_x, self.observed_y, self.test_x, 1, True)\n\n self.assertEqual(np.shape(posterior_trace), tuple((len(self.test_x), 3))) # (number_of_observations, number_of_traces + 2)\n\n detrended_data, test_timepoints, fits = detrending.detrend_data(self.observed_x, self.data, True)\n\n self.assertTrue(np.shape(detrended_data) == np.shape(self.data))\n self.assertTrue(np.shape(fits) == tuple((len(test_timepoints), np.shape(detrended_data)[1])))","title":""},{"docid":"399860a694d3be8648ddbe8390319c17","score":"0.4923434","text":"def isposinf(x=None, y=None):\n return ndarray()","title":""},{"docid":"c9abc08cd4dda955e9fcd10aa58bff1d","score":"0.49133965","text":"def is_continuous_tailing_axis(self, shape, reduce_axis):\n for i in self._origin_op:\n if i[\"op\"] == 'broadcast_for_tensor':\n return False\n\n if len(reduce_axis) > 1:\n index_list = [index for index, _ in enumerate(shape)]\n\n if index_list[-len(reduce_axis):] == reduce_axis:\n return True\n\n return False","title":""},{"docid":"28e5c77a28401d92ac740692b85dda8c","score":"0.4896915","text":"def is_dimensionless(self):\r\n return (self.category == _CATEGORY_UDUNIT and\r\n bool(_ut_is_dimensionless(self.ut_unit)))","title":""},{"docid":"0f0d738d0879325a2eedec941047ebd4","score":"0.48961633","text":"def check_coordinates(data_array: xarray.DataArray):\n missing_dimensions = [d for d in horizontal_spatial_dimensions if d not in data_array.dims]\n if missing_dimensions:\n message = f\"Structural similarity index expects data with dimensions {horizontal_spatial_dimensions}.\\n\" \\\n f\"Dimension/s {missing_dimensions} missing.\"\n raise EnstoolsError(message)","title":""},{"docid":"27442fb24554a40e7b7addc23f608b30","score":"0.48928264","text":"def test_multidim_moving_projection():\n\n x_ = np.random.randn(2000)\n\n x = np.empty((5, 10, 2000))\n x[:] = x_\n y = moving_projection(x, 200, 5/200.)\n\n err = y - y[0, 0]\n assert np.sum(err ** 2) < 1e-8","title":""},{"docid":"87999cf2f95e74ef2b32064f96212ee9","score":"0.48843023","text":"def check_conformity(self, x:np.ndarray) -> np.ndarray:\n ...","title":""},{"docid":"8814c55ab137be6f42bb6ec04e48350c","score":"0.4884279","text":"def test_infinity_handling(self):\n a = np.array([[1, 2], [1, np.inf]])\n b = np.array([3, 4])\n c = np.array([[1, 2], [3, 4]])\n ref = np.array([[[4, 6], [9, 12]],\n [[4, 6], [np.inf, np.inf]]])\n res = gulinalg.update_rank1(a, b, c)\n assert_allclose(res, ref)","title":""},{"docid":"6a1f63711ea06f85ccd8a723f4957e9e","score":"0.48806694","text":"def implicit_constant(x):\n nobs = x.shape[0]\n rank = np.linalg.matrix_rank(np.hstack((np.ones((nobs, 1)), x)))\n return rank == x.shape[1]","title":""},{"docid":"53bc6a2959655332cd00ded4d4538fb3","score":"0.4879","text":"def isreal(self, x):\n return ndarray()","title":""},{"docid":"07cc43428533fcfc16f59765314edd5b","score":"0.4868493","text":"def test_partial_trace_non_square_matrix_dim_2():\n with np.testing.assert_raises(ValueError):\n rho = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n partial_trace(rho, [1], [2])","title":""},{"docid":"9242fe04e4d70c7e783b4a3c718cad16","score":"0.48668632","text":"def integrate(self, t1, t2) -> float:\n ...","title":""},{"docid":"cec740e22520d3afad20cb7362d58130","score":"0.48601013","text":"def isreal(x):\n return ndarray()","title":""},{"docid":"baa17947f07f54a84091e09524365022","score":"0.48562244","text":"def integrate_over_grid(grid_step, F, axis=None):\n if axis is None:\n V = np.prod(grid_step)\n else:\n V = grid_step\n \n return V * np.sum(F, axis=axis)","title":""},{"docid":"a80b25a8427d577786b74f624725a012","score":"0.48501012","text":"def test_interp_inplace():\n x = np.arange(10)\n y_actual = 2 + 5 * x\n\n y_calc = np.empty_like(y_actual)\n y_calc[0] = y_actual[0]\n y_calc[-1] = y_actual[-1]\n\n output = utils._interp_inplace(x, y_calc, y_calc[0], y_calc[-1])\n\n # output should be the same object as the input y array\n assert output is y_calc\n\n assert_allclose(y_calc, y_actual, 1e-12)","title":""},{"docid":"a4f20bdf26e45c92fa051742c6abe47f","score":"0.48459294","text":"def is_monotonic(self):\r\n\r\n if self.ndim != 1:\r\n raise iris.exceptions.CoordinateMultiDimError(self)\r\n\r\n if self.shape == (1,):\r\n return True\r\n\r\n if self.points is not None:\r\n if not iris.util.monotonic(self.points, strict=True):\r\n return False\r\n\r\n if self.bounds is not None:\r\n for b_index in xrange(self.nbounds):\r\n if not iris.util.monotonic(self.bounds[..., b_index],\r\n strict=True):\r\n return False\r\n\r\n return True","title":""},{"docid":"dc87149efa2aa73d40da4d5ce32ff726","score":"0.484416","text":"def test_integraldomain_defaults_is_in_domain():\n pts_01 = [2, 2.0, 4, 4.0, np.array(2), np.array(2.0), np.array(4.0)]\n pts_02 = [2.1, 4.1, np.array(2.1)]\n domain = IntegralDomain()\n in_dom_list_01 = [domain.is_in_domain(pt) for pt in pts_01]\n in_dom_list_02 = [domain.is_in_domain(pt) for pt in pts_02]\n assert all(b is True for b in in_dom_list_01)\n assert all(b is False for b in in_dom_list_02)","title":""},{"docid":"9d57c5ac711b360b6f382f18288f819a","score":"0.48433647","text":"def valid_shape(x):\n return ((isinstance(x, numbers.Integral) and (x > 0)) or\n (x is None) or\n (isinstance(x, (tuple, list)) and\n (len(x) > 0) and\n all([((isinstance(n, numbers.Integral) and (n > 0)) or\n (n is None)) for n in x])))","title":""},{"docid":"e8261c3acfc8c444e14ed380bd05af2c","score":"0.48402226","text":"def test_two_coords(self):\n expected_data = self.cube.collapsed(\n [\"realization\", \"latitude\"], iris.analysis.MEAN\n ).data\n result = collapsed(self.cube, [\"realization\", \"latitude\"], iris.analysis.MEAN)\n self.assertTrue((result.data == expected_data).all())\n self.assertEqual(\n result.coord(\"latitude\").dtype, self.cube.coord(\"latitude\").dtype\n )","title":""},{"docid":"04397426ff10c6fdad78e98a47f1ff16","score":"0.48389104","text":"def valid(self):\n\t\txsq = self.x*self.x\n\t\tysq = self.y*self.y\n\t\treturn (JUBJUB_A * xsq) + ysq == (1 + JUBJUB_D * xsq * ysq)","title":""},{"docid":"d874e823076e12a1bcf13ccb84c39124","score":"0.4837981","text":"def integrate(\n self, data: NumberOrArray, axes: Union[int, Sequence[int], None] = None\n ) -> NumberOrArray:\n # determine the volumes of the individual cells\n if axes is None:\n volume_list = self.cell_volume_data\n else:\n # use stored value for the default case of integrating over all axes\n if isinstance(axes, int):\n axes = (axes,)\n else:\n axes = tuple(axes) # required for numpy.sum\n volume_list = [\n cell_vol if ax in axes else 1\n for ax, cell_vol in enumerate(self.cell_volume_data)\n ]\n cell_volumes = functools.reduce(np.outer, volume_list)\n\n # determine the axes over which we will integrate\n if not isinstance(data, np.ndarray) or data.ndim < self.num_axes:\n # deal with the case where data is not supplied for each support\n # point, e.g., when a single scalar is integrated over the grid\n data = np.broadcast_to(data, self.shape)\n\n elif data.ndim > self.num_axes:\n # deal with the case where more than a single value is provided per\n # support point, e.g., when a tensorial field is integrated\n offset = data.ndim - self.num_axes\n if axes is None:\n # integrate over all axes of the grid\n axes = tuple(range(offset, data.ndim))\n else:\n # shift the indices to account for the data shape\n axes = tuple(offset + i for i in axes)\n\n # calculate integral using a weighted sum along the chosen axes\n integral = (data * cell_volumes).sum(axis=axes)\n\n if self._mesh is None or len(self._mesh) == 1:\n # standard case of a single integral\n return integral # type: ignore\n\n else:\n # we are in a parallel run, so we need to gather the sub-integrals from all\n from mpi4py.MPI import COMM_WORLD\n\n integral_full = np.empty_like(integral)\n COMM_WORLD.Allreduce(integral, integral_full)\n return integral_full # type: ignore","title":""},{"docid":"fd3392f477d8dc6c6bf3cd9018b2ed64","score":"0.48363113","text":"def isIntegral(self):\n return self.precision==0","title":""},{"docid":"4f3609a89a2c6d282b44f49c3d9a66c9","score":"0.48355156","text":"def _validate_normalize_axes(axes, axis, keepdims, input_coredimss, output_coredimss):\n nin = len(input_coredimss)\n nout = 1 if not isinstance(output_coredimss, list) else len(output_coredimss)\n\n if axes is not None and axis is not None:\n raise ValueError(\n \"Only one of `axis` or `axes` keyword arguments should be given\"\n )\n if axes and not isinstance(axes, list):\n raise ValueError(\"`axes` has to be of type list\")\n\n output_coredimss = output_coredimss if nout > 1 else [output_coredimss]\n filtered_core_dims = list(filter(len, input_coredimss))\n nr_outputs_with_coredims = len([True for x in output_coredimss if len(x) > 0])\n\n if keepdims:\n if nr_outputs_with_coredims > 0:\n raise ValueError(\"`keepdims` can only be used for scalar outputs\")\n output_coredimss = len(output_coredimss) * [filtered_core_dims[0]]\n\n core_dims = input_coredimss + output_coredimss\n if axis is not None:\n if not isinstance(axis, int):\n raise ValueError(\"`axis` argument has to be an integer value\")\n if filtered_core_dims:\n cd0 = filtered_core_dims[0]\n if len(cd0) != 1:\n raise ValueError(\n \"`axis` can be used only, if one core dimension is present\"\n )\n for cd in filtered_core_dims:\n if cd0 != cd:\n raise ValueError(\n \"To use `axis`, all core dimensions have to be equal\"\n )\n\n # Expand dafaults or axis\n if axes is None:\n if axis is not None:\n axes = [(axis,) if cd else tuple() for cd in core_dims]\n else:\n axes = [tuple(range(-len(icd), 0)) for icd in core_dims]\n elif not isinstance(axes, list):\n raise ValueError(\"`axes` argument has to be a list\")\n axes = [(a,) if isinstance(a, int) else a for a in axes]\n\n if (\n (nr_outputs_with_coredims == 0)\n and (nin != len(axes))\n and (nin + nout != len(axes))\n ) or ((nr_outputs_with_coredims > 0) and (nin + nout != len(axes))):\n raise ValueError(\n \"The number of `axes` entries is not equal the number of input and output arguments\"\n )\n\n # Treat outputs\n output_axes = axes[nin:]\n output_axes = (\n output_axes\n if output_axes\n else [tuple(range(-len(ocd), 0)) for ocd in output_coredimss]\n )\n input_axes = axes[:nin]\n\n # Assert we have as many axes as output core dimensions\n for idx, (iax, icd) in enumerate(zip(input_axes, input_coredimss)):\n if len(iax) != len(icd):\n raise ValueError(\n \"The number of `axes` entries for argument #{} is not equal \"\n \"the number of respective input core dimensions in signature\".format(\n idx\n )\n )\n if not keepdims:\n for idx, (oax, ocd) in enumerate(zip(output_axes, output_coredimss)):\n if len(oax) != len(ocd):\n raise ValueError(\n \"The number of `axes` entries for argument #{} is not equal \"\n \"the number of respective output core dimensions in signature\".format(\n idx\n )\n )\n else:\n if input_coredimss:\n icd0 = input_coredimss[0]\n for icd in input_coredimss:\n if icd0 != icd:\n raise ValueError(\n \"To use `keepdims`, all core dimensions have to be equal\"\n )\n iax0 = input_axes[0]\n output_axes = [iax0 for _ in output_coredimss]\n\n return input_axes, output_axes","title":""},{"docid":"c98f08f5e0f21de7cdec50c75a58ca90","score":"0.48316625","text":"def _get_out_shape(in_shape, dim = 1, direction = +1, real_transform = False):\n if real_transform:\n if direction == +1:\n return in_shape[:-1] + (in_shape[-1]//2,)\n else:\n return in_shape[:-1] + (in_shape[-1] * 2,)\n else:\n return in_shape","title":""},{"docid":"514c2b5647c2e278a5a0c30afbe05646","score":"0.48250335","text":"def check_exact_dimension_and_auxiliary_matrix(f):\n\n def _check_exact_dimension_and_auxiliary_matrix(*args):\n \"\"\"Wraps the dimension checking in order to provide additional logic.\n\n Returns:\n The wrapped function output.\n\n \"\"\"\n\n # Retrieving the object and the input from arguments\n obj, x = args[0], args[1]\n\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\n try:\n x = np.squeeze(x, axis=1)\n\n except ValueError:\n pass\n\n # If the input dimensions differs from function's allowed dimensions\n if x.shape[0] not in [2, 10, 30, 50]:\n raise e.SizeError(f'{obj.name} input should be 2-, 10-, 30- or 50-dimensional')\n\n if x.shape[0] == 2:\n setattr(obj, 'M', obj.M2)\n\n elif x.shape[0] == 10:\n setattr(obj, 'M', obj.M10)\n\n elif x.shape[0] == 30:\n setattr(obj, 'M', obj.M30)\n\n elif x.shape[0] == 50:\n setattr(obj, 'M', obj.M50)\n\n return f(obj, x)\n\n return _check_exact_dimension_and_auxiliary_matrix","title":""},{"docid":"7850606dbcc5f037fb62d6d85fb5c3aa","score":"0.48239166","text":"def test_s_enhance_errors():\n\n arr = np.arange(28800).reshape((2, 20, 20, 12, 3))\n with pytest.raises(ValueError):\n spatial_coarsening(arr, s_enhance=3)\n with pytest.raises(ValueError):\n spatial_coarsening(arr, s_enhance=7)\n with pytest.raises(ValueError):\n spatial_coarsening(arr, s_enhance=40)\n\n arr = np.ones(10)\n with pytest.raises(ValueError):\n spatial_coarsening(arr, s_enhance=5)\n\n arr = np.ones((4, 4))\n with pytest.raises(ValueError):\n spatial_coarsening(arr, s_enhance=2)","title":""},{"docid":"410883d15336f1f3a76c520679e728a7","score":"0.48204818","text":"def symmetric_2d(self, x: np.array) -> bool:\n return np.allclose(x, x.T, atol=self.tol)","title":""},{"docid":"b1c0c76c648a3134df886607041ad8df","score":"0.4816043","text":"def test_do_interpolate(my_data, method, order):\n loss = do_interpolate(my_data, method=method, order=order)\n assert 0 < loss < np.inf","title":""},{"docid":"f36b2b0491679907dfbb186f7b48abc0","score":"0.48080644","text":"def test_flatten13():\n x = np.ones(shape=[2, 3, 4, 5])\n start_axis = -2\n stop_axis = -1\n res = np.ones(shape=[2, 3, 20])\n obj.run(res=res, x=x, start_axis=start_axis, stop_axis=stop_axis)","title":""},{"docid":"473afe1b65722da72d29c7a97b5a907a","score":"0.4792067","text":"def test_nz_qnorm_irizarry_code(self):\n irizarry_res_sxf = self.nz_qnorm(self.irizarry_ex_sxf)\n res_shape = irizarry_res_sxf.shape\n true_shape = self.irizarry_code_res_sxf.shape\n self.assertEqual(res_shape, true_shape,\n 'Shape mismatch: {} vs {}'.format(res_shape, true_shape))\n nptest.assert_allclose(irizarry_res_sxf, self.irizarry_code_res_sxf, rtol=0.001)\n return","title":""},{"docid":"1dc5556ca32119d2b907494c1da850b9","score":"0.47906113","text":"def test_GridDataInterpolator_2DGrid():\n from pyirf.interpolation import GridDataInterpolator\n\n grid_points = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n target_point = np.array([[0.5, 0.5]])\n\n dummy_data1 = np.array([[[0, 1], [1, 1]], [[0, 2], [2, 3]], [[0, 3], [3, 5]]])\n dummy_data2 = np.array([[[0, 2], [1, 2]], [[0, 4], [2, 6]], [[0, 6], [3, 10]]])\n dummy_data3 = np.array([[[0, 1], [2, 1]], [[0, 2], [4, 3]], [[0, 3], [6, 5]]])\n dummy_data4 = np.array([[[0, 2], [2, 2]], [[0, 4], [4, 6]], [[0, 6], [6, 10]]])\n\n dummy_data = np.array([dummy_data1, dummy_data2, dummy_data3, dummy_data4])\n\n interpolator = GridDataInterpolator(\n grid_points=grid_points, params=dummy_data, method=\"linear\"\n )\n interpolant = interpolator(target_point)\n\n dummy_data_target = 1.5 * dummy_data1\n\n assert np.allclose(interpolant, dummy_data_target)\n assert interpolant.shape == (1, *dummy_data.shape[1:])","title":""},{"docid":"78763b9a86e3d52771e5984f3b1f6a76","score":"0.4786542","text":"def xr_int_along_axis(xa, DZ, axis):\n assert type(axis)==np.dtype(int) or axis in xa.dims\n assert np.shape(xa)==np.shape(DZ)\n assert axis<=len(np.shape(xa))\n \n integral = np.sum(xa*DZ, axis=axis)\n \n return integral","title":""},{"docid":"9e036ebd6367c582f06a4fd396f3e6cb","score":"0.47839087","text":"def integrate(self):\n return self._compute_integral_mean_and_variance()","title":""},{"docid":"c52ca29dd99d25e9977cca19d25fb241","score":"0.47810954","text":"def test_3rd_order_gauss_quadrature(self):\n def fun(y, x):\n \"\"\"\n Integration test function\n \"\"\"\n return x**3 + y**3\n\n element_integral = 0\n for pt, w in zip(Q8.integration_points_3, Q8.integration_weights_3):\n element_integral += w * fun(*pt)\n\n reference_integral = integrate.dblquad(fun, -1, 1, lambda x: -1, lambda x: 1)\n\n self.assertTrue(np.isclose(element_integral, reference_integral[0]))","title":""},{"docid":"5f2eaab26a29bb1d6849f43babdcb2fe","score":"0.47807398","text":"def remove_dimension(trial, param):\n if param == self.param:\n adapted_trial = copy.deepcopy(trial)\n # pylint: disable=protected-access\n del adapted_trial._params[adapted_trial._params.index(self.param)]\n adapted_trials.append(adapted_trial)\n return True\n\n return False","title":""},{"docid":"7766f7f6cb8244f00d52541b1789065b","score":"0.4778484","text":"def _validate(self):\r\n if len(set(self._interp_dims)) != len(self._src_coords):\r\n raise ValueError('Coordinates repeat a data dimension - the '\r\n 'interpolation would be over-specified.')\r\n\r\n for coord in self._src_coords:\r\n if coord.ndim != 1:\r\n raise ValueError('Interpolation coords must be 1-d for '\r\n 'rectilinear interpolation.')\r\n\r\n if not isinstance(coord, DimCoord):\r\n # Check monotonic.\r\n if not iris.util.monotonic(coord.points, strict=True):\r\n msg = 'Cannot interpolate over the non-' \\\r\n 'monotonic coordinate {}.'\r\n raise ValueError(msg.format(coord.name()))","title":""},{"docid":"8df5cea4c2b821411c60ad667f0aaa64","score":"0.47729105","text":"def numerical_integrate_1D(func, domain, *params):\n delta_x = domain[1:] - domain[:-1]\n return sum((func(domain, *params)[:-1]+ func(domain, *params)[1:])/2 * delta_x)","title":""},{"docid":"efa679cc1c8cd8e7af78dc9613713529","score":"0.47696334","text":"def integrationOverXi(self):\n return _libBornAgainCore.InterferenceFunctionFinite2DLattice_integrationOverXi(self)","title":""},{"docid":"4070db5edb7c808f31d23436f7f9c744","score":"0.4767247","text":"def integrate(self, Region = None):\n if ('Uniform' in self.mesh.properties and 'Cartesian' in self.mesh.properties):\n if Region:\n out = self[Region.pindex].sum() \n else: \n out = self.sum()\n \n out *= self.mesh.spacing \n \n return out.astype(self.dtype)","title":""},{"docid":"74cbea9432cf0ecba9cda945e955d9d0","score":"0.47660792","text":"def test_separate_coords(arr, expected1, expected2):\n assert EnsembleKalmanFilter.separate_coords(arr) == (expected1, expected2)","title":""},{"docid":"a74d084a2b4abf3446ac5c289fd09032","score":"0.4765284","text":"def test_get_arr_1d():\n # 1d\n a = np.arange(3)\n arr_1d, i, bc_idx = _get_arr_1d(a, [1], 0)\n assert i == 1\n assert bc_idx == (...,)\n np.testing.assert_equal(arr_1d, a)\n\n # 2d\n a = np.arange(4).reshape(2, 2)\n arr_1d, i, bc_idx = _get_arr_1d(a, [0, 0], 0)\n assert i == 0\n assert bc_idx == (..., 0)\n np.testing.assert_equal(arr_1d, a[:, 0])\n\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1], 1)\n assert i == 1\n assert bc_idx == (..., 1)\n np.testing.assert_equal(arr_1d, a[1, :])\n\n # 3d\n a = np.arange(8).reshape(2, 2, 2)\n arr_1d, i, bc_idx = _get_arr_1d(a, [0, 0, 0], 0)\n assert i == 0\n assert bc_idx == (..., 0, 0)\n np.testing.assert_equal(arr_1d, a[:, 0, 0])\n\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1, 0], 1)\n assert i == 1\n assert bc_idx == (..., 1, 0)\n np.testing.assert_equal(arr_1d, a[1, :, 0])\n\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1, 0], 2)\n assert i == 0\n assert bc_idx == (..., 1, 1)\n np.testing.assert_equal(arr_1d, a[1, 1, :])","title":""}],"string":"[\n {\n \"docid\": \"f41ba7a1922d8fd415d35d28ac8c8643\",\n \"score\": \"0.5874654\",\n \"text\": \"def test_integrate_column_coordinates(self):\\n x = np.linspace(0, 1, 5)\\n t = math.integrate_column(np.arange(5), x)\\n\\n assert(np.isclose(t, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b120bfe091aa6100e43fe2a71643861\",\n \"score\": \"0.58647597\",\n \"text\": \"def test_integrate_column(self):\\n t = math.integrate_column(np.arange(5))\\n\\n assert(np.isclose(t, 8))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2403f079c00970dfe91f9d97fee6694\",\n \"score\": \"0.58519876\",\n \"text\": \"def test_integration_element(two_element_geometries):\\n for geom in two_element_geometries:\\n np.testing.assert_almost_equal(geom.integration_element, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa448548de0c599fc017651fe04c643\",\n \"score\": \"0.56866986\",\n \"text\": \"def ol_integrate_local(\\n arr: np.ndarray,\\n ) -> Callable[[np.ndarray], NumberOrArray]:\\n if arr.ndim == num_axes:\\n # `arr` is a scalar field\\n grid_shape = self.shape\\n\\n def impl(arr: np.ndarray) -> Number:\\n \\\"\\\"\\\"integrate a scalar field\\\"\\\"\\\"\\n assert arr.shape == grid_shape\\n total = 0\\n for i in range(arr.size):\\n total += get_cell_volume(i) * arr.flat[i]\\n return total\\n\\n else:\\n # `arr` is a tensorial field with rank >= 1\\n tensor_shape = (self.dim,) * (arr.ndim - num_axes)\\n data_shape = tensor_shape + self.shape\\n\\n def impl(arr: np.ndarray) -> np.ndarray: # type: ignore\\n \\\"\\\"\\\"integrate a tensorial field\\\"\\\"\\\"\\n assert arr.shape == data_shape\\n total = np.zeros(tensor_shape)\\n for idx in np.ndindex(*tensor_shape):\\n arr_comp = arr[idx]\\n for i in range(arr_comp.size):\\n total[idx] += get_cell_volume(i) * arr_comp.flat[i]\\n return total\\n\\n return impl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda6697dad35f662b41ce1b9205d87d4\",\n \"score\": \"0.5632582\",\n \"text\": \"def test_basic(self):\\n coord_name = \\\"height\\\"\\n result = str(Integration(coord_name))\\n msg = (\\n \\\"<Integration: coord_name_to_integrate: height, \\\"\\n \\\"start_point: None, end_point: None, \\\"\\n \\\"positive_integration: False>\\\"\\n )\\n self.assertEqual(result, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25a133c3619f7bbf3d9826435cbeffc\",\n \"score\": \"0.55956846\",\n \"text\": \"def test_partial_trace_sys_int_dim_int_2():\\n test_input_mat = np.arange(1, 17).reshape(4, 4)\\n\\n expected_res = 34\\n\\n res = partial_trace(test_input_mat, [1], [1, 4])\\n\\n bool_mat = np.isclose(expected_res, res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef3f46e8d656e419521c30f4ab90ba2\",\n \"score\": \"0.5576368\",\n \"text\": \"def test_partial_trace_sys_int_dim_int():\\n test_input_mat = np.arange(1, 17).reshape(4, 4)\\n\\n expected_res = np.array([[7, 11], [23, 27]])\\n\\n res = partial_trace(test_input_mat, [1])\\n\\n bool_mat = np.isclose(expected_res, res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7aaac0d41d38f29f9be73a63ca8dce\",\n \"score\": \"0.5549346\",\n \"text\": \"def _check_exact_dimension(*args):\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the function's number of dimensions is equal to `-1` (n-dimensional)\\n if obj.dims == -1:\\n if x.shape[0] == 0:\\n raise e.SizeError(f'{obj.name} input should be n-dimensional')\\n\\n return f(obj, x)\\n\\n # If the input dimensions is different from function's allowed dimensions\\n if x.shape[0] != obj.dims:\\n raise e.SizeError(f'{obj.name} input should be {obj.dims}-dimensional')\\n\\n return f(obj, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2f200e63ac4e2d44e7bad28ac50cb5\",\n \"score\": \"0.5507682\",\n \"text\": \"def integrate_global(arr: np.ndarray) -> NumberOrArray:\\n integral = integrate_local(arr)\\n return mpi_allreduce(integral) # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9117b82545ed0f06fdb6110e6016a02\",\n \"score\": \"0.54777163\",\n \"text\": \"def _check_dimensions(x, y, solved_shapes):\\n # print(f\\\"{x}x{y}\\\")\\n if (x, y) in solved_shapes.keys():\\n return solved_shapes[(x, y)]\\n elif (y, x) in solved_shapes.keys():\\n return solved_shapes[(y, x)]\\n elif x == 1:\\n solved_shapes[(x, y)] = y + 1\\n return y + 1\\n elif y == 1:\\n solved_shapes[(x, y)] = x + 1\\n return x + 1\\n else:\\n solved_shapes[(x, y)] = _check_dimensions(x - 1, y, solved_shapes) + _check_dimensions(x, y - 1, solved_shapes)\\n return solved_shapes[(x, y)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9bab8e9760f4b44481de0c7f2c202b\",\n \"score\": \"0.54430807\",\n \"text\": \"def test_first_integration(fits_input, fits_output):\\n\\n assert np.all(fits_input['SCI'].data[0] == fits_output['SCI'].data[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91b778437f292faf37a4da4bdc40f11\",\n \"score\": \"0.5376729\",\n \"text\": \"def integrate(self,x1,x2):\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8681ede88cc398aa5ec1703293f0ebdf\",\n \"score\": \"0.5371367\",\n \"text\": \"def unit(x_hat):\\n temp = scipy.squeeze(x_hat)\\n if temp.shape[0] != 3:\\n raise ValueError('input is not in a coordinate system, first dimension must be 3')\\n return temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15d37b135e4fa3524e96b7140e78255\",\n \"score\": \"0.534553\",\n \"text\": \"def check_exact_dimension(f):\\n\\n def _check_exact_dimension(*args):\\n \\\"\\\"\\\"Wraps the dimension checking in order to provide additional logic.\\n\\n Returns:\\n The wrapped function output.\\n\\n \\\"\\\"\\\"\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the function's number of dimensions is equal to `-1` (n-dimensional)\\n if obj.dims == -1:\\n if x.shape[0] == 0:\\n raise e.SizeError(f'{obj.name} input should be n-dimensional')\\n\\n return f(obj, x)\\n\\n # If the input dimensions is different from function's allowed dimensions\\n if x.shape[0] != obj.dims:\\n raise e.SizeError(f'{obj.name} input should be {obj.dims}-dimensional')\\n\\n return f(obj, x)\\n\\n return _check_exact_dimension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38285fa940de30a24c454ad1c29dcdab\",\n \"score\": \"0.53400886\",\n \"text\": \"def IS_integrate(f, g, h, sampler, n):\\n raise NotImplementedError(\\\"Problem 2 Incomplete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ff273b0c290200176e87db6457d7a5\",\n \"score\": \"0.5323855\",\n \"text\": \"def _check_x_dimension(x):\\n x = np.atleast_1d(x)\\n if len(x.shape) > 2 or (len(x.shape) == 2 and x.shape[1] != 1):\\n raise ValueError('Wrong dimension in x.')\\n return x.reshape((-1, ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a2a16c92652ca135f3cb11db89cd92\",\n \"score\": \"0.5306183\",\n \"text\": \"def integrate_local(arr: np.ndarray) -> NumberOrArray:\\n amounts = arr * self.cell_volumes\\n return amounts.sum(axis=tuple(range(-num_axes, 0, 1))) # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba66271f35da3b9791cade5a6da6cc03\",\n \"score\": \"0.5305297\",\n \"text\": \"def _check_less_equal_dimension(*args):\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the input dimensions is different from function's allowed dimensions\\n if x.shape[0] > obj.dims:\\n raise e.SizeError(f'{obj.name} input should be less or equal to {obj.dims}-dimensional')\\n\\n return f(obj, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6172d2fccfdc339980975a36fda0e29f\",\n \"score\": \"0.5293063\",\n \"text\": \"def _check_x_dimension(x):\\n x = np.atleast_1d(x)\\n if len(x.shape) > 2 or (len(x.shape) == 2 and x.shape[1] != 1):\\n raise ValueError('Wrong dimension in x.')\\n return x.reshape((-1,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ae356fd1f6cf6d7dc439d62b3b45b1\",\n \"score\": \"0.5279457\",\n \"text\": \"def requires_2d_input(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1853098cd20e6c1fd093914daa97c81e\",\n \"score\": \"0.5267114\",\n \"text\": \"def test_integraldomain_is_in_domain():\\n dom_params = Namespace(min_max=[(0, 5), (-3.2, 1)])\\n pts_01 = np.array([[0, 1], [0.0, 1.0], [3, -3], [4, -2]])\\n pts_02 = np.array(\\n [[-1, 0], [6, 0], [2, -4], [2, 2], [2.2, 0], [2, 0.3], [2.2, 0.3]]\\n )\\n domain = IntegralDomain(params=dom_params)\\n in_dom_list_01 = [domain.is_in_domain(pt) for pt in pts_01]\\n in_dom_list_02 = [domain.is_in_domain(pt) for pt in pts_02]\\n assert all(b is True for b in in_dom_list_01)\\n assert all(b is False for b in in_dom_list_02)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f34e631f41c84b7961081b1e0c587872\",\n \"score\": \"0.52346665\",\n \"text\": \"def test_partial_trace_sys_int():\\n test_input_mat = np.arange(1, 17).reshape(4, 4)\\n\\n expected_res = np.array([[12, 14], [20, 22]])\\n\\n res = partial_trace(test_input_mat, 0)\\n\\n bool_mat = np.isclose(expected_res, res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f910e715999a68bbdd30eaeadef36a\",\n \"score\": \"0.52221394\",\n \"text\": \"def test_reordering(self):\\n result = self.plugin._slice_over_coordinate(self.cube, \\\"realization\\\")\\n self.assertEqual(len(result), 3)\\n for i in range(3):\\n dim_coord_names = [\\n coord.name() for coord in result[i].coords(dim_coords=True)]\\n self.assertEqual(dim_coord_names[0], \\\"realization\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14329e0b5cbf164863c9cfb52f49cf9\",\n \"score\": \"0.51861185\",\n \"text\": \"def is_in_bound(trial, param):\\n return param.value in self.new_dimension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5cfc3d95510ba8212c4a5787cf2711\",\n \"score\": \"0.51828134\",\n \"text\": \"def _check_x_dimension(x, d=None):\\n x = np.array(x)\\n if len(x.shape) != 2:\\n raise ValueError('Wrong dimension in x.')\\n if (d is not None) and (x.shape[1] != d):\\n raise ValueError('Wrong dimension in x.')\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd2742a208a212bc5190a416d284ced\",\n \"score\": \"0.5182152\",\n \"text\": \"def is_1d(self):\\n if self.shape is None:\\n return False\\n return all([dim == 1 for dim in self.shape[1:]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e870edc231c4f5cb4fad1b7f50cd4276\",\n \"score\": \"0.51817715\",\n \"text\": \"def check_dimension(self, init_x):\\n if self.f == extended_rosenbrock_function and init_x.size % 2 != 0:\\n raise ValueError(\\\"Invalid input dimension.\\\")\\n elif self.f == extended_powell_singular_function and init_x.size % 4 != 0:\\n raise ValueError(\\\"Invalide input dimension\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e870edc231c4f5cb4fad1b7f50cd4276\",\n \"score\": \"0.51817715\",\n \"text\": \"def check_dimension(self, init_x):\\n if self.f == extended_rosenbrock_function and init_x.size % 2 != 0:\\n raise ValueError(\\\"Invalid input dimension.\\\")\\n elif self.f == extended_powell_singular_function and init_x.size % 4 != 0:\\n raise ValueError(\\\"Invalide input dimension\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892edcb4a0236c94c4616e9f9348c173\",\n \"score\": \"0.51809335\",\n \"text\": \"def check_x_dimension(x):\\n array = np.atleast_1d(x)\\n if len(array.shape) > 2 or (len(array.shape) == 2 and array.shape[1] != 1):\\n raise ValueError(\\\"Wrong dimension in x.\\\")\\n return array.reshape((-1,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b895e965073c3393eef1f9844efb7c16\",\n \"score\": \"0.5174387\",\n \"text\": \"def single_elem_2_array_p(one_arg):\\r\\n elem, funct = one_arg\\r\\n intermed = integrate_elem(elem, funct)\\r\\n return intermed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb88e689bdf7606e8041ee04894648c7\",\n \"score\": \"0.5173186\",\n \"text\": \"def make_integrator(self) -> Callable[[np.ndarray], NumberOrArray]:\\n num_axes = self.num_axes\\n # cell volume varies with position\\n get_cell_volume = self.make_cell_volume_compiled(flat_index=True)\\n\\n def integrate_local(arr: np.ndarray) -> NumberOrArray:\\n \\\"\\\"\\\"integrates data over a grid using numpy\\\"\\\"\\\"\\n amounts = arr * self.cell_volumes\\n return amounts.sum(axis=tuple(range(-num_axes, 0, 1))) # type: ignore\\n\\n @overload(integrate_local)\\n def ol_integrate_local(\\n arr: np.ndarray,\\n ) -> Callable[[np.ndarray], NumberOrArray]:\\n \\\"\\\"\\\"integrates data over a grid using numba\\\"\\\"\\\"\\n if arr.ndim == num_axes:\\n # `arr` is a scalar field\\n grid_shape = self.shape\\n\\n def impl(arr: np.ndarray) -> Number:\\n \\\"\\\"\\\"integrate a scalar field\\\"\\\"\\\"\\n assert arr.shape == grid_shape\\n total = 0\\n for i in range(arr.size):\\n total += get_cell_volume(i) * arr.flat[i]\\n return total\\n\\n else:\\n # `arr` is a tensorial field with rank >= 1\\n tensor_shape = (self.dim,) * (arr.ndim - num_axes)\\n data_shape = tensor_shape + self.shape\\n\\n def impl(arr: np.ndarray) -> np.ndarray: # type: ignore\\n \\\"\\\"\\\"integrate a tensorial field\\\"\\\"\\\"\\n assert arr.shape == data_shape\\n total = np.zeros(tensor_shape)\\n for idx in np.ndindex(*tensor_shape):\\n arr_comp = arr[idx]\\n for i in range(arr_comp.size):\\n total[idx] += get_cell_volume(i) * arr_comp.flat[i]\\n return total\\n\\n return impl\\n\\n # deal with MPI multiprocessing\\n if self._mesh is None or len(self._mesh) == 1:\\n # standard case of a single integral\\n @jit\\n def integrate_global(arr: np.ndarray) -> NumberOrArray:\\n \\\"\\\"\\\"integrate data\\n\\n Args:\\n arr (:class:`~numpy.ndarray`): discretized data on grid\\n \\\"\\\"\\\"\\n return integrate_local(arr)\\n\\n else:\\n # we are in a parallel run, so we need to gather the sub-integrals from all\\n # subgrids in the grid mesh\\n from ..tools.mpi import mpi_allreduce\\n\\n @jit\\n def integrate_global(arr: np.ndarray) -> NumberOrArray:\\n \\\"\\\"\\\"integrate data over MPI parallelized grid\\n\\n Args:\\n arr (:class:`~numpy.ndarray`): discretized data on grid\\n \\\"\\\"\\\"\\n integral = integrate_local(arr)\\n return mpi_allreduce(integral) # type: ignore\\n\\n return integrate_global # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4117968108c12c6eed95cae185aea070\",\n \"score\": \"0.516667\",\n \"text\": \"def integrate(self):\\n raise MethodImplementationError(self, 'integrate')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a79cfed83cf9095e9642d723d10238\",\n \"score\": \"0.51280475\",\n \"text\": \"def test_separate_coords_error(arr, expected):\\n with expected:\\n assert EnsembleKalmanFilter.separate_coords(arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9346e0d8e4b6079a708ff5051a7be3f2\",\n \"score\": \"0.51262695\",\n \"text\": \"def check_less_equal_dimension(f):\\n\\n def _check_less_equal_dimension(*args):\\n \\\"\\\"\\\"Wraps the dimension checking in order to provide additional logic.\\n\\n Returns:\\n The wrapped function output.\\n\\n \\\"\\\"\\\"\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the input dimensions is different from function's allowed dimensions\\n if x.shape[0] > obj.dims:\\n raise e.SizeError(f'{obj.name} input should be less or equal to {obj.dims}-dimensional')\\n\\n return f(obj, x)\\n\\n return _check_less_equal_dimension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d62813e5bcc1f28a7488f1809afdfe9\",\n \"score\": \"0.51135397\",\n \"text\": \"def isIntegral():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d62813e5bcc1f28a7488f1809afdfe9\",\n \"score\": \"0.51135397\",\n \"text\": \"def isIntegral():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49d47f6cd107736ff7dfda6e69e4c05\",\n \"score\": \"0.51011837\",\n \"text\": \"def integrate(\\n self: T,\\n *,\\n domain: Optional[DomainRange] = None,\\n ) -> NDArrayFloat:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ff7d7cfd0ad4650d15eb51306fae52\",\n \"score\": \"0.50880814\",\n \"text\": \"def integrate_over_grid(X, Y, Z):\\n int_x = np.trapz(Z, X, axis=1)\\n return np.trapz(int_x, Y[:,0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e33403327e00884f2f90154ddac99ded\",\n \"score\": \"0.5079505\",\n \"text\": \"def test_integrated_variance_fails_with_out_of_domain_test_points(model):\\n space = ParameterSpace([ContinuousParameter('x1', 0, 1), ContinuousParameter('x2', 0, 1)])\\n\\n x_monte_carlo = np.array([[0.5, 20.], [0.2, 0.3]])\\n\\n with pytest.raises(ValueError):\\n IntegratedVarianceReduction(model, space, x_monte_carlo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96be989be73e25c708518b43472c341\",\n \"score\": \"0.5076574\",\n \"text\": \"def test_dim_coords(dat, dat_inst, variable, dat_name, failed):\\n for dim in dat.dims:\\n if (dim not in dat_inst.dims) or (dim == \\\"Time\\\"):\\n continue\\n c = dat[dim].values\\n cr = dat_inst[dim].values\\n if (len(c) != len(cr)) or (c != cr).any():\\n print(\\n \\\"Coordinates for dimension {} in data {} of variable {}\\\"\\n \\\" differs between postprocessed output and WRF output:\\\"\\n \\\"\\\\n {} vs. {}\\\".format(dim, dat_name, variable, c, cr)\\n )\\n f = \\\"FAIL\\\"\\n else:\\n f = \\\"pass\\\"\\n f0 = failed.loc[variable, \\\"dim_coords\\\"]\\n if (f0 == \\\"\\\") or (f0 == \\\"pass\\\"):\\n failed.loc[variable, \\\"dim_coords\\\"] = f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7046a09a8002349f403a18e53f522c6\",\n \"score\": \"0.5072907\",\n \"text\": \"def nd_shape_checking(x, y, mvaxis, traxis):\\n assert x.ndim == y.ndim\\n dims = np.delete(np.arange(x.ndim), -2)\\n assert all([x.shape[k] == y.shape[k] for k in dims])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3c587fc151c58b2e4df2c2d597cba9\",\n \"score\": \"0.5067622\",\n \"text\": \"def test_loc_deph_lindblad_op(dims, site_j, expected):\\n\\n assert np.all(lind.loc_deph_lindblad_op(dims, site_j) == expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87849ac836869c0e4217f7eb9dc6e7ba\",\n \"score\": \"0.5053569\",\n \"text\": \"def test_partial_trace_sys():\\n test_input_mat = np.arange(1, 17).reshape(4, 4)\\n\\n expected_res = np.array([[12, 14], [20, 22]])\\n\\n res = partial_trace(test_input_mat, [0])\\n\\n bool_mat = np.isclose(expected_res, res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd2731d76a5c21abfb2f386dc9c92aa\",\n \"score\": \"0.5021951\",\n \"text\": \"def clearance(self, x):\\n x = scipy.asarray(x)\\n if (x < 0).any():\\n return False\\n if any(i >= j for i, j in zip(x, self.space.shape)):\\n return False\\n return self.space[tuple(x)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafae0acc3d1459d26cc87fa69ff8b8b\",\n \"score\": \"0.5013441\",\n \"text\": \"def test_GridDataInterpolator_1DGrid():\\n from pyirf.interpolation import GridDataInterpolator\\n\\n grid_points = np.array([[0], [1]])\\n target_point = np.array([[0.5]])\\n\\n dummy_data1 = np.array([[[0, 1], [1, 1]], [[0, 2], [2, 3]], [[0, 3], [3, 5]]])\\n dummy_data2 = np.array([[[0, 2], [2, 2]], [[0, 4], [4, 6]], [[0, 6], [6, 10]]])\\n\\n dummy_data = np.array([dummy_data1, dummy_data2])\\n\\n interpolator = GridDataInterpolator(\\n grid_points=grid_points, params=dummy_data, method=\\\"linear\\\"\\n )\\n interpolant = interpolator(target_point)\\n\\n dummy_data_target = 1.5 * dummy_data1\\n\\n assert np.allclose(interpolant, dummy_data_target)\\n assert interpolant.shape == (1, *dummy_data.shape[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6b5fdb3c56f8368080e493e3c06f7e9\",\n \"score\": \"0.4996801\",\n \"text\": \"def test_multidimensional(self):\\r\\n # Get some higher-dimensional test data\\r\\n c1 = istk.realistic_4d()\\r\\n # Chop down to small size, and mask some data\\r\\n c1 = c1[:3, :4, :16, :12]\\r\\n c1.data[:, 2, :, :] = np.ma.masked\\r\\n c1.data[1, 1, 3:9, 4:7] = np.ma.masked\\r\\n # Give it a slightly more challenging indexing order: tzyx --> xzty\\r\\n c1.transpose((3, 1, 0, 2))\\r\\n\\r\\n # Construct a (coarser) target grid of about the same extent\\r\\n c1_cs = c1.coord(axis='x').coord_system\\r\\n xlims = _minmax(c1.coord(axis='x').contiguous_bounds())\\r\\n ylims = _minmax(c1.coord(axis='y').contiguous_bounds())\\r\\n # Reduce the dimensions slightly to avoid NaNs in regridded orography\\r\\n delta = 0.05\\r\\n # || NOTE: this is *not* a small amount. Think there is a bug.\\r\\n # || NOTE: See https://github.com/SciTools/iris/issues/458\\r\\n xlims = np.interp([delta, 1.0 - delta], [0, 1], xlims)\\r\\n ylims = np.interp([delta, 1.0 - delta], [0, 1], ylims)\\r\\n pole_latlon = (c1_cs.grid_north_pole_latitude,\\r\\n c1_cs.grid_north_pole_longitude)\\r\\n c2 = _make_test_cube((7, 8), xlims, ylims, pole_latlon=pole_latlon)\\r\\n\\r\\n # regrid onto new grid\\r\\n c1_to_c2 = regrid_conservative_via_esmpy(c1, c2)\\r\\n\\r\\n # check that all the original coords exist in the new cube\\r\\n # NOTE: this also effectively confirms we haven't lost the orography\\r\\n def list_coord_names(cube):\\r\\n return sorted([coord.name() for coord in cube.coords()])\\r\\n\\r\\n self.assertEqual(list_coord_names(c1_to_c2), list_coord_names(c1))\\r\\n\\r\\n # check that each xy 'slice' has same values as if done on its own.\\r\\n for i_p, i_t in np.ndindex(c1.shape[1:3]):\\r\\n c1_slice = c1[:, i_p, i_t]\\r\\n c2_slice = regrid_conservative_via_esmpy(c1_slice, c2)\\r\\n subcube = c1_to_c2[:, i_p, i_t]\\r\\n self.assertEqual(subcube, c2_slice)\\r\\n\\r\\n # check all other metadata\\r\\n self.assertEqual(c1_to_c2.metadata, c1.metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d15166959b545e79f8ca37f0f4784a\",\n \"score\": \"0.4994784\",\n \"text\": \"def test_shape_of_inflate_array(shape: IntTuple, grid: IntTuple) -> None:\\n grid = grid[: len(shape)]\\n\\n inflated_array = nf.inflate_array(np.empty(shape), grid)\\n\\n assert inflated_array.ndim == len(shape)\\n for i in range(len(grid)):\\n assert inflated_array.shape[i] == grid[i] * shape[i]\\n for i in range(len(grid), inflated_array.ndim):\\n assert inflated_array.shape[i] == shape[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6ccfe2464dfd151dc7447d6ad022ba9\",\n \"score\": \"0.4989598\",\n \"text\": \"def isneginf(x=None, y=None):\\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed399df240d492086b495d7462e728bd\",\n \"score\": \"0.49836963\",\n \"text\": \"def integration_1d():\\n I = integration_1d_sym(x, x0, x1)\\n\\n def P0(f):\\n p = I[0](f(x))\\n return sy.lambdify((x0,), p)\\n\\n def P1(f):\\n p = I[1](f(x))\\n return sy.lambdify((x0, x1), p)\\n\\n return P0, P1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b1a82005f746cbfddf171b05a37fe0\",\n \"score\": \"0.49753493\",\n \"text\": \"def test_x_wrong_ndims():\\n z = np.zeros((0, 2))\\n x = [[np.array([0., 0.]), np.array([[1., 0.]]), np.array([[0., 1.]]), np.array([[1., 1.]])],\\n [z, z, z, z], [z], []]\\n assert_failure(x=x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4a335aad407847b4038eede45c3c6a\",\n \"score\": \"0.49692056\",\n \"text\": \"def first_tensor_invariant(A): \\n return A[0,0] + A[1,1] + A[2,2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7354adf5aac0eb2b3794b8c34791a1e\",\n \"score\": \"0.4966854\",\n \"text\": \"def _check_dimension(params):\\n input_col = params['input_col']\\n output_col = params['output_col']\\n\\n interaction_only = params['interaction_only']\\n degree = params['degree']\\n\\n from sklearn.preprocessing import PolynomialFeatures\\n poly = PolynomialFeatures(degree=degree, interaction_only=interaction_only)\\n tmp = [[0 for _ in range(len(input_col))]]\\n dim = poly.fit_transform(tmp).shape[1]\\n\\n params['output_col'] = ['col{}{}'.format(i, output_col) for i in range(dim)]\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6841e153d617aa50bcb5d13c4cb8f4\",\n \"score\": \"0.49655724\",\n \"text\": \"def test_check_data_array_dimensions_match_unknown_dimension():\\n time_size, depth_size = 4, 3\\n lon_size, lat_size = 10, 15\\n\\n dataset = xarray.Dataset(\\n data_vars={\\n 'botz': ([\\\"lat\\\", \\\"lon\\\"], 50 + 25 * numpy.random.random_sample((lat_size, lon_size))),\\n 'eta': (\\n [\\\"time\\\", \\\"lat\\\", \\\"lon\\\"],\\n numpy.random.random_sample((time_size, lat_size, lon_size)),\\n ),\\n },\\n coords={\\n 'time': ([\\\"time\\\"], pandas.date_range(\\\"2021-12-21\\\", periods=time_size)),\\n 'lon': ([\\\"lon\\\"], numpy.arange(lon_size) + 0.5),\\n 'lat': ([\\\"lat\\\"], numpy.arange(lat_size) + 0.5),\\n }\\n )\\n\\n # Slicing the temperature to get a subset of the lat/lon grid will cause\\n # the dimensions to differ\\n temp = xarray.DataArray(\\n data=15 + 3 * numpy.random.random_sample((time_size, depth_size, lat_size, lon_size)),\\n dims=['time', 'depth', 'lat', 'lon'],\\n )\\n\\n with pytest.raises(ValueError):\\n utils.check_data_array_dimensions_match(dataset, temp)\\n\\n # If you remove the extra dimension, this should be fine\\n surface_temp = temp.isel({'depth': 0}, drop=True)\\n utils.check_data_array_dimensions_match(dataset, surface_temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bed6c0451a6a726bb30ad1aa92fe0c4\",\n \"score\": \"0.49650878\",\n \"text\": \"def is_single_unit(data):\\n try:\\n if isinstance(data[0][0], list) or isinstance(data[0][0], np.ndarray):\\n warnings.warn(\\\"spike times input has too many layers!\\\")\\n if max(np.array(data).shape[:-1]) > 1:\\n # singletons = True\\n return False\\n data = np.squeeze(data)\\n except (IndexError, TypeError):\\n pass\\n try:\\n if isinstance(data[1], list) or isinstance(data[1], np.ndarray):\\n return False\\n except (IndexError, TypeError):\\n pass\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b78db03be1c39ffe970d2456c9497c\",\n \"score\": \"0.4958135\",\n \"text\": \"def _check_exact_dimension_and_auxiliary_matrix(*args):\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the input dimensions differs from function's allowed dimensions\\n if x.shape[0] not in [2, 10, 30, 50]:\\n raise e.SizeError(f'{obj.name} input should be 2-, 10-, 30- or 50-dimensional')\\n\\n if x.shape[0] == 2:\\n setattr(obj, 'M', obj.M2)\\n\\n elif x.shape[0] == 10:\\n setattr(obj, 'M', obj.M10)\\n\\n elif x.shape[0] == 30:\\n setattr(obj, 'M', obj.M30)\\n\\n elif x.shape[0] == 50:\\n setattr(obj, 'M', obj.M50)\\n\\n return f(obj, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ea77bb84f211310b7a6e2db87d88ec\",\n \"score\": \"0.49564505\",\n \"text\": \"def test_partial_trace():\\n test_input_mat = np.arange(1, 17).reshape(4, 4)\\n\\n expected_res = np.array([[7, 11], [23, 27]])\\n\\n res = partial_trace(test_input_mat)\\n\\n bool_mat = np.isclose(expected_res, res)\\n np.testing.assert_equal(np.all(bool_mat), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59eebe69be0d9507ef3c05dae0b81843\",\n \"score\": \"0.49482214\",\n \"text\": \"def test_do_interpolate_func(my_data):\\n loss = do_interpolate(my_data, scipy=False, func=func)\\n assert 0 < loss < np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ad4527f255f97e0c4c7681ce973830\",\n \"score\": \"0.4942044\",\n \"text\": \"def test_addition_centered_grid(self):\\n shape = [32, 27]\\n for boundary in [CLOSED, PERIODIC, OPEN]:\\n domain = Domain(shape, boundaries=(boundary, boundary))\\n centered_grid = CenteredGrid.sample(1, domain)\\n result_array = (centered_grid + centered_grid).data\\n np.testing.assert_array_equal(result_array, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49a5818feafbc1ae551a9cea90fecdf\",\n \"score\": \"0.49379927\",\n \"text\": \"def integrate(func, x_init, x_final, slices=100, mode=\\\"simpson\\\"):\\n\\n h = (x_final - x_init)/float(slices)\\n\\n integral = 0.0\\n if mode == \\\"midpoint\\\":\\n for i in range(slices):\\n x0 = x_init + i*h\\n x1 = x0 + i*h\\n xmid = (x0 + x1)/2.0\\n integral += func(xmid)*h\\n\\n if mode == \\\"trapezoid\\\":\\n for i in range(slices):\\n x0 = x_init + i*h\\n x1 = x0 + h\\n y0 = func(x0)\\n y1 = func(x1)\\n integral += (y0 + y1)/2.0 * h\\n if mode == \\\"simpson\\\":\\n #enforce even number of slices out of laziness\\n for i in range(0, slices, 2): \\n x0 = x_init + i*h\\n x1 = x0 + h\\n x2 = x1 + h\\n y0 = func(x0)\\n y1 = func(x1)\\n y2 = func(x2)\\n integral += (y0 + 4*y1 + y2) * (h/3)\\n\\n return integral\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d15c3a4042e57b988f47d04303f463\",\n \"score\": \"0.49265644\",\n \"text\": \"def test_dimensionality_output(self):\\n\\n detrending = data_prep.Detrending(alpha = 0.001,\\n variance = 0.001,\\n noise = 0.001)\\n\\n matrix = detrending.cov_matrix_SE(self.test_x, self.test_x)\\n prior_trace = detrending.generate_prior_SE_trace(self.test_x, 1)\\n\\n self.assertEqual(np.shape(matrix), tuple((len(self.test_x), len(self.test_x))))\\n self.assertEqual(np.shape(prior_trace), tuple((len(self.test_x), 2))) # (number_of_observations, number_of_traces + 1)\\n # self.assertEqual(prior_trace[:,0], self.test_x)\\n\\n optim = detrending.optimise_SE_trace(0.001, 0.001, 0.001, self.test_x, self.test_y)\\n\\n self.assertTrue(len(optim) == 3) # three parameters optimised\\n\\n posterior_trace = detrending.fit_SE(self.observed_x, self.observed_y, self.test_x, 1, True)\\n\\n self.assertEqual(np.shape(posterior_trace), tuple((len(self.test_x), 3))) # (number_of_observations, number_of_traces + 2)\\n\\n detrended_data, test_timepoints, fits = detrending.detrend_data(self.observed_x, self.data, True)\\n\\n self.assertTrue(np.shape(detrended_data) == np.shape(self.data))\\n self.assertTrue(np.shape(fits) == tuple((len(test_timepoints), np.shape(detrended_data)[1])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"399860a694d3be8648ddbe8390319c17\",\n \"score\": \"0.4923434\",\n \"text\": \"def isposinf(x=None, y=None):\\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9abc08cd4dda955e9fcd10aa58bff1d\",\n \"score\": \"0.49133965\",\n \"text\": \"def is_continuous_tailing_axis(self, shape, reduce_axis):\\n for i in self._origin_op:\\n if i[\\\"op\\\"] == 'broadcast_for_tensor':\\n return False\\n\\n if len(reduce_axis) > 1:\\n index_list = [index for index, _ in enumerate(shape)]\\n\\n if index_list[-len(reduce_axis):] == reduce_axis:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e5c77a28401d92ac740692b85dda8c\",\n \"score\": \"0.4896915\",\n \"text\": \"def is_dimensionless(self):\\r\\n return (self.category == _CATEGORY_UDUNIT and\\r\\n bool(_ut_is_dimensionless(self.ut_unit)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0d738d0879325a2eedec941047ebd4\",\n \"score\": \"0.48961633\",\n \"text\": \"def check_coordinates(data_array: xarray.DataArray):\\n missing_dimensions = [d for d in horizontal_spatial_dimensions if d not in data_array.dims]\\n if missing_dimensions:\\n message = f\\\"Structural similarity index expects data with dimensions {horizontal_spatial_dimensions}.\\\\n\\\" \\\\\\n f\\\"Dimension/s {missing_dimensions} missing.\\\"\\n raise EnstoolsError(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27442fb24554a40e7b7addc23f608b30\",\n \"score\": \"0.48928264\",\n \"text\": \"def test_multidim_moving_projection():\\n\\n x_ = np.random.randn(2000)\\n\\n x = np.empty((5, 10, 2000))\\n x[:] = x_\\n y = moving_projection(x, 200, 5/200.)\\n\\n err = y - y[0, 0]\\n assert np.sum(err ** 2) < 1e-8\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87999cf2f95e74ef2b32064f96212ee9\",\n \"score\": \"0.48843023\",\n \"text\": \"def check_conformity(self, x:np.ndarray) -> np.ndarray:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8814c55ab137be6f42bb6ec04e48350c\",\n \"score\": \"0.4884279\",\n \"text\": \"def test_infinity_handling(self):\\n a = np.array([[1, 2], [1, np.inf]])\\n b = np.array([3, 4])\\n c = np.array([[1, 2], [3, 4]])\\n ref = np.array([[[4, 6], [9, 12]],\\n [[4, 6], [np.inf, np.inf]]])\\n res = gulinalg.update_rank1(a, b, c)\\n assert_allclose(res, ref)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1f63711ea06f85ccd8a723f4957e9e\",\n \"score\": \"0.48806694\",\n \"text\": \"def implicit_constant(x):\\n nobs = x.shape[0]\\n rank = np.linalg.matrix_rank(np.hstack((np.ones((nobs, 1)), x)))\\n return rank == x.shape[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bc6a2959655332cd00ded4d4538fb3\",\n \"score\": \"0.4879\",\n \"text\": \"def isreal(self, x):\\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07cc43428533fcfc16f59765314edd5b\",\n \"score\": \"0.4868493\",\n \"text\": \"def test_partial_trace_non_square_matrix_dim_2():\\n with np.testing.assert_raises(ValueError):\\n rho = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\\n partial_trace(rho, [1], [2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9242fe04e4d70c7e783b4a3c718cad16\",\n \"score\": \"0.48668632\",\n \"text\": \"def integrate(self, t1, t2) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec740e22520d3afad20cb7362d58130\",\n \"score\": \"0.48601013\",\n \"text\": \"def isreal(x):\\n return ndarray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa17947f07f54a84091e09524365022\",\n \"score\": \"0.48562244\",\n \"text\": \"def integrate_over_grid(grid_step, F, axis=None):\\n if axis is None:\\n V = np.prod(grid_step)\\n else:\\n V = grid_step\\n \\n return V * np.sum(F, axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80b25a8427d577786b74f624725a012\",\n \"score\": \"0.48501012\",\n \"text\": \"def test_interp_inplace():\\n x = np.arange(10)\\n y_actual = 2 + 5 * x\\n\\n y_calc = np.empty_like(y_actual)\\n y_calc[0] = y_actual[0]\\n y_calc[-1] = y_actual[-1]\\n\\n output = utils._interp_inplace(x, y_calc, y_calc[0], y_calc[-1])\\n\\n # output should be the same object as the input y array\\n assert output is y_calc\\n\\n assert_allclose(y_calc, y_actual, 1e-12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f20bdf26e45c92fa051742c6abe47f\",\n \"score\": \"0.48459294\",\n \"text\": \"def is_monotonic(self):\\r\\n\\r\\n if self.ndim != 1:\\r\\n raise iris.exceptions.CoordinateMultiDimError(self)\\r\\n\\r\\n if self.shape == (1,):\\r\\n return True\\r\\n\\r\\n if self.points is not None:\\r\\n if not iris.util.monotonic(self.points, strict=True):\\r\\n return False\\r\\n\\r\\n if self.bounds is not None:\\r\\n for b_index in xrange(self.nbounds):\\r\\n if not iris.util.monotonic(self.bounds[..., b_index],\\r\\n strict=True):\\r\\n return False\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc87149efa2aa73d40da4d5ce32ff726\",\n \"score\": \"0.484416\",\n \"text\": \"def test_integraldomain_defaults_is_in_domain():\\n pts_01 = [2, 2.0, 4, 4.0, np.array(2), np.array(2.0), np.array(4.0)]\\n pts_02 = [2.1, 4.1, np.array(2.1)]\\n domain = IntegralDomain()\\n in_dom_list_01 = [domain.is_in_domain(pt) for pt in pts_01]\\n in_dom_list_02 = [domain.is_in_domain(pt) for pt in pts_02]\\n assert all(b is True for b in in_dom_list_01)\\n assert all(b is False for b in in_dom_list_02)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d57c5ac711b360b6f382f18288f819a\",\n \"score\": \"0.48433647\",\n \"text\": \"def valid_shape(x):\\n return ((isinstance(x, numbers.Integral) and (x > 0)) or\\n (x is None) or\\n (isinstance(x, (tuple, list)) and\\n (len(x) > 0) and\\n all([((isinstance(n, numbers.Integral) and (n > 0)) or\\n (n is None)) for n in x])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8261c3acfc8c444e14ed380bd05af2c\",\n \"score\": \"0.48402226\",\n \"text\": \"def test_two_coords(self):\\n expected_data = self.cube.collapsed(\\n [\\\"realization\\\", \\\"latitude\\\"], iris.analysis.MEAN\\n ).data\\n result = collapsed(self.cube, [\\\"realization\\\", \\\"latitude\\\"], iris.analysis.MEAN)\\n self.assertTrue((result.data == expected_data).all())\\n self.assertEqual(\\n result.coord(\\\"latitude\\\").dtype, self.cube.coord(\\\"latitude\\\").dtype\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04397426ff10c6fdad78e98a47f1ff16\",\n \"score\": \"0.48389104\",\n \"text\": \"def valid(self):\\n\\t\\txsq = self.x*self.x\\n\\t\\tysq = self.y*self.y\\n\\t\\treturn (JUBJUB_A * xsq) + ysq == (1 + JUBJUB_D * xsq * ysq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d874e823076e12a1bcf13ccb84c39124\",\n \"score\": \"0.4837981\",\n \"text\": \"def integrate(\\n self, data: NumberOrArray, axes: Union[int, Sequence[int], None] = None\\n ) -> NumberOrArray:\\n # determine the volumes of the individual cells\\n if axes is None:\\n volume_list = self.cell_volume_data\\n else:\\n # use stored value for the default case of integrating over all axes\\n if isinstance(axes, int):\\n axes = (axes,)\\n else:\\n axes = tuple(axes) # required for numpy.sum\\n volume_list = [\\n cell_vol if ax in axes else 1\\n for ax, cell_vol in enumerate(self.cell_volume_data)\\n ]\\n cell_volumes = functools.reduce(np.outer, volume_list)\\n\\n # determine the axes over which we will integrate\\n if not isinstance(data, np.ndarray) or data.ndim < self.num_axes:\\n # deal with the case where data is not supplied for each support\\n # point, e.g., when a single scalar is integrated over the grid\\n data = np.broadcast_to(data, self.shape)\\n\\n elif data.ndim > self.num_axes:\\n # deal with the case where more than a single value is provided per\\n # support point, e.g., when a tensorial field is integrated\\n offset = data.ndim - self.num_axes\\n if axes is None:\\n # integrate over all axes of the grid\\n axes = tuple(range(offset, data.ndim))\\n else:\\n # shift the indices to account for the data shape\\n axes = tuple(offset + i for i in axes)\\n\\n # calculate integral using a weighted sum along the chosen axes\\n integral = (data * cell_volumes).sum(axis=axes)\\n\\n if self._mesh is None or len(self._mesh) == 1:\\n # standard case of a single integral\\n return integral # type: ignore\\n\\n else:\\n # we are in a parallel run, so we need to gather the sub-integrals from all\\n from mpi4py.MPI import COMM_WORLD\\n\\n integral_full = np.empty_like(integral)\\n COMM_WORLD.Allreduce(integral, integral_full)\\n return integral_full # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3392f477d8dc6c6bf3cd9018b2ed64\",\n \"score\": \"0.48363113\",\n \"text\": \"def isIntegral(self):\\n return self.precision==0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3609a89a2c6d282b44f49c3d9a66c9\",\n \"score\": \"0.48355156\",\n \"text\": \"def _validate_normalize_axes(axes, axis, keepdims, input_coredimss, output_coredimss):\\n nin = len(input_coredimss)\\n nout = 1 if not isinstance(output_coredimss, list) else len(output_coredimss)\\n\\n if axes is not None and axis is not None:\\n raise ValueError(\\n \\\"Only one of `axis` or `axes` keyword arguments should be given\\\"\\n )\\n if axes and not isinstance(axes, list):\\n raise ValueError(\\\"`axes` has to be of type list\\\")\\n\\n output_coredimss = output_coredimss if nout > 1 else [output_coredimss]\\n filtered_core_dims = list(filter(len, input_coredimss))\\n nr_outputs_with_coredims = len([True for x in output_coredimss if len(x) > 0])\\n\\n if keepdims:\\n if nr_outputs_with_coredims > 0:\\n raise ValueError(\\\"`keepdims` can only be used for scalar outputs\\\")\\n output_coredimss = len(output_coredimss) * [filtered_core_dims[0]]\\n\\n core_dims = input_coredimss + output_coredimss\\n if axis is not None:\\n if not isinstance(axis, int):\\n raise ValueError(\\\"`axis` argument has to be an integer value\\\")\\n if filtered_core_dims:\\n cd0 = filtered_core_dims[0]\\n if len(cd0) != 1:\\n raise ValueError(\\n \\\"`axis` can be used only, if one core dimension is present\\\"\\n )\\n for cd in filtered_core_dims:\\n if cd0 != cd:\\n raise ValueError(\\n \\\"To use `axis`, all core dimensions have to be equal\\\"\\n )\\n\\n # Expand dafaults or axis\\n if axes is None:\\n if axis is not None:\\n axes = [(axis,) if cd else tuple() for cd in core_dims]\\n else:\\n axes = [tuple(range(-len(icd), 0)) for icd in core_dims]\\n elif not isinstance(axes, list):\\n raise ValueError(\\\"`axes` argument has to be a list\\\")\\n axes = [(a,) if isinstance(a, int) else a for a in axes]\\n\\n if (\\n (nr_outputs_with_coredims == 0)\\n and (nin != len(axes))\\n and (nin + nout != len(axes))\\n ) or ((nr_outputs_with_coredims > 0) and (nin + nout != len(axes))):\\n raise ValueError(\\n \\\"The number of `axes` entries is not equal the number of input and output arguments\\\"\\n )\\n\\n # Treat outputs\\n output_axes = axes[nin:]\\n output_axes = (\\n output_axes\\n if output_axes\\n else [tuple(range(-len(ocd), 0)) for ocd in output_coredimss]\\n )\\n input_axes = axes[:nin]\\n\\n # Assert we have as many axes as output core dimensions\\n for idx, (iax, icd) in enumerate(zip(input_axes, input_coredimss)):\\n if len(iax) != len(icd):\\n raise ValueError(\\n \\\"The number of `axes` entries for argument #{} is not equal \\\"\\n \\\"the number of respective input core dimensions in signature\\\".format(\\n idx\\n )\\n )\\n if not keepdims:\\n for idx, (oax, ocd) in enumerate(zip(output_axes, output_coredimss)):\\n if len(oax) != len(ocd):\\n raise ValueError(\\n \\\"The number of `axes` entries for argument #{} is not equal \\\"\\n \\\"the number of respective output core dimensions in signature\\\".format(\\n idx\\n )\\n )\\n else:\\n if input_coredimss:\\n icd0 = input_coredimss[0]\\n for icd in input_coredimss:\\n if icd0 != icd:\\n raise ValueError(\\n \\\"To use `keepdims`, all core dimensions have to be equal\\\"\\n )\\n iax0 = input_axes[0]\\n output_axes = [iax0 for _ in output_coredimss]\\n\\n return input_axes, output_axes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98f08f5e0f21de7cdec50c75a58ca90\",\n \"score\": \"0.48316625\",\n \"text\": \"def _get_out_shape(in_shape, dim = 1, direction = +1, real_transform = False):\\n if real_transform:\\n if direction == +1:\\n return in_shape[:-1] + (in_shape[-1]//2,)\\n else:\\n return in_shape[:-1] + (in_shape[-1] * 2,)\\n else:\\n return in_shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514c2b5647c2e278a5a0c30afbe05646\",\n \"score\": \"0.48250335\",\n \"text\": \"def check_exact_dimension_and_auxiliary_matrix(f):\\n\\n def _check_exact_dimension_and_auxiliary_matrix(*args):\\n \\\"\\\"\\\"Wraps the dimension checking in order to provide additional logic.\\n\\n Returns:\\n The wrapped function output.\\n\\n \\\"\\\"\\\"\\n\\n # Retrieving the object and the input from arguments\\n obj, x = args[0], args[1]\\n\\n # Tries to squeeze the last dimension of `x` as it might be an array of (dim, 1)\\n try:\\n x = np.squeeze(x, axis=1)\\n\\n except ValueError:\\n pass\\n\\n # If the input dimensions differs from function's allowed dimensions\\n if x.shape[0] not in [2, 10, 30, 50]:\\n raise e.SizeError(f'{obj.name} input should be 2-, 10-, 30- or 50-dimensional')\\n\\n if x.shape[0] == 2:\\n setattr(obj, 'M', obj.M2)\\n\\n elif x.shape[0] == 10:\\n setattr(obj, 'M', obj.M10)\\n\\n elif x.shape[0] == 30:\\n setattr(obj, 'M', obj.M30)\\n\\n elif x.shape[0] == 50:\\n setattr(obj, 'M', obj.M50)\\n\\n return f(obj, x)\\n\\n return _check_exact_dimension_and_auxiliary_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7850606dbcc5f037fb62d6d85fb5c3aa\",\n \"score\": \"0.48239166\",\n \"text\": \"def test_s_enhance_errors():\\n\\n arr = np.arange(28800).reshape((2, 20, 20, 12, 3))\\n with pytest.raises(ValueError):\\n spatial_coarsening(arr, s_enhance=3)\\n with pytest.raises(ValueError):\\n spatial_coarsening(arr, s_enhance=7)\\n with pytest.raises(ValueError):\\n spatial_coarsening(arr, s_enhance=40)\\n\\n arr = np.ones(10)\\n with pytest.raises(ValueError):\\n spatial_coarsening(arr, s_enhance=5)\\n\\n arr = np.ones((4, 4))\\n with pytest.raises(ValueError):\\n spatial_coarsening(arr, s_enhance=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410883d15336f1f3a76c520679e728a7\",\n \"score\": \"0.48204818\",\n \"text\": \"def symmetric_2d(self, x: np.array) -> bool:\\n return np.allclose(x, x.T, atol=self.tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c0c76c648a3134df886607041ad8df\",\n \"score\": \"0.4816043\",\n \"text\": \"def test_do_interpolate(my_data, method, order):\\n loss = do_interpolate(my_data, method=method, order=order)\\n assert 0 < loss < np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36b2b0491679907dfbb186f7b48abc0\",\n \"score\": \"0.48080644\",\n \"text\": \"def test_flatten13():\\n x = np.ones(shape=[2, 3, 4, 5])\\n start_axis = -2\\n stop_axis = -1\\n res = np.ones(shape=[2, 3, 20])\\n obj.run(res=res, x=x, start_axis=start_axis, stop_axis=stop_axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473afe1b65722da72d29c7a97b5a907a\",\n \"score\": \"0.4792067\",\n \"text\": \"def test_nz_qnorm_irizarry_code(self):\\n irizarry_res_sxf = self.nz_qnorm(self.irizarry_ex_sxf)\\n res_shape = irizarry_res_sxf.shape\\n true_shape = self.irizarry_code_res_sxf.shape\\n self.assertEqual(res_shape, true_shape,\\n 'Shape mismatch: {} vs {}'.format(res_shape, true_shape))\\n nptest.assert_allclose(irizarry_res_sxf, self.irizarry_code_res_sxf, rtol=0.001)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc5556ca32119d2b907494c1da850b9\",\n \"score\": \"0.47906113\",\n \"text\": \"def test_GridDataInterpolator_2DGrid():\\n from pyirf.interpolation import GridDataInterpolator\\n\\n grid_points = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\\n target_point = np.array([[0.5, 0.5]])\\n\\n dummy_data1 = np.array([[[0, 1], [1, 1]], [[0, 2], [2, 3]], [[0, 3], [3, 5]]])\\n dummy_data2 = np.array([[[0, 2], [1, 2]], [[0, 4], [2, 6]], [[0, 6], [3, 10]]])\\n dummy_data3 = np.array([[[0, 1], [2, 1]], [[0, 2], [4, 3]], [[0, 3], [6, 5]]])\\n dummy_data4 = np.array([[[0, 2], [2, 2]], [[0, 4], [4, 6]], [[0, 6], [6, 10]]])\\n\\n dummy_data = np.array([dummy_data1, dummy_data2, dummy_data3, dummy_data4])\\n\\n interpolator = GridDataInterpolator(\\n grid_points=grid_points, params=dummy_data, method=\\\"linear\\\"\\n )\\n interpolant = interpolator(target_point)\\n\\n dummy_data_target = 1.5 * dummy_data1\\n\\n assert np.allclose(interpolant, dummy_data_target)\\n assert interpolant.shape == (1, *dummy_data.shape[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78763b9a86e3d52771e5984f3b1f6a76\",\n \"score\": \"0.4786542\",\n \"text\": \"def xr_int_along_axis(xa, DZ, axis):\\n assert type(axis)==np.dtype(int) or axis in xa.dims\\n assert np.shape(xa)==np.shape(DZ)\\n assert axis<=len(np.shape(xa))\\n \\n integral = np.sum(xa*DZ, axis=axis)\\n \\n return integral\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e036ebd6367c582f06a4fd396f3e6cb\",\n \"score\": \"0.47839087\",\n \"text\": \"def integrate(self):\\n return self._compute_integral_mean_and_variance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52ca29dd99d25e9977cca19d25fb241\",\n \"score\": \"0.47810954\",\n \"text\": \"def test_3rd_order_gauss_quadrature(self):\\n def fun(y, x):\\n \\\"\\\"\\\"\\n Integration test function\\n \\\"\\\"\\\"\\n return x**3 + y**3\\n\\n element_integral = 0\\n for pt, w in zip(Q8.integration_points_3, Q8.integration_weights_3):\\n element_integral += w * fun(*pt)\\n\\n reference_integral = integrate.dblquad(fun, -1, 1, lambda x: -1, lambda x: 1)\\n\\n self.assertTrue(np.isclose(element_integral, reference_integral[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2eaab26a29bb1d6849f43babdcb2fe\",\n \"score\": \"0.47807398\",\n \"text\": \"def remove_dimension(trial, param):\\n if param == self.param:\\n adapted_trial = copy.deepcopy(trial)\\n # pylint: disable=protected-access\\n del adapted_trial._params[adapted_trial._params.index(self.param)]\\n adapted_trials.append(adapted_trial)\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7766f7f6cb8244f00d52541b1789065b\",\n \"score\": \"0.4778484\",\n \"text\": \"def _validate(self):\\r\\n if len(set(self._interp_dims)) != len(self._src_coords):\\r\\n raise ValueError('Coordinates repeat a data dimension - the '\\r\\n 'interpolation would be over-specified.')\\r\\n\\r\\n for coord in self._src_coords:\\r\\n if coord.ndim != 1:\\r\\n raise ValueError('Interpolation coords must be 1-d for '\\r\\n 'rectilinear interpolation.')\\r\\n\\r\\n if not isinstance(coord, DimCoord):\\r\\n # Check monotonic.\\r\\n if not iris.util.monotonic(coord.points, strict=True):\\r\\n msg = 'Cannot interpolate over the non-' \\\\\\r\\n 'monotonic coordinate {}.'\\r\\n raise ValueError(msg.format(coord.name()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df5cea4c2b821411c60ad667f0aaa64\",\n \"score\": \"0.47729105\",\n \"text\": \"def numerical_integrate_1D(func, domain, *params):\\n delta_x = domain[1:] - domain[:-1]\\n return sum((func(domain, *params)[:-1]+ func(domain, *params)[1:])/2 * delta_x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa679cc1c8cd8e7af78dc9613713529\",\n \"score\": \"0.47696334\",\n \"text\": \"def integrationOverXi(self):\\n return _libBornAgainCore.InterferenceFunctionFinite2DLattice_integrationOverXi(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4070db5edb7c808f31d23436f7f9c744\",\n \"score\": \"0.4767247\",\n \"text\": \"def integrate(self, Region = None):\\n if ('Uniform' in self.mesh.properties and 'Cartesian' in self.mesh.properties):\\n if Region:\\n out = self[Region.pindex].sum() \\n else: \\n out = self.sum()\\n \\n out *= self.mesh.spacing \\n \\n return out.astype(self.dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cbea9432cf0ecba9cda945e955d9d0\",\n \"score\": \"0.47660792\",\n \"text\": \"def test_separate_coords(arr, expected1, expected2):\\n assert EnsembleKalmanFilter.separate_coords(arr) == (expected1, expected2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74d084a2b4abf3446ac5c289fd09032\",\n \"score\": \"0.4765284\",\n \"text\": \"def test_get_arr_1d():\\n # 1d\\n a = np.arange(3)\\n arr_1d, i, bc_idx = _get_arr_1d(a, [1], 0)\\n assert i == 1\\n assert bc_idx == (...,)\\n np.testing.assert_equal(arr_1d, a)\\n\\n # 2d\\n a = np.arange(4).reshape(2, 2)\\n arr_1d, i, bc_idx = _get_arr_1d(a, [0, 0], 0)\\n assert i == 0\\n assert bc_idx == (..., 0)\\n np.testing.assert_equal(arr_1d, a[:, 0])\\n\\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1], 1)\\n assert i == 1\\n assert bc_idx == (..., 1)\\n np.testing.assert_equal(arr_1d, a[1, :])\\n\\n # 3d\\n a = np.arange(8).reshape(2, 2, 2)\\n arr_1d, i, bc_idx = _get_arr_1d(a, [0, 0, 0], 0)\\n assert i == 0\\n assert bc_idx == (..., 0, 0)\\n np.testing.assert_equal(arr_1d, a[:, 0, 0])\\n\\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1, 0], 1)\\n assert i == 1\\n assert bc_idx == (..., 1, 0)\\n np.testing.assert_equal(arr_1d, a[1, :, 0])\\n\\n arr_1d, i, bc_idx = _get_arr_1d(a, [1, 1, 0], 2)\\n assert i == 0\\n assert bc_idx == (..., 1, 1)\\n np.testing.assert_equal(arr_1d, a[1, 1, :])\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993987,"cells":{"query_id":{"kind":"string","value":"2fe086c3e5a427da90d6431edd2ef5c7"},"query":{"kind":"string","value":"Browse the file system and update the control instance accordingly. If a valid direcorty has already been entered the dialogue will automatically point to this folder, otherwise the current working directory is used."},"positive_passages":{"kind":"list like","value":[{"docid":"897bc328a97b5b014ceb9ef88458331d","score":"0.779447","text":"def onBrowseClicked(control_instance):\n # Get the current directory\n current_control_value = os.path.join(os.getcwd(), os.pardir)\n if DirectoryControlWidget.is_valid(control_instance):\n current_control_value = six.text_type(control_instance.path.text())\n\n # Create a dialog to select a directory\n folder = qt_backend.getExistingDirectory(\n get_ref(control_instance), \"Open directory\", current_control_value,\n QtGui.QFileDialog.ShowDirsOnly\n | QtGui.QFileDialog.DontUseNativeDialog)\n\n # Set the selected directory to the path sub control\n control_instance.path.setText(six.text_type(folder))","title":""}],"string":"[\n {\n \"docid\": \"897bc328a97b5b014ceb9ef88458331d\",\n \"score\": \"0.779447\",\n \"text\": \"def onBrowseClicked(control_instance):\\n # Get the current directory\\n current_control_value = os.path.join(os.getcwd(), os.pardir)\\n if DirectoryControlWidget.is_valid(control_instance):\\n current_control_value = six.text_type(control_instance.path.text())\\n\\n # Create a dialog to select a directory\\n folder = qt_backend.getExistingDirectory(\\n get_ref(control_instance), \\\"Open directory\\\", current_control_value,\\n QtGui.QFileDialog.ShowDirsOnly\\n | QtGui.QFileDialog.DontUseNativeDialog)\\n\\n # Set the selected directory to the path sub control\\n control_instance.path.setText(six.text_type(folder))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5a030732cf12ef84300d8a20743a3891","score":"0.8512092","text":"def _browse(self):\n\n initial_dir = self._variable.get()\n initial_dir = os.path.abspath(os.path.expanduser(initial_dir))\n\n self.options['initialdir'] = initial_dir\n dlg = filedialog.Directory(self.master, **self.options)\n new_dir = dlg.show()\n\n if new_dir:\n new_dir = shorten(new_dir)\n self.value = new_dir","title":""},{"docid":"28ebe030b8c28676ce86a1dddb4994d8","score":"0.7814108","text":"def browseDirectory(self):\n \n path = tk.filedialog.askdirectory(\n title=\"Choose a directory\",\n initialdir=self.controller.destinationDir).replace(\"/\", \"\\\\\")\n\n if path:\n self.saveButton.config(\n background=\"yellow2\",\n activebackground=\"yellow2\")\n\n self.entryText.set(path)","title":""},{"docid":"d956bc3b4ddcc5b85448fe3f10ee62ab","score":"0.7581965","text":"def _browse(self):\n\n initial_file = self._variable.get()\n\n if initial_file:\n initial_dir = os.path.split(initial_file)[0]\n self.options['initialdir'] = initial_dir\n\n if self.mode == 'open':\n dlg = filedialog.Open(self.master, **self.options)\n else:\n dlg = filedialog.SaveAs(self.master, **self.options)\n\n new_file = dlg.show()\n\n if new_file:\n new_file = shorten(new_file)\n self.value = new_file","title":""},{"docid":"4efb6511d238530adfe05bb1a51fc7ae","score":"0.74403816","text":"def open_dir(self, event):\n dlg = wx.DirDialog(self, \"Choose default directory\", style=wx.DD_DEFAULT_STYLE)\n if dlg.ShowModal() == wx.ID_OK:\n self.settings['fp'] = dlg.GetPath()\n self.dir_txt.SetValue(self.settings['fp'])\n dlg.Destroy()\n pub.sendMessage('fp', msg=self.settings['fp'])","title":""},{"docid":"ea2d2a509a0bbae5f6e64ede66353eb4","score":"0.7431","text":"def browseFolder(self):\n # popup folder selection dialog\n dir_path = getAFolder()\n if dir_path != \"\":\n self.filePath = str(dir_path)\n self.selectImageButton.setHidden(True)\n self.selectFolder.setHidden(True)\n self.imageCanvas.setHidden(False)\n self.ignoreFolds = set()\n self.currentFileNumber = 0\n self.onImageChanged()\n self.processFolder()","title":""},{"docid":"0a99dab5d273da38824fddab5293a56a","score":"0.74279505","text":"def input_path_browse_button_clicked():\n directory = QFileDialog.getExistingDirectory(self, \"Find Files\", QDir.currentPath())\n self.input_path_line_edit.setText(directory)","title":""},{"docid":"a1a671f72834a0692b5a8f65955bb502","score":"0.742454","text":"def browse_directory(self, directory_input):\n\n # Open the file dialogue\n directory_name = filedialog.askdirectory(initialdir=self.cwd)\n\n # Don't replace the directory input value if user cancels\n if directory_name is not None and directory_name != '':\n directory_input.delete(0, END) # Clear current value in entry widget\n directory_input.insert(0, directory_name) # Add user-selected value to entry widget","title":""},{"docid":"df465049795f7ad25fc5d7458e0b9b96","score":"0.7378925","text":"def browse(self):\r\n\r\n self.filepath.set(fd.askopenfilename(initialdir=self._initaldir,\r\n filetypes=self._filetypes))","title":""},{"docid":"091eed2108efb32916ee6b5b6dbe1af8","score":"0.73339975","text":"def onDir(self, event):\n dlg = wx.DirDialog(self, \"Choose a directory:\",\n style=wx.DD_DEFAULT_STYLE\n #| wx.DD_DIR_MUST_EXIST\n #| wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.folder=dlg.GetPath()\n dlg.Destroy() \n self.foldadd()","title":""},{"docid":"5f391f64b08f55a6987495c92c3fa145","score":"0.71891147","text":"def dir_browse(self):\n\n if not self.is_running():\n # dirpath will be None or a string\n msg, pth = RegistryHelpers.GetDirFromUserQT(self, RegistryKey='klusterintel',\n Title='Select directory to monitor', AppName='klusterintel')\n if pth is not None:\n self.fil_text.setText(pth)\n else:\n print('You have to stop monitoring before you can change the path')","title":""},{"docid":"07fe581e76af5a38fd30ed911d4df968","score":"0.7138757","text":"def onDir(self, event):\n dlg = wx.DirDialog(self, \"Choose a directory:\",\n style=wx.DD_DEFAULT_STYLE\n #| wx.DD_DIR_MUST_EXIST\n #| wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.folder=dlg.GetPath()\n self.folderadd()\n dlg.Destroy()","title":""},{"docid":"1e60f451f79b10050d6fa641e5f774c6","score":"0.7078267","text":"def open_f(self):\n\n self.dirname = QFileDialog.getExistingDirectory()\n if self.dirname and len(self.dirname) > 0:\n self.text_file.setText(self.dirname)","title":""},{"docid":"d44b9ba513741611784f668f5bac1dcb","score":"0.7068113","text":"def onDiradd(self):\n self.top=1 \n dlg = wx.DirDialog(self, \"Choose a directory:\",\n style=wx.DD_DEFAULT_STYLE\n #| wx.DD_DIR_MUST_EXIST\n #| wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.folder=dlg.GetPath()\n self.foldradd()\n dlg.Destroy()","title":""},{"docid":"194e2cdd6e91278ecabdd2b2d70f4f03","score":"0.7047211","text":"def ask_directory(self):\n self.directory = tkFileDialog.askdirectory(**self.dir_opt)","title":""},{"docid":"2669963175a658f3d01a8ba7ff199e22","score":"0.7013578","text":"def browseFiles(self):\n\n temp_path = filedialog.askopenfilename(title = \"Select a File\", filetypes = ((\"Text Files\", \"*.txt\"), (\"All Files\", \"*\"))) \n if temp_path:\n self.file_path = temp_path\n self.label_file_explorer.configure(text = \"File: \" + self.file_path)\n self.analyze()","title":""},{"docid":"c1f722d0fee5a12e68235869b0856912","score":"0.6981006","text":"def output_path_browse_button_clicked():\n directory = QFileDialog.getExistingDirectory(self, \"Find Files\", QDir.currentPath())\n self.output_path_line_edit.setText(directory)","title":""},{"docid":"a2769fc43798d33f0894ee8024152d6c","score":"0.6971456","text":"def onOpenFile(self, event):\n dlg = wx.FileDialog(\n self, message=\"Choose a file\",\n defaultFile=\"\",\n wildcard=wildcard,\n style=wx.OPEN | wx.MULTIPLE | wx.CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n x = dlg.GetPaths()\n self.paths=x[0]\n self.fileadd() \n dlg.Destroy()","title":""},{"docid":"03053fb6ee60399f4a80d7752d3c6d07","score":"0.69596636","text":"def do_browse_data_directory(self):\n # get the data directory from text line or from default\n if str(self.ui.lineEdit_iptsDir.text()).strip() != '':\n # from IPTS Directory\n default_dir = str(self.ui.lineEdit_iptsDir.text()).strip()\n else:\n # from default\n default_dir = self._homeDir\n\n # user-specified data directory\n data_dir = str(QFileDialog.getExistingDirectory(self, 'Get Directory',\n default_dir))\n self.ui.lineEdit_iptsDir.setText(data_dir)\n self._iptsDir = data_dir\n self._iptsDirFromDir = data_dir\n self._iptsNumber = None\n\n # Enable next step\n self.ui.groupBox_scanIptsInfo.setEnabled(True)\n\n return","title":""},{"docid":"140947aeb5f7e1d9424ad24bda874d82","score":"0.6942863","text":"def ask_path(self):\n\n default_dir = os.path.dirname(self.path)\n\n _dir = QFileDialog.getExistingDirectory(self, dir=default_dir)\n if _dir:\n self.path = _dir","title":""},{"docid":"a4dc3897f08db9e961886ce0af8a8ec6","score":"0.6942444","text":"def selectPath(self):\n dirname = QtWidgets.QFileDialog.getExistingDirectory(self)\n if len(dirname) == 0:\n return\n self.setText(dirname)","title":""},{"docid":"6da3bd7acd4461bd44a51b1885046ed6","score":"0.6936279","text":"def __SelectFolder(self):\n op={}\n op[\"initialdir\"]=os.getcwd()\n f = filedialog.askdirectory(**op)\n if f==\"\":\n return\n filepath = os.path.abspath(f)\n if os.path.isdir(filepath):\n self.__CleanView()\n self.root.title(filepath)\n self.__workdir= os.path.abspath(filepath)\n self.lf.ShowWithoutAnalys(filepath)\n self.__countofLefttree = self.lf.GetCount()\n self.__SetStateofSearchMenu()","title":""},{"docid":"44f29bd750498cee4a9b7d73d8ef4275","score":"0.69354516","text":"def directoryPathChooserClicked(self, textfield):\n path = textfield.text()\n path = QFileDialog.getExistingDirectory(self, \"Select Folder\", path)\n textfield.setText(path)","title":""},{"docid":"90441004285dbc840199a58133807566","score":"0.68388385","text":"def button_browse_callback(self):\n options = {}\n options['initialdir'] = '.'\n options['title'] = 'Select the log directory'\n options['mustexist'] = False\n self.fileName = FileDiag.askdirectory(**options)\n info(\"***%s\" % self.fileName)\n if self.fileName == \"\":\n return None\n else:\n return self.fileName","title":""},{"docid":"ce59e1feaa64f9fc196ae6e9488a760a","score":"0.6827011","text":"def open_directory_dialogue():\n global data_directory\n data_directory = filedialog.askdirectory()","title":""},{"docid":"9b33babacb5ef6cc5147f62b237ae427","score":"0.6822368","text":"def askdirectory(self):\n \n des = tkFileDialog.askdirectory(title = \"The Destination folder is \")\n \n if des:\n self.submit_name(des)","title":""},{"docid":"d939a34f3e990880023cf68db06f6234","score":"0.67647696","text":"def onSetDir(self, event):\n # dialog = wx.TextEntryDialog(None, \"Point to new directory. Currently set to %s\" % self.saveDir,\n # \"Set Save Directory\", \"%s\" % self.saveDir, wx.OK | wx.CANCEL)\n dialog = wx.DirDialog(self, \"Choose a save directory\", defaultPath=\"/home/mro/data\")\n answer = dialog.ShowModal()\n dialog.Destroy()\n\n if answer == wx.ID_OK:\n setTo = str(dialog.GetPath()) + \"/\"\n self.saveDir = setTo\n self.parent.saveDirectoryText.SetLabel(u\"Saving \\u2192 %s\" % self.saveDir)\n self.parent.Layout() # This recenters the static text\n logger.debug(\"Directory: \" + self.saveDir)","title":""},{"docid":"543f4b7dc9b00d829537db1013016bfe","score":"0.67494184","text":"def _create_file_dialog ( self ):\r\n dlg = wx.DirDialog( self.control, message = 'Select a Directory' )\r\n dlg.SetPath( self._file_name.GetValue() )\r\n return dlg","title":""},{"docid":"74aa93866ba310b886088f071bdc6191","score":"0.67004776","text":"def browse(self):\n startdir = self.dest.text() or os.getcwd()\n path = qtw.QFileDialog.getSaveFileName(self, 'Kies een bestand', startdir)\n if path[0]:\n self.dest.setText(path[0])","title":""},{"docid":"e66af338f87cde8c552204146c68d6b9","score":"0.667561","text":"def askdirectory(self):\n return filedialog.askdirectory(title=\"Select A Folder\", mustexist=1)","title":""},{"docid":"8fa4ecfb3ecb2ff02b31f4e644e92d1c","score":"0.66730225","text":"def _showChangePathDialog(self):\n path = self.path()\n\n if not path:\n path = os.path.expanduser(\"~\")\n\n path = QtWidgets.QFileDialog.getExistingDirectory(\n None,\n \"Choose a root folder location\",\n path\n )\n\n return studiolibrary.normPath(path)","title":""},{"docid":"30bcd7e2d09c426b3c598169f2b11d3e","score":"0.66609615","text":"def _open_folder():\n tk.Tk().withdraw()\n return filedialog.askdirectory()","title":""},{"docid":"73540a5dc0c93c289c87cbfe3df43d3f","score":"0.66517067","text":"def get_dir (self, event=None):\n msg = \"Please choose a file in the chosen directory. \"\\\n \"The directory from which the file was taken \"\\\n \"will be chosen as the default directory. \"\\\n \"If you enter an empty entry, an intelligent \"\\\n \"choice of directory will be made automatically at run-time.\"\n tkMessageBox.showinfo (\"Choose directory\", msg)\n tk_fopen = tkFileDialog.askopenfilename\n f_name = tk_fopen (title=\"Choose file to select directory\",\n initialdir=self.cfg.initial_dir,\n filetypes=[(\"All files\", \"*\")])\n if f_name:\n direc = os.path.dirname (f_name)\n self.cfg.initial_dir = direc\n self.initial_dir.set (direc)","title":""},{"docid":"649b45185e27ccf9f07b1356a37beff2","score":"0.66298395","text":"def output_dir_change_action(self):\n fileDialog = QFileDialog()\n directory = fileDialog.getExistingDirectory()\n self.dynamic.output_directory.setText(directory)","title":""},{"docid":"1766d7974f8fae48d55b2ad045380181","score":"0.6627282","text":"def onOpen(self, event):\r\n dlg = wx.DirDialog( self, \"Select Repository Root\", '') # parentWindow, Test, defaultPath ( save the last to make it open to the same one every time )\r\n if dlg.ShowModal() == wx.ID_OK:\r\n self._initTreeView( dlg.GetPath() )","title":""},{"docid":"a4a9888d315746315458c0618a927133","score":"0.66179836","text":"def button_open_downloads_file_chooser_clicked(self, widget, data=None):\n dialog = gtk.FileChooserDialog(_(\"Choose Folder...\"),\n None,\n gtk.FILE_CHOOSER_ACTION_SELECT_FOLDER,\n (gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,\n gtk.STOCK_OPEN, gtk.RESPONSE_OK))\n dialog.set_current_folder(os.path.expanduser(\"~\"))\n dialog.set_default_response(gtk.RESPONSE_OK)\n response = dialog.run()\n if response == gtk.RESPONSE_OK:\n self.downloads_directory = dialog.get_current_folder()\n self.downloads_text_entry.set_text(self.downloads_directory)\n self.db_session.variable_set('downloads_directory', self.downloads_directory)\n dialog.destroy()","title":""},{"docid":"a5fbf1277e66ad98321829ad49b6aaf5","score":"0.65866774","text":"def browser_btn():\n browser_box = Tk()\n browser_box.withdraw()\n browser_box.filename = filedialog.askdirectory(initialdir=os.environ['USERPROFILE'] + \"\\\\Downloads\",\n title=\"Select Download Directory\")\n\n # This is the code to only replace the path if user have select a folder\n if browser_box.filename != \"\":\n path.configure(state=NORMAL)\n path.unbind('<Button-1>')\n path.delete(0, END)\n path.insert(0, str(browser_box.filename))\n\n browser_box.destroy()","title":""},{"docid":"877dce526d4721ad1c6b308b114b1153","score":"0.6584735","text":"def browse_folder(self):\n # Get the file name/path from the user input in the file manager\n filename = tkinter.filedialog.asksaveasfilename(initialdir=\".\", title=\"Save JSON file\",\n filetypes=((\"json files\", \"*.json\"), (\"txt files\", \"*.txt\"),\n (\"All files\", \"*.*\")))\n\n # Update the file name/path to the file name entry\n self.filename_entry.delete(0, tkinter.END)\n self.filename_entry.insert(0, filename)","title":""},{"docid":"f60a6c861a80dd09e941e65640f82582","score":"0.65813696","text":"def onDirIn(self, event):\n dlg = wx.DirDialog(self, \"Choose a directory:\",\n style=wx.DD_DEFAULT_STYLE\n # | wx.DD_DIR_MUST_EXIST\n # | wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.inputDirectory = dlg.GetPath()\n self.inputText.SetLabel(self.inputDirectory)\n self.inputText.SetForegroundColour(\"black\") # set text color\n\n mylist = [f for f in glob.glob(os.path.join(self.inputDirectory, \"*.jpg\"))]\n print(not mylist)\n if not mylist:\n self.genText.SetLabel(\"* Valid images not found\")\n self.genText.SetForegroundColour(\"red\")\n self.inputDirectory = \"\"\n\n else:\n self.files = mylist\n myNames = [os.path.split(f)[1] for f in mylist]\n\n self.num_images = len(mylist)\n self.mainIterator = get_dataset(mylist, np.zeros(self.num_images), (299,299), 1,\n tf.keras.applications.inception_v3.preprocess_input)\n\n self.genText.SetLabel(\"* Found {} validated images\".format(self.num_images))\n self.genText.SetForegroundColour(\"black\")\n\n if not self.num_images * self.sliderValue // 100:\n self.outGenText.SetLabel(\"* No images will be saved. Percentage too low.\")\n self.outGenText.SetForegroundColour(\"red\")\n else:\n self.outGenText.SetLabel(\n \"* Saving {} best images\".format(self.num_images * self.sliderValue // 100))\n self.outGenText.SetForegroundColour(\"black\")\n dlg.Destroy()","title":""},{"docid":"933f2d89283ca17cb4480b50edfe659a","score":"0.6543978","text":"def askdir(self, var, label):\n \n f = tkFileDialog.askdirectory(title='Select Directory', initialdir='R:/soundcast/inputs/lodes')\n \n label.config(text=f)\n update_input_config(var, f)","title":""},{"docid":"d052929cec2d6df3b052a03ae76d4b12","score":"0.6535897","text":"def _add(self):\n filenames = list()\n dlg = sppasFileDialog(self)\n if os.path.exists(self.__current_dir):\n dlg.SetDirectory(self.__current_dir)\n if dlg.ShowModal() == wx.ID_OK:\n filenames = dlg.GetPaths()\n dlg.Destroy()\n\n if len(filenames) > 0:\n added = self.FindWindow(\"filestree\").AddFiles(filenames)\n if added:\n self.__current_dir = os.path.dirname(filenames[0])\n self.notify()","title":""},{"docid":"2fd430ad8a9db44e6a521eae62210dfa","score":"0.6532352","text":"def open_ingestion_directory_selector(self, textbox_widget=None, team=\"root\"):\n directory = QFileDialog.getExistingDirectory(None, 'Select a folder:', 'C:\\\\', QFileDialog.ShowDirsOnly)\n if textbox_widget is None:\n print(directory)\n else:\n self.controller.update_folder_path(team, directory)\n textbox_widget.setPlainText(str(directory))","title":""},{"docid":"62fec91a835a5470bed773c1e329aa3d","score":"0.65273994","text":"def displayFileDialog(self):\n return filedialog.askdirectory()","title":""},{"docid":"f82e5fccac60cb735a14da42d201b6b5","score":"0.6525494","text":"def BrowseForFolder(self):\n pass","title":""},{"docid":"066df63b4c94f56fc2118774fb3e4712","score":"0.65220475","text":"def _select_dir(self):\n with RememberDir(self) as rd:\n self.work_dir, load_ok = rd.get_dir()\n\n if load_ok:\n self._load2gui()","title":""},{"docid":"e5908d8cec6f2a68ecac8f58cc293cd3","score":"0.6507518","text":"def onDirOut(self, event):\n dlg = wx.DirDialog(self, \"Choose a directory:\",\n style=wx.DD_DEFAULT_STYLE\n # | wx.DD_DIR_MUST_EXIST\n # | wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.outputDirectory = dlg.GetPath()\n self.outputText.SetLabel(self.outputDirectory)\n self.outputText.SetForegroundColour(\"black\") # set text color\n dlg.Destroy()","title":""},{"docid":"f8682029c0addf7b83c2da98b7d8f164","score":"0.6504469","text":"def dir_find(self):\n directory = QtWidgets.QFileDialog.getExistingDirectory(self, \"Выберите каталог\")\n self.line_edit_with_buttons.set_text(directory)","title":""},{"docid":"7e579437a3fdca8fb0e763badcb257c8","score":"0.6493883","text":"def update_object ( self, event ):\r\n if self.control is not None:\r\n path = self.control.GetPath()\r\n if isdir( path ):\r\n self.value = path","title":""},{"docid":"a0bdfe0e0cc9941e0985a99f6ed473fc","score":"0.64692336","text":"def on_scan_btn_clicked(self):\n path = QFileDialog.getExistingDirectory(self.base,\n _(\"Select a directory with books or \"\n \"your eReader's drive\"),\n self.base.last_dir,\n QFileDialog.ShowDirsOnly)\n if path:\n self.base.last_dir = path\n self.base.high_list.clear()\n self.base.reload_highlights = True\n self.base.loading_thread(Scanner, path, self.base.kor_text, clear=False)","title":""},{"docid":"e4ec113b1714ab2a156912943fa5d5c6","score":"0.645265","text":"def on_browse_userpath(self, evt):\n try:\n open_file.open_file(pathfinder.user_path())\n except IOError: # file not found\n module_logger.error(\"User folder {0} not found.\".format(pathfinder.user_path()))\n err_msg = \"Unable to find folder '{0}'.\\nPlease ensure the folder exists.\".format(pathfinder.user_path())\n err_dlg = wx.MessageDialog(self.view, message=err_msg,\n caption=\"Unable To Open Folder\", style=wx.ICON_ERROR)\n err_dlg.ShowModal()\n err_dlg.Destroy()\n except OSError as err: # other OS error\n module_logger.error(\"Unidentified OS error {0}\".format(err))\n err_msg = \"Unable to browse to data folder, error reported was:\\n{0}\".format(err)\n err_dlg = wx.MessageDialog(self.view, message=err_msg,\n caption=\"Unable To Open Folder\", style=wx.ICON_ERROR)\n err_dlg.ShowModal()\n err_dlg.Destroy()","title":""},{"docid":"0a2f4b7c37d5a47728df9b5960dba045","score":"0.641189","text":"def open_folder(self):\n self.current_photo = 0\n self.folder = askdirectory()\n if (len(listdir(self.folder)) - 1) != 0:\n self.photos = [\n p\n for p in listdir(self.folder)\n if p.endswith(\".png\") or p.endswith(\".jpg\") or p.endswith(\".gif\")\n ]\n self.total_photos = len(self.photos) - 1\n self.get_photo()\n self.progress_bar()\n else:\n lbl_img.configure(\n text=f\"'{self.folder}' is empty or contains no photos. Choose another folder.\"\n )\n lbl_img.image = f\"'{self.folder}' is empty or contains no photos. Choose another folder.\"","title":""},{"docid":"c1dda33492147b9a06a00c0206d75676","score":"0.6392816","text":"def open_dialog(self):\n # Dialog\n self.file_path = fd.askopenfilename(title=\"Select ASC file\",\n initialdir=self.home,\n filetypes=self.file_types)\n\n # Get the file name -- iterate backwards until /, then reverse\n fname = \"\".join(reversed(self.file_path[\n -1:self.file_path.rfind(\"/\"):-1]))\n \n # Set the tkinter var storing the file name\n self.file_name_var.set(fname)\n\n return None","title":""},{"docid":"481c25b25e29b34ca591cd8a13b9eabd","score":"0.63926655","text":"def chooseOntologyPath(self):\n path = self.path.text()\n path = QFileDialog.getExistingDirectory(self, 'Choose Directory', path)\n self.path.setText(path)","title":""},{"docid":"4abbf1654340d646117d7cb81fef2cba","score":"0.637571","text":"def BrowseForFile(self):\n pass","title":""},{"docid":"92b756921a75e8d14c624f53c4ae582f","score":"0.63712925","text":"def cd(self, directory, bot):\n\t\t# If the back_label, such as \"Back\" is clicked,\n\t\t# we're expected to change directory to ..\n\t\tpath = os.path.join(self.data['path'], directory)\n\t\t# Directories sorted using {*} at the end, gets\n\t\t# their {*} removed. Glob checks if a dir that\n\t\t# ends with {*} exists, and if so, replaces path.\n\t\tif glob(path+' {*}/'):\n\t\t\tpath = glob(path+' {*}')[0]\n\t\tif (directory == args['back_label'] and \n\t\t\tself.data['path'] != args['root']):\n\t\t\t# The first element of os.path.split is the .. directory\n\t\t\tself.data['path'] = os.path.split(self.data['path'])[0]\n\t\t# If file is a directoy, open it\n\t\telif os.path.exists(path):\n\t\t\tself.data['path'] = path\n\t\tself.ls(bot)\n\t\tself.save_data()","title":""},{"docid":"c426932a49491db3290bdfdcc12818cc","score":"0.636276","text":"def select_folder(self, entry_name):\r\n folder_selected = filedialog.askdirectory()\r\n if folder_selected != '':\r\n self.inputs[entry_name].variable.set(folder_selected)\r\n self.inputs['FolderStructure'].update_tree(folder_selected,\r\n sfilter=['.git', '.settings', 'libspecs', '__pycache__',\r\n '.png'])\r\n\r\n self.callbacks['btn_projLocation'](folder_selected)","title":""},{"docid":"f6ccbeba663da9ff17e4620e67e4d91b","score":"0.63622737","text":"def __init__(self, title_txt=\"Seleccione carpeta\", button_txt=\"Abrir\", cls=\"dialog\", path=None):\n\n cls1 = \"filedialog\"\n if not path: self.curdir = os.getcwd()\n else: self.curdir = path\n self.dir_img = gui.basic.Image(\n gui.pguglobals.app.theme.get(cls1+\".folder\", \"\", 'image'))\n self.title = gui.basic.Label(title_txt, cls=cls+\".title.label\")\n self.body = gui.Container(width=700, height=300)\n\n self.body.add(gui.basic.Label(\"Carpeta\"), 10, 10)\n\n self.input_dir = gui.input.Input(size=38)\n self.body.add(self.input_dir, 125, 10)\n\n self.button_ok = gui.button.Button(button_txt)\n self.body.add(self.button_ok, 640, 12)\n self.button_ok.connect(gui.const.CLICK, self._button_okay_clicked_, None)\n\n self.list = gui.area.List(width=690, height=235)\n self.body.add(self.list, 10, 50)\n self._list_dir_()\n self.list.connect(gui.const.CHANGE, self._item_select_changed_, None)\n\n self.value = None\n gui.Dialog.__init__(self, self.title, self.body)","title":""},{"docid":"cc2e18bf5666d62db76df45c5bc448e3","score":"0.6353819","text":"def browse_file(self, file_input, filetypes):\n\n # Open the file dialog\n file_name = filedialog.askopenfilename(initialdir=self.cwd, filetypes=filetypes)\n\n # Don't replace the file input value if user cancels\n if file_name is not None and file_name != '':\n file_input.delete(0, END) # Clear current value in entry widget\n file_input.insert(0, file_name) # Add user-selected value to entry widget","title":""},{"docid":"e0d5d44fc9407f323a31186eb1d18887","score":"0.63331634","text":"def button_openfile(self, event):\n event.Skip()\n actualDir = (os.getcwd() if self.m_comboBox_files.GetValue() else\n os.path.dirname(os.path.abspath(self.m_comboBox_files.GetValue())))\n dlg = wx.FileDialog(self, message=\"Select BOM(s)\", defaultDir=actualDir,\n defaultFile=\"\", wildcard=WILDCARD_BOM,\n style=wx.FD_OPEN | wx.FD_MULTIPLE | wx.FD_CHANGE_DIR)\n if dlg.ShowModal() == wx.ID_OK:\n self.addFile(dlg.GetPaths())\n self.updateOutputFilename() # Update the output file name on GUI text.\n dlg.Destroy()","title":""},{"docid":"8ca30bc35afaa245b3f0d9e716ce6de9","score":"0.6323572","text":"def set_userpath(self):\n try:\n current_user_path = pathfinder.user_path()\n path_dlg = wx.DirDialog(parent=self.view.parent, message=\"Please specify a data folder\",\n defaultPath=current_user_path)\n if path_dlg.ShowModal() == wx.ID_OK:\n new_user_path = path_dlg.GetPath()\n self.model.migrate_user_path(new_user_path)\n self.refresh_data()\n finally:\n path_dlg.Destroy()","title":""},{"docid":"be8d284a3e9ab3cb9455ed3b042f349c","score":"0.63098764","text":"def _open_folder_helper(_None):\n dialog = Gtk.FileChooserDialog(\n action=Gtk.FileChooserAction.SELECT_FOLDER)\n _quick_buttons(dialog)\n dialog.add_button(Gtk.STOCK_OPEN, Gtk.ResponseType.OK)\n dialog.run()\n path = dialog.get_filename()\n dialog.destroy()\n Gtk.main_quit()\n result.append(path)","title":""},{"docid":"4c6946c5df0a97628e582e32587712af","score":"0.63047534","text":"def path_dialog() -> input_dialog:\n return input_dialog(\n title=\"File or Directory Path\", text=\"Input file or directory path: \",\n ).run()","title":""},{"docid":"41359262ae572b7a67bb3af6debbc7c7","score":"0.6301636","text":"def selectDir( self ):\n\n #self.showdialog()\n project = QgsProject.instance()\n if QgsExpressionContextUtils.projectScope(project).variable('project_importpath') == None: \n self.vectorPath = QFileDialog.getExistingDirectory(self.dockWidget, 'Selectvector data path', '/home', QFileDialog.ShowDirsOnly)\n QgsExpressionContextUtils.setProjectVariable(project, 'project_importpath', self.vectorPath)\n self.dockWidget.lineEdit.setText(self.vectorPath)\n #C:\\\\Users\\\\timc7\\\\Dropbox (Personal)\\\\Tim\\\\Employment\\\\WestWind Energy\\\\Work\\\\Projects\\\\GPWF\\\\GIS\\\\VectorData\n #P:\\\\P158_GPWF_GoldenPlainsWindFarm\\\\GIS_Maps\\\\VectorData\n\n else: \n originalVectorPath = QgsExpressionContextUtils.projectScope(project).variable('project_importpath')\n self.vectorPath=originalVectorPath\n self.vectorPath = QFileDialog.getExistingDirectory(self.dockWidget, 'Selectvector data path', self.vectorPath , QFileDialog.ShowDirsOnly)\n \n if (self.vectorPath and self.vectorPath is not None):\n self.dockWidget.lineEdit.setText(self.vectorPath)\n QgsExpressionContextUtils.setProjectVariable(project, 'project_importpath', self.vectorPath)\n else:\n self.dockWidget.lineEdit.setText(originalVectorPath)","title":""},{"docid":"69aeb225990a6b9f3e14db629ce4c074","score":"0.62987274","text":"def handler_ok_changed ( self, info ):\r\n dir = info.object.file_name\r\n if not isdir( dir ):\r\n dir = dirname( dir )\r\n\r\n path = join( dir, self.dir_name )\r\n try:\r\n # Try to create the requested directory:\r\n mkdir( path )\r\n\r\n # Force the file tree view to be refreshed:\r\n info.object.reload = True\r\n\r\n # set the new directory as the currently selected file name:\r\n info.object.file_name = path\r\n\r\n # Close this view:\r\n info.ui.dispose( True )\r\n except:\r\n self.message = \"Could not create the '%s' directory\" % self.dir_name","title":""},{"docid":"35419f9aa50d61cc1ded35b3719fb91e","score":"0.6294492","text":"def folder_dialog(title):\n\n root = tkinter.Tk()\n root.withdraw()\n return filedialog.askdirectory(title=title)","title":""},{"docid":"cb95684489b7461a0f1c9c904afe3a11","score":"0.6282837","text":"def handlerSelectWorkingDir(self):\n workingDir = unicode(QtGui.QFileDialog.getExistingDirectory(self, 'Select Working Directory'))\n \n if workingDir:\n self.lineEditWorkingDir.setText(workingDir)\n \n logging.getLogger(type(self).__name__).info('select working directory: %s', workingDir)","title":""},{"docid":"32a5b4f2906e96d28d20ea4ae1946441","score":"0.62821496","text":"def browseDir(self, event):\n\n fname = str('machinelabels-iter'+str(self.iterationindex)+'.h5')\n self.statusbar.SetStatusText(\"Looking for a folder to start refining...\")\n cwd = os.path.join(os.getcwd(),'labeled-data')\n# dlg = wx.FileDialog(self, \"Choose the machinelabels file for current iteration.\",cwd, \"\",wildcard=fname,style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\n print(platform.system())\n if platform.system()=='Darwin': \n dlg = wx.FileDialog(self, \"Choose the machinelabels file for current iteration.\",cwd, fname ,wildcard=\"(*.h5)|*.h5\",style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) \n else:\n dlg = wx.FileDialog(self, \"Choose the machinelabels file for current iteration.\",cwd, \"\",wildcard=fname,style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\n\n if dlg.ShowModal() == wx.ID_OK:\n self.data_file = dlg.GetPath()\n self.dir = str(Path(self.data_file).parents[0])\n self.fileName = str(Path(self.data_file).stem)\n self.load.Enable(False)\n self.next.Enable(True)\n self.save.Enable(True)\n self.zoom.Enable(True)\n self.pan.Enable(True)\n self.home.Enable(True)\n self.quit.Enable(True)\n else:\n dlg.Destroy()\n self.Destroy()\n dlg.Destroy()\n\n try:\n self.dataname = str(self.data_file)\n\n except:\n print(\"No machinelabels file found!\")\n self.Destroy()\n self.statusbar.SetStatusText('Working on folder: {}'.format(os.path.split(str(self.dir))[-1]))\n self.preview = True\n self.iter = 0\n\n if os.path.isfile(self.dataname):\n self.Dataframe = pd.read_hdf(self.dataname,'df_with_missing')\n self.Dataframe.sort_index(inplace =True)\n self.scorer = self.Dataframe.columns.get_level_values(0)[0]\n\n # bodyParts = self.Dataframe.columns.get_level_values(1)\n # _, idx = np.unique(bodyParts, return_index=True)\n # self.num_joints = len(self.bodyparts)\n # self.bodyparts = bodyParts[np.sort(idx)]\n self.index = list(self.Dataframe.iloc[:,0].index)\n# Reading images\n\n self.img = os.path.join(self.project_path,self.index[self.iter])\n img_name = Path(self.img).name\n self.norm,self.colorIndex = self.image_panel.getColorIndices(self.img,self.bodyparts)\n# Adding Slider and Checkbox\n\n self.choiceBox,self.slider,self.checkBox = self.choice_panel.addCheckBoxSlider(self.bodyparts,self.file,self.markerSize)\n self.slider.Bind(wx.EVT_SLIDER, self.OnSliderScroll)\n self.checkBox.Bind(wx.EVT_CHECKBOX,self.activateSlider)\n self.slider.Enable(False)\n# Show image\n# Setting axis title:dont want to show the threshold as it is not selected yet.\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\n\n instruction = wx.MessageBox('1. Enter the likelihood threshold. \\n\\n2. Each prediction will be shown with a unique color. \\n All the data points above the threshold will be marked as circle filled with a unique color. All the data points below the threshold will be marked with a hollow circle. \\n\\n3. Enable the checkbox to adjust the marker size. \\n\\n4. Hover your mouse over data points to see the labels and their likelihood. \\n\\n5. Left click and drag to move the data points. \\n\\n6. Right click on any data point to remove it. Be careful, you cannot undo this step. \\n Click once on the zoom button to zoom-in the image.The cursor will become cross, click and drag over a point to zoom in. \\n Click on the zoom button again to disable the zooming function and recover the cursor. \\n Use pan button to pan across the image while zoomed in. Use home button to go back to the full;default view. \\n\\n7. When finished click \\'Save\\' to save all the changes. \\n\\n8. Click OK to continue', 'User instructions', wx.OK | wx.ICON_INFORMATION)\n\n if instruction == 4 :\n \"\"\"\n If ok is selected then the image is updated with the thresholded value of the likelihood\n \"\"\"\n textBox = wx.TextEntryDialog(self, \"Select the likelihood threshold\",caption = \"Enter the threshold\",value=\"0.1\")\n textBox.ShowModal()\n self.threshold = float(textBox.GetValue())\n textBox.Destroy()\n self.img = os.path.join(self.project_path,self.index[self.iter])\n img_name = Path(self.img).name\n self.axes.clear()\n self.preview = False\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\n MainFrame.plot(self,self.img)\n MainFrame.saveEachImage(self)\n else:\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\n MainFrame.plot(self,self.img)\n MainFrame.saveEachImage(self)\n\n else:\n msg = wx.MessageBox('No Machinelabels file found! Want to retry?', 'Error!', wx.YES_NO | wx.ICON_WARNING)\n if msg == 2:\n self.load.Enable(True)\n self.next.Enable(False)\n self.save.Enable(False)","title":""},{"docid":"f24d5f3bbf5b82c02cdbc3fc18af74ca","score":"0.6241064","text":"def change_folder_path(self):\n\n if self.set_file_input_le.text() == \"\" or os.path.isdir(self.set_file_input_le.text()) == False:\n current_dir = os.getcwd()\n else:\n current_dir = self.root_path_le.text()\n new_folder_path = str(QtGui.QFileDialog.getExistingDirectory(self, \"Select Directory\", current_dir))\n if str(new_folder_path) != \"\" and str(new_folder_path)[:13] != \"/Applications\" and str(new_folder_path)[\n :10] != \"/Developer\" and str(\n new_folder_path)[:7] != \"/System\" and str(new_folder_path)[:8] != \"/Library\" and str(\n new_folder_path) != \"/\":\n self.set_file_input_le.setText(new_folder_path)\n self.process_setup.set_setup_input_data(new_folder_path, 1)\n self.save_setup()\n elif str(new_folder_path) == \"\":\n return","title":""},{"docid":"f0df307fba580cc5cb70eb03633beaaa","score":"0.62395525","text":"def select_dir():\n root = Tkinter.Tk()\n root.withdraw()\n dirname = tkFileDialog.askdirectory(parent=root, initialdir=\"/\", \n title='Please select a directory')\n mediapath = dirname + '/__MEDIA'\n docpath = dirname + '/__DOCUMENTS'\n miscpath = dirname + '/__MISC'\n \n return dirname","title":""},{"docid":"cf9a22489c4c7eaed1765bed7b002bcb","score":"0.6239309","text":"def browseFile(self):\n cur_attr = self.attr_combo.currentText()\n attr_list = [FileAttr.In, FileAttr.InOut, FileAttr.Out]\n if cur_attr not in [FileAttr.value2str(attr) for attr in attr_list]:\n msg = translate(\"UnitPanel\", \"Please select a value \"\n \"for the `Mode` property above \"\n \"before browsing for the file path.\")\n MessageBox.warning(self.astergui().mainWindow(),\n \"AsterStudy\", msg)\n return\n out_attr = FileAttr.value2str(FileAttr.Out)\n mode = 0 if cur_attr == out_attr else 1\n self._browseFileTemplate(mode)","title":""},{"docid":"f4884e3865b864ecea5e2fa6c5b0f60c","score":"0.6236643","text":"def chooseOntologyPath(self):\n path = self.ontologyPath.text()\n path = QFileDialog.getExistingDirectory(self, 'Choose Directory', path)\n self.ontologyPath.setText(path)","title":""},{"docid":"0fd5d372afd234c79811711a7ad0d145","score":"0.6235732","text":"def update_object ( self, event ):\r\n if self.control is not None:\r\n path = self.control.GetPath()\r\n if self.factory.allow_dir or isfile( path ):\r\n if self.factory.truncate_ext:\r\n path = splitext( path )[0]\r\n\r\n self.value = path","title":""},{"docid":"41f267f1841420c1d439e92adae05613","score":"0.6229492","text":"def browseFile(self):\n file_name = getAFile()\n if file_name != \"\":\n self.onNewFileSelected(str(file_name))\n self.centralWidget.setMinimumSize(700, 500)","title":""},{"docid":"8633ed824c28eccc6d85d6a345578b6a","score":"0.6229477","text":"def on_add_folder(self, evt):\r\n dir_dlg = wx.DirDialog(self.view, \"Please select a folder.\")\r\n if dir_dlg.ShowModal() == wx.ID_OK:\r\n wx.BeginBusyCursor()\r\n self.view.dirtree.add_folder(dir_dlg.GetPath())\r\n wx.EndBusyCursor()","title":""},{"docid":"aca39cf81d1fceef081ca789347e54ea","score":"0.6212621","text":"def openDirectory(self): \n directory = QFileDialog.getExistingDirectory(self, \"Choose Directory\", \"\", \n QFileDialog.Option.ShowDirsOnly) # Specify the file mode to only select directories\n\n if directory:\n self.movie.setFileName(directory)\n # Check if image data is valid before playing\n if self.movie.isValid(): \n # Use setMovie() to set the label's contents as the selected GIF\n self.media_label.setMovie(self.movie)\n self.startMovie() # Call method to begin playing","title":""},{"docid":"535adbf2f2533975e9454f45d5ed1e90","score":"0.6200158","text":"def choose_dir(initialdir=os.path.dirname(__file__)):\n root = Tk.Tk()\n dirpath = tkFileDialog.askdirectory(\n parent=root,\n initialdir=initialdir\n )\n root.destroy()\n return dirpath","title":""},{"docid":"1134e4d5278278cd8c52f742091cf03a","score":"0.619444","text":"def zoekdir(self):\r\n oupad = self.dirnaam.get()\r\n if oupad == \"\":\r\n pad = tkFileDialog.askdirectory()\r\n else:\r\n pad = tkFileDialog.askdirectory(initialdir=oupad)\r\n if pad != \"\":\r\n self.dirnaam.set(pad)","title":""},{"docid":"308afc049e86ce9abf51f99f71d331d8","score":"0.61675054","text":"def file_pick(self):\n old_path = self.Path_Box.text()\n # logger.debug('old path: \"{}\"'.format(old_path))\n # noinspection PyTypeChecker,PyCallByClass\n save_path = QtGui.QFileDialog.getExistingDirectory(self, 'Save Location')\n # logger.debug('selected path: \"{}\"'.format(save_path))\n if save_path == '':\n self.Path_Box.setText(old_path)\n return old_path\n else:\n self.Path_Box.setText(save_path)\n return save_path","title":""},{"docid":"f7d4169f5315baee27156ed198123a94","score":"0.61662483","text":"def onChooseDownloadPath(self, event):\n\n self.logger.info(\"Choose download path event triggered. Opening a dialog for user input now.\")\n\n dirDialog = wx.DirDialog(self, \"Please choose a folder in which to download the file into.\")\n if dirDialog.ShowModal() != wx.ID_CANCEL:\n self.downloadPath = dirDialog.GetPath()\n self.tempDownloadPath = self.downloadPath\n self.radioDownload.SetValue(False)\n self.radioIT.SetValue(False)\n self.logger.info(\"Download path changed. New download path is: {} and new temp download path is: {}\".format(\n self.downloadPath, self.tempDownloadPath))\n self.updateDownloadLabel()\n else:\n self.logger.info(\"Choose download path event canceled.\")","title":""},{"docid":"a5f27cd6638b661666aa8e9f2eef642f","score":"0.6166166","text":"def _create_file_dialog ( self ):\r\n dlg = QtGui.QFileDialog(self.control)\r\n dlg.selectFile(self._file_name.text())\r\n dlg.setFileMode(QtGui.QFileDialog.Directory)\r\n dlg.setOptions(QtGui.QFileDialog.ShowDirsOnly)\r\n\r\n return dlg","title":""},{"docid":"a7158ea2d94cc13138b6c73705abe2ed","score":"0.6158772","text":"def open_file_dialog(self):\n file = QFileDialog.getOpenFileName(self, \"Open file\", getcwd(), \"*.pdf\")\n file_location = file[0].strip(getcwd().replace(\"\\\\\", \"/\"))\n self.entries[\"location\"].setText(file_location)","title":""},{"docid":"9b78599994a2bea00bce0278f7b6a6ad","score":"0.61562693","text":"def show_file_dialog_window(self, line_edit):\n file_path = QtWidgets.QFileDialog.getExistingDirectory(\n self,\n \"Select an install directory\",\n os.path.normpath(os.environ['MAYA_APP_DIR']))\n if file_path:\n line_edit.setText(file_path)","title":""},{"docid":"1be116c13474070a3ccfc831ec9a4d4b","score":"0.6155732","text":"def _open_file_screen(self):\n self._filepath = askdirectory()\n tkinter.Tk(\"Filesize Visualizer\").quit()","title":""},{"docid":"8cd2143047c84e94cdfb6dfc7203f6f4","score":"0.613926","text":"def get_path(self, isfile: bool) -> None:\n if isfile:\n path = QFileDialog.getOpenFileName(self,\n 'Select File',\n os.path.dirname(os.path.abspath(__file__)))\n if path[0]:\n self.scan_path.setText(os.path.abspath(path[0]))\n self.status.setText('✔')\n else:\n path = QFileDialog.getExistingDirectory(self,\n 'Select Directory',\n os.path.dirname(os.path.abspath(__file__)))\n if path:\n self.scan_path.setText(os.path.abspath(path))\n self.status.setText('✔')","title":""},{"docid":"a9c1d6093a9e610624c83ab4332641c4","score":"0.6130713","text":"def choose_directory(self, folder, override=False):\n\n # -f-i-x-m-e-:this doesn't seem to actually show the current folder if there\n # is one...maybe that's a Qt bug, though. Or maybe it's because of the\n # hidden folder in the path?\n\n # update: ok...so the 'default' dialog was crap and didn't work\n # right. For some reason, adding an option (in this case\n # 'DontResolveSymlinks') caused a different dialog to be used\n # (one that looked more familiar to me) that worked MUCH better\n # and started in the correct directory.\n # Wondering if this was perhaps the 'non-native' dialog and the\n # native one was just bad on my system, I changed the options to\n # include 'UseNonNativeDialog'--but this showed a *different*\n # dialog than the other two, which seemed to be between the\n # others as far as functionality went. Presumably the \"good\"\n # dialog was the native one, which is reassuring.\n # Anyway, I still don't really know what's going on, but it\n # seems to work ok for now...\n\n ovrdict = self._override_mapping\n # ovrdict = self._override_paths[self._selected_profile.name]\n\n\n start = self._override_mapping[folder].path if override else self.paths[folder]\n\n # noinspection PyTypeChecker\n chosen = QFileDialog.getExistingDirectory(self,\n \"Select directory\",\n directory=start or \"\",\n options=QFileDialog.DontResolveSymlinks)\n\n if check_path(chosen):\n\n if override:\n self.override_boxes[folder].setText(chosen)\n ovrdict[folder] = ovrdict[folder]._replace(path=chosen)\n else:\n self.path_boxes[folder].setText(chosen)\n if folder in self.indicator_labels:\n self.indicator_labels[folder].setVisible(False)\n # enable apply button if needed\n self._mark_changed()","title":""},{"docid":"7179d1b7b05993b7471fde194b3ac7b1","score":"0.6125794","text":"def onBrowse(self, event):\n dlg = wx.FileDialog(self, \"Choose an image:\",\n style=wx.DD_DEFAULT_STYLE\n # | wx.DD_DIR_MUST_EXIST\n # | wx.DD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n self.inputImg = dlg.GetPath()\n self.inputText.SetLabel(self.inputImg)\n self.inputText.SetForegroundColour(\"black\") # set text color\n self.onView()\n dlg.Destroy()","title":""},{"docid":"c72970213624644967a7922528629a60","score":"0.61231685","text":"def on_miSetRootPath(self):\n if self.filesRootPath is None:\n self.filesRootPath = self.wgAttrFiles.defaultRootPath\n self.fdRootPath = pQt.fileDialog(fdRoot=self.filesRootPath, fdFileMode='DirectoryOnly', fdCmd=self.on_set)\n self.fdRootPath.exec_()","title":""},{"docid":"91f8b22b0125ddfed772942d341b62e6","score":"0.6121621","text":"def ask_user_directory(self):\n return str(\n QtWidgets.QFileDialog.getExistingDirectory(\n self.parent, \"Please select a capa rules directory\", self.rule_path\n )\n )","title":""},{"docid":"76270e305747f0a99fc34f8d3bba1660","score":"0.6119205","text":"def openFileDialog(self):\n self.pathEdit.setText(QFileDialog.getOpenFileName(self, 'Open File', './',\n filter=\"All Files(*.*);;Text Files(*.txt)\")[0])","title":""},{"docid":"813db67a49fe5445d0fc564be9c49940","score":"0.61155754","text":"def _input_file_dialog(self):\r\n\r\n\t\t# Open file dialog\r\n\t\tinputFile = askopenfilename(filetypes=self.inputTypes)\r\n\t\t# If file selected, check if txtInput already has an entry and clear,\r\n\t\t# then insert path of selected file\r\n\t\tif inputFile:\r\n\t\t\tcurTxt = self.txtInput.get()\r\n\t\t\tif curTxt:\r\n\t\t\t\tcurTxtEnd = len(curTxt)\r\n\t\t\t\tself.txtInput.delete(0, curTxtEnd)\r\n\t\t\tself.txtInput.insert(0, inputFile)","title":""},{"docid":"980b4ecb52fec2f698afd85689e85e24","score":"0.60958797","text":"def select_file(_dir=False):\n Tk().withdraw()\n if not _dir:\n return askopenfilename()\n else:\n return askdirectory()","title":""},{"docid":"cf16173be2046f5944ed438809f790a1","score":"0.6092447","text":"def directory_clicked(self, dirname):\n self.chdir(directory=dirname)","title":""},{"docid":"144e6ad0bc6e4b92b1a98b5865c3148d","score":"0.6056002","text":"def _open_file(self):\n file_path, _ext = QFileDialog.getOpenFileName(self, dir=os.environ['HOME'], filter='Folder(.groom)')\n self.ui.groom_package_txf.setText(str(file_path))","title":""},{"docid":"5e7edaee6a56092970308213c3a08d42","score":"0.604943","text":"def set_folder(self):\n\n self.par_folder = filedialog.askdirectory()\n self.entry_folder.delete(0, END)\n self.entry_folder.insert(0, self.par_folder)\n LOGGER.debug('Folder: %s', self.par_folder)\n self.folder_created_by_dialog = True","title":""},{"docid":"227d33323a5f9a10564aea61c3dff835","score":"0.6044199","text":"def on_set(self):\n selPath = self.fdRootPath.selectedFiles()\n if selPath:\n self.filesRootPath = os.path.normpath(str(selPath[0]))\n self.wgAttrFiles.rf_rootPath()\n self.wgVtxFiles.rf_rootPath()","title":""},{"docid":"c8c1491453232d0d955152cc161d9fd7","score":"0.6030077","text":"def save_folder_select(self):\n\n # Remove annoying empty window\n root = Tk()\n root.withdraw()\n\n # Select image from file\n self.folder = filedialog.askdirectory()\n\n if not self.folder:\n print(\"You choosed nothing\")\n else:\n# self.file_path = self.f\n# print(\"Choosed path: \\n\", self.folder, \"\\n\")\n self.edit_dir_save.setText(self.folder)","title":""},{"docid":"9de59e1983cefbda96dca218a17b5265","score":"0.6027771","text":"def new_thread_1(self):\n dir_name = filedialog.askdirectory(parent=self, initialdir=\"/\",\n title='Please select a directory')\n\n if dir_name != \"\":\n self.disable_menu()\n self.path_and_bag.check_if_refresh(dir_name)\n self.config(cursor=\"wait\")\n self.update()\n self.clean_queue()\n GUI.static_queue.put(\"Finding files in chosen folder:\\n\\n\")\n num_files = len([val for sub_list in\n [[os.path.join(i[0], j)for j in i[2]]\n for i in os.walk(dir_name)]\n for val in sub_list])\n rott = tk.Tk()\n app = App(rott, GUI.static_queue, num_files)\n rott.protocol(\"WM_DELETE_WINDOW\", app.on_closing)\n thread = threading.Thread(target=self.open_menu, args=(dir_name,))\n thread.setDaemon(True)\n thread.start()\n app.mainloop()\n else:\n print(\"Action aborted\")","title":""},{"docid":"cc134be69b68707e6688cf64176df5fc","score":"0.6023432","text":"def runOpenDirectoryDialog(self, title, startdir):\n\n dirName = tkFileDialog.askdirectory(\n title=title, initialdir=startdir, mustexist=\"true\")\n\n return dirName","title":""},{"docid":"8c989646d91a20dd143851256579fa63","score":"0.60129404","text":"def update_curdir(self, path):\n self.olddir = self.curdir\n self.curdir = path\n self.prompt = '%s%s> ' % (self.state, path)","title":""},{"docid":"212360e669cc90c29b702772a126800c","score":"0.60106725","text":"def select_folder(textfield, title=\"Select folder\", folder=\".\", master=None):\n if not os.path.isdir(folder):\n print(\"The folder '{}' specified for select_folder does not exist.\".format(folder))\n folder = \".\"\n \n textfield.delete(0, END)\n textfield.insert(0, filedialog.askdirectory(title=title, initialdir=folder, parent=None if master is None else master.tk))","title":""},{"docid":"a946d8fb50201c0025c17fb893e67836","score":"0.59847885","text":"def change_download_directory(self):\n directory = tk_filedialog.askdirectory()\n if directory:\n self.download_directory_entry.config(state=tk.NORMAL)\n self.download_directory_entry.insert(0, directory)\n self.download_directory_entry.config(state=tk.DISABLED)","title":""}],"string":"[\n {\n \"docid\": \"5a030732cf12ef84300d8a20743a3891\",\n \"score\": \"0.8512092\",\n \"text\": \"def _browse(self):\\n\\n initial_dir = self._variable.get()\\n initial_dir = os.path.abspath(os.path.expanduser(initial_dir))\\n\\n self.options['initialdir'] = initial_dir\\n dlg = filedialog.Directory(self.master, **self.options)\\n new_dir = dlg.show()\\n\\n if new_dir:\\n new_dir = shorten(new_dir)\\n self.value = new_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ebe030b8c28676ce86a1dddb4994d8\",\n \"score\": \"0.7814108\",\n \"text\": \"def browseDirectory(self):\\n \\n path = tk.filedialog.askdirectory(\\n title=\\\"Choose a directory\\\",\\n initialdir=self.controller.destinationDir).replace(\\\"/\\\", \\\"\\\\\\\\\\\")\\n\\n if path:\\n self.saveButton.config(\\n background=\\\"yellow2\\\",\\n activebackground=\\\"yellow2\\\")\\n\\n self.entryText.set(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d956bc3b4ddcc5b85448fe3f10ee62ab\",\n \"score\": \"0.7581965\",\n \"text\": \"def _browse(self):\\n\\n initial_file = self._variable.get()\\n\\n if initial_file:\\n initial_dir = os.path.split(initial_file)[0]\\n self.options['initialdir'] = initial_dir\\n\\n if self.mode == 'open':\\n dlg = filedialog.Open(self.master, **self.options)\\n else:\\n dlg = filedialog.SaveAs(self.master, **self.options)\\n\\n new_file = dlg.show()\\n\\n if new_file:\\n new_file = shorten(new_file)\\n self.value = new_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efb6511d238530adfe05bb1a51fc7ae\",\n \"score\": \"0.74403816\",\n \"text\": \"def open_dir(self, event):\\n dlg = wx.DirDialog(self, \\\"Choose default directory\\\", style=wx.DD_DEFAULT_STYLE)\\n if dlg.ShowModal() == wx.ID_OK:\\n self.settings['fp'] = dlg.GetPath()\\n self.dir_txt.SetValue(self.settings['fp'])\\n dlg.Destroy()\\n pub.sendMessage('fp', msg=self.settings['fp'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2d2a509a0bbae5f6e64ede66353eb4\",\n \"score\": \"0.7431\",\n \"text\": \"def browseFolder(self):\\n # popup folder selection dialog\\n dir_path = getAFolder()\\n if dir_path != \\\"\\\":\\n self.filePath = str(dir_path)\\n self.selectImageButton.setHidden(True)\\n self.selectFolder.setHidden(True)\\n self.imageCanvas.setHidden(False)\\n self.ignoreFolds = set()\\n self.currentFileNumber = 0\\n self.onImageChanged()\\n self.processFolder()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a99dab5d273da38824fddab5293a56a\",\n \"score\": \"0.74279505\",\n \"text\": \"def input_path_browse_button_clicked():\\n directory = QFileDialog.getExistingDirectory(self, \\\"Find Files\\\", QDir.currentPath())\\n self.input_path_line_edit.setText(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a671f72834a0692b5a8f65955bb502\",\n \"score\": \"0.742454\",\n \"text\": \"def browse_directory(self, directory_input):\\n\\n # Open the file dialogue\\n directory_name = filedialog.askdirectory(initialdir=self.cwd)\\n\\n # Don't replace the directory input value if user cancels\\n if directory_name is not None and directory_name != '':\\n directory_input.delete(0, END) # Clear current value in entry widget\\n directory_input.insert(0, directory_name) # Add user-selected value to entry widget\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df465049795f7ad25fc5d7458e0b9b96\",\n \"score\": \"0.7378925\",\n \"text\": \"def browse(self):\\r\\n\\r\\n self.filepath.set(fd.askopenfilename(initialdir=self._initaldir,\\r\\n filetypes=self._filetypes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091eed2108efb32916ee6b5b6dbe1af8\",\n \"score\": \"0.73339975\",\n \"text\": \"def onDir(self, event):\\n dlg = wx.DirDialog(self, \\\"Choose a directory:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n #| wx.DD_DIR_MUST_EXIST\\n #| wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.folder=dlg.GetPath()\\n dlg.Destroy() \\n self.foldadd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f391f64b08f55a6987495c92c3fa145\",\n \"score\": \"0.71891147\",\n \"text\": \"def dir_browse(self):\\n\\n if not self.is_running():\\n # dirpath will be None or a string\\n msg, pth = RegistryHelpers.GetDirFromUserQT(self, RegistryKey='klusterintel',\\n Title='Select directory to monitor', AppName='klusterintel')\\n if pth is not None:\\n self.fil_text.setText(pth)\\n else:\\n print('You have to stop monitoring before you can change the path')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fe581e76af5a38fd30ed911d4df968\",\n \"score\": \"0.7138757\",\n \"text\": \"def onDir(self, event):\\n dlg = wx.DirDialog(self, \\\"Choose a directory:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n #| wx.DD_DIR_MUST_EXIST\\n #| wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.folder=dlg.GetPath()\\n self.folderadd()\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e60f451f79b10050d6fa641e5f774c6\",\n \"score\": \"0.7078267\",\n \"text\": \"def open_f(self):\\n\\n self.dirname = QFileDialog.getExistingDirectory()\\n if self.dirname and len(self.dirname) > 0:\\n self.text_file.setText(self.dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44b9ba513741611784f668f5bac1dcb\",\n \"score\": \"0.7068113\",\n \"text\": \"def onDiradd(self):\\n self.top=1 \\n dlg = wx.DirDialog(self, \\\"Choose a directory:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n #| wx.DD_DIR_MUST_EXIST\\n #| wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.folder=dlg.GetPath()\\n self.foldradd()\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"194e2cdd6e91278ecabdd2b2d70f4f03\",\n \"score\": \"0.7047211\",\n \"text\": \"def ask_directory(self):\\n self.directory = tkFileDialog.askdirectory(**self.dir_opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2669963175a658f3d01a8ba7ff199e22\",\n \"score\": \"0.7013578\",\n \"text\": \"def browseFiles(self):\\n\\n temp_path = filedialog.askopenfilename(title = \\\"Select a File\\\", filetypes = ((\\\"Text Files\\\", \\\"*.txt\\\"), (\\\"All Files\\\", \\\"*\\\"))) \\n if temp_path:\\n self.file_path = temp_path\\n self.label_file_explorer.configure(text = \\\"File: \\\" + self.file_path)\\n self.analyze()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f722d0fee5a12e68235869b0856912\",\n \"score\": \"0.6981006\",\n \"text\": \"def output_path_browse_button_clicked():\\n directory = QFileDialog.getExistingDirectory(self, \\\"Find Files\\\", QDir.currentPath())\\n self.output_path_line_edit.setText(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2769fc43798d33f0894ee8024152d6c\",\n \"score\": \"0.6971456\",\n \"text\": \"def onOpenFile(self, event):\\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a file\\\",\\n defaultFile=\\\"\\\",\\n wildcard=wildcard,\\n style=wx.OPEN | wx.MULTIPLE | wx.CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n x = dlg.GetPaths()\\n self.paths=x[0]\\n self.fileadd() \\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03053fb6ee60399f4a80d7752d3c6d07\",\n \"score\": \"0.69596636\",\n \"text\": \"def do_browse_data_directory(self):\\n # get the data directory from text line or from default\\n if str(self.ui.lineEdit_iptsDir.text()).strip() != '':\\n # from IPTS Directory\\n default_dir = str(self.ui.lineEdit_iptsDir.text()).strip()\\n else:\\n # from default\\n default_dir = self._homeDir\\n\\n # user-specified data directory\\n data_dir = str(QFileDialog.getExistingDirectory(self, 'Get Directory',\\n default_dir))\\n self.ui.lineEdit_iptsDir.setText(data_dir)\\n self._iptsDir = data_dir\\n self._iptsDirFromDir = data_dir\\n self._iptsNumber = None\\n\\n # Enable next step\\n self.ui.groupBox_scanIptsInfo.setEnabled(True)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140947aeb5f7e1d9424ad24bda874d82\",\n \"score\": \"0.6942863\",\n \"text\": \"def ask_path(self):\\n\\n default_dir = os.path.dirname(self.path)\\n\\n _dir = QFileDialog.getExistingDirectory(self, dir=default_dir)\\n if _dir:\\n self.path = _dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4dc3897f08db9e961886ce0af8a8ec6\",\n \"score\": \"0.6942444\",\n \"text\": \"def selectPath(self):\\n dirname = QtWidgets.QFileDialog.getExistingDirectory(self)\\n if len(dirname) == 0:\\n return\\n self.setText(dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6da3bd7acd4461bd44a51b1885046ed6\",\n \"score\": \"0.6936279\",\n \"text\": \"def __SelectFolder(self):\\n op={}\\n op[\\\"initialdir\\\"]=os.getcwd()\\n f = filedialog.askdirectory(**op)\\n if f==\\\"\\\":\\n return\\n filepath = os.path.abspath(f)\\n if os.path.isdir(filepath):\\n self.__CleanView()\\n self.root.title(filepath)\\n self.__workdir= os.path.abspath(filepath)\\n self.lf.ShowWithoutAnalys(filepath)\\n self.__countofLefttree = self.lf.GetCount()\\n self.__SetStateofSearchMenu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f29bd750498cee4a9b7d73d8ef4275\",\n \"score\": \"0.69354516\",\n \"text\": \"def directoryPathChooserClicked(self, textfield):\\n path = textfield.text()\\n path = QFileDialog.getExistingDirectory(self, \\\"Select Folder\\\", path)\\n textfield.setText(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90441004285dbc840199a58133807566\",\n \"score\": \"0.68388385\",\n \"text\": \"def button_browse_callback(self):\\n options = {}\\n options['initialdir'] = '.'\\n options['title'] = 'Select the log directory'\\n options['mustexist'] = False\\n self.fileName = FileDiag.askdirectory(**options)\\n info(\\\"***%s\\\" % self.fileName)\\n if self.fileName == \\\"\\\":\\n return None\\n else:\\n return self.fileName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce59e1feaa64f9fc196ae6e9488a760a\",\n \"score\": \"0.6827011\",\n \"text\": \"def open_directory_dialogue():\\n global data_directory\\n data_directory = filedialog.askdirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b33babacb5ef6cc5147f62b237ae427\",\n \"score\": \"0.6822368\",\n \"text\": \"def askdirectory(self):\\n \\n des = tkFileDialog.askdirectory(title = \\\"The Destination folder is \\\")\\n \\n if des:\\n self.submit_name(des)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d939a34f3e990880023cf68db06f6234\",\n \"score\": \"0.67647696\",\n \"text\": \"def onSetDir(self, event):\\n # dialog = wx.TextEntryDialog(None, \\\"Point to new directory. Currently set to %s\\\" % self.saveDir,\\n # \\\"Set Save Directory\\\", \\\"%s\\\" % self.saveDir, wx.OK | wx.CANCEL)\\n dialog = wx.DirDialog(self, \\\"Choose a save directory\\\", defaultPath=\\\"/home/mro/data\\\")\\n answer = dialog.ShowModal()\\n dialog.Destroy()\\n\\n if answer == wx.ID_OK:\\n setTo = str(dialog.GetPath()) + \\\"/\\\"\\n self.saveDir = setTo\\n self.parent.saveDirectoryText.SetLabel(u\\\"Saving \\\\u2192 %s\\\" % self.saveDir)\\n self.parent.Layout() # This recenters the static text\\n logger.debug(\\\"Directory: \\\" + self.saveDir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"543f4b7dc9b00d829537db1013016bfe\",\n \"score\": \"0.67494184\",\n \"text\": \"def _create_file_dialog ( self ):\\r\\n dlg = wx.DirDialog( self.control, message = 'Select a Directory' )\\r\\n dlg.SetPath( self._file_name.GetValue() )\\r\\n return dlg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74aa93866ba310b886088f071bdc6191\",\n \"score\": \"0.67004776\",\n \"text\": \"def browse(self):\\n startdir = self.dest.text() or os.getcwd()\\n path = qtw.QFileDialog.getSaveFileName(self, 'Kies een bestand', startdir)\\n if path[0]:\\n self.dest.setText(path[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66af338f87cde8c552204146c68d6b9\",\n \"score\": \"0.667561\",\n \"text\": \"def askdirectory(self):\\n return filedialog.askdirectory(title=\\\"Select A Folder\\\", mustexist=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa4ecfb3ecb2ff02b31f4e644e92d1c\",\n \"score\": \"0.66730225\",\n \"text\": \"def _showChangePathDialog(self):\\n path = self.path()\\n\\n if not path:\\n path = os.path.expanduser(\\\"~\\\")\\n\\n path = QtWidgets.QFileDialog.getExistingDirectory(\\n None,\\n \\\"Choose a root folder location\\\",\\n path\\n )\\n\\n return studiolibrary.normPath(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30bcd7e2d09c426b3c598169f2b11d3e\",\n \"score\": \"0.66609615\",\n \"text\": \"def _open_folder():\\n tk.Tk().withdraw()\\n return filedialog.askdirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73540a5dc0c93c289c87cbfe3df43d3f\",\n \"score\": \"0.66517067\",\n \"text\": \"def get_dir (self, event=None):\\n msg = \\\"Please choose a file in the chosen directory. \\\"\\\\\\n \\\"The directory from which the file was taken \\\"\\\\\\n \\\"will be chosen as the default directory. \\\"\\\\\\n \\\"If you enter an empty entry, an intelligent \\\"\\\\\\n \\\"choice of directory will be made automatically at run-time.\\\"\\n tkMessageBox.showinfo (\\\"Choose directory\\\", msg)\\n tk_fopen = tkFileDialog.askopenfilename\\n f_name = tk_fopen (title=\\\"Choose file to select directory\\\",\\n initialdir=self.cfg.initial_dir,\\n filetypes=[(\\\"All files\\\", \\\"*\\\")])\\n if f_name:\\n direc = os.path.dirname (f_name)\\n self.cfg.initial_dir = direc\\n self.initial_dir.set (direc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"649b45185e27ccf9f07b1356a37beff2\",\n \"score\": \"0.66298395\",\n \"text\": \"def output_dir_change_action(self):\\n fileDialog = QFileDialog()\\n directory = fileDialog.getExistingDirectory()\\n self.dynamic.output_directory.setText(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1766d7974f8fae48d55b2ad045380181\",\n \"score\": \"0.6627282\",\n \"text\": \"def onOpen(self, event):\\r\\n dlg = wx.DirDialog( self, \\\"Select Repository Root\\\", '') # parentWindow, Test, defaultPath ( save the last to make it open to the same one every time )\\r\\n if dlg.ShowModal() == wx.ID_OK:\\r\\n self._initTreeView( dlg.GetPath() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a9888d315746315458c0618a927133\",\n \"score\": \"0.66179836\",\n \"text\": \"def button_open_downloads_file_chooser_clicked(self, widget, data=None):\\n dialog = gtk.FileChooserDialog(_(\\\"Choose Folder...\\\"),\\n None,\\n gtk.FILE_CHOOSER_ACTION_SELECT_FOLDER,\\n (gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,\\n gtk.STOCK_OPEN, gtk.RESPONSE_OK))\\n dialog.set_current_folder(os.path.expanduser(\\\"~\\\"))\\n dialog.set_default_response(gtk.RESPONSE_OK)\\n response = dialog.run()\\n if response == gtk.RESPONSE_OK:\\n self.downloads_directory = dialog.get_current_folder()\\n self.downloads_text_entry.set_text(self.downloads_directory)\\n self.db_session.variable_set('downloads_directory', self.downloads_directory)\\n dialog.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fbf1277e66ad98321829ad49b6aaf5\",\n \"score\": \"0.65866774\",\n \"text\": \"def browser_btn():\\n browser_box = Tk()\\n browser_box.withdraw()\\n browser_box.filename = filedialog.askdirectory(initialdir=os.environ['USERPROFILE'] + \\\"\\\\\\\\Downloads\\\",\\n title=\\\"Select Download Directory\\\")\\n\\n # This is the code to only replace the path if user have select a folder\\n if browser_box.filename != \\\"\\\":\\n path.configure(state=NORMAL)\\n path.unbind('<Button-1>')\\n path.delete(0, END)\\n path.insert(0, str(browser_box.filename))\\n\\n browser_box.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877dce526d4721ad1c6b308b114b1153\",\n \"score\": \"0.6584735\",\n \"text\": \"def browse_folder(self):\\n # Get the file name/path from the user input in the file manager\\n filename = tkinter.filedialog.asksaveasfilename(initialdir=\\\".\\\", title=\\\"Save JSON file\\\",\\n filetypes=((\\\"json files\\\", \\\"*.json\\\"), (\\\"txt files\\\", \\\"*.txt\\\"),\\n (\\\"All files\\\", \\\"*.*\\\")))\\n\\n # Update the file name/path to the file name entry\\n self.filename_entry.delete(0, tkinter.END)\\n self.filename_entry.insert(0, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60a6c861a80dd09e941e65640f82582\",\n \"score\": \"0.65813696\",\n \"text\": \"def onDirIn(self, event):\\n dlg = wx.DirDialog(self, \\\"Choose a directory:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n # | wx.DD_DIR_MUST_EXIST\\n # | wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.inputDirectory = dlg.GetPath()\\n self.inputText.SetLabel(self.inputDirectory)\\n self.inputText.SetForegroundColour(\\\"black\\\") # set text color\\n\\n mylist = [f for f in glob.glob(os.path.join(self.inputDirectory, \\\"*.jpg\\\"))]\\n print(not mylist)\\n if not mylist:\\n self.genText.SetLabel(\\\"* Valid images not found\\\")\\n self.genText.SetForegroundColour(\\\"red\\\")\\n self.inputDirectory = \\\"\\\"\\n\\n else:\\n self.files = mylist\\n myNames = [os.path.split(f)[1] for f in mylist]\\n\\n self.num_images = len(mylist)\\n self.mainIterator = get_dataset(mylist, np.zeros(self.num_images), (299,299), 1,\\n tf.keras.applications.inception_v3.preprocess_input)\\n\\n self.genText.SetLabel(\\\"* Found {} validated images\\\".format(self.num_images))\\n self.genText.SetForegroundColour(\\\"black\\\")\\n\\n if not self.num_images * self.sliderValue // 100:\\n self.outGenText.SetLabel(\\\"* No images will be saved. Percentage too low.\\\")\\n self.outGenText.SetForegroundColour(\\\"red\\\")\\n else:\\n self.outGenText.SetLabel(\\n \\\"* Saving {} best images\\\".format(self.num_images * self.sliderValue // 100))\\n self.outGenText.SetForegroundColour(\\\"black\\\")\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933f2d89283ca17cb4480b50edfe659a\",\n \"score\": \"0.6543978\",\n \"text\": \"def askdir(self, var, label):\\n \\n f = tkFileDialog.askdirectory(title='Select Directory', initialdir='R:/soundcast/inputs/lodes')\\n \\n label.config(text=f)\\n update_input_config(var, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d052929cec2d6df3b052a03ae76d4b12\",\n \"score\": \"0.6535897\",\n \"text\": \"def _add(self):\\n filenames = list()\\n dlg = sppasFileDialog(self)\\n if os.path.exists(self.__current_dir):\\n dlg.SetDirectory(self.__current_dir)\\n if dlg.ShowModal() == wx.ID_OK:\\n filenames = dlg.GetPaths()\\n dlg.Destroy()\\n\\n if len(filenames) > 0:\\n added = self.FindWindow(\\\"filestree\\\").AddFiles(filenames)\\n if added:\\n self.__current_dir = os.path.dirname(filenames[0])\\n self.notify()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fd430ad8a9db44e6a521eae62210dfa\",\n \"score\": \"0.6532352\",\n \"text\": \"def open_ingestion_directory_selector(self, textbox_widget=None, team=\\\"root\\\"):\\n directory = QFileDialog.getExistingDirectory(None, 'Select a folder:', 'C:\\\\\\\\', QFileDialog.ShowDirsOnly)\\n if textbox_widget is None:\\n print(directory)\\n else:\\n self.controller.update_folder_path(team, directory)\\n textbox_widget.setPlainText(str(directory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62fec91a835a5470bed773c1e329aa3d\",\n \"score\": \"0.65273994\",\n \"text\": \"def displayFileDialog(self):\\n return filedialog.askdirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82e5fccac60cb735a14da42d201b6b5\",\n \"score\": \"0.6525494\",\n \"text\": \"def BrowseForFolder(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066df63b4c94f56fc2118774fb3e4712\",\n \"score\": \"0.65220475\",\n \"text\": \"def _select_dir(self):\\n with RememberDir(self) as rd:\\n self.work_dir, load_ok = rd.get_dir()\\n\\n if load_ok:\\n self._load2gui()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5908d8cec6f2a68ecac8f58cc293cd3\",\n \"score\": \"0.6507518\",\n \"text\": \"def onDirOut(self, event):\\n dlg = wx.DirDialog(self, \\\"Choose a directory:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n # | wx.DD_DIR_MUST_EXIST\\n # | wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.outputDirectory = dlg.GetPath()\\n self.outputText.SetLabel(self.outputDirectory)\\n self.outputText.SetForegroundColour(\\\"black\\\") # set text color\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8682029c0addf7b83c2da98b7d8f164\",\n \"score\": \"0.6504469\",\n \"text\": \"def dir_find(self):\\n directory = QtWidgets.QFileDialog.getExistingDirectory(self, \\\"Выберите каталог\\\")\\n self.line_edit_with_buttons.set_text(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e579437a3fdca8fb0e763badcb257c8\",\n \"score\": \"0.6493883\",\n \"text\": \"def update_object ( self, event ):\\r\\n if self.control is not None:\\r\\n path = self.control.GetPath()\\r\\n if isdir( path ):\\r\\n self.value = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bdfe0e0cc9941e0985a99f6ed473fc\",\n \"score\": \"0.64692336\",\n \"text\": \"def on_scan_btn_clicked(self):\\n path = QFileDialog.getExistingDirectory(self.base,\\n _(\\\"Select a directory with books or \\\"\\n \\\"your eReader's drive\\\"),\\n self.base.last_dir,\\n QFileDialog.ShowDirsOnly)\\n if path:\\n self.base.last_dir = path\\n self.base.high_list.clear()\\n self.base.reload_highlights = True\\n self.base.loading_thread(Scanner, path, self.base.kor_text, clear=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ec113b1714ab2a156912943fa5d5c6\",\n \"score\": \"0.645265\",\n \"text\": \"def on_browse_userpath(self, evt):\\n try:\\n open_file.open_file(pathfinder.user_path())\\n except IOError: # file not found\\n module_logger.error(\\\"User folder {0} not found.\\\".format(pathfinder.user_path()))\\n err_msg = \\\"Unable to find folder '{0}'.\\\\nPlease ensure the folder exists.\\\".format(pathfinder.user_path())\\n err_dlg = wx.MessageDialog(self.view, message=err_msg,\\n caption=\\\"Unable To Open Folder\\\", style=wx.ICON_ERROR)\\n err_dlg.ShowModal()\\n err_dlg.Destroy()\\n except OSError as err: # other OS error\\n module_logger.error(\\\"Unidentified OS error {0}\\\".format(err))\\n err_msg = \\\"Unable to browse to data folder, error reported was:\\\\n{0}\\\".format(err)\\n err_dlg = wx.MessageDialog(self.view, message=err_msg,\\n caption=\\\"Unable To Open Folder\\\", style=wx.ICON_ERROR)\\n err_dlg.ShowModal()\\n err_dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2f4b7c37d5a47728df9b5960dba045\",\n \"score\": \"0.641189\",\n \"text\": \"def open_folder(self):\\n self.current_photo = 0\\n self.folder = askdirectory()\\n if (len(listdir(self.folder)) - 1) != 0:\\n self.photos = [\\n p\\n for p in listdir(self.folder)\\n if p.endswith(\\\".png\\\") or p.endswith(\\\".jpg\\\") or p.endswith(\\\".gif\\\")\\n ]\\n self.total_photos = len(self.photos) - 1\\n self.get_photo()\\n self.progress_bar()\\n else:\\n lbl_img.configure(\\n text=f\\\"'{self.folder}' is empty or contains no photos. Choose another folder.\\\"\\n )\\n lbl_img.image = f\\\"'{self.folder}' is empty or contains no photos. Choose another folder.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dda33492147b9a06a00c0206d75676\",\n \"score\": \"0.6392816\",\n \"text\": \"def open_dialog(self):\\n # Dialog\\n self.file_path = fd.askopenfilename(title=\\\"Select ASC file\\\",\\n initialdir=self.home,\\n filetypes=self.file_types)\\n\\n # Get the file name -- iterate backwards until /, then reverse\\n fname = \\\"\\\".join(reversed(self.file_path[\\n -1:self.file_path.rfind(\\\"/\\\"):-1]))\\n \\n # Set the tkinter var storing the file name\\n self.file_name_var.set(fname)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"481c25b25e29b34ca591cd8a13b9eabd\",\n \"score\": \"0.63926655\",\n \"text\": \"def chooseOntologyPath(self):\\n path = self.path.text()\\n path = QFileDialog.getExistingDirectory(self, 'Choose Directory', path)\\n self.path.setText(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abbf1654340d646117d7cb81fef2cba\",\n \"score\": \"0.637571\",\n \"text\": \"def BrowseForFile(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b756921a75e8d14c624f53c4ae582f\",\n \"score\": \"0.63712925\",\n \"text\": \"def cd(self, directory, bot):\\n\\t\\t# If the back_label, such as \\\"Back\\\" is clicked,\\n\\t\\t# we're expected to change directory to ..\\n\\t\\tpath = os.path.join(self.data['path'], directory)\\n\\t\\t# Directories sorted using {*} at the end, gets\\n\\t\\t# their {*} removed. Glob checks if a dir that\\n\\t\\t# ends with {*} exists, and if so, replaces path.\\n\\t\\tif glob(path+' {*}/'):\\n\\t\\t\\tpath = glob(path+' {*}')[0]\\n\\t\\tif (directory == args['back_label'] and \\n\\t\\t\\tself.data['path'] != args['root']):\\n\\t\\t\\t# The first element of os.path.split is the .. directory\\n\\t\\t\\tself.data['path'] = os.path.split(self.data['path'])[0]\\n\\t\\t# If file is a directoy, open it\\n\\t\\telif os.path.exists(path):\\n\\t\\t\\tself.data['path'] = path\\n\\t\\tself.ls(bot)\\n\\t\\tself.save_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c426932a49491db3290bdfdcc12818cc\",\n \"score\": \"0.636276\",\n \"text\": \"def select_folder(self, entry_name):\\r\\n folder_selected = filedialog.askdirectory()\\r\\n if folder_selected != '':\\r\\n self.inputs[entry_name].variable.set(folder_selected)\\r\\n self.inputs['FolderStructure'].update_tree(folder_selected,\\r\\n sfilter=['.git', '.settings', 'libspecs', '__pycache__',\\r\\n '.png'])\\r\\n\\r\\n self.callbacks['btn_projLocation'](folder_selected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ccbeba663da9ff17e4620e67e4d91b\",\n \"score\": \"0.63622737\",\n \"text\": \"def __init__(self, title_txt=\\\"Seleccione carpeta\\\", button_txt=\\\"Abrir\\\", cls=\\\"dialog\\\", path=None):\\n\\n cls1 = \\\"filedialog\\\"\\n if not path: self.curdir = os.getcwd()\\n else: self.curdir = path\\n self.dir_img = gui.basic.Image(\\n gui.pguglobals.app.theme.get(cls1+\\\".folder\\\", \\\"\\\", 'image'))\\n self.title = gui.basic.Label(title_txt, cls=cls+\\\".title.label\\\")\\n self.body = gui.Container(width=700, height=300)\\n\\n self.body.add(gui.basic.Label(\\\"Carpeta\\\"), 10, 10)\\n\\n self.input_dir = gui.input.Input(size=38)\\n self.body.add(self.input_dir, 125, 10)\\n\\n self.button_ok = gui.button.Button(button_txt)\\n self.body.add(self.button_ok, 640, 12)\\n self.button_ok.connect(gui.const.CLICK, self._button_okay_clicked_, None)\\n\\n self.list = gui.area.List(width=690, height=235)\\n self.body.add(self.list, 10, 50)\\n self._list_dir_()\\n self.list.connect(gui.const.CHANGE, self._item_select_changed_, None)\\n\\n self.value = None\\n gui.Dialog.__init__(self, self.title, self.body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2e18bf5666d62db76df45c5bc448e3\",\n \"score\": \"0.6353819\",\n \"text\": \"def browse_file(self, file_input, filetypes):\\n\\n # Open the file dialog\\n file_name = filedialog.askopenfilename(initialdir=self.cwd, filetypes=filetypes)\\n\\n # Don't replace the file input value if user cancels\\n if file_name is not None and file_name != '':\\n file_input.delete(0, END) # Clear current value in entry widget\\n file_input.insert(0, file_name) # Add user-selected value to entry widget\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d5d44fc9407f323a31186eb1d18887\",\n \"score\": \"0.63331634\",\n \"text\": \"def button_openfile(self, event):\\n event.Skip()\\n actualDir = (os.getcwd() if self.m_comboBox_files.GetValue() else\\n os.path.dirname(os.path.abspath(self.m_comboBox_files.GetValue())))\\n dlg = wx.FileDialog(self, message=\\\"Select BOM(s)\\\", defaultDir=actualDir,\\n defaultFile=\\\"\\\", wildcard=WILDCARD_BOM,\\n style=wx.FD_OPEN | wx.FD_MULTIPLE | wx.FD_CHANGE_DIR)\\n if dlg.ShowModal() == wx.ID_OK:\\n self.addFile(dlg.GetPaths())\\n self.updateOutputFilename() # Update the output file name on GUI text.\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca30bc35afaa245b3f0d9e716ce6de9\",\n \"score\": \"0.6323572\",\n \"text\": \"def set_userpath(self):\\n try:\\n current_user_path = pathfinder.user_path()\\n path_dlg = wx.DirDialog(parent=self.view.parent, message=\\\"Please specify a data folder\\\",\\n defaultPath=current_user_path)\\n if path_dlg.ShowModal() == wx.ID_OK:\\n new_user_path = path_dlg.GetPath()\\n self.model.migrate_user_path(new_user_path)\\n self.refresh_data()\\n finally:\\n path_dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8d284a3e9ab3cb9455ed3b042f349c\",\n \"score\": \"0.63098764\",\n \"text\": \"def _open_folder_helper(_None):\\n dialog = Gtk.FileChooserDialog(\\n action=Gtk.FileChooserAction.SELECT_FOLDER)\\n _quick_buttons(dialog)\\n dialog.add_button(Gtk.STOCK_OPEN, Gtk.ResponseType.OK)\\n dialog.run()\\n path = dialog.get_filename()\\n dialog.destroy()\\n Gtk.main_quit()\\n result.append(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6946c5df0a97628e582e32587712af\",\n \"score\": \"0.63047534\",\n \"text\": \"def path_dialog() -> input_dialog:\\n return input_dialog(\\n title=\\\"File or Directory Path\\\", text=\\\"Input file or directory path: \\\",\\n ).run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41359262ae572b7a67bb3af6debbc7c7\",\n \"score\": \"0.6301636\",\n \"text\": \"def selectDir( self ):\\n\\n #self.showdialog()\\n project = QgsProject.instance()\\n if QgsExpressionContextUtils.projectScope(project).variable('project_importpath') == None: \\n self.vectorPath = QFileDialog.getExistingDirectory(self.dockWidget, 'Selectvector data path', '/home', QFileDialog.ShowDirsOnly)\\n QgsExpressionContextUtils.setProjectVariable(project, 'project_importpath', self.vectorPath)\\n self.dockWidget.lineEdit.setText(self.vectorPath)\\n #C:\\\\\\\\Users\\\\\\\\timc7\\\\\\\\Dropbox (Personal)\\\\\\\\Tim\\\\\\\\Employment\\\\\\\\WestWind Energy\\\\\\\\Work\\\\\\\\Projects\\\\\\\\GPWF\\\\\\\\GIS\\\\\\\\VectorData\\n #P:\\\\\\\\P158_GPWF_GoldenPlainsWindFarm\\\\\\\\GIS_Maps\\\\\\\\VectorData\\n\\n else: \\n originalVectorPath = QgsExpressionContextUtils.projectScope(project).variable('project_importpath')\\n self.vectorPath=originalVectorPath\\n self.vectorPath = QFileDialog.getExistingDirectory(self.dockWidget, 'Selectvector data path', self.vectorPath , QFileDialog.ShowDirsOnly)\\n \\n if (self.vectorPath and self.vectorPath is not None):\\n self.dockWidget.lineEdit.setText(self.vectorPath)\\n QgsExpressionContextUtils.setProjectVariable(project, 'project_importpath', self.vectorPath)\\n else:\\n self.dockWidget.lineEdit.setText(originalVectorPath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69aeb225990a6b9f3e14db629ce4c074\",\n \"score\": \"0.62987274\",\n \"text\": \"def handler_ok_changed ( self, info ):\\r\\n dir = info.object.file_name\\r\\n if not isdir( dir ):\\r\\n dir = dirname( dir )\\r\\n\\r\\n path = join( dir, self.dir_name )\\r\\n try:\\r\\n # Try to create the requested directory:\\r\\n mkdir( path )\\r\\n\\r\\n # Force the file tree view to be refreshed:\\r\\n info.object.reload = True\\r\\n\\r\\n # set the new directory as the currently selected file name:\\r\\n info.object.file_name = path\\r\\n\\r\\n # Close this view:\\r\\n info.ui.dispose( True )\\r\\n except:\\r\\n self.message = \\\"Could not create the '%s' directory\\\" % self.dir_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35419f9aa50d61cc1ded35b3719fb91e\",\n \"score\": \"0.6294492\",\n \"text\": \"def folder_dialog(title):\\n\\n root = tkinter.Tk()\\n root.withdraw()\\n return filedialog.askdirectory(title=title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb95684489b7461a0f1c9c904afe3a11\",\n \"score\": \"0.6282837\",\n \"text\": \"def handlerSelectWorkingDir(self):\\n workingDir = unicode(QtGui.QFileDialog.getExistingDirectory(self, 'Select Working Directory'))\\n \\n if workingDir:\\n self.lineEditWorkingDir.setText(workingDir)\\n \\n logging.getLogger(type(self).__name__).info('select working directory: %s', workingDir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32a5b4f2906e96d28d20ea4ae1946441\",\n \"score\": \"0.62821496\",\n \"text\": \"def browseDir(self, event):\\n\\n fname = str('machinelabels-iter'+str(self.iterationindex)+'.h5')\\n self.statusbar.SetStatusText(\\\"Looking for a folder to start refining...\\\")\\n cwd = os.path.join(os.getcwd(),'labeled-data')\\n# dlg = wx.FileDialog(self, \\\"Choose the machinelabels file for current iteration.\\\",cwd, \\\"\\\",wildcard=fname,style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\\n print(platform.system())\\n if platform.system()=='Darwin': \\n dlg = wx.FileDialog(self, \\\"Choose the machinelabels file for current iteration.\\\",cwd, fname ,wildcard=\\\"(*.h5)|*.h5\\\",style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST) \\n else:\\n dlg = wx.FileDialog(self, \\\"Choose the machinelabels file for current iteration.\\\",cwd, \\\"\\\",wildcard=fname,style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\\n\\n if dlg.ShowModal() == wx.ID_OK:\\n self.data_file = dlg.GetPath()\\n self.dir = str(Path(self.data_file).parents[0])\\n self.fileName = str(Path(self.data_file).stem)\\n self.load.Enable(False)\\n self.next.Enable(True)\\n self.save.Enable(True)\\n self.zoom.Enable(True)\\n self.pan.Enable(True)\\n self.home.Enable(True)\\n self.quit.Enable(True)\\n else:\\n dlg.Destroy()\\n self.Destroy()\\n dlg.Destroy()\\n\\n try:\\n self.dataname = str(self.data_file)\\n\\n except:\\n print(\\\"No machinelabels file found!\\\")\\n self.Destroy()\\n self.statusbar.SetStatusText('Working on folder: {}'.format(os.path.split(str(self.dir))[-1]))\\n self.preview = True\\n self.iter = 0\\n\\n if os.path.isfile(self.dataname):\\n self.Dataframe = pd.read_hdf(self.dataname,'df_with_missing')\\n self.Dataframe.sort_index(inplace =True)\\n self.scorer = self.Dataframe.columns.get_level_values(0)[0]\\n\\n # bodyParts = self.Dataframe.columns.get_level_values(1)\\n # _, idx = np.unique(bodyParts, return_index=True)\\n # self.num_joints = len(self.bodyparts)\\n # self.bodyparts = bodyParts[np.sort(idx)]\\n self.index = list(self.Dataframe.iloc[:,0].index)\\n# Reading images\\n\\n self.img = os.path.join(self.project_path,self.index[self.iter])\\n img_name = Path(self.img).name\\n self.norm,self.colorIndex = self.image_panel.getColorIndices(self.img,self.bodyparts)\\n# Adding Slider and Checkbox\\n\\n self.choiceBox,self.slider,self.checkBox = self.choice_panel.addCheckBoxSlider(self.bodyparts,self.file,self.markerSize)\\n self.slider.Bind(wx.EVT_SLIDER, self.OnSliderScroll)\\n self.checkBox.Bind(wx.EVT_CHECKBOX,self.activateSlider)\\n self.slider.Enable(False)\\n# Show image\\n# Setting axis title:dont want to show the threshold as it is not selected yet.\\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\\n\\n instruction = wx.MessageBox('1. Enter the likelihood threshold. \\\\n\\\\n2. Each prediction will be shown with a unique color. \\\\n All the data points above the threshold will be marked as circle filled with a unique color. All the data points below the threshold will be marked with a hollow circle. \\\\n\\\\n3. Enable the checkbox to adjust the marker size. \\\\n\\\\n4. Hover your mouse over data points to see the labels and their likelihood. \\\\n\\\\n5. Left click and drag to move the data points. \\\\n\\\\n6. Right click on any data point to remove it. Be careful, you cannot undo this step. \\\\n Click once on the zoom button to zoom-in the image.The cursor will become cross, click and drag over a point to zoom in. \\\\n Click on the zoom button again to disable the zooming function and recover the cursor. \\\\n Use pan button to pan across the image while zoomed in. Use home button to go back to the full;default view. \\\\n\\\\n7. When finished click \\\\'Save\\\\' to save all the changes. \\\\n\\\\n8. Click OK to continue', 'User instructions', wx.OK | wx.ICON_INFORMATION)\\n\\n if instruction == 4 :\\n \\\"\\\"\\\"\\n If ok is selected then the image is updated with the thresholded value of the likelihood\\n \\\"\\\"\\\"\\n textBox = wx.TextEntryDialog(self, \\\"Select the likelihood threshold\\\",caption = \\\"Enter the threshold\\\",value=\\\"0.1\\\")\\n textBox.ShowModal()\\n self.threshold = float(textBox.GetValue())\\n textBox.Destroy()\\n self.img = os.path.join(self.project_path,self.index[self.iter])\\n img_name = Path(self.img).name\\n self.axes.clear()\\n self.preview = False\\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\\n MainFrame.plot(self,self.img)\\n MainFrame.saveEachImage(self)\\n else:\\n self.figure,self.axes,self.canvas,self.toolbar = self.image_panel.drawplot(self.img,img_name,self.iter,self.index,self.threshold,self.bodyparts,self.colormap,self.preview)\\n MainFrame.plot(self,self.img)\\n MainFrame.saveEachImage(self)\\n\\n else:\\n msg = wx.MessageBox('No Machinelabels file found! Want to retry?', 'Error!', wx.YES_NO | wx.ICON_WARNING)\\n if msg == 2:\\n self.load.Enable(True)\\n self.next.Enable(False)\\n self.save.Enable(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24d5f3bbf5b82c02cdbc3fc18af74ca\",\n \"score\": \"0.6241064\",\n \"text\": \"def change_folder_path(self):\\n\\n if self.set_file_input_le.text() == \\\"\\\" or os.path.isdir(self.set_file_input_le.text()) == False:\\n current_dir = os.getcwd()\\n else:\\n current_dir = self.root_path_le.text()\\n new_folder_path = str(QtGui.QFileDialog.getExistingDirectory(self, \\\"Select Directory\\\", current_dir))\\n if str(new_folder_path) != \\\"\\\" and str(new_folder_path)[:13] != \\\"/Applications\\\" and str(new_folder_path)[\\n :10] != \\\"/Developer\\\" and str(\\n new_folder_path)[:7] != \\\"/System\\\" and str(new_folder_path)[:8] != \\\"/Library\\\" and str(\\n new_folder_path) != \\\"/\\\":\\n self.set_file_input_le.setText(new_folder_path)\\n self.process_setup.set_setup_input_data(new_folder_path, 1)\\n self.save_setup()\\n elif str(new_folder_path) == \\\"\\\":\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0df307fba580cc5cb70eb03633beaaa\",\n \"score\": \"0.62395525\",\n \"text\": \"def select_dir():\\n root = Tkinter.Tk()\\n root.withdraw()\\n dirname = tkFileDialog.askdirectory(parent=root, initialdir=\\\"/\\\", \\n title='Please select a directory')\\n mediapath = dirname + '/__MEDIA'\\n docpath = dirname + '/__DOCUMENTS'\\n miscpath = dirname + '/__MISC'\\n \\n return dirname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9a22489c4c7eaed1765bed7b002bcb\",\n \"score\": \"0.6239309\",\n \"text\": \"def browseFile(self):\\n cur_attr = self.attr_combo.currentText()\\n attr_list = [FileAttr.In, FileAttr.InOut, FileAttr.Out]\\n if cur_attr not in [FileAttr.value2str(attr) for attr in attr_list]:\\n msg = translate(\\\"UnitPanel\\\", \\\"Please select a value \\\"\\n \\\"for the `Mode` property above \\\"\\n \\\"before browsing for the file path.\\\")\\n MessageBox.warning(self.astergui().mainWindow(),\\n \\\"AsterStudy\\\", msg)\\n return\\n out_attr = FileAttr.value2str(FileAttr.Out)\\n mode = 0 if cur_attr == out_attr else 1\\n self._browseFileTemplate(mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4884e3865b864ecea5e2fa6c5b0f60c\",\n \"score\": \"0.6236643\",\n \"text\": \"def chooseOntologyPath(self):\\n path = self.ontologyPath.text()\\n path = QFileDialog.getExistingDirectory(self, 'Choose Directory', path)\\n self.ontologyPath.setText(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd5d372afd234c79811711a7ad0d145\",\n \"score\": \"0.6235732\",\n \"text\": \"def update_object ( self, event ):\\r\\n if self.control is not None:\\r\\n path = self.control.GetPath()\\r\\n if self.factory.allow_dir or isfile( path ):\\r\\n if self.factory.truncate_ext:\\r\\n path = splitext( path )[0]\\r\\n\\r\\n self.value = path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f267f1841420c1d439e92adae05613\",\n \"score\": \"0.6229492\",\n \"text\": \"def browseFile(self):\\n file_name = getAFile()\\n if file_name != \\\"\\\":\\n self.onNewFileSelected(str(file_name))\\n self.centralWidget.setMinimumSize(700, 500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8633ed824c28eccc6d85d6a345578b6a\",\n \"score\": \"0.6229477\",\n \"text\": \"def on_add_folder(self, evt):\\r\\n dir_dlg = wx.DirDialog(self.view, \\\"Please select a folder.\\\")\\r\\n if dir_dlg.ShowModal() == wx.ID_OK:\\r\\n wx.BeginBusyCursor()\\r\\n self.view.dirtree.add_folder(dir_dlg.GetPath())\\r\\n wx.EndBusyCursor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca39cf81d1fceef081ca789347e54ea\",\n \"score\": \"0.6212621\",\n \"text\": \"def openDirectory(self): \\n directory = QFileDialog.getExistingDirectory(self, \\\"Choose Directory\\\", \\\"\\\", \\n QFileDialog.Option.ShowDirsOnly) # Specify the file mode to only select directories\\n\\n if directory:\\n self.movie.setFileName(directory)\\n # Check if image data is valid before playing\\n if self.movie.isValid(): \\n # Use setMovie() to set the label's contents as the selected GIF\\n self.media_label.setMovie(self.movie)\\n self.startMovie() # Call method to begin playing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535adbf2f2533975e9454f45d5ed1e90\",\n \"score\": \"0.6200158\",\n \"text\": \"def choose_dir(initialdir=os.path.dirname(__file__)):\\n root = Tk.Tk()\\n dirpath = tkFileDialog.askdirectory(\\n parent=root,\\n initialdir=initialdir\\n )\\n root.destroy()\\n return dirpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1134e4d5278278cd8c52f742091cf03a\",\n \"score\": \"0.619444\",\n \"text\": \"def zoekdir(self):\\r\\n oupad = self.dirnaam.get()\\r\\n if oupad == \\\"\\\":\\r\\n pad = tkFileDialog.askdirectory()\\r\\n else:\\r\\n pad = tkFileDialog.askdirectory(initialdir=oupad)\\r\\n if pad != \\\"\\\":\\r\\n self.dirnaam.set(pad)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308afc049e86ce9abf51f99f71d331d8\",\n \"score\": \"0.61675054\",\n \"text\": \"def file_pick(self):\\n old_path = self.Path_Box.text()\\n # logger.debug('old path: \\\"{}\\\"'.format(old_path))\\n # noinspection PyTypeChecker,PyCallByClass\\n save_path = QtGui.QFileDialog.getExistingDirectory(self, 'Save Location')\\n # logger.debug('selected path: \\\"{}\\\"'.format(save_path))\\n if save_path == '':\\n self.Path_Box.setText(old_path)\\n return old_path\\n else:\\n self.Path_Box.setText(save_path)\\n return save_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7d4169f5315baee27156ed198123a94\",\n \"score\": \"0.61662483\",\n \"text\": \"def onChooseDownloadPath(self, event):\\n\\n self.logger.info(\\\"Choose download path event triggered. Opening a dialog for user input now.\\\")\\n\\n dirDialog = wx.DirDialog(self, \\\"Please choose a folder in which to download the file into.\\\")\\n if dirDialog.ShowModal() != wx.ID_CANCEL:\\n self.downloadPath = dirDialog.GetPath()\\n self.tempDownloadPath = self.downloadPath\\n self.radioDownload.SetValue(False)\\n self.radioIT.SetValue(False)\\n self.logger.info(\\\"Download path changed. New download path is: {} and new temp download path is: {}\\\".format(\\n self.downloadPath, self.tempDownloadPath))\\n self.updateDownloadLabel()\\n else:\\n self.logger.info(\\\"Choose download path event canceled.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f27cd6638b661666aa8e9f2eef642f\",\n \"score\": \"0.6166166\",\n \"text\": \"def _create_file_dialog ( self ):\\r\\n dlg = QtGui.QFileDialog(self.control)\\r\\n dlg.selectFile(self._file_name.text())\\r\\n dlg.setFileMode(QtGui.QFileDialog.Directory)\\r\\n dlg.setOptions(QtGui.QFileDialog.ShowDirsOnly)\\r\\n\\r\\n return dlg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7158ea2d94cc13138b6c73705abe2ed\",\n \"score\": \"0.6158772\",\n \"text\": \"def open_file_dialog(self):\\n file = QFileDialog.getOpenFileName(self, \\\"Open file\\\", getcwd(), \\\"*.pdf\\\")\\n file_location = file[0].strip(getcwd().replace(\\\"\\\\\\\\\\\", \\\"/\\\"))\\n self.entries[\\\"location\\\"].setText(file_location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b78599994a2bea00bce0278f7b6a6ad\",\n \"score\": \"0.61562693\",\n \"text\": \"def show_file_dialog_window(self, line_edit):\\n file_path = QtWidgets.QFileDialog.getExistingDirectory(\\n self,\\n \\\"Select an install directory\\\",\\n os.path.normpath(os.environ['MAYA_APP_DIR']))\\n if file_path:\\n line_edit.setText(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be116c13474070a3ccfc831ec9a4d4b\",\n \"score\": \"0.6155732\",\n \"text\": \"def _open_file_screen(self):\\n self._filepath = askdirectory()\\n tkinter.Tk(\\\"Filesize Visualizer\\\").quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd2143047c84e94cdfb6dfc7203f6f4\",\n \"score\": \"0.613926\",\n \"text\": \"def get_path(self, isfile: bool) -> None:\\n if isfile:\\n path = QFileDialog.getOpenFileName(self,\\n 'Select File',\\n os.path.dirname(os.path.abspath(__file__)))\\n if path[0]:\\n self.scan_path.setText(os.path.abspath(path[0]))\\n self.status.setText('✔')\\n else:\\n path = QFileDialog.getExistingDirectory(self,\\n 'Select Directory',\\n os.path.dirname(os.path.abspath(__file__)))\\n if path:\\n self.scan_path.setText(os.path.abspath(path))\\n self.status.setText('✔')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c1d6093a9e610624c83ab4332641c4\",\n \"score\": \"0.6130713\",\n \"text\": \"def choose_directory(self, folder, override=False):\\n\\n # -f-i-x-m-e-:this doesn't seem to actually show the current folder if there\\n # is one...maybe that's a Qt bug, though. Or maybe it's because of the\\n # hidden folder in the path?\\n\\n # update: ok...so the 'default' dialog was crap and didn't work\\n # right. For some reason, adding an option (in this case\\n # 'DontResolveSymlinks') caused a different dialog to be used\\n # (one that looked more familiar to me) that worked MUCH better\\n # and started in the correct directory.\\n # Wondering if this was perhaps the 'non-native' dialog and the\\n # native one was just bad on my system, I changed the options to\\n # include 'UseNonNativeDialog'--but this showed a *different*\\n # dialog than the other two, which seemed to be between the\\n # others as far as functionality went. Presumably the \\\"good\\\"\\n # dialog was the native one, which is reassuring.\\n # Anyway, I still don't really know what's going on, but it\\n # seems to work ok for now...\\n\\n ovrdict = self._override_mapping\\n # ovrdict = self._override_paths[self._selected_profile.name]\\n\\n\\n start = self._override_mapping[folder].path if override else self.paths[folder]\\n\\n # noinspection PyTypeChecker\\n chosen = QFileDialog.getExistingDirectory(self,\\n \\\"Select directory\\\",\\n directory=start or \\\"\\\",\\n options=QFileDialog.DontResolveSymlinks)\\n\\n if check_path(chosen):\\n\\n if override:\\n self.override_boxes[folder].setText(chosen)\\n ovrdict[folder] = ovrdict[folder]._replace(path=chosen)\\n else:\\n self.path_boxes[folder].setText(chosen)\\n if folder in self.indicator_labels:\\n self.indicator_labels[folder].setVisible(False)\\n # enable apply button if needed\\n self._mark_changed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7179d1b7b05993b7471fde194b3ac7b1\",\n \"score\": \"0.6125794\",\n \"text\": \"def onBrowse(self, event):\\n dlg = wx.FileDialog(self, \\\"Choose an image:\\\",\\n style=wx.DD_DEFAULT_STYLE\\n # | wx.DD_DIR_MUST_EXIST\\n # | wx.DD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n self.inputImg = dlg.GetPath()\\n self.inputText.SetLabel(self.inputImg)\\n self.inputText.SetForegroundColour(\\\"black\\\") # set text color\\n self.onView()\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72970213624644967a7922528629a60\",\n \"score\": \"0.61231685\",\n \"text\": \"def on_miSetRootPath(self):\\n if self.filesRootPath is None:\\n self.filesRootPath = self.wgAttrFiles.defaultRootPath\\n self.fdRootPath = pQt.fileDialog(fdRoot=self.filesRootPath, fdFileMode='DirectoryOnly', fdCmd=self.on_set)\\n self.fdRootPath.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f8b22b0125ddfed772942d341b62e6\",\n \"score\": \"0.6121621\",\n \"text\": \"def ask_user_directory(self):\\n return str(\\n QtWidgets.QFileDialog.getExistingDirectory(\\n self.parent, \\\"Please select a capa rules directory\\\", self.rule_path\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76270e305747f0a99fc34f8d3bba1660\",\n \"score\": \"0.6119205\",\n \"text\": \"def openFileDialog(self):\\n self.pathEdit.setText(QFileDialog.getOpenFileName(self, 'Open File', './',\\n filter=\\\"All Files(*.*);;Text Files(*.txt)\\\")[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"813db67a49fe5445d0fc564be9c49940\",\n \"score\": \"0.61155754\",\n \"text\": \"def _input_file_dialog(self):\\r\\n\\r\\n\\t\\t# Open file dialog\\r\\n\\t\\tinputFile = askopenfilename(filetypes=self.inputTypes)\\r\\n\\t\\t# If file selected, check if txtInput already has an entry and clear,\\r\\n\\t\\t# then insert path of selected file\\r\\n\\t\\tif inputFile:\\r\\n\\t\\t\\tcurTxt = self.txtInput.get()\\r\\n\\t\\t\\tif curTxt:\\r\\n\\t\\t\\t\\tcurTxtEnd = len(curTxt)\\r\\n\\t\\t\\t\\tself.txtInput.delete(0, curTxtEnd)\\r\\n\\t\\t\\tself.txtInput.insert(0, inputFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980b4ecb52fec2f698afd85689e85e24\",\n \"score\": \"0.60958797\",\n \"text\": \"def select_file(_dir=False):\\n Tk().withdraw()\\n if not _dir:\\n return askopenfilename()\\n else:\\n return askdirectory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf16173be2046f5944ed438809f790a1\",\n \"score\": \"0.6092447\",\n \"text\": \"def directory_clicked(self, dirname):\\n self.chdir(directory=dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144e6ad0bc6e4b92b1a98b5865c3148d\",\n \"score\": \"0.6056002\",\n \"text\": \"def _open_file(self):\\n file_path, _ext = QFileDialog.getOpenFileName(self, dir=os.environ['HOME'], filter='Folder(.groom)')\\n self.ui.groom_package_txf.setText(str(file_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7edaee6a56092970308213c3a08d42\",\n \"score\": \"0.604943\",\n \"text\": \"def set_folder(self):\\n\\n self.par_folder = filedialog.askdirectory()\\n self.entry_folder.delete(0, END)\\n self.entry_folder.insert(0, self.par_folder)\\n LOGGER.debug('Folder: %s', self.par_folder)\\n self.folder_created_by_dialog = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227d33323a5f9a10564aea61c3dff835\",\n \"score\": \"0.6044199\",\n \"text\": \"def on_set(self):\\n selPath = self.fdRootPath.selectedFiles()\\n if selPath:\\n self.filesRootPath = os.path.normpath(str(selPath[0]))\\n self.wgAttrFiles.rf_rootPath()\\n self.wgVtxFiles.rf_rootPath()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c1491453232d0d955152cc161d9fd7\",\n \"score\": \"0.6030077\",\n \"text\": \"def save_folder_select(self):\\n\\n # Remove annoying empty window\\n root = Tk()\\n root.withdraw()\\n\\n # Select image from file\\n self.folder = filedialog.askdirectory()\\n\\n if not self.folder:\\n print(\\\"You choosed nothing\\\")\\n else:\\n# self.file_path = self.f\\n# print(\\\"Choosed path: \\\\n\\\", self.folder, \\\"\\\\n\\\")\\n self.edit_dir_save.setText(self.folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de59e1983cefbda96dca218a17b5265\",\n \"score\": \"0.6027771\",\n \"text\": \"def new_thread_1(self):\\n dir_name = filedialog.askdirectory(parent=self, initialdir=\\\"/\\\",\\n title='Please select a directory')\\n\\n if dir_name != \\\"\\\":\\n self.disable_menu()\\n self.path_and_bag.check_if_refresh(dir_name)\\n self.config(cursor=\\\"wait\\\")\\n self.update()\\n self.clean_queue()\\n GUI.static_queue.put(\\\"Finding files in chosen folder:\\\\n\\\\n\\\")\\n num_files = len([val for sub_list in\\n [[os.path.join(i[0], j)for j in i[2]]\\n for i in os.walk(dir_name)]\\n for val in sub_list])\\n rott = tk.Tk()\\n app = App(rott, GUI.static_queue, num_files)\\n rott.protocol(\\\"WM_DELETE_WINDOW\\\", app.on_closing)\\n thread = threading.Thread(target=self.open_menu, args=(dir_name,))\\n thread.setDaemon(True)\\n thread.start()\\n app.mainloop()\\n else:\\n print(\\\"Action aborted\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc134be69b68707e6688cf64176df5fc\",\n \"score\": \"0.6023432\",\n \"text\": \"def runOpenDirectoryDialog(self, title, startdir):\\n\\n dirName = tkFileDialog.askdirectory(\\n title=title, initialdir=startdir, mustexist=\\\"true\\\")\\n\\n return dirName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c989646d91a20dd143851256579fa63\",\n \"score\": \"0.60129404\",\n \"text\": \"def update_curdir(self, path):\\n self.olddir = self.curdir\\n self.curdir = path\\n self.prompt = '%s%s> ' % (self.state, path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212360e669cc90c29b702772a126800c\",\n \"score\": \"0.60106725\",\n \"text\": \"def select_folder(textfield, title=\\\"Select folder\\\", folder=\\\".\\\", master=None):\\n if not os.path.isdir(folder):\\n print(\\\"The folder '{}' specified for select_folder does not exist.\\\".format(folder))\\n folder = \\\".\\\"\\n \\n textfield.delete(0, END)\\n textfield.insert(0, filedialog.askdirectory(title=title, initialdir=folder, parent=None if master is None else master.tk))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a946d8fb50201c0025c17fb893e67836\",\n \"score\": \"0.59847885\",\n \"text\": \"def change_download_directory(self):\\n directory = tk_filedialog.askdirectory()\\n if directory:\\n self.download_directory_entry.config(state=tk.NORMAL)\\n self.download_directory_entry.insert(0, directory)\\n self.download_directory_entry.config(state=tk.DISABLED)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993988,"cells":{"query_id":{"kind":"string","value":"8d872fb7864e42a7495f4b21e3bbb084"},"query":{"kind":"string","value":"Parses a media_file path into its component segments."},"positive_passages":{"kind":"list like","value":[{"docid":"a8a29dc123502596e6c9c7d23ad6beec","score":"0.65137494","text":"def parse_media_file_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/mediaFiles/(?P<media_file_id>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""}],"string":"[\n {\n \"docid\": \"a8a29dc123502596e6c9c7d23ad6beec\",\n \"score\": \"0.65137494\",\n \"text\": \"def parse_media_file_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/mediaFiles/(?P<media_file_id>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9f7445ca524d3ea5bd135aa47a25c27f","score":"0.5895364","text":"def split_path(full_path):\n parsed_list = full_path.split('/')[-2:] # directtory, file_name\n \n return parsed_list","title":""},{"docid":"3405777c166d80d631ed05388b7710ab","score":"0.58513534","text":"def SplitPath(self, path):","title":""},{"docid":"daaf27ae5c80ffa70bafe497b98d5f16","score":"0.5751438","text":"def get_video_parts(video_path):\n parts = video_path.split('/')\n filename = parts[-1]\n class_label = filename[0:5]\n seq = filename[5:10]\n dir = '/'.join(parts[0:-1]) + '/'\n return filename, class_label, seq, dir","title":""},{"docid":"09e5c857c612cbee538811e9b105f2af","score":"0.57160467","text":"def _parse_segments_str(\n segments_string: str,\n media_start_time: Optional[int] = 0,\n media_stop_time: Optional[int] = None\n ) -> Optional[List[Tuple[int, int]]]:\n n_early = 0\n n_late = 0\n try:\n segments = []\n start_stops = segments_string.split(\",\")\n for ss in start_stops:\n start, stop = ss.strip().split(\"-\")\n start = int(start)\n stop = int(stop)\n\n # Limit to media start and stop times. If entirely\n # outside the limits, drop the segment\n if media_stop_time is not None and start > media_stop_time:\n n_late += 1\n continue\n if stop < media_start_time:\n n_early += 1\n continue\n start = max(start, media_start_time)\n if media_stop_time is not None:\n stop = min(stop, media_stop_time)\n\n # Offset by media_start_time so that segments are\n # relative to the transcoded waveform\n start -= media_start_time\n stop -= media_start_time\n\n segments.append((start, stop))\n except Exception as e:\n raise mpf.DetectionException(\n 'Exception raised while parsing voiced segments '\n f'\"{segments_string}\": {e}',\n mpf.DetectionError.INVALID_PROPERTY\n )\n\n # If all the voiced segments are outside the time range, signal that\n # we should halt and return an empty list.\n if not segments:\n raise NoInBoundsSpeechSegments(n_early, n_late)\n\n return sorted(segments)","title":""},{"docid":"1f3ee22316bb584da3eebc6e56a55129","score":"0.5584283","text":"def get_video_parts(video_path):\n parts = video_path.split('/')\n filename = parts[1]\n filename_no_ext = filename.split('.')[0]\n classname = parts[0]\n\n return classname, filename_no_ext, filename","title":""},{"docid":"ed9d1478a52685e258124109a7283713","score":"0.5551553","text":"def _parse_path_agnostic(filename):\n filename = filename.replace(\"\\\\\", os.sep).replace(\"/\", os.sep)\n return os.path.split(filename)","title":""},{"docid":"3e9b205b13bfbbb8feae0e88ec96fe9d","score":"0.5472871","text":"def get_video_parts(video_path):\n parts = video_path.split('/')\n filename = parts[-1]\n filename_no_ext = filename\n classname = parts[-1]\n\n return classname, filename_no_ext, filename","title":""},{"docid":"f6ba2878fc531fc4d9c6fe8cc09d56e1","score":"0.5443869","text":"def parse_path(path):\n\n result = []\n split_path = path.split('/')\n for element in split_path:\n if element != '':\n result.append(element)\n return result","title":""},{"docid":"067b228b30fbe42e5c4b67590e1c7086","score":"0.53923386","text":"def parse(self, filepath):","title":""},{"docid":"afef0dd26b9999a307b14c7857661765","score":"0.5352113","text":"def parse(cls, path: str) -> List:\n pass","title":""},{"docid":"6e0d302732ce54557586ba5d04c4bb5b","score":"0.53390574","text":"def divide_video(self):\r\n if self.path and self.split_path:\r\n segment_size = self.get_parts()\r\n print\r\n print ('ffmpeg -i %s -c copy -map 0 -segment_time %s -f segment %s' % (self.path, segment_size, self.split_path) + '%03d.mp4')\r\n print\r\n print\r\n print\r\n #divide video to parts of the calculated length, NOTE: not every part will be exactly at the same length\r\n p = SUB.call('ffmpeg -i %s -c copy -map 0 -segment_time %s -f segment %s' % (self.path, segment_size, self.split_path) + '%03d.mp4')\r\n\r\n n = 0\r\n\r\n files = listdir(self.split_path)\r\n #gets max part\r\n for i in reversed(files):\r\n if len(i) >= 3 and i[:3].isdigit():\r\n max_part = int(i[:3])\r\n break\r\n else:\r\n max_part = -1\r\n\r\n while n <= max_part:\r\n #convert to audio parts\r\n p = SUB.call('ffmpeg -i %s.mp4 %s.wav' % (self.split_path+self.get_part_video_num(n), self.split_path+self.get_part_video_num(n)))\r\n n += 1\r\n\r\n n = 0\r\n while n <= max_part:\r\n #converts to video parts\r\n p = SUB.call('ffmpeg -i %s.mp4 -target ntsc-vcd -vcodec mpeg1video -an %s.mpg' % (self.split_path+self.get_part_video_num(n), self.split_path+self.get_part_video_num(n)))\r\n n += 1\r\n\r\n for f in files:\r\n try:\r\n #deletes the unnecessary original video parts\r\n remove(self.split_path + f)\r\n except:\r\n pass\r\n\r\n print 'nehenaknu im gui'","title":""},{"docid":"667439f8e50f0ac824bb024e56058572","score":"0.5306527","text":"def _split_multipath(pathstr):\n # m is absolute path, M is relative path (or vice versa?)\n if not pathstr[0] in [\"m\",\"M\"]:\n raise ValueError(\"Bad path format: %s\" % pathstr)\n import re\n subpaths = [sp for sp in re.split('[Mm]',pathstr) if len(sp)>0]\n headers = re.findall('[Mm]',pathstr)\n for subpath,header in zip(subpaths,headers):\n # Need further parsing of multi-path strings? perhaps no.\n yield (header + subpath).strip()","title":""},{"docid":"ae75d0cb47ddad1579f9a3a62236a31c","score":"0.52950317","text":"def get_media_url(path):\n if ck_settings.UPLOAD_PREFIX:\n url = ck_settings.UPLOAD_PREFIX + path.replace(ck_settings.UPLOAD_PATH, '')\n else:\n url = os.path.join(settings.MEDIA_URL, path.replace(ck_settings.MEDIA_ROOT, ''))\n\n # Remove multiple forward-slashes from the path portion of the url.\n # Break url into a list.\n url_parts = list(urllib.parse.urlparse(url))\n # Replace two or more slashes with a single slash.\n url_parts[2] = re.sub('\\/+', '/', url_parts[2])\n # Reconstruct the url.\n return urllib.parse.urlunparse(url_parts)","title":""},{"docid":"f406d7394633ae25472c0e3490177110","score":"0.52904373","text":"def _parse_part(self, part, mimetypes):\n\n self._parse_section_three(part, mimetypes)","title":""},{"docid":"747df1745e9302728538aac32172ca22","score":"0.5220871","text":"def parse_full_path(self, full_path):\n parsed_full_path = {'filename': os.path.basename(full_path)}\n return parsed_full_path","title":""},{"docid":"009746872aeb480afbb1a6854596300d","score":"0.51812714","text":"def make_media_file_list(media):\n \n items = []\n\n for item in media:\n if isinstance(item, list):\n #print item[0]\n fname = os.path.basename(item[0])\n #print fname\n items.append(fname)\n else:\n fname = os.path.basename(item)\n #print fname\n items.append(fname)\n \n return items","title":""},{"docid":"3b78823f0ec6fd346e157c235ab074a5","score":"0.5176828","text":"def get_file_parts(path) :\n path,fn = os.path.split(path)\n basename,ext = os.path.splitext(fn)\n return path,fn,basename,ext","title":""},{"docid":"b323a64607a890dd681761eef07762c4","score":"0.51637036","text":"def parse(cls, path):\n pass","title":""},{"docid":"31706697c83c53a0d91fe2050cd1168f","score":"0.51542807","text":"def SplitPath(self, path, qualified=False):\n # type: (str, bool) -> list\n parsed_path = self.hdfs.parse_uri(path)\n # split the path with the path separator and remove empty path segments.\n path_segments = list(filter(None, parsed_path.path.split(self.PATH_SEPARATOR)))\n # if qualified then the first segment is HDFS URI\n if qualified:\n path_segments.insert(0, self.hdfs.make_uri())\n return path_segments","title":""},{"docid":"ba74023ff99d5b0ae8aa590126caf4ff","score":"0.51386625","text":"def __parse(self, dirpath):\n if not dirpath or not isinstance(dirpath, basestring):\n raise ValueError(\n 'path is not defined or not a string. path: {}'.format(\n dirpath))\n if not os.path.exists(dirpath):\n raise ValueError('path does not exist! path: {}'.format(dirpath))\n\n allparts = []\n # Split 4 parts for build, config, cid, size\n # TODO: error checks\n tmp_path = dirpath\n for i in range(0, 4):\n parts = os.path.split(tmp_path)\n assert len(\n parts) == 2, \\\n \"Not enough parts to the path! parts={}, dirpath={}\".format(\n parts, dirpath)\n tmp_path = parts[0]\n allparts.insert(0, parts[1])\n\n self.__build = allparts[0]\n self.config = allparts[1]\n self.cid = str(allparts[2])\n self.size = allparts[3]","title":""},{"docid":"1ed1033701a7f407a26a8af0f1b72ed7","score":"0.51082796","text":"def parse_url(self, url):\n url = url.replace(self._url, '')\n url = url.strip('/').lower()\n url = url.split('/')\n id = '-'.join(url[:-1])\n filename = url[-1]\n return id, filename","title":""},{"docid":"1ed1033701a7f407a26a8af0f1b72ed7","score":"0.51082796","text":"def parse_url(self, url):\n url = url.replace(self._url, '')\n url = url.strip('/').lower()\n url = url.split('/')\n id = '-'.join(url[:-1])\n filename = url[-1]\n return id, filename","title":""},{"docid":"bd6a960f9e58f26f1b41f00e90d6d800","score":"0.510161","text":"def _parse_dest_path(self, path = ''):\n sep = '/'\n if path and path[0] == sep:\n # remove dealing '/'\n path = path[1:]\n if not path: return None\n v = path.split(sep)\n folderId = \"root\"\n mimeType = MIME_TYPE_FOLDER\n i = 0\n # iterates through of the path (array v)\n while i < len(v) and mimeType == MIME_TYPE_FOLDER:\n filename = v[i]\n subfolders = self.list_folders(name = filename, parentId = folderId)\n if subfolders:\n folderId = subfolders[0]['id']\n else:\n if i == len(v) - 1:\n # e.g. 'path/to/folder/foo.txt', then folderId = ID_OF('path/to/folder/'), \n # and filename = 'foo.txt'\n return (folderId, filename)\n else:\n # path not found\n return None\n i += 1\n # this is the case 'path/to/folder/', no filename given\n return (folderId, '')","title":""},{"docid":"7ccd69a11e0bb5709ef8373f66383a60","score":"0.50961405","text":"def parse_path(path):\n\n path_key = path.split(\"/\")[0]\n\n if path_key == \"posts\":\n return PostImporter(path)\n elif path_key == \"pages\":\n return PageImporter(path)","title":""},{"docid":"425f90103303ec8462b2751ca9ce8355","score":"0.50941086","text":"def parse_audience_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/audiences/(?P<audience_id>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"871a5a4072527452f72447e0d9b66420","score":"0.5088982","text":"def explode_path(self, path):\n parts = []\n head = path\n while head:\n # /foo/bar/baz => head=/foo/bar, tail=baz\n head, tail = os.path.split(head)\n parts.append(tail)\n return reversed(parts)","title":""},{"docid":"31eac2a741f55d733144cca3d85fc2c4","score":"0.5079133","text":"def _split(self, file):\r\n\r\n ext_index = str(file).rindex('.')\r\n file_name = file[:ext_index] # get the file name from the full path\r\n extension = file[ext_index + 1:] # get the extension from the full path\r\n\r\n # extract time of all matched points\r\n time_series = [int(row[1] / 1000) for row in self.stop_points]\r\n time_series = [0] + time_series + [int(self.length / 1000)]\r\n print('time_series:', time_series)\r\n\r\n # split the source audio\r\n for i in range(1, len(time_series)):\r\n print('from', time_series[i - 1], 'to', time_series[i])\r\n\r\n cmd = 'ffmpeg -i {} -acodec copy -ss {} -to {} {}_{}.{}'.format(file, time_series[i - 1], time_series[i],\r\n file_name, i, extension)\r\n\r\n print(cmd)\r\n\r\n proc = subprocess.Popen(cmd, shell=True)\r\n proc.communicate()\r\n print('convert is done...')","title":""},{"docid":"4491d85987211fea83c8878cfd796f85","score":"0.50746155","text":"def _get_path_parts(self):\n dn_str = self._remove_format_from_path(self.path, self.api_format)\n return dn_str[1:].split(\"/\")","title":""},{"docid":"df7b50905f406e89cb27216db03f4317","score":"0.50738245","text":"def parse_media(self, tokens):\n mediaquery_tokens = [f for f in tokens if f.type == 'IDENT']\n return mediaquery_tokens","title":""},{"docid":"38cd8303bef843639beeb682b1ee3b45","score":"0.5000593","text":"def parse_asset_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/assets/(?P<asset_id>.+?)$\", path\n )\n return m.groupdict() if m else {}","title":""},{"docid":"cec92281a239ad766f3bbf4f3f057665","score":"0.49627233","text":"def resolv_path(self, path):\n itemx = self.struct_list\n for i in path:\n itemx = itemx[i]\n return itemx","title":""},{"docid":"3b75873003a8627e0e27f163733b45d2","score":"0.492974","text":"def parse_file(self, path):\n with open(path) as file:\n return [line.strip() for line in file]","title":""},{"docid":"74f2d4d55190c544d663fe744615d924","score":"0.4928018","text":"def readSegments( fileName ):\n f = open( fileName, 'r' )\n count = int( f.readline() )\n print \"Files has %d segments\" % ( count )\n segments = []\n for line in f.xreadlines():\n line = line.strip()\n if ( line ):\n print line.strip()\n x1, y1, x2, y2 = map( lambda x: float(x), line.strip().split() )\n segments.append( Segment( Vector3( x1, 0, y1 ), Vector3( x2, 0, y2 ) ) )\n f.close()\n return segments","title":""},{"docid":"7292f66d8a595805fbf73bbca1f47d95","score":"0.49179754","text":"def params_in_path(self):\n # type: () -> List\n return [part[1:] for part in self.path.split('/') if part.startswith(':')]","title":""},{"docid":"eee09a20ff89e03615e976deee670c28","score":"0.49050176","text":"def parse_ad_group_asset_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/adGroupAssets/(?P<ad_group_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"912763bdef029431fc34f379afdf15bb","score":"0.4899388","text":"def split_path(path):\n\n def split(src):\n \"\"\"Core function to split path.\n \"\"\"\n if \"/\" in src:\n return src.rsplit(\"/\", maxsplit=1)\n else:\n # Interpret as path to file in current directory.\n return \"./\", src\n\n if isinstance(path, sb.pretrained.fetching.FetchSource):\n fetch_from, fetch_path = path\n source, filename = split(fetch_path)\n return sb.pretrained.fetching.FetchSource(fetch_from, source), filename\n else:\n return split(path)","title":""},{"docid":"c137998688eefb1903a6f3170a46fead","score":"0.48934528","text":"def parse_asset_group_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/assetGroups/(?P<asset_group_id>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"9757a0db7c7890567adc644ff96a1eb3","score":"0.48931915","text":"def parse_file(self, file_path):\n pass # pragma: nocover","title":""},{"docid":"d300a1f083315523010aff40a75834e3","score":"0.48873222","text":"def _parse_file(self, file_path):\n pass # pragma: nocover","title":""},{"docid":"13288de9f91d0f1eea4434bceafd5063","score":"0.48766533","text":"def parse_file(self, path):\n self.logger.debug(\"Parsing SVD file %s\", path)\n\n with open(path, \"r\") as svd_file:\n svd = svd_file.read()\n return self.parse(svd)","title":""},{"docid":"72fe351adcfa72fa58adec81e6683524","score":"0.48709342","text":"def _split(self):\n if self.is_abspath:\n return self._value[1:].split(self.fs.sep)\n return self._value.split(self.fs.sep)","title":""},{"docid":"424b5ceacffe982f1f0290f9611d6c7c","score":"0.48600677","text":"def __file_parts__(self,file):\n parts = {\n 'path':'',\n 'name':'',\n 'ext':'',\n 'file':'',\n 'full_name':'',\n 'full_path':''\n }\n if not file == \"\" and not file == None:\n parts['path'],parts['full_name'] = os.path.split(file)\n parts['name'],parts['ext'] = parts['full_name'].split('.')\n if not parts['path']:\n parts['path'] = './'\n parts['full_path'] = parts['path']+'/'+parts['name']+'.'+parts['ext']\n else:\n parts['path'] = ''\n parts['name'] = ''\n parts['ext'] = ''\n parts['file'] = ''\n parts['full_path'] = ''\n\n return parts","title":""},{"docid":"f309d66f5daa88e8142525a0f77f5484","score":"0.4854577","text":"def part_parse(self):\n parts = []\n for part in re.split('\\*\\*\\* ([A-Z- ]+) \\*\\*\\*', self.hand_file): # return [ 'part1', 'splitter1', 'part2',..\n parts.append(part)\n\n for i in range(0, len(parts)):\n if i == 0:\n self.part_dict['HEADER'] = parts[i].split('\\n')\n if i % 2 != 0: # number is odd\n # print(type(parts[i]))\n self.part_dict[parts[i]] = parts[i+1].split('\\n')","title":""},{"docid":"60f684ab8a01895a19cc4deb1fb1e82a","score":"0.48466882","text":"def parse_asset_group_asset_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/assetGroupAssets/(?P<asset_group_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"48c1ed092dae14b6dc19400a6fb30d9e","score":"0.48438734","text":"def splitpath(path):\n # type: (bytes) -> List[bytes]\n return path.split(pycompat.ossep)","title":""},{"docid":"c2c4bf3c35e4c059498f17ee9a8273ac","score":"0.4841555","text":"def _split_path(self, path: str) -> Tuple[str, str]:\n parts = path.split(\".\")\n coll = parts.pop(0)\n rest = \".\".join(parts)\n return coll, rest","title":""},{"docid":"d2a4dcdee73b1e93a4005c200ba45cd9","score":"0.4835787","text":"def test_parse_path():\n\n expected_hosts = ['127.0.0.1']\n expected_services = ['45589']\n expected_notes = ['screenshot_web']\n\n pidb = ParserModule.parse_path('tests/server/data/parser-screenshot_web-job.zip')\n\n assert [x.address for x in pidb.hosts] == expected_hosts\n assert [x.port for x in pidb.services] == expected_services\n assert [x.xtype for x in pidb.notes] == expected_notes","title":""},{"docid":"a1dc3aef3880e746a9478211f9ba9c62","score":"0.48354855","text":"def divide_pathstring_parts(pathstring):\n substring = pathstring.strip()\n paths = []\n while 'M' in substring[1:]:\n m_index = substring.find('M', 1)\n if m_index > -1:\n subpath = substring[0:m_index].strip()\n paths.append(subpath)\n substring = substring[m_index:].strip()\n\n paths.append(substring)\n return paths","title":""},{"docid":"26c72d352f9c9219dfb769dce356fd03","score":"0.4830553","text":"def get_media_url(mid):\n return '%s%s' % (config.get('media_files_root'),mid)","title":""},{"docid":"ac117c2a230a2b3e77872f847fc99089","score":"0.4827614","text":"def parse_uri(_, uri):\n uri = str(uri)\n if uri.startswith('file://'):\n uri = uri[len('file://'):]\n return Path(uri).resolve()","title":""},{"docid":"ffb0ba7790fffefdaba8caf9da40c020","score":"0.4827574","text":"def get_mp4_file_path_and_drive_name(h264_filepath):\n # Splits by '.' to get path before extension\n h264_filepath_split_no_ext = h264_filepath.split('.')\n mp4_filepath = h264_filepath_split_no_ext[0] + '.mp4'\n\n # Splits by '/' to get filename at end of path for storage in Drive\n h264_filepath_split_no_slashes = h264_filepath_split_no_ext[0].split('/')\n drive_file_name = h264_filepath_split_no_slashes[len(h264_filepath_split_no_slashes) - 1] + '.mp4'\n\n return mp4_filepath, drive_file_name","title":""},{"docid":"55b2b3c758d564f22bad655b4fb64330","score":"0.48193255","text":"def get_missing_media(self):\n self.make_segment_dir()\n media = self.retrieve_chunklist_manifest()\n for m in media:\n renamed = self.rename_media(m)\n if os.path.isfile(self.segmentDir + '/' + renamed) == False:\n print('INFO: ' + self.segmentDir + '/' + renamed + ' file does not exist')\n status = self.retrieve_media_segment(m, renamed)\n if status:\n self.segments += m # Add to segment list\n self.renamedSegments += renamed # Add to other segment list\n if self.verbose:\n print('INFO: Got new media segment \\t\\t' + m)\n else:\n if self.verbose:\n print('WARN: Failed to get media segment \\t' + m)\n else:\n if self.verbose:\n print('INFO: Segment file ' + m + ' already downloaded, skipping...')\n if self.verbose:\n sdSize = sum(d.stat().st_size for d in os.scandir(self.segmentDir + '/') if d.is_file())\n print('INFO: Segment directory is now at ' + str(sdSize) + ' bytes (' + str(round(sdSize/1000000, 2)) + ' megabytes)')","title":""},{"docid":"6d17f08ae805e98e8aa00a6dd0c19b8d","score":"0.48173803","text":"def format_segment(record, indir='', force_input_dir='', ):\n regex = re.compile(indir)\n file_loc = regex.sub(force_input_dir or indir,\n record['FILE_LOC'])\n logger.debug('Search for file location: %s', file_loc)\n filenames = glob.glob(file_loc)\n if filenames:\n return {k: (v if k != 'FILE_LOC' else filenames[0])\n for k, v in record.items()}\n return None","title":""},{"docid":"954dc0c15e9788913822df5d8ed57732","score":"0.48143667","text":"def read_file(path_segments):\n file_path = os.path.join(here, *path_segments)\n with open(file_path) as f:\n return f.read()","title":""},{"docid":"012d693c2472b1425ceba5c831fe8757","score":"0.48121879","text":"def convert_media_path(html):\n lst = list()\n mcss = re.findall('href=\"(\\S+?\\.css)\"', html)\n lst.extend(list(set(mcss)))\n mjs = re.findall('src=\"([\\w\\./]\\S+?\\.js)\"', html)\n lst.extend(list(set(mjs)))\n msrc = re.findall('<img.*?src=\"([\\w\\./]\\S+?)\".*?>', html)\n lst.extend(list(set(msrc)))\n # print lst\n newlist = ['_' + each.split('/')[-1] for each in lst]\n # print newlist\n for each in zip(lst, newlist):\n html = html.replace(each[0], each[1])\n return unicode(html)","title":""},{"docid":"2bb1357b96aeaf1f6f4f4ef4c8837309","score":"0.4811636","text":"def split_video(self, path):\r\n vidObj = cv2.VideoCapture(path)\r\n images = []\r\n\r\n # Used as counter variable\r\n count = 0\r\n\r\n # checks whether frames were extracted\r\n success = 1\r\n\r\n while success:\r\n # vidObj object calls read\r\n # function extract frames\r\n success, image = vidObj.read()\r\n\r\n # Saves the frames with frame-count\r\n # cv2.imwrite(\"frame%d.jpg\" % count, image)\r\n images.append(image)\r\n count += 1\r\n return images","title":""},{"docid":"9367d762d8b2859ddc9cfb5f2edaa71d","score":"0.4803748","text":"def test_parse_filepath():\n # Given\n path = '/path/to/file.nii.gz'\n # When\n dirname, basename, ext = parse_filepath(path)\n # Then\n assert [dirname, basename, ext] == ['/path/to', 'file', 'nii.gz']","title":""},{"docid":"7b0b14e1e5314eb5318c65fc9802e9fb","score":"0.47905982","text":"def get_video_parts(video_path):\n parts = video_path.split(os.path.sep)\n filename = parts[2]\n filename_no_ext = filename.split('.')[0]\n classname = parts[1]\n train_or_test = parts[0]\n\n return train_or_test, classname, filename_no_ext, filename","title":""},{"docid":"5b2354c46a47cb9233f6ed9d3636b8b1","score":"0.4785013","text":"def parse_module_path(module_path):\n aslist = module_path.split(':', 1)\n if len(aslist) == 1:\n return (aslist[0], [])\n else:\n return (aslist[0], aslist[1].split(','))","title":""},{"docid":"85d1547bb401b417f11e9eda018a3d22","score":"0.47697225","text":"def getSegments(self) -> List[ghidra.app.util.bin.format.omf.OmfSegmentHeader]:\n ...","title":""},{"docid":"abf1a464f041c79bd964dced1c8dc039","score":"0.47663137","text":"def parse(filepath: str):\n paths, _, svg_attributes_dict = svg.svg2paths2(filepath)\n setsofsubpaths = [p.continuous_subpaths() for p in paths]\n paths = itertools.chain.from_iterable(setsofsubpaths)\n\n svg_attributes = SvgAttributes(svg_attributes_dict)\n if svg_attributes.is_portrait:\n return [SVGPath(path, svg_attributes.scale_factor) for path in paths]\n else:\n return [SVGPathRotatedBy90Degrees(path, svg_attributes.scale_factor, svg_attributes.height) for path in paths]","title":""},{"docid":"520b966c488691321f3197c16bb8788a","score":"0.47521687","text":"def parse(cls, path: str) -> List[QuoteModel]:\n ext = path.split(\".\")[-1]\n # First try-except block to catch any exceptions w.r.t.\n # invalid extension.\n try:\n cls.validate_extension(ext)\n except InvalidExtensionError as inv_ext_err:\n print(inv_ext_err)\n else:\n # Second try-except block to catch any exceptions\n # while parsing the file.\n try:\n for ingestor in cls.ingestors:\n if ingestor.can_ingest(path):\n return ingestor.parse(path)\n except Exception:\n print(f\"ParsingError: Error occured while parsing {path}.\")","title":""},{"docid":"8563d693422f86c87b323daf8436f459","score":"0.47483987","text":"def get_segments(self, boundaries, audio_file, before_margin=0.1, after_margin=0.1):\n sample_rate, sig_len = self._get_audio_info(audio_file)\n if sample_rate != self.sample_rate:\n raise ValueError('The detected sample rate is different from that set in the hparam file')\n segments = []\n for i in range(boundaries.shape[0]):\n beg_sample = boundaries[i, 0] * sample_rate\n end_sample = boundaries[i, 1] * sample_rate\n beg_sample = int(max(0, beg_sample - before_margin * sample_rate))\n end_sample = int(min(sig_len, end_sample + after_margin * sample_rate))\n len_seg = end_sample - beg_sample\n vad_segment, fs = torchaudio.load(audio_file, frame_offset=beg_sample, num_frames=len_seg)\n segments.append(vad_segment)\n return segments","title":""},{"docid":"98b29d6ae12ca3b9326ffc6bfa92b315","score":"0.47450432","text":"def _ParseSCCAFile(self, parser_mediator, scca_file):\n format_version = scca_file.format_version\n executable_filename = scca_file.executable_filename\n prefetch_hash = scca_file.prefetch_hash\n run_count = scca_file.run_count\n number_of_volumes = scca_file.number_of_volumes\n\n volume_serial_numbers = []\n volume_device_paths = []\n path_hints = []\n\n for volume_information in iter(scca_file.volumes):\n volume_serial_number = volume_information.serial_number\n volume_device_path = volume_information.device_path\n\n volume_serial_numbers.append(volume_serial_number)\n volume_device_paths.append(volume_device_path)\n\n timestamp = volume_information.get_creation_time_as_integer()\n if timestamp:\n event_data = windows_events.WindowsVolumeEventData()\n event_data.creation_time = dfdatetime_filetime.Filetime(\n timestamp=timestamp)\n event_data.device_path = volume_device_path\n event_data.origin = parser_mediator.GetFilename()\n event_data.serial_number = volume_serial_number\n\n parser_mediator.ProduceEventData(event_data)\n\n for filename in iter(scca_file.filenames):\n if not filename:\n continue\n\n if (filename.startswith(volume_device_path) and\n filename.endswith(executable_filename)):\n _, _, path = filename.partition(volume_device_path)\n path_hints.append(path)\n\n mapped_files = []\n for entry_index, file_metrics in enumerate(scca_file.file_metrics_entries):\n mapped_file_string = file_metrics.filename\n if not mapped_file_string:\n parser_mediator.ProduceExtractionWarning(\n 'missing filename for file metrics entry: {0:d}'.format(\n entry_index))\n continue\n\n file_reference = file_metrics.file_reference\n if file_reference:\n mapped_file_string = (\n '{0:s} [{1:d}-{2:d}]').format(\n mapped_file_string, file_reference & 0xffffffffffff,\n file_reference >> 48)\n\n mapped_files.append(mapped_file_string)\n\n event_data = WinPrefetchExecutionEventData()\n event_data.executable = executable_filename\n event_data.mapped_files = mapped_files\n event_data.number_of_volumes = number_of_volumes\n event_data.path_hints = path_hints\n event_data.prefetch_hash = prefetch_hash\n event_data.run_count = run_count\n event_data.version = format_version\n event_data.volume_device_paths = volume_device_paths\n event_data.volume_serial_numbers = volume_serial_numbers\n\n timestamp = scca_file.get_last_run_time_as_integer(0)\n if timestamp:\n event_data.last_run_time = dfdatetime_filetime.Filetime(\n timestamp=timestamp)\n\n # Check for the 7 older last run time values available since\n # format version 26.\n if format_version >= 26:\n previous_run_times = []\n for last_run_time_index in range(1, 8):\n timestamp = scca_file.get_last_run_time_as_integer(last_run_time_index)\n if timestamp:\n date_time = dfdatetime_filetime.Filetime(timestamp=timestamp)\n previous_run_times.append(date_time)\n\n if previous_run_times:\n event_data.previous_run_times = previous_run_times\n\n parser_mediator.ProduceEventData(event_data)","title":""},{"docid":"078b0965961de34af3dcbf38bb091cb5","score":"0.47448736","text":"def parse(line):\n try:\n url_start = line.index(\" URL:\") + 5\n url_end = line.index(\" [\", url_start)\n url = line[url_start:url_end]\n filepath_start = line.index('-> \"') + 4\n filepath_end = line.index('\" ', filepath_start)\n filepath = line[filepath_start:filepath_end]\n return (url, filepath)\n except ValueError:\n return None","title":""},{"docid":"31209e0164f73d988d1a293455509995","score":"0.47420913","text":"def parse_path(path):\n \n base_name = os.path.splitext( os.path.basename(path) )[0] \n splits = base_name.split(\"_\")\n \n signature = splits[0]\n count = splits[2] \n good = splits[1] == \"g\"\n \n return signature, count, good","title":""},{"docid":"f40c47a119791e6683dd7a578e11ae9d","score":"0.47409058","text":"def __parse_files(self):\n parsed_files = []\n fractures = []\n total_length = 0\n for file in self.files:\n file_length = file[b'length']\n file_path = file[b'path']\n if b'md5sum' in file:\n file_md5sum = file[b'md5sum']\n parsed_files.append({b'length': file_length, b'path': file_path, b'md5sum': file_md5sum})\n else:\n parsed_files.append({b'length': file_length, b'path': file_path})\n\n total_length += file_length\n fractures.append(total_length)\n # print(total_length)\n\n return parsed_files, total_length, fractures","title":""},{"docid":"c6e917068495833d48625feffa531faa","score":"0.47385797","text":"def split(src):\n if \"/\" in src:\n return src.rsplit(\"/\", maxsplit=1)\n else:\n # Interpret as path to file in current directory.\n return \"./\", src","title":""},{"docid":"fcf2590b941bc9d86a343d83971c01f0","score":"0.47325078","text":"def splitPath(self, path):\n split = path.split(',')\n # Strip the individual items of white space and\n # values given after '=' sign.\n for item in split:\n item_index = split.index(item)\n item = item.strip()\n eq_index = item.find('=')\n if eq_index != -1:\n item = item[:eq_index+1]\n split[item_index] = item\n return split","title":""},{"docid":"ea229d20d97d78560f9a25f0349a777f","score":"0.47318658","text":"def split(input_file):\n frames = []\n wave = thinkdsp.read_wave(input_file)\n\n # Some values that we might need\n totframes = len(wave.ts)\n framerate = wave.framerate\n length = totframes / framerate\n framelength = samples_per_frame / framerate\n numframes = int(length / framelength)\n\n # print('Framelength:', framelength, 'seconds')\n # print('Frames to calcuate:', numframes)\n\n for index in range(numframes):\n # print(index)\n currentstart = index * framelength\n frames.append(wave.segment(start=currentstart, duration=framelength))\n\n return frames, framelength","title":""},{"docid":"55ca521e4f80aa5faed7e56931188d6c","score":"0.47306952","text":"def parse_uri_path(self, path):\n options = {}\n db, *_ = path[1:].split(\"/\")\n if db:\n options[\"db\"] = db\n return options","title":""},{"docid":"b705e847f1bdeef28d0ba758c0f4d025","score":"0.47251388","text":"def parse_simout_filepath(filepath):\n split = filepath.split(\"/\")\n run = split[-2]\n opt = split[-3]\n benchmark = split[-4]\n cache_size = split[-5]\n fname = split[-1]\n slice_no = re.search(\"(?<=slice.)[0-9]+\", fname).group(0)\n return (benchmark, opt, run, slice_no, cache_size)","title":""},{"docid":"da2089bd28f8c025c409b0ef36117c04","score":"0.47226387","text":"def parse_campaign_asset_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/campaignAssets/(?P<campaign_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"b688fc24a48f0fbdc32a5162897c6626","score":"0.47190553","text":"def split(self, path: AnyStr) -> Tuple[AnyStr, AnyStr]:\n return self.filesystem.splitpath(path)","title":""},{"docid":"493c01dad3982c90d1913097bf7ef24f","score":"0.47188506","text":"def parse(self, message):\n messages = message.split(' ')\n counter = 0\n segment = ''\n segments = []\n while counter < len(messages):\n counter += 1\n l_s = len(segment)\n chunk = messages[counter - 1]\n segment = chunk if l_s == 0 else '{} {}'.format(segment, chunk)\n if (len(messages[counter]) + l_s) >= lcd_columns:\n # Save string segment and reset for next loop\n segments.append(segment + self.ext(lcd_columns - l_s))\n segment = ''\n # Append final segment:\n segments.append(segment + self.ext(lcd_columns - l_s))\n return segments","title":""},{"docid":"b0dd2ae293dde22a78f83c4855a64804","score":"0.471839","text":"def split_chapters(paths):\n filename = glob.glob(paths[0] + '/*')[0]\n completed_process = subprocess.run(f'ffprobe -i \"{filename}\" -print_format json -show_chapters -loglevel error', stdout=subprocess.PIPE, shell=True)\n chapter_info = json.loads(completed_process.stdout)['chapters']\n if not chapter_info:\n print('Chapters Not Found')\n rmdir(paths[1])\n else:\n _split_file(filename, [(float(entry['start_time']), float(entry['end_time']), join(paths[1], entry['tags']['title'] + '.mp3')) for entry in chapter_info])","title":""},{"docid":"5352ba0d952787236e8338cf830520c2","score":"0.47076356","text":"def _parse(self, file_path):\n print('Reading input file...', end='\\r')\n extension = _get_file_extension(file_path)\n parser = self.get_parser(extension)\n parsed_data = _cast(parser(file_path))\n return parsed_data, extension","title":""},{"docid":"2aca0d285703ee00fe8a8892ab5b564d","score":"0.470492","text":"def _split_path(path, seps=PATH_SEPS):\n if not path:\n return []\n\n for sep in seps:\n if sep in path:\n if path == sep: # Special case, '/' or '.' only.\n return ['']\n return [x for x in path.split(sep) if x]\n\n return [path]","title":""},{"docid":"64b203fc65ae18966baedf2eb2cc0b17","score":"0.47011355","text":"def split_path(path):\n base, has_ext = split(path)\n name, ext = splitext(has_ext)\n return base, name, ext","title":""},{"docid":"1f68199bab8d8d295ff1515f3389bd50","score":"0.47005865","text":"def parse_segs(fid, numseg):\n # temporary storage array\n D = []\n\n # answer array\n R = []\n\n for idx in range(numseg+1):\n aline = fid.readline()\n D.append(parse_segs_data(aline))\n if D[idx][0] == -3:\n R.append((1, D[idx][3], pathlength(D, D[idx][2], D[idx][1])))\n if D[idx][0] == -4:\n R.append((0, D[idx][3], pathlength(D, D[idx][2], D[idx][1])))\n \n return R","title":""},{"docid":"936c078b4657b508328262235237a289","score":"0.4697543","text":"def parse_parts(self, parts):\n _, _, parsed = super().parse_parts(parts)\n if len(parsed) > 0 and parsed[0] == \"/\":\n parsed = parsed[1:]\n return \"\", \"\", parsed","title":""},{"docid":"6411dbf75709116cfaa5c595edbc4702","score":"0.46675193","text":"def parse(self, filename, encoding=None, href=None, media=None):\r\n if not encoding:\r\n encoding = 'css'\r\n return self.parseString(codecs.open(filename, 'r', encoding).read(),\r\n href=href, media=media)","title":""},{"docid":"097fc681c898727f8e726839dee3e7e9","score":"0.46597257","text":"def parse_metadata_from_path(path):\n\n re_name_split = r'.*/newspapers/newspapers/fin/(.{4})/(.{7,9})/.{7,9}\\_.{4}\\-(..)\\-(..)'\n\n split = re.search(re_name_split, path)\n\n year, issn, month, day = split.groups()\n return year, month, day, issn","title":""},{"docid":"d76c58f193d47b4fd8c15682555bdcb4","score":"0.4659066","text":"def get_audio_seg_filenames(audio_dir, segment_dir, p=None):\n\ttemp_filenames = [i for i in sorted(os.listdir(audio_dir)) if \\\n\t\t\t_is_audio_file(i)]\n\taudio_filenames = [os.path.join(audio_dir, i) for i in temp_filenames]\n\ttemp_filenames = [i[:-4] + '.txt' for i in temp_filenames]\n\tseg_filenames = [os.path.join(segment_dir, i) for i in temp_filenames]\n\treturn audio_filenames, seg_filenames","title":""},{"docid":"d28f247b7cae0d52d8b88d2772c77fa3","score":"0.46307933","text":"def parse_files(self):\n pass","title":""},{"docid":"7c30eb1438efb2b40c0763677d765c08","score":"0.4624152","text":"def importable_paths(path):\n chunks = [chunk\n for chunk in path.split(\"/\")\n if chunk and (chunk[0].isalpha() or chunk[0] == \"_\")]\n if chunks:\n chunks[-1] = chunks[-1].rsplit('.', 1)[0]\n return chunks","title":""},{"docid":"eb0affbf620567de141e06935021695a","score":"0.46221396","text":"def fetch_media(song):\n return song.get('media')","title":""},{"docid":"3f7fb2c2005ab053c18810bda2ea114a","score":"0.46147782","text":"def _parse_segments(self):\n reader = csv.reader(open(self._segment_file, 'rU'),\n delimiter='\\t')\n for row in reader:\n if reader.line_num == 1: #skip header\n continue\n sql = '''INSERT INTO segments\n (id, multiplicon, genome, list, first, last, ord)\n VALUES (?,?,?,?,?,?,?)'''\n self._dbconn.execute(sql, row)\n self._dbconn.commit()","title":""},{"docid":"ae061a546f328c2c24b983a1c488e390","score":"0.46123645","text":"def resolve_media_files(document, resource):\n for field in resource_media_fields(document, resource):\n if isinstance(document[field], list):\n resolved_list = []\n for file_id in document[field]:\n resolved_list.append(resolve_one_media(file_id, resource))\n document[field] = resolved_list\n else:\n document[field] = resolve_one_media(document[field], resource)","title":""},{"docid":"fd94ced41c59f23ab6f175adcf835441","score":"0.46119153","text":"def parse_path(path):\n\n parts = path.split('/')\n\n if parts[1] != 'api' or len(parts) != 5:\n return None\n\n return {'version': parts[2],\n 'resource': parts[3],\n 'action': parts[4]}","title":""},{"docid":"6fcdb8b13e6185138229229b47d7c831","score":"0.46079665","text":"def retrieve_segments(track):\n\n track_npz = seg_npz / (track.stem + '.npz')\n if not track_npz.exists():\n track_segs = utility.load_audio_segments(track)\n track_npz.touch()\n np.savez(track_npz, *track_segs)\n\n return np.load(track_npz)","title":""},{"docid":"76847379b64302b82214b587cdc98232","score":"0.46075633","text":"def split_path(path, minsegs=1, maxsegs=None, rest_with_last=False):\r\n if not maxsegs:\r\n maxsegs = minsegs\r\n if minsegs > maxsegs:\r\n raise ValueError('minsegs > maxsegs: %d > %d' % (minsegs, maxsegs))\r\n if rest_with_last:\r\n segs = path.split('/', maxsegs)\r\n minsegs += 1\r\n maxsegs += 1\r\n count = len(segs)\r\n if (segs[0] or count < minsegs or count > maxsegs or\r\n '' in segs[1:minsegs]):\r\n raise ValueError('Invalid path: %s' % quote(path))\r\n else:\r\n minsegs += 1\r\n maxsegs += 1\r\n segs = path.split('/', maxsegs)\r\n count = len(segs)\r\n if (segs[0] or count < minsegs or count > maxsegs + 1 or\r\n '' in segs[1:minsegs] or\r\n (count == maxsegs + 1 and segs[maxsegs])):\r\n raise ValueError('Invalid path: %s' % quote(path))\r\n segs = segs[1:maxsegs]\r\n segs.extend([None] * (maxsegs - 1 - len(segs)))\r\n return segs","title":""},{"docid":"714d61a238a5e3522668bb4302453d11","score":"0.46074203","text":"def _im_and_segm(self, im_file, segm_dir):\n #pdb.set_trace()\n im = sm.Image(im_file)\n segm_list = []\n file_name = os.path.basename(im_file)\n print file_name\n im_segm = sm.Image(os.path.join(segm_dir, file_name))\n self.segm_post_process(im_segm)\n segm_list.append(im_segm)\n return im, segm_list, file_name","title":""},{"docid":"e086e640eac867545cbc9b16b03484c1","score":"0.46065676","text":"def splitpath(path):\n\n head, tail = os.path.split(path)\n if tail == '':\n return head,\n elif head == '':\n return tail,\n else:\n return splitpath(head) + (tail,)","title":""},{"docid":"092cdaa90c350ff8db9f6ea712fac00a","score":"0.46034354","text":"def SplitPath(self, path):\n # Split the path with the path separator and remove empty path segments.\n return list(filter(None, path.split(os.path.sep)))","title":""},{"docid":"815f8936feeeb6e26864822ce6432fd5","score":"0.46002877","text":"def parse_directory(self):\n files = []\n for x in pathlib.Path(self.path).glob('*.m*'):\n if x.is_file():\n x = str(x) # tinytag needs str, not PosixPath\n files.append(x)\n return files","title":""},{"docid":"8f52fd8764ef8913a15b96f9bb8c8fd5","score":"0.4599362","text":"def parse_ad_group_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/adGroups/(?P<ad_group_id>.+?)$\",\n path,\n )\n return m.groupdict() if m else {}","title":""},{"docid":"04ffbe9bd356afe1f77f53a1d0544cc0","score":"0.45965385","text":"def split_path(path):\n if path in '/':\n return '', ''\n path = relpath(path)\n if '/' not in path:\n return path, ''\n return path.split('/', 1)","title":""},{"docid":"58e4bec1d4010ffc3355dac1a719ccb8","score":"0.45908687","text":"def parse_mime_type(mime_type):\r\n parts = mime_type.split(\";\")\r\n params = dict([tuple([s.strip() for s in param.split(\"=\")])\\\r\n for param in parts[1:] ])\r\n (type, subtype) = parts[0].split(\"/\")\r\n return (type.strip(), subtype.strip(), params)","title":""},{"docid":"f0c5d082d420cf3b8458f59cd533469a","score":"0.45839995","text":"def get_plugin_media_path(instance, filename):\n return instance.get_media_path(filename)","title":""}],"string":"[\n {\n \"docid\": \"9f7445ca524d3ea5bd135aa47a25c27f\",\n \"score\": \"0.5895364\",\n \"text\": \"def split_path(full_path):\\n parsed_list = full_path.split('/')[-2:] # directtory, file_name\\n \\n return parsed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3405777c166d80d631ed05388b7710ab\",\n \"score\": \"0.58513534\",\n \"text\": \"def SplitPath(self, path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daaf27ae5c80ffa70bafe497b98d5f16\",\n \"score\": \"0.5751438\",\n \"text\": \"def get_video_parts(video_path):\\n parts = video_path.split('/')\\n filename = parts[-1]\\n class_label = filename[0:5]\\n seq = filename[5:10]\\n dir = '/'.join(parts[0:-1]) + '/'\\n return filename, class_label, seq, dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e5c857c612cbee538811e9b105f2af\",\n \"score\": \"0.57160467\",\n \"text\": \"def _parse_segments_str(\\n segments_string: str,\\n media_start_time: Optional[int] = 0,\\n media_stop_time: Optional[int] = None\\n ) -> Optional[List[Tuple[int, int]]]:\\n n_early = 0\\n n_late = 0\\n try:\\n segments = []\\n start_stops = segments_string.split(\\\",\\\")\\n for ss in start_stops:\\n start, stop = ss.strip().split(\\\"-\\\")\\n start = int(start)\\n stop = int(stop)\\n\\n # Limit to media start and stop times. If entirely\\n # outside the limits, drop the segment\\n if media_stop_time is not None and start > media_stop_time:\\n n_late += 1\\n continue\\n if stop < media_start_time:\\n n_early += 1\\n continue\\n start = max(start, media_start_time)\\n if media_stop_time is not None:\\n stop = min(stop, media_stop_time)\\n\\n # Offset by media_start_time so that segments are\\n # relative to the transcoded waveform\\n start -= media_start_time\\n stop -= media_start_time\\n\\n segments.append((start, stop))\\n except Exception as e:\\n raise mpf.DetectionException(\\n 'Exception raised while parsing voiced segments '\\n f'\\\"{segments_string}\\\": {e}',\\n mpf.DetectionError.INVALID_PROPERTY\\n )\\n\\n # If all the voiced segments are outside the time range, signal that\\n # we should halt and return an empty list.\\n if not segments:\\n raise NoInBoundsSpeechSegments(n_early, n_late)\\n\\n return sorted(segments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3ee22316bb584da3eebc6e56a55129\",\n \"score\": \"0.5584283\",\n \"text\": \"def get_video_parts(video_path):\\n parts = video_path.split('/')\\n filename = parts[1]\\n filename_no_ext = filename.split('.')[0]\\n classname = parts[0]\\n\\n return classname, filename_no_ext, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9d1478a52685e258124109a7283713\",\n \"score\": \"0.5551553\",\n \"text\": \"def _parse_path_agnostic(filename):\\n filename = filename.replace(\\\"\\\\\\\\\\\", os.sep).replace(\\\"/\\\", os.sep)\\n return os.path.split(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9b205b13bfbbb8feae0e88ec96fe9d\",\n \"score\": \"0.5472871\",\n \"text\": \"def get_video_parts(video_path):\\n parts = video_path.split('/')\\n filename = parts[-1]\\n filename_no_ext = filename\\n classname = parts[-1]\\n\\n return classname, filename_no_ext, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ba2878fc531fc4d9c6fe8cc09d56e1\",\n \"score\": \"0.5443869\",\n \"text\": \"def parse_path(path):\\n\\n result = []\\n split_path = path.split('/')\\n for element in split_path:\\n if element != '':\\n result.append(element)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"067b228b30fbe42e5c4b67590e1c7086\",\n \"score\": \"0.53923386\",\n \"text\": \"def parse(self, filepath):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afef0dd26b9999a307b14c7857661765\",\n \"score\": \"0.5352113\",\n \"text\": \"def parse(cls, path: str) -> List:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0d302732ce54557586ba5d04c4bb5b\",\n \"score\": \"0.53390574\",\n \"text\": \"def divide_video(self):\\r\\n if self.path and self.split_path:\\r\\n segment_size = self.get_parts()\\r\\n print\\r\\n print ('ffmpeg -i %s -c copy -map 0 -segment_time %s -f segment %s' % (self.path, segment_size, self.split_path) + '%03d.mp4')\\r\\n print\\r\\n print\\r\\n print\\r\\n #divide video to parts of the calculated length, NOTE: not every part will be exactly at the same length\\r\\n p = SUB.call('ffmpeg -i %s -c copy -map 0 -segment_time %s -f segment %s' % (self.path, segment_size, self.split_path) + '%03d.mp4')\\r\\n\\r\\n n = 0\\r\\n\\r\\n files = listdir(self.split_path)\\r\\n #gets max part\\r\\n for i in reversed(files):\\r\\n if len(i) >= 3 and i[:3].isdigit():\\r\\n max_part = int(i[:3])\\r\\n break\\r\\n else:\\r\\n max_part = -1\\r\\n\\r\\n while n <= max_part:\\r\\n #convert to audio parts\\r\\n p = SUB.call('ffmpeg -i %s.mp4 %s.wav' % (self.split_path+self.get_part_video_num(n), self.split_path+self.get_part_video_num(n)))\\r\\n n += 1\\r\\n\\r\\n n = 0\\r\\n while n <= max_part:\\r\\n #converts to video parts\\r\\n p = SUB.call('ffmpeg -i %s.mp4 -target ntsc-vcd -vcodec mpeg1video -an %s.mpg' % (self.split_path+self.get_part_video_num(n), self.split_path+self.get_part_video_num(n)))\\r\\n n += 1\\r\\n\\r\\n for f in files:\\r\\n try:\\r\\n #deletes the unnecessary original video parts\\r\\n remove(self.split_path + f)\\r\\n except:\\r\\n pass\\r\\n\\r\\n print 'nehenaknu im gui'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667439f8e50f0ac824bb024e56058572\",\n \"score\": \"0.5306527\",\n \"text\": \"def _split_multipath(pathstr):\\n # m is absolute path, M is relative path (or vice versa?)\\n if not pathstr[0] in [\\\"m\\\",\\\"M\\\"]:\\n raise ValueError(\\\"Bad path format: %s\\\" % pathstr)\\n import re\\n subpaths = [sp for sp in re.split('[Mm]',pathstr) if len(sp)>0]\\n headers = re.findall('[Mm]',pathstr)\\n for subpath,header in zip(subpaths,headers):\\n # Need further parsing of multi-path strings? perhaps no.\\n yield (header + subpath).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae75d0cb47ddad1579f9a3a62236a31c\",\n \"score\": \"0.52950317\",\n \"text\": \"def get_media_url(path):\\n if ck_settings.UPLOAD_PREFIX:\\n url = ck_settings.UPLOAD_PREFIX + path.replace(ck_settings.UPLOAD_PATH, '')\\n else:\\n url = os.path.join(settings.MEDIA_URL, path.replace(ck_settings.MEDIA_ROOT, ''))\\n\\n # Remove multiple forward-slashes from the path portion of the url.\\n # Break url into a list.\\n url_parts = list(urllib.parse.urlparse(url))\\n # Replace two or more slashes with a single slash.\\n url_parts[2] = re.sub('\\\\/+', '/', url_parts[2])\\n # Reconstruct the url.\\n return urllib.parse.urlunparse(url_parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f406d7394633ae25472c0e3490177110\",\n \"score\": \"0.52904373\",\n \"text\": \"def _parse_part(self, part, mimetypes):\\n\\n self._parse_section_three(part, mimetypes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"747df1745e9302728538aac32172ca22\",\n \"score\": \"0.5220871\",\n \"text\": \"def parse_full_path(self, full_path):\\n parsed_full_path = {'filename': os.path.basename(full_path)}\\n return parsed_full_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009746872aeb480afbb1a6854596300d\",\n \"score\": \"0.51812714\",\n \"text\": \"def make_media_file_list(media):\\n \\n items = []\\n\\n for item in media:\\n if isinstance(item, list):\\n #print item[0]\\n fname = os.path.basename(item[0])\\n #print fname\\n items.append(fname)\\n else:\\n fname = os.path.basename(item)\\n #print fname\\n items.append(fname)\\n \\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b78823f0ec6fd346e157c235ab074a5\",\n \"score\": \"0.5176828\",\n \"text\": \"def get_file_parts(path) :\\n path,fn = os.path.split(path)\\n basename,ext = os.path.splitext(fn)\\n return path,fn,basename,ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b323a64607a890dd681761eef07762c4\",\n \"score\": \"0.51637036\",\n \"text\": \"def parse(cls, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31706697c83c53a0d91fe2050cd1168f\",\n \"score\": \"0.51542807\",\n \"text\": \"def SplitPath(self, path, qualified=False):\\n # type: (str, bool) -> list\\n parsed_path = self.hdfs.parse_uri(path)\\n # split the path with the path separator and remove empty path segments.\\n path_segments = list(filter(None, parsed_path.path.split(self.PATH_SEPARATOR)))\\n # if qualified then the first segment is HDFS URI\\n if qualified:\\n path_segments.insert(0, self.hdfs.make_uri())\\n return path_segments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba74023ff99d5b0ae8aa590126caf4ff\",\n \"score\": \"0.51386625\",\n \"text\": \"def __parse(self, dirpath):\\n if not dirpath or not isinstance(dirpath, basestring):\\n raise ValueError(\\n 'path is not defined or not a string. path: {}'.format(\\n dirpath))\\n if not os.path.exists(dirpath):\\n raise ValueError('path does not exist! path: {}'.format(dirpath))\\n\\n allparts = []\\n # Split 4 parts for build, config, cid, size\\n # TODO: error checks\\n tmp_path = dirpath\\n for i in range(0, 4):\\n parts = os.path.split(tmp_path)\\n assert len(\\n parts) == 2, \\\\\\n \\\"Not enough parts to the path! parts={}, dirpath={}\\\".format(\\n parts, dirpath)\\n tmp_path = parts[0]\\n allparts.insert(0, parts[1])\\n\\n self.__build = allparts[0]\\n self.config = allparts[1]\\n self.cid = str(allparts[2])\\n self.size = allparts[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed1033701a7f407a26a8af0f1b72ed7\",\n \"score\": \"0.51082796\",\n \"text\": \"def parse_url(self, url):\\n url = url.replace(self._url, '')\\n url = url.strip('/').lower()\\n url = url.split('/')\\n id = '-'.join(url[:-1])\\n filename = url[-1]\\n return id, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed1033701a7f407a26a8af0f1b72ed7\",\n \"score\": \"0.51082796\",\n \"text\": \"def parse_url(self, url):\\n url = url.replace(self._url, '')\\n url = url.strip('/').lower()\\n url = url.split('/')\\n id = '-'.join(url[:-1])\\n filename = url[-1]\\n return id, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6a960f9e58f26f1b41f00e90d6d800\",\n \"score\": \"0.510161\",\n \"text\": \"def _parse_dest_path(self, path = ''):\\n sep = '/'\\n if path and path[0] == sep:\\n # remove dealing '/'\\n path = path[1:]\\n if not path: return None\\n v = path.split(sep)\\n folderId = \\\"root\\\"\\n mimeType = MIME_TYPE_FOLDER\\n i = 0\\n # iterates through of the path (array v)\\n while i < len(v) and mimeType == MIME_TYPE_FOLDER:\\n filename = v[i]\\n subfolders = self.list_folders(name = filename, parentId = folderId)\\n if subfolders:\\n folderId = subfolders[0]['id']\\n else:\\n if i == len(v) - 1:\\n # e.g. 'path/to/folder/foo.txt', then folderId = ID_OF('path/to/folder/'), \\n # and filename = 'foo.txt'\\n return (folderId, filename)\\n else:\\n # path not found\\n return None\\n i += 1\\n # this is the case 'path/to/folder/', no filename given\\n return (folderId, '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ccd69a11e0bb5709ef8373f66383a60\",\n \"score\": \"0.50961405\",\n \"text\": \"def parse_path(path):\\n\\n path_key = path.split(\\\"/\\\")[0]\\n\\n if path_key == \\\"posts\\\":\\n return PostImporter(path)\\n elif path_key == \\\"pages\\\":\\n return PageImporter(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425f90103303ec8462b2751ca9ce8355\",\n \"score\": \"0.50941086\",\n \"text\": \"def parse_audience_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/audiences/(?P<audience_id>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871a5a4072527452f72447e0d9b66420\",\n \"score\": \"0.5088982\",\n \"text\": \"def explode_path(self, path):\\n parts = []\\n head = path\\n while head:\\n # /foo/bar/baz => head=/foo/bar, tail=baz\\n head, tail = os.path.split(head)\\n parts.append(tail)\\n return reversed(parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31eac2a741f55d733144cca3d85fc2c4\",\n \"score\": \"0.5079133\",\n \"text\": \"def _split(self, file):\\r\\n\\r\\n ext_index = str(file).rindex('.')\\r\\n file_name = file[:ext_index] # get the file name from the full path\\r\\n extension = file[ext_index + 1:] # get the extension from the full path\\r\\n\\r\\n # extract time of all matched points\\r\\n time_series = [int(row[1] / 1000) for row in self.stop_points]\\r\\n time_series = [0] + time_series + [int(self.length / 1000)]\\r\\n print('time_series:', time_series)\\r\\n\\r\\n # split the source audio\\r\\n for i in range(1, len(time_series)):\\r\\n print('from', time_series[i - 1], 'to', time_series[i])\\r\\n\\r\\n cmd = 'ffmpeg -i {} -acodec copy -ss {} -to {} {}_{}.{}'.format(file, time_series[i - 1], time_series[i],\\r\\n file_name, i, extension)\\r\\n\\r\\n print(cmd)\\r\\n\\r\\n proc = subprocess.Popen(cmd, shell=True)\\r\\n proc.communicate()\\r\\n print('convert is done...')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4491d85987211fea83c8878cfd796f85\",\n \"score\": \"0.50746155\",\n \"text\": \"def _get_path_parts(self):\\n dn_str = self._remove_format_from_path(self.path, self.api_format)\\n return dn_str[1:].split(\\\"/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7b50905f406e89cb27216db03f4317\",\n \"score\": \"0.50738245\",\n \"text\": \"def parse_media(self, tokens):\\n mediaquery_tokens = [f for f in tokens if f.type == 'IDENT']\\n return mediaquery_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cd8303bef843639beeb682b1ee3b45\",\n \"score\": \"0.5000593\",\n \"text\": \"def parse_asset_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/assets/(?P<asset_id>.+?)$\\\", path\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec92281a239ad766f3bbf4f3f057665\",\n \"score\": \"0.49627233\",\n \"text\": \"def resolv_path(self, path):\\n itemx = self.struct_list\\n for i in path:\\n itemx = itemx[i]\\n return itemx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b75873003a8627e0e27f163733b45d2\",\n \"score\": \"0.492974\",\n \"text\": \"def parse_file(self, path):\\n with open(path) as file:\\n return [line.strip() for line in file]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f2d4d55190c544d663fe744615d924\",\n \"score\": \"0.4928018\",\n \"text\": \"def readSegments( fileName ):\\n f = open( fileName, 'r' )\\n count = int( f.readline() )\\n print \\\"Files has %d segments\\\" % ( count )\\n segments = []\\n for line in f.xreadlines():\\n line = line.strip()\\n if ( line ):\\n print line.strip()\\n x1, y1, x2, y2 = map( lambda x: float(x), line.strip().split() )\\n segments.append( Segment( Vector3( x1, 0, y1 ), Vector3( x2, 0, y2 ) ) )\\n f.close()\\n return segments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7292f66d8a595805fbf73bbca1f47d95\",\n \"score\": \"0.49179754\",\n \"text\": \"def params_in_path(self):\\n # type: () -> List\\n return [part[1:] for part in self.path.split('/') if part.startswith(':')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee09a20ff89e03615e976deee670c28\",\n \"score\": \"0.49050176\",\n \"text\": \"def parse_ad_group_asset_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/adGroupAssets/(?P<ad_group_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912763bdef029431fc34f379afdf15bb\",\n \"score\": \"0.4899388\",\n \"text\": \"def split_path(path):\\n\\n def split(src):\\n \\\"\\\"\\\"Core function to split path.\\n \\\"\\\"\\\"\\n if \\\"/\\\" in src:\\n return src.rsplit(\\\"/\\\", maxsplit=1)\\n else:\\n # Interpret as path to file in current directory.\\n return \\\"./\\\", src\\n\\n if isinstance(path, sb.pretrained.fetching.FetchSource):\\n fetch_from, fetch_path = path\\n source, filename = split(fetch_path)\\n return sb.pretrained.fetching.FetchSource(fetch_from, source), filename\\n else:\\n return split(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c137998688eefb1903a6f3170a46fead\",\n \"score\": \"0.48934528\",\n \"text\": \"def parse_asset_group_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/assetGroups/(?P<asset_group_id>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9757a0db7c7890567adc644ff96a1eb3\",\n \"score\": \"0.48931915\",\n \"text\": \"def parse_file(self, file_path):\\n pass # pragma: nocover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d300a1f083315523010aff40a75834e3\",\n \"score\": \"0.48873222\",\n \"text\": \"def _parse_file(self, file_path):\\n pass # pragma: nocover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13288de9f91d0f1eea4434bceafd5063\",\n \"score\": \"0.48766533\",\n \"text\": \"def parse_file(self, path):\\n self.logger.debug(\\\"Parsing SVD file %s\\\", path)\\n\\n with open(path, \\\"r\\\") as svd_file:\\n svd = svd_file.read()\\n return self.parse(svd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72fe351adcfa72fa58adec81e6683524\",\n \"score\": \"0.48709342\",\n \"text\": \"def _split(self):\\n if self.is_abspath:\\n return self._value[1:].split(self.fs.sep)\\n return self._value.split(self.fs.sep)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424b5ceacffe982f1f0290f9611d6c7c\",\n \"score\": \"0.48600677\",\n \"text\": \"def __file_parts__(self,file):\\n parts = {\\n 'path':'',\\n 'name':'',\\n 'ext':'',\\n 'file':'',\\n 'full_name':'',\\n 'full_path':''\\n }\\n if not file == \\\"\\\" and not file == None:\\n parts['path'],parts['full_name'] = os.path.split(file)\\n parts['name'],parts['ext'] = parts['full_name'].split('.')\\n if not parts['path']:\\n parts['path'] = './'\\n parts['full_path'] = parts['path']+'/'+parts['name']+'.'+parts['ext']\\n else:\\n parts['path'] = ''\\n parts['name'] = ''\\n parts['ext'] = ''\\n parts['file'] = ''\\n parts['full_path'] = ''\\n\\n return parts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f309d66f5daa88e8142525a0f77f5484\",\n \"score\": \"0.4854577\",\n \"text\": \"def part_parse(self):\\n parts = []\\n for part in re.split('\\\\*\\\\*\\\\* ([A-Z- ]+) \\\\*\\\\*\\\\*', self.hand_file): # return [ 'part1', 'splitter1', 'part2',..\\n parts.append(part)\\n\\n for i in range(0, len(parts)):\\n if i == 0:\\n self.part_dict['HEADER'] = parts[i].split('\\\\n')\\n if i % 2 != 0: # number is odd\\n # print(type(parts[i]))\\n self.part_dict[parts[i]] = parts[i+1].split('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60f684ab8a01895a19cc4deb1fb1e82a\",\n \"score\": \"0.48466882\",\n \"text\": \"def parse_asset_group_asset_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/assetGroupAssets/(?P<asset_group_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c1ed092dae14b6dc19400a6fb30d9e\",\n \"score\": \"0.48438734\",\n \"text\": \"def splitpath(path):\\n # type: (bytes) -> List[bytes]\\n return path.split(pycompat.ossep)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c4bf3c35e4c059498f17ee9a8273ac\",\n \"score\": \"0.4841555\",\n \"text\": \"def _split_path(self, path: str) -> Tuple[str, str]:\\n parts = path.split(\\\".\\\")\\n coll = parts.pop(0)\\n rest = \\\".\\\".join(parts)\\n return coll, rest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a4dcdee73b1e93a4005c200ba45cd9\",\n \"score\": \"0.4835787\",\n \"text\": \"def test_parse_path():\\n\\n expected_hosts = ['127.0.0.1']\\n expected_services = ['45589']\\n expected_notes = ['screenshot_web']\\n\\n pidb = ParserModule.parse_path('tests/server/data/parser-screenshot_web-job.zip')\\n\\n assert [x.address for x in pidb.hosts] == expected_hosts\\n assert [x.port for x in pidb.services] == expected_services\\n assert [x.xtype for x in pidb.notes] == expected_notes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1dc3aef3880e746a9478211f9ba9c62\",\n \"score\": \"0.48354855\",\n \"text\": \"def divide_pathstring_parts(pathstring):\\n substring = pathstring.strip()\\n paths = []\\n while 'M' in substring[1:]:\\n m_index = substring.find('M', 1)\\n if m_index > -1:\\n subpath = substring[0:m_index].strip()\\n paths.append(subpath)\\n substring = substring[m_index:].strip()\\n\\n paths.append(substring)\\n return paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c72d352f9c9219dfb769dce356fd03\",\n \"score\": \"0.4830553\",\n \"text\": \"def get_media_url(mid):\\n return '%s%s' % (config.get('media_files_root'),mid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac117c2a230a2b3e77872f847fc99089\",\n \"score\": \"0.4827614\",\n \"text\": \"def parse_uri(_, uri):\\n uri = str(uri)\\n if uri.startswith('file://'):\\n uri = uri[len('file://'):]\\n return Path(uri).resolve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb0ba7790fffefdaba8caf9da40c020\",\n \"score\": \"0.4827574\",\n \"text\": \"def get_mp4_file_path_and_drive_name(h264_filepath):\\n # Splits by '.' to get path before extension\\n h264_filepath_split_no_ext = h264_filepath.split('.')\\n mp4_filepath = h264_filepath_split_no_ext[0] + '.mp4'\\n\\n # Splits by '/' to get filename at end of path for storage in Drive\\n h264_filepath_split_no_slashes = h264_filepath_split_no_ext[0].split('/')\\n drive_file_name = h264_filepath_split_no_slashes[len(h264_filepath_split_no_slashes) - 1] + '.mp4'\\n\\n return mp4_filepath, drive_file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b2b3c758d564f22bad655b4fb64330\",\n \"score\": \"0.48193255\",\n \"text\": \"def get_missing_media(self):\\n self.make_segment_dir()\\n media = self.retrieve_chunklist_manifest()\\n for m in media:\\n renamed = self.rename_media(m)\\n if os.path.isfile(self.segmentDir + '/' + renamed) == False:\\n print('INFO: ' + self.segmentDir + '/' + renamed + ' file does not exist')\\n status = self.retrieve_media_segment(m, renamed)\\n if status:\\n self.segments += m # Add to segment list\\n self.renamedSegments += renamed # Add to other segment list\\n if self.verbose:\\n print('INFO: Got new media segment \\\\t\\\\t' + m)\\n else:\\n if self.verbose:\\n print('WARN: Failed to get media segment \\\\t' + m)\\n else:\\n if self.verbose:\\n print('INFO: Segment file ' + m + ' already downloaded, skipping...')\\n if self.verbose:\\n sdSize = sum(d.stat().st_size for d in os.scandir(self.segmentDir + '/') if d.is_file())\\n print('INFO: Segment directory is now at ' + str(sdSize) + ' bytes (' + str(round(sdSize/1000000, 2)) + ' megabytes)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d17f08ae805e98e8aa00a6dd0c19b8d\",\n \"score\": \"0.48173803\",\n \"text\": \"def format_segment(record, indir='', force_input_dir='', ):\\n regex = re.compile(indir)\\n file_loc = regex.sub(force_input_dir or indir,\\n record['FILE_LOC'])\\n logger.debug('Search for file location: %s', file_loc)\\n filenames = glob.glob(file_loc)\\n if filenames:\\n return {k: (v if k != 'FILE_LOC' else filenames[0])\\n for k, v in record.items()}\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954dc0c15e9788913822df5d8ed57732\",\n \"score\": \"0.48143667\",\n \"text\": \"def read_file(path_segments):\\n file_path = os.path.join(here, *path_segments)\\n with open(file_path) as f:\\n return f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012d693c2472b1425ceba5c831fe8757\",\n \"score\": \"0.48121879\",\n \"text\": \"def convert_media_path(html):\\n lst = list()\\n mcss = re.findall('href=\\\"(\\\\S+?\\\\.css)\\\"', html)\\n lst.extend(list(set(mcss)))\\n mjs = re.findall('src=\\\"([\\\\w\\\\./]\\\\S+?\\\\.js)\\\"', html)\\n lst.extend(list(set(mjs)))\\n msrc = re.findall('<img.*?src=\\\"([\\\\w\\\\./]\\\\S+?)\\\".*?>', html)\\n lst.extend(list(set(msrc)))\\n # print lst\\n newlist = ['_' + each.split('/')[-1] for each in lst]\\n # print newlist\\n for each in zip(lst, newlist):\\n html = html.replace(each[0], each[1])\\n return unicode(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb1357b96aeaf1f6f4f4ef4c8837309\",\n \"score\": \"0.4811636\",\n \"text\": \"def split_video(self, path):\\r\\n vidObj = cv2.VideoCapture(path)\\r\\n images = []\\r\\n\\r\\n # Used as counter variable\\r\\n count = 0\\r\\n\\r\\n # checks whether frames were extracted\\r\\n success = 1\\r\\n\\r\\n while success:\\r\\n # vidObj object calls read\\r\\n # function extract frames\\r\\n success, image = vidObj.read()\\r\\n\\r\\n # Saves the frames with frame-count\\r\\n # cv2.imwrite(\\\"frame%d.jpg\\\" % count, image)\\r\\n images.append(image)\\r\\n count += 1\\r\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9367d762d8b2859ddc9cfb5f2edaa71d\",\n \"score\": \"0.4803748\",\n \"text\": \"def test_parse_filepath():\\n # Given\\n path = '/path/to/file.nii.gz'\\n # When\\n dirname, basename, ext = parse_filepath(path)\\n # Then\\n assert [dirname, basename, ext] == ['/path/to', 'file', 'nii.gz']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b0b14e1e5314eb5318c65fc9802e9fb\",\n \"score\": \"0.47905982\",\n \"text\": \"def get_video_parts(video_path):\\n parts = video_path.split(os.path.sep)\\n filename = parts[2]\\n filename_no_ext = filename.split('.')[0]\\n classname = parts[1]\\n train_or_test = parts[0]\\n\\n return train_or_test, classname, filename_no_ext, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2354c46a47cb9233f6ed9d3636b8b1\",\n \"score\": \"0.4785013\",\n \"text\": \"def parse_module_path(module_path):\\n aslist = module_path.split(':', 1)\\n if len(aslist) == 1:\\n return (aslist[0], [])\\n else:\\n return (aslist[0], aslist[1].split(','))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d1547bb401b417f11e9eda018a3d22\",\n \"score\": \"0.47697225\",\n \"text\": \"def getSegments(self) -> List[ghidra.app.util.bin.format.omf.OmfSegmentHeader]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf1a464f041c79bd964dced1c8dc039\",\n \"score\": \"0.47663137\",\n \"text\": \"def parse(filepath: str):\\n paths, _, svg_attributes_dict = svg.svg2paths2(filepath)\\n setsofsubpaths = [p.continuous_subpaths() for p in paths]\\n paths = itertools.chain.from_iterable(setsofsubpaths)\\n\\n svg_attributes = SvgAttributes(svg_attributes_dict)\\n if svg_attributes.is_portrait:\\n return [SVGPath(path, svg_attributes.scale_factor) for path in paths]\\n else:\\n return [SVGPathRotatedBy90Degrees(path, svg_attributes.scale_factor, svg_attributes.height) for path in paths]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520b966c488691321f3197c16bb8788a\",\n \"score\": \"0.47521687\",\n \"text\": \"def parse(cls, path: str) -> List[QuoteModel]:\\n ext = path.split(\\\".\\\")[-1]\\n # First try-except block to catch any exceptions w.r.t.\\n # invalid extension.\\n try:\\n cls.validate_extension(ext)\\n except InvalidExtensionError as inv_ext_err:\\n print(inv_ext_err)\\n else:\\n # Second try-except block to catch any exceptions\\n # while parsing the file.\\n try:\\n for ingestor in cls.ingestors:\\n if ingestor.can_ingest(path):\\n return ingestor.parse(path)\\n except Exception:\\n print(f\\\"ParsingError: Error occured while parsing {path}.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8563d693422f86c87b323daf8436f459\",\n \"score\": \"0.47483987\",\n \"text\": \"def get_segments(self, boundaries, audio_file, before_margin=0.1, after_margin=0.1):\\n sample_rate, sig_len = self._get_audio_info(audio_file)\\n if sample_rate != self.sample_rate:\\n raise ValueError('The detected sample rate is different from that set in the hparam file')\\n segments = []\\n for i in range(boundaries.shape[0]):\\n beg_sample = boundaries[i, 0] * sample_rate\\n end_sample = boundaries[i, 1] * sample_rate\\n beg_sample = int(max(0, beg_sample - before_margin * sample_rate))\\n end_sample = int(min(sig_len, end_sample + after_margin * sample_rate))\\n len_seg = end_sample - beg_sample\\n vad_segment, fs = torchaudio.load(audio_file, frame_offset=beg_sample, num_frames=len_seg)\\n segments.append(vad_segment)\\n return segments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b29d6ae12ca3b9326ffc6bfa92b315\",\n \"score\": \"0.47450432\",\n \"text\": \"def _ParseSCCAFile(self, parser_mediator, scca_file):\\n format_version = scca_file.format_version\\n executable_filename = scca_file.executable_filename\\n prefetch_hash = scca_file.prefetch_hash\\n run_count = scca_file.run_count\\n number_of_volumes = scca_file.number_of_volumes\\n\\n volume_serial_numbers = []\\n volume_device_paths = []\\n path_hints = []\\n\\n for volume_information in iter(scca_file.volumes):\\n volume_serial_number = volume_information.serial_number\\n volume_device_path = volume_information.device_path\\n\\n volume_serial_numbers.append(volume_serial_number)\\n volume_device_paths.append(volume_device_path)\\n\\n timestamp = volume_information.get_creation_time_as_integer()\\n if timestamp:\\n event_data = windows_events.WindowsVolumeEventData()\\n event_data.creation_time = dfdatetime_filetime.Filetime(\\n timestamp=timestamp)\\n event_data.device_path = volume_device_path\\n event_data.origin = parser_mediator.GetFilename()\\n event_data.serial_number = volume_serial_number\\n\\n parser_mediator.ProduceEventData(event_data)\\n\\n for filename in iter(scca_file.filenames):\\n if not filename:\\n continue\\n\\n if (filename.startswith(volume_device_path) and\\n filename.endswith(executable_filename)):\\n _, _, path = filename.partition(volume_device_path)\\n path_hints.append(path)\\n\\n mapped_files = []\\n for entry_index, file_metrics in enumerate(scca_file.file_metrics_entries):\\n mapped_file_string = file_metrics.filename\\n if not mapped_file_string:\\n parser_mediator.ProduceExtractionWarning(\\n 'missing filename for file metrics entry: {0:d}'.format(\\n entry_index))\\n continue\\n\\n file_reference = file_metrics.file_reference\\n if file_reference:\\n mapped_file_string = (\\n '{0:s} [{1:d}-{2:d}]').format(\\n mapped_file_string, file_reference & 0xffffffffffff,\\n file_reference >> 48)\\n\\n mapped_files.append(mapped_file_string)\\n\\n event_data = WinPrefetchExecutionEventData()\\n event_data.executable = executable_filename\\n event_data.mapped_files = mapped_files\\n event_data.number_of_volumes = number_of_volumes\\n event_data.path_hints = path_hints\\n event_data.prefetch_hash = prefetch_hash\\n event_data.run_count = run_count\\n event_data.version = format_version\\n event_data.volume_device_paths = volume_device_paths\\n event_data.volume_serial_numbers = volume_serial_numbers\\n\\n timestamp = scca_file.get_last_run_time_as_integer(0)\\n if timestamp:\\n event_data.last_run_time = dfdatetime_filetime.Filetime(\\n timestamp=timestamp)\\n\\n # Check for the 7 older last run time values available since\\n # format version 26.\\n if format_version >= 26:\\n previous_run_times = []\\n for last_run_time_index in range(1, 8):\\n timestamp = scca_file.get_last_run_time_as_integer(last_run_time_index)\\n if timestamp:\\n date_time = dfdatetime_filetime.Filetime(timestamp=timestamp)\\n previous_run_times.append(date_time)\\n\\n if previous_run_times:\\n event_data.previous_run_times = previous_run_times\\n\\n parser_mediator.ProduceEventData(event_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078b0965961de34af3dcbf38bb091cb5\",\n \"score\": \"0.47448736\",\n \"text\": \"def parse(line):\\n try:\\n url_start = line.index(\\\" URL:\\\") + 5\\n url_end = line.index(\\\" [\\\", url_start)\\n url = line[url_start:url_end]\\n filepath_start = line.index('-> \\\"') + 4\\n filepath_end = line.index('\\\" ', filepath_start)\\n filepath = line[filepath_start:filepath_end]\\n return (url, filepath)\\n except ValueError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31209e0164f73d988d1a293455509995\",\n \"score\": \"0.47420913\",\n \"text\": \"def parse_path(path):\\n \\n base_name = os.path.splitext( os.path.basename(path) )[0] \\n splits = base_name.split(\\\"_\\\")\\n \\n signature = splits[0]\\n count = splits[2] \\n good = splits[1] == \\\"g\\\"\\n \\n return signature, count, good\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40c47a119791e6683dd7a578e11ae9d\",\n \"score\": \"0.47409058\",\n \"text\": \"def __parse_files(self):\\n parsed_files = []\\n fractures = []\\n total_length = 0\\n for file in self.files:\\n file_length = file[b'length']\\n file_path = file[b'path']\\n if b'md5sum' in file:\\n file_md5sum = file[b'md5sum']\\n parsed_files.append({b'length': file_length, b'path': file_path, b'md5sum': file_md5sum})\\n else:\\n parsed_files.append({b'length': file_length, b'path': file_path})\\n\\n total_length += file_length\\n fractures.append(total_length)\\n # print(total_length)\\n\\n return parsed_files, total_length, fractures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6e917068495833d48625feffa531faa\",\n \"score\": \"0.47385797\",\n \"text\": \"def split(src):\\n if \\\"/\\\" in src:\\n return src.rsplit(\\\"/\\\", maxsplit=1)\\n else:\\n # Interpret as path to file in current directory.\\n return \\\"./\\\", src\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf2590b941bc9d86a343d83971c01f0\",\n \"score\": \"0.47325078\",\n \"text\": \"def splitPath(self, path):\\n split = path.split(',')\\n # Strip the individual items of white space and\\n # values given after '=' sign.\\n for item in split:\\n item_index = split.index(item)\\n item = item.strip()\\n eq_index = item.find('=')\\n if eq_index != -1:\\n item = item[:eq_index+1]\\n split[item_index] = item\\n return split\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea229d20d97d78560f9a25f0349a777f\",\n \"score\": \"0.47318658\",\n \"text\": \"def split(input_file):\\n frames = []\\n wave = thinkdsp.read_wave(input_file)\\n\\n # Some values that we might need\\n totframes = len(wave.ts)\\n framerate = wave.framerate\\n length = totframes / framerate\\n framelength = samples_per_frame / framerate\\n numframes = int(length / framelength)\\n\\n # print('Framelength:', framelength, 'seconds')\\n # print('Frames to calcuate:', numframes)\\n\\n for index in range(numframes):\\n # print(index)\\n currentstart = index * framelength\\n frames.append(wave.segment(start=currentstart, duration=framelength))\\n\\n return frames, framelength\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ca521e4f80aa5faed7e56931188d6c\",\n \"score\": \"0.47306952\",\n \"text\": \"def parse_uri_path(self, path):\\n options = {}\\n db, *_ = path[1:].split(\\\"/\\\")\\n if db:\\n options[\\\"db\\\"] = db\\n return options\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b705e847f1bdeef28d0ba758c0f4d025\",\n \"score\": \"0.47251388\",\n \"text\": \"def parse_simout_filepath(filepath):\\n split = filepath.split(\\\"/\\\")\\n run = split[-2]\\n opt = split[-3]\\n benchmark = split[-4]\\n cache_size = split[-5]\\n fname = split[-1]\\n slice_no = re.search(\\\"(?<=slice.)[0-9]+\\\", fname).group(0)\\n return (benchmark, opt, run, slice_no, cache_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2089bd28f8c025c409b0ef36117c04\",\n \"score\": \"0.47226387\",\n \"text\": \"def parse_campaign_asset_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/campaignAssets/(?P<campaign_id>.+?)~(?P<asset_id>.+?)~(?P<field_type>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b688fc24a48f0fbdc32a5162897c6626\",\n \"score\": \"0.47190553\",\n \"text\": \"def split(self, path: AnyStr) -> Tuple[AnyStr, AnyStr]:\\n return self.filesystem.splitpath(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493c01dad3982c90d1913097bf7ef24f\",\n \"score\": \"0.47188506\",\n \"text\": \"def parse(self, message):\\n messages = message.split(' ')\\n counter = 0\\n segment = ''\\n segments = []\\n while counter < len(messages):\\n counter += 1\\n l_s = len(segment)\\n chunk = messages[counter - 1]\\n segment = chunk if l_s == 0 else '{} {}'.format(segment, chunk)\\n if (len(messages[counter]) + l_s) >= lcd_columns:\\n # Save string segment and reset for next loop\\n segments.append(segment + self.ext(lcd_columns - l_s))\\n segment = ''\\n # Append final segment:\\n segments.append(segment + self.ext(lcd_columns - l_s))\\n return segments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dd2ae293dde22a78f83c4855a64804\",\n \"score\": \"0.471839\",\n \"text\": \"def split_chapters(paths):\\n filename = glob.glob(paths[0] + '/*')[0]\\n completed_process = subprocess.run(f'ffprobe -i \\\"{filename}\\\" -print_format json -show_chapters -loglevel error', stdout=subprocess.PIPE, shell=True)\\n chapter_info = json.loads(completed_process.stdout)['chapters']\\n if not chapter_info:\\n print('Chapters Not Found')\\n rmdir(paths[1])\\n else:\\n _split_file(filename, [(float(entry['start_time']), float(entry['end_time']), join(paths[1], entry['tags']['title'] + '.mp3')) for entry in chapter_info])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5352ba0d952787236e8338cf830520c2\",\n \"score\": \"0.47076356\",\n \"text\": \"def _parse(self, file_path):\\n print('Reading input file...', end='\\\\r')\\n extension = _get_file_extension(file_path)\\n parser = self.get_parser(extension)\\n parsed_data = _cast(parser(file_path))\\n return parsed_data, extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aca0d285703ee00fe8a8892ab5b564d\",\n \"score\": \"0.470492\",\n \"text\": \"def _split_path(path, seps=PATH_SEPS):\\n if not path:\\n return []\\n\\n for sep in seps:\\n if sep in path:\\n if path == sep: # Special case, '/' or '.' only.\\n return ['']\\n return [x for x in path.split(sep) if x]\\n\\n return [path]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b203fc65ae18966baedf2eb2cc0b17\",\n \"score\": \"0.47011355\",\n \"text\": \"def split_path(path):\\n base, has_ext = split(path)\\n name, ext = splitext(has_ext)\\n return base, name, ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f68199bab8d8d295ff1515f3389bd50\",\n \"score\": \"0.47005865\",\n \"text\": \"def parse_segs(fid, numseg):\\n # temporary storage array\\n D = []\\n\\n # answer array\\n R = []\\n\\n for idx in range(numseg+1):\\n aline = fid.readline()\\n D.append(parse_segs_data(aline))\\n if D[idx][0] == -3:\\n R.append((1, D[idx][3], pathlength(D, D[idx][2], D[idx][1])))\\n if D[idx][0] == -4:\\n R.append((0, D[idx][3], pathlength(D, D[idx][2], D[idx][1])))\\n \\n return R\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936c078b4657b508328262235237a289\",\n \"score\": \"0.4697543\",\n \"text\": \"def parse_parts(self, parts):\\n _, _, parsed = super().parse_parts(parts)\\n if len(parsed) > 0 and parsed[0] == \\\"/\\\":\\n parsed = parsed[1:]\\n return \\\"\\\", \\\"\\\", parsed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6411dbf75709116cfaa5c595edbc4702\",\n \"score\": \"0.46675193\",\n \"text\": \"def parse(self, filename, encoding=None, href=None, media=None):\\r\\n if not encoding:\\r\\n encoding = 'css'\\r\\n return self.parseString(codecs.open(filename, 'r', encoding).read(),\\r\\n href=href, media=media)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097fc681c898727f8e726839dee3e7e9\",\n \"score\": \"0.46597257\",\n \"text\": \"def parse_metadata_from_path(path):\\n\\n re_name_split = r'.*/newspapers/newspapers/fin/(.{4})/(.{7,9})/.{7,9}\\\\_.{4}\\\\-(..)\\\\-(..)'\\n\\n split = re.search(re_name_split, path)\\n\\n year, issn, month, day = split.groups()\\n return year, month, day, issn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76c58f193d47b4fd8c15682555bdcb4\",\n \"score\": \"0.4659066\",\n \"text\": \"def get_audio_seg_filenames(audio_dir, segment_dir, p=None):\\n\\ttemp_filenames = [i for i in sorted(os.listdir(audio_dir)) if \\\\\\n\\t\\t\\t_is_audio_file(i)]\\n\\taudio_filenames = [os.path.join(audio_dir, i) for i in temp_filenames]\\n\\ttemp_filenames = [i[:-4] + '.txt' for i in temp_filenames]\\n\\tseg_filenames = [os.path.join(segment_dir, i) for i in temp_filenames]\\n\\treturn audio_filenames, seg_filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28f247b7cae0d52d8b88d2772c77fa3\",\n \"score\": \"0.46307933\",\n \"text\": \"def parse_files(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c30eb1438efb2b40c0763677d765c08\",\n \"score\": \"0.4624152\",\n \"text\": \"def importable_paths(path):\\n chunks = [chunk\\n for chunk in path.split(\\\"/\\\")\\n if chunk and (chunk[0].isalpha() or chunk[0] == \\\"_\\\")]\\n if chunks:\\n chunks[-1] = chunks[-1].rsplit('.', 1)[0]\\n return chunks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0affbf620567de141e06935021695a\",\n \"score\": \"0.46221396\",\n \"text\": \"def fetch_media(song):\\n return song.get('media')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7fb2c2005ab053c18810bda2ea114a\",\n \"score\": \"0.46147782\",\n \"text\": \"def _parse_segments(self):\\n reader = csv.reader(open(self._segment_file, 'rU'),\\n delimiter='\\\\t')\\n for row in reader:\\n if reader.line_num == 1: #skip header\\n continue\\n sql = '''INSERT INTO segments\\n (id, multiplicon, genome, list, first, last, ord)\\n VALUES (?,?,?,?,?,?,?)'''\\n self._dbconn.execute(sql, row)\\n self._dbconn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae061a546f328c2c24b983a1c488e390\",\n \"score\": \"0.46123645\",\n \"text\": \"def resolve_media_files(document, resource):\\n for field in resource_media_fields(document, resource):\\n if isinstance(document[field], list):\\n resolved_list = []\\n for file_id in document[field]:\\n resolved_list.append(resolve_one_media(file_id, resource))\\n document[field] = resolved_list\\n else:\\n document[field] = resolve_one_media(document[field], resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd94ced41c59f23ab6f175adcf835441\",\n \"score\": \"0.46119153\",\n \"text\": \"def parse_path(path):\\n\\n parts = path.split('/')\\n\\n if parts[1] != 'api' or len(parts) != 5:\\n return None\\n\\n return {'version': parts[2],\\n 'resource': parts[3],\\n 'action': parts[4]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fcdb8b13e6185138229229b47d7c831\",\n \"score\": \"0.46079665\",\n \"text\": \"def retrieve_segments(track):\\n\\n track_npz = seg_npz / (track.stem + '.npz')\\n if not track_npz.exists():\\n track_segs = utility.load_audio_segments(track)\\n track_npz.touch()\\n np.savez(track_npz, *track_segs)\\n\\n return np.load(track_npz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76847379b64302b82214b587cdc98232\",\n \"score\": \"0.46075633\",\n \"text\": \"def split_path(path, minsegs=1, maxsegs=None, rest_with_last=False):\\r\\n if not maxsegs:\\r\\n maxsegs = minsegs\\r\\n if minsegs > maxsegs:\\r\\n raise ValueError('minsegs > maxsegs: %d > %d' % (minsegs, maxsegs))\\r\\n if rest_with_last:\\r\\n segs = path.split('/', maxsegs)\\r\\n minsegs += 1\\r\\n maxsegs += 1\\r\\n count = len(segs)\\r\\n if (segs[0] or count < minsegs or count > maxsegs or\\r\\n '' in segs[1:minsegs]):\\r\\n raise ValueError('Invalid path: %s' % quote(path))\\r\\n else:\\r\\n minsegs += 1\\r\\n maxsegs += 1\\r\\n segs = path.split('/', maxsegs)\\r\\n count = len(segs)\\r\\n if (segs[0] or count < minsegs or count > maxsegs + 1 or\\r\\n '' in segs[1:minsegs] or\\r\\n (count == maxsegs + 1 and segs[maxsegs])):\\r\\n raise ValueError('Invalid path: %s' % quote(path))\\r\\n segs = segs[1:maxsegs]\\r\\n segs.extend([None] * (maxsegs - 1 - len(segs)))\\r\\n return segs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714d61a238a5e3522668bb4302453d11\",\n \"score\": \"0.46074203\",\n \"text\": \"def _im_and_segm(self, im_file, segm_dir):\\n #pdb.set_trace()\\n im = sm.Image(im_file)\\n segm_list = []\\n file_name = os.path.basename(im_file)\\n print file_name\\n im_segm = sm.Image(os.path.join(segm_dir, file_name))\\n self.segm_post_process(im_segm)\\n segm_list.append(im_segm)\\n return im, segm_list, file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e086e640eac867545cbc9b16b03484c1\",\n \"score\": \"0.46065676\",\n \"text\": \"def splitpath(path):\\n\\n head, tail = os.path.split(path)\\n if tail == '':\\n return head,\\n elif head == '':\\n return tail,\\n else:\\n return splitpath(head) + (tail,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"092cdaa90c350ff8db9f6ea712fac00a\",\n \"score\": \"0.46034354\",\n \"text\": \"def SplitPath(self, path):\\n # Split the path with the path separator and remove empty path segments.\\n return list(filter(None, path.split(os.path.sep)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815f8936feeeb6e26864822ce6432fd5\",\n \"score\": \"0.46002877\",\n \"text\": \"def parse_directory(self):\\n files = []\\n for x in pathlib.Path(self.path).glob('*.m*'):\\n if x.is_file():\\n x = str(x) # tinytag needs str, not PosixPath\\n files.append(x)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f52fd8764ef8913a15b96f9bb8c8fd5\",\n \"score\": \"0.4599362\",\n \"text\": \"def parse_ad_group_path(path: str) -> Dict[str, str]:\\n m = re.match(\\n r\\\"^customers/(?P<customer_id>.+?)/adGroups/(?P<ad_group_id>.+?)$\\\",\\n path,\\n )\\n return m.groupdict() if m else {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ffbe9bd356afe1f77f53a1d0544cc0\",\n \"score\": \"0.45965385\",\n \"text\": \"def split_path(path):\\n if path in '/':\\n return '', ''\\n path = relpath(path)\\n if '/' not in path:\\n return path, ''\\n return path.split('/', 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e4bec1d4010ffc3355dac1a719ccb8\",\n \"score\": \"0.45908687\",\n \"text\": \"def parse_mime_type(mime_type):\\r\\n parts = mime_type.split(\\\";\\\")\\r\\n params = dict([tuple([s.strip() for s in param.split(\\\"=\\\")])\\\\\\r\\n for param in parts[1:] ])\\r\\n (type, subtype) = parts[0].split(\\\"/\\\")\\r\\n return (type.strip(), subtype.strip(), params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c5d082d420cf3b8458f59cd533469a\",\n \"score\": \"0.45839995\",\n \"text\": \"def get_plugin_media_path(instance, filename):\\n return instance.get_media_path(filename)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993989,"cells":{"query_id":{"kind":"string","value":"7cdb93f03454b8ff65070a1611850f65"},"query":{"kind":"string","value":"Distance from a proposed point to its nearest neighbour (proposed point)"},"positive_passages":{"kind":"list like","value":[{"docid":"2367061b483d48ffb9ef0488832fb63c","score":"0.6574492","text":"def ppn2_distance_to_nearest_neighbour(self):\n # FIXME vectorize loop\n distances = []\n for point in self.im_proposals:\n distances.append(np.partition(np.sum(np.power(point - self.im_proposals, 2), axis=1), 2)[1])\n bins = np.linspace(0, 100, 100)\n self.make_plot(\n distances,\n bins,\n xlabel=\"distance to nearest neighbour\",\n ylabel=\"#proposals\",\n filename='distance_to_nearest_neighbour.png'\n )\n return distances","title":""}],"string":"[\n {\n \"docid\": \"2367061b483d48ffb9ef0488832fb63c\",\n \"score\": \"0.6574492\",\n \"text\": \"def ppn2_distance_to_nearest_neighbour(self):\\n # FIXME vectorize loop\\n distances = []\\n for point in self.im_proposals:\\n distances.append(np.partition(np.sum(np.power(point - self.im_proposals, 2), axis=1), 2)[1])\\n bins = np.linspace(0, 100, 100)\\n self.make_plot(\\n distances,\\n bins,\\n xlabel=\\\"distance to nearest neighbour\\\",\\n ylabel=\\\"#proposals\\\",\\n filename='distance_to_nearest_neighbour.png'\\n )\\n return distances\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f3a2e04fa10b7f690c79eb3d0e02849a","score":"0.80314505","text":"def distance_to_point(self, point):","title":""},{"docid":"2ee7ca52fefe409ffc2a9594fb14a392","score":"0.7305967","text":"def distance(self, p):\n return ((self.x - p.x) ** 2 + (self.y - p.y) ** 2) ** 0.5","title":""},{"docid":"7fd0f91e38f1db51346d47df00e6504a","score":"0.71589077","text":"def PointDistance(*args):\r\n return _pynewton.CollisionGeometry_PointDistance(*args)","title":""},{"docid":"ecca843a9f94e0a410f79053b96dc9c0","score":"0.71313375","text":"def distance(self, point):\n x_diff = self.x - point.x\n y_diff = self.y - point.y\n\n return sqrt(x_diff * x_diff + y_diff * y_diff)","title":""},{"docid":"aba49ea8aafe3739fbcdf2f5d4d92bfb","score":"0.7131221","text":"def point_point_distance(x, y):\n\tassert len(x) == len(y) == 2\n\treturn ((x[0] - y[0]) ** 2 + (x[1] - y[1]) ** 2) ** 0.5","title":""},{"docid":"6a7ecce87cedcf29385beec2febe94d6","score":"0.71158856","text":"def get_nearest_neighbour(self, pointli):\n # Assumes that only valid (projectable, within shell etc) points\n # are in pointli\n mindist = float(sys.maxsize)\n minp = Point()\n for p in pointli:\n if p is not self:\n d = self.dist(p)\n if d < mindist:\n mindist = d\n minp = p\n if not mindist < float(sys.maxsize):\n return None\n else:\n self.nearest_neighbour_dist = mindist\n self.nearest_neighbour_point = minp\n return self.nearest_neighbour_dist","title":""},{"docid":"dd47ac19cdb021392bff12c471dbb044","score":"0.707952","text":"def point_to_point_distance(p1, p2):\n dx = p1.x - p2.x\n dy = p1.y - p2.y\n return math.sqrt(dx**2 + dy**2)","title":""},{"docid":"3f4957fbc4cd2e6f98918464b98c108f","score":"0.7027314","text":"def pointDistance( x1, y1, x2, y2 ):\n\tdist = ( ( float(x2) - float(x1) ) ** 2 + ( float(y2) - float(y1) ) **2 ) ** 0.5\n\treturn dist","title":""},{"docid":"e95df59f5e3c28fcd0e596fa43fcc7b5","score":"0.70109475","text":"def estimated_distance_to_destination(self):\n if self.is_at_destination:\n return 0\n\n position = self.location()\n delta_x = position.x_position - self.destination.x_position\n delta_y = position.y_position - self.destination.y_position\n return sqrt(delta_x**2 + delta_y**2)","title":""},{"docid":"9dbfcdb954669a40431dd1ec525bf1ba","score":"0.6999854","text":"def calculate_distance(self, other_point):\n\n return math.sqrt((self.x - other_point.x) ** 2 + (self.y - other_point.y) ** 2)","title":""},{"docid":"59a0b38f0a54fd6a03a409cf1e584b52","score":"0.69813","text":"def distance(self):\n\n\t\treturn np.sqrt((self.x_kp1 - self.x)**2 + \n\t\t\t\t\t (self.y_kp1 - self.y)**2 )","title":""},{"docid":"4219c86935eaa9d2ae703289ebbd24cb","score":"0.6975208","text":"def dist(p1: Point, p2: Point) -> float:\n return math.sqrt((p2.x - p1.x) ** 2 + (p2.y - p1.y) ** 2)","title":""},{"docid":"4dc518a571a91c5013c88c0a7d94a425","score":"0.69596314","text":"def get_distance(self, p):\n assert len(p) == len(self.x)\n return (sum([(i-j)**2 for i, j in zip(p, self.x)]))**0.5","title":""},{"docid":"7fd582e8853979cc00aa3134c1f00dc6","score":"0.69469345","text":"def direct2dDistance(self, point):\n if not isinstance(point, MapPoint): return 0.0\n return ((self.x-point.x)**2 + (self.y-point.y)**2)**(0.5) # simple distance formula","title":""},{"docid":"2e3b551131d8edac39e226846f4065be","score":"0.6929922","text":"def distance_from_point(self, other):\n # p1, *_, p2 = self.points\n p1 = self.pixels[0]\n x1, y1 = p1.col, p1.row\n p2 = self.pixels[-1]\n x2, y2 = p2.col, p2.row\n\n x0, y0 = other.col, other.row\n\n top = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1-y2*x1)\n bottom = sqrt((y2-y1)**2+(x2-x1)**2)\n\n return top/bottom","title":""},{"docid":"e3e9a8098228a9e5ff4f378c14de95c8","score":"0.6915623","text":"def distanceFrom(self, point = (-1, -1)):\n if (point[0] == -1 or point[1] == -1):\n point = np.array(self.image.size()) / 2\n return spsd.euclidean(point, [self.x, self.y])","title":""},{"docid":"7f8c41b5fe3d269ff16ad7b52f0dcea0","score":"0.69106966","text":"def distance(self, other: \"Point\") -> float:\n #math.hypot(self.x - other.x, self.y-other.y)\n return math.sqrt((self.x - other.x)**2 + (self.y -other.y)**2)","title":""},{"docid":"1d9415c70b5122bd14ec8020eacd7630","score":"0.690273","text":"def distance(self,pt):\n return np.dot(self.normal,pt)-np.dot(self.normal,self.centroid)","title":""},{"docid":"5f47b5cb103ac1ba8d9619910c451659","score":"0.6897161","text":"def calculate_distance(self, other: \"Point\") -> float:\n return math.hypot(self.x - other.x, self.y - other.y)","title":""},{"docid":"6c378dea5a699a72607407efa6956f5c","score":"0.68824846","text":"def distance(p1: Point, p2: Point) -> float:\n return math.sqrt((p1.x - p2.x)**2 + (p1.y - p2.y)**2)","title":""},{"docid":"9e2e2f33686af5361501ba4c1233892b","score":"0.68818855","text":"def distToClosestPoint(self, pixel: tuple):\r\n if len(pixel) == 3:\r\n distance = self._width * self._heightZ\r\n for point in self._points:\r\n dX = pixel[0] - point[0]\r\n dY = pixel[1] - point[1]\r\n dZ = pixel[2] - point[2]\r\n distance = min(distance, sqrt(dX ** 2 + dZ ** 2 + dY ** 2)) \r\n else:\r\n distance = self._width * self._heightZ\r\n for point in self._points:\r\n dX = pixel[0] - point[0]\r\n dZ = pixel[1] - point[2]\r\n distance = min(distance, sqrt(dX ** 2 + dZ ** 2))\r\n return int(distance)","title":""},{"docid":"b4f919e2283866436f21b968bb31d8ee","score":"0.68610597","text":"def distanceFrom(self, point = (-1, -1)):\n if (point[0] == -1 or point[1] == -1 and len(self)):\n point = self[0].image.size()\n \n return spsd.cdist(self.coordinates(), [point])[:,0]","title":""},{"docid":"0328cc3ed67c6796df9a9347c1ff6c63","score":"0.68252766","text":"def distanceFromPoint(self,p):\n p = asarray(p).reshape((3))\n d = self-p\n return sqrt(sum(d*d,-1))","title":""},{"docid":"054551f7f09463007ba32b14941783e9","score":"0.6816136","text":"def pointDistance(p1,p2):\n return math.sqrt(pow(p1[0]-p2[0],2)+pow(p1[1]-p2[1],2))","title":""},{"docid":"031c31a6c5d540627606e757e23425bb","score":"0.68137395","text":"def get_distance(self, other):\n return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)","title":""},{"docid":"74dc6e9092c027c930f20c06c5365ecb","score":"0.68137014","text":"def euclidean_distance(self, point: List[int]) -> float:\n return sqrt(point[0] ** 2 + point[1] ** 2)","title":""},{"docid":"6b82b29ffeabdfa27d719a3586572c9e","score":"0.6758727","text":"def distance(self, another_point):\r\n\t\tif(type(another_point) is Point):\r\n\t\t\tdistance = math.sqrt((another_point.x - self.x)**2 + (another_point.y - self.y)**2)\r\n\t\telse:\r\n\t\t\traise TypeError(\"Arguement 2 must be Point, not \" + type(another_point) + \"!\")\r\n\r\n\t\treturn distance","title":""},{"docid":"12683a9492c248ef487eaa1ef2c6cfa0","score":"0.6758534","text":"def DistanceFromPoint(nodes,pt):\n val = Fn.distanceFromPoint(pt)","title":""},{"docid":"d6fcc91e56e6fdea828ebd507d50893f","score":"0.6755509","text":"def point_dist(a, b):\n return ((a[0]-b[0]) ** 2 + (a[1]-b[1]) ** 2) ** 0.5","title":""},{"docid":"2036e0faef438091b6c1bef89d01673c","score":"0.6754175","text":"def _distance(self, point1, point2):\n return abs(point1[0] - point2[0]) + abs(point1[1] - point2[1])","title":""},{"docid":"e75db45287b4792e650f296ba7687a5c","score":"0.6753394","text":"def _distance(line, point):\n proj_pos = line.project(point, normalized=True)\n proj_point = line.interpolate(proj_pos, normalized=True)\n dist = proj_point.distance(point)\n return dist","title":""},{"docid":"fb5459b0e30b326754d2c6064c79e808","score":"0.67533743","text":"def get_distance(point_1, point_2):\n dist = np.sqrt((point_1.pos_x - point_2.pos_x) ** 2 + (\n point_1.pos_y - point_2.pos_y) ** 2)\n return dist","title":""},{"docid":"4b74c2f28625dd09552f367148f1bec6","score":"0.6749296","text":"def getDistance(x, y):\r\n return sqrt((x ** 2) + (y ** 2))","title":""},{"docid":"0d3248b9c651a8239992bb90735609f3","score":"0.67445344","text":"def getDistance(point1, point2):\n return vincenty(point1, point2).miles","title":""},{"docid":"936ae9f09c190e6eed697a3a512e5d1f","score":"0.673572","text":"def distance_to(self, other):\n dx = other.x - self.x\n dy = other.y - self.y\n return math.sqrt(dx ** 2 + dy ** 2)","title":""},{"docid":"caa51e8b69a03fd7cfd882bfd38ea0a1","score":"0.673538","text":"def distance(self, point):\n assert len(point) == a6dataset.Dataset.getDimension(self._dataset)\n sum = 0\n for x in range(a6dataset.Dataset.getDimension(self._dataset)):\n diff = point[x] - self._centroid[x]\n diff_squared = math.pow(diff,2)\n sum += diff_squared\n euclidean = math.sqrt(sum)\n return euclidean","title":""},{"docid":"523741f9b61042a10d2ead3b7ea0d37a","score":"0.6717078","text":"def get_dist(self):\r\n return sqrt(self.x ** 2 + self.y ** 2)","title":""},{"docid":"283fc018c643d23e681c66d45cdf7b91","score":"0.67122364","text":"def distance_to(self, other):\n dx = other.x - self.x\n dy = other.y - self.y\n return math.sqrt(dx**2 + dy**2)","title":""},{"docid":"3f739227d27bbecfc8fe50ae5be8d000","score":"0.6706389","text":"def distance(self, destination):\n run = destination.x - self.x\n rise = destination.y - self.y\n return math.sqrt(run**2 + rise**2)","title":""},{"docid":"7ffa1de668f630ed46c217327db34254","score":"0.6694731","text":"def distance(p1, p2):\n x = p1.x - p2.x\n y = p1.y - p2.y \n return (x * x + y * y)","title":""},{"docid":"a13dbc39fa986a1e339a3006a143a2ce","score":"0.66871226","text":"def _get_closest_point_distance_brute(self, points):\n point_count = len(points)\n dist = sys.float_info.max\n for i in range(1, point_count):\n for j in range(i):\n dist = min(dist, self._distance_between(points[i],\n points[j]))\n return dist","title":""},{"docid":"ed4b8ce7334ab9d043a851d5af7d6225","score":"0.667693","text":"def distance_to(self, other_station):\n xd = self.x - other_station.x\n yd = self.y - other_station.y\n return sqrt((xd * xd) + (yd * yd))\n pass","title":""},{"docid":"23569462ecead2040f4ec2a5bac6b60a","score":"0.6675885","text":"def dist(self,p):\n if len(self.points)==0:\n return 1e6\n dists=[self.euclidDist(p,point) for point in self.points]\n return min(dists)","title":""},{"docid":"e24b0a1dd9f92dc8dddb37b6e912c775","score":"0.6672284","text":"def get_distance_between_two_points(self, one, two):\n dx = one.x - two.x\n dy = one.y - two.y\n return math.sqrt(dx * dx + dy * dy)","title":""},{"docid":"6bd942442b002c3c5193959e6f2e9b3e","score":"0.66488224","text":"def calcDistance(point1, point2):\r\n x1, y1 = point1.getX(), point1.getY()\r\n x2, y2 = point2.getX(), point2.getY()\r\n return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)","title":""},{"docid":"87a2ff55137466b4612a28d1f3f4acb1","score":"0.6646756","text":"def _get_distance(self):\n return self.dist_","title":""},{"docid":"c0a7395de9c6a74ce79f4c5596032766","score":"0.66427374","text":"def dist(self, x, y):\n pass","title":""},{"docid":"fa3225b958229aba486229148b727c5d","score":"0.6638534","text":"def get_distance_from_point(self, pstart, p_end):\n a = numpy.array((pstart.x, pstart.y, pstart.z))\n b = numpy.array((p_end.x, p_end.y, p_end.z))\n \n distance = numpy.linalg.norm(a - b)\n \n return distance","title":""},{"docid":"ad1ad93637e302928473516fcd2a6a38","score":"0.66374236","text":"def get_distance(point_1, point_2):\n return math.sqrt(\n math.pow(point_2[0] - point_1[0], 2)\n +\n math.pow(point_2[1] - point_1[1], 2)\n )","title":""},{"docid":"df7c4971728edfca878a78c8a1d77117","score":"0.6633415","text":"def get_distance(self, pt1, pt2):\r\n return np.sqrt(np.power(pt1[0] - pt2[0], 2) + np.power(pt1[1] - pt2[1], 2))","title":""},{"docid":"6ad6da72a322dd69073223ffd50b6a49","score":"0.66330475","text":"def distance_formula(self, point):\n distance = (((point.x - self.end.x)**2) + (point.y - self.end.y)**2)**.5\n\n return distance","title":""},{"docid":"37ac359556e7c9dd03d9d681cacb4af0","score":"0.66327953","text":"def point_dist(point1, point2):\n diff = (point1[0] - point2[0], point1[1] - point2[1])\n return diff[0] * diff[0] + diff[1] * diff[1]","title":""},{"docid":"e0658f138922c44872525ff82f37894c","score":"0.6620431","text":"def dist(point_1: Tuple[int, int], point_2: Tuple[int, int]) -> float:\n return np.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] - point_2[1]) ** 2)","title":""},{"docid":"8634045cf25dbb07d440720c4119bc64","score":"0.66097385","text":"def compute_distance(point, center):\n import numpy as np\n if isinstance(point, SparseVector) | isinstance(center, SparseVector):\n p_d = point.toArray()\n c_d = center.toArray()\n return float(np.linalg.norm(p_d - c_d, ord=2))\n else:\n return float(np.linalg.norm(point - center, ord=2))","title":""},{"docid":"7c2cfc11ff8539b398899222ff61d901","score":"0.659397","text":"def getDistance(point1: Tuple[int, int], point2: Tuple[int, int]) -> float:\n try:\n q = point1[0] - point2[0]\n w = point1[1] - point2[1]\n r = q ** 2 + w ** 2\n return Sqrt(r)\n except Exception as e:\n KDS.Logging.AutoError(e)\n return 0","title":""},{"docid":"42ebe45ada088de58023696409674df5","score":"0.6582625","text":"def distance_from_origin(self):\n return (self.x ** 2 + self.y ** 2) ** 0.5","title":""},{"docid":"21ba97948820e9a659c604f525fce1f0","score":"0.65823054","text":"def distance_from_origin(self):\n return ((self.x ** 2) + (self.y ** 2)) ** 0.5","title":""},{"docid":"21ba97948820e9a659c604f525fce1f0","score":"0.65823054","text":"def distance_from_origin(self):\n return ((self.x ** 2) + (self.y ** 2)) ** 0.5","title":""},{"docid":"d8ac31daa85e247d9208dd4e1443e8c2","score":"0.65769446","text":"def getDistanceToNeighbor(self, fromNode, neighbor):\n\t\tedge = fromNode.getEdgeTo(neighbor)\n\t\tassert(fromNode in self.pathDistance.keys())\n\t\treturn self.pathDistance[fromNode] + edge.getCost()","title":""},{"docid":"f4514ca3ca24055618e6dc02efb71426","score":"0.6571196","text":"def pointdistance(pointa, pointb):\n return distance(pointa[0], pointa[1], pointb[0], pointb[1])","title":""},{"docid":"7153de4fe19ca4d487a7952ff19e66cb","score":"0.65652424","text":"def distance(self,q):\n return round(sqrt((self.x-q.x)**2+(self.y-q.y)**2))","title":""},{"docid":"463313830b6161b27f5c765d0d4b69b9","score":"0.6559456","text":"def getDistanceToViewPoint(self, *args):\n return _osg.NodeVisitor_getDistanceToViewPoint(self, *args)","title":""},{"docid":"a9d221b2333a6967f36ebc4c8cf1b973","score":"0.6546633","text":"def _getClosestWaypoint(self, point):\n return min(self.graph, key = lambda k: self._dist2(point, self.graph[k].location))","title":""},{"docid":"32b37e9dc46d94d990c944746bf162db","score":"0.6540587","text":"def distance( point1 = (0,0) , point2= (0,0)):\n return math.sqrt((point2[0] - point1[0]) ** 2 + (point2[1] - point1[1])**2 )","title":""},{"docid":"31d748ac94e052536e315aa2afe39fd5","score":"0.6539546","text":"def _calc_dist_2d(self, px, py, wp_idx):\n return math.sqrt(\n (px - self.waypoints.waypoints[wp_idx].pose.pose.position.x) ** 2 +\n (py - self.waypoints.waypoints[wp_idx].pose.pose.position.y) ** 2)","title":""},{"docid":"87830ab7ad1d2514cc5bf7d32a4a307e","score":"0.6536555","text":"def distance(self):\n return (\n (self.M_PER_LON * (self.dest[0] - self.orig[0])) ** 2\n + (self.M_PER_LAT * (self.dest[1] - self.orig[1])) ** 2\n ) ** 0.5","title":""},{"docid":"87652501dbdac9071f7fcdc000748854","score":"0.6535702","text":"def distance(self, pos):\n return math.sqrt((pos.x - self.x) ** 2 + (pos.y - self.y) ** 2)","title":""},{"docid":"6fefef5f6b76bbaa6cac5c771689b1e5","score":"0.65287113","text":"def get_nearest_lateral_neighbour(self, pointli):\n # Assumes that only valid (projectable, within shell etc) points\n # are in pointli\n mindist = float(sys.maxsize)\n minp = Point()\n for p in pointli:\n if p is not self:\n d = self.lateral_dist_to_point(p, self.profile.path)\n if d < mindist:\n mindist = d\n minp = p\n if not mindist < float(sys.maxsize):\n return None\n else:\n self.nearest_lateral_neighbour_dist = mindist\n self.nearest_lateral_neighbour_point = minp\n return self.nearest_lateral_neighbour_dist","title":""},{"docid":"264e881c4fa27d09e2d85dad18932a58","score":"0.6526864","text":"def distance(pt1, pt2):\n return ((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2) ** 0.5","title":""},{"docid":"01207ce2a7ffa27d91e5f1bc56078247","score":"0.6519585","text":"def calc_distance(x1: float, y1: float, x2: float, y2: float) -> float:\n return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5","title":""},{"docid":"e56be8e15aa3f7c2c96949207e625add","score":"0.651665","text":"def distance(self, x, y):\n return math.sqrt((x - self.x) ** 2 + (y - self.y) ** 2)","title":""},{"docid":"4072ce3a758fc64a250098b45f422d06","score":"0.65109724","text":"def _get_distance(self):\n raise NotImplementedError","title":""},{"docid":"5c1fa13bec2791f2365e112e4f95e39a","score":"0.65055317","text":"def GetDistance(self):\n return np.sqrt((self.pose.x - self.target_x)**2 + \\\n (self.pose.y - self.target_y)**2)","title":""},{"docid":"1b0e01ba5dde7865bc35527dd547a84c","score":"0.6489804","text":"def computeDistance(pt1,pt2):\n return math.sqrt((pt2[1]-pt1[1])**2 + (pt2[0]-pt1[0])**2)","title":""},{"docid":"74768be8b59b9542a94878ef9cf3dfc9","score":"0.64868224","text":"def _get_distance(self, centroid_idx: int, point_coord=None, point_idx=None) -> float:\n centroid_coord = self._temp_centroids[centroid_idx]\n if point_idx is not None and point_coord is None:\n point_coord = self.training_data[point_idx]\n\n distance = 0\n for idx in range(len(point_coord)):\n distance += pow(point_coord[idx] - centroid_coord[idx], 2)\n return round(pow(distance, 0.5), self.ROUND_AFTER_COMA)","title":""},{"docid":"8b54e8ce8c47b172ec04a85872bc2c1d","score":"0.64862436","text":"def closest_point(self, point, maxdist=None):\n return self.xyz","title":""},{"docid":"9bfbb34c6f112439eb0ee19663c39fc2","score":"0.64813703","text":"def distance(point_1=(0, 0), point_2=(0, 0)):\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] - point_2[1]) ** 2)","title":""},{"docid":"57e9b534b566a3e0d2565987f9b21fd2","score":"0.6481139","text":"def dist(self, person):\n distance = math.sqrt(\n (self.position[0] - person.position[0])**2 + (self.position[1] - person.position[1])**2)\n return distance","title":""},{"docid":"9dceb8b839557075b439e82c2be877fe","score":"0.6480697","text":"def _distance(self, x, y):\r\n if self.algorithm == 'gaussian':\r\n return self._euclidean_distance(x, y)\r\n else:\r\n return self._widx_distance(x, y)","title":""},{"docid":"89f4299e461e84b2c311f8491b32db88","score":"0.64795756","text":"def distance(self, ray):\r\n r = self.center - ray.p\r\n top = np.dot(self.normal, r)\r\n bottom = np.dot(ray.k, self.normal)\r\n if np.abs(top) < eps or np.abs(bottom) < eps:\r\n return float('inf')\r\n\r\n d = top / bottom\r\n p = ray.p + d * ray.k\r\n d = d if self.isIntersect(p) else float('inf')\r\n if self.passes:\r\n self.passes -= 1\r\n return float('inf')\r\n else:\r\n return d","title":""},{"docid":"1cab8b335481b143ff69242ef8050d36","score":"0.64735156","text":"def _calculate_distance(self,proposed):\n # simulate the system\n rates = parameterise_rates(self.rate_funcs,proposed)\n stop_time = self.obs[-1][0]\n init_state = self.obs[0][1:]\n sample_trace = gillespie(rates,stop_time,init_state,self.updates)\n # get the distance according to the error metric specified\n return self.dist(sample_trace,self.obs)","title":""},{"docid":"48828d2671e4c56e57871cc25449561c","score":"0.64733195","text":"def distance(point1, point2):\n return np.sqrt(np.sum((point1 - point2)**2))","title":""},{"docid":"48828d2671e4c56e57871cc25449561c","score":"0.64733195","text":"def distance(point1, point2):\n return np.sqrt(np.sum((point1 - point2)**2))","title":""},{"docid":"241acb64f4257cb0507d012616b5d942","score":"0.646772","text":"def get_distance(point_1, point_2):\n delta = point_1 - point_2\n distance = math.sqrt(numpy.dot(delta.transpose(), delta).item())\n\n return distance","title":""},{"docid":"11a06eb9af23d39ba98ec589adfc276f","score":"0.6465166","text":"def distance(self):\n return float()","title":""},{"docid":"36a0e577f1e465eeda199c1a96f3dade","score":"0.646208","text":"def distance(x1, y1, x2, y2):\n return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5","title":""},{"docid":"f07495b5bc5cd9def9664b011cb9409e","score":"0.6461339","text":"def dist(self,x,y):\n xx=self.pZn(x)\n yy=self.pZn(y)\n if (yy<xx): xx,yy=yy,xx\n d=yy-xx # ecart direct\n dd=xx+(self.n-yy) # ecart par les extremites\n if (dd<d): return dd \n else: return d","title":""},{"docid":"69bd18656d07427e115bd12e62ffdee6","score":"0.6460986","text":"def nearest(self, point):\n dist = np.linalg.norm(self.vertices - point, axis=1)\n best = np.argmin(dist)\n return {\"node\": self.vertices[best], \"distance\": dist[best]}","title":""},{"docid":"69bd18656d07427e115bd12e62ffdee6","score":"0.6460986","text":"def nearest(self, point):\n dist = np.linalg.norm(self.vertices - point, axis=1)\n best = np.argmin(dist)\n return {\"node\": self.vertices[best], \"distance\": dist[best]}","title":""},{"docid":"31bb2fd0e2d5352f46c35d687c2d2b50","score":"0.6459042","text":"def calculateDistance(origin, destination):\n\n return float((great_circle(origin, destination).km)*1000)","title":""},{"docid":"855a88f062d59e26c274ef653be31507","score":"0.64561427","text":"def points_distance(self, points, return_nearest=False):\n\n dists = cdist(points, self.points[:-1])\n\n if return_nearest:\n mini = np.argmin(dists, axis=1)\n dists = np.take_along_axis(dists, mini[:, None], axis=1)[:, 0]\n minp = self.points[mini]\n del mini\n else:\n dists = np.min(dists, axis=1)\n\n for pi in range(len(self.points) - 1):\n pA = self.points[pi]\n pB = self.points[pi + 1]\n n = pB - pA\n d = np.linalg.norm(n)\n\n if d > 0:\n n /= d\n q = points - pA[None, :]\n x = np.einsum(\"pd,d->p\", q, n)\n\n sel = (x > 0) & (x < d)\n if np.any(sel):\n x = x[sel]\n y2 = np.maximum(np.linalg.norm(q[sel], axis=1) ** 2 - x**2, 0.0)\n\n dsel = dists[sel]\n dists[sel] = np.minimum(dsel, np.sqrt(y2))\n\n if return_nearest:\n mini = np.argwhere(np.sqrt(y2) < dsel)\n hminp = minp[sel]\n hminp[mini] = pA[None, :] + x[mini, None] * n[None, :]\n minp[sel] = hminp\n del mini, hminp\n\n del y2, dsel\n\n del x, sel\n\n if return_nearest:\n return dists, minp\n else:\n return dists","title":""},{"docid":"75108290afd24b9939feae9156cf2087","score":"0.6455762","text":"def distance(self, x, y):\n dx = x - self.x\n dy = y - self.y\n return math.sqrt(dx**2 + dy**2)","title":""},{"docid":"2753aaefdc3c9f2f2e13acae3ab3f2ea","score":"0.64529335","text":"def distance(x0,y0,x1,y1):\n return sqrt((x1-x0)**2 + (y1-y0)**2)","title":""},{"docid":"337c8ffbf1b98ee56ef6b64d628e77e0","score":"0.6442676","text":"def closest_face_dist_memoized(self, point):\n mn_dist = 1.e9\n obb_faces_3d = self.faces_3d_memoized()\n for face_id in range(obb_faces_3d.shape[2]):\n face = np.array(obb_faces_3d[:, :, face_id].squeeze())\n dist, _ = pointTriangleDistance(face, np.asarray(point))\n if dist < mn_dist:\n mn_dist = dist\n return mn_dist","title":""},{"docid":"9b6f591c99418637611aa13a4dc5efd7","score":"0.64423573","text":"def distance(self):\n detected, point = self.last_read[:2]\n return np.linalg.norm(point) if detected else 1","title":""},{"docid":"eca197aea180f545691656270d332a58","score":"0.6441566","text":"def dist(self):\n dx = list(sorted([abs(x) for x in self.pos]))\n return dx[0] + dx[1]","title":""},{"docid":"236a10aace3ad212d4580e182a3e18d6","score":"0.6441241","text":"def getDistance(point1, point2x, point2y):\n distance = np.sqrt((point2x - point1[0])**2 + (point2y - point1[1])**2)\n return distance","title":""},{"docid":"a13a649abee4006f3147133c8a6da692","score":"0.6433959","text":"def distance(current_x, current_y, goal_x, goal_y):\r\n x_dif = abs(goal_x - current_x) * 10.29\r\n y_dif = abs(goal_y - current_y) * 7.55\r\n return math.sqrt(math.pow(x_dif, 2) +\r\n math.pow(y_dif, 2))","title":""},{"docid":"dcdff36ea13071bffe671cc42adb99d1","score":"0.6427592","text":"def distance(p1, p2):\n return ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5","title":""},{"docid":"64f23b5bd43f1a545f0be94c123acf78","score":"0.6426846","text":"def _distance((x1, y1), (x2, y2)):\n\n\treturn math.sqrt((x2-x1)**2 + (y2-y1)**2)","title":""}],"string":"[\n {\n \"docid\": \"f3a2e04fa10b7f690c79eb3d0e02849a\",\n \"score\": \"0.80314505\",\n \"text\": \"def distance_to_point(self, point):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee7ca52fefe409ffc2a9594fb14a392\",\n \"score\": \"0.7305967\",\n \"text\": \"def distance(self, p):\\n return ((self.x - p.x) ** 2 + (self.y - p.y) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd0f91e38f1db51346d47df00e6504a\",\n \"score\": \"0.71589077\",\n \"text\": \"def PointDistance(*args):\\r\\n return _pynewton.CollisionGeometry_PointDistance(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecca843a9f94e0a410f79053b96dc9c0\",\n \"score\": \"0.71313375\",\n \"text\": \"def distance(self, point):\\n x_diff = self.x - point.x\\n y_diff = self.y - point.y\\n\\n return sqrt(x_diff * x_diff + y_diff * y_diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba49ea8aafe3739fbcdf2f5d4d92bfb\",\n \"score\": \"0.7131221\",\n \"text\": \"def point_point_distance(x, y):\\n\\tassert len(x) == len(y) == 2\\n\\treturn ((x[0] - y[0]) ** 2 + (x[1] - y[1]) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7ecce87cedcf29385beec2febe94d6\",\n \"score\": \"0.71158856\",\n \"text\": \"def get_nearest_neighbour(self, pointli):\\n # Assumes that only valid (projectable, within shell etc) points\\n # are in pointli\\n mindist = float(sys.maxsize)\\n minp = Point()\\n for p in pointli:\\n if p is not self:\\n d = self.dist(p)\\n if d < mindist:\\n mindist = d\\n minp = p\\n if not mindist < float(sys.maxsize):\\n return None\\n else:\\n self.nearest_neighbour_dist = mindist\\n self.nearest_neighbour_point = minp\\n return self.nearest_neighbour_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd47ac19cdb021392bff12c471dbb044\",\n \"score\": \"0.707952\",\n \"text\": \"def point_to_point_distance(p1, p2):\\n dx = p1.x - p2.x\\n dy = p1.y - p2.y\\n return math.sqrt(dx**2 + dy**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4957fbc4cd2e6f98918464b98c108f\",\n \"score\": \"0.7027314\",\n \"text\": \"def pointDistance( x1, y1, x2, y2 ):\\n\\tdist = ( ( float(x2) - float(x1) ) ** 2 + ( float(y2) - float(y1) ) **2 ) ** 0.5\\n\\treturn dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e95df59f5e3c28fcd0e596fa43fcc7b5\",\n \"score\": \"0.70109475\",\n \"text\": \"def estimated_distance_to_destination(self):\\n if self.is_at_destination:\\n return 0\\n\\n position = self.location()\\n delta_x = position.x_position - self.destination.x_position\\n delta_y = position.y_position - self.destination.y_position\\n return sqrt(delta_x**2 + delta_y**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbfcdb954669a40431dd1ec525bf1ba\",\n \"score\": \"0.6999854\",\n \"text\": \"def calculate_distance(self, other_point):\\n\\n return math.sqrt((self.x - other_point.x) ** 2 + (self.y - other_point.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a0b38f0a54fd6a03a409cf1e584b52\",\n \"score\": \"0.69813\",\n \"text\": \"def distance(self):\\n\\n\\t\\treturn np.sqrt((self.x_kp1 - self.x)**2 + \\n\\t\\t\\t\\t\\t (self.y_kp1 - self.y)**2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4219c86935eaa9d2ae703289ebbd24cb\",\n \"score\": \"0.6975208\",\n \"text\": \"def dist(p1: Point, p2: Point) -> float:\\n return math.sqrt((p2.x - p1.x) ** 2 + (p2.y - p1.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc518a571a91c5013c88c0a7d94a425\",\n \"score\": \"0.69596314\",\n \"text\": \"def get_distance(self, p):\\n assert len(p) == len(self.x)\\n return (sum([(i-j)**2 for i, j in zip(p, self.x)]))**0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd582e8853979cc00aa3134c1f00dc6\",\n \"score\": \"0.69469345\",\n \"text\": \"def direct2dDistance(self, point):\\n if not isinstance(point, MapPoint): return 0.0\\n return ((self.x-point.x)**2 + (self.y-point.y)**2)**(0.5) # simple distance formula\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3b551131d8edac39e226846f4065be\",\n \"score\": \"0.6929922\",\n \"text\": \"def distance_from_point(self, other):\\n # p1, *_, p2 = self.points\\n p1 = self.pixels[0]\\n x1, y1 = p1.col, p1.row\\n p2 = self.pixels[-1]\\n x2, y2 = p2.col, p2.row\\n\\n x0, y0 = other.col, other.row\\n\\n top = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1-y2*x1)\\n bottom = sqrt((y2-y1)**2+(x2-x1)**2)\\n\\n return top/bottom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e9a8098228a9e5ff4f378c14de95c8\",\n \"score\": \"0.6915623\",\n \"text\": \"def distanceFrom(self, point = (-1, -1)):\\n if (point[0] == -1 or point[1] == -1):\\n point = np.array(self.image.size()) / 2\\n return spsd.euclidean(point, [self.x, self.y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8c41b5fe3d269ff16ad7b52f0dcea0\",\n \"score\": \"0.69106966\",\n \"text\": \"def distance(self, other: \\\"Point\\\") -> float:\\n #math.hypot(self.x - other.x, self.y-other.y)\\n return math.sqrt((self.x - other.x)**2 + (self.y -other.y)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9415c70b5122bd14ec8020eacd7630\",\n \"score\": \"0.690273\",\n \"text\": \"def distance(self,pt):\\n return np.dot(self.normal,pt)-np.dot(self.normal,self.centroid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f47b5cb103ac1ba8d9619910c451659\",\n \"score\": \"0.6897161\",\n \"text\": \"def calculate_distance(self, other: \\\"Point\\\") -> float:\\n return math.hypot(self.x - other.x, self.y - other.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c378dea5a699a72607407efa6956f5c\",\n \"score\": \"0.68824846\",\n \"text\": \"def distance(p1: Point, p2: Point) -> float:\\n return math.sqrt((p1.x - p2.x)**2 + (p1.y - p2.y)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e2e2f33686af5361501ba4c1233892b\",\n \"score\": \"0.68818855\",\n \"text\": \"def distToClosestPoint(self, pixel: tuple):\\r\\n if len(pixel) == 3:\\r\\n distance = self._width * self._heightZ\\r\\n for point in self._points:\\r\\n dX = pixel[0] - point[0]\\r\\n dY = pixel[1] - point[1]\\r\\n dZ = pixel[2] - point[2]\\r\\n distance = min(distance, sqrt(dX ** 2 + dZ ** 2 + dY ** 2)) \\r\\n else:\\r\\n distance = self._width * self._heightZ\\r\\n for point in self._points:\\r\\n dX = pixel[0] - point[0]\\r\\n dZ = pixel[1] - point[2]\\r\\n distance = min(distance, sqrt(dX ** 2 + dZ ** 2))\\r\\n return int(distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f919e2283866436f21b968bb31d8ee\",\n \"score\": \"0.68610597\",\n \"text\": \"def distanceFrom(self, point = (-1, -1)):\\n if (point[0] == -1 or point[1] == -1 and len(self)):\\n point = self[0].image.size()\\n \\n return spsd.cdist(self.coordinates(), [point])[:,0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0328cc3ed67c6796df9a9347c1ff6c63\",\n \"score\": \"0.68252766\",\n \"text\": \"def distanceFromPoint(self,p):\\n p = asarray(p).reshape((3))\\n d = self-p\\n return sqrt(sum(d*d,-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"054551f7f09463007ba32b14941783e9\",\n \"score\": \"0.6816136\",\n \"text\": \"def pointDistance(p1,p2):\\n return math.sqrt(pow(p1[0]-p2[0],2)+pow(p1[1]-p2[1],2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031c31a6c5d540627606e757e23425bb\",\n \"score\": \"0.68137395\",\n \"text\": \"def get_distance(self, other):\\n return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74dc6e9092c027c930f20c06c5365ecb\",\n \"score\": \"0.68137014\",\n \"text\": \"def euclidean_distance(self, point: List[int]) -> float:\\n return sqrt(point[0] ** 2 + point[1] ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b82b29ffeabdfa27d719a3586572c9e\",\n \"score\": \"0.6758727\",\n \"text\": \"def distance(self, another_point):\\r\\n\\t\\tif(type(another_point) is Point):\\r\\n\\t\\t\\tdistance = math.sqrt((another_point.x - self.x)**2 + (another_point.y - self.y)**2)\\r\\n\\t\\telse:\\r\\n\\t\\t\\traise TypeError(\\\"Arguement 2 must be Point, not \\\" + type(another_point) + \\\"!\\\")\\r\\n\\r\\n\\t\\treturn distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12683a9492c248ef487eaa1ef2c6cfa0\",\n \"score\": \"0.6758534\",\n \"text\": \"def DistanceFromPoint(nodes,pt):\\n val = Fn.distanceFromPoint(pt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6fcc91e56e6fdea828ebd507d50893f\",\n \"score\": \"0.6755509\",\n \"text\": \"def point_dist(a, b):\\n return ((a[0]-b[0]) ** 2 + (a[1]-b[1]) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2036e0faef438091b6c1bef89d01673c\",\n \"score\": \"0.6754175\",\n \"text\": \"def _distance(self, point1, point2):\\n return abs(point1[0] - point2[0]) + abs(point1[1] - point2[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75db45287b4792e650f296ba7687a5c\",\n \"score\": \"0.6753394\",\n \"text\": \"def _distance(line, point):\\n proj_pos = line.project(point, normalized=True)\\n proj_point = line.interpolate(proj_pos, normalized=True)\\n dist = proj_point.distance(point)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5459b0e30b326754d2c6064c79e808\",\n \"score\": \"0.67533743\",\n \"text\": \"def get_distance(point_1, point_2):\\n dist = np.sqrt((point_1.pos_x - point_2.pos_x) ** 2 + (\\n point_1.pos_y - point_2.pos_y) ** 2)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b74c2f28625dd09552f367148f1bec6\",\n \"score\": \"0.6749296\",\n \"text\": \"def getDistance(x, y):\\r\\n return sqrt((x ** 2) + (y ** 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3248b9c651a8239992bb90735609f3\",\n \"score\": \"0.67445344\",\n \"text\": \"def getDistance(point1, point2):\\n return vincenty(point1, point2).miles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936ae9f09c190e6eed697a3a512e5d1f\",\n \"score\": \"0.673572\",\n \"text\": \"def distance_to(self, other):\\n dx = other.x - self.x\\n dy = other.y - self.y\\n return math.sqrt(dx ** 2 + dy ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa51e8b69a03fd7cfd882bfd38ea0a1\",\n \"score\": \"0.673538\",\n \"text\": \"def distance(self, point):\\n assert len(point) == a6dataset.Dataset.getDimension(self._dataset)\\n sum = 0\\n for x in range(a6dataset.Dataset.getDimension(self._dataset)):\\n diff = point[x] - self._centroid[x]\\n diff_squared = math.pow(diff,2)\\n sum += diff_squared\\n euclidean = math.sqrt(sum)\\n return euclidean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523741f9b61042a10d2ead3b7ea0d37a\",\n \"score\": \"0.6717078\",\n \"text\": \"def get_dist(self):\\r\\n return sqrt(self.x ** 2 + self.y ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283fc018c643d23e681c66d45cdf7b91\",\n \"score\": \"0.67122364\",\n \"text\": \"def distance_to(self, other):\\n dx = other.x - self.x\\n dy = other.y - self.y\\n return math.sqrt(dx**2 + dy**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f739227d27bbecfc8fe50ae5be8d000\",\n \"score\": \"0.6706389\",\n \"text\": \"def distance(self, destination):\\n run = destination.x - self.x\\n rise = destination.y - self.y\\n return math.sqrt(run**2 + rise**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ffa1de668f630ed46c217327db34254\",\n \"score\": \"0.6694731\",\n \"text\": \"def distance(p1, p2):\\n x = p1.x - p2.x\\n y = p1.y - p2.y \\n return (x * x + y * y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13dbc39fa986a1e339a3006a143a2ce\",\n \"score\": \"0.66871226\",\n \"text\": \"def _get_closest_point_distance_brute(self, points):\\n point_count = len(points)\\n dist = sys.float_info.max\\n for i in range(1, point_count):\\n for j in range(i):\\n dist = min(dist, self._distance_between(points[i],\\n points[j]))\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4b8ce7334ab9d043a851d5af7d6225\",\n \"score\": \"0.667693\",\n \"text\": \"def distance_to(self, other_station):\\n xd = self.x - other_station.x\\n yd = self.y - other_station.y\\n return sqrt((xd * xd) + (yd * yd))\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23569462ecead2040f4ec2a5bac6b60a\",\n \"score\": \"0.6675885\",\n \"text\": \"def dist(self,p):\\n if len(self.points)==0:\\n return 1e6\\n dists=[self.euclidDist(p,point) for point in self.points]\\n return min(dists)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e24b0a1dd9f92dc8dddb37b6e912c775\",\n \"score\": \"0.6672284\",\n \"text\": \"def get_distance_between_two_points(self, one, two):\\n dx = one.x - two.x\\n dy = one.y - two.y\\n return math.sqrt(dx * dx + dy * dy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd942442b002c3c5193959e6f2e9b3e\",\n \"score\": \"0.66488224\",\n \"text\": \"def calcDistance(point1, point2):\\r\\n x1, y1 = point1.getX(), point1.getY()\\r\\n x2, y2 = point2.getX(), point2.getY()\\r\\n return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a2ff55137466b4612a28d1f3f4acb1\",\n \"score\": \"0.6646756\",\n \"text\": \"def _get_distance(self):\\n return self.dist_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a7395de9c6a74ce79f4c5596032766\",\n \"score\": \"0.66427374\",\n \"text\": \"def dist(self, x, y):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3225b958229aba486229148b727c5d\",\n \"score\": \"0.6638534\",\n \"text\": \"def get_distance_from_point(self, pstart, p_end):\\n a = numpy.array((pstart.x, pstart.y, pstart.z))\\n b = numpy.array((p_end.x, p_end.y, p_end.z))\\n \\n distance = numpy.linalg.norm(a - b)\\n \\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad1ad93637e302928473516fcd2a6a38\",\n \"score\": \"0.66374236\",\n \"text\": \"def get_distance(point_1, point_2):\\n return math.sqrt(\\n math.pow(point_2[0] - point_1[0], 2)\\n +\\n math.pow(point_2[1] - point_1[1], 2)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7c4971728edfca878a78c8a1d77117\",\n \"score\": \"0.6633415\",\n \"text\": \"def get_distance(self, pt1, pt2):\\r\\n return np.sqrt(np.power(pt1[0] - pt2[0], 2) + np.power(pt1[1] - pt2[1], 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad6da72a322dd69073223ffd50b6a49\",\n \"score\": \"0.66330475\",\n \"text\": \"def distance_formula(self, point):\\n distance = (((point.x - self.end.x)**2) + (point.y - self.end.y)**2)**.5\\n\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ac359556e7c9dd03d9d681cacb4af0\",\n \"score\": \"0.66327953\",\n \"text\": \"def point_dist(point1, point2):\\n diff = (point1[0] - point2[0], point1[1] - point2[1])\\n return diff[0] * diff[0] + diff[1] * diff[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0658f138922c44872525ff82f37894c\",\n \"score\": \"0.6620431\",\n \"text\": \"def dist(point_1: Tuple[int, int], point_2: Tuple[int, int]) -> float:\\n return np.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] - point_2[1]) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8634045cf25dbb07d440720c4119bc64\",\n \"score\": \"0.66097385\",\n \"text\": \"def compute_distance(point, center):\\n import numpy as np\\n if isinstance(point, SparseVector) | isinstance(center, SparseVector):\\n p_d = point.toArray()\\n c_d = center.toArray()\\n return float(np.linalg.norm(p_d - c_d, ord=2))\\n else:\\n return float(np.linalg.norm(point - center, ord=2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2cfc11ff8539b398899222ff61d901\",\n \"score\": \"0.659397\",\n \"text\": \"def getDistance(point1: Tuple[int, int], point2: Tuple[int, int]) -> float:\\n try:\\n q = point1[0] - point2[0]\\n w = point1[1] - point2[1]\\n r = q ** 2 + w ** 2\\n return Sqrt(r)\\n except Exception as e:\\n KDS.Logging.AutoError(e)\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ebe45ada088de58023696409674df5\",\n \"score\": \"0.6582625\",\n \"text\": \"def distance_from_origin(self):\\n return (self.x ** 2 + self.y ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ba97948820e9a659c604f525fce1f0\",\n \"score\": \"0.65823054\",\n \"text\": \"def distance_from_origin(self):\\n return ((self.x ** 2) + (self.y ** 2)) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ba97948820e9a659c604f525fce1f0\",\n \"score\": \"0.65823054\",\n \"text\": \"def distance_from_origin(self):\\n return ((self.x ** 2) + (self.y ** 2)) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ac31daa85e247d9208dd4e1443e8c2\",\n \"score\": \"0.65769446\",\n \"text\": \"def getDistanceToNeighbor(self, fromNode, neighbor):\\n\\t\\tedge = fromNode.getEdgeTo(neighbor)\\n\\t\\tassert(fromNode in self.pathDistance.keys())\\n\\t\\treturn self.pathDistance[fromNode] + edge.getCost()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4514ca3ca24055618e6dc02efb71426\",\n \"score\": \"0.6571196\",\n \"text\": \"def pointdistance(pointa, pointb):\\n return distance(pointa[0], pointa[1], pointb[0], pointb[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7153de4fe19ca4d487a7952ff19e66cb\",\n \"score\": \"0.65652424\",\n \"text\": \"def distance(self,q):\\n return round(sqrt((self.x-q.x)**2+(self.y-q.y)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"463313830b6161b27f5c765d0d4b69b9\",\n \"score\": \"0.6559456\",\n \"text\": \"def getDistanceToViewPoint(self, *args):\\n return _osg.NodeVisitor_getDistanceToViewPoint(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9d221b2333a6967f36ebc4c8cf1b973\",\n \"score\": \"0.6546633\",\n \"text\": \"def _getClosestWaypoint(self, point):\\n return min(self.graph, key = lambda k: self._dist2(point, self.graph[k].location))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b37e9dc46d94d990c944746bf162db\",\n \"score\": \"0.6540587\",\n \"text\": \"def distance( point1 = (0,0) , point2= (0,0)):\\n return math.sqrt((point2[0] - point1[0]) ** 2 + (point2[1] - point1[1])**2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d748ac94e052536e315aa2afe39fd5\",\n \"score\": \"0.6539546\",\n \"text\": \"def _calc_dist_2d(self, px, py, wp_idx):\\n return math.sqrt(\\n (px - self.waypoints.waypoints[wp_idx].pose.pose.position.x) ** 2 +\\n (py - self.waypoints.waypoints[wp_idx].pose.pose.position.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87830ab7ad1d2514cc5bf7d32a4a307e\",\n \"score\": \"0.6536555\",\n \"text\": \"def distance(self):\\n return (\\n (self.M_PER_LON * (self.dest[0] - self.orig[0])) ** 2\\n + (self.M_PER_LAT * (self.dest[1] - self.orig[1])) ** 2\\n ) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87652501dbdac9071f7fcdc000748854\",\n \"score\": \"0.6535702\",\n \"text\": \"def distance(self, pos):\\n return math.sqrt((pos.x - self.x) ** 2 + (pos.y - self.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fefef5f6b76bbaa6cac5c771689b1e5\",\n \"score\": \"0.65287113\",\n \"text\": \"def get_nearest_lateral_neighbour(self, pointli):\\n # Assumes that only valid (projectable, within shell etc) points\\n # are in pointli\\n mindist = float(sys.maxsize)\\n minp = Point()\\n for p in pointli:\\n if p is not self:\\n d = self.lateral_dist_to_point(p, self.profile.path)\\n if d < mindist:\\n mindist = d\\n minp = p\\n if not mindist < float(sys.maxsize):\\n return None\\n else:\\n self.nearest_lateral_neighbour_dist = mindist\\n self.nearest_lateral_neighbour_point = minp\\n return self.nearest_lateral_neighbour_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264e881c4fa27d09e2d85dad18932a58\",\n \"score\": \"0.6526864\",\n \"text\": \"def distance(pt1, pt2):\\n return ((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01207ce2a7ffa27d91e5f1bc56078247\",\n \"score\": \"0.6519585\",\n \"text\": \"def calc_distance(x1: float, y1: float, x2: float, y2: float) -> float:\\n return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56be8e15aa3f7c2c96949207e625add\",\n \"score\": \"0.651665\",\n \"text\": \"def distance(self, x, y):\\n return math.sqrt((x - self.x) ** 2 + (y - self.y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4072ce3a758fc64a250098b45f422d06\",\n \"score\": \"0.65109724\",\n \"text\": \"def _get_distance(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c1fa13bec2791f2365e112e4f95e39a\",\n \"score\": \"0.65055317\",\n \"text\": \"def GetDistance(self):\\n return np.sqrt((self.pose.x - self.target_x)**2 + \\\\\\n (self.pose.y - self.target_y)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0e01ba5dde7865bc35527dd547a84c\",\n \"score\": \"0.6489804\",\n \"text\": \"def computeDistance(pt1,pt2):\\n return math.sqrt((pt2[1]-pt1[1])**2 + (pt2[0]-pt1[0])**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74768be8b59b9542a94878ef9cf3dfc9\",\n \"score\": \"0.64868224\",\n \"text\": \"def _get_distance(self, centroid_idx: int, point_coord=None, point_idx=None) -> float:\\n centroid_coord = self._temp_centroids[centroid_idx]\\n if point_idx is not None and point_coord is None:\\n point_coord = self.training_data[point_idx]\\n\\n distance = 0\\n for idx in range(len(point_coord)):\\n distance += pow(point_coord[idx] - centroid_coord[idx], 2)\\n return round(pow(distance, 0.5), self.ROUND_AFTER_COMA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b54e8ce8c47b172ec04a85872bc2c1d\",\n \"score\": \"0.64862436\",\n \"text\": \"def closest_point(self, point, maxdist=None):\\n return self.xyz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bfbb34c6f112439eb0ee19663c39fc2\",\n \"score\": \"0.64813703\",\n \"text\": \"def distance(point_1=(0, 0), point_2=(0, 0)):\\n return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] - point_2[1]) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e9b534b566a3e0d2565987f9b21fd2\",\n \"score\": \"0.6481139\",\n \"text\": \"def dist(self, person):\\n distance = math.sqrt(\\n (self.position[0] - person.position[0])**2 + (self.position[1] - person.position[1])**2)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dceb8b839557075b439e82c2be877fe\",\n \"score\": \"0.6480697\",\n \"text\": \"def _distance(self, x, y):\\r\\n if self.algorithm == 'gaussian':\\r\\n return self._euclidean_distance(x, y)\\r\\n else:\\r\\n return self._widx_distance(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f4299e461e84b2c311f8491b32db88\",\n \"score\": \"0.64795756\",\n \"text\": \"def distance(self, ray):\\r\\n r = self.center - ray.p\\r\\n top = np.dot(self.normal, r)\\r\\n bottom = np.dot(ray.k, self.normal)\\r\\n if np.abs(top) < eps or np.abs(bottom) < eps:\\r\\n return float('inf')\\r\\n\\r\\n d = top / bottom\\r\\n p = ray.p + d * ray.k\\r\\n d = d if self.isIntersect(p) else float('inf')\\r\\n if self.passes:\\r\\n self.passes -= 1\\r\\n return float('inf')\\r\\n else:\\r\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cab8b335481b143ff69242ef8050d36\",\n \"score\": \"0.64735156\",\n \"text\": \"def _calculate_distance(self,proposed):\\n # simulate the system\\n rates = parameterise_rates(self.rate_funcs,proposed)\\n stop_time = self.obs[-1][0]\\n init_state = self.obs[0][1:]\\n sample_trace = gillespie(rates,stop_time,init_state,self.updates)\\n # get the distance according to the error metric specified\\n return self.dist(sample_trace,self.obs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48828d2671e4c56e57871cc25449561c\",\n \"score\": \"0.64733195\",\n \"text\": \"def distance(point1, point2):\\n return np.sqrt(np.sum((point1 - point2)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48828d2671e4c56e57871cc25449561c\",\n \"score\": \"0.64733195\",\n \"text\": \"def distance(point1, point2):\\n return np.sqrt(np.sum((point1 - point2)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241acb64f4257cb0507d012616b5d942\",\n \"score\": \"0.646772\",\n \"text\": \"def get_distance(point_1, point_2):\\n delta = point_1 - point_2\\n distance = math.sqrt(numpy.dot(delta.transpose(), delta).item())\\n\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a06eb9af23d39ba98ec589adfc276f\",\n \"score\": \"0.6465166\",\n \"text\": \"def distance(self):\\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a0e577f1e465eeda199c1a96f3dade\",\n \"score\": \"0.646208\",\n \"text\": \"def distance(x1, y1, x2, y2):\\n return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07495b5bc5cd9def9664b011cb9409e\",\n \"score\": \"0.6461339\",\n \"text\": \"def dist(self,x,y):\\n xx=self.pZn(x)\\n yy=self.pZn(y)\\n if (yy<xx): xx,yy=yy,xx\\n d=yy-xx # ecart direct\\n dd=xx+(self.n-yy) # ecart par les extremites\\n if (dd<d): return dd \\n else: return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69bd18656d07427e115bd12e62ffdee6\",\n \"score\": \"0.6460986\",\n \"text\": \"def nearest(self, point):\\n dist = np.linalg.norm(self.vertices - point, axis=1)\\n best = np.argmin(dist)\\n return {\\\"node\\\": self.vertices[best], \\\"distance\\\": dist[best]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69bd18656d07427e115bd12e62ffdee6\",\n \"score\": \"0.6460986\",\n \"text\": \"def nearest(self, point):\\n dist = np.linalg.norm(self.vertices - point, axis=1)\\n best = np.argmin(dist)\\n return {\\\"node\\\": self.vertices[best], \\\"distance\\\": dist[best]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31bb2fd0e2d5352f46c35d687c2d2b50\",\n \"score\": \"0.6459042\",\n \"text\": \"def calculateDistance(origin, destination):\\n\\n return float((great_circle(origin, destination).km)*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855a88f062d59e26c274ef653be31507\",\n \"score\": \"0.64561427\",\n \"text\": \"def points_distance(self, points, return_nearest=False):\\n\\n dists = cdist(points, self.points[:-1])\\n\\n if return_nearest:\\n mini = np.argmin(dists, axis=1)\\n dists = np.take_along_axis(dists, mini[:, None], axis=1)[:, 0]\\n minp = self.points[mini]\\n del mini\\n else:\\n dists = np.min(dists, axis=1)\\n\\n for pi in range(len(self.points) - 1):\\n pA = self.points[pi]\\n pB = self.points[pi + 1]\\n n = pB - pA\\n d = np.linalg.norm(n)\\n\\n if d > 0:\\n n /= d\\n q = points - pA[None, :]\\n x = np.einsum(\\\"pd,d->p\\\", q, n)\\n\\n sel = (x > 0) & (x < d)\\n if np.any(sel):\\n x = x[sel]\\n y2 = np.maximum(np.linalg.norm(q[sel], axis=1) ** 2 - x**2, 0.0)\\n\\n dsel = dists[sel]\\n dists[sel] = np.minimum(dsel, np.sqrt(y2))\\n\\n if return_nearest:\\n mini = np.argwhere(np.sqrt(y2) < dsel)\\n hminp = minp[sel]\\n hminp[mini] = pA[None, :] + x[mini, None] * n[None, :]\\n minp[sel] = hminp\\n del mini, hminp\\n\\n del y2, dsel\\n\\n del x, sel\\n\\n if return_nearest:\\n return dists, minp\\n else:\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75108290afd24b9939feae9156cf2087\",\n \"score\": \"0.6455762\",\n \"text\": \"def distance(self, x, y):\\n dx = x - self.x\\n dy = y - self.y\\n return math.sqrt(dx**2 + dy**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2753aaefdc3c9f2f2e13acae3ab3f2ea\",\n \"score\": \"0.64529335\",\n \"text\": \"def distance(x0,y0,x1,y1):\\n return sqrt((x1-x0)**2 + (y1-y0)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"337c8ffbf1b98ee56ef6b64d628e77e0\",\n \"score\": \"0.6442676\",\n \"text\": \"def closest_face_dist_memoized(self, point):\\n mn_dist = 1.e9\\n obb_faces_3d = self.faces_3d_memoized()\\n for face_id in range(obb_faces_3d.shape[2]):\\n face = np.array(obb_faces_3d[:, :, face_id].squeeze())\\n dist, _ = pointTriangleDistance(face, np.asarray(point))\\n if dist < mn_dist:\\n mn_dist = dist\\n return mn_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b6f591c99418637611aa13a4dc5efd7\",\n \"score\": \"0.64423573\",\n \"text\": \"def distance(self):\\n detected, point = self.last_read[:2]\\n return np.linalg.norm(point) if detected else 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca197aea180f545691656270d332a58\",\n \"score\": \"0.6441566\",\n \"text\": \"def dist(self):\\n dx = list(sorted([abs(x) for x in self.pos]))\\n return dx[0] + dx[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"236a10aace3ad212d4580e182a3e18d6\",\n \"score\": \"0.6441241\",\n \"text\": \"def getDistance(point1, point2x, point2y):\\n distance = np.sqrt((point2x - point1[0])**2 + (point2y - point1[1])**2)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13a649abee4006f3147133c8a6da692\",\n \"score\": \"0.6433959\",\n \"text\": \"def distance(current_x, current_y, goal_x, goal_y):\\r\\n x_dif = abs(goal_x - current_x) * 10.29\\r\\n y_dif = abs(goal_y - current_y) * 7.55\\r\\n return math.sqrt(math.pow(x_dif, 2) +\\r\\n math.pow(y_dif, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcdff36ea13071bffe671cc42adb99d1\",\n \"score\": \"0.6427592\",\n \"text\": \"def distance(p1, p2):\\n return ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f23b5bd43f1a545f0be94c123acf78\",\n \"score\": \"0.6426846\",\n \"text\": \"def _distance((x1, y1), (x2, y2)):\\n\\n\\treturn math.sqrt((x2-x1)**2 + (y2-y1)**2)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993990,"cells":{"query_id":{"kind":"string","value":"099e314d87cb3a8f39b796e97ab050a8"},"query":{"kind":"string","value":"fixed value comparator test"},"positive_passages":{"kind":"list like","value":[{"docid":"6e63a52c489faa303fcf223b6647ddee","score":"0.6537535","text":"def test_fixed_value_comparator(self, num_state_qubits, value, geq):\n # initialize weighted sum operator factory\n comp = Comparator(num_state_qubits, value, geq)\n\n # initialize circuit\n q = QuantumRegister(num_state_qubits+1)\n if comp.required_ancillas() > 0:\n q_a = QuantumRegister(comp.required_ancillas())\n qc = QuantumCircuit(q, q_a)\n else:\n q_a = None\n qc = QuantumCircuit(q)\n\n # set equal superposition state\n qc.h(q[:num_state_qubits])\n\n # build circuit\n comp.build(qc, q, q_a)\n\n # run simulation\n job = execute(qc, BasicAer.get_backend('statevector_simulator'), shots=1)\n\n for i, s_a in enumerate(job.result().get_statevector()):\n\n prob = np.abs(s_a)**2\n if prob > 1e-6:\n # equal superposition\n self.assertEqual(True, np.isclose(1.0, prob * 2.0**num_state_qubits))\n b_value = '{0:b}'.format(i).rjust(qc.width(), '0')\n x = int(b_value[(-num_state_qubits):], 2)\n comp_result = int(b_value[-num_state_qubits-1], 2)\n if geq:\n self.assertEqual(x >= value, comp_result == 1)\n else:\n self.assertEqual(x < value, comp_result == 1)","title":""}],"string":"[\n {\n \"docid\": \"6e63a52c489faa303fcf223b6647ddee\",\n \"score\": \"0.6537535\",\n \"text\": \"def test_fixed_value_comparator(self, num_state_qubits, value, geq):\\n # initialize weighted sum operator factory\\n comp = Comparator(num_state_qubits, value, geq)\\n\\n # initialize circuit\\n q = QuantumRegister(num_state_qubits+1)\\n if comp.required_ancillas() > 0:\\n q_a = QuantumRegister(comp.required_ancillas())\\n qc = QuantumCircuit(q, q_a)\\n else:\\n q_a = None\\n qc = QuantumCircuit(q)\\n\\n # set equal superposition state\\n qc.h(q[:num_state_qubits])\\n\\n # build circuit\\n comp.build(qc, q, q_a)\\n\\n # run simulation\\n job = execute(qc, BasicAer.get_backend('statevector_simulator'), shots=1)\\n\\n for i, s_a in enumerate(job.result().get_statevector()):\\n\\n prob = np.abs(s_a)**2\\n if prob > 1e-6:\\n # equal superposition\\n self.assertEqual(True, np.isclose(1.0, prob * 2.0**num_state_qubits))\\n b_value = '{0:b}'.format(i).rjust(qc.width(), '0')\\n x = int(b_value[(-num_state_qubits):], 2)\\n comp_result = int(b_value[-num_state_qubits-1], 2)\\n if geq:\\n self.assertEqual(x >= value, comp_result == 1)\\n else:\\n self.assertEqual(x < value, comp_result == 1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a5bff1832536e4806c5a098b17b040b7","score":"0.65169156","text":"def comparator_converter(self, val):\r\n return val","title":""},{"docid":"b5bc04f0cc6c2a68b4b7b8a19a45f85c","score":"0.6449908","text":"def __cmp__(self, p):\n val = self[0].__cmp__(p[0])\n if val != 0:\n return val\n return self[1].__cmp__(p[1])","title":""},{"docid":"c6f284fdfff89dcfc0a6e5a345c98c62","score":"0.63881654","text":"def _CompareValue(self, event_value, filter_value):\n return event_value >= filter_value","title":""},{"docid":"99fee6931967a56a0e3024ff6c7e9168","score":"0.6368352","text":"def _CompareValue(self, event_value, filter_value):\n return event_value > filter_value","title":""},{"docid":"78752964c86c349ff7e2523f896ccc8a","score":"0.6346719","text":"def _CompareValue(self, event_value, filter_value):\n return event_value <= filter_value","title":""},{"docid":"7c4cf6ccf6648377fee84c4def0c120b","score":"0.63366616","text":"def _CompareValue(self, event_value, filter_value):\n return event_value < filter_value","title":""},{"docid":"d3462b5ab0af0ae7bb915e8c6d2f4211","score":"0.62081164","text":"def cmp_func(x, y):\n if x + y > y + x:\n return 1\n elif x == y:\n return 0\n else:\n return -1","title":""},{"docid":"3f1ca994f1b7c37b219649f2ae5486c8","score":"0.60899645","text":"def _comparator(func):\r\n\tdef comparator_wrapper(self, other):\r\n\t\ttry:\r\n\t\t\tassert self.enumtype == other.enumtype\r\n\t\t\tresult = func(self.index, other.index)\r\n\t\texcept (AssertionError, AttributeError):\r\n\t\t\tresult = NotImplemented\r\n\r\n\t\treturn result\r\n\tcomparator_wrapper.__name__ = func.__name__\r\n\tcomparator_wrapper.__doc__ = getattr(float, func.__name__).__doc__\r\n\treturn comparator_wrapper","title":""},{"docid":"fcf5a3b98e3089de5624e7967330b32d","score":"0.6084137","text":"def default_comparator(left, right):\n if left < right:\n return -1\n elif left == right:\n return 0\n else:\n return 1","title":""},{"docid":"8136edea717be14975a0b43b1dce245b","score":"0.60528076","text":"def na_cmp():\n return lambda x, y: int(x) == int(y) == 0","title":""},{"docid":"08a63ffbf6f885847e96c195eede3aef","score":"0.60342187","text":"def cmp(a, b):\n return bool(a > b) - bool(a < b)","title":""},{"docid":"a1dd689bfcd1d8a86ffaa17264aba4aa","score":"0.60299563","text":"def __le__(self, value):\r\n return self._comparison('<=', value)","title":""},{"docid":"c80706325ffb9558cbd92afd32ccda3a","score":"0.5999284","text":"def __ge__(self, value):\r\n return self._comparison('>=', value)","title":""},{"docid":"f029964f95177d4dedc4ab91d6fa47e2","score":"0.5996046","text":"def test_sort_list_with_neg_values(rand_neg):\n key = sorted(rand_neg)\n result = radix_sort(rand_neg)\n assert key == result","title":""},{"docid":"f6ce54c028a5514b9da0af60fb4907aa","score":"0.59779125","text":"def compare_helper(a, b):\n if a > b:\n return 1\n elif a < b:\n return -1\n else:\n return 0","title":""},{"docid":"13857895971d3fe51aaa84560097c7ea","score":"0.595721","text":"def __gt__(self, value):\r\n return self._comparison('>', value)","title":""},{"docid":"a8cba6073f20d55a488ef45b2df2a813","score":"0.5950804","text":"def __contains__(self, value: Real) -> bool:\n return self.lo < value < self.hi","title":""},{"docid":"996cbec666e237ff3553135027069b3e","score":"0.59325933","text":"def three_way_cmp(a: Any, b: Any) -> int:\n return (a > b) - (a < b)","title":""},{"docid":"e508a0b5e6d7765868d956ec5efca79c","score":"0.5932303","text":"def cmp(a, b):\n return (b < a) - (a < b)","title":""},{"docid":"db67f62d799c166427bb8a78c2d94306","score":"0.58524966","text":"def cmp(x, y):\n return (x > y) - (x < y)","title":""},{"docid":"2bcda8c463c95ca4c19a05427d41b577","score":"0.58466846","text":"def cmp(x, y):\n\n return (x > y) - (x < y)","title":""},{"docid":"2bcda8c463c95ca4c19a05427d41b577","score":"0.58466846","text":"def cmp(x, y):\n\n return (x > y) - (x < y)","title":""},{"docid":"06d463f0c3644489c85c5221e65bd5f1","score":"0.58094364","text":"def __gt__(other):","title":""},{"docid":"73ec93137d67af8a0201d330a1a25808","score":"0.5765614","text":"def test_binary_combinator_lt(f, ff):\n assert str(comb.LT(f, ff)) == \"{0} < {1}\".format(str(f), str(ff))","title":""},{"docid":"742df08e8424e2e899d7675df6de7620","score":"0.57641673","text":"def better(x, y):\r\n return x > y","title":""},{"docid":"c5b1472a7726c47b0de73f1c2b940f78","score":"0.57505983","text":"def better(x, y):\r\n return x < y","title":""},{"docid":"3e130a9086d575aef4f8bbdc9c24f0a7","score":"0.5742902","text":"def lessEqual(compare_item):\n\n def callback(item):\n return item <= compare_item\n\n return callback","title":""},{"docid":"dfdd626e90fa1994283b676914e24f29","score":"0.57421297","text":"def compare(a, b):\n if a == b:\n return 0\n elif a > b:\n return 1\n else:\n return -1","title":""},{"docid":"d46b469680fa0f65e19fc7546cde9bab","score":"0.57325137","text":"def _CompareValue(self, event_value, filter_value):\n return event_value != filter_value","title":""},{"docid":"2b3c20d16bc536bd8d84a3b3ff6cd79e","score":"0.57194525","text":"def binarize_function(val, val_low =0, val_up = 100):\n if val_low<= val < val_up :\n return 1 \n else :\n return 0","title":""},{"docid":"96dcad0d9f32dfe81c10d1e1f35b39ae","score":"0.571572","text":"def test_it_sorts_numbers_based_on_their_binary_representation(self):\n self.assertEqual(sort_bits([0, 1, 2, 3, 4, 5, 6, 7, 8]),\n [0, 1, 2, 4, 8, 3, 5, 6, 7])","title":""},{"docid":"9e03e98458043c41bdb6669bdb707a43","score":"0.57104504","text":"def __le__(self, other):\n return self.val <= other.val","title":""},{"docid":"2cb952685517fbb44d6dbcd1df02254c","score":"0.57051355","text":"def _CompareValue(self, event_value, filter_value):","title":""},{"docid":"394ceb205dcafb6af417814966255f8a","score":"0.57043767","text":"def test_binary_combinator_gt(f, ff):\n assert str(comb.GT(f, ff)) == \"{0} > {1}\".format(str(f), str(ff))","title":""},{"docid":"2bda7189d7fae2c927c0a8bfe69429d9","score":"0.5703504","text":"def compare(a, b):\n if a > b:\n return 1\n elif a < b:\n return -1\n else:\n return 0","title":""},{"docid":"0381b880320ea1398e96203788987a48","score":"0.5683629","text":"def get_cmp(table, col_name, value):\r\n col = getattr(table.c, col_name)\r\n original_value = value\r\n\r\n neg = False\r\n cmp_in = False\r\n regex = False\r\n max_ilvl = False\r\n\r\n is_string = isinstance(value, basestring)\r\n\r\n if is_string and value.startswith(\"!\"):\r\n value = value[1:]\r\n neg = True\r\n\r\n if is_string and value.endswith(\"^\"):\r\n value = value[:-1]\r\n assert str(table) == \"item_template\"\r\n # TODO: support other tables where a use case exists for restricting\r\n # results to the MAX/MIN of a specific column\r\n max_ilvl = True\r\n\r\n if is_string and value.startswith(\"/\") and value.endswith(\"/\"):\r\n value = value[1:-1]\r\n regex = True\r\n\r\n elif is_string and value.startswith(\"in \"):\r\n value = value[3:]\r\n cmp_in = True\r\n\r\n flags = get_flags(col_name, value)\r\n if flags is not None:\r\n value = col.in_(flags) if cmp_in else (col == flags)\r\n elif regex:\r\n value = col.op(\"rlike\")(value)\r\n elif is_string and \"%\" in value:\r\n value = col.like(value)\r\n if neg:\r\n value = not_(value)\r\n elif neg:\r\n value = not_(value)\r\n elif flags is None:\r\n value = col == value\r\n\r\n # TODO: actually handle this monkeypatch in the Model object, and stop using\r\n # the AWFUL subquery hack below.\r\n value._max = None\r\n if max_ilvl:\r\n value._max = func.max(table.c.ItemLevel)\r\n subquery = select([\"*\"]).select_from(\r\n select([func.max(table.c.ItemLevel)]).where(value).alias(\"tmp\")\r\n )\r\n value = and_(value, table.c.ItemLevel == subquery)\r\n\r\n return (value, original_value)","title":""},{"docid":"a22797be5947f8c4fa682804bb9a4b34","score":"0.56774604","text":"def test_binary_combinator_lte(f, ff):\n assert str(comb.LTE(f, ff)) == \"{0} <= {1}\".format(str(f), str(ff))","title":""},{"docid":"b803d6f5f3095adde12ddfc40ee22231","score":"0.56651545","text":"def _compare(new_value, old_value):\r\n if isinstance(new_value, (int, float)) and type(new_value) == type(old_value):\r\n # if numeric, compare within a threshold\r\n # TODO\r\n return abs(new_value - old_value) > 0.00001\r\n\r\n elif type(new_value) != type(old_value):\r\n return True\r\n\r\n elif isinstance(new_value, (pd.DataFrame, pd.Series, np.ndarray)) or isinstance(\r\n old_value, (pd.DataFrame, pd.Series, np.ndarray)\r\n ):\r\n return (abs(new_value - old_value) > 0.00001).any()\r\n\r\n return new_value != old_value","title":""},{"docid":"f9162ca6fc614a19da48ecc8feb91f32","score":"0.56622916","text":"def _CompareValue(self, event_value, filter_value):\n return event_value == filter_value","title":""},{"docid":"896aaef9b329c5ced5c0bc7f0b7b953c","score":"0.5658262","text":"def Compare_default(t, x):\n exps = [x.left] + x.comparators\n bools = []\n for i in range(len(x.ops)):\n bools.append(JSBinOp(exps[i], x.ops[i], exps[i + 1]))\n return reduce(lambda x, y: JSBinOp(x, JSOpAnd(), y), bools)","title":""},{"docid":"213dc5165d719ed916636a2fcfc615e6","score":"0.56468433","text":"def is_better_than(self, val1, val2):\n return val1 > val2","title":""},{"docid":"0da955568d85399a99518a95ad6ed93c","score":"0.56459945","text":"def test_pvals(self, pvals):\n x = 0\n for bnd in self.bnds:\n if bnd[0] < pvals[x] < bnd[1]:\n print '%x in bnds' %x\n else:\n print '%x not in bnds' %x\n x += 1\n return pvals","title":""},{"docid":"7be17b2123d6d4d1241ce2125b3a4b74","score":"0.56432354","text":"def compare(self, compare_val):\r\n if compare_val == self._value:\r\n return True\r\n else:\r\n return False","title":""},{"docid":"de0760b4a1cc622311160eec42f18790","score":"0.5639935","text":"def gt(value, arg):\n return long(value) > long(arg)","title":""},{"docid":"e866c6c7615539d0935a952d5165eac6","score":"0.5636675","text":"def gt(value1, value2):\n return value1 > value2","title":""},{"docid":"c392b6f6537290b00453644b24bb970d","score":"0.5634278","text":"def gt(y, x):\n return x > y","title":""},{"docid":"153111ee576d158d501126618fe86e17","score":"0.56247395","text":"def compare(a, b):\n gt = False\n lt = False\n for j, k in zip(a, b):\n gt |= j > k\n lt |= j < k\n if gt and lt:\n break\n return int(gt) - int(lt)","title":""},{"docid":"db673f84f18005e08c8d5471754c3215","score":"0.56242937","text":"def __cmp__(self, other):\n if isinstance(other, (int, long)):\n if other in self:\n return 0\n return cmp(self[0], other)\n return super(LumiblockRange, self).__cmp__(other)","title":""},{"docid":"f927d30d007befe882110e5387821e19","score":"0.56230444","text":"def comparator(operator: Any) -> Any: # -> (self: Unknown, other: Unknown) -> Unknown:\n ...","title":""},{"docid":"b3d1a3e9189771f6a9adefe75726972f","score":"0.5618732","text":"def _is_lower_than(self, item1, item2):","title":""},{"docid":"2453650e6c13a428e9cdd40c10195372","score":"0.5617463","text":"def compare_to(self, obj):\n result = super().compare_to(obj)\n if result == 0:\n return compare_to_boolean(self.__value, obj.get_value())\n return result","title":""},{"docid":"c29653a6354355b2366428d9fb59a9bd","score":"0.56155455","text":"def __gt__(self,other): #>\r\n return float(self)>float(other)","title":""},{"docid":"158e5b5a3f829cf7e0dc73f8a9102a7e","score":"0.5609993","text":"def prefere(a1, a2):\n if a1 < a2:\n return True\n else:\n return False","title":""},{"docid":"69e930e539a9979791ec391ec08e1e78","score":"0.5609589","text":"def compare(val_1, val_2):\n return (val_1 & 0xffff) == (val_2 & 0xffff)","title":""},{"docid":"849ce300660edb7c6a851eb2395b4e54","score":"0.5607712","text":"def compare_pv(arr,idx):\n return True if idx > 0 and arr[idx] > arr[idx - 1] else False # Compares values and checks if idx is at beginning","title":""},{"docid":"8d283a253b518173ea567400526b6dae","score":"0.56054753","text":"def na_cmp():\n return operator.is_","title":""},{"docid":"614626ade817e52619dc28947e8cb947","score":"0.56032354","text":"def test_cmp_sorted(self, obj, result, **kwargs):\n self.assertEqual(metalk8s.cmp_sorted(obj, **kwargs), result)","title":""},{"docid":"ee52f66e628302e4f097e35f48afec73","score":"0.5597271","text":"def test_fixed_list():\n\n fixed = selection_sort([one_list()])\n\n assert all(fixed[i] <= fixed[i+1] for i in range(len(fixed)-1))","title":""},{"docid":"b6695a04b6fa9911651c103d420b456a","score":"0.55863136","text":"def _GetCmpFunc(col):\n if col.startswith('~'):\n col = col[1:]\n descending = True\n else:\n descending = False\n\n field_getter = property_selector.PropertyGetter(col)\n\n def Cmp(r1, r2):\n \"\"\"A comparison function.\"\"\"\n v1 = field_getter.Get(r1)\n v2 = field_getter.Get(r2)\n\n retval = cmp(v1, v2)\n\n # We want None to have the highest ordering, so if either value is\n # None, we have to negate the result of cmp().\n if v1 is None or v2 is None:\n retval *= -1\n\n if descending:\n return retval * -1\n else:\n return retval\n\n return Cmp","title":""},{"docid":"e4a74dda9e46e813a997ccc6f5f739b7","score":"0.5575672","text":"def __ge__(self,other): #>=\r\n return self==other or float(self)>float(other)","title":""},{"docid":"aac22429549a12f1a8e8f5c9e5bc5d52","score":"0.5561724","text":"def __cmp__(self, right):\n if not self.__b:\n # self is oo, which is bigger than everything but oo.\n if not right.__b:\n return 0\n else:\n return 1\n elif not right.__b:\n if not self.__b:\n return 0\n else:\n return -1\n return cmp(self._rational_(), right._rational_())","title":""},{"docid":"f8ae2da2725002d1e2a57765a14f92b2","score":"0.55459964","text":"def _compare(self, first, second):\n pass","title":""},{"docid":"a65edf11d782a7c79bcac99a0e865923","score":"0.5542708","text":"def test_numbers_can_be_in_any_order(self):\n self.assertEqual(sort_bits([8, 0, 1, 3, 5, 4, 2, 6, 7]),\n [0, 1, 2, 4, 8, 3, 5, 6, 7])","title":""},{"docid":"37d1de2c79844ff90e5bc8bd064ca1f0","score":"0.5534672","text":"def __gt__( self, other ) :\n\n return( self.compare( other, 5 ) > 0 )","title":""},{"docid":"cb5353c9ab8e354f13ee4f3294706892","score":"0.552876","text":"def adjustedCompareValue(self, value):\n try:\n return gennumber.GenNumber(value).num\n except ValueError:\n return 0","title":""},{"docid":"9f2881df0b1fe4d2e6a7604d3fc16556","score":"0.55265766","text":"def _equal_to_ (f,v) :\n return opers.__equal_to__(f,v)","title":""},{"docid":"189d37ed1f6656e041f4dc0028de1de4","score":"0.55171615","text":"def greaterEqual(compare_item):\n\n def callback(item):\n return item >= compare_item\n\n return callback","title":""},{"docid":"42a70258809c97981e6e01f33f72a923","score":"0.5515798","text":"def _comp(self, a_node_index, b_node_index):\n if self.min:\n return self._lte(a_node_index, b_node_index)\n else:\n return self._lte(b_node_index, a_node_index)","title":""},{"docid":"9ad9542268df257b4240b1f9cc029b3e","score":"0.55149037","text":"def __cmp__(self, other):\n return 0","title":""},{"docid":"9559de76f6a658f29bc52a83e2483d18","score":"0.5513158","text":"def __ge__(self, other):\n raise NotImplementedError(\"Cannot compare CIs with >=\")","title":""},{"docid":"46c7c9b9e106b846c58e057d783e3925","score":"0.5490949","text":"def test_binary_combinator_gte(f, ff):\n assert str(comb.GTE(f, ff)) == \"{0} >= {1}\".format(str(f), str(ff))","title":""},{"docid":"81fd759b710dc8d2c090eec79a94e60b","score":"0.5489729","text":"def cmp_versions(v1, v2):\n def normalize(v):\n return [int(x) for x in re.sub(r'(\\.0+)*$', '', v).split(\".\")]\n\n n1 = normalize(v1)\n n2 = normalize(v2)\n return (n1 > n2) - (n1 < n2)","title":""},{"docid":"567919d05aed7f80284a87778160982f","score":"0.54817504","text":"def compare (v1, v2):\r\n v1_norm = normalize(v1)\r\n v2_norm = normalize(v2)\r\n if v1_norm < v2_norm:\r\n return -1\r\n if v1_norm > v2_norm:\r\n return 1\r\n return 0","title":""},{"docid":"8c2ae8dc1c1a13197b5984fadfa14ff4","score":"0.54794455","text":"def __gt__(self, other):","title":""},{"docid":"d34190deb3c39bcbf8aeca53366d6c90","score":"0.54788876","text":"def __le__( self, other ) :\n\n return( self.compare( other, 5 ) <= 0 )","title":""},{"docid":"285d24e8fe406eeecfe0b291ed65c41a","score":"0.54756546","text":"def twos_comp(val):\n if(val>=0):\n return val\n else:\n return 2**16+val #not(-val-1)","title":""},{"docid":"84f739ea5cf4160460c8449074b0bd52","score":"0.5471509","text":"def test_range_sorting_in_fh():\n standard_range = ForecastingHorizon(values=range(5))\n assert (standard_range == ForecastingHorizon(values=[0, 3, 4, 1, 2])).all()","title":""},{"docid":"7c3e36ead0bc8c8eb557b65b37ff1c24","score":"0.5447383","text":"def __gt__(self, other):\n return int(self) > int(other)","title":""},{"docid":"9b97e8d904f20c1ace9d77828b6ccb3a","score":"0.54462487","text":"def __ge__( self, other ) :\n\n return( self.compare( other, 5 ) >= 0 )","title":""},{"docid":"af32e7b1d1027bd088aa089d2bc54c41","score":"0.544412","text":"def cmp_f(x, y):\n epsilon = 0.00000001\n # Check absolute precision.\n if -epsilon <= x - y <= epsilon:\n return True\n\n # Is x or y too close to zero?\n if -epsilon <= x <= epsilon or -epsilon <= y <= epsilon:\n return False\n\n # Check relative precision.\n return (-epsilon <= (x - y) / x <= epsilon\n or -epsilon <= (x - y) / y <= epsilon)","title":""},{"docid":"af8f89850de49e93039132140252e230","score":"0.54404444","text":"def __gt__(self, other):\n return self.priority > other.priority or (self.priority == other.priority and self.key > other.key)","title":""},{"docid":"4594671ad0e1e0e5db221bc506eaa0e0","score":"0.5434453","text":"def compare(x, y):\n\tif x > y:\n\t\treturn 1\n\tif x == y:\n\t\treturn 0\n\tif x < y:\n\t\treturn -1","title":""},{"docid":"1d515df6acce5f99006868906f7e06aa","score":"0.54308546","text":"def __gt__(self, other):\n return float(self) > float(other)","title":""},{"docid":"c48fc7134a89a0b65cf8c97b943c776b","score":"0.5416734","text":"def _rvs(cls, U, p):\n return U < p","title":""},{"docid":"1f59e6f10c46aefe21960f7fef429edf","score":"0.5416148","text":"def __gt__(self, other):\n return self.getData() > other","title":""},{"docid":"43d572294c0df536a6e8a1011cda05eb","score":"0.5414917","text":"def compare(self, left, right):\n return cmp(left, right)","title":""},{"docid":"b86d4f0555f42c15f49e6fec3851a769","score":"0.54073787","text":"def comp(a, b):\n for x in range(len(a)):\n if a[x] > b[x]:\n return 1\n elif a[x] < b[x]:\n return 2\n return 0","title":""},{"docid":"7c544bdf9feda5e49991275cead04ac4","score":"0.5407196","text":"def my_compare(x, y):\n if x % 2 == y % 2:\n return compare(x, y)\n if x % 2 == 0:\n return 1\n else:\n return -1","title":""},{"docid":"72ef154778fc7035000ed5b01472113e","score":"0.540564","text":"def precision_at_position_1(sort_data):\n\n if sort_data[0][1] == 1:\n return 1\n else:\n return 0","title":""},{"docid":"a57abcb663bb33eb4fdd0dbbbc7d939c","score":"0.5405529","text":"def __le__(self,other): #<=\r\n return self==other or float(self)<float(other)","title":""},{"docid":"4ef99704374202f212ec75077667eccc","score":"0.53977454","text":"def __ge__(self, other):\r\n return self == other or self > other","title":""},{"docid":"4ef99704374202f212ec75077667eccc","score":"0.53977454","text":"def __ge__(self, other):\r\n return self == other or self > other","title":""},{"docid":"b7359c948ecbbe12d6fae6bb531a21e2","score":"0.5397527","text":"def lte(value, arg):\n return long(value) <= long(arg)","title":""},{"docid":"99456fcc32f4a1bbdd72f18da34cb5aa","score":"0.5394073","text":"def cmp(a, b): # pragma: no cover\n if a is None and b is None:\n return 0\n elif a is None:\n return -1\n elif b is None:\n return 1\n return (a > b) - (a < b)","title":""},{"docid":"7f8dadd3c1bc06b8263560d204fc3c8a","score":"0.53925204","text":"def __le__(self, other):\n return float(self) <= float(other)","title":""},{"docid":"d1db31b77dfc05a913feabf6cda5ae0b","score":"0.53807455","text":"def test_natural_sort(self):\n l = ['100_b','10_bb','100_a','20_aa','500_c', '9_c']\n exp =['9_c','10_bb','20_aa','100_a','100_b','500_c']\n obs = natural_sort(l)\n\n self.assertEqual(obs, exp)","title":""},{"docid":"5aa0db7296e6294052e86816a649e3bf","score":"0.53804195","text":"def _compare(self, other, method):\r\n return method(self.value, other)","title":""},{"docid":"9ee8d2920a6ce72361dc2f6768a13dcb","score":"0.53720444","text":"def test_compare_custom_object(self):\n\n class CustomClass:\n def __init__(self, cmp_result=None):\n self.cmp_result = cmp_result\n\n def generic_result(self):\n if self.cmp_result is None:\n return NotImplemented\n else:\n return self.cmp_result\n\n def __eq__(self, other):\n return self.generic_result()\n\n def __gt__(self, other):\n return self.generic_result()\n\n t = Timedelta(\"1s\")\n\n assert not (t == \"string\")\n assert not (t == 1)\n assert not (t == CustomClass())\n assert not (t == CustomClass(cmp_result=False))\n\n assert t < CustomClass(cmp_result=True)\n assert not (t < CustomClass(cmp_result=False))\n\n assert t == CustomClass(cmp_result=True)","title":""},{"docid":"dc463d83ad508ae00ea36d7b80ad9d36","score":"0.5369304","text":"def compare_to_100(value):\n if value < 100:\n return 'less than 100'\n elif value == 100:\n return 'equal to 100'\n else:\n return 'more than 100'","title":""},{"docid":"1caae99650df61b15245bf72d4735245","score":"0.53589207","text":"def __ge__(self, other):\n return self > other and self == other","title":""}],"string":"[\n {\n \"docid\": \"a5bff1832536e4806c5a098b17b040b7\",\n \"score\": \"0.65169156\",\n \"text\": \"def comparator_converter(self, val):\\r\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5bc04f0cc6c2a68b4b7b8a19a45f85c\",\n \"score\": \"0.6449908\",\n \"text\": \"def __cmp__(self, p):\\n val = self[0].__cmp__(p[0])\\n if val != 0:\\n return val\\n return self[1].__cmp__(p[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f284fdfff89dcfc0a6e5a345c98c62\",\n \"score\": \"0.63881654\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value >= filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99fee6931967a56a0e3024ff6c7e9168\",\n \"score\": \"0.6368352\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value > filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78752964c86c349ff7e2523f896ccc8a\",\n \"score\": \"0.6346719\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value <= filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4cf6ccf6648377fee84c4def0c120b\",\n \"score\": \"0.63366616\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value < filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3462b5ab0af0ae7bb915e8c6d2f4211\",\n \"score\": \"0.62081164\",\n \"text\": \"def cmp_func(x, y):\\n if x + y > y + x:\\n return 1\\n elif x == y:\\n return 0\\n else:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1ca994f1b7c37b219649f2ae5486c8\",\n \"score\": \"0.60899645\",\n \"text\": \"def _comparator(func):\\r\\n\\tdef comparator_wrapper(self, other):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tassert self.enumtype == other.enumtype\\r\\n\\t\\t\\tresult = func(self.index, other.index)\\r\\n\\t\\texcept (AssertionError, AttributeError):\\r\\n\\t\\t\\tresult = NotImplemented\\r\\n\\r\\n\\t\\treturn result\\r\\n\\tcomparator_wrapper.__name__ = func.__name__\\r\\n\\tcomparator_wrapper.__doc__ = getattr(float, func.__name__).__doc__\\r\\n\\treturn comparator_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf5a3b98e3089de5624e7967330b32d\",\n \"score\": \"0.6084137\",\n \"text\": \"def default_comparator(left, right):\\n if left < right:\\n return -1\\n elif left == right:\\n return 0\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8136edea717be14975a0b43b1dce245b\",\n \"score\": \"0.60528076\",\n \"text\": \"def na_cmp():\\n return lambda x, y: int(x) == int(y) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a63ffbf6f885847e96c195eede3aef\",\n \"score\": \"0.60342187\",\n \"text\": \"def cmp(a, b):\\n return bool(a > b) - bool(a < b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1dd689bfcd1d8a86ffaa17264aba4aa\",\n \"score\": \"0.60299563\",\n \"text\": \"def __le__(self, value):\\r\\n return self._comparison('<=', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80706325ffb9558cbd92afd32ccda3a\",\n \"score\": \"0.5999284\",\n \"text\": \"def __ge__(self, value):\\r\\n return self._comparison('>=', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f029964f95177d4dedc4ab91d6fa47e2\",\n \"score\": \"0.5996046\",\n \"text\": \"def test_sort_list_with_neg_values(rand_neg):\\n key = sorted(rand_neg)\\n result = radix_sort(rand_neg)\\n assert key == result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ce54c028a5514b9da0af60fb4907aa\",\n \"score\": \"0.59779125\",\n \"text\": \"def compare_helper(a, b):\\n if a > b:\\n return 1\\n elif a < b:\\n return -1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13857895971d3fe51aaa84560097c7ea\",\n \"score\": \"0.595721\",\n \"text\": \"def __gt__(self, value):\\r\\n return self._comparison('>', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8cba6073f20d55a488ef45b2df2a813\",\n \"score\": \"0.5950804\",\n \"text\": \"def __contains__(self, value: Real) -> bool:\\n return self.lo < value < self.hi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996cbec666e237ff3553135027069b3e\",\n \"score\": \"0.59325933\",\n \"text\": \"def three_way_cmp(a: Any, b: Any) -> int:\\n return (a > b) - (a < b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e508a0b5e6d7765868d956ec5efca79c\",\n \"score\": \"0.5932303\",\n \"text\": \"def cmp(a, b):\\n return (b < a) - (a < b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db67f62d799c166427bb8a78c2d94306\",\n \"score\": \"0.58524966\",\n \"text\": \"def cmp(x, y):\\n return (x > y) - (x < y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bcda8c463c95ca4c19a05427d41b577\",\n \"score\": \"0.58466846\",\n \"text\": \"def cmp(x, y):\\n\\n return (x > y) - (x < y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bcda8c463c95ca4c19a05427d41b577\",\n \"score\": \"0.58466846\",\n \"text\": \"def cmp(x, y):\\n\\n return (x > y) - (x < y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d463f0c3644489c85c5221e65bd5f1\",\n \"score\": \"0.58094364\",\n \"text\": \"def __gt__(other):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ec93137d67af8a0201d330a1a25808\",\n \"score\": \"0.5765614\",\n \"text\": \"def test_binary_combinator_lt(f, ff):\\n assert str(comb.LT(f, ff)) == \\\"{0} < {1}\\\".format(str(f), str(ff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742df08e8424e2e899d7675df6de7620\",\n \"score\": \"0.57641673\",\n \"text\": \"def better(x, y):\\r\\n return x > y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b1472a7726c47b0de73f1c2b940f78\",\n \"score\": \"0.57505983\",\n \"text\": \"def better(x, y):\\r\\n return x < y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e130a9086d575aef4f8bbdc9c24f0a7\",\n \"score\": \"0.5742902\",\n \"text\": \"def lessEqual(compare_item):\\n\\n def callback(item):\\n return item <= compare_item\\n\\n return callback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfdd626e90fa1994283b676914e24f29\",\n \"score\": \"0.57421297\",\n \"text\": \"def compare(a, b):\\n if a == b:\\n return 0\\n elif a > b:\\n return 1\\n else:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46b469680fa0f65e19fc7546cde9bab\",\n \"score\": \"0.57325137\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value != filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3c20d16bc536bd8d84a3b3ff6cd79e\",\n \"score\": \"0.57194525\",\n \"text\": \"def binarize_function(val, val_low =0, val_up = 100):\\n if val_low<= val < val_up :\\n return 1 \\n else :\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96dcad0d9f32dfe81c10d1e1f35b39ae\",\n \"score\": \"0.571572\",\n \"text\": \"def test_it_sorts_numbers_based_on_their_binary_representation(self):\\n self.assertEqual(sort_bits([0, 1, 2, 3, 4, 5, 6, 7, 8]),\\n [0, 1, 2, 4, 8, 3, 5, 6, 7])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e03e98458043c41bdb6669bdb707a43\",\n \"score\": \"0.57104504\",\n \"text\": \"def __le__(self, other):\\n return self.val <= other.val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb952685517fbb44d6dbcd1df02254c\",\n \"score\": \"0.57051355\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"394ceb205dcafb6af417814966255f8a\",\n \"score\": \"0.57043767\",\n \"text\": \"def test_binary_combinator_gt(f, ff):\\n assert str(comb.GT(f, ff)) == \\\"{0} > {1}\\\".format(str(f), str(ff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bda7189d7fae2c927c0a8bfe69429d9\",\n \"score\": \"0.5703504\",\n \"text\": \"def compare(a, b):\\n if a > b:\\n return 1\\n elif a < b:\\n return -1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0381b880320ea1398e96203788987a48\",\n \"score\": \"0.5683629\",\n \"text\": \"def get_cmp(table, col_name, value):\\r\\n col = getattr(table.c, col_name)\\r\\n original_value = value\\r\\n\\r\\n neg = False\\r\\n cmp_in = False\\r\\n regex = False\\r\\n max_ilvl = False\\r\\n\\r\\n is_string = isinstance(value, basestring)\\r\\n\\r\\n if is_string and value.startswith(\\\"!\\\"):\\r\\n value = value[1:]\\r\\n neg = True\\r\\n\\r\\n if is_string and value.endswith(\\\"^\\\"):\\r\\n value = value[:-1]\\r\\n assert str(table) == \\\"item_template\\\"\\r\\n # TODO: support other tables where a use case exists for restricting\\r\\n # results to the MAX/MIN of a specific column\\r\\n max_ilvl = True\\r\\n\\r\\n if is_string and value.startswith(\\\"/\\\") and value.endswith(\\\"/\\\"):\\r\\n value = value[1:-1]\\r\\n regex = True\\r\\n\\r\\n elif is_string and value.startswith(\\\"in \\\"):\\r\\n value = value[3:]\\r\\n cmp_in = True\\r\\n\\r\\n flags = get_flags(col_name, value)\\r\\n if flags is not None:\\r\\n value = col.in_(flags) if cmp_in else (col == flags)\\r\\n elif regex:\\r\\n value = col.op(\\\"rlike\\\")(value)\\r\\n elif is_string and \\\"%\\\" in value:\\r\\n value = col.like(value)\\r\\n if neg:\\r\\n value = not_(value)\\r\\n elif neg:\\r\\n value = not_(value)\\r\\n elif flags is None:\\r\\n value = col == value\\r\\n\\r\\n # TODO: actually handle this monkeypatch in the Model object, and stop using\\r\\n # the AWFUL subquery hack below.\\r\\n value._max = None\\r\\n if max_ilvl:\\r\\n value._max = func.max(table.c.ItemLevel)\\r\\n subquery = select([\\\"*\\\"]).select_from(\\r\\n select([func.max(table.c.ItemLevel)]).where(value).alias(\\\"tmp\\\")\\r\\n )\\r\\n value = and_(value, table.c.ItemLevel == subquery)\\r\\n\\r\\n return (value, original_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22797be5947f8c4fa682804bb9a4b34\",\n \"score\": \"0.56774604\",\n \"text\": \"def test_binary_combinator_lte(f, ff):\\n assert str(comb.LTE(f, ff)) == \\\"{0} <= {1}\\\".format(str(f), str(ff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b803d6f5f3095adde12ddfc40ee22231\",\n \"score\": \"0.56651545\",\n \"text\": \"def _compare(new_value, old_value):\\r\\n if isinstance(new_value, (int, float)) and type(new_value) == type(old_value):\\r\\n # if numeric, compare within a threshold\\r\\n # TODO\\r\\n return abs(new_value - old_value) > 0.00001\\r\\n\\r\\n elif type(new_value) != type(old_value):\\r\\n return True\\r\\n\\r\\n elif isinstance(new_value, (pd.DataFrame, pd.Series, np.ndarray)) or isinstance(\\r\\n old_value, (pd.DataFrame, pd.Series, np.ndarray)\\r\\n ):\\r\\n return (abs(new_value - old_value) > 0.00001).any()\\r\\n\\r\\n return new_value != old_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9162ca6fc614a19da48ecc8feb91f32\",\n \"score\": \"0.56622916\",\n \"text\": \"def _CompareValue(self, event_value, filter_value):\\n return event_value == filter_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"896aaef9b329c5ced5c0bc7f0b7b953c\",\n \"score\": \"0.5658262\",\n \"text\": \"def Compare_default(t, x):\\n exps = [x.left] + x.comparators\\n bools = []\\n for i in range(len(x.ops)):\\n bools.append(JSBinOp(exps[i], x.ops[i], exps[i + 1]))\\n return reduce(lambda x, y: JSBinOp(x, JSOpAnd(), y), bools)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213dc5165d719ed916636a2fcfc615e6\",\n \"score\": \"0.56468433\",\n \"text\": \"def is_better_than(self, val1, val2):\\n return val1 > val2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0da955568d85399a99518a95ad6ed93c\",\n \"score\": \"0.56459945\",\n \"text\": \"def test_pvals(self, pvals):\\n x = 0\\n for bnd in self.bnds:\\n if bnd[0] < pvals[x] < bnd[1]:\\n print '%x in bnds' %x\\n else:\\n print '%x not in bnds' %x\\n x += 1\\n return pvals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be17b2123d6d4d1241ce2125b3a4b74\",\n \"score\": \"0.56432354\",\n \"text\": \"def compare(self, compare_val):\\r\\n if compare_val == self._value:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0760b4a1cc622311160eec42f18790\",\n \"score\": \"0.5639935\",\n \"text\": \"def gt(value, arg):\\n return long(value) > long(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e866c6c7615539d0935a952d5165eac6\",\n \"score\": \"0.5636675\",\n \"text\": \"def gt(value1, value2):\\n return value1 > value2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c392b6f6537290b00453644b24bb970d\",\n \"score\": \"0.5634278\",\n \"text\": \"def gt(y, x):\\n return x > y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153111ee576d158d501126618fe86e17\",\n \"score\": \"0.56247395\",\n \"text\": \"def compare(a, b):\\n gt = False\\n lt = False\\n for j, k in zip(a, b):\\n gt |= j > k\\n lt |= j < k\\n if gt and lt:\\n break\\n return int(gt) - int(lt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db673f84f18005e08c8d5471754c3215\",\n \"score\": \"0.56242937\",\n \"text\": \"def __cmp__(self, other):\\n if isinstance(other, (int, long)):\\n if other in self:\\n return 0\\n return cmp(self[0], other)\\n return super(LumiblockRange, self).__cmp__(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f927d30d007befe882110e5387821e19\",\n \"score\": \"0.56230444\",\n \"text\": \"def comparator(operator: Any) -> Any: # -> (self: Unknown, other: Unknown) -> Unknown:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d1a3e9189771f6a9adefe75726972f\",\n \"score\": \"0.5618732\",\n \"text\": \"def _is_lower_than(self, item1, item2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2453650e6c13a428e9cdd40c10195372\",\n \"score\": \"0.5617463\",\n \"text\": \"def compare_to(self, obj):\\n result = super().compare_to(obj)\\n if result == 0:\\n return compare_to_boolean(self.__value, obj.get_value())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29653a6354355b2366428d9fb59a9bd\",\n \"score\": \"0.56155455\",\n \"text\": \"def __gt__(self,other): #>\\r\\n return float(self)>float(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158e5b5a3f829cf7e0dc73f8a9102a7e\",\n \"score\": \"0.5609993\",\n \"text\": \"def prefere(a1, a2):\\n if a1 < a2:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e930e539a9979791ec391ec08e1e78\",\n \"score\": \"0.5609589\",\n \"text\": \"def compare(val_1, val_2):\\n return (val_1 & 0xffff) == (val_2 & 0xffff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849ce300660edb7c6a851eb2395b4e54\",\n \"score\": \"0.5607712\",\n \"text\": \"def compare_pv(arr,idx):\\n return True if idx > 0 and arr[idx] > arr[idx - 1] else False # Compares values and checks if idx is at beginning\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d283a253b518173ea567400526b6dae\",\n \"score\": \"0.56054753\",\n \"text\": \"def na_cmp():\\n return operator.is_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614626ade817e52619dc28947e8cb947\",\n \"score\": \"0.56032354\",\n \"text\": \"def test_cmp_sorted(self, obj, result, **kwargs):\\n self.assertEqual(metalk8s.cmp_sorted(obj, **kwargs), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee52f66e628302e4f097e35f48afec73\",\n \"score\": \"0.5597271\",\n \"text\": \"def test_fixed_list():\\n\\n fixed = selection_sort([one_list()])\\n\\n assert all(fixed[i] <= fixed[i+1] for i in range(len(fixed)-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6695a04b6fa9911651c103d420b456a\",\n \"score\": \"0.55863136\",\n \"text\": \"def _GetCmpFunc(col):\\n if col.startswith('~'):\\n col = col[1:]\\n descending = True\\n else:\\n descending = False\\n\\n field_getter = property_selector.PropertyGetter(col)\\n\\n def Cmp(r1, r2):\\n \\\"\\\"\\\"A comparison function.\\\"\\\"\\\"\\n v1 = field_getter.Get(r1)\\n v2 = field_getter.Get(r2)\\n\\n retval = cmp(v1, v2)\\n\\n # We want None to have the highest ordering, so if either value is\\n # None, we have to negate the result of cmp().\\n if v1 is None or v2 is None:\\n retval *= -1\\n\\n if descending:\\n return retval * -1\\n else:\\n return retval\\n\\n return Cmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a74dda9e46e813a997ccc6f5f739b7\",\n \"score\": \"0.5575672\",\n \"text\": \"def __ge__(self,other): #>=\\r\\n return self==other or float(self)>float(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac22429549a12f1a8e8f5c9e5bc5d52\",\n \"score\": \"0.5561724\",\n \"text\": \"def __cmp__(self, right):\\n if not self.__b:\\n # self is oo, which is bigger than everything but oo.\\n if not right.__b:\\n return 0\\n else:\\n return 1\\n elif not right.__b:\\n if not self.__b:\\n return 0\\n else:\\n return -1\\n return cmp(self._rational_(), right._rational_())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8ae2da2725002d1e2a57765a14f92b2\",\n \"score\": \"0.55459964\",\n \"text\": \"def _compare(self, first, second):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65edf11d782a7c79bcac99a0e865923\",\n \"score\": \"0.5542708\",\n \"text\": \"def test_numbers_can_be_in_any_order(self):\\n self.assertEqual(sort_bits([8, 0, 1, 3, 5, 4, 2, 6, 7]),\\n [0, 1, 2, 4, 8, 3, 5, 6, 7])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d1de2c79844ff90e5bc8bd064ca1f0\",\n \"score\": \"0.5534672\",\n \"text\": \"def __gt__( self, other ) :\\n\\n return( self.compare( other, 5 ) > 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5353c9ab8e354f13ee4f3294706892\",\n \"score\": \"0.552876\",\n \"text\": \"def adjustedCompareValue(self, value):\\n try:\\n return gennumber.GenNumber(value).num\\n except ValueError:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2881df0b1fe4d2e6a7604d3fc16556\",\n \"score\": \"0.55265766\",\n \"text\": \"def _equal_to_ (f,v) :\\n return opers.__equal_to__(f,v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189d37ed1f6656e041f4dc0028de1de4\",\n \"score\": \"0.55171615\",\n \"text\": \"def greaterEqual(compare_item):\\n\\n def callback(item):\\n return item >= compare_item\\n\\n return callback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a70258809c97981e6e01f33f72a923\",\n \"score\": \"0.5515798\",\n \"text\": \"def _comp(self, a_node_index, b_node_index):\\n if self.min:\\n return self._lte(a_node_index, b_node_index)\\n else:\\n return self._lte(b_node_index, a_node_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad9542268df257b4240b1f9cc029b3e\",\n \"score\": \"0.55149037\",\n \"text\": \"def __cmp__(self, other):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9559de76f6a658f29bc52a83e2483d18\",\n \"score\": \"0.5513158\",\n \"text\": \"def __ge__(self, other):\\n raise NotImplementedError(\\\"Cannot compare CIs with >=\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c7c9b9e106b846c58e057d783e3925\",\n \"score\": \"0.5490949\",\n \"text\": \"def test_binary_combinator_gte(f, ff):\\n assert str(comb.GTE(f, ff)) == \\\"{0} >= {1}\\\".format(str(f), str(ff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81fd759b710dc8d2c090eec79a94e60b\",\n \"score\": \"0.5489729\",\n \"text\": \"def cmp_versions(v1, v2):\\n def normalize(v):\\n return [int(x) for x in re.sub(r'(\\\\.0+)*$', '', v).split(\\\".\\\")]\\n\\n n1 = normalize(v1)\\n n2 = normalize(v2)\\n return (n1 > n2) - (n1 < n2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567919d05aed7f80284a87778160982f\",\n \"score\": \"0.54817504\",\n \"text\": \"def compare (v1, v2):\\r\\n v1_norm = normalize(v1)\\r\\n v2_norm = normalize(v2)\\r\\n if v1_norm < v2_norm:\\r\\n return -1\\r\\n if v1_norm > v2_norm:\\r\\n return 1\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2ae8dc1c1a13197b5984fadfa14ff4\",\n \"score\": \"0.54794455\",\n \"text\": \"def __gt__(self, other):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d34190deb3c39bcbf8aeca53366d6c90\",\n \"score\": \"0.54788876\",\n \"text\": \"def __le__( self, other ) :\\n\\n return( self.compare( other, 5 ) <= 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285d24e8fe406eeecfe0b291ed65c41a\",\n \"score\": \"0.54756546\",\n \"text\": \"def twos_comp(val):\\n if(val>=0):\\n return val\\n else:\\n return 2**16+val #not(-val-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f739ea5cf4160460c8449074b0bd52\",\n \"score\": \"0.5471509\",\n \"text\": \"def test_range_sorting_in_fh():\\n standard_range = ForecastingHorizon(values=range(5))\\n assert (standard_range == ForecastingHorizon(values=[0, 3, 4, 1, 2])).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c3e36ead0bc8c8eb557b65b37ff1c24\",\n \"score\": \"0.5447383\",\n \"text\": \"def __gt__(self, other):\\n return int(self) > int(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b97e8d904f20c1ace9d77828b6ccb3a\",\n \"score\": \"0.54462487\",\n \"text\": \"def __ge__( self, other ) :\\n\\n return( self.compare( other, 5 ) >= 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af32e7b1d1027bd088aa089d2bc54c41\",\n \"score\": \"0.544412\",\n \"text\": \"def cmp_f(x, y):\\n epsilon = 0.00000001\\n # Check absolute precision.\\n if -epsilon <= x - y <= epsilon:\\n return True\\n\\n # Is x or y too close to zero?\\n if -epsilon <= x <= epsilon or -epsilon <= y <= epsilon:\\n return False\\n\\n # Check relative precision.\\n return (-epsilon <= (x - y) / x <= epsilon\\n or -epsilon <= (x - y) / y <= epsilon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8f89850de49e93039132140252e230\",\n \"score\": \"0.54404444\",\n \"text\": \"def __gt__(self, other):\\n return self.priority > other.priority or (self.priority == other.priority and self.key > other.key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4594671ad0e1e0e5db221bc506eaa0e0\",\n \"score\": \"0.5434453\",\n \"text\": \"def compare(x, y):\\n\\tif x > y:\\n\\t\\treturn 1\\n\\tif x == y:\\n\\t\\treturn 0\\n\\tif x < y:\\n\\t\\treturn -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d515df6acce5f99006868906f7e06aa\",\n \"score\": \"0.54308546\",\n \"text\": \"def __gt__(self, other):\\n return float(self) > float(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48fc7134a89a0b65cf8c97b943c776b\",\n \"score\": \"0.5416734\",\n \"text\": \"def _rvs(cls, U, p):\\n return U < p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f59e6f10c46aefe21960f7fef429edf\",\n \"score\": \"0.5416148\",\n \"text\": \"def __gt__(self, other):\\n return self.getData() > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d572294c0df536a6e8a1011cda05eb\",\n \"score\": \"0.5414917\",\n \"text\": \"def compare(self, left, right):\\n return cmp(left, right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86d4f0555f42c15f49e6fec3851a769\",\n \"score\": \"0.54073787\",\n \"text\": \"def comp(a, b):\\n for x in range(len(a)):\\n if a[x] > b[x]:\\n return 1\\n elif a[x] < b[x]:\\n return 2\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c544bdf9feda5e49991275cead04ac4\",\n \"score\": \"0.5407196\",\n \"text\": \"def my_compare(x, y):\\n if x % 2 == y % 2:\\n return compare(x, y)\\n if x % 2 == 0:\\n return 1\\n else:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ef154778fc7035000ed5b01472113e\",\n \"score\": \"0.540564\",\n \"text\": \"def precision_at_position_1(sort_data):\\n\\n if sort_data[0][1] == 1:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57abcb663bb33eb4fdd0dbbbc7d939c\",\n \"score\": \"0.5405529\",\n \"text\": \"def __le__(self,other): #<=\\r\\n return self==other or float(self)<float(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef99704374202f212ec75077667eccc\",\n \"score\": \"0.53977454\",\n \"text\": \"def __ge__(self, other):\\r\\n return self == other or self > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef99704374202f212ec75077667eccc\",\n \"score\": \"0.53977454\",\n \"text\": \"def __ge__(self, other):\\r\\n return self == other or self > other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7359c948ecbbe12d6fae6bb531a21e2\",\n \"score\": \"0.5397527\",\n \"text\": \"def lte(value, arg):\\n return long(value) <= long(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99456fcc32f4a1bbdd72f18da34cb5aa\",\n \"score\": \"0.5394073\",\n \"text\": \"def cmp(a, b): # pragma: no cover\\n if a is None and b is None:\\n return 0\\n elif a is None:\\n return -1\\n elif b is None:\\n return 1\\n return (a > b) - (a < b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8dadd3c1bc06b8263560d204fc3c8a\",\n \"score\": \"0.53925204\",\n \"text\": \"def __le__(self, other):\\n return float(self) <= float(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1db31b77dfc05a913feabf6cda5ae0b\",\n \"score\": \"0.53807455\",\n \"text\": \"def test_natural_sort(self):\\n l = ['100_b','10_bb','100_a','20_aa','500_c', '9_c']\\n exp =['9_c','10_bb','20_aa','100_a','100_b','500_c']\\n obs = natural_sort(l)\\n\\n self.assertEqual(obs, exp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa0db7296e6294052e86816a649e3bf\",\n \"score\": \"0.53804195\",\n \"text\": \"def _compare(self, other, method):\\r\\n return method(self.value, other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee8d2920a6ce72361dc2f6768a13dcb\",\n \"score\": \"0.53720444\",\n \"text\": \"def test_compare_custom_object(self):\\n\\n class CustomClass:\\n def __init__(self, cmp_result=None):\\n self.cmp_result = cmp_result\\n\\n def generic_result(self):\\n if self.cmp_result is None:\\n return NotImplemented\\n else:\\n return self.cmp_result\\n\\n def __eq__(self, other):\\n return self.generic_result()\\n\\n def __gt__(self, other):\\n return self.generic_result()\\n\\n t = Timedelta(\\\"1s\\\")\\n\\n assert not (t == \\\"string\\\")\\n assert not (t == 1)\\n assert not (t == CustomClass())\\n assert not (t == CustomClass(cmp_result=False))\\n\\n assert t < CustomClass(cmp_result=True)\\n assert not (t < CustomClass(cmp_result=False))\\n\\n assert t == CustomClass(cmp_result=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc463d83ad508ae00ea36d7b80ad9d36\",\n \"score\": \"0.5369304\",\n \"text\": \"def compare_to_100(value):\\n if value < 100:\\n return 'less than 100'\\n elif value == 100:\\n return 'equal to 100'\\n else:\\n return 'more than 100'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1caae99650df61b15245bf72d4735245\",\n \"score\": \"0.53589207\",\n \"text\": \"def __ge__(self, other):\\n return self > other and self == other\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993991,"cells":{"query_id":{"kind":"string","value":"c1ef3fb3fca50659a61d3135daea5bf1"},"query":{"kind":"string","value":"Subscribe to the model update request stream."},"positive_passages":{"kind":"list like","value":[{"docid":"f64491422975b5dd6dc00f57e0bff34d","score":"0.7139607","text":"def __listen_to_model_update_stream(self):\n r = alliance.ClientAvailableMessage()\n\n whoami(r.sender, self)\n\n for request in self.orchestrator.ModelUpdateStream(r):\n # A client sent a model update to be handled by the combiner\n if request.client.name != \"reducer\":\n print(\"COMBINER: received model from client! {}\".format(request.client), flush=True)\n self.receive_model_candidate(request.model_update_id)\n print(\"COMBINER: Received model update.\", flush=True)","title":""}],"string":"[\n {\n \"docid\": \"f64491422975b5dd6dc00f57e0bff34d\",\n \"score\": \"0.7139607\",\n \"text\": \"def __listen_to_model_update_stream(self):\\n r = alliance.ClientAvailableMessage()\\n\\n whoami(r.sender, self)\\n\\n for request in self.orchestrator.ModelUpdateStream(r):\\n # A client sent a model update to be handled by the combiner\\n if request.client.name != \\\"reducer\\\":\\n print(\\\"COMBINER: received model from client! {}\\\".format(request.client), flush=True)\\n self.receive_model_candidate(request.model_update_id)\\n print(\\\"COMBINER: Received model update.\\\", flush=True)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8c39a358475085d8a1c88a34ea6e2c7c","score":"0.66264987","text":"def ModelUpdateRequestStream(self, response, context):\n\n client = response.sender\n metadata = context.invocation_metadata()\n if metadata:\n print(\"\\n\\n\\nGOT METADATA: {}\\n\\n\\n\".format(metadata), flush=True)\n\n status = fedn.Status(status=\"Client {} connecting to ModelUpdateRequestStream.\".format(client.name))\n status.log_level = fedn.Status.INFO\n\n self.__whoami(status.sender, self)\n\n self._subscribe_client_to_queue(client, fedn.Channel.MODEL_UPDATE_REQUESTS)\n q = self.__get_queue(client, fedn.Channel.MODEL_UPDATE_REQUESTS)\n\n self._send_status(status)\n\n while True:\n yield q.get()","title":""},{"docid":"b1ee5098ddbc00ef8e0721b7f72bf141","score":"0.6481928","text":"def ModelUpdateRequestStream(self, response, context):\n\n client = response.sender\n metadata = context.invocation_metadata()\n if metadata:\n print(\"\\n\\n\\nGOT METADATA: {}\\n\\n\\n\".format(metadata), flush=True)\n\n status = alliance.Status(status=\"Client {} connecting to ModelUpdateRequestStream.\".format(client.name))\n status.log_level = alliance.Status.INFO\n\n whoami(status.sender, self)\n # print(\"Client {} connecting to ModelUpdateRequestStream.\".format(client))\n\n self._subscribe_client_to_queue(client, alliance.Channel.MODEL_UPDATE_REQUESTS)\n q = self.__get_queue(client, alliance.Channel.MODEL_UPDATE_REQUESTS)\n\n self._send_status(status)\n\n while True:\n yield q.get()","title":""},{"docid":"3fcd60305be3df302a08b2acf57299c6","score":"0.6266932","text":"def notify(self):\n UpdateManager.input_updated(self)","title":""},{"docid":"c6d0cf79b85e47a814e071053b960c59","score":"0.6261632","text":"def UpdateStream(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","title":""},{"docid":"a819b03475e798da2feda1f282224f72","score":"0.61266166","text":"def signal_update():\n signal.update_parameters(model=self.model)","title":""},{"docid":"ae0a1d8eddc952d4ef6333e49c223d44","score":"0.60659707","text":"def update(self) -> None:\n self._filter.handle_internal_messages()","title":""},{"docid":"89abbdcb7ca7f63cf52c43f95b49c370","score":"0.60451084","text":"def updateStream(self):\n response.headers['content-type'] = 'text/event-stream'\n response.status_int = 200\n #updater logic needs to go here...\n context = zmq.Context()\n receiver = context.socket(zmq.SUB)\n receiver.connect(config[\"selectivehearing.updater_url\"])\n receiver.setsockopt(zmq.SUBSCRIBE, \"\")\n def go():\n while True:\n update = receiver.recv()\n #update = '{\"foo\": [[\"bar\", \"broken! %s\", %s]]}' % (time.time(), random.randint(0,4)) #this should actually be an update...\n msg = \"data: %s\\n\\n\" % (update)\n yield msg\n return go()","title":""},{"docid":"2d5a87b4e4b9e2feb0381441347a624a","score":"0.6008182","text":"def model_updated(self,subject=None):\n\n \n raise NotImplementedError","title":""},{"docid":"a2057322549acfbab0877b94a4410d0a","score":"0.59964186","text":"def SendModelUpdate(self, request, context):\n self.combiner.receive_model_candidate(request.model_update_id)\n print(\"ORCHESTRATOR: Received model update\", flush=True)\n\n response = fedn.Response()\n response.response = \"RECEIVED ModelUpdate {} from client {}\".format(response, response.sender.name)\n return response # TODO Fill later","title":""},{"docid":"5c6890c9e95b0c66c9dcf45f5414c456","score":"0.59116393","text":"def SendModelUpdateRequest(self, request, context):\n self._send_request(request, fedn.Channel.MODEL_UPDATE_REQUESTS)\n\n response = fedn.Response()\n response.response = \"CONTROLLER RECEIVED ModelUpdateRequest from client {}\".format(request.sender.name)\n return response # TODO Fill later","title":""},{"docid":"f38afcf24de36764abdb6cdfb88aaa86","score":"0.5898151","text":"def post_update_event(self, request, event, data): # pragma: no cover\n pass","title":""},{"docid":"971a1b52a413b23dedccaddecb3e3e24","score":"0.5887242","text":"def subscribe(self):\n pass","title":""},{"docid":"8f2ac5ba3a7aebc662ff79c1694d74cd","score":"0.5797382","text":"async def subscribe(self, request: WSRequest):\n await self.send(request)","title":""},{"docid":"f3978f4f326323a7569d54d452ab7c7a","score":"0.57791865","text":"def update(self, *args, **kwargs):\n ...","title":""},{"docid":"0146116eb239bc5e60b01401dd74743f","score":"0.57052034","text":"def _update(self, model):\n raise NotImplementedError","title":""},{"docid":"f8689ad05bbfb3d3df73be3f52a6e68b","score":"0.56919247","text":"def update(self, *args, **kwargs) -> None:\n pass","title":""},{"docid":"d82ef6bb821bc5ed0419d82946235053","score":"0.56815875","text":"def model_updated(self):\n raise NotImplementedError","title":""},{"docid":"086bdd4ddc6de3d59c93fd3a4c32a051","score":"0.5653346","text":"def process_update(\n self, stream_name: str, updates: List[DataUpdate],\n resync: bool,\n ):\n raise NotImplementedError()","title":""},{"docid":"ce28fc34bb0ab3cd39d452c133b43288","score":"0.5617974","text":"def SendModelUpdateRequest(self, request, context):\n self._send_request(request, alliance.Channel.MODEL_UPDATE_REQUESTS)\n\n response = alliance.Response()\n response.response = \"CONTROLLER RECEIVED ModelUpdateRequest from client {}\".format(request.sender.name)\n return response # TODO Fill later","title":""},{"docid":"f0faaac9881848cda5244666d2a3e9a9","score":"0.5613765","text":"def callback_subscribe(self, callback):\n self.update_callbacks.append(callback)","title":""},{"docid":"11a075441410e0b9a6567a686b5ba01b","score":"0.5602831","text":"def publish_update(self, payload):\n self._send_message(mtype=RDFStanzaType.UPDATE, payload=payload)","title":""},{"docid":"70d37f68c9e55eeeac6b48abe7d2a588","score":"0.55858904","text":"def partial_update(self, request, *args, **kwargs):\n pass","title":""},{"docid":"a505a0b20ca9413d4da358a144f31412","score":"0.5576473","text":"def update(self):\n ffc.flexflow_model_update(self.handle)","title":""},{"docid":"e479f25eed8e8dff86e01c13fcb6b642","score":"0.55674285","text":"async def handle_subscribe_updates(hass, connection, msg):\n\n @callback\n async def handle_event_wiser_update(hub: str):\n \"\"\"pass data to frontend when backend changes\"\"\"\n connection.send_message(\n {\n \"id\": msg[\"id\"],\n \"type\": \"event\",\n \"event\": {\n \"event\": \"wiser_updated\",\n \"hub\": hub,\n }, # data to pass with event\n }\n )\n\n remove_listener = async_dispatcher_connect(\n hass, \"wiser_update_received\", handle_event_wiser_update\n )\n\n def unsubscribe_listeners():\n \"\"\"unsubscribe listeners when frontend connection closes\"\"\"\n remove_listener()\n\n connection.subscriptions[msg[\"id\"]] = unsubscribe_listeners\n connection.send_result(msg[\"id\"])","title":""},{"docid":"53a6e48d74fc071ae16756b70be054f2","score":"0.5560723","text":"def update(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"23b1bb92da64f1d5b69536a195ad7ed0","score":"0.55462414","text":"def update(self, update_info):\n pass","title":""},{"docid":"d06ab1b9055b73198d635bc1be7449d4","score":"0.554461","text":"def _update_callback(self, msg):\n self.async_update()","title":""},{"docid":"9c275163e70748a88f9ea8c061aea4f3","score":"0.55235994","text":"def UpdateStream(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"719b2096194cf41f6de107e755aa37f9","score":"0.55231196","text":"def process_update(cls, team, date):\n update = model.Update.get_or_insert(team, date=date)\n f = functools.partial(cls.process_subscriber_update,\n update.date)\n map(f, model.Subscriber.subscribed(team))","title":""},{"docid":"ef4bf6200c46a5c385fd4a7b479d585b","score":"0.5516106","text":"def __updateSubscription(self):\n self.__currentSubscription.updateNow()","title":""},{"docid":"d0981cd55022664421cc3cc1575fa4e7","score":"0.55160517","text":"def SendModelUpdate(self, request, context):\n # self._send_request(request,alliance.Channel.MODEL_UPDATES)\n self.orchestrator.receive_model_candidate(request.model_update_id)\n print(\"ORCHESTRATOR: Received model update\", flush=True)\n\n response = alliance.Response()\n response.response = \"RECEIVED ModelUpdate {} from client {}\".format(response, response.sender.name)\n return response # TODO Fill later","title":""},{"docid":"fb66db71e920d4afd423efb440898c8a","score":"0.54947203","text":"def subscribe(self):\n self.mediator.Subscribers.append(self)","title":""},{"docid":"96d7a20d3b407dceef239c8b87d1084a","score":"0.54853505","text":"def sendUpdate(self, info=None):\n for observer in self.observers:\n observer.receiveUpdate(info)","title":""},{"docid":"0bf5a47f3ae67c059102aa18b13abbeb","score":"0.54840416","text":"def modelChanged(self, payload):\n pass","title":""},{"docid":"b372dd9eed21245c7a5cbb7e34bf9328","score":"0.5482174","text":"def update_handler(self, fd, events):\n ...","title":""},{"docid":"3bae08085df7f752a87e49764afecb8e","score":"0.54543567","text":"def __listen_to_model_validation_stream(self):\n r = alliance.ClientAvailableMessage()\n whoami(r.sender, self)\n for validation in self.orchestrator.ModelValidationStream(r):\n # A client sent a model update to be handled by the combiner\n self.receive_validation(validation)\n print(\"COMBINER: Received model validation.\", flush=True)","title":""},{"docid":"790ba6cd8c31c5e065fbd7d8fb7638bc","score":"0.5453752","text":"def handle_subscription_update_event(self, event):\n \n subscription = event.data.object\n price_id = subscription['items']['data'][0].plan.id\n stripe_customer = subscription.customer\n user = MyAccount.objects.get(stripe_customer_id=stripe_customer)\n package = Package.objects.get(stripe_price_id=price_id)\n\n return HttpResponse(content=f'Webhook received: {event[\"type\"]}', status=200)","title":""},{"docid":"9e29b4cdf659a68b989d1d5a0fd8ebf8","score":"0.5436362","text":"def receiveUpdate(self, id, attr, value):\n\n if (self.barrierUp):\n self.communication.objStore.objects[id].update(attr,value)\n else:\n self.incomingUpdates.append((id, attr, value))","title":""},{"docid":"ecfa9422f4ca951941b1296b9f6e5dc3","score":"0.53950834","text":"async def update(self, obj: Model_co):\n pass","title":""},{"docid":"ef49b257afe5f78b5bc67c05f338cda1","score":"0.5394589","text":"def SubscribeCurrentItemChanged(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"a66cbc06b2133cf3f1437719dd00c52e","score":"0.5393619","text":"def do_update(self, _):\n self.fam.handle_events_in_interval(0.1)","title":""},{"docid":"c13c2a488d8528f3e38687b64f24a5ab","score":"0.5392663","text":"def update(self, request, *args, **kwargs):\n response = super().update(request, *args, **kwargs)\n self.get_object().notify_jira_of_change()\n return response","title":""},{"docid":"c418e76256485dc5edb761f732e57d98","score":"0.538968","text":"def update_signal(self, event):\n\n raise NotImplementedError(\"Should implement update_signal()\")","title":""},{"docid":"dcbac044e88c61c5d7b380f910385588","score":"0.5389385","text":"def _listen_source_updates(self) -> None:\n\n @callback\n def _handle_signal(url: str) -> None:\n if self.stream:\n _LOGGER.debug(\"Update stream URL for camera %s\", self.camera_data.name)\n self.stream.update_source(url)\n\n assert self.platform.config_entry\n self.async_on_remove(\n async_dispatcher_connect(\n self.hass,\n f\"{SIGNAL_CAMERA_SOURCE_CHANGED}_{self.platform.config_entry.entry_id}_{self.camera_data.id}\",\n _handle_signal,\n )\n )","title":""},{"docid":"c805c587b9e254fe70a720c696ee39c1","score":"0.53880614","text":"def update(object):\n pass","title":""},{"docid":"a84db9444391ea813c9463f13968a7ce","score":"0.53875816","text":"def on_update(self, state: Dict[str, Any], **kwargs):\n ...","title":""},{"docid":"c670629b654a5fb4b0a6386469454da4","score":"0.53875285","text":"def update(self):\n _LOGGER.debug(\"Super Updating\")\n self._api.update()","title":""},{"docid":"441df0db1a25e99dfa63d2c14569588f","score":"0.5373929","text":"async def async_update(self) -> None:\n return","title":""},{"docid":"6b93ed71d8143e8cdbd521ae00f363ae","score":"0.5372482","text":"async def async_update(self) -> None:","title":""},{"docid":"bb68a6d0319cd00e8120325083389701","score":"0.53702474","text":"async def async_update(self):","title":""},{"docid":"9b939a3a03a8891e5f3393f5f0a09543","score":"0.5347592","text":"def update(self, *args, **kwargs): # real signature unknown\r\n pass","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.53390104","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.53390104","text":"def update(self, data):","title":""},{"docid":"2457c88bcc5d52bec4848a39f4115dcf","score":"0.53390104","text":"def update(self, data):","title":""},{"docid":"cc1d8b3e147d7148764b0b0b7dc49f6f","score":"0.5332301","text":"def process_subscriber_update(cls, date, subscriber):\n subscriber_update = model.SubscriberUpdate.get_or_insert(\n name=subscriber.name,\n mail=subscriber.mail,\n team=subscriber.team,\n date=date)\n sender = cls.get_reply_address(subscriber_update.key.urlsafe())\n message = cls.get_update_message(\n subscriber.team, subscriber.mail, sender, date)\n message.send()\n subscriber_update.sent = True\n subscriber_update.put()","title":""},{"docid":"a92a5ef783618862f56fbe10476d95e4","score":"0.53293395","text":"def update(self, *args, **kwargs): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"b00adbf65b7f25e53847273e3c2a3191","score":"0.5326588","text":"def test_update_message_subscriber(self):\n pass","title":""},{"docid":"1d1beb77daafd104a8b828584a3582d8","score":"0.5305633","text":"def _update(self):\n pass","title":""},{"docid":"1d1beb77daafd104a8b828584a3582d8","score":"0.5305633","text":"def _update(self):\n pass","title":""},{"docid":"f0333f2e7f72ae748f2aebe4cd5cbea0","score":"0.53019804","text":"def watch_model(self, model):\n if self.should_use_remote_logger:\n wandb.watch(models=model, log=\"all\")","title":""},{"docid":"0586bbf5739ffbf6cb003f3d42d7b805","score":"0.52762574","text":"def subscribe():\n\n pub.subscribe(onNode, \"meshtastic.node\")","title":""},{"docid":"42545c97b36854cd8a15633eaaf1a587","score":"0.5275858","text":"def Update(self, data):","title":""},{"docid":"4311e0ed658a8bb2fcc955b2b272c3db","score":"0.5274366","text":"def process_update(self, payload):\n update_id = payload.get('update_id')\n if update_id and update_id > self._update_offset:\n self._update_offset = update_id\n if 'message' in payload:\n self.supervisor.spawn(self.safe_exec, self.on_message,\n payload['message'])\n elif 'callback_query' in payload:\n self.supervisor.spawn(self.safe_exec, self.on_query,\n payload['callback_query'])","title":""},{"docid":"6ffc8345afebddc83c6b279663d75d93","score":"0.5268459","text":"def update(self, request, *args, **kwargs):\n instance = self.get_object()\n serializer = self.get_serializer(instance, data=request.data, partial=True)\n if serializer.is_valid():\n errors_messages = []\n self.perform_update(serializer)\n return Response(data={\"response_code\": 0, \"response_message\": \"Frequency entry updated successfully.\",\n \"obj\": serializer.data, \"errors\": errors_messages},\n status=status.HTTP_201_CREATED)\n else:\n default_errors = serializer.errors # default errors dict\n errors_messages = []\n for field_name, field_errors in default_errors.items():\n for field_error in field_errors:\n error_message = '%s: %s' % (field_name, field_error)\n errors_messages.append(error_message)\n\n return Response(data={\"response_code\": 1, \"response_message\": \"Frequency entry not updated\",\n \"obj\": serializer.data, \"errors\": errors_messages},\n status=status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"0c65c332e0da0ad2b6a364f4c274b4a4","score":"0.52646416","text":"def observe(self):\n pass","title":""},{"docid":"ffb1344d2225ff8722984cad8116bcfd","score":"0.52493244","text":"def notify(self):\n\n print(\"Subject: Notifying observers...\")\n for observer in self._observers:\n observer.update(self)","title":""},{"docid":"245af5a129c7a1bc33b96832654473ca","score":"0.5246309","text":"async def update(self):\r\n await self._client.update()","title":""},{"docid":"54e1f72a280cdb05c24e1d8de1814021","score":"0.5229","text":"def update(self, data, *args, **kwargs):\n self.__getattr__(\"update\", _location=\"remote\")(data, *args, **kwargs)","title":""},{"docid":"9aeda54897245a9d6414115d338ac40c","score":"0.52277863","text":"async def handle_event_wiser_update(hub: str):\n connection.send_message(\n {\n \"id\": msg[\"id\"],\n \"type\": \"event\",\n \"event\": {\n \"event\": \"wiser_updated\",\n \"hub\": hub,\n }, # data to pass with event\n }\n )","title":""},{"docid":"8b8b7c376a484f6cae6493aec8d66487","score":"0.5226176","text":"async def on_connect(self, _):\n self.last_update_success = True","title":""},{"docid":"1ea7548605548b28169fad3bfd1aae7e","score":"0.5221183","text":"def update(self, request, *args, **kwargs):\n partial = kwargs.pop('partial', False)\n instance = self.get_object()\n serializer = UpdateEventModelSerializer(\n instance,\n data=request.data,\n partial=partial\n )\n serializer.is_valid(raise_exception=True)\n event = serializer.save()\n\n data = self.get_serializer(event).data\n return Response(data, status=status.HTTP_200_OK)","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"63705f1ca943ee5589ca4acc99e87352","score":"0.52187914","text":"def update(self):\n pass","title":""},{"docid":"4eb93c971e507cd03871d2b7511995b5","score":"0.5204943","text":"def update(self):\n\n raise NotImplementedError()","title":""},{"docid":"53da9d749cd193a850587ecfd424041a","score":"0.52047443","text":"def _update_model(self, new_state, data):","title":""},{"docid":"f231c97b18562fecc587a028385f33a2","score":"0.51988226","text":"def partial_update(self, request, pk=None):\n raise errors.NotImplemented('HTTP Verb Not Supported')","title":""},{"docid":"52abb06960a074d1975bdcd67e34966d","score":"0.5195938","text":"def update(self, data):\n self._sender.send(data)","title":""},{"docid":"c96eb87e87a10b42d2d4c3a09b3ef6cb","score":"0.51864207","text":"def update_view(self):\n return [\n r'^(?P<pk>.+)/change/$',\n self.get_update_view(),\n '{model_name}_change',\n ]","title":""},{"docid":"d5dde718a6acb79a1e7e43f012586076","score":"0.5185283","text":"def update(self):\n\n self.process_pool()\n self.process_reactor()","title":""},{"docid":"d5dde718a6acb79a1e7e43f012586076","score":"0.5185283","text":"def update(self):\n\n self.process_pool()\n self.process_reactor()","title":""},{"docid":"d24adb6c1a03b10a335576c7599289e8","score":"0.51786363","text":"def update(self) -> None:\n pass","title":""},{"docid":"0083b9ee8a10535774c32c619a2d7010","score":"0.51731795","text":"def update(self):\n raise NotImplementedError","title":""},{"docid":"0083b9ee8a10535774c32c619a2d7010","score":"0.51731795","text":"def update(self):\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"8c39a358475085d8a1c88a34ea6e2c7c\",\n \"score\": \"0.66264987\",\n \"text\": \"def ModelUpdateRequestStream(self, response, context):\\n\\n client = response.sender\\n metadata = context.invocation_metadata()\\n if metadata:\\n print(\\\"\\\\n\\\\n\\\\nGOT METADATA: {}\\\\n\\\\n\\\\n\\\".format(metadata), flush=True)\\n\\n status = fedn.Status(status=\\\"Client {} connecting to ModelUpdateRequestStream.\\\".format(client.name))\\n status.log_level = fedn.Status.INFO\\n\\n self.__whoami(status.sender, self)\\n\\n self._subscribe_client_to_queue(client, fedn.Channel.MODEL_UPDATE_REQUESTS)\\n q = self.__get_queue(client, fedn.Channel.MODEL_UPDATE_REQUESTS)\\n\\n self._send_status(status)\\n\\n while True:\\n yield q.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ee5098ddbc00ef8e0721b7f72bf141\",\n \"score\": \"0.6481928\",\n \"text\": \"def ModelUpdateRequestStream(self, response, context):\\n\\n client = response.sender\\n metadata = context.invocation_metadata()\\n if metadata:\\n print(\\\"\\\\n\\\\n\\\\nGOT METADATA: {}\\\\n\\\\n\\\\n\\\".format(metadata), flush=True)\\n\\n status = alliance.Status(status=\\\"Client {} connecting to ModelUpdateRequestStream.\\\".format(client.name))\\n status.log_level = alliance.Status.INFO\\n\\n whoami(status.sender, self)\\n # print(\\\"Client {} connecting to ModelUpdateRequestStream.\\\".format(client))\\n\\n self._subscribe_client_to_queue(client, alliance.Channel.MODEL_UPDATE_REQUESTS)\\n q = self.__get_queue(client, alliance.Channel.MODEL_UPDATE_REQUESTS)\\n\\n self._send_status(status)\\n\\n while True:\\n yield q.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcd60305be3df302a08b2acf57299c6\",\n \"score\": \"0.6266932\",\n \"text\": \"def notify(self):\\n UpdateManager.input_updated(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d0cf79b85e47a814e071053b960c59\",\n \"score\": \"0.6261632\",\n \"text\": \"def UpdateStream(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a819b03475e798da2feda1f282224f72\",\n \"score\": \"0.61266166\",\n \"text\": \"def signal_update():\\n signal.update_parameters(model=self.model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0a1d8eddc952d4ef6333e49c223d44\",\n \"score\": \"0.60659707\",\n \"text\": \"def update(self) -> None:\\n self._filter.handle_internal_messages()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89abbdcb7ca7f63cf52c43f95b49c370\",\n \"score\": \"0.60451084\",\n \"text\": \"def updateStream(self):\\n response.headers['content-type'] = 'text/event-stream'\\n response.status_int = 200\\n #updater logic needs to go here...\\n context = zmq.Context()\\n receiver = context.socket(zmq.SUB)\\n receiver.connect(config[\\\"selectivehearing.updater_url\\\"])\\n receiver.setsockopt(zmq.SUBSCRIBE, \\\"\\\")\\n def go():\\n while True:\\n update = receiver.recv()\\n #update = '{\\\"foo\\\": [[\\\"bar\\\", \\\"broken! %s\\\", %s]]}' % (time.time(), random.randint(0,4)) #this should actually be an update...\\n msg = \\\"data: %s\\\\n\\\\n\\\" % (update)\\n yield msg\\n return go()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d5a87b4e4b9e2feb0381441347a624a\",\n \"score\": \"0.6008182\",\n \"text\": \"def model_updated(self,subject=None):\\n\\n \\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2057322549acfbab0877b94a4410d0a\",\n \"score\": \"0.59964186\",\n \"text\": \"def SendModelUpdate(self, request, context):\\n self.combiner.receive_model_candidate(request.model_update_id)\\n print(\\\"ORCHESTRATOR: Received model update\\\", flush=True)\\n\\n response = fedn.Response()\\n response.response = \\\"RECEIVED ModelUpdate {} from client {}\\\".format(response, response.sender.name)\\n return response # TODO Fill later\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6890c9e95b0c66c9dcf45f5414c456\",\n \"score\": \"0.59116393\",\n \"text\": \"def SendModelUpdateRequest(self, request, context):\\n self._send_request(request, fedn.Channel.MODEL_UPDATE_REQUESTS)\\n\\n response = fedn.Response()\\n response.response = \\\"CONTROLLER RECEIVED ModelUpdateRequest from client {}\\\".format(request.sender.name)\\n return response # TODO Fill later\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38afcf24de36764abdb6cdfb88aaa86\",\n \"score\": \"0.5898151\",\n \"text\": \"def post_update_event(self, request, event, data): # pragma: no cover\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"971a1b52a413b23dedccaddecb3e3e24\",\n \"score\": \"0.5887242\",\n \"text\": \"def subscribe(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2ac5ba3a7aebc662ff79c1694d74cd\",\n \"score\": \"0.5797382\",\n \"text\": \"async def subscribe(self, request: WSRequest):\\n await self.send(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3978f4f326323a7569d54d452ab7c7a\",\n \"score\": \"0.57791865\",\n \"text\": \"def update(self, *args, **kwargs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0146116eb239bc5e60b01401dd74743f\",\n \"score\": \"0.57052034\",\n \"text\": \"def _update(self, model):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8689ad05bbfb3d3df73be3f52a6e68b\",\n \"score\": \"0.56919247\",\n \"text\": \"def update(self, *args, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82ef6bb821bc5ed0419d82946235053\",\n \"score\": \"0.56815875\",\n \"text\": \"def model_updated(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"086bdd4ddc6de3d59c93fd3a4c32a051\",\n \"score\": \"0.5653346\",\n \"text\": \"def process_update(\\n self, stream_name: str, updates: List[DataUpdate],\\n resync: bool,\\n ):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce28fc34bb0ab3cd39d452c133b43288\",\n \"score\": \"0.5617974\",\n \"text\": \"def SendModelUpdateRequest(self, request, context):\\n self._send_request(request, alliance.Channel.MODEL_UPDATE_REQUESTS)\\n\\n response = alliance.Response()\\n response.response = \\\"CONTROLLER RECEIVED ModelUpdateRequest from client {}\\\".format(request.sender.name)\\n return response # TODO Fill later\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0faaac9881848cda5244666d2a3e9a9\",\n \"score\": \"0.5613765\",\n \"text\": \"def callback_subscribe(self, callback):\\n self.update_callbacks.append(callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a075441410e0b9a6567a686b5ba01b\",\n \"score\": \"0.5602831\",\n \"text\": \"def publish_update(self, payload):\\n self._send_message(mtype=RDFStanzaType.UPDATE, payload=payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d37f68c9e55eeeac6b48abe7d2a588\",\n \"score\": \"0.55858904\",\n \"text\": \"def partial_update(self, request, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a505a0b20ca9413d4da358a144f31412\",\n \"score\": \"0.5576473\",\n \"text\": \"def update(self):\\n ffc.flexflow_model_update(self.handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e479f25eed8e8dff86e01c13fcb6b642\",\n \"score\": \"0.55674285\",\n \"text\": \"async def handle_subscribe_updates(hass, connection, msg):\\n\\n @callback\\n async def handle_event_wiser_update(hub: str):\\n \\\"\\\"\\\"pass data to frontend when backend changes\\\"\\\"\\\"\\n connection.send_message(\\n {\\n \\\"id\\\": msg[\\\"id\\\"],\\n \\\"type\\\": \\\"event\\\",\\n \\\"event\\\": {\\n \\\"event\\\": \\\"wiser_updated\\\",\\n \\\"hub\\\": hub,\\n }, # data to pass with event\\n }\\n )\\n\\n remove_listener = async_dispatcher_connect(\\n hass, \\\"wiser_update_received\\\", handle_event_wiser_update\\n )\\n\\n def unsubscribe_listeners():\\n \\\"\\\"\\\"unsubscribe listeners when frontend connection closes\\\"\\\"\\\"\\n remove_listener()\\n\\n connection.subscriptions[msg[\\\"id\\\"]] = unsubscribe_listeners\\n connection.send_result(msg[\\\"id\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a6e48d74fc071ae16756b70be054f2\",\n \"score\": \"0.5560723\",\n \"text\": \"def update(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b1bb92da64f1d5b69536a195ad7ed0\",\n \"score\": \"0.55462414\",\n \"text\": \"def update(self, update_info):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06ab1b9055b73198d635bc1be7449d4\",\n \"score\": \"0.554461\",\n \"text\": \"def _update_callback(self, msg):\\n self.async_update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c275163e70748a88f9ea8c061aea4f3\",\n \"score\": \"0.55235994\",\n \"text\": \"def UpdateStream(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719b2096194cf41f6de107e755aa37f9\",\n \"score\": \"0.55231196\",\n \"text\": \"def process_update(cls, team, date):\\n update = model.Update.get_or_insert(team, date=date)\\n f = functools.partial(cls.process_subscriber_update,\\n update.date)\\n map(f, model.Subscriber.subscribed(team))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4bf6200c46a5c385fd4a7b479d585b\",\n \"score\": \"0.5516106\",\n \"text\": \"def __updateSubscription(self):\\n self.__currentSubscription.updateNow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0981cd55022664421cc3cc1575fa4e7\",\n \"score\": \"0.55160517\",\n \"text\": \"def SendModelUpdate(self, request, context):\\n # self._send_request(request,alliance.Channel.MODEL_UPDATES)\\n self.orchestrator.receive_model_candidate(request.model_update_id)\\n print(\\\"ORCHESTRATOR: Received model update\\\", flush=True)\\n\\n response = alliance.Response()\\n response.response = \\\"RECEIVED ModelUpdate {} from client {}\\\".format(response, response.sender.name)\\n return response # TODO Fill later\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb66db71e920d4afd423efb440898c8a\",\n \"score\": \"0.54947203\",\n \"text\": \"def subscribe(self):\\n self.mediator.Subscribers.append(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d7a20d3b407dceef239c8b87d1084a\",\n \"score\": \"0.54853505\",\n \"text\": \"def sendUpdate(self, info=None):\\n for observer in self.observers:\\n observer.receiveUpdate(info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bf5a47f3ae67c059102aa18b13abbeb\",\n \"score\": \"0.54840416\",\n \"text\": \"def modelChanged(self, payload):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b372dd9eed21245c7a5cbb7e34bf9328\",\n \"score\": \"0.5482174\",\n \"text\": \"def update_handler(self, fd, events):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bae08085df7f752a87e49764afecb8e\",\n \"score\": \"0.54543567\",\n \"text\": \"def __listen_to_model_validation_stream(self):\\n r = alliance.ClientAvailableMessage()\\n whoami(r.sender, self)\\n for validation in self.orchestrator.ModelValidationStream(r):\\n # A client sent a model update to be handled by the combiner\\n self.receive_validation(validation)\\n print(\\\"COMBINER: Received model validation.\\\", flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790ba6cd8c31c5e065fbd7d8fb7638bc\",\n \"score\": \"0.5453752\",\n \"text\": \"def handle_subscription_update_event(self, event):\\n \\n subscription = event.data.object\\n price_id = subscription['items']['data'][0].plan.id\\n stripe_customer = subscription.customer\\n user = MyAccount.objects.get(stripe_customer_id=stripe_customer)\\n package = Package.objects.get(stripe_price_id=price_id)\\n\\n return HttpResponse(content=f'Webhook received: {event[\\\"type\\\"]}', status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e29b4cdf659a68b989d1d5a0fd8ebf8\",\n \"score\": \"0.5436362\",\n \"text\": \"def receiveUpdate(self, id, attr, value):\\n\\n if (self.barrierUp):\\n self.communication.objStore.objects[id].update(attr,value)\\n else:\\n self.incomingUpdates.append((id, attr, value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecfa9422f4ca951941b1296b9f6e5dc3\",\n \"score\": \"0.53950834\",\n \"text\": \"async def update(self, obj: Model_co):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef49b257afe5f78b5bc67c05f338cda1\",\n \"score\": \"0.5394589\",\n \"text\": \"def SubscribeCurrentItemChanged(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66cbc06b2133cf3f1437719dd00c52e\",\n \"score\": \"0.5393619\",\n \"text\": \"def do_update(self, _):\\n self.fam.handle_events_in_interval(0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13c2a488d8528f3e38687b64f24a5ab\",\n \"score\": \"0.5392663\",\n \"text\": \"def update(self, request, *args, **kwargs):\\n response = super().update(request, *args, **kwargs)\\n self.get_object().notify_jira_of_change()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c418e76256485dc5edb761f732e57d98\",\n \"score\": \"0.538968\",\n \"text\": \"def update_signal(self, event):\\n\\n raise NotImplementedError(\\\"Should implement update_signal()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbac044e88c61c5d7b380f910385588\",\n \"score\": \"0.5389385\",\n \"text\": \"def _listen_source_updates(self) -> None:\\n\\n @callback\\n def _handle_signal(url: str) -> None:\\n if self.stream:\\n _LOGGER.debug(\\\"Update stream URL for camera %s\\\", self.camera_data.name)\\n self.stream.update_source(url)\\n\\n assert self.platform.config_entry\\n self.async_on_remove(\\n async_dispatcher_connect(\\n self.hass,\\n f\\\"{SIGNAL_CAMERA_SOURCE_CHANGED}_{self.platform.config_entry.entry_id}_{self.camera_data.id}\\\",\\n _handle_signal,\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c805c587b9e254fe70a720c696ee39c1\",\n \"score\": \"0.53880614\",\n \"text\": \"def update(object):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84db9444391ea813c9463f13968a7ce\",\n \"score\": \"0.53875816\",\n \"text\": \"def on_update(self, state: Dict[str, Any], **kwargs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c670629b654a5fb4b0a6386469454da4\",\n \"score\": \"0.53875285\",\n \"text\": \"def update(self):\\n _LOGGER.debug(\\\"Super Updating\\\")\\n self._api.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441df0db1a25e99dfa63d2c14569588f\",\n \"score\": \"0.5373929\",\n \"text\": \"async def async_update(self) -> None:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b93ed71d8143e8cdbd521ae00f363ae\",\n \"score\": \"0.5372482\",\n \"text\": \"async def async_update(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb68a6d0319cd00e8120325083389701\",\n \"score\": \"0.53702474\",\n \"text\": \"async def async_update(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b939a3a03a8891e5f3393f5f0a09543\",\n \"score\": \"0.5347592\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.53390104\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.53390104\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2457c88bcc5d52bec4848a39f4115dcf\",\n \"score\": \"0.53390104\",\n \"text\": \"def update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1d8b3e147d7148764b0b0b7dc49f6f\",\n \"score\": \"0.5332301\",\n \"text\": \"def process_subscriber_update(cls, date, subscriber):\\n subscriber_update = model.SubscriberUpdate.get_or_insert(\\n name=subscriber.name,\\n mail=subscriber.mail,\\n team=subscriber.team,\\n date=date)\\n sender = cls.get_reply_address(subscriber_update.key.urlsafe())\\n message = cls.get_update_message(\\n subscriber.team, subscriber.mail, sender, date)\\n message.send()\\n subscriber_update.sent = True\\n subscriber_update.put()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92a5ef783618862f56fbe10476d95e4\",\n \"score\": \"0.53293395\",\n \"text\": \"def update(self, *args, **kwargs): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b00adbf65b7f25e53847273e3c2a3191\",\n \"score\": \"0.5326588\",\n \"text\": \"def test_update_message_subscriber(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d1beb77daafd104a8b828584a3582d8\",\n \"score\": \"0.5305633\",\n \"text\": \"def _update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d1beb77daafd104a8b828584a3582d8\",\n \"score\": \"0.5305633\",\n \"text\": \"def _update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0333f2e7f72ae748f2aebe4cd5cbea0\",\n \"score\": \"0.53019804\",\n \"text\": \"def watch_model(self, model):\\n if self.should_use_remote_logger:\\n wandb.watch(models=model, log=\\\"all\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0586bbf5739ffbf6cb003f3d42d7b805\",\n \"score\": \"0.52762574\",\n \"text\": \"def subscribe():\\n\\n pub.subscribe(onNode, \\\"meshtastic.node\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42545c97b36854cd8a15633eaaf1a587\",\n \"score\": \"0.5275858\",\n \"text\": \"def Update(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4311e0ed658a8bb2fcc955b2b272c3db\",\n \"score\": \"0.5274366\",\n \"text\": \"def process_update(self, payload):\\n update_id = payload.get('update_id')\\n if update_id and update_id > self._update_offset:\\n self._update_offset = update_id\\n if 'message' in payload:\\n self.supervisor.spawn(self.safe_exec, self.on_message,\\n payload['message'])\\n elif 'callback_query' in payload:\\n self.supervisor.spawn(self.safe_exec, self.on_query,\\n payload['callback_query'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ffc8345afebddc83c6b279663d75d93\",\n \"score\": \"0.5268459\",\n \"text\": \"def update(self, request, *args, **kwargs):\\n instance = self.get_object()\\n serializer = self.get_serializer(instance, data=request.data, partial=True)\\n if serializer.is_valid():\\n errors_messages = []\\n self.perform_update(serializer)\\n return Response(data={\\\"response_code\\\": 0, \\\"response_message\\\": \\\"Frequency entry updated successfully.\\\",\\n \\\"obj\\\": serializer.data, \\\"errors\\\": errors_messages},\\n status=status.HTTP_201_CREATED)\\n else:\\n default_errors = serializer.errors # default errors dict\\n errors_messages = []\\n for field_name, field_errors in default_errors.items():\\n for field_error in field_errors:\\n error_message = '%s: %s' % (field_name, field_error)\\n errors_messages.append(error_message)\\n\\n return Response(data={\\\"response_code\\\": 1, \\\"response_message\\\": \\\"Frequency entry not updated\\\",\\n \\\"obj\\\": serializer.data, \\\"errors\\\": errors_messages},\\n status=status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c65c332e0da0ad2b6a364f4c274b4a4\",\n \"score\": \"0.52646416\",\n \"text\": \"def observe(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb1344d2225ff8722984cad8116bcfd\",\n \"score\": \"0.52493244\",\n \"text\": \"def notify(self):\\n\\n print(\\\"Subject: Notifying observers...\\\")\\n for observer in self._observers:\\n observer.update(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"245af5a129c7a1bc33b96832654473ca\",\n \"score\": \"0.5246309\",\n \"text\": \"async def update(self):\\r\\n await self._client.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e1f72a280cdb05c24e1d8de1814021\",\n \"score\": \"0.5229\",\n \"text\": \"def update(self, data, *args, **kwargs):\\n self.__getattr__(\\\"update\\\", _location=\\\"remote\\\")(data, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aeda54897245a9d6414115d338ac40c\",\n \"score\": \"0.52277863\",\n \"text\": \"async def handle_event_wiser_update(hub: str):\\n connection.send_message(\\n {\\n \\\"id\\\": msg[\\\"id\\\"],\\n \\\"type\\\": \\\"event\\\",\\n \\\"event\\\": {\\n \\\"event\\\": \\\"wiser_updated\\\",\\n \\\"hub\\\": hub,\\n }, # data to pass with event\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8b7c376a484f6cae6493aec8d66487\",\n \"score\": \"0.5226176\",\n \"text\": \"async def on_connect(self, _):\\n self.last_update_success = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea7548605548b28169fad3bfd1aae7e\",\n \"score\": \"0.5221183\",\n \"text\": \"def update(self, request, *args, **kwargs):\\n partial = kwargs.pop('partial', False)\\n instance = self.get_object()\\n serializer = UpdateEventModelSerializer(\\n instance,\\n data=request.data,\\n partial=partial\\n )\\n serializer.is_valid(raise_exception=True)\\n event = serializer.save()\\n\\n data = self.get_serializer(event).data\\n return Response(data, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63705f1ca943ee5589ca4acc99e87352\",\n \"score\": \"0.52187914\",\n \"text\": \"def update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb93c971e507cd03871d2b7511995b5\",\n \"score\": \"0.5204943\",\n \"text\": \"def update(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53da9d749cd193a850587ecfd424041a\",\n \"score\": \"0.52047443\",\n \"text\": \"def _update_model(self, new_state, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f231c97b18562fecc587a028385f33a2\",\n \"score\": \"0.51988226\",\n \"text\": \"def partial_update(self, request, pk=None):\\n raise errors.NotImplemented('HTTP Verb Not Supported')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52abb06960a074d1975bdcd67e34966d\",\n \"score\": \"0.5195938\",\n \"text\": \"def update(self, data):\\n self._sender.send(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96eb87e87a10b42d2d4c3a09b3ef6cb\",\n \"score\": \"0.51864207\",\n \"text\": \"def update_view(self):\\n return [\\n r'^(?P<pk>.+)/change/$',\\n self.get_update_view(),\\n '{model_name}_change',\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5dde718a6acb79a1e7e43f012586076\",\n \"score\": \"0.5185283\",\n \"text\": \"def update(self):\\n\\n self.process_pool()\\n self.process_reactor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5dde718a6acb79a1e7e43f012586076\",\n \"score\": \"0.5185283\",\n \"text\": \"def update(self):\\n\\n self.process_pool()\\n self.process_reactor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24adb6c1a03b10a335576c7599289e8\",\n \"score\": \"0.51786363\",\n \"text\": \"def update(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0083b9ee8a10535774c32c619a2d7010\",\n \"score\": \"0.51731795\",\n \"text\": \"def update(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0083b9ee8a10535774c32c619a2d7010\",\n \"score\": \"0.51731795\",\n \"text\": \"def update(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993992,"cells":{"query_id":{"kind":"string","value":"e936c006f2b60cb1447f0caf61b07198"},"query":{"kind":"string","value":"r\"\"\" Return the orientation of the crossings of the link diagram of ``self``."},"positive_passages":{"kind":"list like","value":[{"docid":"eb391244b5bc7f6d2cc3e43b0e0fc07c","score":"0.6693827","text":"def orientation(self):\n directions = self._directions_of_edges()[0]\n orientation = []\n for C in self.pd_code():\n if C[0] == C[1] or C[2] == C[3]:\n orientation.append(-1)\n elif C[1] == C[2] or C[0] == C[3]:\n orientation.append(1)\n elif directions[C[1]] == C:\n orientation.append(-1)\n else:\n orientation.append(1)\n return orientation","title":""}],"string":"[\n {\n \"docid\": \"eb391244b5bc7f6d2cc3e43b0e0fc07c\",\n \"score\": \"0.6693827\",\n \"text\": \"def orientation(self):\\n directions = self._directions_of_edges()[0]\\n orientation = []\\n for C in self.pd_code():\\n if C[0] == C[1] or C[2] == C[3]:\\n orientation.append(-1)\\n elif C[1] == C[2] or C[0] == C[3]:\\n orientation.append(1)\\n elif directions[C[1]] == C:\\n orientation.append(-1)\\n else:\\n orientation.append(1)\\n return orientation\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f1ce0f0417423532c5526f5a36e613ce","score":"0.6612555","text":"def get_orientation(self, visited):\n #print(visited)\n if visited:\n rot = mathutils.Quaternion(self.helical_axisParam, self.get_angle(2))\n a = self.positions[2]\n b = self.positions[3]\n else:\n rot = mathutils.Quaternion(self.helical_axisParam, self.get_angle(0))\n a = self.positions[0]\n b = self.positions[1]\n n = mathutils.Vector(self.east)\n n.rotate(rot)\n return a, b, n","title":""},{"docid":"7d032df59101c5531e7038bcff103b56","score":"0.62240785","text":"def orientation(self):\n\n a, b, b, c = self.covariance.flat\n if a < 0 or c < 0: # negative variance\n return np.nan * u.rad # pragma: no cover\n return 0.5 * np.arctan2(2. * b, (a - c))","title":""},{"docid":"3804bde52098c268274098da0ca6420c","score":"0.6199202","text":"def orientation(self):\n o_tmp = self._orientation + 180\n o_tmp %= 360\n return o_tmp - 180","title":""},{"docid":"dceed3de285eb7e32bdd733bc1aea5cd","score":"0.60408294","text":"def interior_angle(self):\n return (self.edges - 2)*(100/self.edges)","title":""},{"docid":"51564e6580bb05eeca02be5bec95fa8e","score":"0.5991328","text":"def visual_rotation(self):\n if getattr(self, '_body', None) is None:\n return self.rotation\n\n return -np.rad2deg(self._body.angle)","title":""},{"docid":"22b662239cc7e1ca2715e694e4e3022a","score":"0.5961181","text":"def cone_orientation(self):\n if self.is3d:\n orientation = lib.D3DVECTOR()\n _check(\n self._native_buffer3d.GetConeOrientation(ctypes.byref(orientation))\n )\n return orientation.x, orientation.y, orientation.z\n else:\n return 0, 0, 0","title":""},{"docid":"1cfa68b1b4ca59c83845cf36d6f5fc0d","score":"0.5934017","text":"def chiral(self):\n return self.orientation_from_basis(self.basis_from_domains(self.domains))","title":""},{"docid":"d889bd8be8a0754c4092b53db9f850bb","score":"0.59151345","text":"def getOrientation(self):\n return self.orientation","title":""},{"docid":"4b7b3d83e7726ac46530f05bc07cb9ae","score":"0.59147805","text":"def incidence_angle(self):\n return self.get_incidence_angle()","title":""},{"docid":"00a61bb1b7b5fd2a0a9a2f509fd7be6d","score":"0.58855635","text":"def _orientation(side):\r\n a, b = side\r\n if a < b:\r\n return 1\r\n elif a > b:\r\n return -1\r\n else:\r\n raise ValueError('side connects a vertex to itself: ' + str(side))","title":""},{"docid":"9dffd6e8401a42ddf7d3154f8f34c4e3","score":"0.5871174","text":"def angle(self):\n return","title":""},{"docid":"9dffd6e8401a42ddf7d3154f8f34c4e3","score":"0.5871174","text":"def angle(self):\n return","title":""},{"docid":"5b7dfe204d04525a15a36c9e7944705d","score":"0.5828715","text":"def getOrientation(self):\n if self._belt_connection_state != BeltConnectionState.CONNECTED:\n return None\n if self._belt_firm_version<34:\n print(\"BeltController: Unable to know the belt orientation. \" +\n \"Orientation notifications are available only from \"+\n \"firmware version 34.\")\n return\n if self._belt_heading is None or self._belt_heading_offset is None:\n return None\n orientation = (self._belt_heading, self._belt_heading_offset)\n return orientation","title":""},{"docid":"d075472cb4de66e6b0a74cfcd0e228a0","score":"0.5820682","text":"def orientated_lines(self) -> List[str]:\n return self.get_orientated_lines(self.lines, self.flipped, self.rotated)","title":""},{"docid":"b542f277095793890e56ac1e4b6e7931","score":"0.57949173","text":"def getOrientation(self):\n return self._orientation","title":""},{"docid":"7588595274d5a093468ef9b29a381206","score":"0.57776535","text":"def getRotationDirection(self):\n return self.rotationDirection","title":""},{"docid":"f7af541adf1e6d318ac133fe19ac0e86","score":"0.57652414","text":"def _get_end_effector_orientation(self):\n orient_quat = p.getLinkState(\n self.robot,\n linkIndex=8,\n computeForwardKinematics=True,\n physicsClientId=self.id)[1]\n # orient_euler = p.getEulerFromQuaternion(orient_quat)\n return np.array(orient_quat)","title":""},{"docid":"9b0133cc8eb6a2ec28272c319c7c2437","score":"0.5738593","text":"def get_orientation_vector(self):\n return rotate_vector([1, 0, 0][:self.get_dim()], self.orientation)","title":""},{"docid":"8911de800f0563996af7608bc225a09b","score":"0.57322216","text":"def get_orientation(self):\n return tuple(self.x[7:10]) + (self.x[6],)","title":""},{"docid":"9f1643187bd3a2ef3ebe4013533d220f","score":"0.57239","text":"def getAngle(self):\n return _almathswig.Pose2D_getAngle(self)","title":""},{"docid":"16fcaab976971d18dffb06ac30e3c340","score":"0.5717684","text":"def interior_angle(self):\n\t\tif not hasattr(self, '_int_angle'):\n\t\t\tself._int_angle = (self.vertices - 2) * 180 / self.vertices\n\t\treturn self._int_angle","title":""},{"docid":"7c9dbc0446b32dce93f02676c53537ea","score":"0.57156324","text":"def getAngle(self):\n return self.joint_pos","title":""},{"docid":"b4966a3ee04a981b11df3272a3eaf06e","score":"0.57014596","text":"def adjoint(self):\n rotmat = self.rot.as_matrix()\n return np.vstack(\n [np.hstack([rotmat,\n self.RotationType.wedge(self.trans).dot(rotmat)]),\n np.hstack([np.zeros((3, 3)), rotmat])]\n )","title":""},{"docid":"9d45ef89ffee63c47de030b925816160","score":"0.5670013","text":"def __repr__(self):\n return _almathswig.Rotation3D___repr__(self)","title":""},{"docid":"7523e3f83d08a5086f147458a7be83db","score":"0.56660104","text":"def draw_vectors(self):\n arm_v = self.state.heading.cross(Y).unit\n if arm_v == O:\n arm_v = X\n head_v = self.state.heading.rotate(self.state.elevation, about=arm_v)\n return arm_v, head_v","title":""},{"docid":"325b7ed93cc67368794d6d9569677105","score":"0.56347597","text":"def OrientationVector():","title":""},{"docid":"961cac352ffa7cb15f44fe0d19ebe7fc","score":"0.56207556","text":"def __repr__(self):\n return _almathswig.Rotation___repr__(self)","title":""},{"docid":"3eae7508e063079543ad744292681153","score":"0.56200635","text":"def find_rotation(self):\n\n return math.degrees(-abs(STEVE_ROLL - self.face['roll']))","title":""},{"docid":"9f488018c19279799db9855cf4189cb4","score":"0.56190187","text":"def orientation(self, v=None):\n if v is None:\n v = self._DEFAULT_ORIENTATION\n if isinstance(v, str):\n v = v.lower()\n v = {'horizontal': 'h', 0: 'h', 'left-to-right': 'h',\n 'vertical': 'v', 1: 'v', 'top-to-bottom': 'v',\n 'right-to-left': 'hr', 'bottom-to-top': 'vr'}.get(v, v)\n if v not in ('h', 'v', 'hr', 'vr'):\n raise ValueError('%s.orientation got unknown value %r' % (self.id, v))\n return v","title":""},{"docid":"83992eabfff6941718d8e63707819df8","score":"0.5613136","text":"def horizontal_angle(self):\n hypotenuse = math.sqrt(\n (self.topLeft['x'] - self.topRight['x'])^2 + \n (self.topLeft['y'] - self.topRight['y'])^2)\n opposite = self.topRight['x'] - self.topLeft['x']\n\n angle = math.asin(opposite / hypotenuse)\n return math.degrees(angle)","title":""},{"docid":"e98ba986bc9f2ce7c46dd419dcf89c42","score":"0.5608064","text":"def orientation(self) -> bool:\n return self.begin.x != self.end.x","title":""},{"docid":"e2377f4ce40c0aff70a34cf387b56689","score":"0.5578167","text":"def _compute_orientation(self):\n cyl_mat = self.sim.data.body_xmat[self.cyl_body_id]\n cyl_mat.shape = (3, 3)\n cyl_pos = self.sim.data.body_xpos[self.cyl_body_id]\n\n hole_pos = self.sim.data.body_xpos[self.hole_body_id]\n hole_mat = self.sim.data.body_xmat[self.hole_body_id]\n hole_mat.shape = (3, 3)\n\n v = cyl_mat @ np.array([0, 0, 1])\n v = v / np.linalg.norm(v)\n center = hole_pos + hole_mat @ np.array([0.1, 0, 0])\n\n t = (center - cyl_pos) @ v / (np.linalg.norm(v) ** 2)\n d = np.linalg.norm(np.cross(v, cyl_pos - center)) / np.linalg.norm(v)\n\n hole_normal = hole_mat @ np.array([0, 0, 1])\n return (\n t,\n d,\n abs(\n np.dot(hole_normal, v) / np.linalg.norm(hole_normal) / np.linalg.norm(v)\n ),\n )","title":""},{"docid":"a03cb0da6f48bfeca079bb18665f4cb2","score":"0.5549978","text":"def angle(self):\n return self._keypoint.angle","title":""},{"docid":"23e036b235d20ac4ff4b411baab34aa4","score":"0.55357623","text":"def toAngleAxis(self):\n\n nself = self.normalize()\n \n # Clamp nself.w (since the quat has to be normalized it should\n # be between -1 and 1 anyway, but it might be slightly off due\n # to numerical inaccuracies)\n w = max(min(nself.w,1.0),-1.0)\n \n w = math.acos(w)\n s = math.sin(w)\n if s<1E-12:\n return (0.0, _vec3(0.0,0.0,0.0))\n return (2.0*w, _vec3(nself.x/s, nself.y/s, nself.z/s))","title":""},{"docid":"dd86470fe234e2ba507e023b8ebb08fc","score":"0.5532004","text":"def actuator_to_joint_order(self):\n return self._inverse_order","title":""},{"docid":"ad06b00cac072dcf53693f894cb13a5b","score":"0.5528827","text":"def arrows(self):\n Q = self._quiver\n return tuple(self.element_class(self, e[0],e[1], [i]) for i,e in enumerate(self._sorted_edges))","title":""},{"docid":"226051add0982468e58659b5196d6574","score":"0.5528287","text":"def getRotationAngles(self):\n return self.rots","title":""},{"docid":"4a40fd576f01d63c08ea8e6aa239aa06","score":"0.5522791","text":"def get_navigation_angles(self):\n center = len(self.nav_angles[(-0.1 <= self.nav_angles) & (self.nav_angles <= 0.1)])\n left = len(self.nav_angles[self.nav_angles > 0.1])\n right = len(self.nav_angles[self.nav_angles < -0.1])\n\n return left, center, right","title":""},{"docid":"15ed520fd2dff50c2a4b08d6c630e507","score":"0.5520763","text":"def direction(self):\r\n\r\n return self.Direction.ABOVE","title":""},{"docid":"ade181ebdf891e104a38606ab03c92e1","score":"0.5508316","text":"def GetAxisDirection(self):\n ...","title":""},{"docid":"1b153dd300c3d58c433d58311ea5ea1a","score":"0.55073184","text":"def oriented_gauss_code(self):\n if self._oriented_gauss_code is not None:\n return self._oriented_gauss_code\n\n pd = self.pd_code()\n orient = self.orientation()\n crossing_info = {}\n for i, j in enumerate(pd):\n if orient[i] == -1:\n crossing_info[(j[0], -1, i + 1)] = j[2]\n crossing_info[(j[3], 1, i + 1)] = j[1]\n elif orient[i] == 1:\n crossing_info[(j[0], -1, i + 1)] = j[2]\n crossing_info[(j[1], 1, i + 1)] = j[3]\n edges = {}\n cross_number = {}\n for i, j in crossing_info.items():\n edges[i[0]] = [j]\n if i[1] == 1:\n cross_number[i[0]] = i[2]\n elif i[1] == -1:\n cross_number[i[0]] = -i[2]\n edges_graph = DiGraph(edges)\n d = edges_graph.all_simple_cycles()\n code = []\n for i in d:\n l = []\n for j in i:\n l.append(cross_number[j])\n del l[-1]\n code.append(l)\n oriented_code = [code, orient]\n self._oriented_gauss_code = oriented_code\n return self._oriented_gauss_code","title":""},{"docid":"f6797aa8752fd466fb3b9d2fe1f4540c","score":"0.5484768","text":"def rotation (self):\n return self.eman.component_for_entity (self.e, Movement).angle","title":""},{"docid":"734bb9200c70b3b87150063a2ee1c8da","score":"0.54796785","text":"def get_rotation(self):\n return self._properties['axis_rotation']","title":""},{"docid":"ede9516cf0852ed210d68a267f56478a","score":"0.5478329","text":"def get_current_orientation(self):\n res, orientation = vrep.simxGetObjectOrientation(self.clientID, self.robot_handle, -1, vrep.simx_opmode_streaming)\n while(res != vrep.simx_return_ok):\n res, orientation = vrep.simxGetObjectOrientation(self.clientID, self.robot_handle, -1, vrep.simx_opmode_streaming)\n\n return orientation","title":""},{"docid":"f8b7a4323fb538b294a3bb506e720d6e","score":"0.5473131","text":"def angle(self):\n\t\treturn self._angle","title":""},{"docid":"5cf94794e3e17a5d6f446c0f8f375fc8","score":"0.5466589","text":"def angles(self):\n return self.cellpar()[3:].copy()","title":""},{"docid":"854cc397cc6d892bc036b2977f302c34","score":"0.5446664","text":"def direction(self):\n return _vec2(math.cos(self.angle), math.sin(self.angle))","title":""},{"docid":"d73efceabe911795f41f6102c1698a2f","score":"0.5446603","text":"def rotation(self):\n return self.eman.component_for_entity(self.e, Movement).angle","title":""},{"docid":"caaae32d59ec157a2d149be54122932d","score":"0.54398906","text":"def physical_rotation(self):\n if getattr(self, '_body', None) is not None:\n return self._body.angle\n\n return -np.deg2rad(self.rotation)","title":""},{"docid":"b4d6b17ab1a041dd4102cfbd94f159f5","score":"0.54374886","text":"def orientation(self, version=PHOTOS_VERSION_CURRENT):\n imagedata = self._request_image_data(version=version)\n return imagedata.orientation","title":""},{"docid":"51a6bda2532c19e081bffc0ab4f309b0","score":"0.5425652","text":"def orientation(self):\n return self.http.get('orientation').value","title":""},{"docid":"39d11370a639b5b8f3106018f59354ab","score":"0.54195535","text":"def angle(self):\n return self._angle","title":""},{"docid":"33e137e273cc85a7e4c7e78980bc81f3","score":"0.5389049","text":"def getNavigationDirection(self) -> ghidra.app.util.datatype.NavigationDirection:\n ...","title":""},{"docid":"947903ae5462907839df117eee023626","score":"0.5378571","text":"def rotation(self) -> So2:\n return self._rotation","title":""},{"docid":"fad93792bbd7878586f1187b17553653","score":"0.5378019","text":"def orientation(self,p,q,r):\n\t\tval = (q[1] - p[1]) * (r[0] - q[0]) - (q[0] - p[0])*(r[1] - q[1])\n\t\tif val == 0:\n\t\t\treturn 0\n\t\tif val > 0:\n\t\t\treturn 1\n\t\tif val < 0:\n\t\t\treturn 2","title":""},{"docid":"0b7f9ff4bf4cd532029e5cd9b69cb94e","score":"0.5376779","text":"def return_angle(self) -> 'number':\n return arctan2(self.y, self.x)","title":""},{"docid":"10be09e6202314e8d7447eb07c557f56","score":"0.53754926","text":"def get_orientation_from_lines(self, v0, v1):\n # get into an np array\n\t#v1 = invert_vector(v1)\n\t#v0 = invert_vector(v0)\n\tprint \"vectors\"\n v0, v1 = np.array(v0), np.array(v1)\n\t#print \"DOT PRODUCT\"\n\tprint v0\n\tprint v1\n\t#print np.dot(v0,v1)\n # normalize\n v0 = v0 / np.linalg.norm(v0) \n v1 = v1 / np.linalg.norm(v1) \n\tprint v0\n\tprint v1\n # find normal\n n = np.cross(v0, v1)\n\t#print \"NORMAL\"\n\t#print n\n\t#n = np.cross(v1, v0)\n # stack into a matrix\n #rotMat = np.vstack((n, v0, v1)).T\n\trotMat = np.vstack((v0, v1, n)).T\n\t#print \"ROTMAT\"\n\t#print rotMat\n\tmatrix = rotMat\n\tmatrix = linalg.orth(rotMat) #FIXME uncomment\n\t#print \"matrices\"\n\t#print rotMat\n\t#print orthogonalized\n # find the quaternion xyzw\n tbRot = tfx.tb_angles(matrix)\n quat = tbRot.quaternion\n #code.interact(local=locals())\n return list(quat)","title":""},{"docid":"cfc6c801440af8ba96fd83843bb5cfa6","score":"0.5372919","text":"def get_direction(self):\n if self.direction_x == 1:\n return RIGHT\n elif self.direction_x == -1:\n return LEFT\n elif self.direction_y == -1:\n return UP\n elif self.direction_y == 1:\n return DOWN","title":""},{"docid":"dd497a0e86b2e8ef853fcaa254608ee2","score":"0.53591913","text":"def _get_direction(self):\n if self.secondaryjoin is not None:\n return sync.MANYTOMANY\n elif self._is_self_referential():\n # for a self referential mapper, if the \"foreignkey\" is a single or composite primary key,\n # then we are \"many to one\", since the remote site of the relationship identifies a singular entity.\n # otherwise we are \"one to many\".\n if self.remote_side is not None and len(self.remote_side):\n for f in self.foreignkey:\n if f in self.remote_side:\n return sync.ONETOMANY\n else:\n return sync.MANYTOONE\n else:\n for f in self.foreignkey:\n if not f.primary_key:\n return sync.ONETOMANY\n else:\n return sync.MANYTOONE\n else:\n onetomany = len([c for c in self.foreignkey if self.mapper.unjoined_table.corresponding_column(c, False) is not None])\n manytoone = len([c for c in self.foreignkey if self.parent.unjoined_table.corresponding_column(c, False) is not None])\n if not onetomany and not manytoone:\n raise exceptions.ArgumentError(\"Cant determine relation direction for '%s' on mapper '%s' with primary join '%s' - foreign key columns are not present in neither the parent nor the child's mapped tables\" %(self.key, str(self.parent), str(self.primaryjoin)) + str(self.foreignkey))\n elif onetomany and manytoone:\n raise exceptions.ArgumentError(\"Cant determine relation direction for '%s' on mapper '%s' with primary join '%s' - foreign key columns are present in both the parent and the child's mapped tables. Specify 'foreignkey' argument.\" %(self.key, str(self.parent), str(self.primaryjoin)))\n elif onetomany:\n return sync.ONETOMANY\n elif manytoone:\n return sync.MANYTOONE","title":""},{"docid":"6f200472a4e45fdd2fff2d59be74e33a","score":"0.53297985","text":"def angle(self):\n a = self.p1() - self.p0()\n b = self.p1() - self.p2()\n d = np.cross(a, b)[0]\n pi = math.pi\n return pi - np.sign(-d) * math.acos(a.dot(b) / (norm(a) * norm(b)))","title":""},{"docid":"e35fee336f225d304b866f4347491d5a","score":"0.5319846","text":"def rotation(self):\n return self._rotation * 180. / np.pi","title":""},{"docid":"edb431d2b2d417488df53941353f18f5","score":"0.5307419","text":"def angle(self) -> float:\n try:\n return self.cic.hedraAngle[self.ndx]\n except AttributeError:\n return 0.0","title":""},{"docid":"cca5776dfc905da66abfbc620788b856","score":"0.53044343","text":"def getAngle(self):\n\n return math.atan2(self.y, self.x)","title":""},{"docid":"5a42afb926c17971119d3f92c1b7a531","score":"0.53030115","text":"def slip_angle(self):\n if np.count_nonzero(self.mol_a.geom.perp_ax) == 0:\n self.mol_a.calc_axes()\n if np.count_nonzero(self.mol_b.geom.perp_ax) == 0:\n self.mol_b.calc_axes()\n # vector gonig from A to B\n cen_cen = self.mol_b.centroid() - self.mol_a.centroid()\n # test angle with positive cen_cen and negative cen_cen. keep smallest\n slip_angle_a_pos = ao.vec_angle(self.mol_a.geom.perp_ax, cen_cen)\n slip_angle_a_neg = 180 - slip_angle_a_pos\n slip_angle_b_pos = ao.vec_angle(self.mol_b.geom.perp_ax, cen_cen)\n slip_angle_b_neg = 180 - slip_angle_b_pos\n slip_angle_a = min((slip_angle_a_pos,slip_angle_a_neg))\n slip_angle_b = min((slip_angle_b_pos,slip_angle_b_neg))\n\n final_slip = min(slip_angle_a, slip_angle_b)\n return final_slip","title":""},{"docid":"00dcf189db084ce20dbd4f1922536603","score":"0.5300512","text":"def arrowEdge(self):\n return self._state.arrow_edge","title":""},{"docid":"3dac802efa442b9a6711de9b55087884","score":"0.5299564","text":"def _get_orientation(self, origin, p1, p2):\n difference = (\n ((p2.x - origin.x) * (p1.y - origin.y))\n - ((p1.x - origin.x) * (p2.y - origin.y))\n )\n\n return difference","title":""},{"docid":"708b9f64959982a867fbae5e6da1a0cf","score":"0.529511","text":"def _orientation(parents_slot, parents_orientation):\n orientation = int(parents_orientation)\n if orientation == 0:\n if parents_slot == 0:\n return 2\n elif parents_slot == 1:\n return 0\n elif parents_slot == 2:\n return 3\n elif parents_slot == 3:\n return 1\n elif orientation == 1:\n if parents_slot == 0:\n return 3\n elif parents_slot == 1:\n return 1\n elif parents_slot == 2:\n return 0\n elif parents_slot == 3:\n return 2\n elif orientation == 2:\n if parents_slot == 0:\n return 0\n elif parents_slot == 1:\n return 2\n elif parents_slot == 2:\n return 1\n elif parents_slot == 3:\n return 3\n elif orientation == 3:\n if parents_slot == 0:\n return 1\n elif parents_slot == 1:\n return 3\n elif parents_slot == 2:\n return 2\n elif parents_slot == 3:\n return 0\n else:\n err(\"Unsupported parents rotation angle provided.\")","title":""},{"docid":"a9f684835fed799de03657f3eb6f63c7","score":"0.5287697","text":"def angleAdir(self, joints):\r\n\t\tj = joints.reshape((16,2), order = 'F')\r\n\t\tlinks = [(0,1),(1,2),(2,6),(3,6),(4,3),(5,4),(10,11),(11,12),(12,8),(13,8),(14,13),(15,14)]\r\n\t\tangles_l = [(0,1),(1,2),(2,3),(3,4),(4,5),(6,7),(7,8),(8,9),(9,10),(10,11)]\r\n\t\tvects = []\r\n\t\tangles = []\r\n\t\tfor i in range(len(links)):\r\n\t\t\tvects.append(self.joint2Vect( j[links[i][0]], j[links[i][1]]))\r\n\t\tfor i in range(len(angles_l)):\r\n\t\t\tangles.append(self.vect2angle(vects[angles_l[i][0]], vects[angles_l[i][1]]))\r\n\t\treturn vects, np.degrees(angles)","title":""},{"docid":"170affc4424693c029a61d0d6b4050c5","score":"0.5282547","text":"def angle(self, other):\n if isinstance(other, Line3D):\n return self.direction.angle(other.direction)\n elif isinstance(other, Plane3D):\n return self.direction.angle(other.normal)\n\n raise NotImplementedError(\n \"Angle between a Line3D and a %s is not defined\" % other\n )","title":""},{"docid":"26f5d256011319465e9a661b72461a38","score":"0.5281203","text":"def adjacency(self):\n return self.halfedge","title":""},{"docid":"b85f084fb7e8292e4d128f59ae12ac00","score":"0.5280726","text":"def angle(self):\n data_dict = {}\n for name, vectors in self._angles.items():\n data_dict[name]=self.get_angle(self[vectors['left']], self[vectors['right']])\n return pd.DataFrame(data_dict).set_index(self.data.index)","title":""},{"docid":"539d8d96d2a39a5682cc9bd508a5cb68","score":"0.52799267","text":"def orientation(p, q, r):\n val = (float(q.y - p.y) * (r.x - q.x)) - (float(q.x - p.x) * (r.y - q.y))\n if val > 0:\n # Clockwise orientation\n return 1\n elif val < 0:\n # Counterclockwise orientation\n return 2\n else:\n # Colinear orientation\n return 0","title":""},{"docid":"7fd5ffd4d8d9d2f7074ae8c228619244","score":"0.52719665","text":"def get_diagonal(self):\n diagonal1 = (sqrt(pow(self.width, 2) + pow(self.length, 2) -\n 2 * self.width * self.length * cos(radians(self.angle))))\n diagonal2 = (sqrt(self.width ** 2 + self.length ** 2 -\n 2 * self.width * self.length * cos(radians(180 - self.angle))))\n return max(diagonal1, diagonal2)","title":""},{"docid":"69be6631e46a9e7b90918b09e3d54b02","score":"0.52628636","text":"def transpose(self):\n return _almathswig.Rotation_transpose(self)","title":""},{"docid":"170740438b53307402ff6a08982cab9a","score":"0.52611506","text":"def dowker_notation(self):\n pd = self.pd_code()\n orient = self.orientation()\n dn = [(i[0], i[3]) if orient[j] == -1 else (i[0], i[1])\n for j, i in enumerate(pd)]\n return dn","title":""},{"docid":"0fa82c79f5b39cf643cdea5653e6ac7f","score":"0.5247597","text":"def determinant(self):\n return _almathswig.Rotation_determinant(self)","title":""},{"docid":"fe599d0edc7d410a35e26f887d1cb4e5","score":"0.5235638","text":"def getangle(self):\n return self._ang","title":""},{"docid":"428945ca30175820c559f80195c1eea4","score":"0.52339476","text":"def getAngle(self) -> float:\n ...","title":""},{"docid":"19abbd26a30f7809dbeb5a9ccfbc6b3c","score":"0.5231853","text":"def get_aligner_edge(self):\n\n return self._aligner_edge","title":""},{"docid":"2470de906bb465934028b3e5db5248c6","score":"0.5219311","text":"def getRotationAngle(self):\n angle = self.getAttribute('rotation_angle')\n return max(0.0, min(angle, 360.0))","title":""},{"docid":"0e46f23123ec0f1c2606cebe5ca164a0","score":"0.5212095","text":"def horizon(self) -> StratigraphicObject:\n return self.hor","title":""},{"docid":"0e46f23123ec0f1c2606cebe5ca164a0","score":"0.5212095","text":"def horizon(self) -> StratigraphicObject:\n return self.hor","title":""},{"docid":"949760041ce33d91a71b000aeaecb82a","score":"0.5208128","text":"def getRotation(self):\n\t\treturn self._rotation","title":""},{"docid":"0477b20487a0be85b0e41e165efba6d4","score":"0.5206345","text":"def angle(self, other):\n if isinstance(other, Line3D):\n return self.normal.angle(other.direction)\n elif isinstance(other, Plane3D):\n return self.normal.angle(other.normal)\n\n raise NotImplementedError(\n \"Angle between a Line3D and a %s is not defined\" % other\n )","title":""},{"docid":"18175f225d581f2c60cb2c3281c79621","score":"0.52045244","text":"def rotated_intersections(self):\n slices = self.find_active_intersections()\n rotation_angle = slices[0]\n slices = slices - rotation_angle\n\n return rotation_angle, slices + (slices < 0)*2.*np.pi","title":""},{"docid":"67e6be9727dc8fecbb51c2b9161fd7f1","score":"0.5204403","text":"def getAngle(self):\n angle = self.getAttribute('angle')\n return max(0.0, min(angle, 90.0))","title":""},{"docid":"251c76d08235f363308b51f9c5ade9a7","score":"0.52041566","text":"def angleBetween(self, Qt_ScreenOrientation, Qt_ScreenOrientation_1): # real signature unknown; restored from __doc__\n return 0","title":""},{"docid":"33598d80d8d4b1f15bb2dcd9f48a0c47","score":"0.52019775","text":"def get_angle(self):\n return self._header[b'ItalicAngle']","title":""},{"docid":"855f210d4c0d713feb877e8fe9caa980","score":"0.5196006","text":"def convertir_direction_angle(self):\n \n return atan2( self.robot.direction[0], self.robot.direction[1] )","title":""},{"docid":"d0d4df130c603bbfa49f285a775d25db","score":"0.5189529","text":"def angle(self) -> float:\n return math.degrees(math.atan2(self.y, self.x))","title":""},{"docid":"2282585c960cca9c3e58cad6865821a7","score":"0.5188738","text":"def orientation(self) -> int:\n return self._prefs.get(PREF_ORIENTATION, 1)","title":""},{"docid":"e5c90c3892512bd9700182e0682ca2c1","score":"0.51867753","text":"def angles(self):\n if np.count_nonzero(self.mol_a.geom.perp_ax) == 0:\n self.mol_a.calc_axes()\n if np.count_nonzero(self.mol_b.geom.perp_ax) == 0:\n self.mol_b.calc_axes()\n out_lis = [ao.vec_angle(self.mol_a.geom.prin_ax,self.mol_b.geom.prin_ax),\n ao.vec_angle(self.mol_a.geom.sec_ax,self.mol_b.geom.sec_ax),\n ao.vec_angle(self.mol_a.geom.perp_ax,self.mol_b.geom.perp_ax)]\n out_arr = np.array(out_lis)\n\n return out_arr","title":""},{"docid":"865ea4583a62436499221854b7073762","score":"0.5182891","text":"def __str__(self):\n return _almathswig.Rotation___str__(self)","title":""},{"docid":"1e6e2b375ba3d774f2a8cdb93a229020","score":"0.517307","text":"def transpose(self) -> 'TextDiagramDrawer':\n out = TextDiagramDrawer()\n out.entries = {(y, x): v for (x, y), v in self.entries.items()}\n out.vertical_lines = [_VerticalLine(*e)\n for e in self.horizontal_lines]\n out.horizontal_lines = [_HorizontalLine(*e)\n for e in self.vertical_lines]\n return out","title":""},{"docid":"f4805008fa5f16e5072917c24009e48a","score":"0.5163102","text":"def getVerticalAlignment(self):\n return (getLayout()).getVerticalAlignment()","title":""},{"docid":"9ae2f47882714711c2a7ff99afa63a01","score":"0.5155457","text":"def opposite(self):\n\t\treturn Traversal(self.segment, not self.orientation)","title":""},{"docid":"1f479c6079fa88d9df98bfb8fd9a81dd","score":"0.5149696","text":"def r(self) -> So2:\n return self._rotation","title":""},{"docid":"33ea2de217047fbba1daa6ae1067f460","score":"0.5146656","text":"def exterior_angle(self,k):\n return arccos(self.tangent_vector(k+1).inner_product(self.tangent_vector(k)))","title":""},{"docid":"4a43da5dda9dc17659bb340f092cfc95","score":"0.51417583","text":"def getDrawOrder(self):\n\n raise NotImplementedError","title":""},{"docid":"414413852193dc6d416bb3891e168dc4","score":"0.5140761","text":"def getVerticalAlignment(self):\n return verticalAlignment","title":""}],"string":"[\n {\n \"docid\": \"f1ce0f0417423532c5526f5a36e613ce\",\n \"score\": \"0.6612555\",\n \"text\": \"def get_orientation(self, visited):\\n #print(visited)\\n if visited:\\n rot = mathutils.Quaternion(self.helical_axisParam, self.get_angle(2))\\n a = self.positions[2]\\n b = self.positions[3]\\n else:\\n rot = mathutils.Quaternion(self.helical_axisParam, self.get_angle(0))\\n a = self.positions[0]\\n b = self.positions[1]\\n n = mathutils.Vector(self.east)\\n n.rotate(rot)\\n return a, b, n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d032df59101c5531e7038bcff103b56\",\n \"score\": \"0.62240785\",\n \"text\": \"def orientation(self):\\n\\n a, b, b, c = self.covariance.flat\\n if a < 0 or c < 0: # negative variance\\n return np.nan * u.rad # pragma: no cover\\n return 0.5 * np.arctan2(2. * b, (a - c))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3804bde52098c268274098da0ca6420c\",\n \"score\": \"0.6199202\",\n \"text\": \"def orientation(self):\\n o_tmp = self._orientation + 180\\n o_tmp %= 360\\n return o_tmp - 180\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dceed3de285eb7e32bdd733bc1aea5cd\",\n \"score\": \"0.60408294\",\n \"text\": \"def interior_angle(self):\\n return (self.edges - 2)*(100/self.edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51564e6580bb05eeca02be5bec95fa8e\",\n \"score\": \"0.5991328\",\n \"text\": \"def visual_rotation(self):\\n if getattr(self, '_body', None) is None:\\n return self.rotation\\n\\n return -np.rad2deg(self._body.angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b662239cc7e1ca2715e694e4e3022a\",\n \"score\": \"0.5961181\",\n \"text\": \"def cone_orientation(self):\\n if self.is3d:\\n orientation = lib.D3DVECTOR()\\n _check(\\n self._native_buffer3d.GetConeOrientation(ctypes.byref(orientation))\\n )\\n return orientation.x, orientation.y, orientation.z\\n else:\\n return 0, 0, 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfa68b1b4ca59c83845cf36d6f5fc0d\",\n \"score\": \"0.5934017\",\n \"text\": \"def chiral(self):\\n return self.orientation_from_basis(self.basis_from_domains(self.domains))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d889bd8be8a0754c4092b53db9f850bb\",\n \"score\": \"0.59151345\",\n \"text\": \"def getOrientation(self):\\n return self.orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7b3d83e7726ac46530f05bc07cb9ae\",\n \"score\": \"0.59147805\",\n \"text\": \"def incidence_angle(self):\\n return self.get_incidence_angle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a61bb1b7b5fd2a0a9a2f509fd7be6d\",\n \"score\": \"0.58855635\",\n \"text\": \"def _orientation(side):\\r\\n a, b = side\\r\\n if a < b:\\r\\n return 1\\r\\n elif a > b:\\r\\n return -1\\r\\n else:\\r\\n raise ValueError('side connects a vertex to itself: ' + str(side))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dffd6e8401a42ddf7d3154f8f34c4e3\",\n \"score\": \"0.5871174\",\n \"text\": \"def angle(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dffd6e8401a42ddf7d3154f8f34c4e3\",\n \"score\": \"0.5871174\",\n \"text\": \"def angle(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7dfe204d04525a15a36c9e7944705d\",\n \"score\": \"0.5828715\",\n \"text\": \"def getOrientation(self):\\n if self._belt_connection_state != BeltConnectionState.CONNECTED:\\n return None\\n if self._belt_firm_version<34:\\n print(\\\"BeltController: Unable to know the belt orientation. \\\" +\\n \\\"Orientation notifications are available only from \\\"+\\n \\\"firmware version 34.\\\")\\n return\\n if self._belt_heading is None or self._belt_heading_offset is None:\\n return None\\n orientation = (self._belt_heading, self._belt_heading_offset)\\n return orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d075472cb4de66e6b0a74cfcd0e228a0\",\n \"score\": \"0.5820682\",\n \"text\": \"def orientated_lines(self) -> List[str]:\\n return self.get_orientated_lines(self.lines, self.flipped, self.rotated)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b542f277095793890e56ac1e4b6e7931\",\n \"score\": \"0.57949173\",\n \"text\": \"def getOrientation(self):\\n return self._orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7588595274d5a093468ef9b29a381206\",\n \"score\": \"0.57776535\",\n \"text\": \"def getRotationDirection(self):\\n return self.rotationDirection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7af541adf1e6d318ac133fe19ac0e86\",\n \"score\": \"0.57652414\",\n \"text\": \"def _get_end_effector_orientation(self):\\n orient_quat = p.getLinkState(\\n self.robot,\\n linkIndex=8,\\n computeForwardKinematics=True,\\n physicsClientId=self.id)[1]\\n # orient_euler = p.getEulerFromQuaternion(orient_quat)\\n return np.array(orient_quat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0133cc8eb6a2ec28272c319c7c2437\",\n \"score\": \"0.5738593\",\n \"text\": \"def get_orientation_vector(self):\\n return rotate_vector([1, 0, 0][:self.get_dim()], self.orientation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8911de800f0563996af7608bc225a09b\",\n \"score\": \"0.57322216\",\n \"text\": \"def get_orientation(self):\\n return tuple(self.x[7:10]) + (self.x[6],)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1643187bd3a2ef3ebe4013533d220f\",\n \"score\": \"0.57239\",\n \"text\": \"def getAngle(self):\\n return _almathswig.Pose2D_getAngle(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16fcaab976971d18dffb06ac30e3c340\",\n \"score\": \"0.5717684\",\n \"text\": \"def interior_angle(self):\\n\\t\\tif not hasattr(self, '_int_angle'):\\n\\t\\t\\tself._int_angle = (self.vertices - 2) * 180 / self.vertices\\n\\t\\treturn self._int_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c9dbc0446b32dce93f02676c53537ea\",\n \"score\": \"0.57156324\",\n \"text\": \"def getAngle(self):\\n return self.joint_pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4966a3ee04a981b11df3272a3eaf06e\",\n \"score\": \"0.57014596\",\n \"text\": \"def adjoint(self):\\n rotmat = self.rot.as_matrix()\\n return np.vstack(\\n [np.hstack([rotmat,\\n self.RotationType.wedge(self.trans).dot(rotmat)]),\\n np.hstack([np.zeros((3, 3)), rotmat])]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d45ef89ffee63c47de030b925816160\",\n \"score\": \"0.5670013\",\n \"text\": \"def __repr__(self):\\n return _almathswig.Rotation3D___repr__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7523e3f83d08a5086f147458a7be83db\",\n \"score\": \"0.56660104\",\n \"text\": \"def draw_vectors(self):\\n arm_v = self.state.heading.cross(Y).unit\\n if arm_v == O:\\n arm_v = X\\n head_v = self.state.heading.rotate(self.state.elevation, about=arm_v)\\n return arm_v, head_v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325b7ed93cc67368794d6d9569677105\",\n \"score\": \"0.56347597\",\n \"text\": \"def OrientationVector():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961cac352ffa7cb15f44fe0d19ebe7fc\",\n \"score\": \"0.56207556\",\n \"text\": \"def __repr__(self):\\n return _almathswig.Rotation___repr__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eae7508e063079543ad744292681153\",\n \"score\": \"0.56200635\",\n \"text\": \"def find_rotation(self):\\n\\n return math.degrees(-abs(STEVE_ROLL - self.face['roll']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f488018c19279799db9855cf4189cb4\",\n \"score\": \"0.56190187\",\n \"text\": \"def orientation(self, v=None):\\n if v is None:\\n v = self._DEFAULT_ORIENTATION\\n if isinstance(v, str):\\n v = v.lower()\\n v = {'horizontal': 'h', 0: 'h', 'left-to-right': 'h',\\n 'vertical': 'v', 1: 'v', 'top-to-bottom': 'v',\\n 'right-to-left': 'hr', 'bottom-to-top': 'vr'}.get(v, v)\\n if v not in ('h', 'v', 'hr', 'vr'):\\n raise ValueError('%s.orientation got unknown value %r' % (self.id, v))\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83992eabfff6941718d8e63707819df8\",\n \"score\": \"0.5613136\",\n \"text\": \"def horizontal_angle(self):\\n hypotenuse = math.sqrt(\\n (self.topLeft['x'] - self.topRight['x'])^2 + \\n (self.topLeft['y'] - self.topRight['y'])^2)\\n opposite = self.topRight['x'] - self.topLeft['x']\\n\\n angle = math.asin(opposite / hypotenuse)\\n return math.degrees(angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98ba986bc9f2ce7c46dd419dcf89c42\",\n \"score\": \"0.5608064\",\n \"text\": \"def orientation(self) -> bool:\\n return self.begin.x != self.end.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2377f4ce40c0aff70a34cf387b56689\",\n \"score\": \"0.5578167\",\n \"text\": \"def _compute_orientation(self):\\n cyl_mat = self.sim.data.body_xmat[self.cyl_body_id]\\n cyl_mat.shape = (3, 3)\\n cyl_pos = self.sim.data.body_xpos[self.cyl_body_id]\\n\\n hole_pos = self.sim.data.body_xpos[self.hole_body_id]\\n hole_mat = self.sim.data.body_xmat[self.hole_body_id]\\n hole_mat.shape = (3, 3)\\n\\n v = cyl_mat @ np.array([0, 0, 1])\\n v = v / np.linalg.norm(v)\\n center = hole_pos + hole_mat @ np.array([0.1, 0, 0])\\n\\n t = (center - cyl_pos) @ v / (np.linalg.norm(v) ** 2)\\n d = np.linalg.norm(np.cross(v, cyl_pos - center)) / np.linalg.norm(v)\\n\\n hole_normal = hole_mat @ np.array([0, 0, 1])\\n return (\\n t,\\n d,\\n abs(\\n np.dot(hole_normal, v) / np.linalg.norm(hole_normal) / np.linalg.norm(v)\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03cb0da6f48bfeca079bb18665f4cb2\",\n \"score\": \"0.5549978\",\n \"text\": \"def angle(self):\\n return self._keypoint.angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e036b235d20ac4ff4b411baab34aa4\",\n \"score\": \"0.55357623\",\n \"text\": \"def toAngleAxis(self):\\n\\n nself = self.normalize()\\n \\n # Clamp nself.w (since the quat has to be normalized it should\\n # be between -1 and 1 anyway, but it might be slightly off due\\n # to numerical inaccuracies)\\n w = max(min(nself.w,1.0),-1.0)\\n \\n w = math.acos(w)\\n s = math.sin(w)\\n if s<1E-12:\\n return (0.0, _vec3(0.0,0.0,0.0))\\n return (2.0*w, _vec3(nself.x/s, nself.y/s, nself.z/s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd86470fe234e2ba507e023b8ebb08fc\",\n \"score\": \"0.5532004\",\n \"text\": \"def actuator_to_joint_order(self):\\n return self._inverse_order\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad06b00cac072dcf53693f894cb13a5b\",\n \"score\": \"0.5528827\",\n \"text\": \"def arrows(self):\\n Q = self._quiver\\n return tuple(self.element_class(self, e[0],e[1], [i]) for i,e in enumerate(self._sorted_edges))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"226051add0982468e58659b5196d6574\",\n \"score\": \"0.5528287\",\n \"text\": \"def getRotationAngles(self):\\n return self.rots\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a40fd576f01d63c08ea8e6aa239aa06\",\n \"score\": \"0.5522791\",\n \"text\": \"def get_navigation_angles(self):\\n center = len(self.nav_angles[(-0.1 <= self.nav_angles) & (self.nav_angles <= 0.1)])\\n left = len(self.nav_angles[self.nav_angles > 0.1])\\n right = len(self.nav_angles[self.nav_angles < -0.1])\\n\\n return left, center, right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ed520fd2dff50c2a4b08d6c630e507\",\n \"score\": \"0.5520763\",\n \"text\": \"def direction(self):\\r\\n\\r\\n return self.Direction.ABOVE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade181ebdf891e104a38606ab03c92e1\",\n \"score\": \"0.5508316\",\n \"text\": \"def GetAxisDirection(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b153dd300c3d58c433d58311ea5ea1a\",\n \"score\": \"0.55073184\",\n \"text\": \"def oriented_gauss_code(self):\\n if self._oriented_gauss_code is not None:\\n return self._oriented_gauss_code\\n\\n pd = self.pd_code()\\n orient = self.orientation()\\n crossing_info = {}\\n for i, j in enumerate(pd):\\n if orient[i] == -1:\\n crossing_info[(j[0], -1, i + 1)] = j[2]\\n crossing_info[(j[3], 1, i + 1)] = j[1]\\n elif orient[i] == 1:\\n crossing_info[(j[0], -1, i + 1)] = j[2]\\n crossing_info[(j[1], 1, i + 1)] = j[3]\\n edges = {}\\n cross_number = {}\\n for i, j in crossing_info.items():\\n edges[i[0]] = [j]\\n if i[1] == 1:\\n cross_number[i[0]] = i[2]\\n elif i[1] == -1:\\n cross_number[i[0]] = -i[2]\\n edges_graph = DiGraph(edges)\\n d = edges_graph.all_simple_cycles()\\n code = []\\n for i in d:\\n l = []\\n for j in i:\\n l.append(cross_number[j])\\n del l[-1]\\n code.append(l)\\n oriented_code = [code, orient]\\n self._oriented_gauss_code = oriented_code\\n return self._oriented_gauss_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6797aa8752fd466fb3b9d2fe1f4540c\",\n \"score\": \"0.5484768\",\n \"text\": \"def rotation (self):\\n return self.eman.component_for_entity (self.e, Movement).angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734bb9200c70b3b87150063a2ee1c8da\",\n \"score\": \"0.54796785\",\n \"text\": \"def get_rotation(self):\\n return self._properties['axis_rotation']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede9516cf0852ed210d68a267f56478a\",\n \"score\": \"0.5478329\",\n \"text\": \"def get_current_orientation(self):\\n res, orientation = vrep.simxGetObjectOrientation(self.clientID, self.robot_handle, -1, vrep.simx_opmode_streaming)\\n while(res != vrep.simx_return_ok):\\n res, orientation = vrep.simxGetObjectOrientation(self.clientID, self.robot_handle, -1, vrep.simx_opmode_streaming)\\n\\n return orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b7a4323fb538b294a3bb506e720d6e\",\n \"score\": \"0.5473131\",\n \"text\": \"def angle(self):\\n\\t\\treturn self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf94794e3e17a5d6f446c0f8f375fc8\",\n \"score\": \"0.5466589\",\n \"text\": \"def angles(self):\\n return self.cellpar()[3:].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854cc397cc6d892bc036b2977f302c34\",\n \"score\": \"0.5446664\",\n \"text\": \"def direction(self):\\n return _vec2(math.cos(self.angle), math.sin(self.angle))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73efceabe911795f41f6102c1698a2f\",\n \"score\": \"0.5446603\",\n \"text\": \"def rotation(self):\\n return self.eman.component_for_entity(self.e, Movement).angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caaae32d59ec157a2d149be54122932d\",\n \"score\": \"0.54398906\",\n \"text\": \"def physical_rotation(self):\\n if getattr(self, '_body', None) is not None:\\n return self._body.angle\\n\\n return -np.deg2rad(self.rotation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d6b17ab1a041dd4102cfbd94f159f5\",\n \"score\": \"0.54374886\",\n \"text\": \"def orientation(self, version=PHOTOS_VERSION_CURRENT):\\n imagedata = self._request_image_data(version=version)\\n return imagedata.orientation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a6bda2532c19e081bffc0ab4f309b0\",\n \"score\": \"0.5425652\",\n \"text\": \"def orientation(self):\\n return self.http.get('orientation').value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d11370a639b5b8f3106018f59354ab\",\n \"score\": \"0.54195535\",\n \"text\": \"def angle(self):\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e137e273cc85a7e4c7e78980bc81f3\",\n \"score\": \"0.5389049\",\n \"text\": \"def getNavigationDirection(self) -> ghidra.app.util.datatype.NavigationDirection:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947903ae5462907839df117eee023626\",\n \"score\": \"0.5378571\",\n \"text\": \"def rotation(self) -> So2:\\n return self._rotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad93792bbd7878586f1187b17553653\",\n \"score\": \"0.5378019\",\n \"text\": \"def orientation(self,p,q,r):\\n\\t\\tval = (q[1] - p[1]) * (r[0] - q[0]) - (q[0] - p[0])*(r[1] - q[1])\\n\\t\\tif val == 0:\\n\\t\\t\\treturn 0\\n\\t\\tif val > 0:\\n\\t\\t\\treturn 1\\n\\t\\tif val < 0:\\n\\t\\t\\treturn 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7f9ff4bf4cd532029e5cd9b69cb94e\",\n \"score\": \"0.5376779\",\n \"text\": \"def return_angle(self) -> 'number':\\n return arctan2(self.y, self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10be09e6202314e8d7447eb07c557f56\",\n \"score\": \"0.53754926\",\n \"text\": \"def get_orientation_from_lines(self, v0, v1):\\n # get into an np array\\n\\t#v1 = invert_vector(v1)\\n\\t#v0 = invert_vector(v0)\\n\\tprint \\\"vectors\\\"\\n v0, v1 = np.array(v0), np.array(v1)\\n\\t#print \\\"DOT PRODUCT\\\"\\n\\tprint v0\\n\\tprint v1\\n\\t#print np.dot(v0,v1)\\n # normalize\\n v0 = v0 / np.linalg.norm(v0) \\n v1 = v1 / np.linalg.norm(v1) \\n\\tprint v0\\n\\tprint v1\\n # find normal\\n n = np.cross(v0, v1)\\n\\t#print \\\"NORMAL\\\"\\n\\t#print n\\n\\t#n = np.cross(v1, v0)\\n # stack into a matrix\\n #rotMat = np.vstack((n, v0, v1)).T\\n\\trotMat = np.vstack((v0, v1, n)).T\\n\\t#print \\\"ROTMAT\\\"\\n\\t#print rotMat\\n\\tmatrix = rotMat\\n\\tmatrix = linalg.orth(rotMat) #FIXME uncomment\\n\\t#print \\\"matrices\\\"\\n\\t#print rotMat\\n\\t#print orthogonalized\\n # find the quaternion xyzw\\n tbRot = tfx.tb_angles(matrix)\\n quat = tbRot.quaternion\\n #code.interact(local=locals())\\n return list(quat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc6c801440af8ba96fd83843bb5cfa6\",\n \"score\": \"0.5372919\",\n \"text\": \"def get_direction(self):\\n if self.direction_x == 1:\\n return RIGHT\\n elif self.direction_x == -1:\\n return LEFT\\n elif self.direction_y == -1:\\n return UP\\n elif self.direction_y == 1:\\n return DOWN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd497a0e86b2e8ef853fcaa254608ee2\",\n \"score\": \"0.53591913\",\n \"text\": \"def _get_direction(self):\\n if self.secondaryjoin is not None:\\n return sync.MANYTOMANY\\n elif self._is_self_referential():\\n # for a self referential mapper, if the \\\"foreignkey\\\" is a single or composite primary key,\\n # then we are \\\"many to one\\\", since the remote site of the relationship identifies a singular entity.\\n # otherwise we are \\\"one to many\\\".\\n if self.remote_side is not None and len(self.remote_side):\\n for f in self.foreignkey:\\n if f in self.remote_side:\\n return sync.ONETOMANY\\n else:\\n return sync.MANYTOONE\\n else:\\n for f in self.foreignkey:\\n if not f.primary_key:\\n return sync.ONETOMANY\\n else:\\n return sync.MANYTOONE\\n else:\\n onetomany = len([c for c in self.foreignkey if self.mapper.unjoined_table.corresponding_column(c, False) is not None])\\n manytoone = len([c for c in self.foreignkey if self.parent.unjoined_table.corresponding_column(c, False) is not None])\\n if not onetomany and not manytoone:\\n raise exceptions.ArgumentError(\\\"Cant determine relation direction for '%s' on mapper '%s' with primary join '%s' - foreign key columns are not present in neither the parent nor the child's mapped tables\\\" %(self.key, str(self.parent), str(self.primaryjoin)) + str(self.foreignkey))\\n elif onetomany and manytoone:\\n raise exceptions.ArgumentError(\\\"Cant determine relation direction for '%s' on mapper '%s' with primary join '%s' - foreign key columns are present in both the parent and the child's mapped tables. Specify 'foreignkey' argument.\\\" %(self.key, str(self.parent), str(self.primaryjoin)))\\n elif onetomany:\\n return sync.ONETOMANY\\n elif manytoone:\\n return sync.MANYTOONE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f200472a4e45fdd2fff2d59be74e33a\",\n \"score\": \"0.53297985\",\n \"text\": \"def angle(self):\\n a = self.p1() - self.p0()\\n b = self.p1() - self.p2()\\n d = np.cross(a, b)[0]\\n pi = math.pi\\n return pi - np.sign(-d) * math.acos(a.dot(b) / (norm(a) * norm(b)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35fee336f225d304b866f4347491d5a\",\n \"score\": \"0.5319846\",\n \"text\": \"def rotation(self):\\n return self._rotation * 180. / np.pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb431d2b2d417488df53941353f18f5\",\n \"score\": \"0.5307419\",\n \"text\": \"def angle(self) -> float:\\n try:\\n return self.cic.hedraAngle[self.ndx]\\n except AttributeError:\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca5776dfc905da66abfbc620788b856\",\n \"score\": \"0.53044343\",\n \"text\": \"def getAngle(self):\\n\\n return math.atan2(self.y, self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a42afb926c17971119d3f92c1b7a531\",\n \"score\": \"0.53030115\",\n \"text\": \"def slip_angle(self):\\n if np.count_nonzero(self.mol_a.geom.perp_ax) == 0:\\n self.mol_a.calc_axes()\\n if np.count_nonzero(self.mol_b.geom.perp_ax) == 0:\\n self.mol_b.calc_axes()\\n # vector gonig from A to B\\n cen_cen = self.mol_b.centroid() - self.mol_a.centroid()\\n # test angle with positive cen_cen and negative cen_cen. keep smallest\\n slip_angle_a_pos = ao.vec_angle(self.mol_a.geom.perp_ax, cen_cen)\\n slip_angle_a_neg = 180 - slip_angle_a_pos\\n slip_angle_b_pos = ao.vec_angle(self.mol_b.geom.perp_ax, cen_cen)\\n slip_angle_b_neg = 180 - slip_angle_b_pos\\n slip_angle_a = min((slip_angle_a_pos,slip_angle_a_neg))\\n slip_angle_b = min((slip_angle_b_pos,slip_angle_b_neg))\\n\\n final_slip = min(slip_angle_a, slip_angle_b)\\n return final_slip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00dcf189db084ce20dbd4f1922536603\",\n \"score\": \"0.5300512\",\n \"text\": \"def arrowEdge(self):\\n return self._state.arrow_edge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dac802efa442b9a6711de9b55087884\",\n \"score\": \"0.5299564\",\n \"text\": \"def _get_orientation(self, origin, p1, p2):\\n difference = (\\n ((p2.x - origin.x) * (p1.y - origin.y))\\n - ((p1.x - origin.x) * (p2.y - origin.y))\\n )\\n\\n return difference\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708b9f64959982a867fbae5e6da1a0cf\",\n \"score\": \"0.529511\",\n \"text\": \"def _orientation(parents_slot, parents_orientation):\\n orientation = int(parents_orientation)\\n if orientation == 0:\\n if parents_slot == 0:\\n return 2\\n elif parents_slot == 1:\\n return 0\\n elif parents_slot == 2:\\n return 3\\n elif parents_slot == 3:\\n return 1\\n elif orientation == 1:\\n if parents_slot == 0:\\n return 3\\n elif parents_slot == 1:\\n return 1\\n elif parents_slot == 2:\\n return 0\\n elif parents_slot == 3:\\n return 2\\n elif orientation == 2:\\n if parents_slot == 0:\\n return 0\\n elif parents_slot == 1:\\n return 2\\n elif parents_slot == 2:\\n return 1\\n elif parents_slot == 3:\\n return 3\\n elif orientation == 3:\\n if parents_slot == 0:\\n return 1\\n elif parents_slot == 1:\\n return 3\\n elif parents_slot == 2:\\n return 2\\n elif parents_slot == 3:\\n return 0\\n else:\\n err(\\\"Unsupported parents rotation angle provided.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f684835fed799de03657f3eb6f63c7\",\n \"score\": \"0.5287697\",\n \"text\": \"def angleAdir(self, joints):\\r\\n\\t\\tj = joints.reshape((16,2), order = 'F')\\r\\n\\t\\tlinks = [(0,1),(1,2),(2,6),(3,6),(4,3),(5,4),(10,11),(11,12),(12,8),(13,8),(14,13),(15,14)]\\r\\n\\t\\tangles_l = [(0,1),(1,2),(2,3),(3,4),(4,5),(6,7),(7,8),(8,9),(9,10),(10,11)]\\r\\n\\t\\tvects = []\\r\\n\\t\\tangles = []\\r\\n\\t\\tfor i in range(len(links)):\\r\\n\\t\\t\\tvects.append(self.joint2Vect( j[links[i][0]], j[links[i][1]]))\\r\\n\\t\\tfor i in range(len(angles_l)):\\r\\n\\t\\t\\tangles.append(self.vect2angle(vects[angles_l[i][0]], vects[angles_l[i][1]]))\\r\\n\\t\\treturn vects, np.degrees(angles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170affc4424693c029a61d0d6b4050c5\",\n \"score\": \"0.5282547\",\n \"text\": \"def angle(self, other):\\n if isinstance(other, Line3D):\\n return self.direction.angle(other.direction)\\n elif isinstance(other, Plane3D):\\n return self.direction.angle(other.normal)\\n\\n raise NotImplementedError(\\n \\\"Angle between a Line3D and a %s is not defined\\\" % other\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f5d256011319465e9a661b72461a38\",\n \"score\": \"0.5281203\",\n \"text\": \"def adjacency(self):\\n return self.halfedge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b85f084fb7e8292e4d128f59ae12ac00\",\n \"score\": \"0.5280726\",\n \"text\": \"def angle(self):\\n data_dict = {}\\n for name, vectors in self._angles.items():\\n data_dict[name]=self.get_angle(self[vectors['left']], self[vectors['right']])\\n return pd.DataFrame(data_dict).set_index(self.data.index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"539d8d96d2a39a5682cc9bd508a5cb68\",\n \"score\": \"0.52799267\",\n \"text\": \"def orientation(p, q, r):\\n val = (float(q.y - p.y) * (r.x - q.x)) - (float(q.x - p.x) * (r.y - q.y))\\n if val > 0:\\n # Clockwise orientation\\n return 1\\n elif val < 0:\\n # Counterclockwise orientation\\n return 2\\n else:\\n # Colinear orientation\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd5ffd4d8d9d2f7074ae8c228619244\",\n \"score\": \"0.52719665\",\n \"text\": \"def get_diagonal(self):\\n diagonal1 = (sqrt(pow(self.width, 2) + pow(self.length, 2) -\\n 2 * self.width * self.length * cos(radians(self.angle))))\\n diagonal2 = (sqrt(self.width ** 2 + self.length ** 2 -\\n 2 * self.width * self.length * cos(radians(180 - self.angle))))\\n return max(diagonal1, diagonal2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69be6631e46a9e7b90918b09e3d54b02\",\n \"score\": \"0.52628636\",\n \"text\": \"def transpose(self):\\n return _almathswig.Rotation_transpose(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170740438b53307402ff6a08982cab9a\",\n \"score\": \"0.52611506\",\n \"text\": \"def dowker_notation(self):\\n pd = self.pd_code()\\n orient = self.orientation()\\n dn = [(i[0], i[3]) if orient[j] == -1 else (i[0], i[1])\\n for j, i in enumerate(pd)]\\n return dn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa82c79f5b39cf643cdea5653e6ac7f\",\n \"score\": \"0.5247597\",\n \"text\": \"def determinant(self):\\n return _almathswig.Rotation_determinant(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe599d0edc7d410a35e26f887d1cb4e5\",\n \"score\": \"0.5235638\",\n \"text\": \"def getangle(self):\\n return self._ang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428945ca30175820c559f80195c1eea4\",\n \"score\": \"0.52339476\",\n \"text\": \"def getAngle(self) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19abbd26a30f7809dbeb5a9ccfbc6b3c\",\n \"score\": \"0.5231853\",\n \"text\": \"def get_aligner_edge(self):\\n\\n return self._aligner_edge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2470de906bb465934028b3e5db5248c6\",\n \"score\": \"0.5219311\",\n \"text\": \"def getRotationAngle(self):\\n angle = self.getAttribute('rotation_angle')\\n return max(0.0, min(angle, 360.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e46f23123ec0f1c2606cebe5ca164a0\",\n \"score\": \"0.5212095\",\n \"text\": \"def horizon(self) -> StratigraphicObject:\\n return self.hor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e46f23123ec0f1c2606cebe5ca164a0\",\n \"score\": \"0.5212095\",\n \"text\": \"def horizon(self) -> StratigraphicObject:\\n return self.hor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949760041ce33d91a71b000aeaecb82a\",\n \"score\": \"0.5208128\",\n \"text\": \"def getRotation(self):\\n\\t\\treturn self._rotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0477b20487a0be85b0e41e165efba6d4\",\n \"score\": \"0.5206345\",\n \"text\": \"def angle(self, other):\\n if isinstance(other, Line3D):\\n return self.normal.angle(other.direction)\\n elif isinstance(other, Plane3D):\\n return self.normal.angle(other.normal)\\n\\n raise NotImplementedError(\\n \\\"Angle between a Line3D and a %s is not defined\\\" % other\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18175f225d581f2c60cb2c3281c79621\",\n \"score\": \"0.52045244\",\n \"text\": \"def rotated_intersections(self):\\n slices = self.find_active_intersections()\\n rotation_angle = slices[0]\\n slices = slices - rotation_angle\\n\\n return rotation_angle, slices + (slices < 0)*2.*np.pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e6be9727dc8fecbb51c2b9161fd7f1\",\n \"score\": \"0.5204403\",\n \"text\": \"def getAngle(self):\\n angle = self.getAttribute('angle')\\n return max(0.0, min(angle, 90.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251c76d08235f363308b51f9c5ade9a7\",\n \"score\": \"0.52041566\",\n \"text\": \"def angleBetween(self, Qt_ScreenOrientation, Qt_ScreenOrientation_1): # real signature unknown; restored from __doc__\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33598d80d8d4b1f15bb2dcd9f48a0c47\",\n \"score\": \"0.52019775\",\n \"text\": \"def get_angle(self):\\n return self._header[b'ItalicAngle']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855f210d4c0d713feb877e8fe9caa980\",\n \"score\": \"0.5196006\",\n \"text\": \"def convertir_direction_angle(self):\\n \\n return atan2( self.robot.direction[0], self.robot.direction[1] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d4df130c603bbfa49f285a775d25db\",\n \"score\": \"0.5189529\",\n \"text\": \"def angle(self) -> float:\\n return math.degrees(math.atan2(self.y, self.x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2282585c960cca9c3e58cad6865821a7\",\n \"score\": \"0.5188738\",\n \"text\": \"def orientation(self) -> int:\\n return self._prefs.get(PREF_ORIENTATION, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c90c3892512bd9700182e0682ca2c1\",\n \"score\": \"0.51867753\",\n \"text\": \"def angles(self):\\n if np.count_nonzero(self.mol_a.geom.perp_ax) == 0:\\n self.mol_a.calc_axes()\\n if np.count_nonzero(self.mol_b.geom.perp_ax) == 0:\\n self.mol_b.calc_axes()\\n out_lis = [ao.vec_angle(self.mol_a.geom.prin_ax,self.mol_b.geom.prin_ax),\\n ao.vec_angle(self.mol_a.geom.sec_ax,self.mol_b.geom.sec_ax),\\n ao.vec_angle(self.mol_a.geom.perp_ax,self.mol_b.geom.perp_ax)]\\n out_arr = np.array(out_lis)\\n\\n return out_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865ea4583a62436499221854b7073762\",\n \"score\": \"0.5182891\",\n \"text\": \"def __str__(self):\\n return _almathswig.Rotation___str__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6e2b375ba3d774f2a8cdb93a229020\",\n \"score\": \"0.517307\",\n \"text\": \"def transpose(self) -> 'TextDiagramDrawer':\\n out = TextDiagramDrawer()\\n out.entries = {(y, x): v for (x, y), v in self.entries.items()}\\n out.vertical_lines = [_VerticalLine(*e)\\n for e in self.horizontal_lines]\\n out.horizontal_lines = [_HorizontalLine(*e)\\n for e in self.vertical_lines]\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4805008fa5f16e5072917c24009e48a\",\n \"score\": \"0.5163102\",\n \"text\": \"def getVerticalAlignment(self):\\n return (getLayout()).getVerticalAlignment()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae2f47882714711c2a7ff99afa63a01\",\n \"score\": \"0.5155457\",\n \"text\": \"def opposite(self):\\n\\t\\treturn Traversal(self.segment, not self.orientation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f479c6079fa88d9df98bfb8fd9a81dd\",\n \"score\": \"0.5149696\",\n \"text\": \"def r(self) -> So2:\\n return self._rotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ea2de217047fbba1daa6ae1067f460\",\n \"score\": \"0.5146656\",\n \"text\": \"def exterior_angle(self,k):\\n return arccos(self.tangent_vector(k+1).inner_product(self.tangent_vector(k)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a43da5dda9dc17659bb340f092cfc95\",\n \"score\": \"0.51417583\",\n \"text\": \"def getDrawOrder(self):\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"414413852193dc6d416bb3891e168dc4\",\n \"score\": \"0.5140761\",\n \"text\": \"def getVerticalAlignment(self):\\n return verticalAlignment\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993993,"cells":{"query_id":{"kind":"string","value":"05d347d75016454d195ce03d8236430f"},"query":{"kind":"string","value":"Handles input file browsing and selection."},"positive_passages":{"kind":"list like","value":[{"docid":"49171b4d5fc7d7816a1b2a758f052185","score":"0.0","text":"def get_input_file_name(self):\r\n\r\n\t\tentry = self.input_file_E\r\n\t\tentry.delete(0, \"end\")\r\n\t\t\r\n\t\troot.update()\r\n\t\tself.master_input = filedialog.askopenfilename(multiple = True)\r\n\t\troot.update()\r\n\t\t\r\n\t\tif self.master_input == \"\":\r\n\t\t\tself.master_input = (\"\",)\t\r\n\t\t\treturn \r\n\r\n\t\tif len(self.master_input) == 1:\r\n\t\t\tentry.insert(0, self.master_input[0])\r\n\t\t\t\r\n\t\t\ttemp_re = re.search((\".*\\.\"), os.path.basename(os.path.normpath(self.master_input[0])))\r\n\t\t\tif temp_re:\r\n\t\t\t\tself.input_root = temp_re.group(0)[:-1]\r\n\t\t\telse:\r\n\t\t\t\tself.input_root = os.path.basename(os.path.normpath(self.master_input[0]))\r\n\t\t\t\t\t\r\n\t\t\t# Update default names\r\n\t\t\treplace_entry(self.plot_file_E, (self.input_root + \"plot.html\"))\t\t\r\n\t\t\tget_novel_name(self.plot_file_E, (self.input_root + \"plot(NUM).html\"))\r\n\t\t\treplace_entry(self.stats_file_E, (self.input_root + \"Stats.csv\"))\r\n\t\t\tget_novel_name(self.stats_file_E, (self.input_root + \"Stats(NUM).csv\"))\r\n\r\n\t\telse:\r\n\t\t\tentry.insert(0, self.master_input)\r\n\t\t\treplace_entry(self.plot_file_E, \"------------------\")\r\n\t\t\treplace_entry(self.stats_file_E, \"------------------\")","title":""}],"string":"[\n {\n \"docid\": \"49171b4d5fc7d7816a1b2a758f052185\",\n \"score\": \"0.0\",\n \"text\": \"def get_input_file_name(self):\\r\\n\\r\\n\\t\\tentry = self.input_file_E\\r\\n\\t\\tentry.delete(0, \\\"end\\\")\\r\\n\\t\\t\\r\\n\\t\\troot.update()\\r\\n\\t\\tself.master_input = filedialog.askopenfilename(multiple = True)\\r\\n\\t\\troot.update()\\r\\n\\t\\t\\r\\n\\t\\tif self.master_input == \\\"\\\":\\r\\n\\t\\t\\tself.master_input = (\\\"\\\",)\\t\\r\\n\\t\\t\\treturn \\r\\n\\r\\n\\t\\tif len(self.master_input) == 1:\\r\\n\\t\\t\\tentry.insert(0, self.master_input[0])\\r\\n\\t\\t\\t\\r\\n\\t\\t\\ttemp_re = re.search((\\\".*\\\\.\\\"), os.path.basename(os.path.normpath(self.master_input[0])))\\r\\n\\t\\t\\tif temp_re:\\r\\n\\t\\t\\t\\tself.input_root = temp_re.group(0)[:-1]\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tself.input_root = os.path.basename(os.path.normpath(self.master_input[0]))\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t# Update default names\\r\\n\\t\\t\\treplace_entry(self.plot_file_E, (self.input_root + \\\"plot.html\\\"))\\t\\t\\r\\n\\t\\t\\tget_novel_name(self.plot_file_E, (self.input_root + \\\"plot(NUM).html\\\"))\\r\\n\\t\\t\\treplace_entry(self.stats_file_E, (self.input_root + \\\"Stats.csv\\\"))\\r\\n\\t\\t\\tget_novel_name(self.stats_file_E, (self.input_root + \\\"Stats(NUM).csv\\\"))\\r\\n\\r\\n\\t\\telse:\\r\\n\\t\\t\\tentry.insert(0, self.master_input)\\r\\n\\t\\t\\treplace_entry(self.plot_file_E, \\\"------------------\\\")\\r\\n\\t\\t\\treplace_entry(self.stats_file_E, \\\"------------------\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"32ea7dad953fd98a57afaf65664098bb","score":"0.68089163","text":"def input_pick(self):\n file_name = QtWidgets.QFileDialog.getOpenFileName(self, 'Open file', self.vid_file)\n if file_name[0]:\n self.input_load(file_name[0])","title":""},{"docid":"326edf721d5ec1cd4508a2ec8ddd6168","score":"0.67879474","text":"def OnSelectFile(self, evt):\n evid = evt.GetId()\n if evid == self.EVT_SELECT_TRAINING_FILE:\n target = self.fileinput_training\n elif evid == self.EVT_SELECT_PROCESS_FILE:\n target = self.fileinput_analysis\n else:\n return\n fd = wx.FileDialog(self, 'Open file', 'a', 'b',\n 'Excel file (*.xlsx,*.xls)|*.xlsx;*.xls|CSV file (*.csv,*.txt)|*.txt;*.csv', wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\n if self.__datadirectory is None:\n self.__datadirectory = os.path.curdir\n fd.SetDirectory(self.__datadirectory)\n if fd.ShowModal() == wx.ID_OK:#CANCEL:\n self.__datadirectory = os.path.dirname(fd.GetPath())\n target.SetValue(fd.GetPath())\n fd.Destroy()","title":""},{"docid":"dfc9855433998af82d6c194569c2158f","score":"0.671288","text":"def _input_select(self):\n options = QFileDialog.Options()\n file_select, _ = QFileDialog.getOpenFileNames(\n self._view,\n 'Select Input File...',\n '',\n 'RNH Files (*.rnh);;RND Files (*.rnd);;CSV Files (*.csv);;Excel Files (*.xls*);;All Files (*)',\n options=options\n )\n if file_select:\n self._config[\"input\"] = file_select","title":""},{"docid":"6ac19ad5f6676706633999912e840670","score":"0.6612921","text":"def onBrowse(self, event):\n \n fileBrowser = wx.FileDialog(self.GetParent(),\n style=wx.OPEN|wx.MULTIPLE|wx.FILE_MUST_EXIST)\n\n if fileBrowser.ShowModal() == wx.ID_OK:\n\n for filename in fileBrowser.GetPaths():\n # generate an ItemSelected event for each file\n zope.component.handle(\n p6.storage.events.ItemSelected(\n p6.storage.items.FileItem(filename)\n )\n )","title":""},{"docid":"128867df7e6598346cedc4a90ee5cd4c","score":"0.6427338","text":"def browse_file(self):\n filename, _ = QFileDialog.getOpenFileName(self, 'Open file', '.', '*.txt')\n self.inp.setText(filename)\n if os.path.isfile(filename):\n self.write_log(self.logOutput, \"\\nText file: \" + filename)\n self.txt_file = filename\n else:\n self.write_log(self.logOutput, \"\\nError: Text file is not a valid file.\")","title":""},{"docid":"1c82753b53086c7bc09d86dd9d1ea5df","score":"0.64145774","text":"def __open_file(self, event=None) -> None:\n\n filepath = askopenfilename(\n filetypes=[(\"Goethe Files\", \"*.goethe\"), (\"All Files\", \"*.*\")])\n\n if not filepath:\n # User did not select a file\n return\n\n with open(filepath, \"r\") as file:\n self.filepath = filepath\n self.filename = os.path.basename(self.filepath)\n self.editor.delete(1.0, 'end')\n self.editor.insert('end', file.read())\n self.interpreter.set_code(self.__get_text())\n self.__update_widgets()","title":""},{"docid":"437515f5ee52612370c5b24e00358e2e","score":"0.63791525","text":"def browse(self):\n self.load_filename = filedialog.askopenfilename()\n self.pathlabel.config(text=self.load_filename)\n self.reset()\n self.read_raw_data(self.load_filename)","title":""},{"docid":"c219c33a0eaa50082fc4a2ae2278979f","score":"0.63583136","text":"def input_button_clicked(self):\n\t\tfilename = filedialog.askopenfilename(title='Open an input file', initialdir='~/', filetypes=[('Comma-Separated File', '*.csv')])\n\t\tif filename:\n\t\t\tself.input_entry['state'] = \"normal\"\n\t\t\tself.input_entry.delete(0,tk.END)\n\t\t\tself.input_entry.insert(0,filename)\n\t\t\tself.check_input()","title":""},{"docid":"1d7521fa57ddfc2dd57bd24fc5677db0","score":"0.63055557","text":"def selectfile(self):\n self.singleselectfile()\n value = self.component('filename').get()\n self.setfilename(value)\n if value:\n self.okbutton()","title":""},{"docid":"77a9734522cf037b1a787fd1f67beea2","score":"0.62773037","text":"def _process_input(self):\n\n while not self.exit_event.is_set():\n try:\n keypress = self.screen.getkey()\n if keypress == 'q' or keypress == 'Q': # quit program\n self.exit_event.set(0)\n elif keypress == '1': # switch to mode 1\n self.exit_event.set(1)\n elif keypress == '2': # switch to mode 2\n self.exit_event.set(2)\n elif keypress == 'k': # move selected line up\n if self.selected_line != 0:\n self.selected_line -= 1\n self.change_event.set()\n elif keypress == 'j': # move selected line down\n if self.selected_line != len(self.all_plugins) - 1:\n self.selected_line += 1\n self.change_event.set()\n elif keypress == 'c': # toggle selected plugin\n with self.status_lock:\n self.plugin_statuses[self.selected_line] = not self.plugin_statuses[self.selected_line]\n self.change_event.set()\n elif keypress == 's': # send analysis request for currently selected plugins\n self._get_startup()\n elif keypress == 'i':\n stream = self._get_stream_input(keypress)\n elif keypress == 'p':\n while True:\n stream_input = self._get_stream_input(keypress)\n if stream_input.lower() == 'cancel':\n break\n elif os.path.isfile(stream_input):\n self.vimrc_path = stream_input\n break\n logging.debug(keypress)\n except curses.error:\n pass","title":""},{"docid":"12df600c97a6e0a7ca94d4c201f4f07b","score":"0.617765","text":"def unify_tk_controller(self):\n\n self.browse_file_directory()\n self.open_and_read_in_csv_file()\n self.unify_pathway_decider()\n # clearing the parameters so the button can be used again with a new file\n self.filename = ''\n self.csv_file_in_list_format = []","title":""},{"docid":"1d70e73d34b6ed6583ee0f22a31c1153","score":"0.6151189","text":"def fileSelected_callback(self):\n print('=== myTreeWidget.fileSelected_callback()')\n modifiers = QtWidgets.QApplication.keyboardModifiers()\n isShift = modifiers == QtCore.Qt.ShiftModifier\n isControl = modifiers == QtCore.Qt.ControlModifier\n\n print(' isControl:', isControl)\n\n theItems = self.selectedItems()\n if len(theItems) > 0:\n theItem = theItems[0]\n #selectedRow = self.fileList.currentRow() # self.fileList is a QTreeWidget\n filename = theItem.text(self.myColumns['File'])\n #print(' fileSelected_callback()', filename)\n if isControl:\n self.myCanvasWidget.getGraphicsView().zoomSelectedItem(filename)\n # visually select image in canvas with yellow square\n self.myCanvasWidget.getGraphicsView().setSelectedItem(filename)\n else:\n print(' no selected items')","title":""},{"docid":"7f3bc675c44006e6c4690833c3e7c505","score":"0.6136701","text":"def fileButtonClicked(self):\n\t\tfilename = QtGui.QFileDialog.getOpenFileName(self, SELECT_FILE_MESSAGE)\n\t\tif filename:\n\t\t\t# User selected a file\n\t\t\tself.current_file.setText(os.path.basename(str(filename)))\n\t\t\terr = self.source_window.loadSource(str(filename))\n\t\t\tself.assembly_widget.clear()\n\t\t\tself.stack_and_frame_widget.clear()\n\t\t\tself.gdb_process.gdbReset()\n\n\t\t\tif (err > 0):\n\t\t\t\t# User selected a valid C source file\n\t\t\t\tself.compileSource(str(filename))","title":""},{"docid":"b770c6866c5d6e146e75c313b7dd86c6","score":"0.6100576","text":"def open_file(self):\n self.file = askopenfile(mode='r+', filetypes=[('FASTA Files', '*.fasta'), ('FASTA files', '*.fa')])\n # check user has selected a file\n try:\n self.content = SeqIO.read(self.file, 'fasta')\n # reset text_out\n self.text_out.set('')\n # tell user no file was opened if they press cancel\n except AttributeError:\n self.text_out.set('You pressed cancel, no new file was opened')\n # note if a file has already been opened this will stay open","title":""},{"docid":"73a903874df06ccc4cd0ebcdff82854d","score":"0.6097784","text":"def askopenfilename(self, *args, **kw):\n\n self.tk.tk_setPalette('#888888')\n save_update_step = self.update_step\n self.update_step = 0\n filename = tkFileDialog.askopenfilename(parent=self.tk, **file_opt)\n if filename:\n self.readwtf(filename)\n self.wtf = filename\n self.redraw_letters()\n self.update_step = save_update_step\n self.tk.tk_setPalette('#000000')","title":""},{"docid":"1069a751d64ae183721ee6aa889b2e3b","score":"0.60699177","text":"def on_pushButton_DatabaseFileInputSelect_clicked(self):\n ArxmlInputFile, supported_fileKinds = QFileDialog.getOpenFileName(\n self, u'open the file', u'./')\n self.lineEdit_DatabaseFileInput.setText(ArxmlInputFile)\n filePathForWinOS = ArxmlInputFile.replace('/', '\\\\')\n if str(ArxmlInputFile)[-6:] == '.arxml':\n self.ArxmlInputFilePath = filePathForWinOS\n pass\n else:\n l_MessageBox = QMessageBox.information(\n self, u'Tips', 'The file opened is not an arxml file,pls open an arxml file.')\n pass","title":""},{"docid":"e5eded315e3e275ef3516f86bec62a9d","score":"0.6020328","text":"def onOpenFile(self, event):\n dlg = wx.FileDialog(\n self, message=\"Choose a file\",\n defaultDir=self.currentDirectory,\n defaultFile=\"\",\n wildcard=\"Images, PDF (*.pdf,*.jpg,*.png)|*.pdf;*.jpg;*.png|\" \\\n \"All files (*.*)|*.*\",\n style=wx.FD_OPEN | wx.FD_MULTIPLE | wx.FD_CHANGE_DIR\n )\n if dlg.ShowModal() == wx.ID_OK:\n paths = dlg.GetPaths()\n print(\"You chose the following file(s):\")\n self.parent.imagesPaths = []\n for path in paths:\n print(\"choose path: \"+path)\n self.pathsEditText.AppendText(path)\n self.parent.imagesPaths.append(path)\n dlg.Destroy()","title":""},{"docid":"ed67d121ce34e64314ff8f11bc54ee77","score":"0.5993419","text":"def _upload_action(event=None):\n global filename\n filename = filedialog.askopenfilename()\n print('Selected:', filename)","title":""},{"docid":"905e38e93dc44ad6ecd4147cdb58d426","score":"0.5941498","text":"def __openFileInEditor(self):\n itmList = self.getSelectedItems()\n for itm in itmList[:]:\n self.sourceFile.emit(itm.fileName())","title":""},{"docid":"9806b6a347dadd39a12511e792f2b834","score":"0.5905072","text":"def on_open(self):\n text = _(\"Open file dialog.\")\n openFileDialog = wx.FileDialog(\n self,\n _(\"Open\"),\n wildcard=\"Circuit Definition files (*.txt;*.lcdf)|*.txt;*.lcdf\",\n style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\n res = openFileDialog.ShowModal()\n if res == wx.ID_OK: # user selected a file\n self.current_file_path = openFileDialog.GetPath()\n self.clear_log()\n self.log_message(_(\"File opened: {}\")\n .format(self.current_file_path),\n no_new_line=True)\n self.load_file(self.current_file_path)\n self.canvas.render(_(\"Opened file\"))","title":""},{"docid":"2520d775ad0387aa77a265ebb441270b","score":"0.5901012","text":"def _file_dialog_handler(self):\n dirname = QtWidgets.QFileDialog.getExistingDirectory(\n self, self.label, self.dirpath)\n if dirname:\n self.update_path(dirname)","title":""},{"docid":"5ba4284f15a98d2bebba0579276bf76a","score":"0.58996785","text":"async def handle_file_select_action(*, event_data, action_data, logger, app):\n await _confirm_selection(\n event_data=event_data, action_data=action_data, logger=logger, app=app\n )\n\n selected_template = action_data[\"selected_option\"][\"value\"]\n repo = app[\"templatebot/repo\"].get_repo(\n gitref=app[\"root\"][\"templatebot/repoRef\"]\n )\n template = repo[selected_template]\n if len(template.config[\"dialog_fields\"]) == 0:\n await _respond_with_nonconfigurable_content(\n template=template, event_data=event_data, logger=logger, app=app\n )\n else:\n await open_template_dialog(\n template=template,\n callback_id_root=\"templatebot_file_dialog\",\n event_data=event_data,\n logger=logger,\n app=app,\n )","title":""},{"docid":"5711180e7df415fb3a349649cdaabdbe","score":"0.5883519","text":"def _acceptSelection(self, *args, **kwargs):\n if len(args) == 0:\n return\n line = args[0]\n # get path and open it in a window\n path = line.split()[-1]\n # print(path)\n docsetManager.openSearchResult(self._ftype, path)","title":""},{"docid":"eeec14d4745237123aa63e06568ad057","score":"0.58706737","text":"def on_select(self, e):\n style_dirdialog = wx.DD_DEFAULT_STYLE\n dlg = wx.DirDialog(self.pnl, \"Choose the output folder...\", self.src_dir, style=style_dirdialog)\n\n if dlg.ShowModal() == wx.ID_OK:\n self.out_dir = dlg.GetPath()\n\n file_prefix = self.txt_file_prefix.GetValue()\n file_suffix = self.txt_file_suffix.GetValue()\n\n if file_prefix and file_suffix:\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, file_prefix)}{file_suffix}.pdf [...]')\n elif file_prefix:\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, file_prefix)}001.pdf [...]')\n elif file_suffix:\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, self.src_file[:self.src_file.rindex(\".\")])}'\n f'{file_suffix}.pdf [...]')\n else:\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, self.src_file[:self.src_file.rindex(\".\")])}'\n '001.pdf')\n\n # update the statusbar based on the current conditions\n self._statusbar()\n # f.close()\n dlg.Destroy()","title":""},{"docid":"c8bccba6909a493293f571b5ab625411","score":"0.5861975","text":"def on_open(self, e):\n dlg = wx.FileDialog(self.pnl, \"Choose the PDF to split and save to individual pages\", self.src_dir, \"\",\n \"PDF Files (*.pdf)|*.pdf\", wx.FD_OPEN)\n if dlg.ShowModal() == wx.ID_OK: # If the OK button is clicked on the 'Select PDF' dialog\n self.src_file = dlg.GetFilename()\n self.src_dir = dlg.GetDirectory()\n\n self.txt_filename_preview.SetValue(os.path.join(self.src_dir, self.src_file))\n # First, check if the prefix is set or not. If it's not, set it to the prefix of the selected file\n if self.txt_file_prefix.GetValue() == '':\n self.txt_file_prefix.SetValue(self.src_file[:self.src_file.rfind('.')])\n\n # Now, check if the suffix txt box is filled in or not, if not, set it to the standard suffix\n if self.txt_file_suffix.GetValue() == '':\n self.txt_file_suffix.SetValue('001')\n\n # Next, check if the output directory has been selected\n if self.out_dir != '':\n oval = os.path.join(self.out_dir, self.txt_file_prefix.GetValue())\n suffix = f\"{self.txt_file_suffix.GetValue()} [...]\"\n if self.txt_file_suffix.GetValue() != '':\n suffix = self.txt_file_suffix.GetValue()\n self.txt_outputprev.SetValue(f\"{oval}{suffix}.pdf [...]\")\n\n # Now, check if the output directory has not been selected\n elif self.txt_file_prefix.GetValue() and self.txt_file_suffix.GetValue() == '':\n # If not, use the base directory of the source file\n oval = f'{os.path.join(self.src_dir, self.txt_file_prefix.GetValue())}'\n self.txt_outputprev.SetValue(f\"{oval}001.pdf [...]\")\n self.out_dir = self.src_dir\n\n self._statusbar()\n\n dlg.Destroy()","title":""},{"docid":"c1dd43a1603c52d3095a2971aa2269f6","score":"0.5855089","text":"def input_file(self):\n raise NotImplementedError()","title":""},{"docid":"a30b28e216886d86b3a0cef69c21f64c","score":"0.5834874","text":"def select_file_clicked(ui):\n # open file ui\n file_ui = QtGui.QFileDialog()\n file_name = file_ui.getSaveFileName(None, \"Choose output file\", str(expanduser(\"~\")), SELECT_FILTER)\n ui.select_file.setText(file_name)","title":""},{"docid":"a238588968bb9f994722b4f74c0461ae","score":"0.580808","text":"def OnFile(self, event):\r\n if event.Id == wx.ID_OPEN:\r\n self.OpenFile()\r\n elif event.Id == wx.ID_SAVE:\r\n self.SaveFile()\r\n else:\r\n super(FileEditor, self).OnFile(event)","title":""},{"docid":"eef51faa54e56e08ad247e6623723298","score":"0.57987547","text":"def input_check(self, choice):\r\n\r\n if choice == 'U':\r\n print('\\n==========================================')\r\n print('You have chosen to upload a file!')\r\n print('==========================================')\r\n fileName = input('Please enter a filename: ')\r\n category = input('Please enter a category: ')\r\n keywords = input('Please enter keywords: ')\r\n\r\n response = self.user.upload(fileName, category, keywords)\r\n\r\n # results of upload\r\n if response == 1:\r\n print('Successful upload!\\n')\r\n\r\n else: print('A file with that name already exists.\\n')\r\n\r\n elif choice == 'R':\r\n print('\\n==========================================')\r\n print('You have chosen to retrieve a file!')\r\n print('==========================================')\r\n file = str(input('Please enter a filename: '))\r\n\r\n response = self.user.download(file)\r\n if response == 1:\r\n print(\"Success!! file retrieved...\\n\")\r\n else:\r\n print (\"Error!! cannot find file.. \")\r\n\r\n\r\n elif choice == 'S':\r\n print('\\n==========================================')\r\n print('You have chosen to search for a file!')\r\n print('==========================================')\r\n file = str(input('Please enter a keyword to search for: '))\r\n if self.user.search(file):\r\n print(\"\")\r\n\r\n\r\n elif choice == 'D':\r\n print('\\n==========================================')\r\n print('You have chosen to delete an existing file!')\r\n print('==========================================')\r\n file = str(input('Please enter a filename: '))\r\n\r\n self.user.delete(file)\r\n else:\r\n os._exit(0)","title":""},{"docid":"41f267f1841420c1d439e92adae05613","score":"0.5796372","text":"def browseFile(self):\n file_name = getAFile()\n if file_name != \"\":\n self.onNewFileSelected(str(file_name))\n self.centralWidget.setMinimumSize(700, 500)","title":""},{"docid":"8c11002e6b2e7416899e340e887ac87f","score":"0.5775657","text":"def push_Driver_Index_file(self):\n filepath = QFileDialog.getOpenFileName(self)\n self.plainTextEdit_3.setPlainText(filepath[0])\n self.filepath_Driver = filepath[0]","title":""},{"docid":"389a05fcb3bf97b04a6ac375c9aa3e38","score":"0.5752986","text":"def handle_input(self, input):\n pass","title":""},{"docid":"d4e4cc1bc5297fe75dee7cf09d1f6619","score":"0.57359797","text":"def fileBrowserDialog(*args, **kwargs):\n\n pass","title":""},{"docid":"6401a26b5cae191dff94f0ad56319c23","score":"0.5721455","text":"def OnOpen(self, event):\n\t\tdlg = wx.FileDialog(self, \"\", self.aztexEditor.dirname, \"\", \"*.*\", wx.OPEN)\n\t\tif dlg.ShowModal() == wx.ID_OK: # if user clicks OK (if user wants to open a document)\n\t\t\tself.aztexEditor.filename = dlg.GetFilename()\n\t\t\tself.aztexEditor.dirname = dlg.GetDirectory()\n\t\t\tf = open(os.path.join(self.aztexEditor.dirname, self.aztexEditor.filename), 'r')\n\t\t\tself.aztexEditor.SetValue(f.read())\n\t\t\tf.close()\n\t\t\tself.SetTitle(self.aztexEditor.filename)\n\t\tdlg.Destroy()","title":""},{"docid":"af8edc6ab1e42e8c1b13c549357083d1","score":"0.56955665","text":"def pick_file(self):\n fileName = QtGui.QFileDialog.getOpenFileName(self,\n \"Open Session\", __file__, \n \"Session Files ({x})\".format( \\\n x = self.file_extension))\n \n self.pathLE.setText(fileName[0])\n self.textChanged.emit()","title":""},{"docid":"6b245b14402cd1cdbfb0072cfb80ce79","score":"0.5685589","text":"def file_dialog(self):\n\n self.filename = filedialog.askopenfilename(\n initialdir=\"./\", title=\"Select A File\",\n )\n self.label = ttk.Label(self.labelFrame, text=\"\")\n self.label.grid(column=1, row=2)\n self.label.configure(text=self.filename)\n if self.filename:\n self.get_wave_info(self.filename)","title":""},{"docid":"33169b5160e13596b2c90cfb1e7f60df","score":"0.566944","text":"def prepare_input(self, _input, _file):\n return NotImplementedError","title":""},{"docid":"f25569617da9589321d009c6013739f0","score":"0.5661297","text":"def browse_file():\n currdir = os.getcwd()\n Tk().withdraw()\n filepath = tkFileDialog.askopenfilename(parent = root, initialdir=currdir, title='Please select a file')\n if len(filepath) > 0:\n print \"You selected file : %s\" % filepath\n else:\n return browse_file()\n return filepath","title":""},{"docid":"2097e7034e17e328cb54eeed28a3c99d","score":"0.56575316","text":"def open_files(self) :\n\n self.input_file = self._open_root( self.input_file_name )\n if self.input_file is None :\n return self.error_before_loop()\n\n self.input_tree = self._load_tree( self.input_tree_name )\n if self.input_tree is None :\n return self.error_before_loop()\n\n self.output_file = self._create_root( self.output_file_name )\n if self.output_file is None :\n return self.error_before_loop()\n\n return self.ok_before_loop()","title":""},{"docid":"e0657a78066d8a2bc2e5a6f8bdf76acc","score":"0.5649424","text":"def singleselectfile(self):\n cs = self.component('filenamebox').curselection()\n if cs != ():\n value = self.component('filenamebox').get(cs)\n self.setfilename(value)","title":""},{"docid":"30f5151d5d9091bead031835bea19500","score":"0.56484985","text":"def openFile(self):\r\n\t\t\r\n\t\tfList = [(\"Python Files\", \"*.py\"), (\"Text Files\", \"*.txt\")]\r\n\t\tfileName = tkinter.filedialog.askopenfilename(parent = self, filetypes = fList)\r\n\t\t\r\n\t\tif fileName != \"\":\r\n\t\t\tfile = open(fileName, 'r')\r\n\t\t\ttext = file.read()\r\n\t\t\tfile.close()\r\n\t\t\tself.outputArea.setText(text)\r\n\t\t\tself.setTitle(fileName)","title":""},{"docid":"306c52717b5f72ba80aa3e0cbaa36620","score":"0.5645772","text":"def handle_click_open():\n filepath = askopenfilename(\n filetypes=[(\"Python Files\", \"*.py\"), (\"All Files\", \"*.*\")]\n )\n if not filepath:\n return\n textBox.delete(\"1.0\", tk.END)\n with open(filepath, \"r\") as input_file:\n text = input_file.read()\n textBox.insert(tk.END, text)\n window.title(f\"Python IDE - {filepath}\")","title":""},{"docid":"5fc950c94972ad4b1d1998ece5ae5be8","score":"0.564423","text":"def browse_file_directory(self):\n\n self.filename = filedialog.askopenfilename(initialdir=self.files_to_unify_directory)","title":""},{"docid":"46e39275a1a06853439d0db512944448","score":"0.5626472","text":"def _OnOpenFile( self, ev ):\n if ev is not None:\n ev.Skip()\n\n if self.state.dataModelMgr is not None:\n self._UpdateConfig()\n\n dialog = wx.FileDialog(\n\tself, 'Open File', '', '',\n 'HDF5 files (*.h5)|*.h5|VERAView session files (*.vview)|*.vview',\n\twx.FD_OPEN | wx.FD_FILE_MUST_EXIST | wx.FD_CHANGE_DIR\n )\n\n path = None\n if dialog.ShowModal() != wx.ID_CANCEL:\n path = dialog.GetPath()\n dialog.Destroy()\n\n if path:\n path, session = self._ResolveFile( path )\n self.OpenFile( path, session )","title":""},{"docid":"8eaa6cea11398587f200850b0a09fa30","score":"0.560331","text":"def select_signal_handler(self,key):\n if self.file_walker.chdir_into(self.file_walker.join_to_cur(key.get_label())):\n selectable_list_box = self.frame.body.base_widget[0]\n selectable_list_box.body.clear()\n if(self.check_sel_cache()):\n selectable_list_box.body.extend(self.select_mode_walker_cache[self.file_walker.curdir])\n else:\n tracked_item = self.selectable_mode(self.file_walker.get_dir_list(hide_dir_changers=True))\n self.select_mode_walker_cache.update({self.file_walker.curdir:tracked_item })\n selectable_list_box.body.extend(tracked_item)","title":""},{"docid":"4be470f2979fd1dcb08c53373da72a7d","score":"0.55991966","text":"def on_importSelector_activated(self, p0):\n self.importFiles(p0)","title":""},{"docid":"14034a7596c5621f14d30f973e435517","score":"0.559585","text":"def event_db_file_search(self):\n db_dialog = QtWidgets.QFileDialog(self)\n if have_Qt5:\n file_name, _ = db_dialog.getOpenFileName()\n else:\n file_name = db_dialog.getOpenFileName()\n save = self.gui_row\n self.gui_row = 0\n if file_name != '':\n self.top(file_name)\n self.gui_row = save","title":""},{"docid":"f0da0287e20edc06e1a236ed54beb558","score":"0.55887145","text":"def on_hex_file_selection_changed(self, *args):\n self.erase_list.unselect_all()\n\n hex_filename = self.gmap.hex_file_cbutton.get_filename()\n try:\n hex_file = HexFile(hex_filename)\n except (OSError, IOError), e:\n self.goserror(\"Unable to open hex file %(filename)s: %(strerror)s\", e)\n return\n\n micro = self.conf[\"type\"]\n block_ranges = micro_info[micro][\"block_range\"]\n try:\n blocks = hex_file.used_blocks(block_ranges)\n except HexError, e:\n self.ghexerror(\"Error processing hex file %(filename)s at \"\n \"lineno %(lineno)d: %(msg)s\", e)\n return\n self.erase_list.select_blocks(blocks)\n self.used_blocks = blocks","title":""},{"docid":"7e34e3a482a22fc681a63cadbd23abbf","score":"0.558441","text":"def on_file_open(self, event):\n dialog = wx.FileDialog(self, message=\"Select the graph\",\n wildcard=\"DOT files (*.dot)|*.dot|\" \\\n \"All files (*.*)|*.*\",\n style=wx.OPEN | wx.CHANGE_DIR)\n\n if dialog.ShowModal() == wx.ID_OK:\n path = dialog.GetPath()\n self.open_graph = dot.read_graph(path) \n self.open_graph_path = path\n self.SetTitle(path)\n for i in range(self.toolbar.GetToolsCount()):\n self.toolbar.EnableTool(i, True)\n else:\n exit()\n\n dialog.Destroy()","title":""},{"docid":"fb079809bce195e6da5cbd5007877d15","score":"0.5579713","text":"def open_file(self):\n\t\t\n\t\tself.new_file_name = QtGui.QFileDialog.getOpenFileName(self, 'Open file', self.current_file)\n\n\t\tf = open(self.new_file_name, 'r')\n\n\t\twith f: \n\t\t\tself.selected_file = f.read()\n\t\t\tself.current_file = self.new_file_name\n\t\t\tself.file_name.setText(self.current_file)\n\t\t\tself.view_file.setText(self.selected_file)","title":""},{"docid":"757c69079209718970416a4497fe0901","score":"0.5568364","text":"def fileDialog(*args, **kwargs):\n\n pass","title":""},{"docid":"ca45ae73c8fafdca7359592fdbe196fb","score":"0.5567603","text":"def OnFEButton(self, evt):\n \n dlg = wx.FileDialog(\n self, message=\"Choose a file\",\n defaultDir=os.getcwd(), \n defaultFile=\"\",\n wildcard=\"NASTRAN file (*.nas)|*.nas|\" \"All files (*.*)|*.*\",\n style=wx.OPEN | wx.CHANGE_DIR\n )\n \n if dlg.ShowModal() == wx.ID_OK:\n fileName = dlg.GetPath()\n \n self.fem.ReadFile(fileName)\n \n self.Syncronize()\n \n dlg.Destroy()","title":""},{"docid":"f72c1a62aca40140465cd937e90ddc68","score":"0.5567067","text":"def OnSelectFile1(self, event):\n\n # The user can select both file1 and file2 from the file dialog box\n # by using the shift or control key to pick two files. The order in\n # which they are selected determines which is file1 and file2.\n dlg = wx.FileDialog(self,\n message=\"Select 1st Data File\",\n defaultDir=os.getcwd(),\n defaultFile=\"\",\n wildcard=(REFL_FILES+\"|\"+DATA_FILES+\"|\"+\n TEXT_FILES+\"|\"+ALL_FILES),\n style=wx.FD_OPEN|wx.FD_MULTIPLE|wx.FD_CHANGE_DIR)\n # Wait for user to close the dialog.\n sts = dlg.ShowModal()\n if sts == wx.ID_OK:\n paths = dlg.GetPaths()\n dlg.Destroy()\n if sts == wx.ID_CANCEL:\n return # Do nothing\n\n # The user could have selected 1 to n files.\n num_files = len(paths)\n if num_files > 2:\n popup_error_message(\n \"Too Many Files Selected\",\n \"You can select up to two data files, please try again.\")\n return\n elif num_files == 2:\n datafile_1 = paths[1] # files are returned in reverse order!\n datafile_2 = paths[0] # files are returned in reverse order!\n self.TCfile1.SetBackgroundColour(\"WHITE\")\n self.TCfile1.SetValue(datafile_1)\n self.TCfile1.SetInsertionPointEnd()\n self.TCfile2.SetBackgroundColour(\"WHITE\")\n self.TCfile2.SetValue(datafile_2)\n self.TCfile2.SetInsertionPointEnd()\n elif num_files == 1:\n datafile_1 = paths[0]\n self.TCfile1.SetBackgroundColour(\"WHITE\")\n self.TCfile1.SetValue(datafile_1)\n self.TCfile1.SetInsertionPointEnd()\n\n # Plot one or both files.\n self.plot_dataset(self.TCfile1.GetValue(), self.TCfile2.GetValue())","title":""},{"docid":"c56ce40b79d962f3e8a7eb31f24530be","score":"0.55613637","text":"def main():\n file = input(\"Enter File Name: \")\n read_file(file)","title":""},{"docid":"fa5a009218aa731b5e68e859e2e9f064","score":"0.55529666","text":"def chooseFile(self):\n\n global gcode_user_file_path\n options = {}\n\n options['title'] = 'Loading G Code for piece...'\n options['initialdir'] = 'C:\\\\'\n gcode_user_file_path = filedialog.askopenfilename(**options)\n\n self.master.destroy()","title":""},{"docid":"b6bbbc62d1a20490224fd98058904fbf","score":"0.55513495","text":"def __init__(self):\n print \"welcome to file manager...\"\n print \"press to copy data file[1]: \"\n print \"press to delet the file[2]: \"\n print \"perss to change path[3]\"\n print \"press to show file data[4]: \"\n print \"press to longth of data[5]: \"\n print \"press quit to exit\"\n self.chooses()","title":""},{"docid":"4ed4e869bc781fe9d5f3f8ce78b6f519","score":"0.55430955","text":"def open_file(self):\n options = {}\n options['defaultextension'] = '.txt'\n options['filetypes'] = [('all files', '.*'), ('text files', '.txt')]\n options['initialdir'] = '//seavvfile1/Market_SAIntMktg/_Offers/5. In Work'\n options['parent'] = self.root\n options['title'] = \"Open a file\"\n \n with tkFileDialog.askopenfile(mode='r', **options) as f_handle:\n print options\n #for line in f_handle:\n #print line","title":""},{"docid":"214870844d831ec485bc98d421500215","score":"0.5540342","text":"def load_filechooser(self, *args):\n if len(self.objects) != 0:\n fileChooserClass(self)","title":""},{"docid":"9322afd96bb08cafe728b5174bbb637b","score":"0.5522619","text":"def openFileDialog(self,event=None):\r\n global FILE_EXTENSION\r\n options = {}\r\n options['parent'] = self\r\n try:\r\n global CONFIG_NAME\r\n theFile = open(CONFIG_NAME,'rb') #open connection\r\n options['initialdir'] = pickle.load(theFile)[0]\r\n if options['initialdir'] == \"/examples\":\r\n options['initialdir'] = \"examples\"\r\n theFile.close()\r\n except:\r\n options['initialdir'] = \"examples\"\r\n\r\n\r\n\r\n options['defaultextension'] = FILE_EXTENSION\r\n options['filetypes'] = [(\"graph files\",FILE_EXTENSION)]\r\n self.filePath = tkFileDialog.askopenfilename(**options)\r\n self.openFile(self.filePath)","title":""},{"docid":"373b5753924283af2dad996e9de9dadc","score":"0.5519668","text":"def openFile(self):\n\n dialog = gui.QFileDialog(self);\n dialog.setOptions(gui.QFileDialog.DontUseNativeDialog)\n dialog.setWindowTitle(\"Open data file\")\n\n if dialog.exec_():\n if dialog.selectedFiles and dialog.selectedFiles():\n\n # Get file information\n self.filename = str(dialog.selectedFiles()[0])\n \n # Update UI information\n self.updateInfo()\n self.sample = 0\n\n # We have all required information, get data start plotting\n self.updateUI()","title":""},{"docid":"d6a21ad83eaae33bdd444d6356444a7d","score":"0.5515773","text":"def on_convert(self):\n items = self.treeview.get_children()\n if items:\n if self.option_var.get() == 1:\n # merge must include more than 1 file else return error\n if len(items) > 1:\n self.merge_files(items)\n else:\n message = \"Please select 2 or more files to perform a merge.\"\n messagebox.showerror(title='Missing files', message=message)\n else:\n # only 1 file shold be selected when splitting the file\n if len(items) > 1:\n message = \"Select 1 file only when splitting to multiple files.\"\n messagebox.showerror(title='Too many files', message=message)\n else:\n self.split_files(items)\n else:\n message = \"Please select file(s) to continue.\"\n messagebox.showerror(title='Missing files', message=message)","title":""},{"docid":"947adf18498e1e29fef1321ebc083a90","score":"0.55074733","text":"def browseForInput(self):\n settings = QtCore.QSettings()\n startingDir = str(settings.value(\"OsTranslatorII/lastInputFolder\", os.path.expanduser(\"~\")))\n d = str( QFileDialog.getExistingDirectory(None, 'Browse For Input', startingDir) )\n if d != os.sep and d.lower() != 'c:\\\\' and d != '':\n settings.setValue(\"OsTranslatorII/lastInputFolder\", d)\n self.inputLineEdit.setText(d)","title":""},{"docid":"a4487941f990d8ca3ba8595599350286","score":"0.5502798","text":"def clik_on_browse_query(self):\n try:\n self.qry_path = filedialog.askopenfilename()\n self.q_box_from_path.delete('1.0', END)\n self.q_box_from_path.insert(END, self.qry_path)\n except:\n pass","title":""},{"docid":"09488cca1f696b2012fbe6e5fa482be4","score":"0.54970175","text":"def handlerSelectListOfEconomicSector(self):\n file = unicode(QtGui.QFileDialog.getOpenFileName(\n self, 'Select List of Economic Sector', QtCore.QDir.homePath(), 'List of Economic Sector (*{0})'.format(self.main.appSettings['selectCsvfileExt'])))\n \n if file:\n self.lineEditListOfEconomicSector.setText(file)\n \n logging.getLogger(type(self).__name__).info('select file: %s', file)","title":""},{"docid":"e0963c5706cfda83c75512fc4d87da26","score":"0.5495374","text":"async def handle_ui(search_state):\n last_search = ''\n # Event Loop to process \"events\"\n while True:\n event, values = window.read(timeout=1)\n if event in (sg.WIN_CLOSED, 'Exit'):\n sys.exit()\n elif event == '-INPUT-':\n if last_search != values['-INPUT-']:\n window['-RESULT-'].update([])\n last_search = values['-INPUT-']\n if search_state['searching']:\n search_state['interrupting'] = True\n elif event == '-RESULT-':\n file_clicked = values['-RESULT-'][0]\n if search_state['searching']:\n search_state['interrupting'] = True\n os.startfile(file_clicked)\n elif event == '__TIMEOUT__':\n pass\n else:\n print(event, values)\n await asyncio.sleep(0)","title":""},{"docid":"23fb2dde6a9a0d32fbccda3a32f308a2","score":"0.5489173","text":"def open_callback(self):\n\n # Forget welcome screen widgets\n self.parent.open_label.pack_forget() \n self.parent.manual_label.pack_forget()\n self.parent.email_label.pack_forget()\n\n # If a file is already open, close it.\n if Session.file_opened:\n self.close_callback()\n \n file_path = filedialog.askopenfilename(**self.OPEN_OPT)\n if file_path: \n Session.file_path = file_path\n status = FileHandler.open_file(file_path)\n if status:\n Session.file_opened = True\n Session.file_saved = True\n EventHandler.event_generate(OPEN_EVENT)","title":""},{"docid":"cbf5912abbc14706cd84f826a8ccc222","score":"0.5484031","text":"def open_file_dialog(self):\n \n try:\n selected_filename = filedialog.askopenfilename(initialdir = \".\", title = \"Select file\", filetypes = ((\"obj files\",\"*.obj\"), (\"mtl files\",\"*.mtl\"), (\"all files\",\"*.*\")))\n \n if os.path.exists(selected_filename):\n self.upload_file_list.append(selected_filename)\n print(\"File added to upload list:\", selected_filename)\n \n ## Also add the '.mtl' file if not already added\n if selected_filename.endswith('.obj'): \n selected_filename_mtl = selected_filename[:-4]+'.mtl'\n if selected_filename_mtl not in self.upload_file_list:\n if os.path.exists(selected_filename_mtl):\n self.upload_file_list.append(selected_filename_mtl)\n print(\"File added to upload list:\", selected_filename_mtl)\n else:\n print(\"No existing file selected.\")\n pass\n \n except Exception as emsg:\n print(\"EXCEPTION: \"+str(emsg))\n return None\n \n return","title":""},{"docid":"2145ff301c240cf738ecffaf6ccb401a","score":"0.5475936","text":"def _fname_selected(self, fname):\n self.fname_selected.emit(fname.text())","title":""},{"docid":"b0beae70840f936b6c1692a814595718","score":"0.5463578","text":"def _handle_selection_change(self, selection):","title":""},{"docid":"f6e0607e565d906d639d69331ce65932","score":"0.5457833","text":"def __doOpen(self):\n\n if self.__modified == True:\n self.__askForSave()\n\n openname = askopenfilename(initialdir=\"*\",\n title=self.__dicts.getWordFromDict(self.__Config.get_Element(\"Language\"), \"open\"),\n filetypes=(\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\"Language\"), \"fileBoot\"),\n \"*.boo\"),\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\"Language\"), \"fileTxT\"),\n \"*.txt\"),\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\"Language\"), \"fileAll\"),\n \"*.*\"),\n ))\n\n self.__openFile(openname,True)","title":""},{"docid":"0091410e94c46bc712ebae905d7afb34","score":"0.54505515","text":"def invoke (self, context, event):\n context.window_manager.fileselect_add (self)\n return {'RUNNING_MODAL'}","title":""},{"docid":"0091410e94c46bc712ebae905d7afb34","score":"0.54505515","text":"def invoke (self, context, event):\n context.window_manager.fileselect_add (self)\n return {'RUNNING_MODAL'}","title":""},{"docid":"8310268c066a0f320864bce4e5b72242","score":"0.54462266","text":"def _open_file(self):\n file = QFileDialog.getOpenFileName(self, 'Open file', \".\")[0]\n if file:\n file_name = str(file).split('/')[-1] # Need to create version for Windows\n with open(file, 'rt') as text:\n if self.tab.currentWidget().text(): # If current Tab is not empty\n self.tab.addTab(Editor(), file_name)\n self.tab.setCurrentIndex(self.tab.currentIndex() + 1)\n self.tab.currentWidget().setText(text.read())\n else:\n self.tab.currentWidget().setText(text.read())\n self.tab.setTabText(self.tab.currentIndex(), file_name)\n try:\n lexer = lexers.get_lexer_by_ext(file_name)\n self.tab.currentWidget().setLexer(lexer())\n except:\n pass","title":""},{"docid":"99d88108b505f9aa38ead9a6171430ff","score":"0.54447657","text":"def get_user_input(self) -> bool:\r\n tag_list = ['1','2','3']\r\n #currnently only one file\r\n layout = [\r\n [sg.Text('Bat name:')], \r\n [sg.Input(key='-BAT NAME-')],\r\n [sg.Text('Tag')],\r\n [sg.Listbox(tag_list, size=(15, len(tag_list)), key='-TAG-')],\r\n # [sg.Listbox(values=tag_list, size=(20, 12), key='-LIST-', enable_events=False)],\r\n [sg.Text('Select files or a folder to analyze')],\r\n [sg.Text('Files', size=(8, 1)) ,sg.Input(), sg.FilesBrowse(file_types=((\"txt Files\", \"*.txt\"),)) if platform.system() == 'Windows' else sg.FilesBrowse()],\r\n [sg.Text('OR Folder', size=(8, 1)), sg.Input(), sg.FolderBrowse()],\r\n [sg.OK(size=(7,1)), sg.Cancel(size=(7,1))]\r\n ]\r\n\r\n window = sg.Window('Feeder analysis', layout)\r\n event, self.values = window.Read()\r\n window.Close()\r\n return True if event == 'OK' else False","title":""},{"docid":"f1542f0e267c4edf544fe7207aacc7d8","score":"0.5443162","text":"def on_open(self):\n filenames = filedialog.askopenfilenames(parent=self.treeview, filetypes = [(\"TIFF file\", \"*.tif *.tiff\")])\n self.update_idletasks() # without this update the gui becomes temporarily unresponsive\n if filenames:\n for name in filenames:\n short_name = pathlib.Path(name).stem\n self.treeview.insert('', 'end', text=short_name, values=(str(name),))","title":""},{"docid":"da91b24d994d4d033b00be17ba5b264d","score":"0.5441948","text":"def filerSelect(self, *args):\n logger.debug(\"dirchooser.filerSelect called\")\n# self.importImageCallBack(self.guiFiler.filer\").get_current_folder())\n self.guiFiler.filer.close()","title":""},{"docid":"3e230cc776ecbcab099b159492d16004","score":"0.5437073","text":"def OnOpenTxt(self, evt):\n fileDlg = wx.FileDialog(self, message='Choose Pore Radius file...',\n wildcard=DATWILDCARD, style=wx.FD_OPEN)\n if fileDlg.ShowModal() != wx.ID_OK:\n fileDlg.Destroy()\n return\n self.datapath = fileDlg.GetPath()\n fileDlg.Destroy()\n self.open_txt(evt)","title":""},{"docid":"928e11e0ff7a8d7efac0d23a48c7b8d6","score":"0.54310805","text":"def push_CAR_Index_file(self):\n filepath = QFileDialog.getOpenFileName(self)\n self.plainTextEdit_2.setPlainText(filepath[0])\n self.filepath_Car = filepath[0]","title":""},{"docid":"4e611d75846c34c52ce5fd80dd6150d2","score":"0.5416835","text":"def Openfile5(self, event):\n dlg = wx.FileDialog(\n self, message=\"Choose a Specie\",\n defaultFile=\"\",\n wildcard=wildcard1,\n style=wx.FD_OPEN\n )\n if dlg.ShowModal() == wx.ID_OK:\n tmp=\"\"\n #paths = dlg.GetPaths()\n paths = dlg.GetPaths()\n #print \"You chose the following file(s):\"\n for path in paths:\n tmp=tmp+path\n #set the value of TextCtrl[filename]\n \n #set the value to the TextCtrl[contents]\n \n self.aa = self.readtable(tmp)\n self.contents1.SetValue(str(self.aa[0]))\n self.contents2.SetValue(str(self.aa[1]))\n self.contents3.SetValue(str(self.aa[2]))\n# self.contents4.SetValue(str(a[3]))\n \n dlg.Destroy()\n return self.aa","title":""},{"docid":"5fd5729a6dc2a79ea0f09688e2c82801","score":"0.54084235","text":"def onFileOpen(self, path=None):\n if path:\n self.fileMenu.currFile = path\n if self.fileMenu.currFile and os.path.exists(self.fileMenu.currFile):\n self.cmdFile = self.fileMenu.currFile\n self.onExit(quit=False)","title":""},{"docid":"0178abd40f7651d2422c68c7e75bb773","score":"0.5407632","text":"def on_button_click(self):\n prev = self.filename.get()\n initialdir = os.path.dirname(prev) if prev else _INITIALDIR\n\n filename = filedialog.askopenfilename(\n parent=self, initialdir=initialdir, title='Choose a file')\n if filename:\n self.filename.set(filename)","title":""},{"docid":"0577bb5de24d2bdd21caff7766c1d7e3","score":"0.54021037","text":"def openFile(self):\n fList = [(\"Text files\", \"*.txt\")]\n fileName = tkinter.filedialog.askopenfilename(parent = self, filetypes = fList)\n\n if fileName != \"\":\n file = open(fileName, 'r')\n text = file.read()\n file.close()\n self.outputArea.setText(text)\n self.setTitle(fileName)","title":""},{"docid":"14731fb3610163e16972482b21c053a7","score":"0.53979987","text":"def __init__(self):\n self.infile = open(input())","title":""},{"docid":"f27550350b8137809cfe267442203050","score":"0.5395495","text":"def file_open(self):\n\n # Stop any running calculation\n if self.calculation_thread is not None:\n if self.calculation_thread.isRunning():\n # Cancel the running calculation\n self.interrupt_calculation()\n\n filename, _chosen_extension = QFileDialog.getOpenFileName(\n parent=self,\n caption=\"Open File\",\n filter=\"MagnetiCalc INI File (*.ini)\",\n options=QFileDialog.DontUseNativeDialog\n )\n\n if filename != \"\":\n\n self.model.invalidate()\n\n self.config.close()\n self.config.set_filename(filename)\n self.config.load()\n\n self.sidebar_left.wire_widget.reinitialize()\n self.sidebar_left.sampling_volume_widget.reinitialize()\n self.sidebar_right.field_widget.reinitialize()\n self.sidebar_right.metric_widget.reinitialize()\n # Parameters_Widget doesn't need reinitialization as it does not access the configuration\n # Perspective_Widget doesn't need reinitialization as it does not access the configuration\n self.sidebar_right.display_widget.reinitialize()\n\n self.menu.reinitialize()\n self.statusbar.reinitialize()\n\n self.vispy_canvas.load_perspective()\n\n if self.config.get_bool(\"auto_calculation\"):\n self.recalculate()","title":""},{"docid":"813177ce00fbc8abf7c010ac4676ab23","score":"0.5393413","text":"def get_file_items(self, window, sel_items):\n if len(sel_items) != 1 or sel_items[0].is_directory() or sel_items[0].get_uri_scheme() != 'file':\n return\n uri_raw = sel_items[0].get_uri()\n if len(uri_raw) < 7: return\n source_path = urllib.unquote(uri_raw[7:])\n filetype = subprocess.Popen(\"file -i %s\" % re.escape(source_path), shell=True, stdout=subprocess.PIPE).communicate()[0]\n\tprint filetype\n\n if \"application\" or \"text\" in filetype:\n item = Nautilus.MenuItem(name='NautilusPython::preferences-nautilus-optirun',\n label=_('Run via Optirun'),\n tip=_('Run the selected file on discrete video card'),\n icon='preferences-nautilus-optirun')\n item.connect('activate', self.run, source_path)\n return item,","title":""},{"docid":"e7ca99011908043a81dee284849bb031","score":"0.5393086","text":"def run(self) -> None:\n user_input = \"\"\n if not self._logo_displayed:\n self.cls()\n print(base64.b64decode(self.LOGO).decode(\"ascii\"))\n self._logo_displayed = True\n self.display_menu()\n while True:\n user_input = input(\"\\nYour selection: \")\n try:\n user_input = int(user_input)\n if user_input <= 0 or user_input > len(self.options):\n self.display_error()\n else:\n if callable(self.options[user_input][1]):\n self.selected_option = user_input\n self.options[user_input][1]()\n if self.options[user_input][2]:\n return\n else:\n self.display_menu()\n elif self.options[user_input][1]:\n self.selected_option = user_input\n self.options[user_input][1].run()\n self.display_menu(clear_screen=True)\n else:\n return\n except ValueError:\n self.display_error()","title":""},{"docid":"a8307a6c17981445509b374d903d469c","score":"0.5392842","text":"def load(self, selection):\n self.file_path = str(selection[0])\n self.popup.dismiss()\n self.load_action()","title":""},{"docid":"bd21cb2b3b0431f19d28c8b5bb89ba2d","score":"0.5392435","text":"def Openfile4(self, event):\n dlg = wx.FileDialog(\n self, message=\"Choose a Specie\",\n defaultFile=\"\",\n wildcard=wildcard1,\n style=wx.FD_OPEN\n )\n if dlg.ShowModal() == wx.ID_OK:\n tmp=\"\"\n #paths = dlg.GetPaths()\n paths = dlg.GetPaths()\n #print \"You chose the following file(s):\"\n for path in paths:\n tmp=tmp+path\n \n #set the value to the TextCtrl[contents]\n self.a4 = self.readtable(tmp)\n self.contents4.SetValue(str(self.a4))\n \n dlg.Destroy()\n return self.a4","title":""},{"docid":"1bf673ac509152b4339e9f8fc384d5cc","score":"0.5390938","text":"def openFileDialog(self):\r\n filename, filter = QFileDialog.getOpenFileName(dir=os.path.dirname(self.filename))\r\n if not filename == '':\r\n self.filename = filename","title":""},{"docid":"73314dd050fdee0f0d1f59f65153aec0","score":"0.5389249","text":"def browse(self):\n qfd = QFileDialog()\n file_filter = 'VHDL file(*.vhd);;All files(*)'\n self.input_file_path = QFileDialog.getOpenFileName(qfd, 'Open source file', 'C:/', file_filter)[0]\n self.input_file_name = self.input_file_path.split(\"/\")[-1]\n self.project_path = '/'.join(self.input_file_path.split(\"/\")[:-1]) + '/'\n self.xdc_ports = {}\n if self.input_file_path != '':\n ports, module_name, libs = gen_and_parse.get_stuff(self.input_file_path)\n\n for key, value in ports.items():\n self.xdc_ports[key] = value\n try:\n num1, num2 = re.search(r'(\\d+).*(\\d+)', value[1]).groups()\n del self.xdc_ports[key]\n\n num1 = int(num1)\n num2 = int(num2)\n if num1 > num2:\n for i in range(num2, num1 + 1):\n self.xdc_ports[f'{key}[{i}]'] = ''\n else:\n for i in range(num1, num2 + 1):\n self.xdc_ports[f'{key}[{i}]'] = ''\n except:\n pass\n\n self.tableWidget.setRowCount(len(self.xdc_ports.keys()))\n global ddr_chosen\n for i, port in enumerate(self.xdc_ports, start=0):\n temp_w = QTableWidgetItem(port)\n self.tableWidget.setItem(i, 0, temp_w)\n self.set_items_in_table(i)","title":""},{"docid":"5312a6cf2c9f24787b7fd89c9c225805","score":"0.5380366","text":"def execute(self, context):\n\n filename, extension = os.path.splitext(self.filepath)\n\n print('Selected file:', self.filepath)\n print('File name:', filename)\n print('File extension:', extension)\n rigify(self.filepath)\n\n return {'FINISHED'}","title":""},{"docid":"f73f202f712477728d73a6d33a733c5d","score":"0.5370303","text":"def fileHandler(value):\r\n if value.lower() == \"exit\":\r\n print(\"Exiting the program..\")\r\n sys.exit()\r\n \r\n if(value == \"1\"): #if the user enters 1\r\n filename = \"C:/Users/shruti/source/repos/Hw2/StemAndLeaf1.txt\" #file path for SteamAndLeaf1.txt file\r\n with open(filename) as f:\r\n fileData = f.read().splitlines() #reads the file data\r\n \r\n\r\n if(value == \"2\"):\r\n filename = \"C:/Users/shruti/source/repos/Hw2/StemAndLeaf2.txt\"\r\n with open(filename) as f:\r\n fileData = f.read().splitlines()\r\n \r\n if(value == \"3\"):\r\n filename = \"C:/Users/shruti/source/repos/Hw2/StemAndLeaf3.txt\"\r\n with open(filename) as f:\r\n fileData = f.read().splitlines()\r\n \r\n\r\n return fileData","title":""},{"docid":"1e6fa296d6154d939919d64038a5ff9d","score":"0.53696764","text":"def read_input(self):","title":""},{"docid":"66823154aeefc417924f6f0a3147c1d0","score":"0.53628767","text":"def browse_function(first_frame):\n global raw_filenames\n root.filename = \\\n filedialog.askopenfilenames(initialdir=\"/\", title=\"Select Image\")\n first_file = root.filename[0]\n if first_file.lower().endswith('zip') is True:\n zf = zipfile.ZipFile(root.filename[0], 'r')\n raw_filenames = zf.namelist()\n num_files = len(raw_filenames)\n else:\n raw_filenames = root.filename\n num_files = len(raw_filenames)\n file_label = ttk.Label(first_frame,\n text=\"{} file(s) uploaded\".format(num_files))\n file_label.grid(column=0, row=3)","title":""},{"docid":"2a5f38d80d9e8be7db2458c88e48c7a9","score":"0.53509563","text":"def outFileBrowse(self):\n \n outFilename = Utilities.saveFileDialog(self) \n if not outFilename:\n return\n self.outputFilename.setText(outFilename)","title":""},{"docid":"a629bde03fa71f19539ba455ff463050","score":"0.53497124","text":"def _output_select(self):\n options = QFileDialog.Options()\n file_select, _ = QFileDialog.getSaveFileName(\n self._view,\n 'Save Output...',\n '',\n # 'RNH Files (*.rnh);;RND Files (*.rnd);;CSV Files (*.csv);;Excel Files (*.xls*);;All Files (*)',\n options=options\n )\n if file_select:\n self._config[\"output\"] = [file_select]","title":""},{"docid":"64cc3e7e6e0287f55526e5300f02127a","score":"0.5345172","text":"def _openItem(self):\n itmList = self.getSelectedItems(\n [BrowserFileItem, BrowserClassItem,\n BrowserMethodItem, BrowserClassAttributeItem,\n BrowserImportItem])\n \n if not self._activating:\n self._activating = True\n for itm in itmList:\n if isinstance(itm, BrowserFileItem):\n if itm.isPython2File():\n self.sourceFile[str].emit(itm.fileName())\n elif itm.isPython3File():\n self.sourceFile[str].emit(itm.fileName())\n elif itm.isRubyFile():\n self.sourceFile[str, int, str].emit(\n itm.fileName(), -1, \"Ruby\")\n elif itm.isDFile():\n self.sourceFile[str, int, str].emit(\n itm.fileName(), -1, \"D\")\n elif itm.isDesignerFile():\n self.designerFile.emit(itm.fileName())\n elif itm.isLinguistFile():\n if itm.fileExt() == '.ts':\n self.linguistFile.emit(itm.fileName())\n else:\n self.trpreview.emit([itm.fileName()])\n elif itm.isProjectFile():\n self.projectFile.emit(itm.fileName())\n elif itm.isMultiProjectFile():\n self.multiProjectFile.emit(itm.fileName())\n elif itm.isIdlFile():\n self.sourceFile[str].emit(itm.fileName())\n elif itm.isProtobufFile():\n self.sourceFile[str].emit(itm.fileName())\n elif itm.isResourcesFile():\n self.sourceFile[str].emit(itm.fileName())\n elif itm.isSvgFile():\n self.svgFile.emit(itm.fileName())\n elif itm.isPixmapFile():\n self.pixmapFile.emit(itm.fileName())\n else:\n if Utilities.MimeTypes.isTextFile(itm.fileName()):\n self.sourceFile[str].emit(itm.fileName())\n else:\n QDesktopServices.openUrl(QUrl(itm.fileName()))\n elif isinstance(itm, BrowserClassItem):\n self.sourceFile[str, int].emit(\n itm.fileName(), itm.classObject().lineno)\n elif isinstance(itm, BrowserMethodItem):\n self.sourceFile[str, int].emit(\n itm.fileName(), itm.functionObject().lineno)\n elif isinstance(itm, BrowserClassAttributeItem):\n self.sourceFile[str, int].emit(\n itm.fileName(), itm.attributeObject().lineno)\n elif isinstance(itm, BrowserImportItem):\n self.sourceFile[str, list].emit(\n itm.fileName(), itm.linenos())\n self._activating = False","title":""},{"docid":"d773be7d07d2e4959c0f2c6980e91983","score":"0.53407794","text":"def select_files(self, directory):\n filenames_loc=fd.askopenfilenames(initialdir=directory, title='Select files', filetypes=(('text files', '*.txt'), ('all files', '*.*')))\n filenames_loc=list(filenames_loc)\n \n if len(filenames_loc)>0: #prevents error from ocurring if user doesn't actually select any files\n self.folder=os.path.dirname(filenames_loc[0]) #remembers last used directory\n \n return filenames_loc","title":""},{"docid":"27e9f1a6a8879aad6793d44be555ad70","score":"0.53387815","text":"def file_inputs(self):\n for i in range(self.controller.inputs['num_variables_input']):\n if self.controller.inputs['is_included_in_file_input'][i]==0:\n self.controller.inputs['num_txt_files']*=self.controller.inputs['num_datapoints_input'][i] #finds the product of the length of all the unincluded dimensions\n \n #uploads files \n if self.controller.inputs['track_file_input']==1 and self.controller.inputs['unincluded_dimensions']!=[]: #checks for whether the user would like to and is able to track their files \n self.tracker_child_frame.destroy()\n self.tracker_child_frame=tk.Frame(self.tracker_frame)\n self.tracker_child_frame.config(highlightbackground='blue', highlightcolor='blue', highlightthickness=1, height=20, width=70)\n self.tracker_child_frame.pack() \n self.shared_func.tracker(frame=self.tracker_child_frame, \n dimensions=self.controller.inputs['unincluded_dimensions'], \n dim_name=self.controller.inputs['unincluded_dim_name'], \n row=0, \n column=0) \n elif self.controller.inputs['track_file_input']==1 and self.controller.inputs['unincluded_dimensions']==[]: #if the user would like to track their files but there is only one file, show error\n mb.showerror('Error', 'Cannot track only one file. Please uncheck tracking file option') \n else: #creates file list without tracker function \n while len(self.shared_func.file_list)<self.controller.inputs['num_txt_files']:\n add_file=self.shared_func.select_files(self.shared_func.folder)\n if add_file==[]: #if user cancels adding files, assumes the user would like to start everything over\n break\n add_file.append(add_file.pop(0)) #puts the first file selected back to the beginning because tkinter is weird like that (tkinter will put the first file selected at the end)\n self.shared_func.file_list+=add_file \n \n #Check to make sure the file list is the right size\n if len(self.shared_func.file_list)!=self.controller.inputs['num_txt_files']: \n mb.showerror(\"Error\", \"File list incompatible with amount of data specified, please reconfirm inputs and press reset\") \n \n #Widgets for loading text from the .txt files** \n self.shared_func.file_format_widgets(frame=self.file_format_child_frame, row=0, column=0)\n\n #Button that will compile all the information and .txt files into an n-dimensional matrix\n compile_button=ttk.Button(self.file_format_child_frame, \n text='Compile', \n command= lambda comments=self.shared_func.comments_entry, \n delimiter=self.shared_func.delimiter_entry,\n skiprows=self.shared_func.skiprows_entry, \n usecolumns=self.shared_func.usecols_entry: self.inputs_create_matrix(comments, \n delimiter, \n skiprows, \n usecolumns))\n compile_button.grid(row=0, column=8)","title":""},{"docid":"47ed733ca6ac5391f4e3773b7937abe8","score":"0.5332661","text":"def _browse_vfk_files(self):\n \n title = u'Vyberte VFK soubor.'\n filters = u'.vfk (*.vfk)'\n \n filePath = self.dW.open_file_dialog(title, filters, True)\n \n if filePath:\n self.set_text_browseVfkLineEdit.emit(filePath)","title":""},{"docid":"4b00f3d8854c7bf991c84fd1a917756a","score":"0.5327411","text":"def _handle(self, inputs, **options):\n pass","title":""}],"string":"[\n {\n \"docid\": \"32ea7dad953fd98a57afaf65664098bb\",\n \"score\": \"0.68089163\",\n \"text\": \"def input_pick(self):\\n file_name = QtWidgets.QFileDialog.getOpenFileName(self, 'Open file', self.vid_file)\\n if file_name[0]:\\n self.input_load(file_name[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326edf721d5ec1cd4508a2ec8ddd6168\",\n \"score\": \"0.67879474\",\n \"text\": \"def OnSelectFile(self, evt):\\n evid = evt.GetId()\\n if evid == self.EVT_SELECT_TRAINING_FILE:\\n target = self.fileinput_training\\n elif evid == self.EVT_SELECT_PROCESS_FILE:\\n target = self.fileinput_analysis\\n else:\\n return\\n fd = wx.FileDialog(self, 'Open file', 'a', 'b',\\n 'Excel file (*.xlsx,*.xls)|*.xlsx;*.xls|CSV file (*.csv,*.txt)|*.txt;*.csv', wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\\n if self.__datadirectory is None:\\n self.__datadirectory = os.path.curdir\\n fd.SetDirectory(self.__datadirectory)\\n if fd.ShowModal() == wx.ID_OK:#CANCEL:\\n self.__datadirectory = os.path.dirname(fd.GetPath())\\n target.SetValue(fd.GetPath())\\n fd.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc9855433998af82d6c194569c2158f\",\n \"score\": \"0.671288\",\n \"text\": \"def _input_select(self):\\n options = QFileDialog.Options()\\n file_select, _ = QFileDialog.getOpenFileNames(\\n self._view,\\n 'Select Input File...',\\n '',\\n 'RNH Files (*.rnh);;RND Files (*.rnd);;CSV Files (*.csv);;Excel Files (*.xls*);;All Files (*)',\\n options=options\\n )\\n if file_select:\\n self._config[\\\"input\\\"] = file_select\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac19ad5f6676706633999912e840670\",\n \"score\": \"0.6612921\",\n \"text\": \"def onBrowse(self, event):\\n \\n fileBrowser = wx.FileDialog(self.GetParent(),\\n style=wx.OPEN|wx.MULTIPLE|wx.FILE_MUST_EXIST)\\n\\n if fileBrowser.ShowModal() == wx.ID_OK:\\n\\n for filename in fileBrowser.GetPaths():\\n # generate an ItemSelected event for each file\\n zope.component.handle(\\n p6.storage.events.ItemSelected(\\n p6.storage.items.FileItem(filename)\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128867df7e6598346cedc4a90ee5cd4c\",\n \"score\": \"0.6427338\",\n \"text\": \"def browse_file(self):\\n filename, _ = QFileDialog.getOpenFileName(self, 'Open file', '.', '*.txt')\\n self.inp.setText(filename)\\n if os.path.isfile(filename):\\n self.write_log(self.logOutput, \\\"\\\\nText file: \\\" + filename)\\n self.txt_file = filename\\n else:\\n self.write_log(self.logOutput, \\\"\\\\nError: Text file is not a valid file.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c82753b53086c7bc09d86dd9d1ea5df\",\n \"score\": \"0.64145774\",\n \"text\": \"def __open_file(self, event=None) -> None:\\n\\n filepath = askopenfilename(\\n filetypes=[(\\\"Goethe Files\\\", \\\"*.goethe\\\"), (\\\"All Files\\\", \\\"*.*\\\")])\\n\\n if not filepath:\\n # User did not select a file\\n return\\n\\n with open(filepath, \\\"r\\\") as file:\\n self.filepath = filepath\\n self.filename = os.path.basename(self.filepath)\\n self.editor.delete(1.0, 'end')\\n self.editor.insert('end', file.read())\\n self.interpreter.set_code(self.__get_text())\\n self.__update_widgets()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437515f5ee52612370c5b24e00358e2e\",\n \"score\": \"0.63791525\",\n \"text\": \"def browse(self):\\n self.load_filename = filedialog.askopenfilename()\\n self.pathlabel.config(text=self.load_filename)\\n self.reset()\\n self.read_raw_data(self.load_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c219c33a0eaa50082fc4a2ae2278979f\",\n \"score\": \"0.63583136\",\n \"text\": \"def input_button_clicked(self):\\n\\t\\tfilename = filedialog.askopenfilename(title='Open an input file', initialdir='~/', filetypes=[('Comma-Separated File', '*.csv')])\\n\\t\\tif filename:\\n\\t\\t\\tself.input_entry['state'] = \\\"normal\\\"\\n\\t\\t\\tself.input_entry.delete(0,tk.END)\\n\\t\\t\\tself.input_entry.insert(0,filename)\\n\\t\\t\\tself.check_input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7521fa57ddfc2dd57bd24fc5677db0\",\n \"score\": \"0.63055557\",\n \"text\": \"def selectfile(self):\\n self.singleselectfile()\\n value = self.component('filename').get()\\n self.setfilename(value)\\n if value:\\n self.okbutton()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a9734522cf037b1a787fd1f67beea2\",\n \"score\": \"0.62773037\",\n \"text\": \"def _process_input(self):\\n\\n while not self.exit_event.is_set():\\n try:\\n keypress = self.screen.getkey()\\n if keypress == 'q' or keypress == 'Q': # quit program\\n self.exit_event.set(0)\\n elif keypress == '1': # switch to mode 1\\n self.exit_event.set(1)\\n elif keypress == '2': # switch to mode 2\\n self.exit_event.set(2)\\n elif keypress == 'k': # move selected line up\\n if self.selected_line != 0:\\n self.selected_line -= 1\\n self.change_event.set()\\n elif keypress == 'j': # move selected line down\\n if self.selected_line != len(self.all_plugins) - 1:\\n self.selected_line += 1\\n self.change_event.set()\\n elif keypress == 'c': # toggle selected plugin\\n with self.status_lock:\\n self.plugin_statuses[self.selected_line] = not self.plugin_statuses[self.selected_line]\\n self.change_event.set()\\n elif keypress == 's': # send analysis request for currently selected plugins\\n self._get_startup()\\n elif keypress == 'i':\\n stream = self._get_stream_input(keypress)\\n elif keypress == 'p':\\n while True:\\n stream_input = self._get_stream_input(keypress)\\n if stream_input.lower() == 'cancel':\\n break\\n elif os.path.isfile(stream_input):\\n self.vimrc_path = stream_input\\n break\\n logging.debug(keypress)\\n except curses.error:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12df600c97a6e0a7ca94d4c201f4f07b\",\n \"score\": \"0.617765\",\n \"text\": \"def unify_tk_controller(self):\\n\\n self.browse_file_directory()\\n self.open_and_read_in_csv_file()\\n self.unify_pathway_decider()\\n # clearing the parameters so the button can be used again with a new file\\n self.filename = ''\\n self.csv_file_in_list_format = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d70e73d34b6ed6583ee0f22a31c1153\",\n \"score\": \"0.6151189\",\n \"text\": \"def fileSelected_callback(self):\\n print('=== myTreeWidget.fileSelected_callback()')\\n modifiers = QtWidgets.QApplication.keyboardModifiers()\\n isShift = modifiers == QtCore.Qt.ShiftModifier\\n isControl = modifiers == QtCore.Qt.ControlModifier\\n\\n print(' isControl:', isControl)\\n\\n theItems = self.selectedItems()\\n if len(theItems) > 0:\\n theItem = theItems[0]\\n #selectedRow = self.fileList.currentRow() # self.fileList is a QTreeWidget\\n filename = theItem.text(self.myColumns['File'])\\n #print(' fileSelected_callback()', filename)\\n if isControl:\\n self.myCanvasWidget.getGraphicsView().zoomSelectedItem(filename)\\n # visually select image in canvas with yellow square\\n self.myCanvasWidget.getGraphicsView().setSelectedItem(filename)\\n else:\\n print(' no selected items')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3bc675c44006e6c4690833c3e7c505\",\n \"score\": \"0.6136701\",\n \"text\": \"def fileButtonClicked(self):\\n\\t\\tfilename = QtGui.QFileDialog.getOpenFileName(self, SELECT_FILE_MESSAGE)\\n\\t\\tif filename:\\n\\t\\t\\t# User selected a file\\n\\t\\t\\tself.current_file.setText(os.path.basename(str(filename)))\\n\\t\\t\\terr = self.source_window.loadSource(str(filename))\\n\\t\\t\\tself.assembly_widget.clear()\\n\\t\\t\\tself.stack_and_frame_widget.clear()\\n\\t\\t\\tself.gdb_process.gdbReset()\\n\\n\\t\\t\\tif (err > 0):\\n\\t\\t\\t\\t# User selected a valid C source file\\n\\t\\t\\t\\tself.compileSource(str(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b770c6866c5d6e146e75c313b7dd86c6\",\n \"score\": \"0.6100576\",\n \"text\": \"def open_file(self):\\n self.file = askopenfile(mode='r+', filetypes=[('FASTA Files', '*.fasta'), ('FASTA files', '*.fa')])\\n # check user has selected a file\\n try:\\n self.content = SeqIO.read(self.file, 'fasta')\\n # reset text_out\\n self.text_out.set('')\\n # tell user no file was opened if they press cancel\\n except AttributeError:\\n self.text_out.set('You pressed cancel, no new file was opened')\\n # note if a file has already been opened this will stay open\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a903874df06ccc4cd0ebcdff82854d\",\n \"score\": \"0.6097784\",\n \"text\": \"def askopenfilename(self, *args, **kw):\\n\\n self.tk.tk_setPalette('#888888')\\n save_update_step = self.update_step\\n self.update_step = 0\\n filename = tkFileDialog.askopenfilename(parent=self.tk, **file_opt)\\n if filename:\\n self.readwtf(filename)\\n self.wtf = filename\\n self.redraw_letters()\\n self.update_step = save_update_step\\n self.tk.tk_setPalette('#000000')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1069a751d64ae183721ee6aa889b2e3b\",\n \"score\": \"0.60699177\",\n \"text\": \"def on_pushButton_DatabaseFileInputSelect_clicked(self):\\n ArxmlInputFile, supported_fileKinds = QFileDialog.getOpenFileName(\\n self, u'open the file', u'./')\\n self.lineEdit_DatabaseFileInput.setText(ArxmlInputFile)\\n filePathForWinOS = ArxmlInputFile.replace('/', '\\\\\\\\')\\n if str(ArxmlInputFile)[-6:] == '.arxml':\\n self.ArxmlInputFilePath = filePathForWinOS\\n pass\\n else:\\n l_MessageBox = QMessageBox.information(\\n self, u'Tips', 'The file opened is not an arxml file,pls open an arxml file.')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5eded315e3e275ef3516f86bec62a9d\",\n \"score\": \"0.6020328\",\n \"text\": \"def onOpenFile(self, event):\\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a file\\\",\\n defaultDir=self.currentDirectory,\\n defaultFile=\\\"\\\",\\n wildcard=\\\"Images, PDF (*.pdf,*.jpg,*.png)|*.pdf;*.jpg;*.png|\\\" \\\\\\n \\\"All files (*.*)|*.*\\\",\\n style=wx.FD_OPEN | wx.FD_MULTIPLE | wx.FD_CHANGE_DIR\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n paths = dlg.GetPaths()\\n print(\\\"You chose the following file(s):\\\")\\n self.parent.imagesPaths = []\\n for path in paths:\\n print(\\\"choose path: \\\"+path)\\n self.pathsEditText.AppendText(path)\\n self.parent.imagesPaths.append(path)\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed67d121ce34e64314ff8f11bc54ee77\",\n \"score\": \"0.5993419\",\n \"text\": \"def _upload_action(event=None):\\n global filename\\n filename = filedialog.askopenfilename()\\n print('Selected:', filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905e38e93dc44ad6ecd4147cdb58d426\",\n \"score\": \"0.5941498\",\n \"text\": \"def __openFileInEditor(self):\\n itmList = self.getSelectedItems()\\n for itm in itmList[:]:\\n self.sourceFile.emit(itm.fileName())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9806b6a347dadd39a12511e792f2b834\",\n \"score\": \"0.5905072\",\n \"text\": \"def on_open(self):\\n text = _(\\\"Open file dialog.\\\")\\n openFileDialog = wx.FileDialog(\\n self,\\n _(\\\"Open\\\"),\\n wildcard=\\\"Circuit Definition files (*.txt;*.lcdf)|*.txt;*.lcdf\\\",\\n style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST)\\n res = openFileDialog.ShowModal()\\n if res == wx.ID_OK: # user selected a file\\n self.current_file_path = openFileDialog.GetPath()\\n self.clear_log()\\n self.log_message(_(\\\"File opened: {}\\\")\\n .format(self.current_file_path),\\n no_new_line=True)\\n self.load_file(self.current_file_path)\\n self.canvas.render(_(\\\"Opened file\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2520d775ad0387aa77a265ebb441270b\",\n \"score\": \"0.5901012\",\n \"text\": \"def _file_dialog_handler(self):\\n dirname = QtWidgets.QFileDialog.getExistingDirectory(\\n self, self.label, self.dirpath)\\n if dirname:\\n self.update_path(dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba4284f15a98d2bebba0579276bf76a\",\n \"score\": \"0.58996785\",\n \"text\": \"async def handle_file_select_action(*, event_data, action_data, logger, app):\\n await _confirm_selection(\\n event_data=event_data, action_data=action_data, logger=logger, app=app\\n )\\n\\n selected_template = action_data[\\\"selected_option\\\"][\\\"value\\\"]\\n repo = app[\\\"templatebot/repo\\\"].get_repo(\\n gitref=app[\\\"root\\\"][\\\"templatebot/repoRef\\\"]\\n )\\n template = repo[selected_template]\\n if len(template.config[\\\"dialog_fields\\\"]) == 0:\\n await _respond_with_nonconfigurable_content(\\n template=template, event_data=event_data, logger=logger, app=app\\n )\\n else:\\n await open_template_dialog(\\n template=template,\\n callback_id_root=\\\"templatebot_file_dialog\\\",\\n event_data=event_data,\\n logger=logger,\\n app=app,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5711180e7df415fb3a349649cdaabdbe\",\n \"score\": \"0.5883519\",\n \"text\": \"def _acceptSelection(self, *args, **kwargs):\\n if len(args) == 0:\\n return\\n line = args[0]\\n # get path and open it in a window\\n path = line.split()[-1]\\n # print(path)\\n docsetManager.openSearchResult(self._ftype, path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeec14d4745237123aa63e06568ad057\",\n \"score\": \"0.58706737\",\n \"text\": \"def on_select(self, e):\\n style_dirdialog = wx.DD_DEFAULT_STYLE\\n dlg = wx.DirDialog(self.pnl, \\\"Choose the output folder...\\\", self.src_dir, style=style_dirdialog)\\n\\n if dlg.ShowModal() == wx.ID_OK:\\n self.out_dir = dlg.GetPath()\\n\\n file_prefix = self.txt_file_prefix.GetValue()\\n file_suffix = self.txt_file_suffix.GetValue()\\n\\n if file_prefix and file_suffix:\\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, file_prefix)}{file_suffix}.pdf [...]')\\n elif file_prefix:\\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, file_prefix)}001.pdf [...]')\\n elif file_suffix:\\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, self.src_file[:self.src_file.rindex(\\\".\\\")])}'\\n f'{file_suffix}.pdf [...]')\\n else:\\n self.txt_outputprev.SetValue(f'{os.path.join(self.out_dir, self.src_file[:self.src_file.rindex(\\\".\\\")])}'\\n '001.pdf')\\n\\n # update the statusbar based on the current conditions\\n self._statusbar()\\n # f.close()\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bccba6909a493293f571b5ab625411\",\n \"score\": \"0.5861975\",\n \"text\": \"def on_open(self, e):\\n dlg = wx.FileDialog(self.pnl, \\\"Choose the PDF to split and save to individual pages\\\", self.src_dir, \\\"\\\",\\n \\\"PDF Files (*.pdf)|*.pdf\\\", wx.FD_OPEN)\\n if dlg.ShowModal() == wx.ID_OK: # If the OK button is clicked on the 'Select PDF' dialog\\n self.src_file = dlg.GetFilename()\\n self.src_dir = dlg.GetDirectory()\\n\\n self.txt_filename_preview.SetValue(os.path.join(self.src_dir, self.src_file))\\n # First, check if the prefix is set or not. If it's not, set it to the prefix of the selected file\\n if self.txt_file_prefix.GetValue() == '':\\n self.txt_file_prefix.SetValue(self.src_file[:self.src_file.rfind('.')])\\n\\n # Now, check if the suffix txt box is filled in or not, if not, set it to the standard suffix\\n if self.txt_file_suffix.GetValue() == '':\\n self.txt_file_suffix.SetValue('001')\\n\\n # Next, check if the output directory has been selected\\n if self.out_dir != '':\\n oval = os.path.join(self.out_dir, self.txt_file_prefix.GetValue())\\n suffix = f\\\"{self.txt_file_suffix.GetValue()} [...]\\\"\\n if self.txt_file_suffix.GetValue() != '':\\n suffix = self.txt_file_suffix.GetValue()\\n self.txt_outputprev.SetValue(f\\\"{oval}{suffix}.pdf [...]\\\")\\n\\n # Now, check if the output directory has not been selected\\n elif self.txt_file_prefix.GetValue() and self.txt_file_suffix.GetValue() == '':\\n # If not, use the base directory of the source file\\n oval = f'{os.path.join(self.src_dir, self.txt_file_prefix.GetValue())}'\\n self.txt_outputprev.SetValue(f\\\"{oval}001.pdf [...]\\\")\\n self.out_dir = self.src_dir\\n\\n self._statusbar()\\n\\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dd43a1603c52d3095a2971aa2269f6\",\n \"score\": \"0.5855089\",\n \"text\": \"def input_file(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30b28e216886d86b3a0cef69c21f64c\",\n \"score\": \"0.5834874\",\n \"text\": \"def select_file_clicked(ui):\\n # open file ui\\n file_ui = QtGui.QFileDialog()\\n file_name = file_ui.getSaveFileName(None, \\\"Choose output file\\\", str(expanduser(\\\"~\\\")), SELECT_FILTER)\\n ui.select_file.setText(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a238588968bb9f994722b4f74c0461ae\",\n \"score\": \"0.580808\",\n \"text\": \"def OnFile(self, event):\\r\\n if event.Id == wx.ID_OPEN:\\r\\n self.OpenFile()\\r\\n elif event.Id == wx.ID_SAVE:\\r\\n self.SaveFile()\\r\\n else:\\r\\n super(FileEditor, self).OnFile(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef51faa54e56e08ad247e6623723298\",\n \"score\": \"0.57987547\",\n \"text\": \"def input_check(self, choice):\\r\\n\\r\\n if choice == 'U':\\r\\n print('\\\\n==========================================')\\r\\n print('You have chosen to upload a file!')\\r\\n print('==========================================')\\r\\n fileName = input('Please enter a filename: ')\\r\\n category = input('Please enter a category: ')\\r\\n keywords = input('Please enter keywords: ')\\r\\n\\r\\n response = self.user.upload(fileName, category, keywords)\\r\\n\\r\\n # results of upload\\r\\n if response == 1:\\r\\n print('Successful upload!\\\\n')\\r\\n\\r\\n else: print('A file with that name already exists.\\\\n')\\r\\n\\r\\n elif choice == 'R':\\r\\n print('\\\\n==========================================')\\r\\n print('You have chosen to retrieve a file!')\\r\\n print('==========================================')\\r\\n file = str(input('Please enter a filename: '))\\r\\n\\r\\n response = self.user.download(file)\\r\\n if response == 1:\\r\\n print(\\\"Success!! file retrieved...\\\\n\\\")\\r\\n else:\\r\\n print (\\\"Error!! cannot find file.. \\\")\\r\\n\\r\\n\\r\\n elif choice == 'S':\\r\\n print('\\\\n==========================================')\\r\\n print('You have chosen to search for a file!')\\r\\n print('==========================================')\\r\\n file = str(input('Please enter a keyword to search for: '))\\r\\n if self.user.search(file):\\r\\n print(\\\"\\\")\\r\\n\\r\\n\\r\\n elif choice == 'D':\\r\\n print('\\\\n==========================================')\\r\\n print('You have chosen to delete an existing file!')\\r\\n print('==========================================')\\r\\n file = str(input('Please enter a filename: '))\\r\\n\\r\\n self.user.delete(file)\\r\\n else:\\r\\n os._exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f267f1841420c1d439e92adae05613\",\n \"score\": \"0.5796372\",\n \"text\": \"def browseFile(self):\\n file_name = getAFile()\\n if file_name != \\\"\\\":\\n self.onNewFileSelected(str(file_name))\\n self.centralWidget.setMinimumSize(700, 500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c11002e6b2e7416899e340e887ac87f\",\n \"score\": \"0.5775657\",\n \"text\": \"def push_Driver_Index_file(self):\\n filepath = QFileDialog.getOpenFileName(self)\\n self.plainTextEdit_3.setPlainText(filepath[0])\\n self.filepath_Driver = filepath[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"389a05fcb3bf97b04a6ac375c9aa3e38\",\n \"score\": \"0.5752986\",\n \"text\": \"def handle_input(self, input):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e4cc1bc5297fe75dee7cf09d1f6619\",\n \"score\": \"0.57359797\",\n \"text\": \"def fileBrowserDialog(*args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6401a26b5cae191dff94f0ad56319c23\",\n \"score\": \"0.5721455\",\n \"text\": \"def OnOpen(self, event):\\n\\t\\tdlg = wx.FileDialog(self, \\\"\\\", self.aztexEditor.dirname, \\\"\\\", \\\"*.*\\\", wx.OPEN)\\n\\t\\tif dlg.ShowModal() == wx.ID_OK: # if user clicks OK (if user wants to open a document)\\n\\t\\t\\tself.aztexEditor.filename = dlg.GetFilename()\\n\\t\\t\\tself.aztexEditor.dirname = dlg.GetDirectory()\\n\\t\\t\\tf = open(os.path.join(self.aztexEditor.dirname, self.aztexEditor.filename), 'r')\\n\\t\\t\\tself.aztexEditor.SetValue(f.read())\\n\\t\\t\\tf.close()\\n\\t\\t\\tself.SetTitle(self.aztexEditor.filename)\\n\\t\\tdlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8edc6ab1e42e8c1b13c549357083d1\",\n \"score\": \"0.56955665\",\n \"text\": \"def pick_file(self):\\n fileName = QtGui.QFileDialog.getOpenFileName(self,\\n \\\"Open Session\\\", __file__, \\n \\\"Session Files ({x})\\\".format( \\\\\\n x = self.file_extension))\\n \\n self.pathLE.setText(fileName[0])\\n self.textChanged.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b245b14402cd1cdbfb0072cfb80ce79\",\n \"score\": \"0.5685589\",\n \"text\": \"def file_dialog(self):\\n\\n self.filename = filedialog.askopenfilename(\\n initialdir=\\\"./\\\", title=\\\"Select A File\\\",\\n )\\n self.label = ttk.Label(self.labelFrame, text=\\\"\\\")\\n self.label.grid(column=1, row=2)\\n self.label.configure(text=self.filename)\\n if self.filename:\\n self.get_wave_info(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33169b5160e13596b2c90cfb1e7f60df\",\n \"score\": \"0.566944\",\n \"text\": \"def prepare_input(self, _input, _file):\\n return NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25569617da9589321d009c6013739f0\",\n \"score\": \"0.5661297\",\n \"text\": \"def browse_file():\\n currdir = os.getcwd()\\n Tk().withdraw()\\n filepath = tkFileDialog.askopenfilename(parent = root, initialdir=currdir, title='Please select a file')\\n if len(filepath) > 0:\\n print \\\"You selected file : %s\\\" % filepath\\n else:\\n return browse_file()\\n return filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2097e7034e17e328cb54eeed28a3c99d\",\n \"score\": \"0.56575316\",\n \"text\": \"def open_files(self) :\\n\\n self.input_file = self._open_root( self.input_file_name )\\n if self.input_file is None :\\n return self.error_before_loop()\\n\\n self.input_tree = self._load_tree( self.input_tree_name )\\n if self.input_tree is None :\\n return self.error_before_loop()\\n\\n self.output_file = self._create_root( self.output_file_name )\\n if self.output_file is None :\\n return self.error_before_loop()\\n\\n return self.ok_before_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0657a78066d8a2bc2e5a6f8bdf76acc\",\n \"score\": \"0.5649424\",\n \"text\": \"def singleselectfile(self):\\n cs = self.component('filenamebox').curselection()\\n if cs != ():\\n value = self.component('filenamebox').get(cs)\\n self.setfilename(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f5151d5d9091bead031835bea19500\",\n \"score\": \"0.56484985\",\n \"text\": \"def openFile(self):\\r\\n\\t\\t\\r\\n\\t\\tfList = [(\\\"Python Files\\\", \\\"*.py\\\"), (\\\"Text Files\\\", \\\"*.txt\\\")]\\r\\n\\t\\tfileName = tkinter.filedialog.askopenfilename(parent = self, filetypes = fList)\\r\\n\\t\\t\\r\\n\\t\\tif fileName != \\\"\\\":\\r\\n\\t\\t\\tfile = open(fileName, 'r')\\r\\n\\t\\t\\ttext = file.read()\\r\\n\\t\\t\\tfile.close()\\r\\n\\t\\t\\tself.outputArea.setText(text)\\r\\n\\t\\t\\tself.setTitle(fileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"306c52717b5f72ba80aa3e0cbaa36620\",\n \"score\": \"0.5645772\",\n \"text\": \"def handle_click_open():\\n filepath = askopenfilename(\\n filetypes=[(\\\"Python Files\\\", \\\"*.py\\\"), (\\\"All Files\\\", \\\"*.*\\\")]\\n )\\n if not filepath:\\n return\\n textBox.delete(\\\"1.0\\\", tk.END)\\n with open(filepath, \\\"r\\\") as input_file:\\n text = input_file.read()\\n textBox.insert(tk.END, text)\\n window.title(f\\\"Python IDE - {filepath}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fc950c94972ad4b1d1998ece5ae5be8\",\n \"score\": \"0.564423\",\n \"text\": \"def browse_file_directory(self):\\n\\n self.filename = filedialog.askopenfilename(initialdir=self.files_to_unify_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e39275a1a06853439d0db512944448\",\n \"score\": \"0.5626472\",\n \"text\": \"def _OnOpenFile( self, ev ):\\n if ev is not None:\\n ev.Skip()\\n\\n if self.state.dataModelMgr is not None:\\n self._UpdateConfig()\\n\\n dialog = wx.FileDialog(\\n\\tself, 'Open File', '', '',\\n 'HDF5 files (*.h5)|*.h5|VERAView session files (*.vview)|*.vview',\\n\\twx.FD_OPEN | wx.FD_FILE_MUST_EXIST | wx.FD_CHANGE_DIR\\n )\\n\\n path = None\\n if dialog.ShowModal() != wx.ID_CANCEL:\\n path = dialog.GetPath()\\n dialog.Destroy()\\n\\n if path:\\n path, session = self._ResolveFile( path )\\n self.OpenFile( path, session )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eaa6cea11398587f200850b0a09fa30\",\n \"score\": \"0.560331\",\n \"text\": \"def select_signal_handler(self,key):\\n if self.file_walker.chdir_into(self.file_walker.join_to_cur(key.get_label())):\\n selectable_list_box = self.frame.body.base_widget[0]\\n selectable_list_box.body.clear()\\n if(self.check_sel_cache()):\\n selectable_list_box.body.extend(self.select_mode_walker_cache[self.file_walker.curdir])\\n else:\\n tracked_item = self.selectable_mode(self.file_walker.get_dir_list(hide_dir_changers=True))\\n self.select_mode_walker_cache.update({self.file_walker.curdir:tracked_item })\\n selectable_list_box.body.extend(tracked_item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be470f2979fd1dcb08c53373da72a7d\",\n \"score\": \"0.55991966\",\n \"text\": \"def on_importSelector_activated(self, p0):\\n self.importFiles(p0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14034a7596c5621f14d30f973e435517\",\n \"score\": \"0.559585\",\n \"text\": \"def event_db_file_search(self):\\n db_dialog = QtWidgets.QFileDialog(self)\\n if have_Qt5:\\n file_name, _ = db_dialog.getOpenFileName()\\n else:\\n file_name = db_dialog.getOpenFileName()\\n save = self.gui_row\\n self.gui_row = 0\\n if file_name != '':\\n self.top(file_name)\\n self.gui_row = save\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0da0287e20edc06e1a236ed54beb558\",\n \"score\": \"0.55887145\",\n \"text\": \"def on_hex_file_selection_changed(self, *args):\\n self.erase_list.unselect_all()\\n\\n hex_filename = self.gmap.hex_file_cbutton.get_filename()\\n try:\\n hex_file = HexFile(hex_filename)\\n except (OSError, IOError), e:\\n self.goserror(\\\"Unable to open hex file %(filename)s: %(strerror)s\\\", e)\\n return\\n\\n micro = self.conf[\\\"type\\\"]\\n block_ranges = micro_info[micro][\\\"block_range\\\"]\\n try:\\n blocks = hex_file.used_blocks(block_ranges)\\n except HexError, e:\\n self.ghexerror(\\\"Error processing hex file %(filename)s at \\\"\\n \\\"lineno %(lineno)d: %(msg)s\\\", e)\\n return\\n self.erase_list.select_blocks(blocks)\\n self.used_blocks = blocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e34e3a482a22fc681a63cadbd23abbf\",\n \"score\": \"0.558441\",\n \"text\": \"def on_file_open(self, event):\\n dialog = wx.FileDialog(self, message=\\\"Select the graph\\\",\\n wildcard=\\\"DOT files (*.dot)|*.dot|\\\" \\\\\\n \\\"All files (*.*)|*.*\\\",\\n style=wx.OPEN | wx.CHANGE_DIR)\\n\\n if dialog.ShowModal() == wx.ID_OK:\\n path = dialog.GetPath()\\n self.open_graph = dot.read_graph(path) \\n self.open_graph_path = path\\n self.SetTitle(path)\\n for i in range(self.toolbar.GetToolsCount()):\\n self.toolbar.EnableTool(i, True)\\n else:\\n exit()\\n\\n dialog.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb079809bce195e6da5cbd5007877d15\",\n \"score\": \"0.5579713\",\n \"text\": \"def open_file(self):\\n\\t\\t\\n\\t\\tself.new_file_name = QtGui.QFileDialog.getOpenFileName(self, 'Open file', self.current_file)\\n\\n\\t\\tf = open(self.new_file_name, 'r')\\n\\n\\t\\twith f: \\n\\t\\t\\tself.selected_file = f.read()\\n\\t\\t\\tself.current_file = self.new_file_name\\n\\t\\t\\tself.file_name.setText(self.current_file)\\n\\t\\t\\tself.view_file.setText(self.selected_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757c69079209718970416a4497fe0901\",\n \"score\": \"0.5568364\",\n \"text\": \"def fileDialog(*args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca45ae73c8fafdca7359592fdbe196fb\",\n \"score\": \"0.5567603\",\n \"text\": \"def OnFEButton(self, evt):\\n \\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a file\\\",\\n defaultDir=os.getcwd(), \\n defaultFile=\\\"\\\",\\n wildcard=\\\"NASTRAN file (*.nas)|*.nas|\\\" \\\"All files (*.*)|*.*\\\",\\n style=wx.OPEN | wx.CHANGE_DIR\\n )\\n \\n if dlg.ShowModal() == wx.ID_OK:\\n fileName = dlg.GetPath()\\n \\n self.fem.ReadFile(fileName)\\n \\n self.Syncronize()\\n \\n dlg.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72c1a62aca40140465cd937e90ddc68\",\n \"score\": \"0.5567067\",\n \"text\": \"def OnSelectFile1(self, event):\\n\\n # The user can select both file1 and file2 from the file dialog box\\n # by using the shift or control key to pick two files. The order in\\n # which they are selected determines which is file1 and file2.\\n dlg = wx.FileDialog(self,\\n message=\\\"Select 1st Data File\\\",\\n defaultDir=os.getcwd(),\\n defaultFile=\\\"\\\",\\n wildcard=(REFL_FILES+\\\"|\\\"+DATA_FILES+\\\"|\\\"+\\n TEXT_FILES+\\\"|\\\"+ALL_FILES),\\n style=wx.FD_OPEN|wx.FD_MULTIPLE|wx.FD_CHANGE_DIR)\\n # Wait for user to close the dialog.\\n sts = dlg.ShowModal()\\n if sts == wx.ID_OK:\\n paths = dlg.GetPaths()\\n dlg.Destroy()\\n if sts == wx.ID_CANCEL:\\n return # Do nothing\\n\\n # The user could have selected 1 to n files.\\n num_files = len(paths)\\n if num_files > 2:\\n popup_error_message(\\n \\\"Too Many Files Selected\\\",\\n \\\"You can select up to two data files, please try again.\\\")\\n return\\n elif num_files == 2:\\n datafile_1 = paths[1] # files are returned in reverse order!\\n datafile_2 = paths[0] # files are returned in reverse order!\\n self.TCfile1.SetBackgroundColour(\\\"WHITE\\\")\\n self.TCfile1.SetValue(datafile_1)\\n self.TCfile1.SetInsertionPointEnd()\\n self.TCfile2.SetBackgroundColour(\\\"WHITE\\\")\\n self.TCfile2.SetValue(datafile_2)\\n self.TCfile2.SetInsertionPointEnd()\\n elif num_files == 1:\\n datafile_1 = paths[0]\\n self.TCfile1.SetBackgroundColour(\\\"WHITE\\\")\\n self.TCfile1.SetValue(datafile_1)\\n self.TCfile1.SetInsertionPointEnd()\\n\\n # Plot one or both files.\\n self.plot_dataset(self.TCfile1.GetValue(), self.TCfile2.GetValue())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56ce40b79d962f3e8a7eb31f24530be\",\n \"score\": \"0.55613637\",\n \"text\": \"def main():\\n file = input(\\\"Enter File Name: \\\")\\n read_file(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5a009218aa731b5e68e859e2e9f064\",\n \"score\": \"0.55529666\",\n \"text\": \"def chooseFile(self):\\n\\n global gcode_user_file_path\\n options = {}\\n\\n options['title'] = 'Loading G Code for piece...'\\n options['initialdir'] = 'C:\\\\\\\\'\\n gcode_user_file_path = filedialog.askopenfilename(**options)\\n\\n self.master.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bbbc62d1a20490224fd98058904fbf\",\n \"score\": \"0.55513495\",\n \"text\": \"def __init__(self):\\n print \\\"welcome to file manager...\\\"\\n print \\\"press to copy data file[1]: \\\"\\n print \\\"press to delet the file[2]: \\\"\\n print \\\"perss to change path[3]\\\"\\n print \\\"press to show file data[4]: \\\"\\n print \\\"press to longth of data[5]: \\\"\\n print \\\"press quit to exit\\\"\\n self.chooses()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed4e869bc781fe9d5f3f8ce78b6f519\",\n \"score\": \"0.55430955\",\n \"text\": \"def open_file(self):\\n options = {}\\n options['defaultextension'] = '.txt'\\n options['filetypes'] = [('all files', '.*'), ('text files', '.txt')]\\n options['initialdir'] = '//seavvfile1/Market_SAIntMktg/_Offers/5. In Work'\\n options['parent'] = self.root\\n options['title'] = \\\"Open a file\\\"\\n \\n with tkFileDialog.askopenfile(mode='r', **options) as f_handle:\\n print options\\n #for line in f_handle:\\n #print line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214870844d831ec485bc98d421500215\",\n \"score\": \"0.5540342\",\n \"text\": \"def load_filechooser(self, *args):\\n if len(self.objects) != 0:\\n fileChooserClass(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9322afd96bb08cafe728b5174bbb637b\",\n \"score\": \"0.5522619\",\n \"text\": \"def openFileDialog(self,event=None):\\r\\n global FILE_EXTENSION\\r\\n options = {}\\r\\n options['parent'] = self\\r\\n try:\\r\\n global CONFIG_NAME\\r\\n theFile = open(CONFIG_NAME,'rb') #open connection\\r\\n options['initialdir'] = pickle.load(theFile)[0]\\r\\n if options['initialdir'] == \\\"/examples\\\":\\r\\n options['initialdir'] = \\\"examples\\\"\\r\\n theFile.close()\\r\\n except:\\r\\n options['initialdir'] = \\\"examples\\\"\\r\\n\\r\\n\\r\\n\\r\\n options['defaultextension'] = FILE_EXTENSION\\r\\n options['filetypes'] = [(\\\"graph files\\\",FILE_EXTENSION)]\\r\\n self.filePath = tkFileDialog.askopenfilename(**options)\\r\\n self.openFile(self.filePath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373b5753924283af2dad996e9de9dadc\",\n \"score\": \"0.5519668\",\n \"text\": \"def openFile(self):\\n\\n dialog = gui.QFileDialog(self);\\n dialog.setOptions(gui.QFileDialog.DontUseNativeDialog)\\n dialog.setWindowTitle(\\\"Open data file\\\")\\n\\n if dialog.exec_():\\n if dialog.selectedFiles and dialog.selectedFiles():\\n\\n # Get file information\\n self.filename = str(dialog.selectedFiles()[0])\\n \\n # Update UI information\\n self.updateInfo()\\n self.sample = 0\\n\\n # We have all required information, get data start plotting\\n self.updateUI()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a21ad83eaae33bdd444d6356444a7d\",\n \"score\": \"0.5515773\",\n \"text\": \"def on_convert(self):\\n items = self.treeview.get_children()\\n if items:\\n if self.option_var.get() == 1:\\n # merge must include more than 1 file else return error\\n if len(items) > 1:\\n self.merge_files(items)\\n else:\\n message = \\\"Please select 2 or more files to perform a merge.\\\"\\n messagebox.showerror(title='Missing files', message=message)\\n else:\\n # only 1 file shold be selected when splitting the file\\n if len(items) > 1:\\n message = \\\"Select 1 file only when splitting to multiple files.\\\"\\n messagebox.showerror(title='Too many files', message=message)\\n else:\\n self.split_files(items)\\n else:\\n message = \\\"Please select file(s) to continue.\\\"\\n messagebox.showerror(title='Missing files', message=message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947adf18498e1e29fef1321ebc083a90\",\n \"score\": \"0.55074733\",\n \"text\": \"def browseForInput(self):\\n settings = QtCore.QSettings()\\n startingDir = str(settings.value(\\\"OsTranslatorII/lastInputFolder\\\", os.path.expanduser(\\\"~\\\")))\\n d = str( QFileDialog.getExistingDirectory(None, 'Browse For Input', startingDir) )\\n if d != os.sep and d.lower() != 'c:\\\\\\\\' and d != '':\\n settings.setValue(\\\"OsTranslatorII/lastInputFolder\\\", d)\\n self.inputLineEdit.setText(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4487941f990d8ca3ba8595599350286\",\n \"score\": \"0.5502798\",\n \"text\": \"def clik_on_browse_query(self):\\n try:\\n self.qry_path = filedialog.askopenfilename()\\n self.q_box_from_path.delete('1.0', END)\\n self.q_box_from_path.insert(END, self.qry_path)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09488cca1f696b2012fbe6e5fa482be4\",\n \"score\": \"0.54970175\",\n \"text\": \"def handlerSelectListOfEconomicSector(self):\\n file = unicode(QtGui.QFileDialog.getOpenFileName(\\n self, 'Select List of Economic Sector', QtCore.QDir.homePath(), 'List of Economic Sector (*{0})'.format(self.main.appSettings['selectCsvfileExt'])))\\n \\n if file:\\n self.lineEditListOfEconomicSector.setText(file)\\n \\n logging.getLogger(type(self).__name__).info('select file: %s', file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0963c5706cfda83c75512fc4d87da26\",\n \"score\": \"0.5495374\",\n \"text\": \"async def handle_ui(search_state):\\n last_search = ''\\n # Event Loop to process \\\"events\\\"\\n while True:\\n event, values = window.read(timeout=1)\\n if event in (sg.WIN_CLOSED, 'Exit'):\\n sys.exit()\\n elif event == '-INPUT-':\\n if last_search != values['-INPUT-']:\\n window['-RESULT-'].update([])\\n last_search = values['-INPUT-']\\n if search_state['searching']:\\n search_state['interrupting'] = True\\n elif event == '-RESULT-':\\n file_clicked = values['-RESULT-'][0]\\n if search_state['searching']:\\n search_state['interrupting'] = True\\n os.startfile(file_clicked)\\n elif event == '__TIMEOUT__':\\n pass\\n else:\\n print(event, values)\\n await asyncio.sleep(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fb2dde6a9a0d32fbccda3a32f308a2\",\n \"score\": \"0.5489173\",\n \"text\": \"def open_callback(self):\\n\\n # Forget welcome screen widgets\\n self.parent.open_label.pack_forget() \\n self.parent.manual_label.pack_forget()\\n self.parent.email_label.pack_forget()\\n\\n # If a file is already open, close it.\\n if Session.file_opened:\\n self.close_callback()\\n \\n file_path = filedialog.askopenfilename(**self.OPEN_OPT)\\n if file_path: \\n Session.file_path = file_path\\n status = FileHandler.open_file(file_path)\\n if status:\\n Session.file_opened = True\\n Session.file_saved = True\\n EventHandler.event_generate(OPEN_EVENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf5912abbc14706cd84f826a8ccc222\",\n \"score\": \"0.5484031\",\n \"text\": \"def open_file_dialog(self):\\n \\n try:\\n selected_filename = filedialog.askopenfilename(initialdir = \\\".\\\", title = \\\"Select file\\\", filetypes = ((\\\"obj files\\\",\\\"*.obj\\\"), (\\\"mtl files\\\",\\\"*.mtl\\\"), (\\\"all files\\\",\\\"*.*\\\")))\\n \\n if os.path.exists(selected_filename):\\n self.upload_file_list.append(selected_filename)\\n print(\\\"File added to upload list:\\\", selected_filename)\\n \\n ## Also add the '.mtl' file if not already added\\n if selected_filename.endswith('.obj'): \\n selected_filename_mtl = selected_filename[:-4]+'.mtl'\\n if selected_filename_mtl not in self.upload_file_list:\\n if os.path.exists(selected_filename_mtl):\\n self.upload_file_list.append(selected_filename_mtl)\\n print(\\\"File added to upload list:\\\", selected_filename_mtl)\\n else:\\n print(\\\"No existing file selected.\\\")\\n pass\\n \\n except Exception as emsg:\\n print(\\\"EXCEPTION: \\\"+str(emsg))\\n return None\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2145ff301c240cf738ecffaf6ccb401a\",\n \"score\": \"0.5475936\",\n \"text\": \"def _fname_selected(self, fname):\\n self.fname_selected.emit(fname.text())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0beae70840f936b6c1692a814595718\",\n \"score\": \"0.5463578\",\n \"text\": \"def _handle_selection_change(self, selection):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e0607e565d906d639d69331ce65932\",\n \"score\": \"0.5457833\",\n \"text\": \"def __doOpen(self):\\n\\n if self.__modified == True:\\n self.__askForSave()\\n\\n openname = askopenfilename(initialdir=\\\"*\\\",\\n title=self.__dicts.getWordFromDict(self.__Config.get_Element(\\\"Language\\\"), \\\"open\\\"),\\n filetypes=(\\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\\\"Language\\\"), \\\"fileBoot\\\"),\\n \\\"*.boo\\\"),\\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\\\"Language\\\"), \\\"fileTxT\\\"),\\n \\\"*.txt\\\"),\\n (self.__dicts.getWordFromDict(self.__Config.get_Element(\\\"Language\\\"), \\\"fileAll\\\"),\\n \\\"*.*\\\"),\\n ))\\n\\n self.__openFile(openname,True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0091410e94c46bc712ebae905d7afb34\",\n \"score\": \"0.54505515\",\n \"text\": \"def invoke (self, context, event):\\n context.window_manager.fileselect_add (self)\\n return {'RUNNING_MODAL'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0091410e94c46bc712ebae905d7afb34\",\n \"score\": \"0.54505515\",\n \"text\": \"def invoke (self, context, event):\\n context.window_manager.fileselect_add (self)\\n return {'RUNNING_MODAL'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8310268c066a0f320864bce4e5b72242\",\n \"score\": \"0.54462266\",\n \"text\": \"def _open_file(self):\\n file = QFileDialog.getOpenFileName(self, 'Open file', \\\".\\\")[0]\\n if file:\\n file_name = str(file).split('/')[-1] # Need to create version for Windows\\n with open(file, 'rt') as text:\\n if self.tab.currentWidget().text(): # If current Tab is not empty\\n self.tab.addTab(Editor(), file_name)\\n self.tab.setCurrentIndex(self.tab.currentIndex() + 1)\\n self.tab.currentWidget().setText(text.read())\\n else:\\n self.tab.currentWidget().setText(text.read())\\n self.tab.setTabText(self.tab.currentIndex(), file_name)\\n try:\\n lexer = lexers.get_lexer_by_ext(file_name)\\n self.tab.currentWidget().setLexer(lexer())\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d88108b505f9aa38ead9a6171430ff\",\n \"score\": \"0.54447657\",\n \"text\": \"def get_user_input(self) -> bool:\\r\\n tag_list = ['1','2','3']\\r\\n #currnently only one file\\r\\n layout = [\\r\\n [sg.Text('Bat name:')], \\r\\n [sg.Input(key='-BAT NAME-')],\\r\\n [sg.Text('Tag')],\\r\\n [sg.Listbox(tag_list, size=(15, len(tag_list)), key='-TAG-')],\\r\\n # [sg.Listbox(values=tag_list, size=(20, 12), key='-LIST-', enable_events=False)],\\r\\n [sg.Text('Select files or a folder to analyze')],\\r\\n [sg.Text('Files', size=(8, 1)) ,sg.Input(), sg.FilesBrowse(file_types=((\\\"txt Files\\\", \\\"*.txt\\\"),)) if platform.system() == 'Windows' else sg.FilesBrowse()],\\r\\n [sg.Text('OR Folder', size=(8, 1)), sg.Input(), sg.FolderBrowse()],\\r\\n [sg.OK(size=(7,1)), sg.Cancel(size=(7,1))]\\r\\n ]\\r\\n\\r\\n window = sg.Window('Feeder analysis', layout)\\r\\n event, self.values = window.Read()\\r\\n window.Close()\\r\\n return True if event == 'OK' else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1542f0e267c4edf544fe7207aacc7d8\",\n \"score\": \"0.5443162\",\n \"text\": \"def on_open(self):\\n filenames = filedialog.askopenfilenames(parent=self.treeview, filetypes = [(\\\"TIFF file\\\", \\\"*.tif *.tiff\\\")])\\n self.update_idletasks() # without this update the gui becomes temporarily unresponsive\\n if filenames:\\n for name in filenames:\\n short_name = pathlib.Path(name).stem\\n self.treeview.insert('', 'end', text=short_name, values=(str(name),))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da91b24d994d4d033b00be17ba5b264d\",\n \"score\": \"0.5441948\",\n \"text\": \"def filerSelect(self, *args):\\n logger.debug(\\\"dirchooser.filerSelect called\\\")\\n# self.importImageCallBack(self.guiFiler.filer\\\").get_current_folder())\\n self.guiFiler.filer.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e230cc776ecbcab099b159492d16004\",\n \"score\": \"0.5437073\",\n \"text\": \"def OnOpenTxt(self, evt):\\n fileDlg = wx.FileDialog(self, message='Choose Pore Radius file...',\\n wildcard=DATWILDCARD, style=wx.FD_OPEN)\\n if fileDlg.ShowModal() != wx.ID_OK:\\n fileDlg.Destroy()\\n return\\n self.datapath = fileDlg.GetPath()\\n fileDlg.Destroy()\\n self.open_txt(evt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928e11e0ff7a8d7efac0d23a48c7b8d6\",\n \"score\": \"0.54310805\",\n \"text\": \"def push_CAR_Index_file(self):\\n filepath = QFileDialog.getOpenFileName(self)\\n self.plainTextEdit_2.setPlainText(filepath[0])\\n self.filepath_Car = filepath[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e611d75846c34c52ce5fd80dd6150d2\",\n \"score\": \"0.5416835\",\n \"text\": \"def Openfile5(self, event):\\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a Specie\\\",\\n defaultFile=\\\"\\\",\\n wildcard=wildcard1,\\n style=wx.FD_OPEN\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n tmp=\\\"\\\"\\n #paths = dlg.GetPaths()\\n paths = dlg.GetPaths()\\n #print \\\"You chose the following file(s):\\\"\\n for path in paths:\\n tmp=tmp+path\\n #set the value of TextCtrl[filename]\\n \\n #set the value to the TextCtrl[contents]\\n \\n self.aa = self.readtable(tmp)\\n self.contents1.SetValue(str(self.aa[0]))\\n self.contents2.SetValue(str(self.aa[1]))\\n self.contents3.SetValue(str(self.aa[2]))\\n# self.contents4.SetValue(str(a[3]))\\n \\n dlg.Destroy()\\n return self.aa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd5729a6dc2a79ea0f09688e2c82801\",\n \"score\": \"0.54084235\",\n \"text\": \"def onFileOpen(self, path=None):\\n if path:\\n self.fileMenu.currFile = path\\n if self.fileMenu.currFile and os.path.exists(self.fileMenu.currFile):\\n self.cmdFile = self.fileMenu.currFile\\n self.onExit(quit=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0178abd40f7651d2422c68c7e75bb773\",\n \"score\": \"0.5407632\",\n \"text\": \"def on_button_click(self):\\n prev = self.filename.get()\\n initialdir = os.path.dirname(prev) if prev else _INITIALDIR\\n\\n filename = filedialog.askopenfilename(\\n parent=self, initialdir=initialdir, title='Choose a file')\\n if filename:\\n self.filename.set(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0577bb5de24d2bdd21caff7766c1d7e3\",\n \"score\": \"0.54021037\",\n \"text\": \"def openFile(self):\\n fList = [(\\\"Text files\\\", \\\"*.txt\\\")]\\n fileName = tkinter.filedialog.askopenfilename(parent = self, filetypes = fList)\\n\\n if fileName != \\\"\\\":\\n file = open(fileName, 'r')\\n text = file.read()\\n file.close()\\n self.outputArea.setText(text)\\n self.setTitle(fileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14731fb3610163e16972482b21c053a7\",\n \"score\": \"0.53979987\",\n \"text\": \"def __init__(self):\\n self.infile = open(input())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27550350b8137809cfe267442203050\",\n \"score\": \"0.5395495\",\n \"text\": \"def file_open(self):\\n\\n # Stop any running calculation\\n if self.calculation_thread is not None:\\n if self.calculation_thread.isRunning():\\n # Cancel the running calculation\\n self.interrupt_calculation()\\n\\n filename, _chosen_extension = QFileDialog.getOpenFileName(\\n parent=self,\\n caption=\\\"Open File\\\",\\n filter=\\\"MagnetiCalc INI File (*.ini)\\\",\\n options=QFileDialog.DontUseNativeDialog\\n )\\n\\n if filename != \\\"\\\":\\n\\n self.model.invalidate()\\n\\n self.config.close()\\n self.config.set_filename(filename)\\n self.config.load()\\n\\n self.sidebar_left.wire_widget.reinitialize()\\n self.sidebar_left.sampling_volume_widget.reinitialize()\\n self.sidebar_right.field_widget.reinitialize()\\n self.sidebar_right.metric_widget.reinitialize()\\n # Parameters_Widget doesn't need reinitialization as it does not access the configuration\\n # Perspective_Widget doesn't need reinitialization as it does not access the configuration\\n self.sidebar_right.display_widget.reinitialize()\\n\\n self.menu.reinitialize()\\n self.statusbar.reinitialize()\\n\\n self.vispy_canvas.load_perspective()\\n\\n if self.config.get_bool(\\\"auto_calculation\\\"):\\n self.recalculate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"813177ce00fbc8abf7c010ac4676ab23\",\n \"score\": \"0.5393413\",\n \"text\": \"def get_file_items(self, window, sel_items):\\n if len(sel_items) != 1 or sel_items[0].is_directory() or sel_items[0].get_uri_scheme() != 'file':\\n return\\n uri_raw = sel_items[0].get_uri()\\n if len(uri_raw) < 7: return\\n source_path = urllib.unquote(uri_raw[7:])\\n filetype = subprocess.Popen(\\\"file -i %s\\\" % re.escape(source_path), shell=True, stdout=subprocess.PIPE).communicate()[0]\\n\\tprint filetype\\n\\n if \\\"application\\\" or \\\"text\\\" in filetype:\\n item = Nautilus.MenuItem(name='NautilusPython::preferences-nautilus-optirun',\\n label=_('Run via Optirun'),\\n tip=_('Run the selected file on discrete video card'),\\n icon='preferences-nautilus-optirun')\\n item.connect('activate', self.run, source_path)\\n return item,\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ca99011908043a81dee284849bb031\",\n \"score\": \"0.5393086\",\n \"text\": \"def run(self) -> None:\\n user_input = \\\"\\\"\\n if not self._logo_displayed:\\n self.cls()\\n print(base64.b64decode(self.LOGO).decode(\\\"ascii\\\"))\\n self._logo_displayed = True\\n self.display_menu()\\n while True:\\n user_input = input(\\\"\\\\nYour selection: \\\")\\n try:\\n user_input = int(user_input)\\n if user_input <= 0 or user_input > len(self.options):\\n self.display_error()\\n else:\\n if callable(self.options[user_input][1]):\\n self.selected_option = user_input\\n self.options[user_input][1]()\\n if self.options[user_input][2]:\\n return\\n else:\\n self.display_menu()\\n elif self.options[user_input][1]:\\n self.selected_option = user_input\\n self.options[user_input][1].run()\\n self.display_menu(clear_screen=True)\\n else:\\n return\\n except ValueError:\\n self.display_error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8307a6c17981445509b374d903d469c\",\n \"score\": \"0.5392842\",\n \"text\": \"def load(self, selection):\\n self.file_path = str(selection[0])\\n self.popup.dismiss()\\n self.load_action()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd21cb2b3b0431f19d28c8b5bb89ba2d\",\n \"score\": \"0.5392435\",\n \"text\": \"def Openfile4(self, event):\\n dlg = wx.FileDialog(\\n self, message=\\\"Choose a Specie\\\",\\n defaultFile=\\\"\\\",\\n wildcard=wildcard1,\\n style=wx.FD_OPEN\\n )\\n if dlg.ShowModal() == wx.ID_OK:\\n tmp=\\\"\\\"\\n #paths = dlg.GetPaths()\\n paths = dlg.GetPaths()\\n #print \\\"You chose the following file(s):\\\"\\n for path in paths:\\n tmp=tmp+path\\n \\n #set the value to the TextCtrl[contents]\\n self.a4 = self.readtable(tmp)\\n self.contents4.SetValue(str(self.a4))\\n \\n dlg.Destroy()\\n return self.a4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf673ac509152b4339e9f8fc384d5cc\",\n \"score\": \"0.5390938\",\n \"text\": \"def openFileDialog(self):\\r\\n filename, filter = QFileDialog.getOpenFileName(dir=os.path.dirname(self.filename))\\r\\n if not filename == '':\\r\\n self.filename = filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73314dd050fdee0f0d1f59f65153aec0\",\n \"score\": \"0.5389249\",\n \"text\": \"def browse(self):\\n qfd = QFileDialog()\\n file_filter = 'VHDL file(*.vhd);;All files(*)'\\n self.input_file_path = QFileDialog.getOpenFileName(qfd, 'Open source file', 'C:/', file_filter)[0]\\n self.input_file_name = self.input_file_path.split(\\\"/\\\")[-1]\\n self.project_path = '/'.join(self.input_file_path.split(\\\"/\\\")[:-1]) + '/'\\n self.xdc_ports = {}\\n if self.input_file_path != '':\\n ports, module_name, libs = gen_and_parse.get_stuff(self.input_file_path)\\n\\n for key, value in ports.items():\\n self.xdc_ports[key] = value\\n try:\\n num1, num2 = re.search(r'(\\\\d+).*(\\\\d+)', value[1]).groups()\\n del self.xdc_ports[key]\\n\\n num1 = int(num1)\\n num2 = int(num2)\\n if num1 > num2:\\n for i in range(num2, num1 + 1):\\n self.xdc_ports[f'{key}[{i}]'] = ''\\n else:\\n for i in range(num1, num2 + 1):\\n self.xdc_ports[f'{key}[{i}]'] = ''\\n except:\\n pass\\n\\n self.tableWidget.setRowCount(len(self.xdc_ports.keys()))\\n global ddr_chosen\\n for i, port in enumerate(self.xdc_ports, start=0):\\n temp_w = QTableWidgetItem(port)\\n self.tableWidget.setItem(i, 0, temp_w)\\n self.set_items_in_table(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5312a6cf2c9f24787b7fd89c9c225805\",\n \"score\": \"0.5380366\",\n \"text\": \"def execute(self, context):\\n\\n filename, extension = os.path.splitext(self.filepath)\\n\\n print('Selected file:', self.filepath)\\n print('File name:', filename)\\n print('File extension:', extension)\\n rigify(self.filepath)\\n\\n return {'FINISHED'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73f202f712477728d73a6d33a733c5d\",\n \"score\": \"0.5370303\",\n \"text\": \"def fileHandler(value):\\r\\n if value.lower() == \\\"exit\\\":\\r\\n print(\\\"Exiting the program..\\\")\\r\\n sys.exit()\\r\\n \\r\\n if(value == \\\"1\\\"): #if the user enters 1\\r\\n filename = \\\"C:/Users/shruti/source/repos/Hw2/StemAndLeaf1.txt\\\" #file path for SteamAndLeaf1.txt file\\r\\n with open(filename) as f:\\r\\n fileData = f.read().splitlines() #reads the file data\\r\\n \\r\\n\\r\\n if(value == \\\"2\\\"):\\r\\n filename = \\\"C:/Users/shruti/source/repos/Hw2/StemAndLeaf2.txt\\\"\\r\\n with open(filename) as f:\\r\\n fileData = f.read().splitlines()\\r\\n \\r\\n if(value == \\\"3\\\"):\\r\\n filename = \\\"C:/Users/shruti/source/repos/Hw2/StemAndLeaf3.txt\\\"\\r\\n with open(filename) as f:\\r\\n fileData = f.read().splitlines()\\r\\n \\r\\n\\r\\n return fileData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6fa296d6154d939919d64038a5ff9d\",\n \"score\": \"0.53696764\",\n \"text\": \"def read_input(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66823154aeefc417924f6f0a3147c1d0\",\n \"score\": \"0.53628767\",\n \"text\": \"def browse_function(first_frame):\\n global raw_filenames\\n root.filename = \\\\\\n filedialog.askopenfilenames(initialdir=\\\"/\\\", title=\\\"Select Image\\\")\\n first_file = root.filename[0]\\n if first_file.lower().endswith('zip') is True:\\n zf = zipfile.ZipFile(root.filename[0], 'r')\\n raw_filenames = zf.namelist()\\n num_files = len(raw_filenames)\\n else:\\n raw_filenames = root.filename\\n num_files = len(raw_filenames)\\n file_label = ttk.Label(first_frame,\\n text=\\\"{} file(s) uploaded\\\".format(num_files))\\n file_label.grid(column=0, row=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5f38d80d9e8be7db2458c88e48c7a9\",\n \"score\": \"0.53509563\",\n \"text\": \"def outFileBrowse(self):\\n \\n outFilename = Utilities.saveFileDialog(self) \\n if not outFilename:\\n return\\n self.outputFilename.setText(outFilename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a629bde03fa71f19539ba455ff463050\",\n \"score\": \"0.53497124\",\n \"text\": \"def _output_select(self):\\n options = QFileDialog.Options()\\n file_select, _ = QFileDialog.getSaveFileName(\\n self._view,\\n 'Save Output...',\\n '',\\n # 'RNH Files (*.rnh);;RND Files (*.rnd);;CSV Files (*.csv);;Excel Files (*.xls*);;All Files (*)',\\n options=options\\n )\\n if file_select:\\n self._config[\\\"output\\\"] = [file_select]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cc3e7e6e0287f55526e5300f02127a\",\n \"score\": \"0.5345172\",\n \"text\": \"def _openItem(self):\\n itmList = self.getSelectedItems(\\n [BrowserFileItem, BrowserClassItem,\\n BrowserMethodItem, BrowserClassAttributeItem,\\n BrowserImportItem])\\n \\n if not self._activating:\\n self._activating = True\\n for itm in itmList:\\n if isinstance(itm, BrowserFileItem):\\n if itm.isPython2File():\\n self.sourceFile[str].emit(itm.fileName())\\n elif itm.isPython3File():\\n self.sourceFile[str].emit(itm.fileName())\\n elif itm.isRubyFile():\\n self.sourceFile[str, int, str].emit(\\n itm.fileName(), -1, \\\"Ruby\\\")\\n elif itm.isDFile():\\n self.sourceFile[str, int, str].emit(\\n itm.fileName(), -1, \\\"D\\\")\\n elif itm.isDesignerFile():\\n self.designerFile.emit(itm.fileName())\\n elif itm.isLinguistFile():\\n if itm.fileExt() == '.ts':\\n self.linguistFile.emit(itm.fileName())\\n else:\\n self.trpreview.emit([itm.fileName()])\\n elif itm.isProjectFile():\\n self.projectFile.emit(itm.fileName())\\n elif itm.isMultiProjectFile():\\n self.multiProjectFile.emit(itm.fileName())\\n elif itm.isIdlFile():\\n self.sourceFile[str].emit(itm.fileName())\\n elif itm.isProtobufFile():\\n self.sourceFile[str].emit(itm.fileName())\\n elif itm.isResourcesFile():\\n self.sourceFile[str].emit(itm.fileName())\\n elif itm.isSvgFile():\\n self.svgFile.emit(itm.fileName())\\n elif itm.isPixmapFile():\\n self.pixmapFile.emit(itm.fileName())\\n else:\\n if Utilities.MimeTypes.isTextFile(itm.fileName()):\\n self.sourceFile[str].emit(itm.fileName())\\n else:\\n QDesktopServices.openUrl(QUrl(itm.fileName()))\\n elif isinstance(itm, BrowserClassItem):\\n self.sourceFile[str, int].emit(\\n itm.fileName(), itm.classObject().lineno)\\n elif isinstance(itm, BrowserMethodItem):\\n self.sourceFile[str, int].emit(\\n itm.fileName(), itm.functionObject().lineno)\\n elif isinstance(itm, BrowserClassAttributeItem):\\n self.sourceFile[str, int].emit(\\n itm.fileName(), itm.attributeObject().lineno)\\n elif isinstance(itm, BrowserImportItem):\\n self.sourceFile[str, list].emit(\\n itm.fileName(), itm.linenos())\\n self._activating = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d773be7d07d2e4959c0f2c6980e91983\",\n \"score\": \"0.53407794\",\n \"text\": \"def select_files(self, directory):\\n filenames_loc=fd.askopenfilenames(initialdir=directory, title='Select files', filetypes=(('text files', '*.txt'), ('all files', '*.*')))\\n filenames_loc=list(filenames_loc)\\n \\n if len(filenames_loc)>0: #prevents error from ocurring if user doesn't actually select any files\\n self.folder=os.path.dirname(filenames_loc[0]) #remembers last used directory\\n \\n return filenames_loc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e9f1a6a8879aad6793d44be555ad70\",\n \"score\": \"0.53387815\",\n \"text\": \"def file_inputs(self):\\n for i in range(self.controller.inputs['num_variables_input']):\\n if self.controller.inputs['is_included_in_file_input'][i]==0:\\n self.controller.inputs['num_txt_files']*=self.controller.inputs['num_datapoints_input'][i] #finds the product of the length of all the unincluded dimensions\\n \\n #uploads files \\n if self.controller.inputs['track_file_input']==1 and self.controller.inputs['unincluded_dimensions']!=[]: #checks for whether the user would like to and is able to track their files \\n self.tracker_child_frame.destroy()\\n self.tracker_child_frame=tk.Frame(self.tracker_frame)\\n self.tracker_child_frame.config(highlightbackground='blue', highlightcolor='blue', highlightthickness=1, height=20, width=70)\\n self.tracker_child_frame.pack() \\n self.shared_func.tracker(frame=self.tracker_child_frame, \\n dimensions=self.controller.inputs['unincluded_dimensions'], \\n dim_name=self.controller.inputs['unincluded_dim_name'], \\n row=0, \\n column=0) \\n elif self.controller.inputs['track_file_input']==1 and self.controller.inputs['unincluded_dimensions']==[]: #if the user would like to track their files but there is only one file, show error\\n mb.showerror('Error', 'Cannot track only one file. Please uncheck tracking file option') \\n else: #creates file list without tracker function \\n while len(self.shared_func.file_list)<self.controller.inputs['num_txt_files']:\\n add_file=self.shared_func.select_files(self.shared_func.folder)\\n if add_file==[]: #if user cancels adding files, assumes the user would like to start everything over\\n break\\n add_file.append(add_file.pop(0)) #puts the first file selected back to the beginning because tkinter is weird like that (tkinter will put the first file selected at the end)\\n self.shared_func.file_list+=add_file \\n \\n #Check to make sure the file list is the right size\\n if len(self.shared_func.file_list)!=self.controller.inputs['num_txt_files']: \\n mb.showerror(\\\"Error\\\", \\\"File list incompatible with amount of data specified, please reconfirm inputs and press reset\\\") \\n \\n #Widgets for loading text from the .txt files** \\n self.shared_func.file_format_widgets(frame=self.file_format_child_frame, row=0, column=0)\\n\\n #Button that will compile all the information and .txt files into an n-dimensional matrix\\n compile_button=ttk.Button(self.file_format_child_frame, \\n text='Compile', \\n command= lambda comments=self.shared_func.comments_entry, \\n delimiter=self.shared_func.delimiter_entry,\\n skiprows=self.shared_func.skiprows_entry, \\n usecolumns=self.shared_func.usecols_entry: self.inputs_create_matrix(comments, \\n delimiter, \\n skiprows, \\n usecolumns))\\n compile_button.grid(row=0, column=8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47ed733ca6ac5391f4e3773b7937abe8\",\n \"score\": \"0.5332661\",\n \"text\": \"def _browse_vfk_files(self):\\n \\n title = u'Vyberte VFK soubor.'\\n filters = u'.vfk (*.vfk)'\\n \\n filePath = self.dW.open_file_dialog(title, filters, True)\\n \\n if filePath:\\n self.set_text_browseVfkLineEdit.emit(filePath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b00f3d8854c7bf991c84fd1a917756a\",\n \"score\": \"0.5327411\",\n \"text\": \"def _handle(self, inputs, **options):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993994,"cells":{"query_id":{"kind":"string","value":"abc3539d6a4d16fc36b94d707e583008"},"query":{"kind":"string","value":"Return triangle area given its vertices' coordinates."},"positive_passages":{"kind":"list like","value":[{"docid":"0add7d83ad7bf33b1cf10765399b27f0","score":"0.70234346","text":"def get_triangle_area(point1: Point, point2: Point, point3: Point):\n\t\tprod1 = (point1[0] - point3[0]) * (point2[1] - point1[1])\n\t\tprod2 = (point1[0] - point2[0]) * (point3[1] - point1[1])\n\n\t\ttriangle_area = 1/2 * abs(prod1 - prod2)\n\n\t\treturn triangle_area","title":""}],"string":"[\n {\n \"docid\": \"0add7d83ad7bf33b1cf10765399b27f0\",\n \"score\": \"0.70234346\",\n \"text\": \"def get_triangle_area(point1: Point, point2: Point, point3: Point):\\n\\t\\tprod1 = (point1[0] - point3[0]) * (point2[1] - point1[1])\\n\\t\\tprod2 = (point1[0] - point2[0]) * (point3[1] - point1[1])\\n\\n\\t\\ttriangle_area = 1/2 * abs(prod1 - prod2)\\n\\n\\t\\treturn triangle_area\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ce87c52ea5d3ae5d6cadd5a7d6f1c8a6","score":"0.8274563","text":"def area_of_triangle(*args):\n if len(args) != 1:\n print('ERROR: one argument expected, got {}.'.format(len(args)))\n return None\n vertexes = args[0]\n if not isinstance(vertexes, list):\n print('ERROR: list of vertexes expected as an argument, got {}.'.format(type(vertexes)))\n return None\n if len(vertexes) != 6:\n print('ERROR: 6 coordinates expected, got {}.'.format(len(vertexes)))\n return None\n for coordinate in vertexes:\n if not (isinstance(coordinate, int) or isinstance(coordinate, float)):\n print('ERROR: type int or float expected, got {}.'.format(type(coordinate)))\n return None\n ver = [[vertexes[0], vertexes[1]], [vertexes[2], vertexes[3]], [vertexes[4], vertexes[5]]]\n a = distance(ver[0], ver[1])\n b = distance(ver[1], ver[2])\n c = distance(ver[2], ver[0])\n p = (a + b + c) / 2\n return (p * (p - a) * (p - b) * (p - c)) ** 0.5","title":""},{"docid":"4a3537ea3fce9eaa695688c0a9bd4ba9","score":"0.7953434","text":"def get_area(vertices):\n area = 0.0\n for i in range(len(vertices)):\n j = (i + 1) % len(vertices)\n area += vertices[i][0] * vertices[j][1] - vertices[j][0] * vertices[i][1]\n return abs(area) / 2.0","title":""},{"docid":"8e9e7e7fcb60d5d687bc91ed0ca60cf0","score":"0.78157234","text":"def polygon_area(vertices: Sequence[Sequence[float]]) -> float:\n if len(vertices) == 3:\n # Specialized, fast implementation for triangles, since this is heavily\n # used in `smallest_enclosing_circle`\n dx1 = vertices[1][0] - vertices[0][0]\n dy1 = vertices[1][1] - vertices[0][1]\n dx2 = vertices[2][0] - vertices[1][0]\n dy2 = vertices[2][1] - vertices[1][1]\n return (dx1 * dy2 - dy1 * dx2) / 2\n\n x, y = np.vstack((vertices[-1], vertices)).T\n return np.sum((x[1:] + x[:-1]) * (y[1:] - y[:-1])) / 2","title":""},{"docid":"cdb866be487eb3b7cad6ce2f957b0d0c","score":"0.757878","text":"def area_of_triangle(x1, y1, x2, y2, x3, y3):\n area = abs((x1 * (y2 - y3) + x2 * (y3 - y1) + x3 * (y1 - y2)) / 2.0)\n\n return area","title":""},{"docid":"639d2fa5d4b924aa263a32215afbe1a8","score":"0.74019337","text":"def area_of_triangle_with(self, v):\n\t\treturn .5*self.area_of_parallelogram(v)","title":""},{"docid":"c121e4114d613be50ab749deff4081b2","score":"0.7401034","text":"def triangleArea(a, b, c):\n\treturn (a[0]*b[1] - a[1]*b[0] + a[1]*c[0]\n\t\t\t- a[0]*c[1] + b[0]*c[1] - c[0]*b[1]) / 2.0","title":""},{"docid":"9c1038134adbd7b858778ca4ab99e5a5","score":"0.7334358","text":"def area_of_triangle(base, height):\n return 0","title":""},{"docid":"322df138966e862ab85aac6336a18cc3","score":"0.7289267","text":"def polygon_area(coords):\n Xs = coords[:,0]\n Ys = coords[:,1]\n\n # Ignore orientation\n return 0.5*abs(sum(Xs[:-1]*Ys[1:] - Xs[1:]*Ys[:-1]))","title":""},{"docid":"fd3bd8e34edd08d3fd4530f77f79ecca","score":"0.7232928","text":"def area_for_poly(vertices, radius=0):\r\n vs = list(map(tuple, vertices))\r\n return cp.cpAreaForPoly(len(vertices), vs, radius)","title":""},{"docid":"912743698ebe28c24a622abef68b6657","score":"0.71538067","text":"def triangle_area(base, height):\n # You have to code here\n # REMEMBER: Tests first!!!\n area = (base * height) / 2\n return area","title":""},{"docid":"718d4216605cbec2c7ed2d2b1e0d013a","score":"0.71321255","text":"def triangle_area(a, h):\n return (a*h)/2","title":""},{"docid":"eda08222a39e5f48bf5397c18209b2dd","score":"0.7088057","text":"def area_of_triangle(p1, p2, p3):\n return norm(cross((p2 - p1), (p3 - p1))) / 2.0","title":""},{"docid":"8bcc1aaebdc0c7793e824d3218898414","score":"0.69998634","text":"def calculate_triangle_area(base, height):\n return 0.5 * base * height","title":""},{"docid":"432bb08ac25c321de1d54a602f9524da","score":"0.69147646","text":"def square_of_triangle(vertexes):\n if not isinstance(vertexes, list):\n print('ERROR: list of vertexes expected as an argument, got {}'.format(type(vertexes)))\n return None\n if len(vertexes) != 6:\n print('ERROR: 6 coordinates expected, got {}'.format(len(vertexes)))\n return None\n for coordinate in vertexes:\n if not (isinstance(coordinate, int) or isinstance(coordinate, float)):\n print('ERROR: type int or float expected, got {}'.format(type(coordinate)))\n return None\n a = ((vertexes[0] - vertexes[2]) ** 2 + (vertexes[1] - vertexes[3]) ** 2) ** 0.5\n b = ((vertexes[2] - vertexes[4]) ** 2 + (vertexes[3] - vertexes[5]) ** 2) ** 0.5\n c = ((vertexes[0] - vertexes[4]) ** 2 + (vertexes[1] - vertexes[5]) ** 2) ** 0.5\n p = (a + b + c) / 2\n return (p * (p - a) * (p - b) * (p - c)) ** 0.5","title":""},{"docid":"3fce82b2dcd8c55b00edd24be3dbc2f8","score":"0.6842898","text":"def areaTriangle(ptx1,pty1,ptx2,pty2,ptx3,pty3):\r\n # This uses Heron's Formula - Google it if you want to know more\r\n side1 = distance(ptx1,pty1,ptx2,pty2)\r\n side2 = distance(ptx2,pty2,ptx3,pty3)\r\n side3 = distance(ptx3,pty3,ptx1,pty1)\r\n p = (side1 + side2 + side3)/2\r\n t1 = p-side1\r\n t2 = p-side2\r\n t3 = p-side2\r\n if t1==0 or t2==0 or t3==0:\r\n print(\"Does not form a triangle\")\r\n return None\r\n area = math.sqrt( p*(p-side1)*(p-side2)*(p-side3) )\r\n return(area)","title":""},{"docid":"53741ada61b2749f4f8c694b22353f84","score":"0.6834161","text":"def triangle(vert_a, vert_b, vert_c):\n vert_a_x, vert_a_y = vert_a\n vert_b_x, vert_b_y = vert_b\n vert_c_x, vert_c_y = vert_c\n a_tri = abs((vert_a_x * (vert_b_y - vert_c_y) +\n vert_b_x * (vert_c_y - vert_a_y) +\n vert_c_x * (vert_a_y - vert_b_y)) / 2.0)\n # area = (1/2)b*h\n # /\\A\n # / \\\n # / \\\n # B/______\\C\n\n return a_tri","title":""},{"docid":"a10a13cdcbdcdae3ccb7370d09b886bc","score":"0.6788807","text":"def triangle_area(base=None, height=None, side_a=None, side_b=None, side_c=None):\n if (base is not None) & (height is not None):\n return 0.5 * base * height\n elif (side_a is not None) & (side_b is not None) & (side_c is not None):\n if (side_a + side_b) > side_c & (side_b + side_c) > side_a & (side_a + side_c) > side_b:\n s = (side_a + side_b + side_c) * 0.5\n return (s*(s-side_a)*(s-side_b)*(s-side_c)) ** 0.5\n else:\n return\"The sum of two sides must be greater than the third side of a triangle\"\n else:\n return \"no such triangle\"","title":""},{"docid":"b888b0733977f59da0f0d0aed909e12d","score":"0.67785037","text":"def poly_area(verts):#Tartley - Jonathan Hartley, http://tartley.com\n\taccum = 0.0\n\tfor i in range(len(verts)):\n\t\tj = (i + 1) % len(verts)\n\t\taccum += verts[j][0] * verts[i][1] - verts[i][0] * verts[j][1]\n\treturn accum / 2","title":""},{"docid":"726b1bd7ca7feb04765715b09876e7aa","score":"0.6737634","text":"def compute_tri_area(side):\n return side * side * math.sqrt(3)/4","title":""},{"docid":"83e30e76b185b71cb7c0984721e3e715","score":"0.67302614","text":"def get_area_triangle(p1, p2, p3):\n det_jac = (p1[0] - p3[0]) * (p2[1] - p3[1]) - (p2[0] - p3[0]) * (p1[1] - p3[1])\n return 0.5 * abs(det_jac)","title":""},{"docid":"196547415b5ca299c2f8e3725bde47e7","score":"0.67248505","text":"def calculate_area(coords):\r\n return max(0, coords[1] - coords[0]) * max(0, coords[3] - coords[2])","title":""},{"docid":"058642431a02403e92e2ba67c9028891","score":"0.672136","text":"def get_area(self):\n\n # We use the \"shoelace\" algorithm to calculate the area, since the\n # polygon has no holes or self-intersections.\n s1 = s2 = 0\n for i in range(self.n):\n j = (i+1) % self.n\n s1 += self.vertices[i].x * self.vertices[j].y\n s2 += self.vertices[i].y * self.vertices[j].x\n return abs(s1 - s2) / 2","title":""},{"docid":"82e34d37ead553aa7b651813d03a1437","score":"0.6668018","text":"def polygon_area(points):\n res = 0\n for i in range(len(points)):\n j = (i+1) % len(points)\n res = res + points[i].x * points[j].y\n res = res - points[j].x * points[i].y\n return abs(res) / 2","title":""},{"docid":"d10c3e647d3b5eb263df75040d991580","score":"0.666083","text":"def triangle_area(height=None, base=None) -> Number:\n if dim_validate3(base) and dim_validate3(height):\n if dim_validate2(base) and dim_validate2(height):\n if dim_validate1(base) and dim_validate1(height):\n return base * height / 2\n else:\n raise ValueError(\"side must be positive: \")\n raise TypeError('value is a string')\n raise AttributeError(\"The dimension is not provided\")","title":""},{"docid":"92cadaf8de68f99cd322dfbca611f6f5","score":"0.66544366","text":"def area(vs):\n a = 0\n x0, y0 = vs[0]\n for [x1, y1] in vs[1:]:\n dy = y1 - y0\n a += x0*dy\n x0 = x1\n y0 = y1\n return a","title":""},{"docid":"cb71486155d37251644a40732f1a369b","score":"0.6585298","text":"def triangle_area(breadth, height: Number) -> Number:\n return 0.5*breadth*height","title":""},{"docid":"35d25fb3d05464f4a863121e998417b4","score":"0.6577138","text":"def area_triangle(b, h: Number) -> Number:\n return 1/2*b*h","title":""},{"docid":"c3708370821e0c77c95fd606c8af6e9f","score":"0.6539446","text":"def heron_triangle_area_sides(a, b, c):\n if triangle_exists(a, b, c):\n return 0.25*math.sqrt((a + (b + c))*(c - (a - b))*(c + (a - b))*(a + (b - c)))\n else:\n return 0","title":""},{"docid":"5afeb340572a8e455cf98e66681a7c02","score":"0.6485532","text":"def calculate_area(pts):\n pts = perspective.order_points(pts)\n a = euclid_dist(pts[2], pts[3])\n b = euclid_dist(pts[3], pts[0])\n c = euclid_dist(pts[0], pts[1])\n d = euclid_dist(pts[1], pts[2])\n t = 0.5 * (a + b + c + d)\n angle1 = angle_betw_lines(\n np.array([pts[2], pts[3]]), np.array([pts[3], pts[0]]))\n angle2 = angle_betw_lines(\n np.array([pts[0], pts[1]]), np.array([pts[1], pts[2]]))\n area = math.sqrt(((t - a) * (t - b) * (t - c) * (t - d))\n - (a * b * c * d * ((math.cos((angle1 + angle2)/2)) ** 2)))\n return area","title":""},{"docid":"b46a305074f292a2cc456664802e70bd","score":"0.6464238","text":"def line_vertex_area(X, L):\n a = line_edge_area(X, L)\n _, ind0 = np.unique(L[:,0], return_index=True)\n _, ind1 = np.unique(L[:,1], return_index=True)\n ind = np.column_stack((ind0,ind1))\n a = np.sum(a[ind], axis=1) / 2\n return a","title":""},{"docid":"8e1d20bf728ff68ccae47672fdfc363b","score":"0.6463007","text":"def triangleArea(triangleSet):\n\n\ttriangleAreaSet = []\n\n\tfor i in range(len(triangleSet)):\n\t\tv1 = triangleSet[i][1] - triangleSet[i][0]\n\t\tv2 = triangleSet[i][2] - triangleSet[i][0]\n\t\tarea = np.linalg.norm(np.cross(v1, v2))/2\n\t\ttriangleAreaSet.append(area)\n\n\treturn triangleAreaSet","title":""},{"docid":"fe04a806cf7cd09bb431b73441692284","score":"0.6447586","text":"def polygon_area(polygon):\n\n w=0\n for count in range(len(polygon)-1):\n y = polygon[count+1][1] + polygon[count][1]\n x = polygon[count+1][0] - polygon[count][0]\n z = y * x\n w += z\n return abs(w/2.0)","title":""},{"docid":"7d07499360c23269b42c987a1d62f28f","score":"0.6436004","text":"def area(self) -> float:\n p = 0.5 * sum(self.sides)\n a, b, c = self.sides\n return math.sqrt(p * (p - a) * (p - b) * (p - c))","title":""},{"docid":"34c03ef9f25e74907b28598356fd06c8","score":"0.6430339","text":"def area_triangle(x=None, y=None, z=None, h=None, theta=None) ->Number:\n if (x is not None) & (y is not None) & (z is not None):\n s = (x + y + z)/2\n area = (s*(s - x)*(s - y)*(s - z))**(1/2)\n return area\n elif (x is not None) & (y is not None) & (theta is not None):\n area = 1/2*x*y*sin(theta)\n return area\n elif (x is not None) & (h is not None):\n area = x*h/2\n return area\n else:\n return \"the argument given are inadequate to compute area\"","title":""},{"docid":"61db44bdfd46b354c352239b44b10203","score":"0.64260465","text":"def polygon_center(vertices: Sequence[Sequence[float]],\n area: Optional[float] = None) -> Tuple[float, float]:\n if area is None:\n area = polygon_area(vertices)\n\n x, y = np.vstack((vertices[-1], vertices)).T\n fact = x[:-1] * y[1:] - x[1:] * y[:-1]\n x_c = np.sum((x[:-1] + x[1:]) * fact) / (6 * area)\n y_c = np.sum((y[:-1] + y[1:]) * fact) / (6 * area)\n\n return x_c, y_c","title":""},{"docid":"8f36582619a174b292f21e15bb1af89c","score":"0.6413507","text":"def polygon_area(corners):\n n = len(corners) # of corners\n area = 0.0\n for i in range(n):\n j = (i + 1) % n\n area += corners[i][0] * corners[j][1]\n area -= corners[j][0] * corners[i][1]\n area = abs(area) / 2.0\n return area","title":""},{"docid":"4e47b75ca6c90c9289deed7edb6e025d","score":"0.64095837","text":"def trapezoidal_area(xyz):\n d = scipy.spatial.Delaunay(xyz[:,:2])\n tri = xyz[d.vertices]\n\n a = tri[:,0,:2] - tri[:,1,:2]\n b = tri[:,0,:2] - tri[:,2,:2]\n proj_area = np.cross(a, b).sum(axis=-1)\n zavg = tri[:,:,2].sum(axis=1)\n vol = zavg * np.abs(proj_area) / 6.0\n return vol.sum()","title":""},{"docid":"4e47b75ca6c90c9289deed7edb6e025d","score":"0.64095837","text":"def trapezoidal_area(xyz):\n d = scipy.spatial.Delaunay(xyz[:,:2])\n tri = xyz[d.vertices]\n\n a = tri[:,0,:2] - tri[:,1,:2]\n b = tri[:,0,:2] - tri[:,2,:2]\n proj_area = np.cross(a, b).sum(axis=-1)\n zavg = tri[:,:,2].sum(axis=1)\n vol = zavg * np.abs(proj_area) / 6.0\n return vol.sum()","title":""},{"docid":"a0c499fb1dbc9aa22065e28f35df4cef","score":"0.63906235","text":"def triangleSignedAreaE2(A, B, C):\n return 0.5 * (A.x * (B.y - C.y) + A.y * (C.x - B.x) + (B.x * C.y - B.y * C.x))","title":""},{"docid":"9130dc322cdfc40a30028ae6ab5b1361","score":"0.6388903","text":"def get_triangle_area(self, other):\n return self.get_parallelogram_area(other) / 2","title":""},{"docid":"e7a263c5abdabb27b6b0b6554d6d8100","score":"0.63794124","text":"def get_area_polygon(polygon): \n area_outer = get_area_single_polygon(polygon[0])\n area_inner = 0\n for poly in polygon[1:]:\n area_inner += get_area_single_polygon(poly)\n return area_outer - area_inner","title":""},{"docid":"c4a2b3f87aa3f70661d49bdcbaf94547","score":"0.63707113","text":"def EqTriArea(side):\n return ((3**0.5) / 4) * (side**2)","title":""},{"docid":"564722d326ad4c414fcf7616910d4dc5","score":"0.63513863","text":"def area(x1, y1, x2, y2, x3, y3): \n return abs((x1 * (y2 - y3) + x2 * (y3 - y1) \n + x3 * (y1 - y2)) / 2.0)","title":""},{"docid":"a08d601b818be6249b4f136671d3076a","score":"0.6299496","text":"def area(self):\n # Length x breadth\n if self.is_valid():\n return self.sides[0] * self.sides[1]","title":""},{"docid":"d11ccf4ee16a69f294b378f04c788a00","score":"0.6232965","text":"def getArea(self):\n a, b, c = self.a, self.b, self.c\n return abs(a.x*(b.y-c.y)+b.x*(c.y-a.y)+c.x*(a.y-b.y))/2","title":""},{"docid":"bb8757be74feed95fd61d974d19e0e6f","score":"0.619349","text":"def largestTriangleArea(self, # pylint: disable=invalid-name\n\t\t\t\t\t\t\tpoints: Points) -> float:\n\t\tconvex_hull = self.get_convex_hull(points)\n\n\t\tmax_triangle_area = 0\n\t\tconvex_hull_len = len(convex_hull)\n\n\t\tfor idx1 in range(convex_hull_len):\n\t\t\t# Combine idx1 with every index to its right to avoid repeats\n\t\t\tfor idx2 in range(idx1, convex_hull_len):\n\t\t\t\t# Combine idx2 with every index to its right to avoid repeats\n\t\t\t\tfor idx3 in range(idx2, convex_hull_len):\n\t\t\t\t\ttriple = (\n\t\t\t\t\t\tconvex_hull[idx1],\n\t\t\t\t\t\tconvex_hull[idx2],\n\t\t\t\t\t\tconvex_hull[idx3]\n\t\t\t\t\t)\n\t\t\t\t\ttriangle_area = self.get_triangle_area(*triple)\n\t\t\t\t\tmax_triangle_area = max(max_triangle_area, triangle_area)\n\n\t\treturn max_triangle_area","title":""},{"docid":"ec86d9c791eac13f45e6c898853c20e1","score":"0.6110272","text":"def area(self):\n s = self.perimeter() / 2.0\n return sqrt(s * (s - self.a) * (s - self.b) * (s - self.c))","title":""},{"docid":"ab897ec99d62d5ef82651f8379ef969f","score":"0.6084797","text":"def area_of_polygon_wrt_point(self, centroid):\n x, y = [i for i in centroid]\n\n area_of_polygon = 0.0\n\n for i in range(len(self._coords) - 1):\n x1, y1 = [i for i in self._coords[i]]\n x2, y2 = [i for i in self._coords[i + 1]]\n area_of_polygon += self.area_of_triangle(x, y, x1, y1, x2, y2)\n\n x1, y1 = [i for i in self._coords[-1]]\n x2, y2 = [i for i in self._coords[0]]\n\n area_of_polygon += self.area_of_triangle(x, y, x1, y1, x2, y2)\n\n return area_of_polygon","title":""},{"docid":"d12ac51e8fd6eaa74c0bdae55789723d","score":"0.6058393","text":"def get_area_single_polygon(single_polygon):\n x,y = [c[0] for c in single_polygon], [c[1] for c in single_polygon]\n area = 0\n for i in range(len(single_polygon) - 1):\n area += 0.5 * (x[i] * y[i + 1] - x[i + 1] * y[i])\n return abs(area)","title":""},{"docid":"434ed129e22036c18fab34a93b30950a","score":"0.60412085","text":"def getArea(self):\n return _libBornAgainCore.Polygon_getArea(self)","title":""},{"docid":"ae37851a036528138d3cb2fb34d5d55f","score":"0.60280424","text":"def polyArea(self, x, y=None):\r\n if self.isnone(y) and not shape(x)[1]==2:\r\n raise ValueError('input must be 2d!')\r\n elif self.isnone(y):\r\n y = x[:,0]\r\n x = x[:,1]\r\n elif len(shape(x))>1 and not shape(x)[1]==1:\r\n raise ValueError('\\'x\\' must be a 1d vector of first coordinates!')\r\n elif not len(x)==len(y):\r\n raise ValueError('\\'x\\' and \\'y\\' must be the same length!')\r\n else:\r\n x = reshape(x, len(x))\r\n y = reshape(y, len(x))\r\n \r\n return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))","title":""},{"docid":"9c3adb7a011c95dd55a6b2ccc5a39e28","score":"0.6014797","text":"def area_of_parallelogram_with(self, v):\n\t\treturn (self*v).magnitude","title":""},{"docid":"9bf29a6c63f8c31b9f1dd6468dab98d1","score":"0.59926504","text":"def test_polygon_area():\n result = polygon_area(15, sides=3)\n assert math.isclose(result, 6.495190528383289, abs_tol=1e-5)","title":""},{"docid":"24310e87683beb216ed9c44a1b6e13fc","score":"0.5974369","text":"def area(l, w):\n return l*w","title":""},{"docid":"080757f8762c508732d76684ea03c576","score":"0.5959136","text":"def area(self):\n return self.b.distance(self.a) * self.b.distance(self.c)","title":""},{"docid":"3b08f8003dde6bb4b0b652f1326a2bb3","score":"0.59469944","text":"def area(boxes):\n\n xmin, ymin, xmax, ymax = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]\n return (ymax - ymin) * (xmax - xmin)","title":""},{"docid":"10e4731f2ba426bb6296be6da1eb3f6e","score":"0.5918353","text":"def _vertices_barycenters_areas(self) -> np.ndarray:\n Avf = self.vertex_face_adjacency()\n faces_areas = self.areas\n return (1 / 3) * Avf * faces_areas","title":""},{"docid":"97b3b4868245e6fe35d5cc64e5675e14","score":"0.5886943","text":"def elem_area(xn):\n nnodes = len(xn[:,0]) # numero de nodos\n if nnodes == 3:\n # elemento triangular lineal\n x1, x2, x3 = xn[0, 0], xn[1, 0], xn[2, 0]\n y1, y2, y3 = xn[0, 1], xn[1, 1], xn[2, 1]\n A = 1/2*(x1*y2 - x1*y3 + x2*y3 - x2*y1 + x3*y1 - x3*y2)\n else:\n # elemento cuadrilateral bilineal\n x1, x2 = xn[0, 0], xn[1, 0]\n y1, y4 = xn[0, 1], xn[3, 1]\n a = 1/2 * (x2 - x1)\n b = 1/2 * (y4 - y1)\n A = 4*a*b\n \n return A","title":""},{"docid":"3cbe3a58c2c1ea14592c1e622f3d4a71","score":"0.5884567","text":"def area_of_polygon(geom):\n geod = pyproj.Geod(ellps=\"WGS84\")\n\n poly_area, poly_perimeter = geod.geometry_area_perimeter(\n geom\n )\n\n return abs(poly_area)","title":""},{"docid":"ea96d8031b42e389718a3d945ab96c05","score":"0.5877812","text":"def regular_polygon_area(perimeter, apothem):\n # You have to code here\n # REMEMBER: Tests first!!!\n area = (perimeter * apothem) / 2\n return area","title":""},{"docid":"db3f30c7319d62ed8c97810ac33ad819","score":"0.5867328","text":"def get_area(self):\n self._area = vector.length(self._vec_cross) * 0.5\n return self._area","title":""},{"docid":"b6f360327b485398eeff22f3cd230f6f","score":"0.58475584","text":"def area(self):\n self.area = ((3**0.5)/4)* self.__class__.edge_length**2\n return self.area","title":""},{"docid":"b20b55b6ee61050fe6fedbc56057e6f7","score":"0.58466566","text":"def triArea(base,height):\n return 0.5*base*height","title":""},{"docid":"efa452c36bfd5bee936705367af0f1e9","score":"0.5829293","text":"def area_of_square(side_length):\n return 0","title":""},{"docid":"f9dd43a0d71604c0a9ed91665fc6dadb","score":"0.58236367","text":"def area_4vector(self, jet):\n return _fastjet.ClusterSequenceVoronoiArea_area_4vector(self, jet)","title":""},{"docid":"b020f16239850044187cd26656b3eb2a","score":"0.5821677","text":"def get_area(self):\n return (self.length ** 2 * math.sqrt(3)) / 4","title":""},{"docid":"a748deb31d5fa5be35495e549d53069d","score":"0.5820992","text":"def area(self):\n return Fraction(1, 2) \\\n * self.edges \\\n * self.edge_length \\\n * self.apothem","title":""},{"docid":"998a906789149ba30c6d7eab5e832ef4","score":"0.5802728","text":"def get_area(self):\n coords = self.polygon.centroid.coords\n self.polygon.transform(EPSG_NY, clone=False)\n return self.polygon.area, coords","title":""},{"docid":"85c58d67239c22b45e6b24e94cc7a9c4","score":"0.5795838","text":"def area(x,y):\n return x * y","title":""},{"docid":"1f66cfe6c7107d0f46adc11f8cc7f4dd","score":"0.5773974","text":"def get_area_from_points(\n mesh: meshio.Mesh, points: List[int], min_value: float = 1e-10\n) -> float:\n a: np.ndarray\n b: np.ndarray\n c: np.ndarray\n a, b, c = mesh.points[points[0]], mesh.points[points[1]], mesh.points[points[2]]\n return max(0.5 * np.linalg.norm((np.cross(a - c, b - c))), min_value)","title":""},{"docid":"84fe01cc011adc230b6b00790a236e0b","score":"0.5737316","text":"def perimeter_triangle(a, b, c: Number) -> Number:\n return a + b + c","title":""},{"docid":"592825a105125e940a7a78e8c1b8e4e2","score":"0.57358825","text":"def triangle(self):\n\t\t# noinspection PyTypeChecker\n\t\treturn distance.squareform(self.squareform().values)","title":""},{"docid":"b50f521e0774d2bd1d0c83972cd711c7","score":"0.5726993","text":"def triangle_area(): \n print_title(\"OPTION 1-TRIANGLE AREA\")\n h = input_int(\"Triangle height: \")\n b = input_int(\"Triangle base: \")\n precision = input(\"Do you want Decimals?: [y/n]\")\n if precision == \"y\":\n r = (b * h) / 2\n else:\n r = (b * h) // 2\n print(f\"AREA: {r}\")","title":""},{"docid":"e004eca4920b218f61c2e77ebfe9aa45","score":"0.5710562","text":"def calculate_area(a: float, b: float, c: float):\n p = (a + b + c) / 2.0\n area = math.sqrt(p * (p - a) * (p - b) * (p - c))\n return area","title":""},{"docid":"8d37377186448db25cdc06998c1c6797","score":"0.57068974","text":"def trapezoid_area(base_minor, base_major, height):\n # You have to code here\n # REMEMBER: Tests first!!!\n area = height * ((base_minor + base_major)/ 2)\n return area","title":""},{"docid":"577cd84b19f63ee0964d7b4329d649eb","score":"0.56994534","text":"def area_rect(l, w):\n a = l * w\n return a","title":""},{"docid":"214fec990a0d7baa96a80c0f5d96efd0","score":"0.56805736","text":"def area_box(self):\n return (self.x_max - self.x_min) * (self.y_max - self.y_min)","title":""},{"docid":"08eb9112a80e6c06f3aa68c807bbc5ba","score":"0.56796795","text":"def _area(bboxes):\n size = bboxes[:, 2:4] - bboxes[:, 0:2]\n area = size[:, 0] * size[:, 1]\n return area","title":""},{"docid":"151b8dfbf83c2b216ab154ea5304fcc3","score":"0.5647638","text":"def area(self) -> float:\n return self.w * self.h","title":""},{"docid":"a9c99d1dfaa0d8eb2fd86b2c17d692b3","score":"0.56459033","text":"def NetPolygonsArea(polys):\n area = 0.0\n for i in range(len(polys)):\n area += SignedPolygonArea(polys[i])\n return area","title":""},{"docid":"68391f00de257e3966934c1481e8bed5","score":"0.564241","text":"def _get_triangleCount(self) -> \"int\" :\n return _fusion.PolygonMesh__get_triangleCount(self)","title":""},{"docid":"785077d51f4491d96c6f94911ebb05ed","score":"0.56256735","text":"def area(self):\r\n return (self.ymax - self.ymin) * (self.xmax - self.xmin)","title":""},{"docid":"c0de8bf31616b557df3cc13f8696d22c","score":"0.5624591","text":"def _ComputeArea(cutPoly):\n area = []\n for i in range(cutPoly.GetNumberOfCells()):\n tri=polydata.GetCell(i)\n points=tri.GetPoints()\n area.append(tri.ComputeArea())\n\n print('Area is {}'.format(sum(area)))\n print('Diameter is {}'.format(sqrt(4*sum(area)/pi**2)))","title":""},{"docid":"9b0034de00bf9744946176ffb81e7ccb","score":"0.56236637","text":"def area(self):\n return len(self.coordinates)","title":""},{"docid":"ccc1ad5a37fc0aa7bcac87e7b6b63818","score":"0.56100273","text":"def rotatedRectangularArea(self):\n return polygonArea(self.rotatedBox)","title":""},{"docid":"6ed77555c1c0fa93a1e2d7d36137471e","score":"0.5603313","text":"def _faces_areas(self) -> np.ndarray:\n vertices = np.array(self.v)\n faces = np.array(self.f)\n faces_vertices = [\n (\n vertices[faces[f][0], :],\n vertices[faces[f][1], :],\n vertices[faces[f][2], :],\n )\n for f in range(faces.shape[0])]\n\n triangles = np.concatenate([\n np.expand_dims(np.array((\n np.sqrt(np.sum(np.power((tri[0] - tri[1]), 2))),\n np.sqrt(np.sum(np.power((tri[0] - tri[2]), 2))),\n np.sqrt(np.sum(np.power((tri[1] - tri[2]), 2))),\n )), 0) for tri in faces_vertices], 0)\n\n # Heron's formula\n s = np.sum(triangles, 1) / 2\n\n # area of each face\n areas = np.array([np.sqrt((s[t] * (s[t] - triangles[t, 0]) * (s[t] - triangles[t, 1]) * (s[t] - triangles[t, 2])))\n for t, tri in enumerate(triangles)])\n return areas","title":""},{"docid":"384cdd28cb3a64c46de6d8dfdd481b3b","score":"0.56016064","text":"def area(self, jet):\n return _fastjet.ClusterSequenceVoronoiArea_area(self, jet)","title":""},{"docid":"d47956cd22e845a7b683d17705b78307","score":"0.5575096","text":"def panels_area(tris):\n Ni = np.cross( tris[::,1 ] - tris[::,0] , tris[::,2 ] - tris[::,0] )\n Area = np.sqrt(Ni[:,0]**2 + Ni[:,1]**2 + Ni[:,2]**2) * 0.5\n \n return Area","title":""},{"docid":"16f0e81cc1f4d7d27bcedb076fa93cff","score":"0.55578417","text":"def compute_square_area(side):\n return side ** 2","title":""},{"docid":"92365ea6439c9c55f499e2bd23b08402","score":"0.5556692","text":"def _area(vset):\n # Initialize A as it could be calculated iteratively\n A = 0\n # Check multiple exteriors\n if type(vset[0][0]) == list:\n # Calc every exterior separately\n for i in range(len(vset)):\n A += pyclipper.scale_from_clipper(pyclipper.scale_from_clipper(pyclipper.Area(pyclipper.scale_to_clipper(vset[i]))))\n else:\n # Single exterior\n A = pyclipper.scale_from_clipper(pyclipper.scale_from_clipper(pyclipper.Area(pyclipper.scale_to_clipper(vset))))\n return A","title":""},{"docid":"d388d7f51da6988d3fac85e55cd10151","score":"0.5549856","text":"def surface_area(self):\n l, w, h = self.all\n return 2 * l * w + 2 * w * h + 2 * l * h","title":""},{"docid":"dae9bde064f6d9940845fd0779cbb040","score":"0.554556","text":"def _triangle_vertices(self, angle, center=0):\n\n angle = radians(self._hue_degrees)\n hx = floor(center + 0.5 + (cos(angle) * self._triangle_radius))\n hy = floor(center + 0.5 - (sin(angle) * self._triangle_radius))\n sx = floor(center + 0.5 + (cos(angle + (2.0 * pi / 3.0)) * self._triangle_radius))\n sy = floor(center + 0.5 - (sin(angle + (2.0 * pi / 3.0)) * self._triangle_radius))\n vx = floor(center + 0.5 + (cos(angle + (4.0 * pi / 3.0)) * self._triangle_radius))\n vy = floor(center + 0.5 - (sin(angle + (4.0 * pi / 3.0)) * self._triangle_radius))\n\n return hx, hy, sx, sy, vx, vy","title":""},{"docid":"15b3baa328c9318a8e151c811bc69364","score":"0.55320925","text":"def area_rectangle(length, width) -> Number:\n return length*width","title":""},{"docid":"07366dac9361fdbce0e8066efcf00d2b","score":"0.55202895","text":"def cuboid_surface_area(length, width, height):\n return 2*(length*width+length*height+width*height)","title":""},{"docid":"03de21e137b65cf4abaab224cb89dfa1","score":"0.5515626","text":"def geometry_area_weights(cube, geometry, normalize=False):\r\n # Validate the input parameters\r\n if not cube.coords(axis='x') or not cube.coords(axis='y'):\r\n raise ValueError('The cube must contain x and y axes.')\r\n\r\n x_coords = cube.coords(axis='x')\r\n y_coords = cube.coords(axis='y')\r\n if len(x_coords) != 1 or len(y_coords) != 1:\r\n raise ValueError('The cube must contain one, and only one, coordinate for each of the x and y axes.')\r\n\r\n x_coord = x_coords[0]\r\n y_coord = y_coords[0]\r\n if not (x_coord.has_bounds() and y_coord.has_bounds()):\r\n raise ValueError('Both horizontal coordinates must have bounds.')\r\n\r\n if x_coord.ndim != 1:\r\n raise iris.exceptions.CoordinateMultiDimError(x_coord)\r\n if y_coord.ndim != 1:\r\n raise iris.exceptions.CoordinateMultiDimError(y_coord)\r\n\r\n # Figure out the shape of the horizontal dimensions\r\n shape = [1] * len(cube.shape)\r\n x_dim = cube.coord_dims(x_coord)[0]\r\n y_dim = cube.coord_dims(y_coord)[0]\r\n shape[x_dim] = x_coord.shape[0]\r\n shape[y_dim] = y_coord.shape[0]\r\n weights = np.empty(shape, np.float32)\r\n\r\n # Calculate the area weights\r\n x_bounds = x_coord.bounds\r\n y_bounds = y_coord.bounds\r\n for nd_index in np.ndindex(weights.shape):\r\n xi = nd_index[x_dim]\r\n yi = nd_index[y_dim]\r\n x0, x1 = x_bounds[xi]\r\n y0, y1 = y_bounds[yi]\r\n polygon = Polygon([(x0, y0), (x0, y1), (x1, y1), (x1, y0)])\r\n if normalize:\r\n weights[nd_index] = polygon.intersection(geometry).area / polygon.area\r\n else:\r\n weights[nd_index] = polygon.intersection(geometry).area\r\n\r\n # Fix for the limitation of iris.analysis.MEAN weights handling.\r\n # Broadcast the array to the full shape of the cube\r\n weights = np.broadcast_arrays(weights, cube.data)[0]\r\n\r\n return weights","title":""},{"docid":"c5dd08439deb56fc5730ac6d0cd5c625","score":"0.5493793","text":"def area(self) -> np.float64:\n return 0.5 * self.normal().norm()","title":""},{"docid":"91c28923172d53c582927b6915bfb53e","score":"0.5489234","text":"def area_trapezoid(base_1, base_2, height) -> Number:\n return 1/2*(base_1 + base_2)*height","title":""},{"docid":"5d63a0cd5fc1091ee830998703a7cc72","score":"0.5483533","text":"def area_of(self, left_top, right_bottom):\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]","title":""},{"docid":"286e67fe41b4c7e219d124457fb4d3c4","score":"0.5470709","text":"def SignedPolygonArea(poly):\n n = len(poly) # of poly\n area = 0.0\n for i in range(n):\n j = (i + 1) % n\n area += poly[i][0] * poly[j][1]\n area -= poly[j][0] * poly[i][1]\n area = area / 2.0\n\n\n if np.isfinite(area):\n if area > 0:\n cornercorrection = 0.5\n elif area == 0:\n cornercorrection = 0.\n elif area < 0:\n cornercorrection = -0.5\n return area + cornercorrection\n else: \n print(\"[polytools] WARNING nan area encountered\")\n return 0.","title":""},{"docid":"1cfff9454f645495b5392315dd9512a3","score":"0.5466223","text":"def _e_area(a):\r\n x0, y1 = (a.T)[:, 1:]\r\n x1, y0 = (a.T)[:, :-1]\r\n e0 = np.einsum('...i,...i->...i', x0, y0)\r\n e1 = np.einsum('...i,...i->...i', x1, y1)\r\n return np.nansum((e0-e1)*0.5)","title":""}],"string":"[\n {\n \"docid\": \"ce87c52ea5d3ae5d6cadd5a7d6f1c8a6\",\n \"score\": \"0.8274563\",\n \"text\": \"def area_of_triangle(*args):\\n if len(args) != 1:\\n print('ERROR: one argument expected, got {}.'.format(len(args)))\\n return None\\n vertexes = args[0]\\n if not isinstance(vertexes, list):\\n print('ERROR: list of vertexes expected as an argument, got {}.'.format(type(vertexes)))\\n return None\\n if len(vertexes) != 6:\\n print('ERROR: 6 coordinates expected, got {}.'.format(len(vertexes)))\\n return None\\n for coordinate in vertexes:\\n if not (isinstance(coordinate, int) or isinstance(coordinate, float)):\\n print('ERROR: type int or float expected, got {}.'.format(type(coordinate)))\\n return None\\n ver = [[vertexes[0], vertexes[1]], [vertexes[2], vertexes[3]], [vertexes[4], vertexes[5]]]\\n a = distance(ver[0], ver[1])\\n b = distance(ver[1], ver[2])\\n c = distance(ver[2], ver[0])\\n p = (a + b + c) / 2\\n return (p * (p - a) * (p - b) * (p - c)) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3537ea3fce9eaa695688c0a9bd4ba9\",\n \"score\": \"0.7953434\",\n \"text\": \"def get_area(vertices):\\n area = 0.0\\n for i in range(len(vertices)):\\n j = (i + 1) % len(vertices)\\n area += vertices[i][0] * vertices[j][1] - vertices[j][0] * vertices[i][1]\\n return abs(area) / 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9e7e7fcb60d5d687bc91ed0ca60cf0\",\n \"score\": \"0.78157234\",\n \"text\": \"def polygon_area(vertices: Sequence[Sequence[float]]) -> float:\\n if len(vertices) == 3:\\n # Specialized, fast implementation for triangles, since this is heavily\\n # used in `smallest_enclosing_circle`\\n dx1 = vertices[1][0] - vertices[0][0]\\n dy1 = vertices[1][1] - vertices[0][1]\\n dx2 = vertices[2][0] - vertices[1][0]\\n dy2 = vertices[2][1] - vertices[1][1]\\n return (dx1 * dy2 - dy1 * dx2) / 2\\n\\n x, y = np.vstack((vertices[-1], vertices)).T\\n return np.sum((x[1:] + x[:-1]) * (y[1:] - y[:-1])) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb866be487eb3b7cad6ce2f957b0d0c\",\n \"score\": \"0.757878\",\n \"text\": \"def area_of_triangle(x1, y1, x2, y2, x3, y3):\\n area = abs((x1 * (y2 - y3) + x2 * (y3 - y1) + x3 * (y1 - y2)) / 2.0)\\n\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639d2fa5d4b924aa263a32215afbe1a8\",\n \"score\": \"0.74019337\",\n \"text\": \"def area_of_triangle_with(self, v):\\n\\t\\treturn .5*self.area_of_parallelogram(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c121e4114d613be50ab749deff4081b2\",\n \"score\": \"0.7401034\",\n \"text\": \"def triangleArea(a, b, c):\\n\\treturn (a[0]*b[1] - a[1]*b[0] + a[1]*c[0]\\n\\t\\t\\t- a[0]*c[1] + b[0]*c[1] - c[0]*b[1]) / 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1038134adbd7b858778ca4ab99e5a5\",\n \"score\": \"0.7334358\",\n \"text\": \"def area_of_triangle(base, height):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322df138966e862ab85aac6336a18cc3\",\n \"score\": \"0.7289267\",\n \"text\": \"def polygon_area(coords):\\n Xs = coords[:,0]\\n Ys = coords[:,1]\\n\\n # Ignore orientation\\n return 0.5*abs(sum(Xs[:-1]*Ys[1:] - Xs[1:]*Ys[:-1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3bd8e34edd08d3fd4530f77f79ecca\",\n \"score\": \"0.7232928\",\n \"text\": \"def area_for_poly(vertices, radius=0):\\r\\n vs = list(map(tuple, vertices))\\r\\n return cp.cpAreaForPoly(len(vertices), vs, radius)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912743698ebe28c24a622abef68b6657\",\n \"score\": \"0.71538067\",\n \"text\": \"def triangle_area(base, height):\\n # You have to code here\\n # REMEMBER: Tests first!!!\\n area = (base * height) / 2\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718d4216605cbec2c7ed2d2b1e0d013a\",\n \"score\": \"0.71321255\",\n \"text\": \"def triangle_area(a, h):\\n return (a*h)/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda08222a39e5f48bf5397c18209b2dd\",\n \"score\": \"0.7088057\",\n \"text\": \"def area_of_triangle(p1, p2, p3):\\n return norm(cross((p2 - p1), (p3 - p1))) / 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bcc1aaebdc0c7793e824d3218898414\",\n \"score\": \"0.69998634\",\n \"text\": \"def calculate_triangle_area(base, height):\\n return 0.5 * base * height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"432bb08ac25c321de1d54a602f9524da\",\n \"score\": \"0.69147646\",\n \"text\": \"def square_of_triangle(vertexes):\\n if not isinstance(vertexes, list):\\n print('ERROR: list of vertexes expected as an argument, got {}'.format(type(vertexes)))\\n return None\\n if len(vertexes) != 6:\\n print('ERROR: 6 coordinates expected, got {}'.format(len(vertexes)))\\n return None\\n for coordinate in vertexes:\\n if not (isinstance(coordinate, int) or isinstance(coordinate, float)):\\n print('ERROR: type int or float expected, got {}'.format(type(coordinate)))\\n return None\\n a = ((vertexes[0] - vertexes[2]) ** 2 + (vertexes[1] - vertexes[3]) ** 2) ** 0.5\\n b = ((vertexes[2] - vertexes[4]) ** 2 + (vertexes[3] - vertexes[5]) ** 2) ** 0.5\\n c = ((vertexes[0] - vertexes[4]) ** 2 + (vertexes[1] - vertexes[5]) ** 2) ** 0.5\\n p = (a + b + c) / 2\\n return (p * (p - a) * (p - b) * (p - c)) ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fce82b2dcd8c55b00edd24be3dbc2f8\",\n \"score\": \"0.6842898\",\n \"text\": \"def areaTriangle(ptx1,pty1,ptx2,pty2,ptx3,pty3):\\r\\n # This uses Heron's Formula - Google it if you want to know more\\r\\n side1 = distance(ptx1,pty1,ptx2,pty2)\\r\\n side2 = distance(ptx2,pty2,ptx3,pty3)\\r\\n side3 = distance(ptx3,pty3,ptx1,pty1)\\r\\n p = (side1 + side2 + side3)/2\\r\\n t1 = p-side1\\r\\n t2 = p-side2\\r\\n t3 = p-side2\\r\\n if t1==0 or t2==0 or t3==0:\\r\\n print(\\\"Does not form a triangle\\\")\\r\\n return None\\r\\n area = math.sqrt( p*(p-side1)*(p-side2)*(p-side3) )\\r\\n return(area)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53741ada61b2749f4f8c694b22353f84\",\n \"score\": \"0.6834161\",\n \"text\": \"def triangle(vert_a, vert_b, vert_c):\\n vert_a_x, vert_a_y = vert_a\\n vert_b_x, vert_b_y = vert_b\\n vert_c_x, vert_c_y = vert_c\\n a_tri = abs((vert_a_x * (vert_b_y - vert_c_y) +\\n vert_b_x * (vert_c_y - vert_a_y) +\\n vert_c_x * (vert_a_y - vert_b_y)) / 2.0)\\n # area = (1/2)b*h\\n # /\\\\A\\n # / \\\\\\n # / \\\\\\n # B/______\\\\C\\n\\n return a_tri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10a13cdcbdcdae3ccb7370d09b886bc\",\n \"score\": \"0.6788807\",\n \"text\": \"def triangle_area(base=None, height=None, side_a=None, side_b=None, side_c=None):\\n if (base is not None) & (height is not None):\\n return 0.5 * base * height\\n elif (side_a is not None) & (side_b is not None) & (side_c is not None):\\n if (side_a + side_b) > side_c & (side_b + side_c) > side_a & (side_a + side_c) > side_b:\\n s = (side_a + side_b + side_c) * 0.5\\n return (s*(s-side_a)*(s-side_b)*(s-side_c)) ** 0.5\\n else:\\n return\\\"The sum of two sides must be greater than the third side of a triangle\\\"\\n else:\\n return \\\"no such triangle\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b888b0733977f59da0f0d0aed909e12d\",\n \"score\": \"0.67785037\",\n \"text\": \"def poly_area(verts):#Tartley - Jonathan Hartley, http://tartley.com\\n\\taccum = 0.0\\n\\tfor i in range(len(verts)):\\n\\t\\tj = (i + 1) % len(verts)\\n\\t\\taccum += verts[j][0] * verts[i][1] - verts[i][0] * verts[j][1]\\n\\treturn accum / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726b1bd7ca7feb04765715b09876e7aa\",\n \"score\": \"0.6737634\",\n \"text\": \"def compute_tri_area(side):\\n return side * side * math.sqrt(3)/4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e30e76b185b71cb7c0984721e3e715\",\n \"score\": \"0.67302614\",\n \"text\": \"def get_area_triangle(p1, p2, p3):\\n det_jac = (p1[0] - p3[0]) * (p2[1] - p3[1]) - (p2[0] - p3[0]) * (p1[1] - p3[1])\\n return 0.5 * abs(det_jac)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"196547415b5ca299c2f8e3725bde47e7\",\n \"score\": \"0.67248505\",\n \"text\": \"def calculate_area(coords):\\r\\n return max(0, coords[1] - coords[0]) * max(0, coords[3] - coords[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058642431a02403e92e2ba67c9028891\",\n \"score\": \"0.672136\",\n \"text\": \"def get_area(self):\\n\\n # We use the \\\"shoelace\\\" algorithm to calculate the area, since the\\n # polygon has no holes or self-intersections.\\n s1 = s2 = 0\\n for i in range(self.n):\\n j = (i+1) % self.n\\n s1 += self.vertices[i].x * self.vertices[j].y\\n s2 += self.vertices[i].y * self.vertices[j].x\\n return abs(s1 - s2) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e34d37ead553aa7b651813d03a1437\",\n \"score\": \"0.6668018\",\n \"text\": \"def polygon_area(points):\\n res = 0\\n for i in range(len(points)):\\n j = (i+1) % len(points)\\n res = res + points[i].x * points[j].y\\n res = res - points[j].x * points[i].y\\n return abs(res) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10c3e647d3b5eb263df75040d991580\",\n \"score\": \"0.666083\",\n \"text\": \"def triangle_area(height=None, base=None) -> Number:\\n if dim_validate3(base) and dim_validate3(height):\\n if dim_validate2(base) and dim_validate2(height):\\n if dim_validate1(base) and dim_validate1(height):\\n return base * height / 2\\n else:\\n raise ValueError(\\\"side must be positive: \\\")\\n raise TypeError('value is a string')\\n raise AttributeError(\\\"The dimension is not provided\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cadaf8de68f99cd322dfbca611f6f5\",\n \"score\": \"0.66544366\",\n \"text\": \"def area(vs):\\n a = 0\\n x0, y0 = vs[0]\\n for [x1, y1] in vs[1:]:\\n dy = y1 - y0\\n a += x0*dy\\n x0 = x1\\n y0 = y1\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb71486155d37251644a40732f1a369b\",\n \"score\": \"0.6585298\",\n \"text\": \"def triangle_area(breadth, height: Number) -> Number:\\n return 0.5*breadth*height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d25fb3d05464f4a863121e998417b4\",\n \"score\": \"0.6577138\",\n \"text\": \"def area_triangle(b, h: Number) -> Number:\\n return 1/2*b*h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3708370821e0c77c95fd606c8af6e9f\",\n \"score\": \"0.6539446\",\n \"text\": \"def heron_triangle_area_sides(a, b, c):\\n if triangle_exists(a, b, c):\\n return 0.25*math.sqrt((a + (b + c))*(c - (a - b))*(c + (a - b))*(a + (b - c)))\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5afeb340572a8e455cf98e66681a7c02\",\n \"score\": \"0.6485532\",\n \"text\": \"def calculate_area(pts):\\n pts = perspective.order_points(pts)\\n a = euclid_dist(pts[2], pts[3])\\n b = euclid_dist(pts[3], pts[0])\\n c = euclid_dist(pts[0], pts[1])\\n d = euclid_dist(pts[1], pts[2])\\n t = 0.5 * (a + b + c + d)\\n angle1 = angle_betw_lines(\\n np.array([pts[2], pts[3]]), np.array([pts[3], pts[0]]))\\n angle2 = angle_betw_lines(\\n np.array([pts[0], pts[1]]), np.array([pts[1], pts[2]]))\\n area = math.sqrt(((t - a) * (t - b) * (t - c) * (t - d))\\n - (a * b * c * d * ((math.cos((angle1 + angle2)/2)) ** 2)))\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46a305074f292a2cc456664802e70bd\",\n \"score\": \"0.6464238\",\n \"text\": \"def line_vertex_area(X, L):\\n a = line_edge_area(X, L)\\n _, ind0 = np.unique(L[:,0], return_index=True)\\n _, ind1 = np.unique(L[:,1], return_index=True)\\n ind = np.column_stack((ind0,ind1))\\n a = np.sum(a[ind], axis=1) / 2\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1d20bf728ff68ccae47672fdfc363b\",\n \"score\": \"0.6463007\",\n \"text\": \"def triangleArea(triangleSet):\\n\\n\\ttriangleAreaSet = []\\n\\n\\tfor i in range(len(triangleSet)):\\n\\t\\tv1 = triangleSet[i][1] - triangleSet[i][0]\\n\\t\\tv2 = triangleSet[i][2] - triangleSet[i][0]\\n\\t\\tarea = np.linalg.norm(np.cross(v1, v2))/2\\n\\t\\ttriangleAreaSet.append(area)\\n\\n\\treturn triangleAreaSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe04a806cf7cd09bb431b73441692284\",\n \"score\": \"0.6447586\",\n \"text\": \"def polygon_area(polygon):\\n\\n w=0\\n for count in range(len(polygon)-1):\\n y = polygon[count+1][1] + polygon[count][1]\\n x = polygon[count+1][0] - polygon[count][0]\\n z = y * x\\n w += z\\n return abs(w/2.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d07499360c23269b42c987a1d62f28f\",\n \"score\": \"0.6436004\",\n \"text\": \"def area(self) -> float:\\n p = 0.5 * sum(self.sides)\\n a, b, c = self.sides\\n return math.sqrt(p * (p - a) * (p - b) * (p - c))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c03ef9f25e74907b28598356fd06c8\",\n \"score\": \"0.6430339\",\n \"text\": \"def area_triangle(x=None, y=None, z=None, h=None, theta=None) ->Number:\\n if (x is not None) & (y is not None) & (z is not None):\\n s = (x + y + z)/2\\n area = (s*(s - x)*(s - y)*(s - z))**(1/2)\\n return area\\n elif (x is not None) & (y is not None) & (theta is not None):\\n area = 1/2*x*y*sin(theta)\\n return area\\n elif (x is not None) & (h is not None):\\n area = x*h/2\\n return area\\n else:\\n return \\\"the argument given are inadequate to compute area\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61db44bdfd46b354c352239b44b10203\",\n \"score\": \"0.64260465\",\n \"text\": \"def polygon_center(vertices: Sequence[Sequence[float]],\\n area: Optional[float] = None) -> Tuple[float, float]:\\n if area is None:\\n area = polygon_area(vertices)\\n\\n x, y = np.vstack((vertices[-1], vertices)).T\\n fact = x[:-1] * y[1:] - x[1:] * y[:-1]\\n x_c = np.sum((x[:-1] + x[1:]) * fact) / (6 * area)\\n y_c = np.sum((y[:-1] + y[1:]) * fact) / (6 * area)\\n\\n return x_c, y_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f36582619a174b292f21e15bb1af89c\",\n \"score\": \"0.6413507\",\n \"text\": \"def polygon_area(corners):\\n n = len(corners) # of corners\\n area = 0.0\\n for i in range(n):\\n j = (i + 1) % n\\n area += corners[i][0] * corners[j][1]\\n area -= corners[j][0] * corners[i][1]\\n area = abs(area) / 2.0\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e47b75ca6c90c9289deed7edb6e025d\",\n \"score\": \"0.64095837\",\n \"text\": \"def trapezoidal_area(xyz):\\n d = scipy.spatial.Delaunay(xyz[:,:2])\\n tri = xyz[d.vertices]\\n\\n a = tri[:,0,:2] - tri[:,1,:2]\\n b = tri[:,0,:2] - tri[:,2,:2]\\n proj_area = np.cross(a, b).sum(axis=-1)\\n zavg = tri[:,:,2].sum(axis=1)\\n vol = zavg * np.abs(proj_area) / 6.0\\n return vol.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e47b75ca6c90c9289deed7edb6e025d\",\n \"score\": \"0.64095837\",\n \"text\": \"def trapezoidal_area(xyz):\\n d = scipy.spatial.Delaunay(xyz[:,:2])\\n tri = xyz[d.vertices]\\n\\n a = tri[:,0,:2] - tri[:,1,:2]\\n b = tri[:,0,:2] - tri[:,2,:2]\\n proj_area = np.cross(a, b).sum(axis=-1)\\n zavg = tri[:,:,2].sum(axis=1)\\n vol = zavg * np.abs(proj_area) / 6.0\\n return vol.sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c499fb1dbc9aa22065e28f35df4cef\",\n \"score\": \"0.63906235\",\n \"text\": \"def triangleSignedAreaE2(A, B, C):\\n return 0.5 * (A.x * (B.y - C.y) + A.y * (C.x - B.x) + (B.x * C.y - B.y * C.x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9130dc322cdfc40a30028ae6ab5b1361\",\n \"score\": \"0.6388903\",\n \"text\": \"def get_triangle_area(self, other):\\n return self.get_parallelogram_area(other) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a263c5abdabb27b6b0b6554d6d8100\",\n \"score\": \"0.63794124\",\n \"text\": \"def get_area_polygon(polygon): \\n area_outer = get_area_single_polygon(polygon[0])\\n area_inner = 0\\n for poly in polygon[1:]:\\n area_inner += get_area_single_polygon(poly)\\n return area_outer - area_inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a2b3f87aa3f70661d49bdcbaf94547\",\n \"score\": \"0.63707113\",\n \"text\": \"def EqTriArea(side):\\n return ((3**0.5) / 4) * (side**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564722d326ad4c414fcf7616910d4dc5\",\n \"score\": \"0.63513863\",\n \"text\": \"def area(x1, y1, x2, y2, x3, y3): \\n return abs((x1 * (y2 - y3) + x2 * (y3 - y1) \\n + x3 * (y1 - y2)) / 2.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08d601b818be6249b4f136671d3076a\",\n \"score\": \"0.6299496\",\n \"text\": \"def area(self):\\n # Length x breadth\\n if self.is_valid():\\n return self.sides[0] * self.sides[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11ccf4ee16a69f294b378f04c788a00\",\n \"score\": \"0.6232965\",\n \"text\": \"def getArea(self):\\n a, b, c = self.a, self.b, self.c\\n return abs(a.x*(b.y-c.y)+b.x*(c.y-a.y)+c.x*(a.y-b.y))/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb8757be74feed95fd61d974d19e0e6f\",\n \"score\": \"0.619349\",\n \"text\": \"def largestTriangleArea(self, # pylint: disable=invalid-name\\n\\t\\t\\t\\t\\t\\t\\tpoints: Points) -> float:\\n\\t\\tconvex_hull = self.get_convex_hull(points)\\n\\n\\t\\tmax_triangle_area = 0\\n\\t\\tconvex_hull_len = len(convex_hull)\\n\\n\\t\\tfor idx1 in range(convex_hull_len):\\n\\t\\t\\t# Combine idx1 with every index to its right to avoid repeats\\n\\t\\t\\tfor idx2 in range(idx1, convex_hull_len):\\n\\t\\t\\t\\t# Combine idx2 with every index to its right to avoid repeats\\n\\t\\t\\t\\tfor idx3 in range(idx2, convex_hull_len):\\n\\t\\t\\t\\t\\ttriple = (\\n\\t\\t\\t\\t\\t\\tconvex_hull[idx1],\\n\\t\\t\\t\\t\\t\\tconvex_hull[idx2],\\n\\t\\t\\t\\t\\t\\tconvex_hull[idx3]\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\ttriangle_area = self.get_triangle_area(*triple)\\n\\t\\t\\t\\t\\tmax_triangle_area = max(max_triangle_area, triangle_area)\\n\\n\\t\\treturn max_triangle_area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec86d9c791eac13f45e6c898853c20e1\",\n \"score\": \"0.6110272\",\n \"text\": \"def area(self):\\n s = self.perimeter() / 2.0\\n return sqrt(s * (s - self.a) * (s - self.b) * (s - self.c))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab897ec99d62d5ef82651f8379ef969f\",\n \"score\": \"0.6084797\",\n \"text\": \"def area_of_polygon_wrt_point(self, centroid):\\n x, y = [i for i in centroid]\\n\\n area_of_polygon = 0.0\\n\\n for i in range(len(self._coords) - 1):\\n x1, y1 = [i for i in self._coords[i]]\\n x2, y2 = [i for i in self._coords[i + 1]]\\n area_of_polygon += self.area_of_triangle(x, y, x1, y1, x2, y2)\\n\\n x1, y1 = [i for i in self._coords[-1]]\\n x2, y2 = [i for i in self._coords[0]]\\n\\n area_of_polygon += self.area_of_triangle(x, y, x1, y1, x2, y2)\\n\\n return area_of_polygon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12ac51e8fd6eaa74c0bdae55789723d\",\n \"score\": \"0.6058393\",\n \"text\": \"def get_area_single_polygon(single_polygon):\\n x,y = [c[0] for c in single_polygon], [c[1] for c in single_polygon]\\n area = 0\\n for i in range(len(single_polygon) - 1):\\n area += 0.5 * (x[i] * y[i + 1] - x[i + 1] * y[i])\\n return abs(area)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434ed129e22036c18fab34a93b30950a\",\n \"score\": \"0.60412085\",\n \"text\": \"def getArea(self):\\n return _libBornAgainCore.Polygon_getArea(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae37851a036528138d3cb2fb34d5d55f\",\n \"score\": \"0.60280424\",\n \"text\": \"def polyArea(self, x, y=None):\\r\\n if self.isnone(y) and not shape(x)[1]==2:\\r\\n raise ValueError('input must be 2d!')\\r\\n elif self.isnone(y):\\r\\n y = x[:,0]\\r\\n x = x[:,1]\\r\\n elif len(shape(x))>1 and not shape(x)[1]==1:\\r\\n raise ValueError('\\\\'x\\\\' must be a 1d vector of first coordinates!')\\r\\n elif not len(x)==len(y):\\r\\n raise ValueError('\\\\'x\\\\' and \\\\'y\\\\' must be the same length!')\\r\\n else:\\r\\n x = reshape(x, len(x))\\r\\n y = reshape(y, len(x))\\r\\n \\r\\n return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3adb7a011c95dd55a6b2ccc5a39e28\",\n \"score\": \"0.6014797\",\n \"text\": \"def area_of_parallelogram_with(self, v):\\n\\t\\treturn (self*v).magnitude\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf29a6c63f8c31b9f1dd6468dab98d1\",\n \"score\": \"0.59926504\",\n \"text\": \"def test_polygon_area():\\n result = polygon_area(15, sides=3)\\n assert math.isclose(result, 6.495190528383289, abs_tol=1e-5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24310e87683beb216ed9c44a1b6e13fc\",\n \"score\": \"0.5974369\",\n \"text\": \"def area(l, w):\\n return l*w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080757f8762c508732d76684ea03c576\",\n \"score\": \"0.5959136\",\n \"text\": \"def area(self):\\n return self.b.distance(self.a) * self.b.distance(self.c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b08f8003dde6bb4b0b652f1326a2bb3\",\n \"score\": \"0.59469944\",\n \"text\": \"def area(boxes):\\n\\n xmin, ymin, xmax, ymax = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]\\n return (ymax - ymin) * (xmax - xmin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e4731f2ba426bb6296be6da1eb3f6e\",\n \"score\": \"0.5918353\",\n \"text\": \"def _vertices_barycenters_areas(self) -> np.ndarray:\\n Avf = self.vertex_face_adjacency()\\n faces_areas = self.areas\\n return (1 / 3) * Avf * faces_areas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b3b4868245e6fe35d5cc64e5675e14\",\n \"score\": \"0.5886943\",\n \"text\": \"def elem_area(xn):\\n nnodes = len(xn[:,0]) # numero de nodos\\n if nnodes == 3:\\n # elemento triangular lineal\\n x1, x2, x3 = xn[0, 0], xn[1, 0], xn[2, 0]\\n y1, y2, y3 = xn[0, 1], xn[1, 1], xn[2, 1]\\n A = 1/2*(x1*y2 - x1*y3 + x2*y3 - x2*y1 + x3*y1 - x3*y2)\\n else:\\n # elemento cuadrilateral bilineal\\n x1, x2 = xn[0, 0], xn[1, 0]\\n y1, y4 = xn[0, 1], xn[3, 1]\\n a = 1/2 * (x2 - x1)\\n b = 1/2 * (y4 - y1)\\n A = 4*a*b\\n \\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbe3a58c2c1ea14592c1e622f3d4a71\",\n \"score\": \"0.5884567\",\n \"text\": \"def area_of_polygon(geom):\\n geod = pyproj.Geod(ellps=\\\"WGS84\\\")\\n\\n poly_area, poly_perimeter = geod.geometry_area_perimeter(\\n geom\\n )\\n\\n return abs(poly_area)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea96d8031b42e389718a3d945ab96c05\",\n \"score\": \"0.5877812\",\n \"text\": \"def regular_polygon_area(perimeter, apothem):\\n # You have to code here\\n # REMEMBER: Tests first!!!\\n area = (perimeter * apothem) / 2\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3f30c7319d62ed8c97810ac33ad819\",\n \"score\": \"0.5867328\",\n \"text\": \"def get_area(self):\\n self._area = vector.length(self._vec_cross) * 0.5\\n return self._area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f360327b485398eeff22f3cd230f6f\",\n \"score\": \"0.58475584\",\n \"text\": \"def area(self):\\n self.area = ((3**0.5)/4)* self.__class__.edge_length**2\\n return self.area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20b55b6ee61050fe6fedbc56057e6f7\",\n \"score\": \"0.58466566\",\n \"text\": \"def triArea(base,height):\\n return 0.5*base*height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa452c36bfd5bee936705367af0f1e9\",\n \"score\": \"0.5829293\",\n \"text\": \"def area_of_square(side_length):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9dd43a0d71604c0a9ed91665fc6dadb\",\n \"score\": \"0.58236367\",\n \"text\": \"def area_4vector(self, jet):\\n return _fastjet.ClusterSequenceVoronoiArea_area_4vector(self, jet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b020f16239850044187cd26656b3eb2a\",\n \"score\": \"0.5821677\",\n \"text\": \"def get_area(self):\\n return (self.length ** 2 * math.sqrt(3)) / 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a748deb31d5fa5be35495e549d53069d\",\n \"score\": \"0.5820992\",\n \"text\": \"def area(self):\\n return Fraction(1, 2) \\\\\\n * self.edges \\\\\\n * self.edge_length \\\\\\n * self.apothem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998a906789149ba30c6d7eab5e832ef4\",\n \"score\": \"0.5802728\",\n \"text\": \"def get_area(self):\\n coords = self.polygon.centroid.coords\\n self.polygon.transform(EPSG_NY, clone=False)\\n return self.polygon.area, coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c58d67239c22b45e6b24e94cc7a9c4\",\n \"score\": \"0.5795838\",\n \"text\": \"def area(x,y):\\n return x * y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f66cfe6c7107d0f46adc11f8cc7f4dd\",\n \"score\": \"0.5773974\",\n \"text\": \"def get_area_from_points(\\n mesh: meshio.Mesh, points: List[int], min_value: float = 1e-10\\n) -> float:\\n a: np.ndarray\\n b: np.ndarray\\n c: np.ndarray\\n a, b, c = mesh.points[points[0]], mesh.points[points[1]], mesh.points[points[2]]\\n return max(0.5 * np.linalg.norm((np.cross(a - c, b - c))), min_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fe01cc011adc230b6b00790a236e0b\",\n \"score\": \"0.5737316\",\n \"text\": \"def perimeter_triangle(a, b, c: Number) -> Number:\\n return a + b + c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592825a105125e940a7a78e8c1b8e4e2\",\n \"score\": \"0.57358825\",\n \"text\": \"def triangle(self):\\n\\t\\t# noinspection PyTypeChecker\\n\\t\\treturn distance.squareform(self.squareform().values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50f521e0774d2bd1d0c83972cd711c7\",\n \"score\": \"0.5726993\",\n \"text\": \"def triangle_area(): \\n print_title(\\\"OPTION 1-TRIANGLE AREA\\\")\\n h = input_int(\\\"Triangle height: \\\")\\n b = input_int(\\\"Triangle base: \\\")\\n precision = input(\\\"Do you want Decimals?: [y/n]\\\")\\n if precision == \\\"y\\\":\\n r = (b * h) / 2\\n else:\\n r = (b * h) // 2\\n print(f\\\"AREA: {r}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e004eca4920b218f61c2e77ebfe9aa45\",\n \"score\": \"0.5710562\",\n \"text\": \"def calculate_area(a: float, b: float, c: float):\\n p = (a + b + c) / 2.0\\n area = math.sqrt(p * (p - a) * (p - b) * (p - c))\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d37377186448db25cdc06998c1c6797\",\n \"score\": \"0.57068974\",\n \"text\": \"def trapezoid_area(base_minor, base_major, height):\\n # You have to code here\\n # REMEMBER: Tests first!!!\\n area = height * ((base_minor + base_major)/ 2)\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577cd84b19f63ee0964d7b4329d649eb\",\n \"score\": \"0.56994534\",\n \"text\": \"def area_rect(l, w):\\n a = l * w\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214fec990a0d7baa96a80c0f5d96efd0\",\n \"score\": \"0.56805736\",\n \"text\": \"def area_box(self):\\n return (self.x_max - self.x_min) * (self.y_max - self.y_min)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08eb9112a80e6c06f3aa68c807bbc5ba\",\n \"score\": \"0.56796795\",\n \"text\": \"def _area(bboxes):\\n size = bboxes[:, 2:4] - bboxes[:, 0:2]\\n area = size[:, 0] * size[:, 1]\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151b8dfbf83c2b216ab154ea5304fcc3\",\n \"score\": \"0.5647638\",\n \"text\": \"def area(self) -> float:\\n return self.w * self.h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c99d1dfaa0d8eb2fd86b2c17d692b3\",\n \"score\": \"0.56459033\",\n \"text\": \"def NetPolygonsArea(polys):\\n area = 0.0\\n for i in range(len(polys)):\\n area += SignedPolygonArea(polys[i])\\n return area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68391f00de257e3966934c1481e8bed5\",\n \"score\": \"0.564241\",\n \"text\": \"def _get_triangleCount(self) -> \\\"int\\\" :\\n return _fusion.PolygonMesh__get_triangleCount(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785077d51f4491d96c6f94911ebb05ed\",\n \"score\": \"0.56256735\",\n \"text\": \"def area(self):\\r\\n return (self.ymax - self.ymin) * (self.xmax - self.xmin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0de8bf31616b557df3cc13f8696d22c\",\n \"score\": \"0.5624591\",\n \"text\": \"def _ComputeArea(cutPoly):\\n area = []\\n for i in range(cutPoly.GetNumberOfCells()):\\n tri=polydata.GetCell(i)\\n points=tri.GetPoints()\\n area.append(tri.ComputeArea())\\n\\n print('Area is {}'.format(sum(area)))\\n print('Diameter is {}'.format(sqrt(4*sum(area)/pi**2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0034de00bf9744946176ffb81e7ccb\",\n \"score\": \"0.56236637\",\n \"text\": \"def area(self):\\n return len(self.coordinates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc1ad5a37fc0aa7bcac87e7b6b63818\",\n \"score\": \"0.56100273\",\n \"text\": \"def rotatedRectangularArea(self):\\n return polygonArea(self.rotatedBox)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ed77555c1c0fa93a1e2d7d36137471e\",\n \"score\": \"0.5603313\",\n \"text\": \"def _faces_areas(self) -> np.ndarray:\\n vertices = np.array(self.v)\\n faces = np.array(self.f)\\n faces_vertices = [\\n (\\n vertices[faces[f][0], :],\\n vertices[faces[f][1], :],\\n vertices[faces[f][2], :],\\n )\\n for f in range(faces.shape[0])]\\n\\n triangles = np.concatenate([\\n np.expand_dims(np.array((\\n np.sqrt(np.sum(np.power((tri[0] - tri[1]), 2))),\\n np.sqrt(np.sum(np.power((tri[0] - tri[2]), 2))),\\n np.sqrt(np.sum(np.power((tri[1] - tri[2]), 2))),\\n )), 0) for tri in faces_vertices], 0)\\n\\n # Heron's formula\\n s = np.sum(triangles, 1) / 2\\n\\n # area of each face\\n areas = np.array([np.sqrt((s[t] * (s[t] - triangles[t, 0]) * (s[t] - triangles[t, 1]) * (s[t] - triangles[t, 2])))\\n for t, tri in enumerate(triangles)])\\n return areas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384cdd28cb3a64c46de6d8dfdd481b3b\",\n \"score\": \"0.56016064\",\n \"text\": \"def area(self, jet):\\n return _fastjet.ClusterSequenceVoronoiArea_area(self, jet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47956cd22e845a7b683d17705b78307\",\n \"score\": \"0.5575096\",\n \"text\": \"def panels_area(tris):\\n Ni = np.cross( tris[::,1 ] - tris[::,0] , tris[::,2 ] - tris[::,0] )\\n Area = np.sqrt(Ni[:,0]**2 + Ni[:,1]**2 + Ni[:,2]**2) * 0.5\\n \\n return Area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f0e81cc1f4d7d27bcedb076fa93cff\",\n \"score\": \"0.55578417\",\n \"text\": \"def compute_square_area(side):\\n return side ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92365ea6439c9c55f499e2bd23b08402\",\n \"score\": \"0.5556692\",\n \"text\": \"def _area(vset):\\n # Initialize A as it could be calculated iteratively\\n A = 0\\n # Check multiple exteriors\\n if type(vset[0][0]) == list:\\n # Calc every exterior separately\\n for i in range(len(vset)):\\n A += pyclipper.scale_from_clipper(pyclipper.scale_from_clipper(pyclipper.Area(pyclipper.scale_to_clipper(vset[i]))))\\n else:\\n # Single exterior\\n A = pyclipper.scale_from_clipper(pyclipper.scale_from_clipper(pyclipper.Area(pyclipper.scale_to_clipper(vset))))\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388d7f51da6988d3fac85e55cd10151\",\n \"score\": \"0.5549856\",\n \"text\": \"def surface_area(self):\\n l, w, h = self.all\\n return 2 * l * w + 2 * w * h + 2 * l * h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae9bde064f6d9940845fd0779cbb040\",\n \"score\": \"0.554556\",\n \"text\": \"def _triangle_vertices(self, angle, center=0):\\n\\n angle = radians(self._hue_degrees)\\n hx = floor(center + 0.5 + (cos(angle) * self._triangle_radius))\\n hy = floor(center + 0.5 - (sin(angle) * self._triangle_radius))\\n sx = floor(center + 0.5 + (cos(angle + (2.0 * pi / 3.0)) * self._triangle_radius))\\n sy = floor(center + 0.5 - (sin(angle + (2.0 * pi / 3.0)) * self._triangle_radius))\\n vx = floor(center + 0.5 + (cos(angle + (4.0 * pi / 3.0)) * self._triangle_radius))\\n vy = floor(center + 0.5 - (sin(angle + (4.0 * pi / 3.0)) * self._triangle_radius))\\n\\n return hx, hy, sx, sy, vx, vy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b3baa328c9318a8e151c811bc69364\",\n \"score\": \"0.55320925\",\n \"text\": \"def area_rectangle(length, width) -> Number:\\n return length*width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07366dac9361fdbce0e8066efcf00d2b\",\n \"score\": \"0.55202895\",\n \"text\": \"def cuboid_surface_area(length, width, height):\\n return 2*(length*width+length*height+width*height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03de21e137b65cf4abaab224cb89dfa1\",\n \"score\": \"0.5515626\",\n \"text\": \"def geometry_area_weights(cube, geometry, normalize=False):\\r\\n # Validate the input parameters\\r\\n if not cube.coords(axis='x') or not cube.coords(axis='y'):\\r\\n raise ValueError('The cube must contain x and y axes.')\\r\\n\\r\\n x_coords = cube.coords(axis='x')\\r\\n y_coords = cube.coords(axis='y')\\r\\n if len(x_coords) != 1 or len(y_coords) != 1:\\r\\n raise ValueError('The cube must contain one, and only one, coordinate for each of the x and y axes.')\\r\\n\\r\\n x_coord = x_coords[0]\\r\\n y_coord = y_coords[0]\\r\\n if not (x_coord.has_bounds() and y_coord.has_bounds()):\\r\\n raise ValueError('Both horizontal coordinates must have bounds.')\\r\\n\\r\\n if x_coord.ndim != 1:\\r\\n raise iris.exceptions.CoordinateMultiDimError(x_coord)\\r\\n if y_coord.ndim != 1:\\r\\n raise iris.exceptions.CoordinateMultiDimError(y_coord)\\r\\n\\r\\n # Figure out the shape of the horizontal dimensions\\r\\n shape = [1] * len(cube.shape)\\r\\n x_dim = cube.coord_dims(x_coord)[0]\\r\\n y_dim = cube.coord_dims(y_coord)[0]\\r\\n shape[x_dim] = x_coord.shape[0]\\r\\n shape[y_dim] = y_coord.shape[0]\\r\\n weights = np.empty(shape, np.float32)\\r\\n\\r\\n # Calculate the area weights\\r\\n x_bounds = x_coord.bounds\\r\\n y_bounds = y_coord.bounds\\r\\n for nd_index in np.ndindex(weights.shape):\\r\\n xi = nd_index[x_dim]\\r\\n yi = nd_index[y_dim]\\r\\n x0, x1 = x_bounds[xi]\\r\\n y0, y1 = y_bounds[yi]\\r\\n polygon = Polygon([(x0, y0), (x0, y1), (x1, y1), (x1, y0)])\\r\\n if normalize:\\r\\n weights[nd_index] = polygon.intersection(geometry).area / polygon.area\\r\\n else:\\r\\n weights[nd_index] = polygon.intersection(geometry).area\\r\\n\\r\\n # Fix for the limitation of iris.analysis.MEAN weights handling.\\r\\n # Broadcast the array to the full shape of the cube\\r\\n weights = np.broadcast_arrays(weights, cube.data)[0]\\r\\n\\r\\n return weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5dd08439deb56fc5730ac6d0cd5c625\",\n \"score\": \"0.5493793\",\n \"text\": \"def area(self) -> np.float64:\\n return 0.5 * self.normal().norm()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c28923172d53c582927b6915bfb53e\",\n \"score\": \"0.5489234\",\n \"text\": \"def area_trapezoid(base_1, base_2, height) -> Number:\\n return 1/2*(base_1 + base_2)*height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d63a0cd5fc1091ee830998703a7cc72\",\n \"score\": \"0.5483533\",\n \"text\": \"def area_of(self, left_top, right_bottom):\\n hw = np.clip(right_bottom - left_top, 0.0, None)\\n return hw[..., 0] * hw[..., 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286e67fe41b4c7e219d124457fb4d3c4\",\n \"score\": \"0.5470709\",\n \"text\": \"def SignedPolygonArea(poly):\\n n = len(poly) # of poly\\n area = 0.0\\n for i in range(n):\\n j = (i + 1) % n\\n area += poly[i][0] * poly[j][1]\\n area -= poly[j][0] * poly[i][1]\\n area = area / 2.0\\n\\n\\n if np.isfinite(area):\\n if area > 0:\\n cornercorrection = 0.5\\n elif area == 0:\\n cornercorrection = 0.\\n elif area < 0:\\n cornercorrection = -0.5\\n return area + cornercorrection\\n else: \\n print(\\\"[polytools] WARNING nan area encountered\\\")\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfff9454f645495b5392315dd9512a3\",\n \"score\": \"0.5466223\",\n \"text\": \"def _e_area(a):\\r\\n x0, y1 = (a.T)[:, 1:]\\r\\n x1, y0 = (a.T)[:, :-1]\\r\\n e0 = np.einsum('...i,...i->...i', x0, y0)\\r\\n e1 = np.einsum('...i,...i->...i', x1, y1)\\r\\n return np.nansum((e0-e1)*0.5)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993995,"cells":{"query_id":{"kind":"string","value":"4128657e337d1d76f50983275de1d03c"},"query":{"kind":"string","value":"Method to convert assessor label into a dictionary"},"positive_passages":{"kind":"list like","value":[{"docid":"08d533d9dece6896af31f5254a0b5073","score":"0.7269525","text":"def get_assessor_dict(assessor_label):\n assessor_dict = dict()\n labels = assessor_label.split('-x-')\n if len(labels) == 1:\n print'ERROR: WRONG PROCESS LABEL: the assessor label can not be set (ERROR no \"-x-\" in the name)'\n print' -> Skipping the processor %s' % (assessor_label)\n else:\n assessor_dict['project_id'] = labels[0]\n assessor_dict['subject_label'] = labels[1]\n assessor_dict['session_label'] = labels[2]\n assessor_dict['label'] = assessor_label\n assessor_dict['proctype'] = labels[-1]\n if assessor_dict['proctype'] == 'FS' and XnatUtils.has_fs_datatypes:\n assessor_dict['xsiType'] = XnatUtils.DEFAULT_FS_DATATYPE\n else:\n assessor_dict['xsiType'] = XnatUtils.DEFAULT_DATATYPE\n return assessor_dict","title":""}],"string":"[\n {\n \"docid\": \"08d533d9dece6896af31f5254a0b5073\",\n \"score\": \"0.7269525\",\n \"text\": \"def get_assessor_dict(assessor_label):\\n assessor_dict = dict()\\n labels = assessor_label.split('-x-')\\n if len(labels) == 1:\\n print'ERROR: WRONG PROCESS LABEL: the assessor label can not be set (ERROR no \\\"-x-\\\" in the name)'\\n print' -> Skipping the processor %s' % (assessor_label)\\n else:\\n assessor_dict['project_id'] = labels[0]\\n assessor_dict['subject_label'] = labels[1]\\n assessor_dict['session_label'] = labels[2]\\n assessor_dict['label'] = assessor_label\\n assessor_dict['proctype'] = labels[-1]\\n if assessor_dict['proctype'] == 'FS' and XnatUtils.has_fs_datatypes:\\n assessor_dict['xsiType'] = XnatUtils.DEFAULT_FS_DATATYPE\\n else:\\n assessor_dict['xsiType'] = XnatUtils.DEFAULT_DATATYPE\\n return assessor_dict\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"807e2a0238177386f84ae6d97f5c6d81","score":"0.61845165","text":"def get_labels(self):\n\t\tld = {}\n\t\tfor label in self.labels:\n\t\t\ttokens = label.split(\":\")\n\t\t\tld[tokens[0]] = tokens[1]\n\t\treturn ld","title":""},{"docid":"c791b0778493c3bc4f7b391e1407d05c","score":"0.6141668","text":"def get_labels_dict(acquisitions, separator='_', detectPosition=True):\n labels_dict = {}\n value = 0\n for key in acquisitions.keys():\n key = key.split('_')[0]\n key = key.split(separator)\n if key[0] == \"Normal\" or detectPosition:\n label = key[0]\n else:\n label = key[0][2:]\n if not label in labels_dict:\n labels_dict[label] = value\n value += 1\n return labels_dict","title":""},{"docid":"f6b7519e610a19fd27d48885f4a0a8d7","score":"0.6018663","text":"def __label_mapper(label):\n mapper = {'Normal': 0, 'PVC': 1#, 'SPB': 2\n }\n \n y = mapper[label]\n \n return y","title":""},{"docid":"59fa727603cef8874026c2214b348b10","score":"0.6017599","text":"def _label_task(input_dataframe: DataFrame) -> dict:\n # Stupid but it works\n # True must be turned into 'true'\n json_value = input_dataframe.label.value_counts().to_json()\n return json.loads(json_value)","title":""},{"docid":"e78a5d42f110eec87903a1d0ffa72bcd","score":"0.5934875","text":"def __label_mapper(label):\n mapper = {'N': 0, 'PVC': 1 # , 'SPB': 2\n }\n\n y = mapper[label]\n\n return y","title":""},{"docid":"6a15de1af46eecf1fe5467f8495ef664","score":"0.59233665","text":"def __label_mapper(label):\n\n mapper = {'Normal': 0, 'PVC': 1}#'SPB': 1, 'PVC': 2\n y = mapper[label]\n\n return y","title":""},{"docid":"5a3a0969bac8fc2a141a6bd66605301c","score":"0.5861572","text":"def __label_mapper(label):\n mapper = {'Normal': 0, 'SPB': 1, 'PVC': 2}\n\n y = mapper[label]\n\n return y","title":""},{"docid":"5a3a0969bac8fc2a141a6bd66605301c","score":"0.5861572","text":"def __label_mapper(label):\n mapper = {'Normal': 0, 'SPB': 1, 'PVC': 2}\n\n y = mapper[label]\n\n return y","title":""},{"docid":"3a274a971464dcea39d6f7e4557fdfc6","score":"0.5835935","text":"def parse_labels(response):\n return {\n label[\"Name\"]: round(label[\"Confidence\"], 2) for label in response[\"Labels\"]\n }","title":""},{"docid":"85cd0b50d4f7e56e86fb4174627622a4","score":"0.57522","text":"def createDictLabels(labels):\n\n # Re-arange the Target vectors between [0..nClasses_train]\n labels = labels.numpy()\n unique_labels = np.unique(labels)\n dictLabels = {val: i for i, val in enumerate(unique_labels)}\n dictLabelsInverse = {i: val for i, val in enumerate(unique_labels)}\n return dictLabels,dictLabelsInverse","title":""},{"docid":"01f672874e892f63aa6ea3d583e73676","score":"0.5692806","text":"def pred2dict(self, data_sample: KIEDataSample) -> Dict:\n result = {}\n pred = data_sample.pred_instances\n result['scores'] = pred.scores.cpu().numpy().tolist()\n result['edge_scores'] = pred.edge_scores.cpu().numpy().tolist()\n result['edge_labels'] = pred.edge_labels.cpu().numpy().tolist()\n result['labels'] = pred.labels.cpu().numpy().tolist()\n return result","title":""},{"docid":"5e8597e07a428f06a2edb6628c46ea55","score":"0.56730324","text":"def security_label_map(self):\n return {\n 'tlp:white': 'marking-definition--613f2e26-407d-48c7-9eca-b8e91df99dc9',\n 'tlp:green': 'marking-definition--34098fce-860f-48ae-8e50-ebd3cc5e41da',\n 'tlp:amber': 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82',\n 'tlp:red': 'marking-definition--5e57c739-391a-4eb3-b6be-7d15ca92d5ed',\n }","title":""},{"docid":"4097ce2e488537020e67eeefb95a1c9f","score":"0.5661316","text":"def labels_(self):\n return {self.LABEL_LICIT: 0, \n self.LABEL_ILLICIT: 1}","title":""},{"docid":"76f5ff631670f96d4c94921c3e71f283","score":"0.5631069","text":"def label_to_data_id(self):\n return {\"Altitude\": DataEntryIds.CALCULATED_ALTITUDE,\n \"MaxAltitude\": None,\n \"State\": DataEntryIds.STATE,\n \"Pressure\": DataEntryIds.PRESSURE,\n \"Acceleration\": [DataEntryIds.ACCELERATION_X, DataEntryIds.ACCELERATION_Y,\n DataEntryIds.ACCELERATION_Z]\n }","title":""},{"docid":"5f52dd44d9e4a78fb4e3c24dab789e3f","score":"0.56184167","text":"def get_inference(self) -> dict:\n return {'output': self.label_pred.argmax(dim=1).cpu().numpy(), 'image_id': self.image_id,\n 'label_orig': self.label_original.cpu().numpy()}","title":""},{"docid":"94a41357c324e035f8e83729d700bbaa","score":"0.55440646","text":"def _to_dict(self):\r\n _dict = {}\r\n if hasattr(self, 'label') and self.label is not None:\r\n _dict['label'] = self.label\r\n if hasattr(self, 'value') and self.value is not None:\r\n _dict['value'] = self.value._to_dict()\r\n return _dict","title":""},{"docid":"9905b0d3ec8a3befe5a04470e86619a8","score":"0.5542545","text":"def map_labels(self, pgrm: List[Instr]) -> dict:\n label_addrs = {}\n for addr in range(len(pgrm)):\n ins = pgrm[addr]\n if ins.isLabel():\n # duplicate labels are not allowed\n if ins.label in label_addrs:\n raise DuplicateLabel(self, ins.label)\n label_addrs[ins.label] = addr + 1\n return label_addrs","title":""},{"docid":"d2c8e34141f389ddf01a7c14f6bc89b3","score":"0.5541204","text":"def process_label(intents, w2v):\n class_dict = {}\n label_vec = []\n class_id = 0\n for line in intents:\n # check whether all the words in w2v dict\n if line == 'Troubleshooting_Technical_Issues':\n line = 'Trouble_shooting_Technical_Issues'\n if '_' in line:\n label = [w.lower().strip() for w in line.split('_')]\n else:\n label = [w.lower().strip() for w in line.split(' ')]\n for w in label:\n if not w2v.vocab.has_key(w.lower()):\n print(\"not in w2v dict\", w)\n # compute label vec\n label_sum = np.sum([w2v[w] for w in label], axis = 0)\n label_vec.append(label_sum)\n # store class names => index\n class_dict[' '.join(label)] = class_id\n class_id = class_id + 1\n return class_dict, np.asarray(label_vec)","title":""},{"docid":"c1acb9afb5c2f34b77cc683d1452008c","score":"0.55404854","text":"def get_dict(self):\n return dict(label=self.__label, color=self.__color)","title":""},{"docid":"25168796eef5e37b31fe841a8f68e388","score":"0.55311346","text":"def as_dict(self, use_count: int) -> Dict[Text, Any]:\n\n return {\"value\": self.name, \"id\": self.id, \"nlu_examples_count\": use_count}","title":""},{"docid":"b42b52f557f135ded8d1b421c50a6594","score":"0.55262387","text":"def get_label(example):\n return example['label'].numpy()","title":""},{"docid":"d91f8ce2c9ac71f3c7b8b9fec37150b0","score":"0.5513511","text":"def to_dict():","title":""},{"docid":"9a02ec62382cafc3e89bdfb4b805a48d","score":"0.54940736","text":"def convert_to_dict(self) -> dict:","title":""},{"docid":"da846cc22ec06c8d636d33a3ad7729c1","score":"0.5470197","text":"def get_k8s_labels_for_job(job: Job) -> Dict[str, str]:\n return {\n \"job_id\": job.id,\n \"job_type\": job.target_type,\n }","title":""},{"docid":"bd642233c29bb7ac5a501f947aa94740","score":"0.546146","text":"def label(self):\n return self.sess_element.get('label')","title":""},{"docid":"5358922f256e49e14047fa9b7f03fd7d","score":"0.54249454","text":"def parse_lexicon_entry(lexicon_entry: str) -> dict:\n functional_labels = {}\n lexicon_entry = lexicon_entry.replace(\"[\", \"\").replace(\"]\", \"\").replace(\";\", \"\")\n labels = lexicon_entry.split()\n for label in labels:\n parts = label.split('=')\n if len(parts) == 2:\n functional_labels[parts[0].strip()] = parts[1].strip()\n return functional_labels","title":""},{"docid":"2b56c3f021e318b3d2bd4911f0c259b0","score":"0.54211986","text":"def dict_from_pbtxt_file(fname):\n lines = [line.rstrip('\\n').strip() for line in open(fname)]\n label_map = {}\n curr_label = ''\n curr_id = 0\n\n for l in lines:\n \n if l.startswith( 'display_name: '):\n curr_label = l.split(' ')[1]\n\n if l.startswith( 'id: '):\n curr_id = int(l.split(' ')[1])\n\n if l.startswith( '}'):\n # print(curr_id, curr_label)\n label_map[curr_id] = curr_label.replace(\"\\\"\", \"\")\n\n return label_map","title":""},{"docid":"c591496302f43289dbcfe9f64290c4dc","score":"0.54123926","text":"def _get_labels(self, truth_dict: Dict[str, Any]) -> Dict[str, Any]:\n if \"pid\" in truth_dict.keys():\n abs_pid = abs(truth_dict[\"pid\"])\n sim_type = truth_dict[\"sim_type\"]\n\n labels_dict = {\n self._index_column: truth_dict[self._index_column],\n \"muon\": int(abs_pid == 13),\n \"muon_stopped\": int(truth_dict.get(\"stopped_muon\") == 1),\n \"noise\": int((abs_pid == 1) & (sim_type != \"data\")),\n \"neutrino\": int(\n (abs_pid != 13) & (abs_pid != 1)\n ), # @TODO: `abs_pid in [12,14,16]`?\n \"v_e\": int(abs_pid == 12),\n \"v_u\": int(abs_pid == 14),\n \"v_t\": int(abs_pid == 16),\n \"track\": int(\n (abs_pid == 14) & (truth_dict[\"interaction_type\"] == 1)\n ),\n \"dbang\": self._get_dbang_label(truth_dict),\n \"corsika\": int(abs_pid > 20),\n }\n else:\n labels_dict = {\n self._index_column: truth_dict[self._index_column],\n \"muon\": -1,\n \"muon_stopped\": -1,\n \"noise\": -1,\n \"neutrino\": -1,\n \"v_e\": -1,\n \"v_u\": -1,\n \"v_t\": -1,\n \"track\": -1,\n \"dbang\": -1,\n \"corsika\": -1,\n }\n return labels_dict","title":""},{"docid":"128ab4da939efdf9dd0148cafa3655af","score":"0.5407699","text":"def set_labels(self,label:dict):\n self.label_dict = label\n print(\"[INFO] Label dictionary : \",label)","title":""},{"docid":"539fab3c7267134f9aaf80dd4114975a","score":"0.54033065","text":"def create_ans2label(occurence, name, cache_root):\n label, label2ans, ans2label = 0, [], {}\n for answer in occurence:\n label2ans.append(answer)\n ans2label[answer] = label\n label += 1\n\n utils.create_dir(cache_root)\n\n cache_file = os.path.join(cache_root, name+'_ans2label.json')\n json.dump(ans2label, open(cache_file, 'w'))\n cache_file = os.path.join(cache_root, name+'_label2ans.json')\n json.dump(label2ans, open(cache_file, 'w'))\n return ans2label","title":""},{"docid":"3eb6f8f2e01bea7c93bc65ef696e2e90","score":"0.5385623","text":"def ark2dict(arkfile):","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"c66257bd0326b0b14c54f97588f3cdf7","score":"0.5378722","text":"def labels(self) -> Mapping[str, str]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"ed628b0e2b48af84e68ad32360623284","score":"0.5369858","text":"def get_label_yml(label):\n text = f' - name: \"{label[\"name\"]}\"\\n'\n text += f' color: \"{label[\"color\"]}\"\\n'\n text += f' description: \"{label[\"description\"]}\"\\n'\n return text","title":""},{"docid":"17632f7e3ac20d81e99fa2040dab8ec7","score":"0.5362394","text":"def predict(self, exemple, label):","title":""},{"docid":"17632f7e3ac20d81e99fa2040dab8ec7","score":"0.5362394","text":"def predict(self, exemple, label):","title":""},{"docid":"682cd2798e228da6619b8bebc9773bac","score":"0.536196","text":"def get_labels(*args):\n return {name: value for name, value in LABELS.items() if name in args}","title":""},{"docid":"54f67b8e2e2fab8e2bb8627eaa87a10a","score":"0.53590834","text":"def aspen_version_label_dict(self):\n # 先以 version name 找到對應的 version label\n l_list = []\n for version_name in self.__version_list:\n h_key = r.HKEY_CLASSES_ROOT(fr\"{version_name}\\shell\")\n for item in h_key.subkeys():\n if re.match(\"Open with Aspen Plus V\\d+.\", item.name): l_list.append(item.name)\n\n # 然後再將 name 與 label 變成字典\n self.__version_label_dict = dict(zip(self.__version_list, l_list))","title":""},{"docid":"027bcc9fb054a27e1ac17e2efcdc9b6e","score":"0.53537124","text":"def labels(self) -> Mapping[str, Any]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"027bcc9fb054a27e1ac17e2efcdc9b6e","score":"0.53537124","text":"def labels(self) -> Mapping[str, Any]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"ded56ba607f6d753abe84f7f517123c7","score":"0.5336088","text":"def get_labels(G: nx.classes.graph.Graph,\n ml_target: str) -> Dict:\n\n labels = {}\n for n in G.nodes(data=True):\n labels[n[1]['id']] = n[1][ml_target]\n return labels","title":""},{"docid":"e352369c936c8f498d275251444e6afd","score":"0.5334917","text":"def my_fn(x):\n input_label = tf.strings.join([input_feature, ':'], separator='')\n inputs = tf.strings.join(\n [prefix, input_label, x[input_feature]], separator=' ')\n\n class_label = tf.gather(output_classes, x['label'])\n if drop_explanations:\n targets = class_label\n else:\n targets = _explanation_targets(class_label, x['evidences'])\n\n return {'inputs': inputs, 'targets': targets}","title":""},{"docid":"1f0ced40754aa6036772658610bc2bf9","score":"0.5326014","text":"def labelToDescription(label) :\n descriptions = [\"top\", \"trouser\", \"pullover\",\n \"dress\", \"coat\", \"sandal\",\n \"shirt\", \"sneaker\", \"bag\",\n \"ankle boot\"]\n return descriptions[label]","title":""},{"docid":"40cd4eca35432c103fcc9faebc180417","score":"0.5324759","text":"def as_dict(self) -> Dict[str, Any]:","title":""},{"docid":"7eeb479fb6f86530b46754b1b755a6e8","score":"0.53247446","text":"def load_labels(\n location: PathLike = DEV_DATA,\n expr_type: ExperimentType = ExperimentType.ils,\n) -> Dict:\n location = pathlib.Path(location) / f\"task-{expr_type.value}\"\n # specify `latin-1` encoding due to an encoding error\n reader = CSVReader(location, DataType.labels, encoding=\"latin-1\")\n data = reader.read_all()\n # an encoding error results in a bad column\n del data[\"Unnamed: 0\"]\n # the conventions for Subject, Date, and Run do not match folder names\n # so we update them here to match the other data\n pilot = data.Subject.apply(lambda x: f\"sub-cp{int(x):03d}\")\n session = data.Date.apply(lambda x: f\"ses-{int(x)}\")\n run = data.Run.apply(lambda x: f\"run-{int(x):03d}\")\n del data[\"Subject\"]\n del data[\"Date\"]\n del data[\"Run\"]\n data[\"pilot\"] = pilot\n data[\"session\"] = session\n data[\"run\"] = run\n data = data.astype(\n {\n \"pilot\": \"category\",\n \"session\": \"category\",\n \"run\": \"category\",\n }\n )\n return {\"labels\": data}","title":""},{"docid":"e3d79df5fea94e0393e2bcedf3986bb7","score":"0.53228766","text":"def getMetadataLabels(self):\n return { \"title\": self.label }","title":""},{"docid":"600b0fc605315140ae662443197931a9","score":"0.5321642","text":"def to_dict(self) -> t.Dict[str, t.Any]:\n return {k: getattr(self, k) for k in self._get_annotations()}","title":""},{"docid":"934aea56fc6b067c8c106134a50cdd38","score":"0.5319331","text":"def make_metadata(self, margin, best_label):\n return {\n 'confidence': float(f'{margin: 1.2f}'),\n 'job-name': self.job_name,\n 'class-name': self.label_names[self.get_label_index(best_label)],\n 'human-annotated': 'no',\n 'creation-date': datetime.utcnow().strftime('%Y-%m-%dT%H:%m:%S.%f'),\n 'type': JOB_TYPE\n }","title":""},{"docid":"b5f23662056a15ac30ce32db31294c94","score":"0.53111476","text":"def labels(observation):\n raise NotImplementedError","title":""},{"docid":"9472a095408517cb9521788c519c2d04","score":"0.5311099","text":"def asdict(self) -> Dict[str, Any]:","title":""},{"docid":"0559ca65b68dc65a0ca91e319d6b497e","score":"0.5298789","text":"def individual_to_params(individual):\n return dict(zip(GENE_LABELS, individual))","title":""},{"docid":"bfc19f087b25f791a49a04260b29e73f","score":"0.52983963","text":"def __init__(self, label):\n self.assessor_label = label\n self.is_session_assessor = False\n self.is_scan_assessor = False\n if len(re.findall('-x-', label)) == 3:\n labels = label.split('-x-')\n self.project_id = labels[0]\n self.subject_label = labels[1]\n self.session_label = labels[2]\n self.proctype = labels[3]\n self.scan_id = None\n self.is_session_assessor = True\n elif len(re.findall('-x-', label)) == 4:\n labels = label.split('-x-')\n self.project_id = labels[0]\n self.subject_label = labels[1]\n self.session_label = labels[2]\n self.scan_id = labels[3]\n self.proctype = labels[4]\n self.is_scan_assessor = True\n else:\n self.assessor_label = None","title":""},{"docid":"a220b6a1e2e7c48fe9e2cb49e1260583","score":"0.52759767","text":"def get_label_text(label_number):\n mapping = {}\n with open(CLASS_MAPPING_FILE, 'r') as fp:\n for _, line in enumerate(fp):\n terms = line.split('\\t')\n # mapping","title":""},{"docid":"1b0d9280e9290e6a138216bd7dfa277c","score":"0.52754885","text":"def assignVar2label(node2var):\n node2label = {}\n for node in node2var.keys():\n label = node2var[node].replace(\"x{0}?\".format(node),\"\")\n node2label[node] = label\n return node2label","title":""},{"docid":"5ed5eff77bad17151c399a09e9fa59fc","score":"0.52524656","text":"def getLabelInfo(self, label) -> retval:\n ...","title":""},{"docid":"052f415ba3539cbf324771c2a51e3ade","score":"0.5244683","text":"def predict_label(text: str):\n # Step 01: Predict the label\n prediction_result = Classifier().get_instance().predict(text)\n\n # Step 02: Parse the prediction result.\n predicted_label = str(prediction_result[0][0])\n predicted_label = predicted_label.replace(\"__label__\", \"\").strip()\n confidence = round(100 * prediction_result[1][0], 2)\n\n # Step 03: Return the result.\n return predicted_label, confidence","title":""},{"docid":"bccd7e38987d2f844bcc6165b7134aa2","score":"0.52344626","text":"def _impact_labels():\n # Prevent reset upon auto reload in jupyter notebook\n if not '_impact_labels' in builtins.__dict__:\n builtins._impact_labels = dict()\n\n return builtins._impact_labels","title":""},{"docid":"4ccfedb796d6d1e66eefa1e4f82c2bf3","score":"0.5218931","text":"def get_label(self):","title":""},{"docid":"77f94c9e9cde815255bc44bbd40625af","score":"0.5216753","text":"def _collect_label(self, file_id, content):\n label_dressed = dict()\n label_dressed[file_id] = {cls:[] for cls in self.classes[1:]}\n for line in content:\n cls = line['field_name']\n if cls in self.classes:\n #identity = line.get('identity', 0) \n label_dressed[file_id][cls].append( {'key_id':[], 'value_id':[], 'key_text':'', 'value_text':''} )\n label_dressed[file_id][cls][-1]['key_id'] = line.get('key_id', [])\n label_dressed[file_id][cls][-1]['value_id'] = line['value_id'] # value_id\n label_dressed[file_id][cls][-1]['key_text'] = line.get('key_text', []) \n label_dressed[file_id][cls][-1]['value_text'] = line['value_text'] # value_text\n \n # handle corrupted data\n for cls in label_dressed[file_id]: \n for idx, label in enumerate(label_dressed[file_id][cls]):\n if len(label) == 0: # no relevant class in sample @file_id\n continue\n if (len(label['key_text'])>0 and len(label['key_id'])==0) or \\\n (len(label['value_text'])>0 and len(label['value_id'])==0):\n return None\n \n return label_dressed","title":""},{"docid":"f65bbd161c3afec09e3b8f08ca88e803","score":"0.51999146","text":"def read_img_labels(img_label_path):\n with open(img_label_path,\"r\") as img_label_file:\n data_lines = img_label_file.readlines()\n x1,y1,x2,y2 = data_lines[1].split(\"\\t\")\n img_label_file.close()\n return {\"x1\":int(x1),\"y1\":int(y1),\"x2\":int(x2),\"y2\":int(y2)}","title":""},{"docid":"4087ce8dd5025c28aaf1b2839e13fbc7","score":"0.5198736","text":"def info(self):\n sess_info = {}\n\n sess_info['ID'] = self.get('ID')\n sess_info['label'] = self.get('label')\n sess_info['note'] = self.get('xnat:note')\n sess_info['session_type'] = self.get('session_type')\n sess_info['project_id'] = self.project\n sess_info['original'] = self.get('original')\n sess_info['modality'] = self.get('modality')\n sess_info['UID'] = self.get('UID')\n sess_info['subject_id'] = self.get('xnat:subject_ID')\n sess_info['subject_label'] = self.subject\n sess_info['project_label'] = sess_info['project_id']\n sess_info['project'] = sess_info['project_id']\n sess_info['subject_ID'] = self.get('xnat:subject_ID')\n sess_info['URI'] = '/data/experiments/%s' % sess_info['ID']\n sess_info['session_label'] = sess_info['label']\n sess_info['last_updated'] = sess_info['original']\n sess_info['type'] = sess_info['modality']\n\n return sess_info","title":""},{"docid":"a37a930b620268ef093cfc437aed7126","score":"0.519552","text":"def create_edge_labels(game_board):\n edge_labels = {}\n for edge in game_board:\n edge_labels[(edge[0][1],edge[1][1])] = edge[2]\n return edge_labels","title":""},{"docid":"f814b51d76d48c8bb18edcfb568456a4","score":"0.51852524","text":"def prepare_labels(labels):\n d = {}\n count = 0\n setlabels = set(labels)\n for w in setlabels:\n d[w] = count\n count += 1\n idxlabels = np.array([d[w] for w in labels])\n return idxlabels","title":""},{"docid":"e3517bee022723f6ccec853140d1105a","score":"0.51809204","text":"def load_labels():","title":""},{"docid":"fbf76a8614dd0971da88b77905e2fc4e","score":"0.5176966","text":"def parse_possible_labels(word_and_labels):\n possible_labels = {}\n for i, word_label in enumerate(word_and_labels):\n word, label = word_label\n if not word in possible_labels:\n possible_labels[word] = set([label])\n else:\n possible_labels[word].add(label)\n return possible_labels","title":""},{"docid":"a1602671a0b225c0112a10ad9d168985","score":"0.516992","text":"def get_event_label_dict(self, pattern=None):\n return self._parser_obj.get_event_label_dict(pattern=pattern)","title":""},{"docid":"c106850bdecb0e3ed2f0672749e8c39a","score":"0.51476675","text":"def getAnnotationDict_old(self):\n html = unicode(self.toHtml())\n parser = etree.HTMLParser()\n tree = etree.fromstring(html, parser)\n aElements = tree.findall(\".//a[@name]\")\n ret = {}\n for aElement in aElements:\n idAnnotation = aElement.attrib[\"name\"]\n annotation = \"\"\n if idAnnotation == \"title\":\n for t in aElement.getparent().itertext():\n annotation = annotation + t\n else:\n for t in aElement.getparent().getparent().getnext().itertext():\n annotation = annotation + t\n annotation = annotation.strip()\n if annotation == self.strEmptyCharacter:\n annotation = \"\"\n ret[idAnnotation] = annotation\n #print \"%s: %s\" % (idAnnotation, annotation)\n return ret","title":""},{"docid":"35c13318b1839e66f856baf62bdbc2a8","score":"0.5146432","text":"def _predict(self, X):\n y_pred = self._automl.predict(X)\n y_pred = self.dataset.inverse_transform_labels(y_pred)\n return {self.Keys.PREDICTED: y_pred}","title":""},{"docid":"03d85f314da1c9918ef18cb4a44f6f2d","score":"0.5137802","text":"def _inputs_to_dict(**kwords):\n return kwords","title":""},{"docid":"99e225bb141c6c4534b5ede3fdc1325f","score":"0.5136677","text":"def convert_label_to_idx(self, label):\n label = label.strip()\n return class2label[label]","title":""},{"docid":"b0c1dc74b5197cd2a93f1f6ff98a39d3","score":"0.5126444","text":"def get_label_mapping(label_file):\n with open(label_file, 'r') as f:\n id2label = f.readlines()\n id2label = [l.strip() for l in id2label]\n label2id = {}\n count = 0\n for label in id2label:\n label2id[label] = count\n count += 1\n return id2label, label2id","title":""},{"docid":"dc824fa2eaf11763eb5ac1c3e652f035","score":"0.51264375","text":"def state_dict(self) -> Dict:","title":""},{"docid":"3d521c56ad86ea4755e8f57bb22249b2","score":"0.5117194","text":"def pipeline_dict(request) -> dict:\n pipeline_dict = {\n \"name\": \"transformers_tokenizer_test\",\n \"features\": {\n \"transformers\": {\"model_name\": \"sshleifer/tiny-distilroberta-base\"}\n },\n \"head\": {\n \"type\": \"TextClassification\",\n \"labels\": [\"a\", \"b\"],\n },\n }\n return pipeline_dict","title":""},{"docid":"273cdf8275aee5da47809feb999008a6","score":"0.5114229","text":"def assignLabel2Var(node2label):\n node2var = {}\n for node in node2label.keys():\n var = \"x{0}?{1}\".format(node,node2label[node])\n node2var[node] = var\n return node2var","title":""},{"docid":"fd120c04e7b42408e98947a8f1313525","score":"0.5107142","text":"def pred2dict(self, data_sample: InstanceData) -> Dict:\n result = {}\n if 'pred_instances_3d' in data_sample:\n pred_instances_3d = data_sample.pred_instances_3d.numpy()\n result = {\n 'bboxes_3d': pred_instances_3d.bboxes_3d.tensor.cpu().tolist(),\n 'labels_3d': pred_instances_3d.labels_3d.tolist(),\n 'scores_3d': pred_instances_3d.scores_3d.tolist()\n }\n\n if 'pred_pts_seg' in data_sample:\n pred_pts_seg = data_sample.pred_pts_seg.numpy()\n result['pts_semantic_mask'] = \\\n pred_pts_seg.pts_semantic_mask.tolist()\n\n return result","title":""},{"docid":"bf2513229a6782c77f4434092e65b031","score":"0.5099719","text":"def prepare_labels(self, dots):\n # The expected decoding is just the input.\n labels = dots.copy()\n labels[\"decode\"] = self._small_eye\n # We don't really care about the encoded representation, so we can just set\n # the labels to what the output already is.\n labels[\"encode\"] = self.__encoded\n return labels","title":""},{"docid":"dde4846796372364d21a33bce5154237","score":"0.50925577","text":"def _convert_labels_to_label_mapping(labels, requires_zero_mapping):\n if isinstance(labels, dict):\n return labels\n\n # if list\n start_index = 0 if requires_zero_mapping else 1\n return dict(zip(labels, list(\n range(start_index, start_index + len(labels)))))","title":""},{"docid":"06ff4d84e6e133ea25962d8bbbd9e4f7","score":"0.50896394","text":"def __getitem__(self, index):\n\n\t\treturn {'domain_a': super(MNISTG4LANDDataset, self).__getitem__(self.domain_a[index]),\\\n\t\t\t\t 'domain_b': super(MNISTG4LANDDataset, self).__getitem__(self.domain_b[index]), \\\n\t\t\t\t 'y_label': self.to_one_of_k(int(self.y_label[index]), 2)}","title":""},{"docid":"0200eed5a69c4be12aabfeacd1717b34","score":"0.50820607","text":"def asDict(self):\n return {\n \"predominant_emotion\": self.predominateEmotion.name.lower(),\n \"estimations\": {\n \"anger\": self.anger,\n \"disgust\": self.disgust,\n \"fear\": self.fear,\n \"happiness\": self.happiness,\n \"sadness\": self.sadness,\n \"surprise\": self.surprise,\n \"neutral\": self.neutral,\n },\n }","title":""},{"docid":"c964b69b4ddebf65a91b4aad2ebff6b6","score":"0.50816333","text":"def get_labels(self, encoding='shift-jis'):\n label_dict = dict()\n label_dict[0] = 'NULL'\n data_length = len(self._data)\n label_base_offset = data_length + len(self._p1_list) * 4 + \\\n len(self._p2_list) * 8\n for pointer1 in self._p1_list:\n ptr = unpack('<I', self._data[pointer1:pointer1 + 0x4])[0]\n if ptr < data_length:\n continue\n offset = ptr - label_base_offset\n length = 0\n while self._labels[offset + length] != '\\0':\n length += 1\n if encoding == 'shift-jis':\n label_dict[ptr] = self._labels[offset:offset + length]\n else:\n label_dict[ptr] = self._labels[offset:offset + length]\\\n .decode('shift-jis').encode(encoding)\n return label_dict","title":""},{"docid":"2d72f8114f2d918cfe22f97aaa30e6ef","score":"0.5076148","text":"def get_withLabel(self):\r\n \r\n return self.obj_dict['withLabel']","title":""},{"docid":"01411627b12e7d7719c93e3676f31c15","score":"0.50739354","text":"def encode_label(self, label):\n if label not in self.labels_dict:\n self.labels_dict[label] = self.labels[0].tolist()\n self.labels = self.labels[1:]\n return self.labels_dict[label]","title":""},{"docid":"ef6eba6101b0ac3a47779d426833d933","score":"0.5068386","text":"def read_labels(file_path):\n with open(file_path, \"r\", encoding=\"utf-8\") as label_file:\n lines = label_file.readlines()\n labels = {}\n for line in lines:\n pair = line.strip().split(maxsplit=1)\n labels[int(pair[0])] = pair[1].strip()\n return labels","title":""},{"docid":"8d1c6b6ed925378a4b3a95ce633f4fb7","score":"0.5066627","text":"def to_dict(self):\n return {\n \"acc\": \"None\" if self.acc is None else \"%.2f%%\" % self.acc,\n \"beatmap\": map_str(self.beatmap) if self.beatmap else \"None\",\n \"mode\": \"Unknown\"\n if self.mode is None\n else consts.mode2str[self.mode], # noqa\n \"mods\": combine_mods(self.mods),\n \"player\": self.player.username if self.player else \"None\",\n \"guest mapper\": self.guest_mapper.username\n if self.guest_mapper\n else \"None\", # noqa\n }","title":""},{"docid":"ff22da51da5c377222eef8857f62027f","score":"0.5066222","text":"def convert_to_dict(data):\n pass #TODO implement","title":""},{"docid":"9506ff1bd08b0c355c97b2ac92baa8be","score":"0.5057204","text":"def copa_preformat_fn(ex: Dict[str, Any]) -> Dict[str, Any]:\n premise, question = ex['premise'].decode(), ex['question'].decode()\n input_prompt = f'{premise} What was the {question} of this?'\n choices = {\n ex['choice1'].decode(): -ex['label'] + 1,\n ex['choice2'].decode(): ex['label']\n }\n return {'input': input_prompt, 'target_scores': choices}","title":""},{"docid":"91a70b02fe14b7c7385276179d9a27dd","score":"0.5053743","text":"def state(self) -> Dict[str, Any]:","title":""},{"docid":"28eda5082edf2ef8912608cde15eb812","score":"0.5053538","text":"def labels_str_to_rule_format(labels_string: str, gc_api: RESTManagementAPI) -> Dict[str, List[Dict[str, List[str]]]]:\r\n structured_labels = {\"or_labels\": list()}\r\n # Normalize spaces\r\n labels_string = labels_string.replace(\", \", \",\").replace(\r\n \" ,\", \",\").replace(\"& \", \"&\").replace(\" &\", \"&\")\r\n labels_string = labels_string.replace(\": \", \":\").replace(\" :\", \":\").strip()\r\n\r\n for or_label in labels_string.split(','):\r\n and_labels = {\"and_labels\": list()}\r\n for and_label in or_label.split('&'):\r\n key, value = and_label.split(':')\r\n and_labels[\"and_labels\"].append(get_label_id(key, value, gc_api))\r\n structured_labels[\"or_labels\"].append(and_labels)\r\n return structured_labels","title":""},{"docid":"2a277cd529f0f474490c6356f7b55901","score":"0.5050749","text":"def labels(self) -> pulumi.Output[Mapping[str, str]]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"2a277cd529f0f474490c6356f7b55901","score":"0.5050749","text":"def labels(self) -> pulumi.Output[Mapping[str, str]]:\n return pulumi.get(self, \"labels\")","title":""},{"docid":"b45af93715635948522e972981dd6120","score":"0.5049098","text":"def state_to_dict(self):\n self.subsetDict = {}\n for butTyp, buttons in self.toggleStates.items():\n l = []\n for butName, state in buttons.items():\n if state is True:\n label = self.builder.get_object(butName).get_label()\n l.append(label)\n self.subsetDict[butTyp] = l\n\n self.subsetDict['MeanFit'] = \\\n self.builder.get_object('meanFitSpinButton').get_value()","title":""},{"docid":"095634c58a7f68b63677c96733401452","score":"0.5043671","text":"def to_dict(self, target):\n\n\t\tret_dic = {}\n\n\t\tret_dic[\"name\"] = target.get_name()\n\n\t\treturn ret_dic","title":""},{"docid":"1eb70d06191a636f50c62fbed2d84c5e","score":"0.5039761","text":"def label_to_name(self, label):\n return self.labels[label]","title":""}],"string":"[\n {\n \"docid\": \"807e2a0238177386f84ae6d97f5c6d81\",\n \"score\": \"0.61845165\",\n \"text\": \"def get_labels(self):\\n\\t\\tld = {}\\n\\t\\tfor label in self.labels:\\n\\t\\t\\ttokens = label.split(\\\":\\\")\\n\\t\\t\\tld[tokens[0]] = tokens[1]\\n\\t\\treturn ld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c791b0778493c3bc4f7b391e1407d05c\",\n \"score\": \"0.6141668\",\n \"text\": \"def get_labels_dict(acquisitions, separator='_', detectPosition=True):\\n labels_dict = {}\\n value = 0\\n for key in acquisitions.keys():\\n key = key.split('_')[0]\\n key = key.split(separator)\\n if key[0] == \\\"Normal\\\" or detectPosition:\\n label = key[0]\\n else:\\n label = key[0][2:]\\n if not label in labels_dict:\\n labels_dict[label] = value\\n value += 1\\n return labels_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b7519e610a19fd27d48885f4a0a8d7\",\n \"score\": \"0.6018663\",\n \"text\": \"def __label_mapper(label):\\n mapper = {'Normal': 0, 'PVC': 1#, 'SPB': 2\\n }\\n \\n y = mapper[label]\\n \\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fa727603cef8874026c2214b348b10\",\n \"score\": \"0.6017599\",\n \"text\": \"def _label_task(input_dataframe: DataFrame) -> dict:\\n # Stupid but it works\\n # True must be turned into 'true'\\n json_value = input_dataframe.label.value_counts().to_json()\\n return json.loads(json_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78a5d42f110eec87903a1d0ffa72bcd\",\n \"score\": \"0.5934875\",\n \"text\": \"def __label_mapper(label):\\n mapper = {'N': 0, 'PVC': 1 # , 'SPB': 2\\n }\\n\\n y = mapper[label]\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a15de1af46eecf1fe5467f8495ef664\",\n \"score\": \"0.59233665\",\n \"text\": \"def __label_mapper(label):\\n\\n mapper = {'Normal': 0, 'PVC': 1}#'SPB': 1, 'PVC': 2\\n y = mapper[label]\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3a0969bac8fc2a141a6bd66605301c\",\n \"score\": \"0.5861572\",\n \"text\": \"def __label_mapper(label):\\n mapper = {'Normal': 0, 'SPB': 1, 'PVC': 2}\\n\\n y = mapper[label]\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3a0969bac8fc2a141a6bd66605301c\",\n \"score\": \"0.5861572\",\n \"text\": \"def __label_mapper(label):\\n mapper = {'Normal': 0, 'SPB': 1, 'PVC': 2}\\n\\n y = mapper[label]\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a274a971464dcea39d6f7e4557fdfc6\",\n \"score\": \"0.5835935\",\n \"text\": \"def parse_labels(response):\\n return {\\n label[\\\"Name\\\"]: round(label[\\\"Confidence\\\"], 2) for label in response[\\\"Labels\\\"]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85cd0b50d4f7e56e86fb4174627622a4\",\n \"score\": \"0.57522\",\n \"text\": \"def createDictLabels(labels):\\n\\n # Re-arange the Target vectors between [0..nClasses_train]\\n labels = labels.numpy()\\n unique_labels = np.unique(labels)\\n dictLabels = {val: i for i, val in enumerate(unique_labels)}\\n dictLabelsInverse = {i: val for i, val in enumerate(unique_labels)}\\n return dictLabels,dictLabelsInverse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f672874e892f63aa6ea3d583e73676\",\n \"score\": \"0.5692806\",\n \"text\": \"def pred2dict(self, data_sample: KIEDataSample) -> Dict:\\n result = {}\\n pred = data_sample.pred_instances\\n result['scores'] = pred.scores.cpu().numpy().tolist()\\n result['edge_scores'] = pred.edge_scores.cpu().numpy().tolist()\\n result['edge_labels'] = pred.edge_labels.cpu().numpy().tolist()\\n result['labels'] = pred.labels.cpu().numpy().tolist()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8597e07a428f06a2edb6628c46ea55\",\n \"score\": \"0.56730324\",\n \"text\": \"def security_label_map(self):\\n return {\\n 'tlp:white': 'marking-definition--613f2e26-407d-48c7-9eca-b8e91df99dc9',\\n 'tlp:green': 'marking-definition--34098fce-860f-48ae-8e50-ebd3cc5e41da',\\n 'tlp:amber': 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82',\\n 'tlp:red': 'marking-definition--5e57c739-391a-4eb3-b6be-7d15ca92d5ed',\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4097ce2e488537020e67eeefb95a1c9f\",\n \"score\": \"0.5661316\",\n \"text\": \"def labels_(self):\\n return {self.LABEL_LICIT: 0, \\n self.LABEL_ILLICIT: 1}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76f5ff631670f96d4c94921c3e71f283\",\n \"score\": \"0.5631069\",\n \"text\": \"def label_to_data_id(self):\\n return {\\\"Altitude\\\": DataEntryIds.CALCULATED_ALTITUDE,\\n \\\"MaxAltitude\\\": None,\\n \\\"State\\\": DataEntryIds.STATE,\\n \\\"Pressure\\\": DataEntryIds.PRESSURE,\\n \\\"Acceleration\\\": [DataEntryIds.ACCELERATION_X, DataEntryIds.ACCELERATION_Y,\\n DataEntryIds.ACCELERATION_Z]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52dd44d9e4a78fb4e3c24dab789e3f\",\n \"score\": \"0.56184167\",\n \"text\": \"def get_inference(self) -> dict:\\n return {'output': self.label_pred.argmax(dim=1).cpu().numpy(), 'image_id': self.image_id,\\n 'label_orig': self.label_original.cpu().numpy()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a41357c324e035f8e83729d700bbaa\",\n \"score\": \"0.55440646\",\n \"text\": \"def _to_dict(self):\\r\\n _dict = {}\\r\\n if hasattr(self, 'label') and self.label is not None:\\r\\n _dict['label'] = self.label\\r\\n if hasattr(self, 'value') and self.value is not None:\\r\\n _dict['value'] = self.value._to_dict()\\r\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9905b0d3ec8a3befe5a04470e86619a8\",\n \"score\": \"0.5542545\",\n \"text\": \"def map_labels(self, pgrm: List[Instr]) -> dict:\\n label_addrs = {}\\n for addr in range(len(pgrm)):\\n ins = pgrm[addr]\\n if ins.isLabel():\\n # duplicate labels are not allowed\\n if ins.label in label_addrs:\\n raise DuplicateLabel(self, ins.label)\\n label_addrs[ins.label] = addr + 1\\n return label_addrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c8e34141f389ddf01a7c14f6bc89b3\",\n \"score\": \"0.5541204\",\n \"text\": \"def process_label(intents, w2v):\\n class_dict = {}\\n label_vec = []\\n class_id = 0\\n for line in intents:\\n # check whether all the words in w2v dict\\n if line == 'Troubleshooting_Technical_Issues':\\n line = 'Trouble_shooting_Technical_Issues'\\n if '_' in line:\\n label = [w.lower().strip() for w in line.split('_')]\\n else:\\n label = [w.lower().strip() for w in line.split(' ')]\\n for w in label:\\n if not w2v.vocab.has_key(w.lower()):\\n print(\\\"not in w2v dict\\\", w)\\n # compute label vec\\n label_sum = np.sum([w2v[w] for w in label], axis = 0)\\n label_vec.append(label_sum)\\n # store class names => index\\n class_dict[' '.join(label)] = class_id\\n class_id = class_id + 1\\n return class_dict, np.asarray(label_vec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1acb9afb5c2f34b77cc683d1452008c\",\n \"score\": \"0.55404854\",\n \"text\": \"def get_dict(self):\\n return dict(label=self.__label, color=self.__color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25168796eef5e37b31fe841a8f68e388\",\n \"score\": \"0.55311346\",\n \"text\": \"def as_dict(self, use_count: int) -> Dict[Text, Any]:\\n\\n return {\\\"value\\\": self.name, \\\"id\\\": self.id, \\\"nlu_examples_count\\\": use_count}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b42b52f557f135ded8d1b421c50a6594\",\n \"score\": \"0.55262387\",\n \"text\": \"def get_label(example):\\n return example['label'].numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91f8ce2c9ac71f3c7b8b9fec37150b0\",\n \"score\": \"0.5513511\",\n \"text\": \"def to_dict():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a02ec62382cafc3e89bdfb4b805a48d\",\n \"score\": \"0.54940736\",\n \"text\": \"def convert_to_dict(self) -> dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da846cc22ec06c8d636d33a3ad7729c1\",\n \"score\": \"0.5470197\",\n \"text\": \"def get_k8s_labels_for_job(job: Job) -> Dict[str, str]:\\n return {\\n \\\"job_id\\\": job.id,\\n \\\"job_type\\\": job.target_type,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd642233c29bb7ac5a501f947aa94740\",\n \"score\": \"0.546146\",\n \"text\": \"def label(self):\\n return self.sess_element.get('label')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5358922f256e49e14047fa9b7f03fd7d\",\n \"score\": \"0.54249454\",\n \"text\": \"def parse_lexicon_entry(lexicon_entry: str) -> dict:\\n functional_labels = {}\\n lexicon_entry = lexicon_entry.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\").replace(\\\";\\\", \\\"\\\")\\n labels = lexicon_entry.split()\\n for label in labels:\\n parts = label.split('=')\\n if len(parts) == 2:\\n functional_labels[parts[0].strip()] = parts[1].strip()\\n return functional_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b56c3f021e318b3d2bd4911f0c259b0\",\n \"score\": \"0.54211986\",\n \"text\": \"def dict_from_pbtxt_file(fname):\\n lines = [line.rstrip('\\\\n').strip() for line in open(fname)]\\n label_map = {}\\n curr_label = ''\\n curr_id = 0\\n\\n for l in lines:\\n \\n if l.startswith( 'display_name: '):\\n curr_label = l.split(' ')[1]\\n\\n if l.startswith( 'id: '):\\n curr_id = int(l.split(' ')[1])\\n\\n if l.startswith( '}'):\\n # print(curr_id, curr_label)\\n label_map[curr_id] = curr_label.replace(\\\"\\\\\\\"\\\", \\\"\\\")\\n\\n return label_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c591496302f43289dbcfe9f64290c4dc\",\n \"score\": \"0.54123926\",\n \"text\": \"def _get_labels(self, truth_dict: Dict[str, Any]) -> Dict[str, Any]:\\n if \\\"pid\\\" in truth_dict.keys():\\n abs_pid = abs(truth_dict[\\\"pid\\\"])\\n sim_type = truth_dict[\\\"sim_type\\\"]\\n\\n labels_dict = {\\n self._index_column: truth_dict[self._index_column],\\n \\\"muon\\\": int(abs_pid == 13),\\n \\\"muon_stopped\\\": int(truth_dict.get(\\\"stopped_muon\\\") == 1),\\n \\\"noise\\\": int((abs_pid == 1) & (sim_type != \\\"data\\\")),\\n \\\"neutrino\\\": int(\\n (abs_pid != 13) & (abs_pid != 1)\\n ), # @TODO: `abs_pid in [12,14,16]`?\\n \\\"v_e\\\": int(abs_pid == 12),\\n \\\"v_u\\\": int(abs_pid == 14),\\n \\\"v_t\\\": int(abs_pid == 16),\\n \\\"track\\\": int(\\n (abs_pid == 14) & (truth_dict[\\\"interaction_type\\\"] == 1)\\n ),\\n \\\"dbang\\\": self._get_dbang_label(truth_dict),\\n \\\"corsika\\\": int(abs_pid > 20),\\n }\\n else:\\n labels_dict = {\\n self._index_column: truth_dict[self._index_column],\\n \\\"muon\\\": -1,\\n \\\"muon_stopped\\\": -1,\\n \\\"noise\\\": -1,\\n \\\"neutrino\\\": -1,\\n \\\"v_e\\\": -1,\\n \\\"v_u\\\": -1,\\n \\\"v_t\\\": -1,\\n \\\"track\\\": -1,\\n \\\"dbang\\\": -1,\\n \\\"corsika\\\": -1,\\n }\\n return labels_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128ab4da939efdf9dd0148cafa3655af\",\n \"score\": \"0.5407699\",\n \"text\": \"def set_labels(self,label:dict):\\n self.label_dict = label\\n print(\\\"[INFO] Label dictionary : \\\",label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"539fab3c7267134f9aaf80dd4114975a\",\n \"score\": \"0.54033065\",\n \"text\": \"def create_ans2label(occurence, name, cache_root):\\n label, label2ans, ans2label = 0, [], {}\\n for answer in occurence:\\n label2ans.append(answer)\\n ans2label[answer] = label\\n label += 1\\n\\n utils.create_dir(cache_root)\\n\\n cache_file = os.path.join(cache_root, name+'_ans2label.json')\\n json.dump(ans2label, open(cache_file, 'w'))\\n cache_file = os.path.join(cache_root, name+'_label2ans.json')\\n json.dump(label2ans, open(cache_file, 'w'))\\n return ans2label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb6f8f2e01bea7c93bc65ef696e2e90\",\n \"score\": \"0.5385623\",\n \"text\": \"def ark2dict(arkfile):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66257bd0326b0b14c54f97588f3cdf7\",\n \"score\": \"0.5378722\",\n \"text\": \"def labels(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed628b0e2b48af84e68ad32360623284\",\n \"score\": \"0.5369858\",\n \"text\": \"def get_label_yml(label):\\n text = f' - name: \\\"{label[\\\"name\\\"]}\\\"\\\\n'\\n text += f' color: \\\"{label[\\\"color\\\"]}\\\"\\\\n'\\n text += f' description: \\\"{label[\\\"description\\\"]}\\\"\\\\n'\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17632f7e3ac20d81e99fa2040dab8ec7\",\n \"score\": \"0.5362394\",\n \"text\": \"def predict(self, exemple, label):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17632f7e3ac20d81e99fa2040dab8ec7\",\n \"score\": \"0.5362394\",\n \"text\": \"def predict(self, exemple, label):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682cd2798e228da6619b8bebc9773bac\",\n \"score\": \"0.536196\",\n \"text\": \"def get_labels(*args):\\n return {name: value for name, value in LABELS.items() if name in args}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f67b8e2e2fab8e2bb8627eaa87a10a\",\n \"score\": \"0.53590834\",\n \"text\": \"def aspen_version_label_dict(self):\\n # 先以 version name 找到對應的 version label\\n l_list = []\\n for version_name in self.__version_list:\\n h_key = r.HKEY_CLASSES_ROOT(fr\\\"{version_name}\\\\shell\\\")\\n for item in h_key.subkeys():\\n if re.match(\\\"Open with Aspen Plus V\\\\d+.\\\", item.name): l_list.append(item.name)\\n\\n # 然後再將 name 與 label 變成字典\\n self.__version_label_dict = dict(zip(self.__version_list, l_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027bcc9fb054a27e1ac17e2efcdc9b6e\",\n \"score\": \"0.53537124\",\n \"text\": \"def labels(self) -> Mapping[str, Any]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027bcc9fb054a27e1ac17e2efcdc9b6e\",\n \"score\": \"0.53537124\",\n \"text\": \"def labels(self) -> Mapping[str, Any]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded56ba607f6d753abe84f7f517123c7\",\n \"score\": \"0.5336088\",\n \"text\": \"def get_labels(G: nx.classes.graph.Graph,\\n ml_target: str) -> Dict:\\n\\n labels = {}\\n for n in G.nodes(data=True):\\n labels[n[1]['id']] = n[1][ml_target]\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e352369c936c8f498d275251444e6afd\",\n \"score\": \"0.5334917\",\n \"text\": \"def my_fn(x):\\n input_label = tf.strings.join([input_feature, ':'], separator='')\\n inputs = tf.strings.join(\\n [prefix, input_label, x[input_feature]], separator=' ')\\n\\n class_label = tf.gather(output_classes, x['label'])\\n if drop_explanations:\\n targets = class_label\\n else:\\n targets = _explanation_targets(class_label, x['evidences'])\\n\\n return {'inputs': inputs, 'targets': targets}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0ced40754aa6036772658610bc2bf9\",\n \"score\": \"0.5326014\",\n \"text\": \"def labelToDescription(label) :\\n descriptions = [\\\"top\\\", \\\"trouser\\\", \\\"pullover\\\",\\n \\\"dress\\\", \\\"coat\\\", \\\"sandal\\\",\\n \\\"shirt\\\", \\\"sneaker\\\", \\\"bag\\\",\\n \\\"ankle boot\\\"]\\n return descriptions[label]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cd4eca35432c103fcc9faebc180417\",\n \"score\": \"0.5324759\",\n \"text\": \"def as_dict(self) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eeb479fb6f86530b46754b1b755a6e8\",\n \"score\": \"0.53247446\",\n \"text\": \"def load_labels(\\n location: PathLike = DEV_DATA,\\n expr_type: ExperimentType = ExperimentType.ils,\\n) -> Dict:\\n location = pathlib.Path(location) / f\\\"task-{expr_type.value}\\\"\\n # specify `latin-1` encoding due to an encoding error\\n reader = CSVReader(location, DataType.labels, encoding=\\\"latin-1\\\")\\n data = reader.read_all()\\n # an encoding error results in a bad column\\n del data[\\\"Unnamed: 0\\\"]\\n # the conventions for Subject, Date, and Run do not match folder names\\n # so we update them here to match the other data\\n pilot = data.Subject.apply(lambda x: f\\\"sub-cp{int(x):03d}\\\")\\n session = data.Date.apply(lambda x: f\\\"ses-{int(x)}\\\")\\n run = data.Run.apply(lambda x: f\\\"run-{int(x):03d}\\\")\\n del data[\\\"Subject\\\"]\\n del data[\\\"Date\\\"]\\n del data[\\\"Run\\\"]\\n data[\\\"pilot\\\"] = pilot\\n data[\\\"session\\\"] = session\\n data[\\\"run\\\"] = run\\n data = data.astype(\\n {\\n \\\"pilot\\\": \\\"category\\\",\\n \\\"session\\\": \\\"category\\\",\\n \\\"run\\\": \\\"category\\\",\\n }\\n )\\n return {\\\"labels\\\": data}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d79df5fea94e0393e2bcedf3986bb7\",\n \"score\": \"0.53228766\",\n \"text\": \"def getMetadataLabels(self):\\n return { \\\"title\\\": self.label }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600b0fc605315140ae662443197931a9\",\n \"score\": \"0.5321642\",\n \"text\": \"def to_dict(self) -> t.Dict[str, t.Any]:\\n return {k: getattr(self, k) for k in self._get_annotations()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934aea56fc6b067c8c106134a50cdd38\",\n \"score\": \"0.5319331\",\n \"text\": \"def make_metadata(self, margin, best_label):\\n return {\\n 'confidence': float(f'{margin: 1.2f}'),\\n 'job-name': self.job_name,\\n 'class-name': self.label_names[self.get_label_index(best_label)],\\n 'human-annotated': 'no',\\n 'creation-date': datetime.utcnow().strftime('%Y-%m-%dT%H:%m:%S.%f'),\\n 'type': JOB_TYPE\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f23662056a15ac30ce32db31294c94\",\n \"score\": \"0.53111476\",\n \"text\": \"def labels(observation):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9472a095408517cb9521788c519c2d04\",\n \"score\": \"0.5311099\",\n \"text\": \"def asdict(self) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0559ca65b68dc65a0ca91e319d6b497e\",\n \"score\": \"0.5298789\",\n \"text\": \"def individual_to_params(individual):\\n return dict(zip(GENE_LABELS, individual))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc19f087b25f791a49a04260b29e73f\",\n \"score\": \"0.52983963\",\n \"text\": \"def __init__(self, label):\\n self.assessor_label = label\\n self.is_session_assessor = False\\n self.is_scan_assessor = False\\n if len(re.findall('-x-', label)) == 3:\\n labels = label.split('-x-')\\n self.project_id = labels[0]\\n self.subject_label = labels[1]\\n self.session_label = labels[2]\\n self.proctype = labels[3]\\n self.scan_id = None\\n self.is_session_assessor = True\\n elif len(re.findall('-x-', label)) == 4:\\n labels = label.split('-x-')\\n self.project_id = labels[0]\\n self.subject_label = labels[1]\\n self.session_label = labels[2]\\n self.scan_id = labels[3]\\n self.proctype = labels[4]\\n self.is_scan_assessor = True\\n else:\\n self.assessor_label = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a220b6a1e2e7c48fe9e2cb49e1260583\",\n \"score\": \"0.52759767\",\n \"text\": \"def get_label_text(label_number):\\n mapping = {}\\n with open(CLASS_MAPPING_FILE, 'r') as fp:\\n for _, line in enumerate(fp):\\n terms = line.split('\\\\t')\\n # mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0d9280e9290e6a138216bd7dfa277c\",\n \"score\": \"0.52754885\",\n \"text\": \"def assignVar2label(node2var):\\n node2label = {}\\n for node in node2var.keys():\\n label = node2var[node].replace(\\\"x{0}?\\\".format(node),\\\"\\\")\\n node2label[node] = label\\n return node2label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed5eff77bad17151c399a09e9fa59fc\",\n \"score\": \"0.52524656\",\n \"text\": \"def getLabelInfo(self, label) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052f415ba3539cbf324771c2a51e3ade\",\n \"score\": \"0.5244683\",\n \"text\": \"def predict_label(text: str):\\n # Step 01: Predict the label\\n prediction_result = Classifier().get_instance().predict(text)\\n\\n # Step 02: Parse the prediction result.\\n predicted_label = str(prediction_result[0][0])\\n predicted_label = predicted_label.replace(\\\"__label__\\\", \\\"\\\").strip()\\n confidence = round(100 * prediction_result[1][0], 2)\\n\\n # Step 03: Return the result.\\n return predicted_label, confidence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccd7e38987d2f844bcc6165b7134aa2\",\n \"score\": \"0.52344626\",\n \"text\": \"def _impact_labels():\\n # Prevent reset upon auto reload in jupyter notebook\\n if not '_impact_labels' in builtins.__dict__:\\n builtins._impact_labels = dict()\\n\\n return builtins._impact_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ccfedb796d6d1e66eefa1e4f82c2bf3\",\n \"score\": \"0.5218931\",\n \"text\": \"def get_label(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f94c9e9cde815255bc44bbd40625af\",\n \"score\": \"0.5216753\",\n \"text\": \"def _collect_label(self, file_id, content):\\n label_dressed = dict()\\n label_dressed[file_id] = {cls:[] for cls in self.classes[1:]}\\n for line in content:\\n cls = line['field_name']\\n if cls in self.classes:\\n #identity = line.get('identity', 0) \\n label_dressed[file_id][cls].append( {'key_id':[], 'value_id':[], 'key_text':'', 'value_text':''} )\\n label_dressed[file_id][cls][-1]['key_id'] = line.get('key_id', [])\\n label_dressed[file_id][cls][-1]['value_id'] = line['value_id'] # value_id\\n label_dressed[file_id][cls][-1]['key_text'] = line.get('key_text', []) \\n label_dressed[file_id][cls][-1]['value_text'] = line['value_text'] # value_text\\n \\n # handle corrupted data\\n for cls in label_dressed[file_id]: \\n for idx, label in enumerate(label_dressed[file_id][cls]):\\n if len(label) == 0: # no relevant class in sample @file_id\\n continue\\n if (len(label['key_text'])>0 and len(label['key_id'])==0) or \\\\\\n (len(label['value_text'])>0 and len(label['value_id'])==0):\\n return None\\n \\n return label_dressed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65bbd161c3afec09e3b8f08ca88e803\",\n \"score\": \"0.51999146\",\n \"text\": \"def read_img_labels(img_label_path):\\n with open(img_label_path,\\\"r\\\") as img_label_file:\\n data_lines = img_label_file.readlines()\\n x1,y1,x2,y2 = data_lines[1].split(\\\"\\\\t\\\")\\n img_label_file.close()\\n return {\\\"x1\\\":int(x1),\\\"y1\\\":int(y1),\\\"x2\\\":int(x2),\\\"y2\\\":int(y2)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4087ce8dd5025c28aaf1b2839e13fbc7\",\n \"score\": \"0.5198736\",\n \"text\": \"def info(self):\\n sess_info = {}\\n\\n sess_info['ID'] = self.get('ID')\\n sess_info['label'] = self.get('label')\\n sess_info['note'] = self.get('xnat:note')\\n sess_info['session_type'] = self.get('session_type')\\n sess_info['project_id'] = self.project\\n sess_info['original'] = self.get('original')\\n sess_info['modality'] = self.get('modality')\\n sess_info['UID'] = self.get('UID')\\n sess_info['subject_id'] = self.get('xnat:subject_ID')\\n sess_info['subject_label'] = self.subject\\n sess_info['project_label'] = sess_info['project_id']\\n sess_info['project'] = sess_info['project_id']\\n sess_info['subject_ID'] = self.get('xnat:subject_ID')\\n sess_info['URI'] = '/data/experiments/%s' % sess_info['ID']\\n sess_info['session_label'] = sess_info['label']\\n sess_info['last_updated'] = sess_info['original']\\n sess_info['type'] = sess_info['modality']\\n\\n return sess_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37a930b620268ef093cfc437aed7126\",\n \"score\": \"0.519552\",\n \"text\": \"def create_edge_labels(game_board):\\n edge_labels = {}\\n for edge in game_board:\\n edge_labels[(edge[0][1],edge[1][1])] = edge[2]\\n return edge_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f814b51d76d48c8bb18edcfb568456a4\",\n \"score\": \"0.51852524\",\n \"text\": \"def prepare_labels(labels):\\n d = {}\\n count = 0\\n setlabels = set(labels)\\n for w in setlabels:\\n d[w] = count\\n count += 1\\n idxlabels = np.array([d[w] for w in labels])\\n return idxlabels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3517bee022723f6ccec853140d1105a\",\n \"score\": \"0.51809204\",\n \"text\": \"def load_labels():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf76a8614dd0971da88b77905e2fc4e\",\n \"score\": \"0.5176966\",\n \"text\": \"def parse_possible_labels(word_and_labels):\\n possible_labels = {}\\n for i, word_label in enumerate(word_and_labels):\\n word, label = word_label\\n if not word in possible_labels:\\n possible_labels[word] = set([label])\\n else:\\n possible_labels[word].add(label)\\n return possible_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1602671a0b225c0112a10ad9d168985\",\n \"score\": \"0.516992\",\n \"text\": \"def get_event_label_dict(self, pattern=None):\\n return self._parser_obj.get_event_label_dict(pattern=pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c106850bdecb0e3ed2f0672749e8c39a\",\n \"score\": \"0.51476675\",\n \"text\": \"def getAnnotationDict_old(self):\\n html = unicode(self.toHtml())\\n parser = etree.HTMLParser()\\n tree = etree.fromstring(html, parser)\\n aElements = tree.findall(\\\".//a[@name]\\\")\\n ret = {}\\n for aElement in aElements:\\n idAnnotation = aElement.attrib[\\\"name\\\"]\\n annotation = \\\"\\\"\\n if idAnnotation == \\\"title\\\":\\n for t in aElement.getparent().itertext():\\n annotation = annotation + t\\n else:\\n for t in aElement.getparent().getparent().getnext().itertext():\\n annotation = annotation + t\\n annotation = annotation.strip()\\n if annotation == self.strEmptyCharacter:\\n annotation = \\\"\\\"\\n ret[idAnnotation] = annotation\\n #print \\\"%s: %s\\\" % (idAnnotation, annotation)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c13318b1839e66f856baf62bdbc2a8\",\n \"score\": \"0.5146432\",\n \"text\": \"def _predict(self, X):\\n y_pred = self._automl.predict(X)\\n y_pred = self.dataset.inverse_transform_labels(y_pred)\\n return {self.Keys.PREDICTED: y_pred}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03d85f314da1c9918ef18cb4a44f6f2d\",\n \"score\": \"0.5137802\",\n \"text\": \"def _inputs_to_dict(**kwords):\\n return kwords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e225bb141c6c4534b5ede3fdc1325f\",\n \"score\": \"0.5136677\",\n \"text\": \"def convert_label_to_idx(self, label):\\n label = label.strip()\\n return class2label[label]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c1dc74b5197cd2a93f1f6ff98a39d3\",\n \"score\": \"0.5126444\",\n \"text\": \"def get_label_mapping(label_file):\\n with open(label_file, 'r') as f:\\n id2label = f.readlines()\\n id2label = [l.strip() for l in id2label]\\n label2id = {}\\n count = 0\\n for label in id2label:\\n label2id[label] = count\\n count += 1\\n return id2label, label2id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc824fa2eaf11763eb5ac1c3e652f035\",\n \"score\": \"0.51264375\",\n \"text\": \"def state_dict(self) -> Dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d521c56ad86ea4755e8f57bb22249b2\",\n \"score\": \"0.5117194\",\n \"text\": \"def pipeline_dict(request) -> dict:\\n pipeline_dict = {\\n \\\"name\\\": \\\"transformers_tokenizer_test\\\",\\n \\\"features\\\": {\\n \\\"transformers\\\": {\\\"model_name\\\": \\\"sshleifer/tiny-distilroberta-base\\\"}\\n },\\n \\\"head\\\": {\\n \\\"type\\\": \\\"TextClassification\\\",\\n \\\"labels\\\": [\\\"a\\\", \\\"b\\\"],\\n },\\n }\\n return pipeline_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"273cdf8275aee5da47809feb999008a6\",\n \"score\": \"0.5114229\",\n \"text\": \"def assignLabel2Var(node2label):\\n node2var = {}\\n for node in node2label.keys():\\n var = \\\"x{0}?{1}\\\".format(node,node2label[node])\\n node2var[node] = var\\n return node2var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd120c04e7b42408e98947a8f1313525\",\n \"score\": \"0.5107142\",\n \"text\": \"def pred2dict(self, data_sample: InstanceData) -> Dict:\\n result = {}\\n if 'pred_instances_3d' in data_sample:\\n pred_instances_3d = data_sample.pred_instances_3d.numpy()\\n result = {\\n 'bboxes_3d': pred_instances_3d.bboxes_3d.tensor.cpu().tolist(),\\n 'labels_3d': pred_instances_3d.labels_3d.tolist(),\\n 'scores_3d': pred_instances_3d.scores_3d.tolist()\\n }\\n\\n if 'pred_pts_seg' in data_sample:\\n pred_pts_seg = data_sample.pred_pts_seg.numpy()\\n result['pts_semantic_mask'] = \\\\\\n pred_pts_seg.pts_semantic_mask.tolist()\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2513229a6782c77f4434092e65b031\",\n \"score\": \"0.5099719\",\n \"text\": \"def prepare_labels(self, dots):\\n # The expected decoding is just the input.\\n labels = dots.copy()\\n labels[\\\"decode\\\"] = self._small_eye\\n # We don't really care about the encoded representation, so we can just set\\n # the labels to what the output already is.\\n labels[\\\"encode\\\"] = self.__encoded\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde4846796372364d21a33bce5154237\",\n \"score\": \"0.50925577\",\n \"text\": \"def _convert_labels_to_label_mapping(labels, requires_zero_mapping):\\n if isinstance(labels, dict):\\n return labels\\n\\n # if list\\n start_index = 0 if requires_zero_mapping else 1\\n return dict(zip(labels, list(\\n range(start_index, start_index + len(labels)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ff4d84e6e133ea25962d8bbbd9e4f7\",\n \"score\": \"0.50896394\",\n \"text\": \"def __getitem__(self, index):\\n\\n\\t\\treturn {'domain_a': super(MNISTG4LANDDataset, self).__getitem__(self.domain_a[index]),\\\\\\n\\t\\t\\t\\t 'domain_b': super(MNISTG4LANDDataset, self).__getitem__(self.domain_b[index]), \\\\\\n\\t\\t\\t\\t 'y_label': self.to_one_of_k(int(self.y_label[index]), 2)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0200eed5a69c4be12aabfeacd1717b34\",\n \"score\": \"0.50820607\",\n \"text\": \"def asDict(self):\\n return {\\n \\\"predominant_emotion\\\": self.predominateEmotion.name.lower(),\\n \\\"estimations\\\": {\\n \\\"anger\\\": self.anger,\\n \\\"disgust\\\": self.disgust,\\n \\\"fear\\\": self.fear,\\n \\\"happiness\\\": self.happiness,\\n \\\"sadness\\\": self.sadness,\\n \\\"surprise\\\": self.surprise,\\n \\\"neutral\\\": self.neutral,\\n },\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c964b69b4ddebf65a91b4aad2ebff6b6\",\n \"score\": \"0.50816333\",\n \"text\": \"def get_labels(self, encoding='shift-jis'):\\n label_dict = dict()\\n label_dict[0] = 'NULL'\\n data_length = len(self._data)\\n label_base_offset = data_length + len(self._p1_list) * 4 + \\\\\\n len(self._p2_list) * 8\\n for pointer1 in self._p1_list:\\n ptr = unpack('<I', self._data[pointer1:pointer1 + 0x4])[0]\\n if ptr < data_length:\\n continue\\n offset = ptr - label_base_offset\\n length = 0\\n while self._labels[offset + length] != '\\\\0':\\n length += 1\\n if encoding == 'shift-jis':\\n label_dict[ptr] = self._labels[offset:offset + length]\\n else:\\n label_dict[ptr] = self._labels[offset:offset + length]\\\\\\n .decode('shift-jis').encode(encoding)\\n return label_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d72f8114f2d918cfe22f97aaa30e6ef\",\n \"score\": \"0.5076148\",\n \"text\": \"def get_withLabel(self):\\r\\n \\r\\n return self.obj_dict['withLabel']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01411627b12e7d7719c93e3676f31c15\",\n \"score\": \"0.50739354\",\n \"text\": \"def encode_label(self, label):\\n if label not in self.labels_dict:\\n self.labels_dict[label] = self.labels[0].tolist()\\n self.labels = self.labels[1:]\\n return self.labels_dict[label]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6eba6101b0ac3a47779d426833d933\",\n \"score\": \"0.5068386\",\n \"text\": \"def read_labels(file_path):\\n with open(file_path, \\\"r\\\", encoding=\\\"utf-8\\\") as label_file:\\n lines = label_file.readlines()\\n labels = {}\\n for line in lines:\\n pair = line.strip().split(maxsplit=1)\\n labels[int(pair[0])] = pair[1].strip()\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d1c6b6ed925378a4b3a95ce633f4fb7\",\n \"score\": \"0.5066627\",\n \"text\": \"def to_dict(self):\\n return {\\n \\\"acc\\\": \\\"None\\\" if self.acc is None else \\\"%.2f%%\\\" % self.acc,\\n \\\"beatmap\\\": map_str(self.beatmap) if self.beatmap else \\\"None\\\",\\n \\\"mode\\\": \\\"Unknown\\\"\\n if self.mode is None\\n else consts.mode2str[self.mode], # noqa\\n \\\"mods\\\": combine_mods(self.mods),\\n \\\"player\\\": self.player.username if self.player else \\\"None\\\",\\n \\\"guest mapper\\\": self.guest_mapper.username\\n if self.guest_mapper\\n else \\\"None\\\", # noqa\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff22da51da5c377222eef8857f62027f\",\n \"score\": \"0.5066222\",\n \"text\": \"def convert_to_dict(data):\\n pass #TODO implement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9506ff1bd08b0c355c97b2ac92baa8be\",\n \"score\": \"0.5057204\",\n \"text\": \"def copa_preformat_fn(ex: Dict[str, Any]) -> Dict[str, Any]:\\n premise, question = ex['premise'].decode(), ex['question'].decode()\\n input_prompt = f'{premise} What was the {question} of this?'\\n choices = {\\n ex['choice1'].decode(): -ex['label'] + 1,\\n ex['choice2'].decode(): ex['label']\\n }\\n return {'input': input_prompt, 'target_scores': choices}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a70b02fe14b7c7385276179d9a27dd\",\n \"score\": \"0.5053743\",\n \"text\": \"def state(self) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28eda5082edf2ef8912608cde15eb812\",\n \"score\": \"0.5053538\",\n \"text\": \"def labels_str_to_rule_format(labels_string: str, gc_api: RESTManagementAPI) -> Dict[str, List[Dict[str, List[str]]]]:\\r\\n structured_labels = {\\\"or_labels\\\": list()}\\r\\n # Normalize spaces\\r\\n labels_string = labels_string.replace(\\\", \\\", \\\",\\\").replace(\\r\\n \\\" ,\\\", \\\",\\\").replace(\\\"& \\\", \\\"&\\\").replace(\\\" &\\\", \\\"&\\\")\\r\\n labels_string = labels_string.replace(\\\": \\\", \\\":\\\").replace(\\\" :\\\", \\\":\\\").strip()\\r\\n\\r\\n for or_label in labels_string.split(','):\\r\\n and_labels = {\\\"and_labels\\\": list()}\\r\\n for and_label in or_label.split('&'):\\r\\n key, value = and_label.split(':')\\r\\n and_labels[\\\"and_labels\\\"].append(get_label_id(key, value, gc_api))\\r\\n structured_labels[\\\"or_labels\\\"].append(and_labels)\\r\\n return structured_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a277cd529f0f474490c6356f7b55901\",\n \"score\": \"0.5050749\",\n \"text\": \"def labels(self) -> pulumi.Output[Mapping[str, str]]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a277cd529f0f474490c6356f7b55901\",\n \"score\": \"0.5050749\",\n \"text\": \"def labels(self) -> pulumi.Output[Mapping[str, str]]:\\n return pulumi.get(self, \\\"labels\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45af93715635948522e972981dd6120\",\n \"score\": \"0.5049098\",\n \"text\": \"def state_to_dict(self):\\n self.subsetDict = {}\\n for butTyp, buttons in self.toggleStates.items():\\n l = []\\n for butName, state in buttons.items():\\n if state is True:\\n label = self.builder.get_object(butName).get_label()\\n l.append(label)\\n self.subsetDict[butTyp] = l\\n\\n self.subsetDict['MeanFit'] = \\\\\\n self.builder.get_object('meanFitSpinButton').get_value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095634c58a7f68b63677c96733401452\",\n \"score\": \"0.5043671\",\n \"text\": \"def to_dict(self, target):\\n\\n\\t\\tret_dic = {}\\n\\n\\t\\tret_dic[\\\"name\\\"] = target.get_name()\\n\\n\\t\\treturn ret_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb70d06191a636f50c62fbed2d84c5e\",\n \"score\": \"0.5039761\",\n \"text\": \"def label_to_name(self, label):\\n return self.labels[label]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993996,"cells":{"query_id":{"kind":"string","value":"7ce80f9f8797379e4a6aa6862d2a224d"},"query":{"kind":"string","value":"Update the cell with new input x_t = the new input"},"positive_passages":{"kind":"list like","value":[{"docid":"181e455170603e85e323fcba7a64b0ba","score":"0.78416604","text":"def update(self, x_t):\n # the output of the previous update\n h_t_0 = self.h_t\n \n # the previous cell content \n C_t_0 = self.C_t\n\n # step 1: Decide what information to forget\n f_t = forget_gate(h_t_0, x_t, self.W_f, self.U_f, self.b_f)\n\n # step 2: Decide what information to put in the cell\n # 2.1 input gate decides which values to update\n # 2.2 tanh layer creates vector of new candidate values\n i_t, Ctilde_t = input_gate(h_t_0, x_t, self.W_i, self.U_i, self.b_i, self.W_c, self.U_c, self.b_c)\n\n # step 3: Update old cell staet Ct-1 into new cell state Ct\n # 3.1 multiply old state by ft to forget\n # 3.2 add it * C_t, new scaled candidates\n C_t = update_cell_state(f_t, C_t_0, i_t, Ctilde_t)\n\n # step 4: Decide what the output is\n # 4.1 Run a signmoid layer to decide what part to output\n # 4.2 Put a cell through tanh -> transform to range [-1, 1]\n h_t = output_gate(h_t_0, x_t, self.W_o, self.U_o, self.b_o, C_t)\n\n # Update cell values, output and the time\n self.h_t = h_t\n self.C_t = C_t\n self.t += 1","title":""}],"string":"[\n {\n \"docid\": \"181e455170603e85e323fcba7a64b0ba\",\n \"score\": \"0.78416604\",\n \"text\": \"def update(self, x_t):\\n # the output of the previous update\\n h_t_0 = self.h_t\\n \\n # the previous cell content \\n C_t_0 = self.C_t\\n\\n # step 1: Decide what information to forget\\n f_t = forget_gate(h_t_0, x_t, self.W_f, self.U_f, self.b_f)\\n\\n # step 2: Decide what information to put in the cell\\n # 2.1 input gate decides which values to update\\n # 2.2 tanh layer creates vector of new candidate values\\n i_t, Ctilde_t = input_gate(h_t_0, x_t, self.W_i, self.U_i, self.b_i, self.W_c, self.U_c, self.b_c)\\n\\n # step 3: Update old cell staet Ct-1 into new cell state Ct\\n # 3.1 multiply old state by ft to forget\\n # 3.2 add it * C_t, new scaled candidates\\n C_t = update_cell_state(f_t, C_t_0, i_t, Ctilde_t)\\n\\n # step 4: Decide what the output is\\n # 4.1 Run a signmoid layer to decide what part to output\\n # 4.2 Put a cell through tanh -> transform to range [-1, 1]\\n h_t = output_gate(h_t_0, x_t, self.W_o, self.U_o, self.b_o, C_t)\\n\\n # Update cell values, output and the time\\n self.h_t = h_t\\n self.C_t = C_t\\n self.t += 1\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9b44555f42e0d42190d1746033231b46","score":"0.70777196","text":"def Updatev(Cell,dt):","title":""},{"docid":"c6a57999dc4ecee8ddd36a67f5fc584f","score":"0.680216","text":"def recurrent_cell(self, st, t):\n\n\t\tz = self.z[t-par['latency'],..., cp.newaxis]\n\t\tx = self.input_data[t,:,:,cp.newaxis]\n\n\t\t# Update the input traces based on presynaptic spikes\n\t\tcurr_beta = self.con_dict[par['spike_model']]['beta']\n\t\tst['ia'] = curr_beta * st['ia'] + (1-curr_beta) * self.eff_var['W_in'] * x\n\t\tst['ir'] = curr_beta * st['ir'] + (1-curr_beta) * self.eff_var['W_rnn'] * st['sx'] * st['su'] * z\n\n\t\tst['ja'] = curr_beta * st['ja'] + (1-curr_beta) * x\n\t\tst['jr'] = curr_beta * st['jr'] + (1-curr_beta) * st['sx'] * st['su'] * z\n\n\t\t#print( 'I', cp.mean(st['ia']), cp.mean(st['ir']))\n\n\t\t# Update the synaptic plasticity state (recurrent only; input is static)\n\t\tst['sx'], st['su'] = \\\n\t\t\tsynaptic_plasticity(st['sx'], st['su'], z, self.con_dict, par['use_stp'])\n\n\t\t# Sum the input currents into shape [batch x postsynaptic]\n\t\tI = cp.sum(st['ia'], axis=1, keepdims=True) + cp.sum(st['ir'], axis=1, keepdims=True)\n\n\t\t# Update the AdEx cell state with the input current\n\t\tst['v'], st['w'], self.z[t,...] = run_spike_model(st['v'], st['w'], I, par['spike_model'], self.con_dict[par['spike_model']])\n\n\t\t# Update output trace based on postsynaptic cell state (Eq. 12)\n\t\tself.y[t,...] = self.con_dict[par['spike_model']]['kappa'] * self.y[t-1,...] + self.z[t,...] @ self.eff_var['W_out'] + self.eff_var['b_out']\n\n\t\t# Calculate h, the pseudo-derivative (Eq. 5, ~24, 20/21)\n\t\t# Bellec et al., 2018b\n\t\tif par['spike_model'] == 'adex':\n\t\t\tT = self.con_dict['adex']['V_T'] + par['betagrad']\n\t\telif par['spike_model'] == 'izhi':\n\t\t\tT = self.con_dict['izhi']['c'] + par['betagrad']\n\t\telse:\n\t\t\traise Exception('Unimplemented pseudo-derivative.')\n\n\t\tself.h[t,...] = cp.squeeze(par['gamma_psd'] * cp.maximum(0., \\\n\t\t\t1 - cp.abs(st['v'] - T)/par['pseudo_th']))\n\n\n\t\t#h = par['gamma_psd'] * cp.maximum(0., 1 - cp.abs((st['v'] + 40e-3)/par['pseudo_th']))\n\t\t#h = par['gamma_psd'] * cp.ones_like(h)\n\t\treturn st, I","title":""},{"docid":"dc0f3904f07f7bcde140e1aaaf940c80","score":"0.6566709","text":"def updateX(self):\n pass","title":""},{"docid":"c1e2463db041448fceeabccb2a4a6b88","score":"0.646216","text":"def update_state(s_t, x_t1_col):\n x_t1 = resize(rgb2gray(x_t1_col), (84, 84))\n x_t1 = np.reshape(x_t1, (84, 84, 1))\n s_t1 = np.append(x_t1, s_t[:,:,:3], axis = 2)\n return s_t1","title":""},{"docid":"78134ac058a0b38a33fce84ad5e1877e","score":"0.64159375","text":"def update(self, t=1):\n self.pos = np.add(self.pos, t * self.v + self.acc * (1/2 * t ** 2))\n self.v = np.add(self.v, t * self.acc)","title":""},{"docid":"a3f40f73b1c51c8cff1c780e09077448","score":"0.64041686","text":"def update_x(self):\n self.x += self.v*self.dt\n return self.x","title":""},{"docid":"9046340e02eafb4ee9a95513082431df","score":"0.63605344","text":"def update(self, x: numbers.Number):\n raise NotImplementedError","title":""},{"docid":"aaaf263f71ab36ff80241bcb1aae0332","score":"0.6114706","text":"def update(self, x=None, y=None):\n if x:\n self.x = x\n elif y:\n self.y = y\n self.t += 1","title":""},{"docid":"53229c5c445f360c702bd26bf38780a2","score":"0.5980773","text":"def update_x(x_current_l, v_current_l, a_current_l, dt_l):\n\n x_new = x_current_l + v_current_l * dt_l + 0.5 * a_current_l * (dt_l ** 2)\n\n return x_new","title":""},{"docid":"358eb0c7aac3a64c2245a51548dee07b","score":"0.5942913","text":"def update(self, t, dt):\n\n festim.update_expressions(self.expressions, t)\n\n converged = False\n u_ = Function(self.u.function_space())\n u_.assign(self.u)\n while converged is False:\n self.u.assign(u_)\n nb_it, converged = self.solve_once()\n if dt.adaptive_stepsize is not None:\n dt.adapt(t, nb_it, converged)\n\n # Update previous solutions\n self.update_previous_solutions()\n\n # Solve extrinsic traps formulation\n self.traps.solve_extrinsic_traps()","title":""},{"docid":"1043adf7a7ebbc786aaddcd414d062dc","score":"0.59254026","text":"def update(self, value, t):\n self._freshen(t)\n self.weight += value","title":""},{"docid":"c63c597d065ebc59a11a45c8ccd2a338","score":"0.59201956","text":"def propogate_state(x_t_prev, u_t, d_t):\n \"\"\"STUDENT CODE START\"\"\"\n #x_bar_t = np.empty()\n \"\"\"STUDENT CODE END\"\"\"\n\n return #x_bar_t","title":""},{"docid":"2be5a9de279fd42c61aef124a0fa10d7","score":"0.58918077","text":"def update(self, input_val, t_step):\n \n self.ode.set_f_params(input_val)\n self.ode.integrate(self.ode.t + t_step)\n return self.ode.successful()","title":""},{"docid":"21f29b7ea1ef341c8abb8e33dab2025a","score":"0.58527356","text":"def update(self, x, y):","title":""},{"docid":"21f29b7ea1ef341c8abb8e33dab2025a","score":"0.58527356","text":"def update(self, x, y):","title":""},{"docid":"21f29b7ea1ef341c8abb8e33dab2025a","score":"0.58527356","text":"def update(self, x, y):","title":""},{"docid":"8c2b004e391c01b02d326434c0cc5f7d","score":"0.58463764","text":"def UpdateU(Cell,dt):\n\tfor i in Cell:\n\t\tfac = dt/Cell[i][\"V\"]\n\t\t\n\t\tCell[i][\"Unew\"] = Cell[i][\"U\"]-fac*(\n\t\tCell[i][\"Side2\"][4]*Cell[i][\"Normalvector2\"][0]+\n\t\tCell[i][\"Side4\"][4]*Cell[i][\"Normalvector4\"][0]\n\t\t)\n\t\t\n\t\t\n\t\t#print(\"Side2 -> {}\".format(Cell[i][\"Side2\"][4]*Cell[i][\"Normalvector2\"][0]))\n\n\t\t#print(\"Side4 -> {}\".format(Cell[i][\"Side4\"][4]*Cell[i][\"Normalvector4\"][0]))\n\n\t\n\tfor i in Cell:\n\t\tCell[i][\"U\"] = Cell[i][\"Unew\"]","title":""},{"docid":"66c7ab07460389fabcb05cd0664548a7","score":"0.5781401","text":"def set_cval(self, x, y, val):\n self.t.update_cell(y, x, val)","title":""},{"docid":"a18f43ed60b5e10dbaf0526a5f080d39","score":"0.57679224","text":"def update(self, X, accepted, S, r):\n self.t += S + accepted*X # update time state","title":""},{"docid":"a18f43ed60b5e10dbaf0526a5f080d39","score":"0.57679224","text":"def update(self, X, accepted, S, r):\n self.t += S + accepted*X # update time state","title":""},{"docid":"02810d5df0bf0c908f57547b38bd13db","score":"0.575226","text":"def b_node_set_button(self, event):\n\n new_x = float(self.x_input.GetValue())\n new_y = float(self.y_input.GetValue())\n\n if self.i == self.node_layer_reference:\n xt = np.array(self.plotx)\n yt = np.array(self.ploty)\n\n if self.boundary_lock_list[self.i] == 0 and self.index_node is not None:\n if xt[self.index_node] == 0 and yt[self.index_node] != 0.001:\n xt[self.index_node] = 0 # REPLACE OLD X WITH NEW X\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\n elif xt[self.index_node] == self.x2 and yt[self.index_node] != 0.001:\n xt[self.index_node] = self.x2 # REPLACE OLD X WITH NEW X\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\n elif xt[self.index_node] == 0 and yt[self.index_node] == 0.001:\n xt[self.index_node] = 0 # REPLACE OLD X WITH NEW X\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\n elif xt[self.index_node] == self.x2 and yt[self.index_node] == 0.001:\n xt[self.index_node] = self.x2 # REPLACE OLD X WITH NEW X\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\n elif new_y <= 0:\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\n else:\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\n elif self.boundary_lock_list[self.i] == 1:\n if new_y <= 0:\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\n else:\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\n\n # DEAL WITH PINCHED NODE\n if self.pinch_switch != 0:\n for k in range(0, len(self.index_arg2_list)):\n if self.index_arg2_list[k] is not None:\n next_x_list = self.plotx_list[k]\n next_y_list = self.ploty_list[k] # GET THE NODE LIST OF THE NEXT LAYER\n next_x_list[self.index_arg2_list[k]] = new_x\n next_y_list[self.index_arg2_list[k]] = new_y # REPLACE THE PINCHED NODE WITH THE NEW NODE\n self.plotx_list[k] = next_x_list\n self.ploty_list[k] = next_y_list # OVERWRITE THE NODE LIST WITH UPDATED LIST\n\n self.plotx = xt\n self.ploty = yt\n self.polyline.set_data(self.plotx, self.ploty)\n\n # COLOR CURRENTLY SELECTED NODE RED\n self.current_node.set_offsets([new_x, new_y])\n else:\n pass\n\n # UPDATE LAYER DATA\n self.set_density(self)\n self.set_susceptibility(self)\n self.set_angle_a(self)\n self.set_angle_b(self)\n\n # UPDATE GMG\n self.update_layer_data()\n self.run_algorithms()","title":""},{"docid":"f8e0915abc6df761f4b67f43fe5e2885","score":"0.5723508","text":"def _update_eta(self, t) -> None:\n # self.eta *= DTYPE((t + 1) / (t + 2))","title":""},{"docid":"e4d2e0cd8669d4dcb3fefc565c9a8bb8","score":"0.5714379","text":"def OnCellEdit(self,event):\n row,col = (event.GetRow(),event.GetCol())\n cval = self.GetCellValue(row,col)\n if cval.startswith(\"=\"):\n try:\n cval = str(eval(cval[1:]))\n self.SetCellValue(row,col,cval)\n except:\n pass\n try:\n cval = float(cval)\n except ValueError:\n cval = np.nan\n self.SetCellValue(row,col,str(cval))","title":""},{"docid":"ed9714fab24a235615465ff53a2f338b","score":"0.56973976","text":"def insert_number(self, x,y,value):\n M = self.actual_state.reshape((9,9))\n M[x][y] = value\n self.actual_state= M.reshape(-1)","title":""},{"docid":"b4b0c92854b38870a16f8441e55bd9e3","score":"0.5661427","text":"def update_temperature(self):\n self.T = self.T - (self.T0 / self.iterations)\n\n # Exponential would look like this:\n # alpha = 0.99\n # self.T = self.T * alpha\n\n # where alpha can be any value below 1","title":""},{"docid":"c6e87a26a2018dcdce811aa7e50dffda","score":"0.5647182","text":"def update(self, t, x):\n # For readability\n g = self.g\n m_p, m_c = self.m_p, self.m_c\n l = self.l\n s, c = np.sin(x[2]), np.cos(x[2])\n u = self.u(t, x)\n dx = np.zeros((4,))\n\n if not self.manipulator_eqns:\n den = (m_c + m_p*s**2)\n q_ddot = np.zeros((2,1))\n q_ddot[0] = (1/den)*(u + m_p*s*(l*x[3]**2 + g*c))\n q_ddot[1] = (1/(l*den))*(-u*c - m_p*l*x[3]**2*c*s - (m_c + m_p)*g*s)\n else:\n # Mass matrix\n M = np.array([[ m_c + m_p, m_p*l*c ],\n [ m_p*l*c, m_p*l**2]])\n # Coriolis terms\n C = np.array([[ 0, -m_p*l*x[3]*s],\n [ 0, 0 ]])\n # Torque\n tau_g = -g*np.array([[ 0 ],\n [ m_p*l*s]])\n # B\n B = np.array([[ 1],\n [ 0]])\n # Calculate angular acceleration\n q_dot = np.array([[x[1]],\n [x[3]]])\n Cv = np.dot(C, q_dot)\n q_ddot = np.dot(np.linalg.inv(M), (tau_g + np.dot(B, u) - Cv))\n\n dx[0] = x[1]\n dx[1] = q_ddot[0]\n dx[2] = x[3]\n dx[3] = q_ddot[1]\n return dx","title":""},{"docid":"2edbb8665b5b87c014d43afdb8d4f83c","score":"0.5644153","text":"def xt_calculate(self):\r\n\r\n for i in range(0, len(self.obs.obt_g)):\r\n t = float(self.obs.obt[i]) / self.nu_t\r\n self.t.append(t)\r\n self.t_err.append(0.5 * (1.0 / 2.0) / self.nu_t) # 0.5 is bar\r\n x = float(self.obs.obx[i]) / self.nu_x\r\n self.x.append(x)\r\n print 'coordinate calculated'","title":""},{"docid":"c7acaecb0f5998785d0f1ead39ebe967","score":"0.5632091","text":"def setCell(self, x, y, val):\r\n\t\tself.list[x][y].set(val)","title":""},{"docid":"569abfee28be703d1ad95a8bad198937","score":"0.56252295","text":"def update_matrix(self):\r\n Xleft, Xright = self.X[1:-1,:-2], self.X[1:-1,2:]\r\n Xtop, Xbottom = self.X[:-2,1:-1], self.X[2:,1:-1]\r\n self.X[1:-1,1:-1] += self.dt*(Xleft+Xright+Xtop+Xbottom-4*self.X[1:-1,1:-1])/self.dx**2","title":""},{"docid":"72041ce1f73d7550243e90e61300dc6b","score":"0.562036","text":"def update(self,x,win,t):\n # eta(t) = eta(0) / (1 + t/T)\n # keeps the learning rate nearly constant for the first T iterations and then adjusts it\n eta = self.learning_rate/(1+t/self.T)\n sig = self.sigma/(1+t/self.T) # sigma and learning rate decrease with the same rule\n g = self.neighborhood(win,sig)*eta # improves the performances\n it = nditer(g, flags=['multi_index'])\n while not it.finished:\n # eta * neighborhood_function * (x-w)\n self.weights[it.multi_index] += g[it.multi_index]*(x-self.weights[it.multi_index])\n # normalization\n #self.weights[it.multi_index] = self.weights[it.multi_index] / linalg.norm(self.weights[it.multi_index])\n it.iternext()","title":""},{"docid":"ede566b0e2fd8a6f7ae19e7896196b71","score":"0.5601493","text":"def update(self):\n self.x += self.vx","title":""},{"docid":"ede566b0e2fd8a6f7ae19e7896196b71","score":"0.5601493","text":"def update(self):\n self.x += self.vx","title":""},{"docid":"4d8bb3ff7e80fbcb029346c58621d855","score":"0.55964744","text":"def update(self, x):\n if self.first_pass:\n self.means = np.mean(x, axis=0)\n self.vars = np.var(x, axis=0)\n self.m = x.shape[0]\n self.first_pass = False\n else:\n n = x.shape[0]\n new_data_var = np.var(x, axis=0)\n new_data_mean = np.mean(x, axis=0)\n new_data_mean_sq = np.square(new_data_mean)\n new_means = ((self.means * self.m) + (new_data_mean * n)) / (self.m + n)\n self.vars = (((self.m * (self.vars + np.square(self.means))) +\n (n * (new_data_var + new_data_mean_sq))) / (self.m + n) -\n np.square(new_means))\n self.vars = np.maximum(0.0, self.vars)\n self.means = new_means\n self.m += n","title":""},{"docid":"b93147f44ff41df185549d0c27733cc5","score":"0.55931485","text":"def update_tau(self, r, s, val):\n self.tau_mat[r][s] = val","title":""},{"docid":"b8f4733e6b2fac54e3b85a9f88c33070","score":"0.55798584","text":"def _update1D(self, x, output):\n msg = \"Plot updating ... \"\n wx.PostEvent(self.parent, StatusEvent(status=msg, type=\"update\"))","title":""},{"docid":"8c0b4365f11b4a262e9ef84d0057a8ca","score":"0.5555576","text":"def update(self, t):\n x = np.zeros((3,))\n x_dot = np.zeros((3,))\n x_ddot = np.zeros((3,))\n x_dddot = np.zeros((3,))\n x_ddddot = np.zeros((3,))\n yaw = 0\n yaw_dot = 0\n\n # STUDENT CODE HERE\n\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\n 'yaw': yaw, 'yaw_dot': yaw_dot}\n\n num_points = len(self.points)\n distances = np.zeros((num_points - 1, 3)) # matrix of distances between points\n for i in range(num_points - 1): # calculate distances between points\n distances[i] = self.points[i + 1] - self.points[i]\n\n times = np.linalg.norm(distances, axis=1) # calculate times btwn points based on distance\n times = np.cumsum(times)\n # times = times.reshape(-1, 1)\n times = np.insert(times, 0, 0, axis=0) # add t = 0 to first element of time vector\n times = times / 3.0\n\n for ind, j in enumerate(times):\n if t == times[0]: # QC at initial position at t = 0\n x = self.points[0]\n break\n elif t >= times[-1]: # if actual time is greater than time for final position, keep QC at final position\n x = self.points[-1]\n break\n elif ind == self.points.shape[0] - 1:\n break\n elif j < t < times[ind + 1]:\n x[0], x_dot[0], x_ddot[0] = self.posvel(ind, self.points[ind], times, t, flat_output, 'x')\n x[1], x_dot[1], x_ddot[1] = self.posvel(ind, self.points[ind], times, t, flat_output, 'y')\n x[2], x_dot[2], x_ddot[2] = self.posvel(ind, self.points[ind], times, t, flat_output, 'z')\n # x[0], x_dot[0] = self.posvel(ind, self.points[ind], times, t, flat_output, 'x')\n # x[1], x_dot[1] = self.posvel(ind, self.points[ind], times, t, flat_output, 'y')\n # x[2], x_dot[2] = self.posvel(ind, self.points[ind], times, t, flat_output, 'z')\n\n x_ddot = x_ddot.clip(max=3)\n x_ddot = x_ddot.clip(min=-3)\n x_dot = x_dot.clip(max=2)\n x_dot = x_dot.clip(min=-2)\n\n\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\n 'yaw': yaw, 'yaw_dot': yaw_dot}\n return flat_output","title":""},{"docid":"a03f5fc1aa9a5753b03d466b43b55ce6","score":"0.55434877","text":"def update(self):\n for i in range(len(self.previous)-1):\n xi = self.previous[i]\n xip1 = self.previous[i+1]\n for field in xi:\n field.assign(xip1(field.name()))\n for field in self.n:\n field.assign(self.np1(field.name()))","title":""},{"docid":"ab6e6d4682da055accac164032d4f637","score":"0.55409485","text":"def sketch(self, val):\n row, col = self.selected\n self.cells[row][col].set_temp(val)","title":""},{"docid":"10cb68592baf69606f5ada6df7624261","score":"0.55309683","text":"def set_x(self, new_x):\r\n check_value(new_x)\r\n self.x = new_x","title":""},{"docid":"704829bdd6fd42e5a71e54f9314ead05","score":"0.5528827","text":"def update(self, x):\n if self.first_pass:\n self.means = np.mean(x, axis=0)\n self.vars = np.var(x, axis=0)\n self.m = x.shape[0]\n self.first_pass = False\n else:\n n = x.shape[0]\n new_data_var = np.var(x, axis=0)\n new_data_mean = np.mean(x, axis=0)\n new_data_mean_sq = np.square(new_data_mean)\n new_means = ((self.means * self.m) + (new_data_mean * n)) / (self.m + n)\n self.vars = (((self.m * (self.vars + np.square(self.means))) +\n (n * (new_data_var + new_data_mean_sq))) / (self.m + n) -\n np.square(new_means))\n self.vars = np.maximum(0.0, self.vars) # occasionally goes negative, clip\n self.means = new_means\n self.m += n","title":""},{"docid":"979b53148475dbc04cce2d71376eb002","score":"0.5515013","text":"def update(self, x):\n diff = (x - self.W) # compute difference matrix\n bmu_idx = self.find_bmu(x, diff_matrix=diff)\n u = self.W[bmu_idx,:]\n\n for n in range(self.W.shape[0]):\n v = tf.expand_dims(self.W[n,:],axis=0)\n dist_uv = float( tf.norm(u - v, ord=\"euclidean\") )\n if dist_uv < (self.radius * self.radius):\n oh = tf.expand_dims(tf.one_hot(n, depth=self.W.shape[0]),axis=1)\n wght = self.calc_neighborhood_weights(u, v, sigma=self.radius)\n dW = tf.matmul(oh, (x - v) * wght * self.eta)\n self.W = self.W + dW\n self.s += 1","title":""},{"docid":"18f7bfeca0c45840d82cb6e8cfb06333","score":"0.5498006","text":"def set_cell(self, x):\n if isinstance(x, str): \n apart = None # letter part of a1-style reference\n npart = None # numeric part of a1-style reference \n \n #SN_1 shorter version\n search_result = re.search(\"^(\\D*)(\\d*)$\", x)\n if search_result is not None:\n apart = search_result.group(1)\n npart = search_result.group(2)\n # ***\n \n if apart and npart:\n a1ref = apart.upper() + npart\n self.rowx, self.colx = xlsxwriter.utility.xl_cell_to_rowcol(a1ref) \n elif npart:\n rowx = int(npart) \n elif apart:\n _proxy_cell = apart.upper() + '1'\n self.colx = xlsxwriter.utility.xl_cell_to_rowcol(_proxy_cell)[1] \n elif isinstance(x,int):\n self.rowx = x - 1","title":""},{"docid":"fe26268a8253b9ea378461263852bffe","score":"0.54898095","text":"def ForcePoint(self,x_next):\n \n # change position of interface and get resulting y-value\n self.mi.setX(x_next)\n if(self.acq_func[0] == 'testEI'):\n (x_new, y_new) = (x_next, self.acq_func[2].iloc[ind,-1])\n else:\n (x_new, y_new) = self.mi.getState()\n # add new entry to observed data\n self.X_obs = np.concatenate((self.X_obs,x_new),axis=0)\n self.Y_obs.append(y_new)\n \n # update the model (may want to add noise if using testEI)\n self.model.update(x_new, y_new)","title":""},{"docid":"4a4daa4082d05f1271091a4966977ed4","score":"0.5485281","text":"def update(self, xnew, ynew):\n y = np.concatenate((self.y, ynew), axis=0)\n X = np.concatenate((self.X, xnew), axis=0)\n self.fit(X, y)","title":""},{"docid":"4a4daa4082d05f1271091a4966977ed4","score":"0.5485281","text":"def update(self, xnew, ynew):\n y = np.concatenate((self.y, ynew), axis=0)\n X = np.concatenate((self.X, xnew), axis=0)\n self.fit(X, y)","title":""},{"docid":"1c91b80ef69c13a91c9b8795ceb8ac16","score":"0.54694533","text":"def update_hyperparams(self):\n self.eta_t = self.eta_t * self.f\n if self.epoch < self.T:\n self.p_t = (1 - ((self.epoch + 1) / self.T)) * self.p_i + \\\n ((self.epoch + 1) / self.T) * self.p_f\n else:\n self.p_t = self.p_f","title":""},{"docid":"f81cb9ba3bd0fd01e4f7ffce7e1cc973","score":"0.546051","text":"def update(self, x, win, t):\n eta = self._decay_function(self.learning_rate, t, self.T)\n sig = self._decay_function(self.sigma, t, self.T) # sigma and learning rate decrease with the same rule\n g = self.neighborhood(win, sig)*eta # improves the performances\n it = nditer(g, flags=['multi_index'])\n while not it.finished:\n # eta * neighborhood_function * (x-w)\n self.weights[it.multi_index] += g[it.multi_index]*(x-self.weights[it.multi_index])\n # normalization\n self.weights[it.multi_index] = self.weights[it.multi_index] / fast_norm(self.weights[it.multi_index])\n it.iternext()","title":""},{"docid":"689988474378fdff280486b1f5479684","score":"0.54528725","text":"def setCell(self, x, y, val, updateGUI):\r\n\t\t# Set the grid value for the puzzle at x-y to val\r\n\t\tself.grid[x][y] = val\r\n\t\tif val != 0:\r\n\t\t\t# Only update the nodes expanded if we are not resetting the cell value back to 0\r\n\t\t\tself.nodesExpanded += 1\r\n\t\tif updateGUI and not self.commandLine:\r\n\t\t\t# If asked, update the gui with new cell values too\r\n\t\t\tself.gui.setCell(x,y,val)\r\n\t\t\tself.update_gui()","title":""},{"docid":"75ee58d618b9dd67a78f3a9fb947287c","score":"0.54461056","text":"def set_tile(self, row, col, value):\r\n # replace with your code\r\n self._cells[row][col] = value","title":""},{"docid":"94e880159ec2e10310cdc73222be0a89","score":"0.54388535","text":"def save_X(self, x):\n self.x = x","title":""},{"docid":"c721f14d2b1912f0b3c13b9efa8e3138","score":"0.5433696","text":"def update_state(self, clock, new_data):\r\n # self.system_stat.iloc[clock, 1:9] = self.system_stat.iloc[clock, 1:9] #+ new_data\r\n self.system_stat.iloc[clock,\r\n ] = self.system_stat.iloc[clock, ] + new_data\r\n # self.system_stat.loc[clock, 'E'] = E\r","title":""},{"docid":"a37e2653d996282d28256017fef8e0b5","score":"0.54266226","text":"def propagate_qttt_to_ttt(self):\n self.ttt.update_ttt_from_qttt(self.board)","title":""},{"docid":"85bd27b2c1ed95b032f0173ff0607dcc","score":"0.5407221","text":"def update(self,t=1):\n force=Force.sum(self.forces)\n print(\"MyMaterialPoint:force:\",force)\n x,y=force\n acceleration=Vector(x,y)/self.mass\n self.motion.setAcceleration(acceleration)\n self.motion.update(t)","title":""},{"docid":"efded9597a8faab8809539e764fbcb9d","score":"0.5395012","text":"def update(self):\n self.Tax = self.tw*self.w*self.L + self.tr*self.r*(self.A+self.D)\n self.G = self.gy*self.C\n self.Tr = (self.Tax+self.Beq-self.G)/self.Pt\n self.tb = self.zeta*(1-self.tw)*self.Pr/(self.L+self.zeta*self.Pr)\n self.b = self.zeta*(1-self.tw-self.tb)*self.w\n self.p = array([self.r, self.w, self.b, self.tr, self.tw, self.tb, \n self.Tr, self.qh, self.qr])","title":""},{"docid":"ba910dde69e271e3d23f81df2685b86c","score":"0.53934026","text":"def put_x(self, x, y):\r\n self.field[y - 1][x - 1] = 1","title":""},{"docid":"c3beedb2149ba231696661cbf88c7aa7","score":"0.5391946","text":"def move_poly(self, xt=1000, yt=1000):\n index = self.get_id_input()\n if index:\n self.df.loc[self.df.index == index, self.columns_x] = \\\n self.df.loc[self.df.index == index].apply(lambda x: x[self.columns_x]+xt, axis=1)\n self.df.loc[self.df.index == index, self.columns_y] = \\\n self.df.loc[self.df.index == index].apply(lambda x: x[self.columns_y]+yt, axis=1)\n return self.df.loc[self.df.index == index]\n return None","title":""},{"docid":"9fcf259bbcdce1a1391428b80c7e9461","score":"0.5386673","text":"def __update_t(t_opt, window_size):\n global t_min\n t_min = max(t0_min, round(t_opt - window_size//2))\n global t_max\n t_max = round(t_opt + window_size//2)\n global n\n n = t_max*2","title":""},{"docid":"c8d521b8098318c0e49273eac6fb04c6","score":"0.538591","text":"def eval_cell(self, value, x, ufc_cell):\n\t\tglobal area_fracs\n\t\tvalue[0] = area_fracs[ufc_cell.index]","title":""},{"docid":"cc02479d90ad7f17585bc353ab12b2a6","score":"0.5382476","text":"def update(self, x, y, d):\n raise NotImplementedError","title":""},{"docid":"e1936a0b8bc9e2279b587fc30ca1872a","score":"0.53799874","text":"def update_cell(self, row, col, val):\n self.update_acell( Worksheet.get_addr_int(row,col), val=unicode(val))","title":""},{"docid":"e4a4bcdc8d885829ea34bc8c1ba9c31b","score":"0.5379168","text":"def update_orig(self, inp=None):\n \n if inp is None:\n inp = self.arrtemp\n inp[self.arrmask < MASK_LIM] = np.nan\n \n self.arrorig = inp.copy()\n self.ptsorig = copy.deepcopy(self.ptstemp)\n self.oriorig = copy.deepcopy(self.oritemp)\n self.parsorig = copy.deepcopy(self.parstemp)\n \n self.reset_temp()","title":""},{"docid":"b11f63001f61f77d9ff28c208dae81be","score":"0.5374063","text":"def xt_accurate(self):\r\n\r\n self.t_acc.append(0.0)\r\n err = 0.0\r\n self.t_local_err.append(err)\r\n for i in range(1, len(self.obs.obt_g)):\r\n an = math.sqrt(float(self.obs.obt_g[i]) ** 2 + self.x[i]\r\n ** 2)\r\n self.t_acc.append(an)\r\n err = math.fabs(100.0 * (an - self.t[i]) / an)\r\n self.t_local_err.append(err)\r\n print 'accurate coordinate calculated'","title":""},{"docid":"b072f35d4b1e79933de5a836c1b48a0a","score":"0.5371576","text":"def set_cells(self, x: int, y: int, kind: CellType, w = 1, h = 1):\n for col in range(x, x + w):\n for row in range(y, y + h):\n self.grid[row * self.rows + col] = CellType(kind.value)\n self.updates.append((col, row, kind))","title":""},{"docid":"ed834a351e2fd8425b22e4f882e91ae4","score":"0.5368172","text":"def tick(self):\n self.setup_cells()\n\n updated = []\n for i in range(self.rows):\n this_row = []\n for j in range(self.cols):\n this_cell = self.get_cell(i, j)\n result_identity = None\n if not this_cell.static: # not only inactive cells surrounding\n for rule in RULES[this_cell.identity]:\n result_identity = rule(this_cell)\n if result_identity: # breaks on first identity change\n break\n result_identity = result_identity or this_cell.identity # prioritizes result identity\n this_cell.ticks += 1\n this_row.append(CELLS_INDEX[result_identity])\n updated.append(this_row)\n\n self.initialize(updated)\n self.ticks += 1","title":""},{"docid":"33aae1e67dcd78a279912000cf14e3ed","score":"0.5357971","text":"def update_model(self):\n self.model = [[self.cells[i][j].value for j in range(self.cols)] for i in range(self.rows)]","title":""},{"docid":"f9533f0a837753ef5c86a307e3ddd38d","score":"0.53511006","text":"def opcode_Td(renderer, t_x, t_y):\n renderer.ts.m = PdfMatrix(1, 0, 0, 1, t_x, t_y)*renderer.ts.lm\n renderer.ts.reset_lm()","title":""},{"docid":"597edfaa72307449bc62102045c73ea9","score":"0.5340474","text":"def set_tile(self, row, col, value): \n self.cells[row][col] = value","title":""},{"docid":"11f37521d970b24987d40927023ee202","score":"0.5324432","text":"def _tnet_update(self):\n with tf.variable_scope('qnet'): \n self.update_opt = [t.assign(q) for t, q in zip(self.tnet.params, self.params)]","title":""},{"docid":"ce855b59ff59026ea5670f0bdb6cb850","score":"0.53202856","text":"def update_params(self, X):\n batchsize = X.shape[0]\n self.deltaC = self.p_t * self.deltaC - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dC\n self.deltaB = self.p_t * self.deltaB - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.db\n self.deltaM = self.p_t * self.deltaM - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dM\n self.deltaJ = self.p_t * self.deltaJ - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dJ\n self.deltaBj = self.p_t * self.deltaBj - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dBj\n self.deltaWhf = self.p_t * self.deltaWhf - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWhf\n self.deltaWfv = self.p_t * self.deltaWfv - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWfv\n self.deltaWfx = self.p_t * self.deltaWfx - \\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWfx\n\n self.C = self.C + self.deltaC\n self.bw = self.bw + self.deltaB\n self.M = self.M + self.deltaM\n self.J = self.J + self.deltaJ\n self.bj = self.bj + self.deltaBj\n self.Wfv = self.Wfv + self.deltaWfv\n self.Wfx = self.Wfx + self.deltaWfx\n self.Whf = self.Whf + self.deltaWhf","title":""},{"docid":"69276eef642e3d487fdea0d0180d62b6","score":"0.53150904","text":"def set_cell(self, x: int, y: int, state: CellState):\n if 0 <= y < len(self.grid) and 0 <= x < len(self.grid[y]):\n self.grid[y][x] = state","title":""},{"docid":"923375c33f8e50856675541e3bb10837","score":"0.5313385","text":"def _update_pert_row(\n self,\n rxn_table_id,\n rxn_no,\n rxn,\n k_i,\n ind_len_west,\n ind_len_gav,\n ind_len_ng,\n cell_size_west,\n cell_size_gav,\n cell_size_ng,\n sens_ind_len_west,\n sens_ind_len_gav,\n sens_ind_len_ng,\n sens_cell_size_west,\n sens_cell_size_gav,\n sens_cell_size_ng,\n ):\n with self.con as con:\n cur = con.cursor()\n cur.execute(\n \"\"\"\n UPDATE {:s} SET \n k_i = :k_i,\n ind_len_west = :ind_len_west,\n ind_len_gav = :ind_len_gav,\n ind_len_ng = :ind_len_ng,\n cell_size_west = :cell_size_west,\n cell_size_gav = :cell_size_gav,\n cell_size_ng = :cell_size_ng,\n sens_ind_len_west = :sens_ind_len_west,\n sens_ind_len_gav = :sens_ind_len_gav,\n sens_ind_len_ng = :sens_ind_len_ng,\n sens_cell_size_west = :sens_cell_size_west,\n sens_cell_size_gav = :sens_cell_size_gav,\n sens_cell_size_ng = :sens_cell_size_ng\n WHERE\n rxn_no = :rxn_no AND\n rxn = :rxn\n \"\"\".format(rxn_table_id),\n {\n 'rxn_no': rxn_no,\n 'rxn': rxn,\n 'k_i': k_i,\n 'ind_len_west': ind_len_west,\n 'ind_len_gav': ind_len_gav,\n 'ind_len_ng': ind_len_ng,\n 'cell_size_west': cell_size_west,\n 'cell_size_gav': cell_size_gav,\n 'cell_size_ng': cell_size_ng,\n 'sens_ind_len_west': sens_ind_len_west,\n 'sens_ind_len_gav': sens_ind_len_gav,\n 'sens_ind_len_ng': sens_ind_len_ng,\n 'sens_cell_size_west': sens_cell_size_west,\n 'sens_cell_size_gav': sens_cell_size_gav,\n 'sens_cell_size_ng': sens_cell_size_ng,\n }\n )","title":""},{"docid":"e5429677997cadd71eb2727325c66f16","score":"0.5309254","text":"def set_cell(frame, data):\n with data.cell_:\n data.cell_[:,0] = [L, 0., 0.]\n data.cell_[:,1] = [0., L, 0.]\n data.cell_[:,2] = [0., 0., L]\n #cell origin\n data.cell_[:,3] = [0, 0 , 0]\n #set periodic boundary conditions\n data.cell_.pbc = (True, True, True)","title":""},{"docid":"96846cc85d43990e9709b6d2c3c47e53","score":"0.53056103","text":"def update(self):\n vals = np.zeros(len(self.table_model._TableModel__x))\n for ipt in self.onPtSrc:\n vals += [\n self.likelihoodModel.pointSources[ipt].spectralModel.photonFlux(\n bounds[0], bounds[1]\n )\n for bounds in zip(self.e_lo, self.e_hi)\n ]\n # integrated fluxes over same energy bins as for dataset, according to Sherpa TableModel specs, TBV\n self.table_model._TableModel__y = vals","title":""},{"docid":"1dfed14778d7ed29e0e4c6fa39ebe9dd","score":"0.5283027","text":"def set_value(self, tuple, item):\r\n self.cells[tuple[0]][tuple[1]] = item","title":""},{"docid":"2adaf6996a7708274ac2b20e3b5fb1cd","score":"0.5274169","text":"def update_state(state, kernel, learning_rate, x_i, y_i):\n # *** START CODE HERE ***\n # Append to the last column\n length = len(state[0])\n\n # add a new row\n state[0].append(x_i)\n state[1].append(y_i)\n state[2].append([0.0])\n state[3].append([])\n index = 0 \n while index < length + 1:\n kernEnt = kernel(state[0][index], x_i)\n oldMargin = state[2][length][index] \n state[2][length].append(oldMargin + learning_rate * (state[1][index] - sign(state[2][index][index])) * kernEnt)\n state[3][length].append(kernEnt)\n index = index + 1\n\n index1 = 0 \n while index1 < length:\n kernEnt = kernel(x_i, state[0][index1])\n oldMargin = state[2][index1][length] \n #state[2][index] = oldMargin + learning_rate * (y_i - sign(oldMargin)) * kernEnt\n state[2][index1].append(oldMargin + learning_rate * (y_i - sign(state[2][length][length])) * kernEnt)\n state[3][index1].append(kernEnt)\n index1 = index1 + 1\n # *** END CODE HERE ***","title":""},{"docid":"e222f5ab4ad18cba94fed235fcc9199a","score":"0.52676314","text":"def update_grid(self):\n for var in self.model.variables():\n if var.name[0:1] == 'x':\n coor = int(var.name[1:])\n xCoor = (coor - 1) % self.cols\n yCoor = math.floor((coor - 1) / self.cols)\n if self.grid[yCoor][xCoor] == 0:\n self.grid[yCoor][xCoor] = \"0\"\n continue\n if var.value() == 1:\n self.grid[yCoor][xCoor] = \"x\"\n else:\n self.grid[yCoor][xCoor] = \"1\"","title":""},{"docid":"688dcec9c00d0f5213022276113c9504","score":"0.52493984","text":"def modify_cell_with(self, sheet_name, column, row, op, val):\n pass\n # my_sheet_index = self.workbook.sheet_names().index(sheet_name)\n # cell = self.workbook.get_sheet(my_sheet_index).cell(int(row), int(column))\n # curval = cell.value\n # if cell.ctype is XL_CELL_NUMBER:\n # self.workbook.sheets()\n # if not self.copied_workbook:\n # self.copied_workbook = copy(self.workbook)\n # plain = easyxf('')\n # modexpr = str(curval) + op + val\n # self.copied_workbook.get_sheet(my_sheet_index).write(int(row), int(column), eval(modexpr), plain)","title":""},{"docid":"96e950612e1513a9f8b73fadd5da46d9","score":"0.5247235","text":"def update(self, t, state, flat_output):\r\n\r\n \"First extract the current (actual) position of the robot and the desired position derived from the waypoints\"\r\n ri = state[\"x\"] #expressed in inertial frame\r\n rT = flat_output[\"x\"] #expressed in inertial frame\r\n \"actual velocity of robot and desired velocity from waypoints\"\r\n vi = state[\"v\"] #expressed in inertial frame\r\n vT = flat_output[\"x_dot\"] #expressed in inertial frame\r\n \"Create the position and velocity error vector between those two\"\r\n e_pos = ri - rT\r\n e_vel = vi - vT\r\n rdotdotT = flat_output[\"x_ddot\"] #acceleration as returned from waypoints\r\n psiT = flat_output[\"yaw\"]\r\n\r\n \"From quaternions to euler angles\"\r\n rot = Rotation.from_quat(state[\"q\"])\r\n eulerAngles = rot.as_euler('zyx')\r\n psi = eulerAngles[0]\r\n theta = eulerAngles[1]\r\n phi = eulerAngles[2]\r\n\r\n kd = 5 #tune\r\n kp = 5 #tune\r\n\r\n kdMatrix = np.diagflat(kd*np.ones((1,3)))\r\n kpMatrix = np.diagflat(kp*np.ones((1,3)))\r\n\r\n rdotdotdes = rdotdotT - kdMatrix@e_vel - kpMatrix@e_pos\r\n\r\n \"Now we can compute u1 (thrust), θDes and φDes\"\r\n u1 = self.mass*(self.g + rdotdotdes[2]) #thrust control\r\n phiDes = rdotdotdes[0]*np.sin(psiT) - rdotdotdes[1]*np.cos(psiT)\r\n thetaDes = (rdotdotdes[0] - phiDes*np.sin(psiT)) / self.g*np.cos(psiT)\r\n\r\n \"Now we can compute u2\"\r\n I = np.diagflat([self.Ixx, self.Iyy, self.Izz])\r\n u2 = I@np.array([[-kp*(phi - phiDes)-kd*(state[\"w\"][0])],\r\n [-kp*(theta - thetaDes)-kd*(state[\"w\"][1])],\r\n [-kp*(psi - psiT)-kd*(state[\"w\"][2])]])\r\n\r\n \"From u2 to forces\"\r\n gamma = self.k_drag / self.k_thrust\r\n A = np.array([[1, 1, 1, 1],\r\n [0, self.arm_length, 0, -self.arm_length],\r\n [-self.arm_length, 0, self.arm_length, 0],\r\n [gamma, -gamma, gamma, -gamma]])\r\n\r\n forces = np.linalg.inv(A)@np.insert(u2,0,u1)\r\n\r\n \"From forces to motor speeds\"\r\n cmd_motor_speeds = np.squeeze(np.sqrt(1/self.k_thrust * forces))\r\n cmd_thrust = np.sum(forces)\r\n\r\n \"From motor speeds to moments\"\r\n cmd_moment = u2\r\n cmd_q = np.zeros((4,))\r\n\r\n # STUDENT CODE HERE\r\n\r\n control_input = {'cmd_motor_speeds':cmd_motor_speeds,\r\n 'cmd_thrust':cmd_thrust,\r\n 'cmd_moment':cmd_moment,\r\n 'cmd_q':cmd_q}\r\n return control_input","title":""},{"docid":"0b744236c1978264254e5b5720381ad3","score":"0.52438533","text":"def __call__(self, t, x):\n #TODO: might want to integrate/average over the input so it isn't only based on the last timestep\n\n # Only perform a state transition if enough time has passed since the last one\n if t - self.last_t >= self.time_interval:\n # Get the action id of the closest action vector to the noisy input vector\n action = self.find_closest_action(x)\n # Perform that action to update the current state\n self.make_action(action)\n\n self.last_t = t\n\n return self.index_to_state_vector[self.current_state]","title":""},{"docid":"f24268841eaaa34790f133c0f3ab2704","score":"0.5241271","text":"def _update(self, vals, params, dt):\n pass","title":""},{"docid":"0683e66d002a85076de659838e02764f","score":"0.5240061","text":"def update_problem(self, x0, Xm):\n self.update_x0(x0)\n self.update_Xm(Xm)\n return","title":""},{"docid":"3bee19d67300ffe0791ed32f7ba82db2","score":"0.52332383","text":"def rearrange(self, cell):\r\n pass","title":""},{"docid":"4c75b247b5b86cf34e78da36650fc8e7","score":"0.5227867","text":"def set_initial_value(self, y, t=0.0):\n self.t = t\n self.y = y\n return self","title":""},{"docid":"f630973511a4bf858f555f38f023e3ef","score":"0.5221748","text":"def rebase_to_one(self):\n self.x_data = self.x_data.T\n list_of_first_elem_price = [i[0] for i in self.x_data]\n shape_row, shape_column = self.x_data.shape[0], self.x_data.shape[1]\n\n for i in range(shape_row):\n for j in range(shape_column):\n self.x_data[i][j] /= list_of_first_elem_price[i]\n self.x_data = self.x_data.T","title":""},{"docid":"bf81e8bcc5fae4af7f6700581a39586a","score":"0.52135056","text":"def update_cell(self, i: int, j: int, new_value: Cell) -> None:\n try:\n self.__cells[j][i] = new_value\n except IndexError:\n print(\"Trying to update a cell with index out of bounds\")","title":""},{"docid":"ef0d64b2c6688a85479f74dc436ddcb8","score":"0.51921713","text":"def update_dynamics_model(self, x, u):\n if x.ndim < 2:\n x = np.expand_dims(x, axis=1) # shape of x should be (N,1), not (N,)\n if u.ndim < 2:\n u = np.expand_dims(u, axis=1)\n # ===== Model parameters ===== #\n\n # num of state, action\n nx = self.nx\n nu = self.nu\n\n m = self.m # i30\n\n width = self.width\n length = self.length\n\n l_f = self.l_f\n l_r = self.l_r\n wheelbase = self.wheelbase\n turning_circle = self.turning_circle\n max_steer = self.max_steer\n\n Iz = self.Iz\n\n # Iw = 1.8 # wheel inertia\n # rw = 0.3 # wheel radius\n\n roh = self.roh # density of air [kg/m3]\n C_d = self.C_d # drag coefficient\n A_f = self.A_f # vehicle frontal area [m2]\n C_roll = self.C_roll # rolling resistance coefficient\n\n dt = self.dt # sampling time. [sec]\n\n \n \"\"\"\n Pacejka lateral tire model params\n \n \"\"\"\n Fz_f = 9.81 * (m * l_r/wheelbase) * 0.001 # vertical force at front axle. [kN]\n\n a_lat_f = [-22.1, 1011, 1078, 1.82, 0.208, 0.000, -0.354, 0.707] # for Fy\n C_lat_f = 1.30\n D_lat_f = a_lat_f[0]*Fz_f**2 + a_lat_f[1]*Fz_f\n BCD_lat_f = a_lat_f[2]*math.sin(a_lat_f[3]*math.atan(a_lat_f[4]*Fz_f)) # before, atan\n # B_lat_f = BCD_lat_f/(C_lat_f*D_lat_f)\n B_lat_f = BCD_lat_f/(C_lat_f*D_lat_f) * 180/np.pi # for radian sideslip angle\n E_lat_f = a_lat_f[5]*Fz_f**2 + a_lat_f[6]*Fz_f + a_lat_f[7]\n\n Fz_r = 9.81 * (m * l_f/wheelbase) * 0.001 # vertical force at rear axle. [kN]\n\n a_lat_r = [-22.1, 1011, 1078, 1.82, 0.208, 0.000, -0.354, 0.707] # for Fy\n C_lat_r = 1.30\n D_lat_r = a_lat_r[0]*Fz_r**2 + a_lat_r[1]*Fz_r\n BCD_lat_r = a_lat_r[2]*math.sin(a_lat_r[3]*math.atan(a_lat_r[4]*Fz_r)) # berore, atan\n # B_lat_r = BCD_lat_r/(C_lat_r*D_lat_r)\n B_lat_r = BCD_lat_r/(C_lat_r*D_lat_r) * 180/np.pi # for radian sideslip angle\n E_lat_r = a_lat_r[5]*Fz_r**2 + a_lat_r[6]*Fz_r + a_lat_r[7]\n\n \"\"\"\n ===== Discretize Linearized Dynamics model =====\n \"\"\"\n\n # normalize angle\n # x[2] = normalize_angle(x[2])\n\n # Avoiding zero denominator (for slip angle, expm in discretization procedure)\n # before 19.07.31, 0.5 m/s\n if x[3,0] >=0 and x[3,0] < 0.5:\n # x[3] = 0.0\n x[4,0] = 0. # v_y\n x[5,0] = 0. # yaw_rate\n u[0,0] = 0. # steer\n if x[3,0] < 0.3:\n x[3,0] = 0.3 # v_x\n print(\"Avoiding zero denominator\")\n\n if x[3,0] > -0.5 and x[3,0] < 0:\n # x[3] = 0.\n x[4,0] = 0.\n x[5,0] = 0.\n u[0,0] = 0.\n if x[3,0] > -0.3:\n x[3,0] = -0.3\n print(\"Avoiding zero denominator\")\n\n # States\n yaw = x[2,0] # [0] for scalar data\n v_x = x[3,0]\n v_y = x[4,0]\n yaw_rate = x[5,0]\n\n steer = u[0,0]\n accel_track = u[1,0]\n\n # Dynamics model\n # Slip angle [deg]\n # alpha_f = np.rad2deg(-math.atan2( l_f*yaw_rate + v_y,v_x) + steer)\n # alpha_r = np.rad2deg(-math.atan2(-l_r*yaw_rate + v_y,v_x))\n\n alpha_f = -math.atan2( l_f*yaw_rate + v_y,v_x) + steer\n alpha_r = -math.atan2(-l_r*yaw_rate + v_y,v_x)\n\n # Lateral force (front & rear)\n # Fy_f = D_lat_f * math.sin(C_lat_f * math.atan2(B_lat_f * alpha_f, 1)) # before was atan\n # Fy_r = D_lat_r * math.sin(C_lat_r * math.atan2(B_lat_r * alpha_r, 1)) # before was atan\n Fy_f = D_lat_f * math.sin(C_lat_f * math.atan(B_lat_f * alpha_f))\n Fy_r = D_lat_r * math.sin(C_lat_r * math.atan(B_lat_r * alpha_r))\n\n # Longitudinal force\n\n # for both forward and backward driving.\n R_roll = C_roll * m * 9.81 * np.sign(v_x) # rolling resistance. [N] f*(Fzf+Fzr) = f*(mg)\n F_aero = 0.5*roh*C_d*A_f*v_x**2 * np.sign(v_x) # aero dynamics drag. [N] 0.5*rho*cd*A.\n Fx_f = m*accel_track - F_aero - R_roll\n\n # Next state\n x_dot = np.array([[v_x*math.cos(yaw) - v_y*math.sin(yaw)],\n [v_y*math.cos(yaw) + v_x*math.sin(yaw)],\n [yaw_rate],\n [1./m*(Fx_f*math.cos(steer) - Fy_f*math.sin(steer) + m*v_y*yaw_rate)],\n [1./m*(Fx_f*math.sin(steer) + Fy_r + Fy_f*math.cos(steer) - m*v_x*yaw_rate)],\n [1./Iz*(Fx_f*l_f*math.sin(steer) + Fy_f*l_f*math.cos(steer)- Fy_r*l_r)]])\n\n x_next = x + x_dot * dt\n\n return x_next, alpha_f, alpha_r","title":""},{"docid":"f22189c24f6422b20fa1f36876bb54b1","score":"0.51817864","text":"def x(self, x: float):\n\n self._x = x","title":""},{"docid":"1540b88ab29d2321c269c45e2692cad7","score":"0.51742435","text":"def _update_times(self):\n self._times = self.tmin + (self.tstep * np.arange(self.shape[-1]))\n self._times.flags.writeable = False","title":""},{"docid":"a7cdae8715195477e2ffb0c6edd76ea4","score":"0.517007","text":"def replace_cell(self, new, row, col):\n self.cells[row][col] = new","title":""},{"docid":"19fe5bb797175119e8f90b55d380bfdb","score":"0.5166559","text":"def t_value(self, kx: float, ky: float) : # This is t_ij(k_tilde)\n t = self.t ; tp = self.tp; tpp = self.tpp\n t_val = np.zeros((4, 4), dtype=complex)\n ex = np.exp(-2.0j*kx) ; emx = np.conjugate(ex)\n ey = np.exp(-2.0j*ky) ; emy = np.conjugate(ey)\n tloc = np.array([[0.0, -t, -t, -tp],\n [-t, 0.0, 0.0, -t],\n [-t, 0.0, 0.0, -t],\n [-tp, -t, -t, 0.0]])\n\n\n t_val += tloc\n\n t_val[0, 0] += -tpp*(ex+emx+ey+emy); t_val[0, 1] += -t*ex; t_val[0, 3] += -tp*(ex + ey + ex*ey); t_val[0, 2] += -t*ey\n t_val[1, 0] += -t*emx; t_val[1, 1] += -tpp*(ex+emx+ey+emy); t_val[1, 3] += -t*ey; t_val[1, 2] += -tp*(emx + ey + emx*ey)\n t_val[3, 0] += -tp*(emx + emy + emx*emy); t_val[3, 1] += -t*emy; t_val[3, 3] += -tpp*(ex+emx+ey+emy); t_val[3, 2] += -t*emx\n t_val[2, 0] += -t*emy; t_val[2, 1] += -tp*(ex + emy + ex*emy); t_val[2, 3] += -t*ex; t_val[2, 2] += -tpp*(ex+emx+ey+emy)\n\n return (t_val)","title":""},{"docid":"974b3ecf0a5291de32ca1bd4bd4b6fd0","score":"0.51663643","text":"def actuate(self, cell):\n pass","title":""},{"docid":"24ba997272ffe57e1ed08a5d0c888ded","score":"0.51662946","text":"def updateValues(self, tickData):\n pass","title":""},{"docid":"36b7e0ae60f6c23fe61dc8554e253380","score":"0.5149533","text":"def set(self, name, value, ex=None, px=None, nx=False, xx=False):","title":""},{"docid":"8bb4ae34502eb110565d6ca7018d2431","score":"0.5140047","text":"def updateTable(self, item: KnapsackItem, x: int, y: int) -> None:\n ((self.table)[y][x]).extend(item)","title":""},{"docid":"e39080875e3288f70ca8b812ba350c13","score":"0.51353663","text":"def post_integration(self):\n # Cache the values\n self.sim.derivs(self.t0, self.xold)\n self.__store_values()\n \n V, dV = self.sim.CVs.volumes(self.t0)\n Nexist = self.sim.CVs.Nexist\n if sorted(self.sim.stateVariables) == ['D','T']:\n self.sim.CVs.updateStates('T',self.xnew[0:Nexist],'D',self.xnew[Nexist:2*Nexist])\n elif sorted(self.sim.stateVariables) == ['M','T']:\n self.sim.CVs.updateStates('T',self.xnew[0:Nexist],'D',self.xnew[Nexist:2*Nexist]/V)\n else:\n raise NotImplementedError","title":""},{"docid":"485da1b2d4554809aa38ca7fe336d418","score":"0.5130694","text":"def changespeed(self, x):\r\n self.change_x += x","title":""},{"docid":"485da1b2d4554809aa38ca7fe336d418","score":"0.5130694","text":"def changespeed(self, x):\r\n self.change_x += x","title":""},{"docid":"485da1b2d4554809aa38ca7fe336d418","score":"0.5130694","text":"def changespeed(self, x):\r\n self.change_x += x","title":""},{"docid":"c177146ecff0c7731b66f65a03b57895","score":"0.51288164","text":"def setX(self, x):\n self.x = x","title":""},{"docid":"c177146ecff0c7731b66f65a03b57895","score":"0.51288164","text":"def setX(self, x):\n self.x = x","title":""}],"string":"[\n {\n \"docid\": \"9b44555f42e0d42190d1746033231b46\",\n \"score\": \"0.70777196\",\n \"text\": \"def Updatev(Cell,dt):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a57999dc4ecee8ddd36a67f5fc584f\",\n \"score\": \"0.680216\",\n \"text\": \"def recurrent_cell(self, st, t):\\n\\n\\t\\tz = self.z[t-par['latency'],..., cp.newaxis]\\n\\t\\tx = self.input_data[t,:,:,cp.newaxis]\\n\\n\\t\\t# Update the input traces based on presynaptic spikes\\n\\t\\tcurr_beta = self.con_dict[par['spike_model']]['beta']\\n\\t\\tst['ia'] = curr_beta * st['ia'] + (1-curr_beta) * self.eff_var['W_in'] * x\\n\\t\\tst['ir'] = curr_beta * st['ir'] + (1-curr_beta) * self.eff_var['W_rnn'] * st['sx'] * st['su'] * z\\n\\n\\t\\tst['ja'] = curr_beta * st['ja'] + (1-curr_beta) * x\\n\\t\\tst['jr'] = curr_beta * st['jr'] + (1-curr_beta) * st['sx'] * st['su'] * z\\n\\n\\t\\t#print( 'I', cp.mean(st['ia']), cp.mean(st['ir']))\\n\\n\\t\\t# Update the synaptic plasticity state (recurrent only; input is static)\\n\\t\\tst['sx'], st['su'] = \\\\\\n\\t\\t\\tsynaptic_plasticity(st['sx'], st['su'], z, self.con_dict, par['use_stp'])\\n\\n\\t\\t# Sum the input currents into shape [batch x postsynaptic]\\n\\t\\tI = cp.sum(st['ia'], axis=1, keepdims=True) + cp.sum(st['ir'], axis=1, keepdims=True)\\n\\n\\t\\t# Update the AdEx cell state with the input current\\n\\t\\tst['v'], st['w'], self.z[t,...] = run_spike_model(st['v'], st['w'], I, par['spike_model'], self.con_dict[par['spike_model']])\\n\\n\\t\\t# Update output trace based on postsynaptic cell state (Eq. 12)\\n\\t\\tself.y[t,...] = self.con_dict[par['spike_model']]['kappa'] * self.y[t-1,...] + self.z[t,...] @ self.eff_var['W_out'] + self.eff_var['b_out']\\n\\n\\t\\t# Calculate h, the pseudo-derivative (Eq. 5, ~24, 20/21)\\n\\t\\t# Bellec et al., 2018b\\n\\t\\tif par['spike_model'] == 'adex':\\n\\t\\t\\tT = self.con_dict['adex']['V_T'] + par['betagrad']\\n\\t\\telif par['spike_model'] == 'izhi':\\n\\t\\t\\tT = self.con_dict['izhi']['c'] + par['betagrad']\\n\\t\\telse:\\n\\t\\t\\traise Exception('Unimplemented pseudo-derivative.')\\n\\n\\t\\tself.h[t,...] = cp.squeeze(par['gamma_psd'] * cp.maximum(0., \\\\\\n\\t\\t\\t1 - cp.abs(st['v'] - T)/par['pseudo_th']))\\n\\n\\n\\t\\t#h = par['gamma_psd'] * cp.maximum(0., 1 - cp.abs((st['v'] + 40e-3)/par['pseudo_th']))\\n\\t\\t#h = par['gamma_psd'] * cp.ones_like(h)\\n\\t\\treturn st, I\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0f3904f07f7bcde140e1aaaf940c80\",\n \"score\": \"0.6566709\",\n \"text\": \"def updateX(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e2463db041448fceeabccb2a4a6b88\",\n \"score\": \"0.646216\",\n \"text\": \"def update_state(s_t, x_t1_col):\\n x_t1 = resize(rgb2gray(x_t1_col), (84, 84))\\n x_t1 = np.reshape(x_t1, (84, 84, 1))\\n s_t1 = np.append(x_t1, s_t[:,:,:3], axis = 2)\\n return s_t1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78134ac058a0b38a33fce84ad5e1877e\",\n \"score\": \"0.64159375\",\n \"text\": \"def update(self, t=1):\\n self.pos = np.add(self.pos, t * self.v + self.acc * (1/2 * t ** 2))\\n self.v = np.add(self.v, t * self.acc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f40f73b1c51c8cff1c780e09077448\",\n \"score\": \"0.64041686\",\n \"text\": \"def update_x(self):\\n self.x += self.v*self.dt\\n return self.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9046340e02eafb4ee9a95513082431df\",\n \"score\": \"0.63605344\",\n \"text\": \"def update(self, x: numbers.Number):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaaf263f71ab36ff80241bcb1aae0332\",\n \"score\": \"0.6114706\",\n \"text\": \"def update(self, x=None, y=None):\\n if x:\\n self.x = x\\n elif y:\\n self.y = y\\n self.t += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53229c5c445f360c702bd26bf38780a2\",\n \"score\": \"0.5980773\",\n \"text\": \"def update_x(x_current_l, v_current_l, a_current_l, dt_l):\\n\\n x_new = x_current_l + v_current_l * dt_l + 0.5 * a_current_l * (dt_l ** 2)\\n\\n return x_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358eb0c7aac3a64c2245a51548dee07b\",\n \"score\": \"0.5942913\",\n \"text\": \"def update(self, t, dt):\\n\\n festim.update_expressions(self.expressions, t)\\n\\n converged = False\\n u_ = Function(self.u.function_space())\\n u_.assign(self.u)\\n while converged is False:\\n self.u.assign(u_)\\n nb_it, converged = self.solve_once()\\n if dt.adaptive_stepsize is not None:\\n dt.adapt(t, nb_it, converged)\\n\\n # Update previous solutions\\n self.update_previous_solutions()\\n\\n # Solve extrinsic traps formulation\\n self.traps.solve_extrinsic_traps()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1043adf7a7ebbc786aaddcd414d062dc\",\n \"score\": \"0.59254026\",\n \"text\": \"def update(self, value, t):\\n self._freshen(t)\\n self.weight += value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63c597d065ebc59a11a45c8ccd2a338\",\n \"score\": \"0.59201956\",\n \"text\": \"def propogate_state(x_t_prev, u_t, d_t):\\n \\\"\\\"\\\"STUDENT CODE START\\\"\\\"\\\"\\n #x_bar_t = np.empty()\\n \\\"\\\"\\\"STUDENT CODE END\\\"\\\"\\\"\\n\\n return #x_bar_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be5a9de279fd42c61aef124a0fa10d7\",\n \"score\": \"0.58918077\",\n \"text\": \"def update(self, input_val, t_step):\\n \\n self.ode.set_f_params(input_val)\\n self.ode.integrate(self.ode.t + t_step)\\n return self.ode.successful()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f29b7ea1ef341c8abb8e33dab2025a\",\n \"score\": \"0.58527356\",\n \"text\": \"def update(self, x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f29b7ea1ef341c8abb8e33dab2025a\",\n \"score\": \"0.58527356\",\n \"text\": \"def update(self, x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f29b7ea1ef341c8abb8e33dab2025a\",\n \"score\": \"0.58527356\",\n \"text\": \"def update(self, x, y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2b004e391c01b02d326434c0cc5f7d\",\n \"score\": \"0.58463764\",\n \"text\": \"def UpdateU(Cell,dt):\\n\\tfor i in Cell:\\n\\t\\tfac = dt/Cell[i][\\\"V\\\"]\\n\\t\\t\\n\\t\\tCell[i][\\\"Unew\\\"] = Cell[i][\\\"U\\\"]-fac*(\\n\\t\\tCell[i][\\\"Side2\\\"][4]*Cell[i][\\\"Normalvector2\\\"][0]+\\n\\t\\tCell[i][\\\"Side4\\\"][4]*Cell[i][\\\"Normalvector4\\\"][0]\\n\\t\\t)\\n\\t\\t\\n\\t\\t\\n\\t\\t#print(\\\"Side2 -> {}\\\".format(Cell[i][\\\"Side2\\\"][4]*Cell[i][\\\"Normalvector2\\\"][0]))\\n\\n\\t\\t#print(\\\"Side4 -> {}\\\".format(Cell[i][\\\"Side4\\\"][4]*Cell[i][\\\"Normalvector4\\\"][0]))\\n\\n\\t\\n\\tfor i in Cell:\\n\\t\\tCell[i][\\\"U\\\"] = Cell[i][\\\"Unew\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c7ab07460389fabcb05cd0664548a7\",\n \"score\": \"0.5781401\",\n \"text\": \"def set_cval(self, x, y, val):\\n self.t.update_cell(y, x, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18f43ed60b5e10dbaf0526a5f080d39\",\n \"score\": \"0.57679224\",\n \"text\": \"def update(self, X, accepted, S, r):\\n self.t += S + accepted*X # update time state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18f43ed60b5e10dbaf0526a5f080d39\",\n \"score\": \"0.57679224\",\n \"text\": \"def update(self, X, accepted, S, r):\\n self.t += S + accepted*X # update time state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02810d5df0bf0c908f57547b38bd13db\",\n \"score\": \"0.575226\",\n \"text\": \"def b_node_set_button(self, event):\\n\\n new_x = float(self.x_input.GetValue())\\n new_y = float(self.y_input.GetValue())\\n\\n if self.i == self.node_layer_reference:\\n xt = np.array(self.plotx)\\n yt = np.array(self.ploty)\\n\\n if self.boundary_lock_list[self.i] == 0 and self.index_node is not None:\\n if xt[self.index_node] == 0 and yt[self.index_node] != 0.001:\\n xt[self.index_node] = 0 # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\\n elif xt[self.index_node] == self.x2 and yt[self.index_node] != 0.001:\\n xt[self.index_node] = self.x2 # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\\n elif xt[self.index_node] == 0 and yt[self.index_node] == 0.001:\\n xt[self.index_node] = 0 # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\\n elif xt[self.index_node] == self.x2 and yt[self.index_node] == 0.001:\\n xt[self.index_node] = self.x2 # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\\n elif new_y <= 0:\\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\\n else:\\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\\n elif self.boundary_lock_list[self.i] == 1:\\n if new_y <= 0:\\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = 0.001 # REPLACE OLD Y WITH NEW Y\\n else:\\n xt[self.index_node] = new_x # REPLACE OLD X WITH NEW X\\n yt[self.index_node] = new_y # REPLACE OLD Y WITH NEW Y\\n\\n # DEAL WITH PINCHED NODE\\n if self.pinch_switch != 0:\\n for k in range(0, len(self.index_arg2_list)):\\n if self.index_arg2_list[k] is not None:\\n next_x_list = self.plotx_list[k]\\n next_y_list = self.ploty_list[k] # GET THE NODE LIST OF THE NEXT LAYER\\n next_x_list[self.index_arg2_list[k]] = new_x\\n next_y_list[self.index_arg2_list[k]] = new_y # REPLACE THE PINCHED NODE WITH THE NEW NODE\\n self.plotx_list[k] = next_x_list\\n self.ploty_list[k] = next_y_list # OVERWRITE THE NODE LIST WITH UPDATED LIST\\n\\n self.plotx = xt\\n self.ploty = yt\\n self.polyline.set_data(self.plotx, self.ploty)\\n\\n # COLOR CURRENTLY SELECTED NODE RED\\n self.current_node.set_offsets([new_x, new_y])\\n else:\\n pass\\n\\n # UPDATE LAYER DATA\\n self.set_density(self)\\n self.set_susceptibility(self)\\n self.set_angle_a(self)\\n self.set_angle_b(self)\\n\\n # UPDATE GMG\\n self.update_layer_data()\\n self.run_algorithms()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e0915abc6df761f4b67f43fe5e2885\",\n \"score\": \"0.5723508\",\n \"text\": \"def _update_eta(self, t) -> None:\\n # self.eta *= DTYPE((t + 1) / (t + 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d2e0cd8669d4dcb3fefc565c9a8bb8\",\n \"score\": \"0.5714379\",\n \"text\": \"def OnCellEdit(self,event):\\n row,col = (event.GetRow(),event.GetCol())\\n cval = self.GetCellValue(row,col)\\n if cval.startswith(\\\"=\\\"):\\n try:\\n cval = str(eval(cval[1:]))\\n self.SetCellValue(row,col,cval)\\n except:\\n pass\\n try:\\n cval = float(cval)\\n except ValueError:\\n cval = np.nan\\n self.SetCellValue(row,col,str(cval))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9714fab24a235615465ff53a2f338b\",\n \"score\": \"0.56973976\",\n \"text\": \"def insert_number(self, x,y,value):\\n M = self.actual_state.reshape((9,9))\\n M[x][y] = value\\n self.actual_state= M.reshape(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4b0c92854b38870a16f8441e55bd9e3\",\n \"score\": \"0.5661427\",\n \"text\": \"def update_temperature(self):\\n self.T = self.T - (self.T0 / self.iterations)\\n\\n # Exponential would look like this:\\n # alpha = 0.99\\n # self.T = self.T * alpha\\n\\n # where alpha can be any value below 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6e87a26a2018dcdce811aa7e50dffda\",\n \"score\": \"0.5647182\",\n \"text\": \"def update(self, t, x):\\n # For readability\\n g = self.g\\n m_p, m_c = self.m_p, self.m_c\\n l = self.l\\n s, c = np.sin(x[2]), np.cos(x[2])\\n u = self.u(t, x)\\n dx = np.zeros((4,))\\n\\n if not self.manipulator_eqns:\\n den = (m_c + m_p*s**2)\\n q_ddot = np.zeros((2,1))\\n q_ddot[0] = (1/den)*(u + m_p*s*(l*x[3]**2 + g*c))\\n q_ddot[1] = (1/(l*den))*(-u*c - m_p*l*x[3]**2*c*s - (m_c + m_p)*g*s)\\n else:\\n # Mass matrix\\n M = np.array([[ m_c + m_p, m_p*l*c ],\\n [ m_p*l*c, m_p*l**2]])\\n # Coriolis terms\\n C = np.array([[ 0, -m_p*l*x[3]*s],\\n [ 0, 0 ]])\\n # Torque\\n tau_g = -g*np.array([[ 0 ],\\n [ m_p*l*s]])\\n # B\\n B = np.array([[ 1],\\n [ 0]])\\n # Calculate angular acceleration\\n q_dot = np.array([[x[1]],\\n [x[3]]])\\n Cv = np.dot(C, q_dot)\\n q_ddot = np.dot(np.linalg.inv(M), (tau_g + np.dot(B, u) - Cv))\\n\\n dx[0] = x[1]\\n dx[1] = q_ddot[0]\\n dx[2] = x[3]\\n dx[3] = q_ddot[1]\\n return dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edbb8665b5b87c014d43afdb8d4f83c\",\n \"score\": \"0.5644153\",\n \"text\": \"def xt_calculate(self):\\r\\n\\r\\n for i in range(0, len(self.obs.obt_g)):\\r\\n t = float(self.obs.obt[i]) / self.nu_t\\r\\n self.t.append(t)\\r\\n self.t_err.append(0.5 * (1.0 / 2.0) / self.nu_t) # 0.5 is bar\\r\\n x = float(self.obs.obx[i]) / self.nu_x\\r\\n self.x.append(x)\\r\\n print 'coordinate calculated'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7acaecb0f5998785d0f1ead39ebe967\",\n \"score\": \"0.5632091\",\n \"text\": \"def setCell(self, x, y, val):\\r\\n\\t\\tself.list[x][y].set(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569abfee28be703d1ad95a8bad198937\",\n \"score\": \"0.56252295\",\n \"text\": \"def update_matrix(self):\\r\\n Xleft, Xright = self.X[1:-1,:-2], self.X[1:-1,2:]\\r\\n Xtop, Xbottom = self.X[:-2,1:-1], self.X[2:,1:-1]\\r\\n self.X[1:-1,1:-1] += self.dt*(Xleft+Xright+Xtop+Xbottom-4*self.X[1:-1,1:-1])/self.dx**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72041ce1f73d7550243e90e61300dc6b\",\n \"score\": \"0.562036\",\n \"text\": \"def update(self,x,win,t):\\n # eta(t) = eta(0) / (1 + t/T)\\n # keeps the learning rate nearly constant for the first T iterations and then adjusts it\\n eta = self.learning_rate/(1+t/self.T)\\n sig = self.sigma/(1+t/self.T) # sigma and learning rate decrease with the same rule\\n g = self.neighborhood(win,sig)*eta # improves the performances\\n it = nditer(g, flags=['multi_index'])\\n while not it.finished:\\n # eta * neighborhood_function * (x-w)\\n self.weights[it.multi_index] += g[it.multi_index]*(x-self.weights[it.multi_index])\\n # normalization\\n #self.weights[it.multi_index] = self.weights[it.multi_index] / linalg.norm(self.weights[it.multi_index])\\n it.iternext()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede566b0e2fd8a6f7ae19e7896196b71\",\n \"score\": \"0.5601493\",\n \"text\": \"def update(self):\\n self.x += self.vx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede566b0e2fd8a6f7ae19e7896196b71\",\n \"score\": \"0.5601493\",\n \"text\": \"def update(self):\\n self.x += self.vx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8bb3ff7e80fbcb029346c58621d855\",\n \"score\": \"0.55964744\",\n \"text\": \"def update(self, x):\\n if self.first_pass:\\n self.means = np.mean(x, axis=0)\\n self.vars = np.var(x, axis=0)\\n self.m = x.shape[0]\\n self.first_pass = False\\n else:\\n n = x.shape[0]\\n new_data_var = np.var(x, axis=0)\\n new_data_mean = np.mean(x, axis=0)\\n new_data_mean_sq = np.square(new_data_mean)\\n new_means = ((self.means * self.m) + (new_data_mean * n)) / (self.m + n)\\n self.vars = (((self.m * (self.vars + np.square(self.means))) +\\n (n * (new_data_var + new_data_mean_sq))) / (self.m + n) -\\n np.square(new_means))\\n self.vars = np.maximum(0.0, self.vars)\\n self.means = new_means\\n self.m += n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93147f44ff41df185549d0c27733cc5\",\n \"score\": \"0.55931485\",\n \"text\": \"def update_tau(self, r, s, val):\\n self.tau_mat[r][s] = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f4733e6b2fac54e3b85a9f88c33070\",\n \"score\": \"0.55798584\",\n \"text\": \"def _update1D(self, x, output):\\n msg = \\\"Plot updating ... \\\"\\n wx.PostEvent(self.parent, StatusEvent(status=msg, type=\\\"update\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0b4365f11b4a262e9ef84d0057a8ca\",\n \"score\": \"0.5555576\",\n \"text\": \"def update(self, t):\\n x = np.zeros((3,))\\n x_dot = np.zeros((3,))\\n x_ddot = np.zeros((3,))\\n x_dddot = np.zeros((3,))\\n x_ddddot = np.zeros((3,))\\n yaw = 0\\n yaw_dot = 0\\n\\n # STUDENT CODE HERE\\n\\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\\n 'yaw': yaw, 'yaw_dot': yaw_dot}\\n\\n num_points = len(self.points)\\n distances = np.zeros((num_points - 1, 3)) # matrix of distances between points\\n for i in range(num_points - 1): # calculate distances between points\\n distances[i] = self.points[i + 1] - self.points[i]\\n\\n times = np.linalg.norm(distances, axis=1) # calculate times btwn points based on distance\\n times = np.cumsum(times)\\n # times = times.reshape(-1, 1)\\n times = np.insert(times, 0, 0, axis=0) # add t = 0 to first element of time vector\\n times = times / 3.0\\n\\n for ind, j in enumerate(times):\\n if t == times[0]: # QC at initial position at t = 0\\n x = self.points[0]\\n break\\n elif t >= times[-1]: # if actual time is greater than time for final position, keep QC at final position\\n x = self.points[-1]\\n break\\n elif ind == self.points.shape[0] - 1:\\n break\\n elif j < t < times[ind + 1]:\\n x[0], x_dot[0], x_ddot[0] = self.posvel(ind, self.points[ind], times, t, flat_output, 'x')\\n x[1], x_dot[1], x_ddot[1] = self.posvel(ind, self.points[ind], times, t, flat_output, 'y')\\n x[2], x_dot[2], x_ddot[2] = self.posvel(ind, self.points[ind], times, t, flat_output, 'z')\\n # x[0], x_dot[0] = self.posvel(ind, self.points[ind], times, t, flat_output, 'x')\\n # x[1], x_dot[1] = self.posvel(ind, self.points[ind], times, t, flat_output, 'y')\\n # x[2], x_dot[2] = self.posvel(ind, self.points[ind], times, t, flat_output, 'z')\\n\\n x_ddot = x_ddot.clip(max=3)\\n x_ddot = x_ddot.clip(min=-3)\\n x_dot = x_dot.clip(max=2)\\n x_dot = x_dot.clip(min=-2)\\n\\n\\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\\n 'yaw': yaw, 'yaw_dot': yaw_dot}\\n return flat_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03f5fc1aa9a5753b03d466b43b55ce6\",\n \"score\": \"0.55434877\",\n \"text\": \"def update(self):\\n for i in range(len(self.previous)-1):\\n xi = self.previous[i]\\n xip1 = self.previous[i+1]\\n for field in xi:\\n field.assign(xip1(field.name()))\\n for field in self.n:\\n field.assign(self.np1(field.name()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab6e6d4682da055accac164032d4f637\",\n \"score\": \"0.55409485\",\n \"text\": \"def sketch(self, val):\\n row, col = self.selected\\n self.cells[row][col].set_temp(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10cb68592baf69606f5ada6df7624261\",\n \"score\": \"0.55309683\",\n \"text\": \"def set_x(self, new_x):\\r\\n check_value(new_x)\\r\\n self.x = new_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704829bdd6fd42e5a71e54f9314ead05\",\n \"score\": \"0.5528827\",\n \"text\": \"def update(self, x):\\n if self.first_pass:\\n self.means = np.mean(x, axis=0)\\n self.vars = np.var(x, axis=0)\\n self.m = x.shape[0]\\n self.first_pass = False\\n else:\\n n = x.shape[0]\\n new_data_var = np.var(x, axis=0)\\n new_data_mean = np.mean(x, axis=0)\\n new_data_mean_sq = np.square(new_data_mean)\\n new_means = ((self.means * self.m) + (new_data_mean * n)) / (self.m + n)\\n self.vars = (((self.m * (self.vars + np.square(self.means))) +\\n (n * (new_data_var + new_data_mean_sq))) / (self.m + n) -\\n np.square(new_means))\\n self.vars = np.maximum(0.0, self.vars) # occasionally goes negative, clip\\n self.means = new_means\\n self.m += n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979b53148475dbc04cce2d71376eb002\",\n \"score\": \"0.5515013\",\n \"text\": \"def update(self, x):\\n diff = (x - self.W) # compute difference matrix\\n bmu_idx = self.find_bmu(x, diff_matrix=diff)\\n u = self.W[bmu_idx,:]\\n\\n for n in range(self.W.shape[0]):\\n v = tf.expand_dims(self.W[n,:],axis=0)\\n dist_uv = float( tf.norm(u - v, ord=\\\"euclidean\\\") )\\n if dist_uv < (self.radius * self.radius):\\n oh = tf.expand_dims(tf.one_hot(n, depth=self.W.shape[0]),axis=1)\\n wght = self.calc_neighborhood_weights(u, v, sigma=self.radius)\\n dW = tf.matmul(oh, (x - v) * wght * self.eta)\\n self.W = self.W + dW\\n self.s += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f7bfeca0c45840d82cb6e8cfb06333\",\n \"score\": \"0.5498006\",\n \"text\": \"def set_cell(self, x):\\n if isinstance(x, str): \\n apart = None # letter part of a1-style reference\\n npart = None # numeric part of a1-style reference \\n \\n #SN_1 shorter version\\n search_result = re.search(\\\"^(\\\\D*)(\\\\d*)$\\\", x)\\n if search_result is not None:\\n apart = search_result.group(1)\\n npart = search_result.group(2)\\n # ***\\n \\n if apart and npart:\\n a1ref = apart.upper() + npart\\n self.rowx, self.colx = xlsxwriter.utility.xl_cell_to_rowcol(a1ref) \\n elif npart:\\n rowx = int(npart) \\n elif apart:\\n _proxy_cell = apart.upper() + '1'\\n self.colx = xlsxwriter.utility.xl_cell_to_rowcol(_proxy_cell)[1] \\n elif isinstance(x,int):\\n self.rowx = x - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe26268a8253b9ea378461263852bffe\",\n \"score\": \"0.54898095\",\n \"text\": \"def ForcePoint(self,x_next):\\n \\n # change position of interface and get resulting y-value\\n self.mi.setX(x_next)\\n if(self.acq_func[0] == 'testEI'):\\n (x_new, y_new) = (x_next, self.acq_func[2].iloc[ind,-1])\\n else:\\n (x_new, y_new) = self.mi.getState()\\n # add new entry to observed data\\n self.X_obs = np.concatenate((self.X_obs,x_new),axis=0)\\n self.Y_obs.append(y_new)\\n \\n # update the model (may want to add noise if using testEI)\\n self.model.update(x_new, y_new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4daa4082d05f1271091a4966977ed4\",\n \"score\": \"0.5485281\",\n \"text\": \"def update(self, xnew, ynew):\\n y = np.concatenate((self.y, ynew), axis=0)\\n X = np.concatenate((self.X, xnew), axis=0)\\n self.fit(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4daa4082d05f1271091a4966977ed4\",\n \"score\": \"0.5485281\",\n \"text\": \"def update(self, xnew, ynew):\\n y = np.concatenate((self.y, ynew), axis=0)\\n X = np.concatenate((self.X, xnew), axis=0)\\n self.fit(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c91b80ef69c13a91c9b8795ceb8ac16\",\n \"score\": \"0.54694533\",\n \"text\": \"def update_hyperparams(self):\\n self.eta_t = self.eta_t * self.f\\n if self.epoch < self.T:\\n self.p_t = (1 - ((self.epoch + 1) / self.T)) * self.p_i + \\\\\\n ((self.epoch + 1) / self.T) * self.p_f\\n else:\\n self.p_t = self.p_f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81cb9ba3bd0fd01e4f7ffce7e1cc973\",\n \"score\": \"0.546051\",\n \"text\": \"def update(self, x, win, t):\\n eta = self._decay_function(self.learning_rate, t, self.T)\\n sig = self._decay_function(self.sigma, t, self.T) # sigma and learning rate decrease with the same rule\\n g = self.neighborhood(win, sig)*eta # improves the performances\\n it = nditer(g, flags=['multi_index'])\\n while not it.finished:\\n # eta * neighborhood_function * (x-w)\\n self.weights[it.multi_index] += g[it.multi_index]*(x-self.weights[it.multi_index])\\n # normalization\\n self.weights[it.multi_index] = self.weights[it.multi_index] / fast_norm(self.weights[it.multi_index])\\n it.iternext()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689988474378fdff280486b1f5479684\",\n \"score\": \"0.54528725\",\n \"text\": \"def setCell(self, x, y, val, updateGUI):\\r\\n\\t\\t# Set the grid value for the puzzle at x-y to val\\r\\n\\t\\tself.grid[x][y] = val\\r\\n\\t\\tif val != 0:\\r\\n\\t\\t\\t# Only update the nodes expanded if we are not resetting the cell value back to 0\\r\\n\\t\\t\\tself.nodesExpanded += 1\\r\\n\\t\\tif updateGUI and not self.commandLine:\\r\\n\\t\\t\\t# If asked, update the gui with new cell values too\\r\\n\\t\\t\\tself.gui.setCell(x,y,val)\\r\\n\\t\\t\\tself.update_gui()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ee58d618b9dd67a78f3a9fb947287c\",\n \"score\": \"0.54461056\",\n \"text\": \"def set_tile(self, row, col, value):\\r\\n # replace with your code\\r\\n self._cells[row][col] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e880159ec2e10310cdc73222be0a89\",\n \"score\": \"0.54388535\",\n \"text\": \"def save_X(self, x):\\n self.x = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c721f14d2b1912f0b3c13b9efa8e3138\",\n \"score\": \"0.5433696\",\n \"text\": \"def update_state(self, clock, new_data):\\r\\n # self.system_stat.iloc[clock, 1:9] = self.system_stat.iloc[clock, 1:9] #+ new_data\\r\\n self.system_stat.iloc[clock,\\r\\n ] = self.system_stat.iloc[clock, ] + new_data\\r\\n # self.system_stat.loc[clock, 'E'] = E\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37e2653d996282d28256017fef8e0b5\",\n \"score\": \"0.54266226\",\n \"text\": \"def propagate_qttt_to_ttt(self):\\n self.ttt.update_ttt_from_qttt(self.board)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bd27b2c1ed95b032f0173ff0607dcc\",\n \"score\": \"0.5407221\",\n \"text\": \"def update(self,t=1):\\n force=Force.sum(self.forces)\\n print(\\\"MyMaterialPoint:force:\\\",force)\\n x,y=force\\n acceleration=Vector(x,y)/self.mass\\n self.motion.setAcceleration(acceleration)\\n self.motion.update(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efded9597a8faab8809539e764fbcb9d\",\n \"score\": \"0.5395012\",\n \"text\": \"def update(self):\\n self.Tax = self.tw*self.w*self.L + self.tr*self.r*(self.A+self.D)\\n self.G = self.gy*self.C\\n self.Tr = (self.Tax+self.Beq-self.G)/self.Pt\\n self.tb = self.zeta*(1-self.tw)*self.Pr/(self.L+self.zeta*self.Pr)\\n self.b = self.zeta*(1-self.tw-self.tb)*self.w\\n self.p = array([self.r, self.w, self.b, self.tr, self.tw, self.tb, \\n self.Tr, self.qh, self.qr])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba910dde69e271e3d23f81df2685b86c\",\n \"score\": \"0.53934026\",\n \"text\": \"def put_x(self, x, y):\\r\\n self.field[y - 1][x - 1] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3beedb2149ba231696661cbf88c7aa7\",\n \"score\": \"0.5391946\",\n \"text\": \"def move_poly(self, xt=1000, yt=1000):\\n index = self.get_id_input()\\n if index:\\n self.df.loc[self.df.index == index, self.columns_x] = \\\\\\n self.df.loc[self.df.index == index].apply(lambda x: x[self.columns_x]+xt, axis=1)\\n self.df.loc[self.df.index == index, self.columns_y] = \\\\\\n self.df.loc[self.df.index == index].apply(lambda x: x[self.columns_y]+yt, axis=1)\\n return self.df.loc[self.df.index == index]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fcf259bbcdce1a1391428b80c7e9461\",\n \"score\": \"0.5386673\",\n \"text\": \"def __update_t(t_opt, window_size):\\n global t_min\\n t_min = max(t0_min, round(t_opt - window_size//2))\\n global t_max\\n t_max = round(t_opt + window_size//2)\\n global n\\n n = t_max*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8d521b8098318c0e49273eac6fb04c6\",\n \"score\": \"0.538591\",\n \"text\": \"def eval_cell(self, value, x, ufc_cell):\\n\\t\\tglobal area_fracs\\n\\t\\tvalue[0] = area_fracs[ufc_cell.index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc02479d90ad7f17585bc353ab12b2a6\",\n \"score\": \"0.5382476\",\n \"text\": \"def update(self, x, y, d):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1936a0b8bc9e2279b587fc30ca1872a\",\n \"score\": \"0.53799874\",\n \"text\": \"def update_cell(self, row, col, val):\\n self.update_acell( Worksheet.get_addr_int(row,col), val=unicode(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a4bcdc8d885829ea34bc8c1ba9c31b\",\n \"score\": \"0.5379168\",\n \"text\": \"def update_orig(self, inp=None):\\n \\n if inp is None:\\n inp = self.arrtemp\\n inp[self.arrmask < MASK_LIM] = np.nan\\n \\n self.arrorig = inp.copy()\\n self.ptsorig = copy.deepcopy(self.ptstemp)\\n self.oriorig = copy.deepcopy(self.oritemp)\\n self.parsorig = copy.deepcopy(self.parstemp)\\n \\n self.reset_temp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11f63001f61f77d9ff28c208dae81be\",\n \"score\": \"0.5374063\",\n \"text\": \"def xt_accurate(self):\\r\\n\\r\\n self.t_acc.append(0.0)\\r\\n err = 0.0\\r\\n self.t_local_err.append(err)\\r\\n for i in range(1, len(self.obs.obt_g)):\\r\\n an = math.sqrt(float(self.obs.obt_g[i]) ** 2 + self.x[i]\\r\\n ** 2)\\r\\n self.t_acc.append(an)\\r\\n err = math.fabs(100.0 * (an - self.t[i]) / an)\\r\\n self.t_local_err.append(err)\\r\\n print 'accurate coordinate calculated'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b072f35d4b1e79933de5a836c1b48a0a\",\n \"score\": \"0.5371576\",\n \"text\": \"def set_cells(self, x: int, y: int, kind: CellType, w = 1, h = 1):\\n for col in range(x, x + w):\\n for row in range(y, y + h):\\n self.grid[row * self.rows + col] = CellType(kind.value)\\n self.updates.append((col, row, kind))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed834a351e2fd8425b22e4f882e91ae4\",\n \"score\": \"0.5368172\",\n \"text\": \"def tick(self):\\n self.setup_cells()\\n\\n updated = []\\n for i in range(self.rows):\\n this_row = []\\n for j in range(self.cols):\\n this_cell = self.get_cell(i, j)\\n result_identity = None\\n if not this_cell.static: # not only inactive cells surrounding\\n for rule in RULES[this_cell.identity]:\\n result_identity = rule(this_cell)\\n if result_identity: # breaks on first identity change\\n break\\n result_identity = result_identity or this_cell.identity # prioritizes result identity\\n this_cell.ticks += 1\\n this_row.append(CELLS_INDEX[result_identity])\\n updated.append(this_row)\\n\\n self.initialize(updated)\\n self.ticks += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33aae1e67dcd78a279912000cf14e3ed\",\n \"score\": \"0.5357971\",\n \"text\": \"def update_model(self):\\n self.model = [[self.cells[i][j].value for j in range(self.cols)] for i in range(self.rows)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9533f0a837753ef5c86a307e3ddd38d\",\n \"score\": \"0.53511006\",\n \"text\": \"def opcode_Td(renderer, t_x, t_y):\\n renderer.ts.m = PdfMatrix(1, 0, 0, 1, t_x, t_y)*renderer.ts.lm\\n renderer.ts.reset_lm()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597edfaa72307449bc62102045c73ea9\",\n \"score\": \"0.5340474\",\n \"text\": \"def set_tile(self, row, col, value): \\n self.cells[row][col] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f37521d970b24987d40927023ee202\",\n \"score\": \"0.5324432\",\n \"text\": \"def _tnet_update(self):\\n with tf.variable_scope('qnet'): \\n self.update_opt = [t.assign(q) for t, q in zip(self.tnet.params, self.params)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce855b59ff59026ea5670f0bdb6cb850\",\n \"score\": \"0.53202856\",\n \"text\": \"def update_params(self, X):\\n batchsize = X.shape[0]\\n self.deltaC = self.p_t * self.deltaC - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dC\\n self.deltaB = self.p_t * self.deltaB - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.db\\n self.deltaM = self.p_t * self.deltaM - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dM\\n self.deltaJ = self.p_t * self.deltaJ - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dJ\\n self.deltaBj = self.p_t * self.deltaBj - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dBj\\n self.deltaWhf = self.p_t * self.deltaWhf - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWhf\\n self.deltaWfv = self.p_t * self.deltaWfv - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWfv\\n self.deltaWfx = self.p_t * self.deltaWfx - \\\\\\n (1 - self.p_t) * (self.eta_t / batchsize) * self.dWfx\\n\\n self.C = self.C + self.deltaC\\n self.bw = self.bw + self.deltaB\\n self.M = self.M + self.deltaM\\n self.J = self.J + self.deltaJ\\n self.bj = self.bj + self.deltaBj\\n self.Wfv = self.Wfv + self.deltaWfv\\n self.Wfx = self.Wfx + self.deltaWfx\\n self.Whf = self.Whf + self.deltaWhf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69276eef642e3d487fdea0d0180d62b6\",\n \"score\": \"0.53150904\",\n \"text\": \"def set_cell(self, x: int, y: int, state: CellState):\\n if 0 <= y < len(self.grid) and 0 <= x < len(self.grid[y]):\\n self.grid[y][x] = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923375c33f8e50856675541e3bb10837\",\n \"score\": \"0.5313385\",\n \"text\": \"def _update_pert_row(\\n self,\\n rxn_table_id,\\n rxn_no,\\n rxn,\\n k_i,\\n ind_len_west,\\n ind_len_gav,\\n ind_len_ng,\\n cell_size_west,\\n cell_size_gav,\\n cell_size_ng,\\n sens_ind_len_west,\\n sens_ind_len_gav,\\n sens_ind_len_ng,\\n sens_cell_size_west,\\n sens_cell_size_gav,\\n sens_cell_size_ng,\\n ):\\n with self.con as con:\\n cur = con.cursor()\\n cur.execute(\\n \\\"\\\"\\\"\\n UPDATE {:s} SET \\n k_i = :k_i,\\n ind_len_west = :ind_len_west,\\n ind_len_gav = :ind_len_gav,\\n ind_len_ng = :ind_len_ng,\\n cell_size_west = :cell_size_west,\\n cell_size_gav = :cell_size_gav,\\n cell_size_ng = :cell_size_ng,\\n sens_ind_len_west = :sens_ind_len_west,\\n sens_ind_len_gav = :sens_ind_len_gav,\\n sens_ind_len_ng = :sens_ind_len_ng,\\n sens_cell_size_west = :sens_cell_size_west,\\n sens_cell_size_gav = :sens_cell_size_gav,\\n sens_cell_size_ng = :sens_cell_size_ng\\n WHERE\\n rxn_no = :rxn_no AND\\n rxn = :rxn\\n \\\"\\\"\\\".format(rxn_table_id),\\n {\\n 'rxn_no': rxn_no,\\n 'rxn': rxn,\\n 'k_i': k_i,\\n 'ind_len_west': ind_len_west,\\n 'ind_len_gav': ind_len_gav,\\n 'ind_len_ng': ind_len_ng,\\n 'cell_size_west': cell_size_west,\\n 'cell_size_gav': cell_size_gav,\\n 'cell_size_ng': cell_size_ng,\\n 'sens_ind_len_west': sens_ind_len_west,\\n 'sens_ind_len_gav': sens_ind_len_gav,\\n 'sens_ind_len_ng': sens_ind_len_ng,\\n 'sens_cell_size_west': sens_cell_size_west,\\n 'sens_cell_size_gav': sens_cell_size_gav,\\n 'sens_cell_size_ng': sens_cell_size_ng,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5429677997cadd71eb2727325c66f16\",\n \"score\": \"0.5309254\",\n \"text\": \"def set_cell(frame, data):\\n with data.cell_:\\n data.cell_[:,0] = [L, 0., 0.]\\n data.cell_[:,1] = [0., L, 0.]\\n data.cell_[:,2] = [0., 0., L]\\n #cell origin\\n data.cell_[:,3] = [0, 0 , 0]\\n #set periodic boundary conditions\\n data.cell_.pbc = (True, True, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96846cc85d43990e9709b6d2c3c47e53\",\n \"score\": \"0.53056103\",\n \"text\": \"def update(self):\\n vals = np.zeros(len(self.table_model._TableModel__x))\\n for ipt in self.onPtSrc:\\n vals += [\\n self.likelihoodModel.pointSources[ipt].spectralModel.photonFlux(\\n bounds[0], bounds[1]\\n )\\n for bounds in zip(self.e_lo, self.e_hi)\\n ]\\n # integrated fluxes over same energy bins as for dataset, according to Sherpa TableModel specs, TBV\\n self.table_model._TableModel__y = vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dfed14778d7ed29e0e4c6fa39ebe9dd\",\n \"score\": \"0.5283027\",\n \"text\": \"def set_value(self, tuple, item):\\r\\n self.cells[tuple[0]][tuple[1]] = item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2adaf6996a7708274ac2b20e3b5fb1cd\",\n \"score\": \"0.5274169\",\n \"text\": \"def update_state(state, kernel, learning_rate, x_i, y_i):\\n # *** START CODE HERE ***\\n # Append to the last column\\n length = len(state[0])\\n\\n # add a new row\\n state[0].append(x_i)\\n state[1].append(y_i)\\n state[2].append([0.0])\\n state[3].append([])\\n index = 0 \\n while index < length + 1:\\n kernEnt = kernel(state[0][index], x_i)\\n oldMargin = state[2][length][index] \\n state[2][length].append(oldMargin + learning_rate * (state[1][index] - sign(state[2][index][index])) * kernEnt)\\n state[3][length].append(kernEnt)\\n index = index + 1\\n\\n index1 = 0 \\n while index1 < length:\\n kernEnt = kernel(x_i, state[0][index1])\\n oldMargin = state[2][index1][length] \\n #state[2][index] = oldMargin + learning_rate * (y_i - sign(oldMargin)) * kernEnt\\n state[2][index1].append(oldMargin + learning_rate * (y_i - sign(state[2][length][length])) * kernEnt)\\n state[3][index1].append(kernEnt)\\n index1 = index1 + 1\\n # *** END CODE HERE ***\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e222f5ab4ad18cba94fed235fcc9199a\",\n \"score\": \"0.52676314\",\n \"text\": \"def update_grid(self):\\n for var in self.model.variables():\\n if var.name[0:1] == 'x':\\n coor = int(var.name[1:])\\n xCoor = (coor - 1) % self.cols\\n yCoor = math.floor((coor - 1) / self.cols)\\n if self.grid[yCoor][xCoor] == 0:\\n self.grid[yCoor][xCoor] = \\\"0\\\"\\n continue\\n if var.value() == 1:\\n self.grid[yCoor][xCoor] = \\\"x\\\"\\n else:\\n self.grid[yCoor][xCoor] = \\\"1\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"688dcec9c00d0f5213022276113c9504\",\n \"score\": \"0.52493984\",\n \"text\": \"def modify_cell_with(self, sheet_name, column, row, op, val):\\n pass\\n # my_sheet_index = self.workbook.sheet_names().index(sheet_name)\\n # cell = self.workbook.get_sheet(my_sheet_index).cell(int(row), int(column))\\n # curval = cell.value\\n # if cell.ctype is XL_CELL_NUMBER:\\n # self.workbook.sheets()\\n # if not self.copied_workbook:\\n # self.copied_workbook = copy(self.workbook)\\n # plain = easyxf('')\\n # modexpr = str(curval) + op + val\\n # self.copied_workbook.get_sheet(my_sheet_index).write(int(row), int(column), eval(modexpr), plain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e950612e1513a9f8b73fadd5da46d9\",\n \"score\": \"0.5247235\",\n \"text\": \"def update(self, t, state, flat_output):\\r\\n\\r\\n \\\"First extract the current (actual) position of the robot and the desired position derived from the waypoints\\\"\\r\\n ri = state[\\\"x\\\"] #expressed in inertial frame\\r\\n rT = flat_output[\\\"x\\\"] #expressed in inertial frame\\r\\n \\\"actual velocity of robot and desired velocity from waypoints\\\"\\r\\n vi = state[\\\"v\\\"] #expressed in inertial frame\\r\\n vT = flat_output[\\\"x_dot\\\"] #expressed in inertial frame\\r\\n \\\"Create the position and velocity error vector between those two\\\"\\r\\n e_pos = ri - rT\\r\\n e_vel = vi - vT\\r\\n rdotdotT = flat_output[\\\"x_ddot\\\"] #acceleration as returned from waypoints\\r\\n psiT = flat_output[\\\"yaw\\\"]\\r\\n\\r\\n \\\"From quaternions to euler angles\\\"\\r\\n rot = Rotation.from_quat(state[\\\"q\\\"])\\r\\n eulerAngles = rot.as_euler('zyx')\\r\\n psi = eulerAngles[0]\\r\\n theta = eulerAngles[1]\\r\\n phi = eulerAngles[2]\\r\\n\\r\\n kd = 5 #tune\\r\\n kp = 5 #tune\\r\\n\\r\\n kdMatrix = np.diagflat(kd*np.ones((1,3)))\\r\\n kpMatrix = np.diagflat(kp*np.ones((1,3)))\\r\\n\\r\\n rdotdotdes = rdotdotT - kdMatrix@e_vel - kpMatrix@e_pos\\r\\n\\r\\n \\\"Now we can compute u1 (thrust), θDes and φDes\\\"\\r\\n u1 = self.mass*(self.g + rdotdotdes[2]) #thrust control\\r\\n phiDes = rdotdotdes[0]*np.sin(psiT) - rdotdotdes[1]*np.cos(psiT)\\r\\n thetaDes = (rdotdotdes[0] - phiDes*np.sin(psiT)) / self.g*np.cos(psiT)\\r\\n\\r\\n \\\"Now we can compute u2\\\"\\r\\n I = np.diagflat([self.Ixx, self.Iyy, self.Izz])\\r\\n u2 = I@np.array([[-kp*(phi - phiDes)-kd*(state[\\\"w\\\"][0])],\\r\\n [-kp*(theta - thetaDes)-kd*(state[\\\"w\\\"][1])],\\r\\n [-kp*(psi - psiT)-kd*(state[\\\"w\\\"][2])]])\\r\\n\\r\\n \\\"From u2 to forces\\\"\\r\\n gamma = self.k_drag / self.k_thrust\\r\\n A = np.array([[1, 1, 1, 1],\\r\\n [0, self.arm_length, 0, -self.arm_length],\\r\\n [-self.arm_length, 0, self.arm_length, 0],\\r\\n [gamma, -gamma, gamma, -gamma]])\\r\\n\\r\\n forces = np.linalg.inv(A)@np.insert(u2,0,u1)\\r\\n\\r\\n \\\"From forces to motor speeds\\\"\\r\\n cmd_motor_speeds = np.squeeze(np.sqrt(1/self.k_thrust * forces))\\r\\n cmd_thrust = np.sum(forces)\\r\\n\\r\\n \\\"From motor speeds to moments\\\"\\r\\n cmd_moment = u2\\r\\n cmd_q = np.zeros((4,))\\r\\n\\r\\n # STUDENT CODE HERE\\r\\n\\r\\n control_input = {'cmd_motor_speeds':cmd_motor_speeds,\\r\\n 'cmd_thrust':cmd_thrust,\\r\\n 'cmd_moment':cmd_moment,\\r\\n 'cmd_q':cmd_q}\\r\\n return control_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b744236c1978264254e5b5720381ad3\",\n \"score\": \"0.52438533\",\n \"text\": \"def __call__(self, t, x):\\n #TODO: might want to integrate/average over the input so it isn't only based on the last timestep\\n\\n # Only perform a state transition if enough time has passed since the last one\\n if t - self.last_t >= self.time_interval:\\n # Get the action id of the closest action vector to the noisy input vector\\n action = self.find_closest_action(x)\\n # Perform that action to update the current state\\n self.make_action(action)\\n\\n self.last_t = t\\n\\n return self.index_to_state_vector[self.current_state]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24268841eaaa34790f133c0f3ab2704\",\n \"score\": \"0.5241271\",\n \"text\": \"def _update(self, vals, params, dt):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0683e66d002a85076de659838e02764f\",\n \"score\": \"0.5240061\",\n \"text\": \"def update_problem(self, x0, Xm):\\n self.update_x0(x0)\\n self.update_Xm(Xm)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bee19d67300ffe0791ed32f7ba82db2\",\n \"score\": \"0.52332383\",\n \"text\": \"def rearrange(self, cell):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c75b247b5b86cf34e78da36650fc8e7\",\n \"score\": \"0.5227867\",\n \"text\": \"def set_initial_value(self, y, t=0.0):\\n self.t = t\\n self.y = y\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f630973511a4bf858f555f38f023e3ef\",\n \"score\": \"0.5221748\",\n \"text\": \"def rebase_to_one(self):\\n self.x_data = self.x_data.T\\n list_of_first_elem_price = [i[0] for i in self.x_data]\\n shape_row, shape_column = self.x_data.shape[0], self.x_data.shape[1]\\n\\n for i in range(shape_row):\\n for j in range(shape_column):\\n self.x_data[i][j] /= list_of_first_elem_price[i]\\n self.x_data = self.x_data.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf81e8bcc5fae4af7f6700581a39586a\",\n \"score\": \"0.52135056\",\n \"text\": \"def update_cell(self, i: int, j: int, new_value: Cell) -> None:\\n try:\\n self.__cells[j][i] = new_value\\n except IndexError:\\n print(\\\"Trying to update a cell with index out of bounds\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef0d64b2c6688a85479f74dc436ddcb8\",\n \"score\": \"0.51921713\",\n \"text\": \"def update_dynamics_model(self, x, u):\\n if x.ndim < 2:\\n x = np.expand_dims(x, axis=1) # shape of x should be (N,1), not (N,)\\n if u.ndim < 2:\\n u = np.expand_dims(u, axis=1)\\n # ===== Model parameters ===== #\\n\\n # num of state, action\\n nx = self.nx\\n nu = self.nu\\n\\n m = self.m # i30\\n\\n width = self.width\\n length = self.length\\n\\n l_f = self.l_f\\n l_r = self.l_r\\n wheelbase = self.wheelbase\\n turning_circle = self.turning_circle\\n max_steer = self.max_steer\\n\\n Iz = self.Iz\\n\\n # Iw = 1.8 # wheel inertia\\n # rw = 0.3 # wheel radius\\n\\n roh = self.roh # density of air [kg/m3]\\n C_d = self.C_d # drag coefficient\\n A_f = self.A_f # vehicle frontal area [m2]\\n C_roll = self.C_roll # rolling resistance coefficient\\n\\n dt = self.dt # sampling time. [sec]\\n\\n \\n \\\"\\\"\\\"\\n Pacejka lateral tire model params\\n \\n \\\"\\\"\\\"\\n Fz_f = 9.81 * (m * l_r/wheelbase) * 0.001 # vertical force at front axle. [kN]\\n\\n a_lat_f = [-22.1, 1011, 1078, 1.82, 0.208, 0.000, -0.354, 0.707] # for Fy\\n C_lat_f = 1.30\\n D_lat_f = a_lat_f[0]*Fz_f**2 + a_lat_f[1]*Fz_f\\n BCD_lat_f = a_lat_f[2]*math.sin(a_lat_f[3]*math.atan(a_lat_f[4]*Fz_f)) # before, atan\\n # B_lat_f = BCD_lat_f/(C_lat_f*D_lat_f)\\n B_lat_f = BCD_lat_f/(C_lat_f*D_lat_f) * 180/np.pi # for radian sideslip angle\\n E_lat_f = a_lat_f[5]*Fz_f**2 + a_lat_f[6]*Fz_f + a_lat_f[7]\\n\\n Fz_r = 9.81 * (m * l_f/wheelbase) * 0.001 # vertical force at rear axle. [kN]\\n\\n a_lat_r = [-22.1, 1011, 1078, 1.82, 0.208, 0.000, -0.354, 0.707] # for Fy\\n C_lat_r = 1.30\\n D_lat_r = a_lat_r[0]*Fz_r**2 + a_lat_r[1]*Fz_r\\n BCD_lat_r = a_lat_r[2]*math.sin(a_lat_r[3]*math.atan(a_lat_r[4]*Fz_r)) # berore, atan\\n # B_lat_r = BCD_lat_r/(C_lat_r*D_lat_r)\\n B_lat_r = BCD_lat_r/(C_lat_r*D_lat_r) * 180/np.pi # for radian sideslip angle\\n E_lat_r = a_lat_r[5]*Fz_r**2 + a_lat_r[6]*Fz_r + a_lat_r[7]\\n\\n \\\"\\\"\\\"\\n ===== Discretize Linearized Dynamics model =====\\n \\\"\\\"\\\"\\n\\n # normalize angle\\n # x[2] = normalize_angle(x[2])\\n\\n # Avoiding zero denominator (for slip angle, expm in discretization procedure)\\n # before 19.07.31, 0.5 m/s\\n if x[3,0] >=0 and x[3,0] < 0.5:\\n # x[3] = 0.0\\n x[4,0] = 0. # v_y\\n x[5,0] = 0. # yaw_rate\\n u[0,0] = 0. # steer\\n if x[3,0] < 0.3:\\n x[3,0] = 0.3 # v_x\\n print(\\\"Avoiding zero denominator\\\")\\n\\n if x[3,0] > -0.5 and x[3,0] < 0:\\n # x[3] = 0.\\n x[4,0] = 0.\\n x[5,0] = 0.\\n u[0,0] = 0.\\n if x[3,0] > -0.3:\\n x[3,0] = -0.3\\n print(\\\"Avoiding zero denominator\\\")\\n\\n # States\\n yaw = x[2,0] # [0] for scalar data\\n v_x = x[3,0]\\n v_y = x[4,0]\\n yaw_rate = x[5,0]\\n\\n steer = u[0,0]\\n accel_track = u[1,0]\\n\\n # Dynamics model\\n # Slip angle [deg]\\n # alpha_f = np.rad2deg(-math.atan2( l_f*yaw_rate + v_y,v_x) + steer)\\n # alpha_r = np.rad2deg(-math.atan2(-l_r*yaw_rate + v_y,v_x))\\n\\n alpha_f = -math.atan2( l_f*yaw_rate + v_y,v_x) + steer\\n alpha_r = -math.atan2(-l_r*yaw_rate + v_y,v_x)\\n\\n # Lateral force (front & rear)\\n # Fy_f = D_lat_f * math.sin(C_lat_f * math.atan2(B_lat_f * alpha_f, 1)) # before was atan\\n # Fy_r = D_lat_r * math.sin(C_lat_r * math.atan2(B_lat_r * alpha_r, 1)) # before was atan\\n Fy_f = D_lat_f * math.sin(C_lat_f * math.atan(B_lat_f * alpha_f))\\n Fy_r = D_lat_r * math.sin(C_lat_r * math.atan(B_lat_r * alpha_r))\\n\\n # Longitudinal force\\n\\n # for both forward and backward driving.\\n R_roll = C_roll * m * 9.81 * np.sign(v_x) # rolling resistance. [N] f*(Fzf+Fzr) = f*(mg)\\n F_aero = 0.5*roh*C_d*A_f*v_x**2 * np.sign(v_x) # aero dynamics drag. [N] 0.5*rho*cd*A.\\n Fx_f = m*accel_track - F_aero - R_roll\\n\\n # Next state\\n x_dot = np.array([[v_x*math.cos(yaw) - v_y*math.sin(yaw)],\\n [v_y*math.cos(yaw) + v_x*math.sin(yaw)],\\n [yaw_rate],\\n [1./m*(Fx_f*math.cos(steer) - Fy_f*math.sin(steer) + m*v_y*yaw_rate)],\\n [1./m*(Fx_f*math.sin(steer) + Fy_r + Fy_f*math.cos(steer) - m*v_x*yaw_rate)],\\n [1./Iz*(Fx_f*l_f*math.sin(steer) + Fy_f*l_f*math.cos(steer)- Fy_r*l_r)]])\\n\\n x_next = x + x_dot * dt\\n\\n return x_next, alpha_f, alpha_r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22189c24f6422b20fa1f36876bb54b1\",\n \"score\": \"0.51817864\",\n \"text\": \"def x(self, x: float):\\n\\n self._x = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1540b88ab29d2321c269c45e2692cad7\",\n \"score\": \"0.51742435\",\n \"text\": \"def _update_times(self):\\n self._times = self.tmin + (self.tstep * np.arange(self.shape[-1]))\\n self._times.flags.writeable = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7cdae8715195477e2ffb0c6edd76ea4\",\n \"score\": \"0.517007\",\n \"text\": \"def replace_cell(self, new, row, col):\\n self.cells[row][col] = new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fe5bb797175119e8f90b55d380bfdb\",\n \"score\": \"0.5166559\",\n \"text\": \"def t_value(self, kx: float, ky: float) : # This is t_ij(k_tilde)\\n t = self.t ; tp = self.tp; tpp = self.tpp\\n t_val = np.zeros((4, 4), dtype=complex)\\n ex = np.exp(-2.0j*kx) ; emx = np.conjugate(ex)\\n ey = np.exp(-2.0j*ky) ; emy = np.conjugate(ey)\\n tloc = np.array([[0.0, -t, -t, -tp],\\n [-t, 0.0, 0.0, -t],\\n [-t, 0.0, 0.0, -t],\\n [-tp, -t, -t, 0.0]])\\n\\n\\n t_val += tloc\\n\\n t_val[0, 0] += -tpp*(ex+emx+ey+emy); t_val[0, 1] += -t*ex; t_val[0, 3] += -tp*(ex + ey + ex*ey); t_val[0, 2] += -t*ey\\n t_val[1, 0] += -t*emx; t_val[1, 1] += -tpp*(ex+emx+ey+emy); t_val[1, 3] += -t*ey; t_val[1, 2] += -tp*(emx + ey + emx*ey)\\n t_val[3, 0] += -tp*(emx + emy + emx*emy); t_val[3, 1] += -t*emy; t_val[3, 3] += -tpp*(ex+emx+ey+emy); t_val[3, 2] += -t*emx\\n t_val[2, 0] += -t*emy; t_val[2, 1] += -tp*(ex + emy + ex*emy); t_val[2, 3] += -t*ex; t_val[2, 2] += -tpp*(ex+emx+ey+emy)\\n\\n return (t_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"974b3ecf0a5291de32ca1bd4bd4b6fd0\",\n \"score\": \"0.51663643\",\n \"text\": \"def actuate(self, cell):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ba997272ffe57e1ed08a5d0c888ded\",\n \"score\": \"0.51662946\",\n \"text\": \"def updateValues(self, tickData):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b7e0ae60f6c23fe61dc8554e253380\",\n \"score\": \"0.5149533\",\n \"text\": \"def set(self, name, value, ex=None, px=None, nx=False, xx=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb4ae34502eb110565d6ca7018d2431\",\n \"score\": \"0.5140047\",\n \"text\": \"def updateTable(self, item: KnapsackItem, x: int, y: int) -> None:\\n ((self.table)[y][x]).extend(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e39080875e3288f70ca8b812ba350c13\",\n \"score\": \"0.51353663\",\n \"text\": \"def post_integration(self):\\n # Cache the values\\n self.sim.derivs(self.t0, self.xold)\\n self.__store_values()\\n \\n V, dV = self.sim.CVs.volumes(self.t0)\\n Nexist = self.sim.CVs.Nexist\\n if sorted(self.sim.stateVariables) == ['D','T']:\\n self.sim.CVs.updateStates('T',self.xnew[0:Nexist],'D',self.xnew[Nexist:2*Nexist])\\n elif sorted(self.sim.stateVariables) == ['M','T']:\\n self.sim.CVs.updateStates('T',self.xnew[0:Nexist],'D',self.xnew[Nexist:2*Nexist]/V)\\n else:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485da1b2d4554809aa38ca7fe336d418\",\n \"score\": \"0.5130694\",\n \"text\": \"def changespeed(self, x):\\r\\n self.change_x += x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485da1b2d4554809aa38ca7fe336d418\",\n \"score\": \"0.5130694\",\n \"text\": \"def changespeed(self, x):\\r\\n self.change_x += x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485da1b2d4554809aa38ca7fe336d418\",\n \"score\": \"0.5130694\",\n \"text\": \"def changespeed(self, x):\\r\\n self.change_x += x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c177146ecff0c7731b66f65a03b57895\",\n \"score\": \"0.51288164\",\n \"text\": \"def setX(self, x):\\n self.x = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c177146ecff0c7731b66f65a03b57895\",\n \"score\": \"0.51288164\",\n \"text\": \"def setX(self, x):\\n self.x = x\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993997,"cells":{"query_id":{"kind":"string","value":"08221d73799e78906b30f1464d647dc0"},"query":{"kind":"string","value":"Computes how the interaction hamiltonian acts over a given state"},"positive_passages":{"kind":"list like","value":[{"docid":"a69fab8c6a837f99f24123569f0e0dad","score":"0.55249983","text":"def Hint(state_ini):\n states = []\n coefs = []\n for k in range(len(state_ini)):\n for l in range(len(state_ini)):\n for p in range(len(state_ini)):\n for q in range(len(state_ini)):\n if not (I(k,l,p,q)==0):\n state1,coef1,stop1 = anihilation(p,state_ini)\n if not stop1: \n state2,coef2,stop2 = anihilation(q,state1)\n if not stop2: \n state3,coef3 = creation(l,state2)\n state4,coef4 = creation(k,state3)\n states.append(state4)\n coefs.append(I(k,l,p,q)*coef1*coef2*coef3*coef4)\n \n return states,coefs","title":""}],"string":"[\n {\n \"docid\": \"a69fab8c6a837f99f24123569f0e0dad\",\n \"score\": \"0.55249983\",\n \"text\": \"def Hint(state_ini):\\n states = []\\n coefs = []\\n for k in range(len(state_ini)):\\n for l in range(len(state_ini)):\\n for p in range(len(state_ini)):\\n for q in range(len(state_ini)):\\n if not (I(k,l,p,q)==0):\\n state1,coef1,stop1 = anihilation(p,state_ini)\\n if not stop1: \\n state2,coef2,stop2 = anihilation(q,state1)\\n if not stop2: \\n state3,coef3 = creation(l,state2)\\n state4,coef4 = creation(k,state3)\\n states.append(state4)\\n coefs.append(I(k,l,p,q)*coef1*coef2*coef3*coef4)\\n \\n return states,coefs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"97f2aec5ed08627489e932504160126d","score":"0.7395203","text":"def actHam(state, N, J, U):\n t1, t2 = [], []\n # First term\n for i in range(len(state.vector)-1):\n t1.append(state.create(i+1, N).destroy(i))\n t1.append(state.create(i, N).destroy(i+1))\n # Second term\n for i in range(len(state.vector)):\n PREFACTOR = state.num(i) * (state.num(i) - 1)\n temp3 = deepcopy(state)\n temp3.prefactor *= (PREFACTOR*U/2)\n t2.append(temp3)\n\n for state in t1:\n state.prefactor *= (-1 * J)\n\n return np.r_[t1, t2]","title":""},{"docid":"49e2bcfe0fdee658320bbefd516c24dd","score":"0.6854056","text":"def HMF(state, Delta, N):\n\n\n\t#kinetic term: sum_i(eps(i)*(n_i,up + n_i,down))\n\tkinetic_state = dict_list_sum(\n\t\t[dict_prod(eps(i, N), dict_sum(number_op(state, i, 0, N), number_op(state, i, 1, N))) for i in range(N)])\n\n\t#interaction term: sum_i( Delta c_iUP^dag c_iDOWN^dag + conj.(Delta) c_iDOWN c_iUP )\n\tinteraction_state = dict_list_sum(\n\t\t[dict_sum(dict_prod(Delta, cr(cr(state, i, 1, N), i, 0, N)), dict_prod(np.conj(Delta), an(an(state, i, 0, N), i, 1, N))) for i in range(N)])\n\n\treturn dict_sum(kinetic_state, interaction_state)","title":""},{"docid":"6b219e360df4b439f5375946a9b7bea7","score":"0.6688797","text":"def make_Hamiltonian(p, state_table):\n # dim = len(state_table)\n # row = []\n # col = []\n # data = []\n\n # H = sparse.csr_matrix((data, (row, col)), shape=(dim, dim), dtype=complex)\n H = transverse_field_matrix(p, state_table)\n H += longitudinal_field_matrix(p, state_table)\n H += ising_interaction_matrix(p, state_table)\n\n return H","title":""},{"docid":"6efcd98dcfc3dca2bcb8789e628b922d","score":"0.65604424","text":"def Hexact(state, N):\n\n\t#kinetic term: sum_i(eps(i)*(n_i,up + n_i,down))\n\tkinetic_state = dict_list_sum(\n\t\t[dict_prod(eps(i, N), dict_sum(number_op(state, i, 0, N), number_op(state, i, 1, N))) for i in range(N)])\n\n\t#interaction term: sum_ij(c_iUP^dag c_iDOWN^dag c_jDOWN c_jUP)\n\tinteraction_state = dict_list_sum([\n\t\t\t\t\t\t\tdict_list_sum([\n\t\t\t\t\t\t\t\tcr(cr(an(an(state, j, 1, N), j, 0, N), i, 0, N), i, 1, N) for i in range(N)]) for j in range(N)])\n\t\n\tg=1/N\n\tinteraction_state = dict_prod(-g, interaction_state)\n\treturn dict_sum(kinetic_state, interaction_state)","title":""},{"docid":"0305491888b297f37caaeb325dcefbf6","score":"0.6484014","text":"def calc_h(self, a_state):\n # this trivial version returns 0, a trivial estimate, but consistent and admissible\n count = 0\n for i in range(len(a_state.new)):\n if a_state.new[i] != self._goal[i]:\n count+=1\n return count","title":""},{"docid":"b9e281db8139379581670b477e5238c1","score":"0.6339773","text":"def getHamMatrix(N, M, J, U):\n basis = getBasisStates(N, M)\n ham_matrix = np.zeros((len(basis), len(basis)))\n for state in basis:\n # Act ham on each basis state\n acted = actHam(state, N, J, U)\n for x in acted:\n for b in basis:\n # Find the matrix location of the 'acted' state and enter into\n # Hamiltonian matrix.\n if(np.all(x.vector == b.vector)):\n ham_matrix[state.idx][b.idx] += x.prefactor\n # Return ham matrix and basis\n return ham_matrix, basis","title":""},{"docid":"a8305c1a560f3afeb326b5427c2f1241","score":"0.6338933","text":"def Hsp_element(state,omega):\n res= 0.0\n for i in range(len(state)):\n e = 1+i*(1-omega)\n res=res+state[i]*e\n return res","title":""},{"docid":"f65b36235f3bf9b3a02b24c0e43b90cb","score":"0.6199688","text":"def ising_interaction_matrix(p, state_table):\n if p['J'].shape[0] != p['N'] or p['J'].shape[1] != p['N']:\n warnings.warn('J does NOT have dimensions LxL!')\n if not np.array_equal(p['J'], p['J'].conj().T):\n warnings.warn('J is NOT hermitian!')\n\n dim = len(state_table)\n row = []\n col = []\n data = []\n\n for In in range(dim):\n state = int_to_state(p, state_table[In])\n\n # eigenvalue of |0> is 1 and |1> is -1 so transform state (binary code) to spin basis\n spin = 1 - np.multiply(2, state)\n\n # construct matrix which has all the ZiZj products as elements (i,j)\n ZiZj = np.tensordot(spin, spin, axes=0)\n\n # get matrix element\n matrixelement = -1.0 * np.sum(np.multiply(p['J'], ZiZj)) / 2\n\n # store matrix element (note hz is diagonal so Out = In)\n row.append(In)\n col.append(In)\n data.append(matrixelement)\n\n del matrixelement\n\n ising_interaction = sparse.csr_matrix((data, (row, col)),\n shape=(dim, dim), dtype=complex)\n return ising_interaction","title":""},{"docid":"17ae9c4c8f13e19e763b1423af3ef7ee","score":"0.6149006","text":"def manhattan_heuristic(state):\n goal = 0\n mis = 0\n\n for y in state:\n for x in y:\n mis+= (abs(goal - x) //3) + (abs(abs(goal - x) % 3))\n goal += 1\n \n return(mis) # replace this","title":""},{"docid":"24a4aeba011380cdc6b192337e00c463","score":"0.6114106","text":"def h(state: tuple):\n return 6 - state[0] - state[1]","title":""},{"docid":"93419913bb220264de1c247a0db38e34","score":"0.61005616","text":"def simulate(self, input_state: ndarray) -> List[int]:\n phi_0 = self.__prepare_initial_state(input_state)\n evolved_state = dot(self.interferometer_matrix, phi_0)\n probabilities = self.__calculate_probabilities(evolved_state)\n return self.__calculate_approximation_of_boson_sampling_outcome(probabilities)","title":""},{"docid":"3ca828b1d7a60c67bf048f70740ecb16","score":"0.60028356","text":"def make_trotter_Hamiltonian(p, state_table):\n H_list = []\n H_list.append(longitudinal_field_matrix(p, state_table) +\n ising_interaction_matrix(p, state_table))\n H_list.append(transverse_field_matrix(p, state_table))\n return H_list","title":""},{"docid":"297ed502535f64ed50f9102b6fd39d2f","score":"0.600123","text":"def call(self, inputs, state):\n\n inputs_u, inputs_i = tf.split(value=inputs, num_or_size_splits=2, axis=1)\n gate_inputs = math_ops.matmul(\n array_ops.concat([inputs, state], 1), self._gate_kernel)\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias)\n\n value = math_ops.sigmoid(gate_inputs)\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\n\n r_state = r * state\n\n candidate = math_ops.matmul(\n array_ops.concat([inputs, r_state], 1), self._candidate_kernel)\n candidate = nn_ops.bias_add(candidate, self._candidate_bias)\n\n c = self._activation(candidate)\n new_h = u * state + (1 - u) * c\n return new_h, new_h","title":""},{"docid":"82f30d60bc9c67b7bedc9ccd4bd67323","score":"0.59757584","text":"def H(self, state):\n\t\tH = state.EX + state.EV\n\t\treturn H","title":""},{"docid":"14f67be23c2a4e9414e54490713d35c5","score":"0.59659684","text":"def state_obs_mat(ns,g_cords,state_obs):\n state = sym.Matrix(np.zeros(2*len(g_cords)))\n\n for i in range(len(g_cords)):\n state[i] = sym.symbols(g_cords[i], real = True)\n state[i+len(g_cords)] = sym.symbols(g_cords[i] + '_dot', real = True)\n\n state_obs_s = sym.sympify(state_obs, locals = ns)\n\n Htilde_s = state_obs_s.jacobian(state)\n\n t = sym.symbols('t', real = True)\n state_time[0] = t\n for i in range(len(g_cords)):\n state_time[i + 1] = sym.symbols(g_cords[i], real = True)\n state_time[i + len(g_cords)+ 1] = sym.symbols(g_cords[i]+'_dot', real = True)\n Htilde = sym.lambdify(state_time,Htilde_s, modules='numpy')\n\n return Htilde","title":""},{"docid":"a4bb356fd591a18e8d4600fcbdce38b1","score":"0.59569496","text":"def call(self, inputs, state):\n with vs.variable_scope(\"gates\"):\n h = state\n args = array_ops.concat([inputs, h], 1)\n concat = self._linear(args, 2)\n\n z, r = array_ops.split(value=concat, num_or_size_splits=2, axis=1)\n if self._layer_norm:\n z = self._norm(z, \"update\")\n r = self._norm(r, \"reset\")\n\n with vs.variable_scope(\"candidate\"):\n args = array_ops.concat([inputs, math_ops.sigmoid(r) * h], 1)\n new_c = self._linear(args, 1)\n if self._layer_norm:\n new_c = self._norm(new_c, \"state\")\n new_h = self._activation(new_c) * math_ops.sigmoid(z) + \\\n (1 - math_ops.sigmoid(z)) * h\n return new_h, new_h","title":""},{"docid":"0ecabccba7e3b7662aa5d2de5dc93c41","score":"0.59503686","text":"def call(self, inputs, state):\n sigmoid = math_ops.sigmoid\n one = constant_op.constant(1, dtype=dtypes.int32)\n\n # Parameters of gates are concatenated into one multiply for efficiency.\n c, h, h_skip, h_cnt = state\n n_skip = self._n_skip\n if n_skip:\n skip_bool = h_cnt % self._n_skip == 0\n\n gate_inputs = math_ops.matmul(\n array_ops.concat([inputs, h], 1), self._kernel)\n gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)\n\n # i = input_gate, j = new_input, f = forget_gate, o = output_gate\n i, j, f, o = array_ops.split(\n value=gate_inputs, num_or_size_splits=4, axis=one)\n forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)\n\n # Note that using `add` and `multiply` instead of `+` and `*` gives a\n # performance improvement. So using those at the cost of readability.\n add = math_ops.add\n multiply = math_ops.multiply\n\n first = multiply(c, sigmoid(add(f, forget_bias_tensor)))\n new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))),\n multiply(sigmoid(i), self._activation(j)))\n if n_skip:\n new_h = multiply(self._activation(new_c), sigmoid(o)) + skip_bool * 1 * h_skip\n h_skip = h_skip * (1-skip_bool) + new_h * skip_bool\n else:\n new_h = multiply(self._activation(new_c), sigmoid(o)) \n h_skip = new_h \n\n h_cnt += 1\n new_state = SCLSTMStateTuple(new_h, new_c, h_skip, h_cnt)\n return new_h, new_state","title":""},{"docid":"87868cbe1affefbb21921f94e9d7c122","score":"0.59469837","text":"def forward(self, state):\n x = self.fc1(state)\n x = self.act(x)\n x = self.bn1(x)\n x1 = self.fc2(x)\n x1 = self.act(x1)\n x1 = self.bn2(x1)\n x1 = self.fc3(x1)\n x1 = self.act(x1)\n x1 = self.bn3(x1)\n x = torch.add(x1,self.act(self.fc1_skip(x)))\n x1 = self.fc4(x)\n x1 = self.act(x1)\n x1 = self.bn4(x1)\n x1 = self.fc5(x1)\n x1 = self.act(x1)\n x1 = self.bn5(x1)\n x = torch.add(x1,self.act(self.fc2_skip(x)))\n x = self.fc6(x)\n x = self.act(x)\n x = self.bn6(x)\n return torch.add(self.fc8_1(self.act(self.fc7_1(x))),self.fc8_2(self.act(self.fc7_2(x))) - torch.mean(self.fc8_2(self.act(self.fc7_2(x)))))","title":""},{"docid":"ef75533465394cff98e4f38ee8b87da1","score":"0.59223974","text":"def act(self, state):\n action = np.zeros(self.action_size)\n if np.random.rand() <= self.epsilon:\n action[random.randrange(self.action_size)]=1\n else:\n state = state.reshape(1,self.state_size)\n act_values = self.brain.predict(state)\n action[np.argmax(act_values[0])] = 1\n return action","title":""},{"docid":"359cc813b316231a152502e83dc6f585","score":"0.58734393","text":"def act(self, state):\n state = np.reshape(state, [-1, self.state_size])\n action = self.actor_local.model.predict(state)[0]\n actions = list(action + self.noise.sample()) # add some noise for exploration\n #actions = list(action) # no noise for exploration\n actions = np.maximum(actions, self.action_low)\n actions = np.minimum(actions, self.action_high)\n return actions","title":""},{"docid":"9cefa3e8e7750d39eaf74143397f1406","score":"0.5870848","text":"def hamiltonian(self):\n return self.kineticmat() + self.potentialmat()","title":""},{"docid":"b2684bd508eb3345dfcab6e3c76ab878","score":"0.5867485","text":"def process(state):\n \n ## normalized vector\n return np.array([state[0]/(max(env.state_space)[0]), state[1]/(max(env.state_space)[1]), state[2]]).reshape((3,1))\n \n ## one-hot (dim = 280,1)","title":""},{"docid":"100d19d84cea3e371fb5d96e6839cf35","score":"0.58647317","text":"def calc_eq_of_state(pyom, n):\n density_args = (pyom, pyom.salt[..., n], pyom.temp[..., n], np.abs(pyom.zt))\n\n \"\"\"\n calculate new density\n \"\"\"\n pyom.rho[..., n] = density.get_rho(*density_args) * pyom.maskT\n\n if pyom.enable_conserve_energy:\n \"\"\"\n calculate new dynamic enthalpy and derivatives\n \"\"\"\n pyom.Hd[..., n] = density.get_dyn_enthalpy(*density_args) * pyom.maskT\n pyom.int_drhodT[..., n] = density.get_int_drhodT(*density_args)\n pyom.int_drhodS[..., n] = density.get_int_drhodS(*density_args)\n\n \"\"\"\n new stability frequency\n \"\"\"\n fxa = -pyom.grav / pyom.rho_0 / pyom.dzw[np.newaxis, np.newaxis, :-1] * pyom.maskW[:, :, :-1]\n pyom.Nsqr[:, :, :-1, n] = fxa * (density.get_rho(pyom, pyom.salt[:,:,1:,n], pyom.temp[:,:,1:,n], np.abs(pyom.zt[:-1])) - pyom.rho[:,:,:-1,n])\n pyom.Nsqr[:, :, -1, n] = pyom.Nsqr[:,:,-2,n]","title":""},{"docid":"72d2c0e6fcd90d2f4171a96ac0741f60","score":"0.57848847","text":"def test_state_dependent_exploration_grad():\n n_states = 2\n state_dim = 3\n action_dim = 10\n sigma_hat = th.ones(state_dim, action_dim, requires_grad=True)\n # Reduce the number of parameters\n # sigma_ = th.ones(state_dim, action_dim) * sigma_\n # weights_dist = Normal(th.zeros_like(log_sigma), th.exp(log_sigma))\n th.manual_seed(2)\n weights_dist = Normal(th.zeros_like(sigma_hat), sigma_hat)\n weights = weights_dist.rsample()\n\n state = th.rand(n_states, state_dim)\n mu = th.ones(action_dim)\n noise = th.mm(state, weights)\n\n action = mu + noise\n\n variance = th.mm(state**2, sigma_hat**2)\n action_dist = Normal(mu, th.sqrt(variance))\n\n # Sum over the action dimension because we assume they are independent\n loss = action_dist.log_prob(action.detach()).sum(dim=-1).mean()\n loss.backward()\n\n # From Rueckstiess paper: check that the computed gradient\n # correspond to the analytical form\n grad = th.zeros_like(sigma_hat)\n for j in range(action_dim):\n # sigma_hat is the std of the gaussian distribution of the noise matrix weights\n # sigma_j = sum_j(state_i **2 * sigma_hat_ij ** 2)\n # sigma_j is the standard deviation of the policy gaussian distribution\n sigma_j = th.sqrt(variance[:, j])\n for i in range(state_dim):\n # Derivative of the log probability of the jth component of the action\n # w.r.t. the standard deviation sigma_j\n d_log_policy_j = (noise[:, j] ** 2 - sigma_j**2) / sigma_j**3\n # Derivative of sigma_j w.r.t. sigma_hat_ij\n d_log_sigma_j = (state[:, i] ** 2 * sigma_hat[i, j]) / sigma_j\n # Chain rule, average over the minibatch\n grad[i, j] = (d_log_policy_j * d_log_sigma_j).mean()\n\n # sigma.grad should be equal to grad\n assert sigma_hat.grad.allclose(grad)","title":""},{"docid":"d1b4036704b90211b08319d6f2ff190e","score":"0.5756763","text":"def inv_chol(self, state):\n return self.lmult_inv_chol(state, np.eye(state.mom.shape[0]))","title":""},{"docid":"9925893a3fb16894a61d097ea3da8225","score":"0.5751316","text":"def state_symmetry_exploit(state):\n player_id, board = np.split(state, [1])\n assert bool(player_id)\n return board*player_id","title":""},{"docid":"e36e221f33fb516ebb392e5bc61ca1ab","score":"0.5737062","text":"def formulate_state(state):\n\n # Initalize all necessary infomration\n arena = state['arena'].copy()\n crates_arena = np.maximum(arena, 0)\n for (cx, cy) in state['coins']:\n crates_arena[cx, cy] = 2\n crates_arena = crates_arena.T\n\n x, y, _, bombs_left, _ = state['self']\n bombs = state['bombs']\n others = [(xo, yo) for (xo, yo, _, _, _) in state['others']]\n\n # First Information: Direction to nearest coin\n diglist = list()\n if len(state['coins']) == 0:\n diglist.append(0)\n else:\n closest_coin = sorted(state['coins'], key=lambda k: abs(k[0] - x) + abs(k[1] - y))[0]\n best_orientation = np.argmin([(closest_coin[0] - mx)**2 + (closest_coin[1] - my)**2 for (mx, my) in\n [(x, y-1), (x-1, y-1), (x-1, y), (x-1, y+1),\n (x, y+1), (x+1, y+1), (x+1, y), (x+1, y-1)]]) + 1\n diglist.append(best_orientation)\n\n # Second Information: Direction to more crates\n if np.sum(crates_arena) == 0:\n diglist.append(0)\n else:\n q1map = np.sum(crates_arena[1:6, 1:6])\n q2map = np.sum(crates_arena[1:6, 6:11])\n q3map = np.sum(crates_arena[1:6, 11:16])\n q4map = np.sum(crates_arena[6:11, 1:6])\n q5map = np.sum(crates_arena[6:11, 6:11])\n q6map = np.sum(crates_arena[6:11, 11:16])\n q7map = np.sum(crates_arena[11:16, 1:6])\n q8map = np.sum(crates_arena[11:16, 6:11])\n q9map = np.sum(crates_arena[11:16, 11:16])\n diglist.append(np.argmax([q1map, q2map, q3map, q4map, q5map, q6map, q7map, q8map, q9map]) + 1)\n\n # Thrid Information: Direction to nearest opponent\n if len(state['others']) == 0:\n diglist.append(0)\n else:\n closest_p = sorted(state['others'], key=lambda k: abs(k[0] - x) + abs(k[1] - y))[0]\n closest_orientation = np.argmin([abs(closest_p[0] - mx) + abs(closest_p[1] - my) for (mx, my) in\n [(x, y - 1), (x - 1, y - 1), (x - 1, y), (x - 1, y + 1), (x, y + 1),\n (x + 1, y + 1), (x + 1, y), (x + 1, y - 1)]]) + 1\n diglist.append(closest_orientation)\n\n # Fourth Information: Number of bombs available\n diglist.append(bombs_left)\n\n # 5-45th Information: Information in the 4-step vision\n for (i, j) in [( 0, -4), (-1, -3), ( 0, -3), ( 1, -3),\n (-2, -2), (-1, -2), ( 0, -2), ( 1, -2), ( 2, -2),\n (-3, -1), (-2, -1), (-1, -1), ( 0, -1), ( 1, -1),\n ( 2, -1), ( 3, -1), (-4, 0), (-3, 0), (-2, 0),\n (-1, 0), (0 , 0), ( 1, 0), ( 2, 0), ( 3, 0),\n ( 4, 0), (-3, 1), (-2, 1), (-1, 1), ( 0, 1),\n ( 1, 1), ( 2, 1), ( 3, 1), (-2, 2), (-1, 2),\n ( 0, 2), ( 1, 2), ( 2, 2), (-1, 3), ( 0, 3),\n ( 1, 3), ( 0, 4)]:\n\n if (x + i) < 0 or (x + i) > 16 or (y + j) < 0 or (y + j) > 16:\n diglist.append(0)\n elif (x + i, y + j) in state['coins']:\n diglist.append(300)\n elif state['explosions'][x + i, y + j] == 1:\n diglist.append(10)\n elif state['explosions'][x + i, y + j] == 2:\n diglist.append(9)\n elif (x + i, y + j, 4) in bombs:\n if (x + i, y + j) in others:\n diglist.append(40)\n else:\n diglist.append(4)\n elif (x + i, y + j, 3) in bombs:\n if (x + i, y + j) in others:\n diglist.append(50)\n else:\n diglist.append(5)\n elif (x + i, y + j, 2) in bombs:\n if (x + i, y + j) in others:\n diglist.append(60)\n else:\n diglist.append(6)\n elif (x + i, y + j, 1) in bombs:\n if (x + i, y + j) in others:\n diglist.append(70)\n else:\n diglist.append(7)\n elif (x + i, y + j, 0) in bombs:\n if (x + i, y + j) in others:\n diglist.append(80)\n else:\n diglist.append(8)\n elif (x + i, y + j) in others:\n diglist.append(100)\n else:\n diglist.append(arena[x + i, y + j] + 1) # 0, 1, 2\n\n state = np.array(diglist)\n return state.reshape((1, 45))","title":""},{"docid":"17ce22e2f65d211f2d3efb91ab50f002","score":"0.57361174","text":"def construct_H(state_bra,state_ket):\n states,coefs=Hint(state_ket)\n res = 0\n for i in range(len(states)):\n if (state_bra==states[i]):\n res = res+ coefs[i]\n return 0.5*res","title":""},{"docid":"793ad6bfa6bc966e871e36ac947b51cc","score":"0.5724917","text":"def likelihood_and_decoding_hmm(*args,**kwargs):\n num_of_states = kwargs['num_of_states']\n time_grid = kwargs['time_grid']\n num_of_steps = len(time_grid) # |W| - 1\n log_alphas = np.zeros((num_of_steps,num_of_states),dtype=np.float)\n log_viterbi = np.zeros((num_of_steps,num_of_states),dtype=np.float)\n backpointers = np.zeros((num_of_steps),dtype=np.float)\n init_probs = kwargs['init_probs'] # distribution over initial states\n pi = kwargs['pi'] # state transition; \"parameters\" HMM (rows x cols ~ origin x destination)\n e = kwargs['e'] # list of functions size (num_of_states); likelihood of obs from each state\n O = kwargs['O'] # list of observations size (num_of_steps)\n\n # init forward pass\n time_next = time_grid[0] # the first point in the time_grid, |W|, is _not_ zero in general.\n # but it is zero currently.... not great.\n for state_idx in range(num_of_states):\n obs = [O[0,time_next],time_next]\n log_alphas[0,state_idx] = np.ma.log([init_probs.l(state_idx)]).filled(-np.infty) +\\\n np.ma.log([e(obs)[state_idx]]).filled(-np.infty)\n log_viterbi[0,state_idx] = np.ma.log([init_probs.l(state_idx)]).filled(-np.infty) +\\\n np.ma.log([e(obs)[state_idx]]).filled(-np.infty)\n \n \"\"\"\n The augmented state space is actually the issue; we don't need to iterate over delta_w_enum\n What we actually need is for the augmented state space to _include_ all the different\n enumerations of delta_w. E.g. NOT [STATE,GRID] rather [STATE,DELTA_W]. \n I do ~not~ think this should impact \"smjp_transition()\", but let's watch out.\n \"\"\"\n # run the forward\n # print(\"---\")\n index_range = range(1,len(time_grid))\n print(len(index_range),len(time_grid[1:-1]),len(time_grid[0:-1]),len(time_grid))\n for alpha_index,time in zip(index_range,time_grid[0:-1]): # {w_0,...,w_{|W|-1}}\n # print(alpha_index)\n time_n = time_grid[alpha_index] # update the next time.\n delta_w = time_n - time \n delta_w_l = [delta_w]\n # delta_w_enumeration = create_delta_w_enumeration(time_n,time_grid)\n for delta_w in delta_w_l: # todo: remove this loop\n obs = [O[time,time_n],time_n]\n for state_idx in range(num_of_states):\n for state_prime_idx in range(num_of_states):\n #print(pi(delta_w)[state_prime_idx,state_idx],e(obs)[state_idx])\n # HMM: pi(t,t_next) = pi; does not depend on time difference\n # sMJP: pi(t,t_next) =/= pi; depends on time difference\n log_alpha = log_alphas[alpha_index-1,state_prime_idx]\n log_transition = pi([time,time_n])[state_prime_idx,state_idx]\n log_obs = np.ma.log([e(obs)[state_idx]]).filled(-np.infty)[0]\n #print(log_alpha,log_transition,log_obs)\n alpha_new = np.exp(log_alpha + log_transition + log_obs)\n if alpha_index == index_range[-1] and state_prime_idx == 0 and state_idx in [0,1] and False:\n print(log_alpha,log_transition,log_obs)\n print(np.sum([log_alpha,log_transition,log_obs]))\n\n #print(alpha_new)\n if state_prime_idx == 0 and state_idx == 1 and alpha_index > 2 and False:\n print(\"[[state_prime_idx == 0 and state_idx == 1]]\")\n print(log_alpha,log_transition,log_obs)\n print(log_alphas[alpha_index-1,:])\n print(log_alphas[alpha_index-1,state_prime_idx])\n print('dw',delta_w,time,time_n)\n print(pi.state_space[state_prime_idx])\n print(pi.state_space[state_idx])\n print(\"==========================\")\n exit()\n if do_we_thin(pi.state_space,state_prime_idx,state_idx,delta_w) and False:\n print(pi.state_space)\n print(alpha_index-1)\n print(log_alphas[alpha_index-1,:])\n print(state_prime_idx,state_idx)\n print(log_alpha)\n print(\"THN\")\n v,l = pi.state_space[state_prime_idx]\n vp,lp = pi.state_space[state_idx]\n print(log_alphas[alpha_index-1,state_prime_idx])\n print(delta_w)\n print(pi.state_space[state_prime_idx])\n print(pi.state_space[state_idx])\n if np.any(np.isnan(alpha_new)):\n print(\"found a nan\")\n quit()\n #print('alpha_new',alpha_new,log_alpha,state_idx)\n log_alphas[alpha_index,state_idx] += alpha_new\n log_alphas[alpha_index,:] = np.ma.log([log_alphas[alpha_index,:]]).filled(-np.infty)\n # print(log_alphas)\n # exit()\n # print(alpha_index,time)\n # print(time,alphas[time,:])\n backpath = None # trace_backward_path(backpointers)\n \n # print(log_alphas)\n # print(len(index_range),len(time_grid[1:-1]),len(time_grid[0:-1]),len(time_grid))\n # print(time_grid)\n # exit()\n backpath = None\n output_prob = np.sum(np.exp(log_alphas[-1,:]))\n \n return log_alphas,output_prob,log_viterbi,backpath","title":""},{"docid":"45e97c50e1486c399512fd98baac5ade","score":"0.5722083","text":"def produce_action_and_action_info(self, state):\n actor_output = self.actor_local(state)\n mean, log_std = actor_output[:, :self.action_size], actor_output[:, self.action_size:]\n std = log_std.exp()\n normal = Normal(mean, std)\n x_t = normal.rsample() #rsample means it is sampled using reparameterisation trick\n action = torch.tanh(x_t)\n log_prob = normal.log_prob(x_t)\n log_prob -= torch.log(1 - action.pow(2) + 1e-6)\n log_prob = log_prob.sum(1, keepdim=True)\n return action, log_prob, torch.tanh(mean)","title":""},{"docid":"6516b51969a54d09829e27c15134f1ab","score":"0.57211965","text":"def hamiltonian(n):\n N = 2**n\n binaryFactor = np.array([1, 2, 4])\n row = []\n col = []\n value = []\n for j in range(n-1):\n for i in range(N):\n spin1 = (i % binaryFactor[1]) // binaryFactor[0]\n spin2 = (i % binaryFactor[2]) // binaryFactor[1]\n if spin1 != spin2:\n row.append(i)\n col.append(i)\n value.append(-1)\n \n row.append(i)\n value.append(2)\n if spin1 == 1:\n col.append((i + binaryFactor[0]) % N)\n else:\n col.append((i - binaryFactor[0]) % N)\n else:\n row.append(i)\n col.append(i)\n value.append(1)\n binaryFactor *= 2\n return scipy.sparse.coo_matrix((value, (row, col)), shape = (N, N), dtype = np.float64)","title":""},{"docid":"d8bdcb0ad63e10f92e1349c816964aeb","score":"0.5719273","text":"def nextState(self):\n newstate = np.zeros(self.N, dtype=int)\n i = 0\n for adj in self.adjmat:\n input_ind = np.where(adj == 1)\n inputs = [self.state[ind] for ind in input_ind[0]]\n if np.random.rand() < self.LUT[tuple(inputs)]:\n newstate[i] = 0\n else:\n newstate[i] = 1\n i += 1\n return newstate","title":""},{"docid":"f6f8eff0394c3192c08efb19daa74409","score":"0.57086587","text":"def call(self, state):\n z = tf.cast(state, tf.float64)\n h1 = tf.nn.leaky_relu(tf.matmul(z, self.w1) + self.b1)\n h2 = tf.nn.relu(tf.matmul(h1, self.w2) + self.b2)\n \n h2 = tf.nn.relu(tf.matmul(h2, self.w21) + self.b21)\n h2 = tf.nn.relu(tf.matmul(h2, self.w22) + self.b22)\n \n if self.deeper:\n h2 = tf.nn.relu(tf.matmul(h2, self.w23) + self.b23)\n h2 = tf.nn.relu(tf.matmul(h2, self.w24) + self.b24)\n \n out = (tf.matmul(h2, self.w3) + self.b3) #/10\n # out = - tf.math.log(1.01 + tf.exp(out))\n return out","title":""},{"docid":"2c62d308827bc5056be49da43a36ce1e","score":"0.5694486","text":"def forward(self, state):\n x = state\n x = self.hidden_activation(self.fc0(x))\n state_value = self.hidden_activation(self.fc1_s(x))\n state_value = self.fc2_s(state_value)\n advantage_values = self.hidden_activation(self.fc1_a(x))\n advantage_values = self.fc2_a(advantage_values)\n q = state_value + advantage_values - advantage_values.mean(dim=1, keepdim=True)\n\n return q","title":""},{"docid":"b2d9d36d9a411ebe08b75567bee5c300","score":"0.5693643","text":"def call(self, inputs, state):\n gate_inputs = tf.matmul(\n tf.concat([inputs, state], 1), self._gate_kernel)\n gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias)\n\n if self._layer_norm:\n gate_inputs = common_layers.split_last_dim(gate_inputs, 3)\n mean = tf.reduce_mean(gate_inputs, axis=[-1], keepdims=True)\n variance = tf.reduce_mean(tf.square(gate_inputs - mean), axis=[-1], keepdims=True)\n norm_x = (gate_inputs - mean) * tf.rsqrt(variance + self._ln_epsilon)\n norm_x = common_layers.combine_last_two_dims(norm_x)\n gate_inputs = norm_x * self._ln_scale + self._ln_bias\n\n value = tf.sigmoid(gate_inputs)\n r, u, l = tf.split(value=value, num_or_size_splits=3, axis=1)\n\n r_state = r * state\n\n candidate = tf.matmul(\n tf.concat([inputs, r_state], 1), self._candidate_kernel)\n candidate = tf.nn.bias_add(candidate, self._candidate_bias)\n\n c = self._activation(candidate)\n c += l * tf.matmul(inputs, self._linear_kernel)\n if self._dropout_rate:\n c = tf.nn.dropout(c, keep_prob=1-self._dropout_rate)\n new_h = u * state + (1 - u) * c\n return new_h, new_h","title":""},{"docid":"966c4f025e93f8e5a8db02bea7be46c8","score":"0.5692773","text":"def hamiltonian(self, state, time, value, grad_value):\n del time, value # unused\n control = self.optimal_control(state, grad_value)\n return grad_value @ self.dynamics(state, control)","title":""},{"docid":"a61d7e95f8edaf1a53f2cbc094b743bc","score":"0.5683809","text":"def binary_mera_energy(hamiltonian, state, isometry, disentangler):\n backend = \"jax\"\n\n out = []\n for dirn in ('left', 'right'):\n iso_c = tensornetwork.Node(isometry, backend=backend)\n iso_r = tensornetwork.Node(isometry, backend=backend)\n\n iso_l_con = tensornetwork.linalg.node_linalg.conj(iso_l)\n #iso_c_con = tensornetwork.linalg.node_linalg.conj(iso_c)\n iso_r_con = tensornetwork.linalg.node_linalg.conj(iso_r)\n\n op = tensornetwork.Node(hamiltonian, backend=backend)\n rho = tensornetwork.Node(state, backend=backend)\n\n un_l = tensornetwork.Node(disentangler, backend=backend)\n un_l_con = tensornetwork.linalg.node_linalg.conj(un_l)\n\n un_r = tensornetwork.Node(disentangler, backend=backend)\n un_r_con = tensornetwork.linalg.node_linalg.conj(un_r)\n\n tensornetwork.connect(iso_l[2], rho[0])\n tensornetwork.connect(iso_c[2], rho[1])\n tensornetwork.connect(iso_r[2], rho[2])\n\n tensornetwork.connect(iso_l[0], iso_l_con[0])\n tensornetwork.connect(iso_l[1], un_l[2])\n tensornetwork.connect(iso_c[0], un_l[3])\n tensornetwork.connect(iso_c[1], un_r[2])\n tensornetwork.connect(iso_r[0], un_r[3])\n tensornetwork.connect(iso_r[1], iso_r_con[1])\n\n if dirn == 'right':\n tensornetwork.connect(un_l[0], un_l_con[0])\n tensornetwork.connect(un_l[1], op[3])\n tensornetwork.connect(un_r[0], op[4])\n tensornetwork.connect(un_r[1], op[5])\n tensornetwork.connect(op[0], un_l_con[1])\n tensornetwork.connect(op[1], un_r_con[0])\n tensornetwork.connect(op[2], un_r_con[1])\n elif dirn == 'left':\n tensornetwork.connect(un_l[0], op[3])\n tensornetwork.connect(un_l[1], op[4])\n tensornetwork.connect(un_r[0], op[5])\n tensornetwork.connect(un_r[1], un_r_con[1])\n tensornetwork.connect(op[0], un_l_con[0])\n tensornetwork.connect(op[1], un_l_con[1])\n tensornetwork.connect(op[2], un_r_con[0])\n\n tensornetwork.connect(un_l_con[2], iso_l_con[1])\n tensornetwork.connect(un_l_con[3], iso_c_con[0])\n tensornetwork.connect(un_r_con[2], iso_c_con[1])\n tensornetwork.connect(un_r_con[3], iso_r_con[0])\n\n #tensornetwork.connect(iso_l_con[2], rho[3])\n tensornetwork.connect(iso_c_con[2], rho[4])\n tensornetwork.connect(iso_r_con[2], rho[5])\n\n # FIXME: Check that this is giving us a good path!\n out.append(\n contractors.branch(tensornetwork.reachable(rho),\n nbranch=2).get_tensor())\n\n return 0.5 * sum(out)","title":""},{"docid":"651ee5c4589e0381a6652cfc29f180ca","score":"0.56658435","text":"def forward(self, state):\n \n h_relu1 = F.relu(self.fc1(state))\n h_relu2 = F.relu(self.fc2(h_relu1))\n yhat = self.fc3(h_relu2)\n return yhat","title":""},{"docid":"2dff52b429a3171a910ecd3c9c529c69","score":"0.5662271","text":"def random_action(state):\n cible = [int(random.uniform(0, w-1)), int(random.uniform(0, h-1))]\n dist = Env.dist(state,cible)\n if dist == 0 :\n dist = 1\n norm = min(100,dist)\n return [int(norm/dist*(cible[0]-state[0])),int(norm/dist*(cible[1]-state[1]))]","title":""},{"docid":"6903789fa0af81b1bc4d1dfc8d497914","score":"0.56595314","text":"def get_action_features(phi_state, action, n_actions):\n if len(phi_state.shape) > 1:\n assert phi_state.shape[0] == action.shape[0]\n\n phi = np.ones((phi_state.shape[0], n_actions * phi_state[0].size))\n i = 0\n for s, a in zip(phi_state, action):\n start = s.size * int(a[0])\n stop = start + s.size\n\n phi_sa = np.zeros(n_actions * s.size)\n phi_sa[start:stop] = s\n\n phi[i] = phi_sa\n\n i += 1\n else:\n start = phi_state.size * action[0]\n stop = start + phi_state.size\n\n phi = np.zeros(n_actions * phi_state.size)\n phi[start:stop] = phi_state\n\n return phi","title":""},{"docid":"32e40a7f569426fba704921e3ab3c635","score":"0.5657275","text":"def phi(self, state, action):\n state_ = self._domain.states[state]\n return np.array([self._feature_puddle(state_),\n self._feature_goal_distance(state_)])","title":""},{"docid":"83218e52feb5a7fbbe502c234abea26c","score":"0.56513274","text":"def act(self, state):\n return self.brain.predict_ensemble(state)","title":""},{"docid":"537b3d8dbada89e8c081ee8316d4ac87","score":"0.5630806","text":"def groundstate(H):\n \"\"\"find the gs of a Hamiltonian matrix\"\"\"\n Hinv = H.I\n E, wf = sparse.linalg.eigsh(H, k = 1)\n return 1/E, wf","title":""},{"docid":"1edd0af8ac092bb0c7bb7fb9573ae0c0","score":"0.5627552","text":"def act_target(self, state):\n state = np.reshape(state, [-1, self.state_size])\n action = self.actor_target.model.predict(state)[0]\n #actions = list(action + self.noise.sample()) # add some noise for exploration\n actions = list(action) # no noise for exploration\n return actions","title":""},{"docid":"893b7d3ead52d4d28f9514e003ec8c20","score":"0.56271374","text":"def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = self.model.predict(state)\n\n return np.argmax(act_values[0])","title":""},{"docid":"5d19c1e930f0816aa24b6d4e78ac6fb7","score":"0.56209517","text":"def call(self, inputs, state):\n # Using Attention Wrapper\n print(\"self.input_depth:\",self.input_depth)\n inputs, context = array_ops.split(value=inputs, num_or_size_splits=[int(self.input_depth), self._num_units], axis=-1)\n #_, _, state = array_ops.split(value=state, num_or_size_splits=[int(self.input_depth), self._num_units, self._num_units], axis=-1)\n \n # First round\n gate_inputs = math_ops.matmul(\n array_ops.concat([inputs, state], 1), self._gate_kernel_1)\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias_1)\n\n value = math_ops.sigmoid(gate_inputs)\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\n\n r_state = r * state\n\n candidate = math_ops.matmul(\n array_ops.concat([inputs, r_state], 1), self._candidate_kernel_1)\n candidate = nn_ops.bias_add(candidate, self._candidate_bias_1)\n\n c = self._activation(candidate)\n new_h_prime = u * state + (1 - u) * c\n \n # Final round\n\n gate_inputs = math_ops.matmul(\n array_ops.concat([context, new_h_prime], 1), self._gate_kernel_2)\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias_2)\n\n value = math_ops.sigmoid(gate_inputs)\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\n\n r_state = r * new_h_prime\n\n candidate = math_ops.matmul(\n array_ops.concat([context, r_state], 1), self._candidate_kernel_2)\n candidate = nn_ops.bias_add(candidate, self._candidate_bias_2)\n\n c = self._activation(candidate)\n new_h = u * new_h_prime + (1 - u) * c\n\n return new_h, new_h","title":""},{"docid":"38dfe1a26dc675e1c0401065753cb1c6","score":"0.5617738","text":"def call(self, inputs, state):\n sigmoid = math_ops.sigmoid\n add = math_ops.add\n multiply = math_ops.multiply\n\n transformed_input = math_ops.matmul(inputs, self._kernel)\n transformed_input = nn_ops.bias_add(transformed_input, self._bias)\n in_all = array_ops.split(value=transformed_input, num_or_size_splits=self._k_depth, axis=1)\n\n h = state[0]\n y = h\n for idx in range(self._k_depth):\n hy = math_ops.matmul(y, self._U_dict[idx])\n y = sigmoid(in_all[idx] + hy)\n\n new_h = self._activation(add(h, y))\n new_state = RRNStateTuple(new_h)\n\n return new_h, new_state","title":""},{"docid":"54439a91aa40e49339a02dc35813b2fc","score":"0.5617707","text":"def ComputeInteractionState(self, p_int, p_int_1, p_int_2):\n ...","title":""},{"docid":"54439a91aa40e49339a02dc35813b2fc","score":"0.5617707","text":"def ComputeInteractionState(self, p_int, p_int_1, p_int_2):\n ...","title":""},{"docid":"e30cc5c9983124e06c8afce2afc2e92a","score":"0.5616494","text":"def Viterbi(all_possible_hidden_states,\r\n all_possible_observed_states,\r\n prior_distribution,\r\n transition_model,\r\n observation_model,\r\n observations):\r\n\r\n # TODO: Write your code here\r\n \r\n num_time_steps = len(observations)\r\n w = [None] * num_time_steps\r\n phi = [None] * (num_time_steps-1)\r\n estimated_hidden_states = [None] * num_time_steps\r\n \r\n # computing the transition matrix p(zn|zn-1)\r\n transition_matrix = rover.Distribution()\r\n \r\n # computing the observation matrix p(x,y | z)\r\n observation_matrix = rover.Distribution()\r\n \r\n for state in all_possible_hidden_states:\r\n transition_matrix[state] = transition_model(state)\r\n \r\n observation_matrix[state] = observation_model(state)\r\n\r\n # initial w(z1)\r\n initial_post = observation_matrix[ ( (observations[0]) + (\"stay\",) ) ]\r\n \r\n result = rover.Distribution()\r\n \r\n for state in initial_post:\r\n result[ (state) + (\"stay\",) ] = np.log(initial_post[state] * prior_distribution[state + (\"stay\",)])\r\n \r\n\r\n w[0] = result\r\n \r\n \r\n \r\n # loop\r\n for i in range(1, num_time_steps):\r\n \r\n w[i] = rover.Distribution()\r\n \r\n phi[i-1] = rover.Distribution()\r\n \r\n # loop through all states possible\r\n \r\n # i is zn and i-1 is zn-1\r\n \r\n # get the possible states of zn\r\n for zn in all_possible_hidden_states:\r\n \r\n # set to large negative number\r\n inner_term = -90000000\r\n \r\n # loop through zn-1 values\r\n for zn1 in w[i-1]:\r\n \r\n # probability p(z_n | z_n-1)\r\n probZZ = transition_matrix[zn1]\r\n \r\n # check if in the p(zn|zn-1)\r\n if zn not in probZZ:\r\n continue\r\n \r\n if w[i-1][zn1] == 0:\r\n continue\r\n\r\n \r\n # max term of forward message \r\n if (np.log(probZZ[zn]) + w[i-1][zn1] > inner_term):\r\n inner_term = np.log(probZZ[zn]) + w[i-1][zn1]\r\n phi[i-1][zn] = zn1\r\n \r\n \r\n # once gone through all values of z_n-1, multiply by p(x|z_n)\r\n post_xy = observation_model(zn)\r\n \r\n if (observations[i] == None):\r\n probxy = 1\r\n else:\r\n if observations[i] not in post_xy:\r\n continue\r\n probxy = post_xy[observations[i]]\r\n \r\n \r\n \r\n result = np.log(probxy) + inner_term\r\n \r\n if (inner_term != -90000000):\r\n w[i][zn] = result\r\n \r\n # find max z_n\r\n maximum = -90000\r\n arg_max = None\r\n \r\n estimated_hidden_states[-1] = rover.Distribution()\r\n \r\n for key in w[-1]:\r\n if w[-1][key] > maximum:\r\n arg_max = key\r\n maximum = w[-1][key]\r\n \r\n estimated_hidden_states[-1] = arg_max\r\n \r\n\r\n # backtrack\r\n for i in range(num_time_steps-2, -1, -1):\r\n estimated_hidden_states[i] = rover.Distribution()\r\n \r\n estimated_hidden_states[i] = phi[i][arg_max]\r\n \r\n # next term used to backtrack\r\n arg_max = phi[i][arg_max]\r\n \r\n \r\n return estimated_hidden_states","title":""},{"docid":"b1527e8d7df30709aee57f1fe8bfaf88","score":"0.5615736","text":"def act(self, state: np.ndarray): \n state = tupleTensor(state)\n \n with torch.no_grad():\n outputs,self.dqn_hidden_state,self.dqn_cell_state =\\\n self.dqn(state,self.dqn_hidden_state,self.dqn_cell_state)\n action = torch.argmax(outputs)\n return action.item()","title":""},{"docid":"12a564bc5a1453914d4e404d12321c31","score":"0.5614487","text":"def act(self, state):\n\n state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)\n probs, embed = self.forward(state)\n probs = probs.cpu()\n embed = embed.cpu()\n m = Categorical(probs)\n action = m.sample()\n return action.item(), m.log_prob(action), embed","title":""},{"docid":"02da9e841926f3df293097e7a5faf2d8","score":"0.56090236","text":"def transition(self, state):\n #Find the label for the state\n label = self.dict[state]\n #For when I redfine the dictionary in the other part of the lab\n if type(label) is int:\n# print(self.mat[label])\n# print(state)\n result = np.random.multinomial(1, self.mat[:,label])\n index = np.argmax(result)\n for l, i in self.dict.items():\n if index == i:\n return l\n \n #For when I don't need to redefine the dictionary\n for s in label:\n #Transition\n result = np.random.multinomial(1, self.mat[:,s])\n index = np.argmax(result)\n #Check where the transition took me\n for l, i in self.dict.items():\n if index in i:\n return l\n \n \n raise NotImplementedError(\"Problem 2 Incomplete\")","title":""},{"docid":"40e77c78593475d34dbc7fd6b833fe62","score":"0.559867","text":"def _dense_ham_term(H):\n h1, (h2L, h2R) = H\n D = h1.shape[0]\n dtype = h1.dtype\n\n E = backend.eye(D, dtype=dtype)\n\n h = backend.ncon([h1, E], [(-1, -3), (-2, -4)])\n for (hl, hr) in zip(h2L, h2R):\n h += backend.ncon([hl, hr], [(-1, -3), (-2, -4)])\n\n return h","title":""},{"docid":"43c9f4ea47f07462371de3763b127fc4","score":"0.5591284","text":"def act(self, state):\n if np.random.rand() < self.epsilon:\n return self.action_space.sample() #sample() is a function of spaces.Discrete()\n else:\n state_interval = self.make_interval(state)\n\n q_values = self.q_network.predict(state_interval)\n action = np.argmax(q_values[0])\n return action","title":""},{"docid":"c7c29856a7dc483236b4127bae92ae11","score":"0.5582921","text":"def actions(self, state):\n sick = []\n healthy = []\n for i, line in enumerate(state):\n for j, (value, _) in enumerate(line):\n if value == 'S':\n sick.append((Q_action, (i, j)))\n elif value == 'H':\n healthy.append((V_action, (i, j)))\n\n sick_permutations = list(itertools.combinations(sick, min(self.police, len(sick))))\n healthy_permutations = list(itertools.combinations(healthy, min(self.medics, len(healthy))))\n actions = [sick_perm + healthy_perm\n for sick_perm in sick_permutations\n for healthy_perm in healthy_permutations]\n return actions","title":""},{"docid":"ed11b945f14fb39bcc7a2e7888525efc","score":"0.5581947","text":"def __call__(self, inputs, state, scope=None):\n with tf.variable_scope(scope or type(self).__name__):\n c, h = state\n\n # change bias argument to False since LN will add bias via shift\n concat = tf.nn.rnn_cell._linear([inputs, h], 4 * self._num_units, False)\n\n i, j, f, o = tf.split(1, 4, concat)\n\n # add layer normalization to each gate\n i = ln(i, scope = 'i/')\n j = ln(j, scope = 'j/')\n f = ln(f, scope = 'f/')\n o = ln(o, scope = 'o/')\n\n new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) *\n self._activation(j))\n\n # add layer_normalization in calculation of new hidden state\n new_h = self._activation(ln(new_c, scope = 'new_h/')) * tf.nn.sigmoid(o)\n new_state = tf.nn.rnn_cell.LSTMStateTuple(new_c, new_h)\n\n return new_h, new_state","title":""},{"docid":"e85ff62fff976096c3a0516060ae9662","score":"0.5581648","text":"def ham_ising():\n E = np.array([[1, 0], [0, 1]])\n X = np.array([[0, 1], [1, 0]])\n Z = np.array([[1, 0], [0, -1]])\n # hmat = np.kron(X, np.kron(Z, X))\n hmat -= 0.5 * (np.kron(np.kron(X, X), E) + np.kron(E, np.kron(X, X)))\n return np.reshape(hmat, [2] * 6)","title":""},{"docid":"04116fb31389501f4d6b3b71b20f0eba","score":"0.5579655","text":"def lift_state(self):\n init_state = self.x0\n ex0 = np.array(self.x0)\n for mu in self.centers:\n init_state.append(logistic(np.linalg.norm(ex0 - mu)**2, self.alpha))\n return np.hstack([np.array([1]), init_state])","title":""},{"docid":"3cbd4c21e63889886f3afd106d678c0a","score":"0.55784917","text":"def no_actions_first_and_second_clauses(states_encoding, t):\n\n if t != 1 and t != 2:\n print(\"timestamp does not match clauses generation method\")\n return\n\n formula = []\n formula += unique_state_clauses(states_encoding, t)\n H = states_encoding[\"H\"]\n S = states_encoding[\"S\"]\n U = states_encoding[\"U\"]\n\n X = H.shape[0]\n Y = H.shape[1]\n for x in range(X):\n for y in range(Y):\n\n # unpopulated[t] ==> unpopulated[t-1]\n # unpopulated[t-1] ==> unpopulated[t]\n formula.append([-U[x, y, t], U[x, y, t-1]])\n formula.append([-U[x, y, t-1], U[x, y, t]])\n\n # healthy[t] ==> healthy[t-1]\n formula.append([-H[x, y, t], H[x, y, t-1]])\n\n # sick[t-1] ==> sick[t]\n formula.append([-S[x, y, t-1], S[x, y, t]])\n # sick[t] ==> sick[t-1] | healthy[t-1]\n formula.append([-S[x, y, t], S[x, y, t-1], H[x, y, t-1]])\n\n adjacent_neighbors = neighbors((x, y), (X, Y))\n # construct gradually the clause of (was_sick(t-1) OR one_of_neighbor_was_sick(t-1))\n sick_neighbors = []\n\n for neighbor_x, neighbor_y in adjacent_neighbors:\n # healthy[t] ==> ~sick_neighbor[t-1]\n formula.append([-H[x, y, t], -S[neighbor_x, neighbor_y, t-1]])\n # sick_neighbor[t-1] & healthy[t-1] ==> S[t]\n formula.append([-S[neighbor_x, neighbor_y, t-1], -H[x, y, t-1], S[x, y, t]])\n sick_neighbors.append(S[neighbor_x, neighbor_y, t-1])\n\n formula.append([-S[x, y, t], S[x, y, t-1]] + sick_neighbors)\n # H[t-1] & ~S[x-1, y, t-1] & ~S[x+1, y, t-1] & ~ S[x, y+1, t-1] ... ==> H[t]\n formula.append([-H[x, y, t-1], H[x, y, t]]+sick_neighbors)\n\n return formula","title":""},{"docid":"80b55e4efe62148cf2b7ec59db507e7a","score":"0.5577992","text":"def get_dyn_theory(env, state):\n assert env.problem == OneMax\n assert env.action_description == \"lbd\"\n assert env.state_description == \"n, f(x)\"\n assert len(env.state_functions) == 2\n\n return np.asarray([np.sqrt(env.x.n / (env.x.n - state[1]))], dtype=np.float32)","title":""},{"docid":"6d5725a613f769b99f01d4b4469638ed","score":"0.55727875","text":"def getActionProbabilities(self, state: int) -> np.ndarray:\n distribution = []\n denominator = sum([np.exp(self._sigma * self._theta[state][i]) for i in range(self.numActions)])\n for i in range(self.numActions):\n numerator = np.exp(self._sigma * self._theta[state][i])\n distribution.append(numerator / denominator)\n return np.array(distribution)","title":""},{"docid":"9eba66fff22090b2f8011c4272d08818","score":"0.5571411","text":"def _heom_state_dictionaries(dims, excitations):\n nstates = 0\n state2idx = {}\n idx2state = {}\n\n for state in state_number_enumerate(dims, excitations):\n state2idx[state] = nstates\n idx2state[nstates] = state\n nstates += 1\n\n return nstates, state2idx, idx2state","title":""},{"docid":"cef9f9492f2834e707b480176989d1be","score":"0.55690783","text":"def action_distribution(self, state):\n raise NotImplementedError()","title":""},{"docid":"4183db0d99516a080c915486f81dfc74","score":"0.5557254","text":"def reward(self, state):\n test_pixels = self.get_underlying_pixels(state)\n sim = self.similarity(self.agent[self.indices], test_pixels) #TODO scale this? \n return sim","title":""},{"docid":"160bcf209043a339d65ccfb731152936","score":"0.5556321","text":"def __call__(self, inputs, state, scope=None):\n with tf.variable_scope(scope or type(self).__name__):\n c, h = state\n\n # change bias argument to False since LN will add bias via shift\n concat = tf.nn.rnn_cell._linear(\n [inputs, h], 4 * self._num_units, False)\n # ipdb.set_trace()\n\n i, j, f, o = tf.split(1, 4, concat)\n\n # add layer normalization to each gate\n i = ln(i, scope='i/')\n j = ln(j, scope='j/')\n f = ln(f, scope='f/')\n o = ln(o, scope='o/')\n\n new_c = (c * tf.nn.sigmoid(f + self._forget_bias) +\n tf.nn.sigmoid(i) * self._activation(j))\n\n # add layer_normalization in calculation of new hidden state\n new_h = self._activation(\n ln(new_c, scope='new_h/')) * tf.nn.sigmoid(o)\n new_state = tf.nn.rnn_cell.LSTMStateTuple(new_c, new_h)\n return new_h, new_state","title":""},{"docid":"439a23fc1be390bc1b8f84b153324a71","score":"0.5550651","text":"def act(self, state):\n if np.random.rand() < self.epsilon:\n return random.randrange(self.action_size)\n return np.argmax(self.predict(state)[0])","title":""},{"docid":"5d2bc3d257b1d21c5e56b2b4979ea457","score":"0.5548322","text":"def run_baum_welch_algorithm(self, state_features, timeseries_observations, cycle_states):\n\n # create the initial transition matrix\n transition_matrix = self.create_transition_matrix()\n\n # set cumulative probability, this will be used as a breaking criteria for the EM algorithm\n cummulative_probability = np.inf\n\n for i in range(10):\n # create the observation probabilities given the initial features\n observation_probabilities = self.create_observation_probabilities(state_features, timeseries_observations, cycle_states)\n\n # run forward and backward pass through\n forward_probabilities, forward_trellis = self.run_forward_pass(transition_matrix, observation_probabilities)\n backward_probabilities, backward_trellis = self.run_backward_pass(transition_matrix,\n observation_probabilities)\n backward_trellis.reverse()\n backward_probabilities.reverse()\n\n # update lambda parameter (probability of state i, time j)\n numerator = np.multiply(np.array(forward_probabilities), np.array(backward_probabilities))\n\n denominator = sum(np.multiply(np.array(forward_probabilities), np.array(backward_probabilities)).T)\n _lambda = []\n for j in range(len(numerator)):\n _lambda.append((numerator[j, :].T / denominator[j]).T)\n\n # update epsilon parameter (probability of moving for state i to state j)\n numerator = np.multiply(forward_trellis[1:], backward_trellis[:-1])\n epsilon = []\n for g in range(len(numerator)):\n denominator = np.sum(numerator[g, :, :])\n epsilon.append((numerator[g, :, :].T / denominator).T)\n\n # Update the transition matrix and observation probabilities for the next iteration\n transition_matrix = ((sum(epsilon) / sum(_lambda))).T / sum((sum(epsilon) / sum(_lambda)))\n\n # Update the state space parameters\n observation_probabilities = _lambda\n state_ind = 0\n for state in state_features:\n param_set = state_features[state]\n state_weight = [0]*len(set(cycle_states))\n state_var = [0]*len(set(cycle_states))\n state_sum = [0]*len(set(cycle_states))\n\n for ind in range(len(timeseries_observations)):\n cycle = cycle_states[ind]\n state_weight[cycle] += _lambda[ind][state_ind]\n state_sum[cycle] += timeseries_observations[ind] * _lambda[ind][state_ind]\n state_var[cycle] += _lambda[ind][state_ind] * np.sqrt(\n (timeseries_observations[ind] - param_set.loc[param_set['CYCLE']==cycle, 'SEASONALITY_MU'].item()) ** 2)\n\n state_mu_set = np.divide(state_sum, state_weight).tolist()\n state_sigma_set = np.divide(state_var, state_weight).tolist()\n cycle_ind = list(set(cycle_states))\n cycle_ind.sort()\n param_set_new = pd.DataFrame(columns=['CYCLE', 'SEASONALITY_MU', 'SEASONALITY_SIGMA'], data=\n np.array([cycle_ind, state_mu_set, state_sigma_set]).T)\n\n state_features.update({state: param_set_new})\n\n state_ind += 1\n\n cummulative_probability_new = np.sum(_lambda)\n pcnt_change = (cummulative_probability_new-cummulative_probability)/cummulative_probability\n if pcnt_change < 0.01:\n break\n else:\n cummulative_probability = cummulative_probability_new\n\n print('Fitted transition matrix: ')\n print(transition_matrix)\n print('Fitted state features: ')\n print(state_features)\n\n # multiply the probabilities to get the overall probability. Convert to state using MAP\n observation_probabilities = _lambda\n\n return transition_matrix, state_features, observation_probabilities","title":""},{"docid":"08d35e4730c3dcdb9f69f6eb2563a4da","score":"0.55421877","text":"def compute_state(self):\r\n\r\n self.predicted_state[:, [0]] = self.vehicle.state\r\n u = []\r\n for idx in range(1, self.horizon+1):\r\n dstate, control = self.propagate(self.predicted_state[:, [0]])\r\n\r\n self.predicted_state[:, [idx]] = np.matmul(self.vehicle.system_matrix(), self.predicted_state[:, [idx-1]])\\\r\n + dstate\r\n u.append(control)\r\n self.update_time()\r\n print(\"----------------------------------\")\r\n print(\"Current initial state and horizon: \\n\")\r\n print(self.predicted_state[self.xidx, :], \"\\n\", self.predicted_state[self.yidx, :])\r\n print(\"----------------------------------\")\r\n current_initial = self.predicted_state[:self.num_states, [1]]\r\n\r\n self.vehicle.update(current_initial, u[0])\r\n return self.predicted_state","title":""},{"docid":"10207053dd706df12c49568975759a62","score":"0.5530769","text":"def no_actions_clauses(states_encoding, t):\n if t < 3:\n print(\"timestamp does not match clauses generation method\")\n return\n\n formula = []\n formula += unique_state_clauses(states_encoding, t)\n\n H = states_encoding[\"H\"]\n S = states_encoding[\"S\"]\n U = states_encoding[\"U\"]\n\n X = H.shape[0]\n Y = H.shape[1]\n\n for x in range(X):\n for y in range(Y):\n\n # if unpopulated[t] ==> unpopulated[t-1]\n formula.append([-U[x, y, t], U[x, y, t-1]])\n # if unpopulated[t-1] ==> unpopulated[t]\n formula.append([-U[x, y, t-1], U[x, y, t]])\n\n # S[t-1] & S[t-2] & S[t-3] ==> H[t]\n formula.append([-S[x, y, t-1], -S[x, y, t-2], -S[x, y, t-3], H[x, y, t]])\n\n # S[t-1] & ~S[t-2] ==> S[t]\n formula.append([-S[x, y, t-1], S[x, y, t-2], S[x, y, t]])\n\n adjacent_neighbors = neighbors((x, y), (X, Y))\n\n # H[x, y, t] ==> (S[t-1] & S[t-2] & S[t-3]) | (H[x, y, t-1] & ~S[x+1, y, t-1] & ~S[x-1, y, t-1]...)\n healthy_condition_prefixes = [[-H[x, y, t], S[x, y, t-1]], [-H[x, y, t], S[x, y, t-2]],\n [-H[x, y, t], S[x, y, t-3]]]\n healthy_condition_suffixed = [[H[x, y, t-1]]]\n sick_neighbors = []\n for neighbor_x, neighbor_y in adjacent_neighbors:\n healthy_condition_suffixed.append([-S[neighbor_x, neighbor_y, t-1]])\n\n # H[t-1] & sick_neighbor[t-1] ==> S[t]\n formula.append([-H[x, y, t-1], -S[neighbor_x, neighbor_y, t-1], S[x, y, t]])\n\n # build or clause between sick neighbors sick conditions\n sick_neighbors.append(S[neighbor_x, neighbor_y, t-1])\n\n formula += [prefix + suffix for prefix in healthy_condition_prefixes\n for suffix in healthy_condition_suffixed]\n\n # S[t] ==> (S[t-1] & (~S[t-2] | ~ S[t-3]) | (H[t-1] & (S[x+1, y, t-1] | S[x-1, y, t-1]...))\n sick_condition_prefixes = [[-S[x, y, t], S[x, y, t-1]], [-S[x, y, t], -S[x, y, t-2], -S[x, y, t-3]]]\n sick_condition_suffixes = [[H[x, y, t-1]], sick_neighbors]\n formula += [prefix + suffix for prefix in sick_condition_prefixes\n for suffix in sick_condition_suffixes]\n\n return formula","title":""},{"docid":"f0f44953b32b476eb41e4acc072e8c7d","score":"0.5526509","text":"def call(self, inputs, state):\n\n gate_inputs = tf.matmul(state, self._gate_kernel)\n gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias)\n\n if self._layer_norm:\n gate_inputs = common_layers.split_last_dim(gate_inputs, 2)\n mean = tf.reduce_mean(gate_inputs, axis=[-1], keepdims=True)\n variance = tf.reduce_mean(tf.square(gate_inputs - mean), axis=[-1], keepdims=True)\n norm_x = (gate_inputs - mean) * tf.rsqrt(variance + self._ln_epsilon)\n norm_x = common_layers.combine_last_two_dims(norm_x)\n gate_inputs = norm_x * self._ln_scale + self._ln_bias\n\n value = tf.sigmoid(gate_inputs)\n r, u = tf.split(value=value, num_or_size_splits=2, axis=1)\n\n r_state = r * state\n\n candidate = tf.matmul(r_state, self._candidate_kernel)\n candidate = tf.nn.bias_add(candidate, self._candidate_bias)\n\n c = self._activation(candidate)\n if self._dropout_rate:\n c = tf.nn.dropout(c, keep_prob=1-self._dropout_rate)\n new_h = u * state + (1 - u) * c\n return new_h, new_h","title":""},{"docid":"54d666e7c4479fe5d688c66d2f7f8fe4","score":"0.5525123","text":"def actions(self, state):\n # find all s and then insert the new ss to 'seakness_index_map'\n\n # make S's index list\n comb_list_S = get_comb_list(self, 'S', state,2,self.zoc)\n comb_list_S1 = get_comb_list(self, 'S', state, 1, self.zoc)\n # make H's index list\n comb_list_H = get_comb_list(self, 'H', state,1,self.zoc)\n\n # combined S an H\n if comb_list_H==-1 or comb_list_S==-1:\n combHS2=[]\n else:\n combHS2 = list(product(comb_list_S, comb_list_H))\n if comb_list_H == -1 or comb_list_S1 == -1:\n combHS1 = []\n else:\n combHS1 = list(product(comb_list_S1, comb_list_H))\n if combHS1 != -1:\n combHS2.extend(combHS1)\n if comb_list_S != -1:\n combHS2.extend(comb_list_S)\n if comb_list_S != -1:\n combHS2.extend(comb_list_S)\n if comb_list_H!=-1:\n combHS2.extend(comb_list_H)\n return tuple(combHS2)","title":""},{"docid":"d70d9aa325da8a50a773e69ebbf86b94","score":"0.55226743","text":"def IMU_3DOF(state_t0,state_t1,T):\n # num_state = len(state_t0)\n sigma_theta = 0.001\n sigma_x = 0.01\n sigma_y = 0.01\n dtheta = state_t1[5] + sigma_theta*np.random.randn(1,1)\n accel_x = (state_t1[3] - state_t0([3]))/T + sigma_x*np.random.randn(1,1)\n accel_y = (state_t1[5] - state_t0([5]))/T + sigma_y*np.random.randn(1,1)\n\n return (dtheta,accel_x,accel_y)","title":""},{"docid":"6aef5f47114586753cfc1bdfd2a3077f","score":"0.55225915","text":"def GetAction(state):\n p = 1/actionSize\n if len(state.shape)==3:\n probs =np.full((1,actionSize),p)\n else:\n probs =np.full((state.shape[0],actionSize),p)\n actions = np.array([np.random.choice(probs.shape[1], p=prob / sum(prob)) for prob in probs])\n return actions","title":""},{"docid":"4607d651d7e6712bc381744d33b876b0","score":"0.55015206","text":"def result(self, state, actions):\n state = state_to_list(state)\n\n for action, (i, j) in actions:\n if action == Q_action:\n state[i][j] = ('Q', 3)\n\n elif action == V_action:\n state[i][j] = ('I', 0)\n\n infect = []\n for i, row in enumerate(state):\n for j, value in enumerate(row):\n if value[0] == 'S':\n for k, l in self.get_neighbors(i, j):\n if state[k][l][0] == 'H':\n infect.append((k, l))\n\n for i, j in infect:\n state[i][j] = ('S', 4) # 4 since this is going to be demoted immediately\n\n for i, row in enumerate(state):\n for j, (value, days) in enumerate(row):\n if value in ['S', 'Q']:\n if days == 1:\n state[i][j] = ('H', 0)\n else:\n state[i][j] = (value, days - 1)\n\n return state_to_tuple(state)","title":""},{"docid":"a2d2defb15a4080a1c22e131ad238d03","score":"0.54844165","text":"def calculate_gs(p):\n state_table = generate_state_table(p)\n H = make_Hamiltonian(p, state_table)\n w, v = scipy.sparse.linalg.eigsh(H, k=1, which='SA')\n\n return w[0], v[:, 0], state_table","title":""},{"docid":"f90ab98973a5df59e1f8c07afdfbac95","score":"0.5483582","text":"def forward(observed_states):\n L = len(observed_states[0])\n matrix = [[] for i in range (0, L)]\n for i in range (0, L):\n for j in range(0, 4):\n if j == 0: state = \"coding\"\n if j == 1: state = \"regulatory\"\n if j == 2: state = \"hetero\"\n if j == 3: state = \"other\"\n emission = math.log10(E1[state][observed_states[0][i]]) + math.log10(E2[state][observed_states[1][i]])\n # emission = math.pow(10,emission)\n if i == 0:\n ## matrix[i][j] = P[state]\n # matrix[i].insert(j, emission * P[state])\n init = emission + math.log10(P[state])\n init = math.pow(10, init)\n matrix[i].insert(j, init)\n else:\n # print(\"first, \", E1[state][observed_states[0][i]], \"second, \", E2[state][observed_states[1][i]])\n summation = 0\n prevState = \"\"\n for k in range(0, 4):\n if k == 0: prevState = \"coding\"\n if k == 1: prevState = \"regulatory\"\n if k == 2: prevState = \"hetero\"\n if k == 3: prevState = \"other\"\n\n # summation += matrix[i - 1][k] * A[prevState][state]\n if matrix[i - 1][k] == 0 or A[prevState][state] == 0:\n summation += 0\n else:\n product = math.log10(matrix[i - 1][k]) + math.log10(A[prevState][state])\n product = math.pow(10, product)\n summation += product\n\n # matrix[i].insert(j, emission * summation)\n if summation == 0:\n entry = 0\n else:\n entry = emission + math.log10(summation)\n entry = math.pow(10, entry)\n matrix[i].insert(j, entry)\n\n likelihood = 0\n for i in range(0, 4):\n likelihood += matrix[L - 1][i]\n print(\"likelihood forward\", likelihood)\n\n return matrix","title":""},{"docid":"301646394b9e378532d8b3ecb1e2853a","score":"0.5477483","text":"def creation(i,state_in):\n coef = np.sqrt(state_in[i]+1)\n state_out=state_in.copy()\n state_out[i] = state_out[i]+1\n return state_out,coef","title":""},{"docid":"3580525e6888641ab93969a2a71b9259","score":"0.5475777","text":"def __call__(self, inputs, state, scope=None):\n with vs.variable_scope(scope or type(self).__name__): # \"BasicLSTMCell\"\n # Parameters of gates are concatenated into one multiply for efficiency.\n if self._state_is_tuple:\n # batch_size X num_cand * hidden_size, batch_size X num_cand * hidden_size\n c, h = state\n else:\n c, h = array_ops.split(1, 2, state)\n hidden_size = int(self._num_units / self._num_cand)\n # batch_size X num_cand X hidden_size\n h_flat = array_ops.reshape(h, [-1, self._num_cand, hidden_size])\n # batch_size X hidden_size\n h_sum = tf.reduce_mean(h_flat, reduction_indices=1)\n # batch_size * num_cand X hidden_size\n with vs.variable_scope('forget_gate'):\n W_f = tf.get_variable(\"W_f\", [hidden_size, hidden_size])\n U_f = tf.get_variable(\"U_f\", [hidden_size, hidden_size])\n b_f = tf.get_variable(\"b_f\", [hidden_size],\n initializer=tf.constant_initializer(1.0))\n f_x = tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_f)\n f_h = tf.reshape(tf.matmul(tf.reshape(h_flat,\n [-1, hidden_size]),\n U_f),\n [-1, 1, self._num_cand, hidden_size])\n f_x = tf.tile(tf.reshape(f_x,\n [-1, self._num_cand, 1, hidden_size]),\n [1, 1, self._num_cand, 1])\n # batch_size * num_cand * num_cand * hidden_size\n f_xh = sigmoid(f_x + f_h + b_f)\n # f_x = _linear(array_ops.reshape(inputs, [-1, hidden_size]),\n # hidden_size, True, 1.0, 'InputGate1')\n # batch_size * num_cand X hidden_size\n # f_h = array_ops.reshape(_linear(array_ops.reshape(h,\n # [-1, hidden_size]),\n # hidden_size, True, 1.0, 'InputGate2'),\n # [-1, self._num_cand, hidden_size])\n #\n # f_x = array_ops.reshape(array_ops.tile(f_x, [1, self._num_cand]),\n # [-1, self._num_cand, self._num_cand, hidden_size])\n # f_h = array_ops.reshape(array_ops.tile(f_h, [1, self._num_cand, 1]),\n # [-1, self._num_cand, self._num_cand, hidden_size])\n # f_xh = array_ops.transpose(f_x + f_h, [1, 0, 2, 3])\n\n with vs.variable_scope('update'):\n W_a = tf.get_variable(\"W_a\", [hidden_size, hidden_size * 3])\n U_a = tf.get_variable(\"U_a\", [hidden_size, hidden_size * 3])\n b_in = tf.get_variable(\"b_in\", [hidden_size])\n b_o = tf.get_variable(\"b_o\", [hidden_size])\n b_u = tf.get_variable(\"b_u\", [hidden_size])\n a_x = tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_a)\n a_h = tf.matmul(h_sum, U_a)\n i_x, o_x, u_x = tf.split(1, 3, a_x)\n i_h, o_h, u_h = tf.split(1, 3, a_h)\n # batch_size X num_cand X hidden_size\n i_xh = sigmoid(tf.reshape(i_x, [-1, self._num_cand, hidden_size]) \\\n + tf.reshape(i_h, [-1, 1, hidden_size]) + b_in)\n o_xh = sigmoid(tf.reshape(o_x, [-1, self._num_cand, hidden_size]) \\\n + tf.reshape(o_h, [-1, 1, hidden_size]) + b_o)\n u_xh = tanh(tf.reshape(u_x, [-1, self._num_cand, hidden_size]) \\\n + tf.reshape(u_h, [-1, 1, hidden_size]) + b_u)\n\n # o_x = _linear(array_ops.reshape(inputs, [-1, hidden_size]), 3 * hidden_size, True)\n # with vs.variable_scope('UpdateGates2'):\n # o_h = _linear(array_ops.reshape(h_sum, [-1, hidden_size]), 3 * hidden_size, True)\n # batch_size * num_cand * hidden_size\n # o = array_ops.reshape(o_x, [-1, self._num_cand, 3 * hidden_size]) \\\n # + array_ops.reshape(tf.tile(o_h, [1, self._num_cand]),\n # [-1, self._num_cand, 3 * hidden_size])\n # i_xh, o_xh, u_xh = array_ops.split(2, 3, o)\n # i = input_gate, j = new_input, f = forget_gate, o = output_gate\n new_c =tf.reduce_sum(\n array_ops.reshape(c,[-1, 1, self._num_cand, hidden_size])\n * f_xh, reduction_indices=2) + i_xh * u_xh\n new_h = tanh(new_c) * o_xh\n new_c = array_ops.reshape(new_c, [-1, hidden_size * self._num_cand])\n new_h = array_ops.reshape(new_h, [-1, hidden_size * self._num_cand])\n if self._state_is_tuple:\n new_state = LSTMStateTuple(new_c, new_h)\n else:\n new_state = array_ops.concat(1, [new_c, new_h])\n return new_h, new_state","title":""},{"docid":"f177517e3d49689d4bcbb9f45fa70695","score":"0.54749775","text":"def forward(self, state):\n x = np.dot(state, self.w)\n # softmax\n x = np.exp(x)/sum(np.exp(x))\n return x","title":""},{"docid":"b0ab2c7256a25ab59f166aee41eb5d66","score":"0.5473587","text":"def evolve(p, state_table, state, kind=\"list\", trotterised=False, xinit=False, xmeas=False):\n if kind == \"ket\":\n psi0 = state\n elif kind == \"list\":\n # if we parsed a product state, construct ket by identifying the\n # corresponding number of the basis state and putting a 1 into the ket\n psi0 = np.zeros((len(state_table), 1), dtype=complex)\n psi0[state_table.index(state_to_int(p, state))] = 1.\n elif kind == \"int\":\n psi0 = np.zeros((len(state_table), 1), dtype=complex)\n psi0[state_table.index(state)] = 1.\n\n if xinit:\n H_matrix = local_H_matrix(p, state_table)\n psi0 = H_matrix.dot(psi0)\n\n time = np.linspace(p['t_initial'], p['t_final'],\n int(p['t_final'] / p['dt'] + 1))\n\n # make dictionary with measurement operators\n meas = {}\n for i in range(int(p['N'])):\n meas['Zi' + ' Site ' + str(i)\n ] = Zi_matrix(p, i, state_table)\n meas['Yi' + ' Site ' + str(i)\n ] = Yi_matrix(p, i, state_table)\n meas['Xi' + ' Site ' + str(i)\n ] = Xi_matrix(p, i, state_table)\n\n sim = {}\n sim['Time'] = time\n for key in meas.keys():\n sim[key] = np.zeros(np.shape(time))\n sim['Total Z'] = np.zeros(np.shape(time))\n sim['Total Y'] = np.zeros(np.shape(time))\n sim['Total X'] = np.zeros(np.shape(time))\n\n if trotterised:\n H_list = make_trotter_Hamiltonian(p, state_table)\n else:\n H_list = [make_Hamiltonian(p, state_table)]\n\n # construct time-evolution operators for a single time-step\n U_list = [LA.expm(-1.j * H.tocsc() * p['dt']) for H in H_list]\n\n # Time Evolution\n for i in range(len(time)):\n # define initial (t=0) state\n if i == 0:\n psi = psi0\n\n # measurements\n for operator in meas.keys():\n expct = expct_val(meas[operator], psi) # [0][0]\n\n if np.imag(expct) < 1e-12:\n sim[operator][i] = np.real(expct)\n else:\n print(\"Imaginary Measurement %s\" % (operator))\n\n # apply U to current state psi to get psi(t+dt) = U * psi(t)\n for U in U_list:\n psi = U.dot(psi)\n\n for key in sim.keys():\n if key[:2] == \"Zi\":\n sim['Total Z'] += sim[key]\n elif key[:2] == \"Yi\":\n sim['Total Y'] += sim[key]\n elif key[:2] == \"Xi\":\n sim['Total X'] += sim[key]\n\n return sim, state_table","title":""},{"docid":"040bb8c1c8154722b89b7726987bed2b","score":"0.5473407","text":"def anihilation(i,state_in):\n if not (state_in[i] == 0):\n coef = np.sqrt(state_in[i])\n state_out=state_in.copy()\n state_out[i]=state_out[i]-1\n stop = False\n return state_out,coef,stop\n else:\n #print('This state cant be lowered at', i,'!', )\n stop = True \n state_out= []\n coef=0\n return state_out,coef,stop","title":""},{"docid":"12badc73be8c3bd67d63bf51657ec725","score":"0.5472945","text":"def H(state: SparseSim,\n qubit: int) -> None:\n\n stabs = state.stabs\n\n # Change the sign appropriately\n\n # X and Z -> -1\n # ----------\n stabs.signs_minus ^= stabs.col_x[qubit] & stabs.col_z[qubit]\n\n # Update Paulis\n # -------------------------------------------------------------------\n for g in state.gens:\n\n # Swap X and Z for rows\n xonly = g.col_x[qubit] - g.col_z[qubit]\n\n zonly = g.col_z[qubit] - g.col_x[qubit]\n\n for i in xonly:\n g.row_x[i].discard(qubit)\n g.row_z[i].add(qubit)\n\n for i in zonly:\n g.row_z[i].discard(qubit)\n g.row_x[i].add(qubit)\n\n # Swap X and Z for cols\n g.col_x[qubit], g.col_z[qubit] = g.col_z[qubit], g.col_x[qubit]","title":""},{"docid":"fcea6487e36d1cecda3c5f71129f43eb","score":"0.5468132","text":"def getManhattanDistanceComponents(self, state):\n\t\treshaped_state = np.reshape(state, self.outdim)\n\t\t# np_state = np.array([[j for j in i.split('\\t')] for i in state.splitlines()])\n\t\tavatar = 1\n\t\t## Example: to find what ID a box would have, you'd just do ...index(\"box\"). This is the\n\t\t## Schaul function for figuring the sprite IDs.\n\t\tgoal = 2**(1+sorted(self._obstypes.keys())[::-1].index(\"goal\"))\n\t\tavatar_loc = None\n\t\tgoal_loc = None\n\t\tnumRows, numCols = self.outdim\n\t\tfor i in range(numRows): \n\t\t\tfor j in range(numCols):\n\t\t\t\tif (reshaped_state[i,j] / goal) % 2 == 1:\n\t\t\t\t\tgoal_loc = (i,j)\n\n\t\t\t\tif (reshaped_state[i,j]/ avatar) % 2 == 1:\n\t\t\t\t\tavatar_loc = (i,j)\n\n\t\treturn avatar_loc[0]-goal_loc[0], avatar_loc[1] - goal_loc[1]","title":""},{"docid":"9a44f0b3553645e8f860d46854bc194f","score":"0.545734","text":"def tclab_step(self, state, time, action, dist):\n heater_pwm = action\n sensor_temp, heater_temp = state\n\n dth = (self.surf_area * self.beta1 * (self.k_d * dist) ** self.beta2 * (\n self.amb_temp - heater_temp) + self.emissivity * self.boltzmann\n * self.surf_area * (self.amb_temp ** 4 - heater_temp ** 4)\n + self.alpha * heater_pwm) / (self.mass * self.cp)\n dtc = (-sensor_temp + heater_temp) / self.tau_hc\n\n new_state = np.zeros(2)\n new_state[0] = dtc\n new_state[1] = dth\n return new_state","title":""},{"docid":"6e51e3c8410985c94b5d12315ff7fb4d","score":"0.54566973","text":"def act(self, state):\n state = np.reshape(state, [-1, self.state_size])\n action = self.actor_local.model.predict(state)[0]\n # add some noise for exploration\n return list(action + self.noise.sample())\n return action","title":""},{"docid":"37ef0a1dda4a5f208c9530c289563c2b","score":"0.54542744","text":"def get_action(self, state:np.ndarray)->int:\n x = self.basis.encode(state) # Computes the basis function representation of the state features\n p = self.get_action_probabilities(x) # computes the probabilities of each action\n a = int(np.random.choice(range(p.shape[0]), p=p, size=1)) # samples the action from p\n\n return a","title":""},{"docid":"27bb87e54eb058ab5f0a4061306398be","score":"0.5453973","text":"def state_transform(state):\n return state.mean(2)[None, ...] / 256.0","title":""},{"docid":"7428bdc2ef95a73f70b247fbf9a89c44","score":"0.544846","text":"def local_H_matrix(p, state_table):\n single_H = np.array([[1., 1.], [1., -1.]]) / np.sqrt(2)\n H_mat = np.eye(1)\n for i in range(p['N']):\n H_mat = np.kron(single_H, H_mat)\n return H_mat","title":""},{"docid":"7fa34013afda72da1e3fc9477101db97","score":"0.54361","text":"def h1(state: State):\n delta = [state.board.end_state[i] - state.board.tiles[i] for i in state.board.tiles]\n return sum(x != 0 for x in delta)","title":""},{"docid":"b1a41231b13a3b6866f87753f1e287da","score":"0.54301536","text":"def work(self):\r\n return self.m_act * self.eff_mech * (self.inlet.h - self.outlet.h)","title":""},{"docid":"b5002d9c33efb936bd11ed68682a96be","score":"0.5418533","text":"def act(self, state, sess):\n probs = sess.run(self.a_prob, {self.states_St: np.expand_dims(state.flatten(), axis=0)})\n a_t = 1 * (np.random.rand() < probs)\n return a_t.squeeze()","title":""},{"docid":"e4d3a28d5d011def3ab71de89c1198fb","score":"0.5418465","text":"def get_action(self, state):\n tens = torch.tensor(state, dtype = torch.float32).permute(2,0,1)\n logits, _ = self.net(tens)\n probs = torch.softmax(logits, dim=-1)\n probs = probs.squeeze()\n return np.random.choice(4, p = probs.detach().numpy())","title":""},{"docid":"ef3e281d233f2a0acae83413984ffc6c","score":"0.54177237","text":"def airportMDP():\n # States -------------------------------------------\n # order of states goes: G_1, G_2, ... T_1, T_2,...\n # gate ordered from concourse A/B/C/D satellite N/S \n # -------------------------------------------\n S = 6 + 77;\n \n # Actions -------------------------------------------\n # actions: going within each concourse/satellite, \n # going to neighbouring concourse/satellite\n # action order: first action is to stay in concourse\n # subsequent actions are to move to neighbouring state\n # -------------------------------------------\n A = np.zeros(S);\n # concourse A\n A[0] = 3;\n # concourse B\n A[1] = 4;\n # concourse C\n A[2] = 4;\n # concourse D\n A[3] = 3;\n # satellite N\n A[4] = 3;\n # satellite S\n A[5] = 3;\n # the gate states T_i\n A[6:] = 1;\n# print (A)\n actionSize = int( max(A));\n # corresponding terminal states\n Terminals = np.array([14,12,12, 11,14,14]);\n # neighbouring states \n Neighbours = [[1,5], # A is connected to (B, S)\n [0,2,5], # B is connected to (A, C, S)\n [1,3,4], # C is connected to (B, D, N),\n [2,4], # D is connected to (C, N)\n [2,3], # N is connected to (C, D)\n [0,1]]; # S is connected to (A, B)\n \n # probability kernel -------------------------------------------\n P = np.zeros((S,S,actionSize));\n for s in range(S):\n if s <= 5: # state is a concourse state\n # action 0: state in concourse state\n P[s,s,0] = 0.5;\n termStart = int(6 + sum(Terminals[0:s]));\n termEnd = termStart + Terminals[s];\n P[termStart:termEnd, s, 0] = 0.5/Terminals[s];\n# print (\"state \", s );\n# print (P[:,s,0])\n # other actions: going to neighbouring concourses\n neighbourAction = 1;\n neighbours = len(Neighbours[s]) - 1;\n for desti in Neighbours[s]:\n P[desti, s, neighbourAction] = 0.9;\n for otherNeighB in Neighbours[s]:\n if otherNeighB != desti:\n P[otherNeighB, s, neighbourAction] = 0.1/neighbours;\n neighbourAction += 1;\n else: # state is a gate state\n P[s,s,0] = 1;\n \n # cost -------------------------------------------\n # l(y) = Cy + D, C: S x A, D : S x A\n C = np.zeros((S,actionSize));\n D = np.zeros((S,actionSize));\n # cost of non-existent actions is infinity \n for s in range(S):\n action = 0;\n # cost of feasible action\n while action < A[s]:\n if action == 0:\n if s <= 5: # cost of staying in concourse s\n C[s, action]= 50;\n else: # cost of staying in gate s\n C[s, action] = 0;\n D[s, action] = 1;\n else:\n # going to neighbouring states depends on how \n # many other planes are traversing the road\n C[s, action] = 1; \n # gas money of going else where\n D[s, action] = 3;\n action += 1;\n # cost of non-existent actions is infinity\n someInf = 10000; # apparently np.inf doesn't work\n while action < actionSize: \n C[s, action] = 0;\n D[s, action] = someInf;\n action +=1; \n return P, C, D, S, actionSize","title":""},{"docid":"d36c2cd1d7924bb1a36c987ca0628d94","score":"0.5408316","text":"def get_action(self, state):\n\t\tq_vals = self._get_q_vals(state)\n\t\tnum = np.random.uniform()\n\n\t\tif num < self._epsilon:\n\t\t\treturn np.random.choice(self.k)\n\t\telse:\n\t\t\treturn np.argmax(q_vals)","title":""},{"docid":"567e3d4ae89622d60a370fdd7c747826","score":"0.5407528","text":"def solve(t, state):\n \n p= state[0]; q= state[1]; r= state[2]; vA= state[3]; vB= state[4]\n pd= state[5]; qd= state[6]; rd= state[7]; vAd= state[8]; vBd= state[9]\n wA= state[10]; wB= state[11] \n \n bA= cross(wA, IA.dot(wA))-mA*cross(g*z+cross(wA, cross(wA, p)), p)-mB*cross(g*z+cross(wA, cross(wA, q))+cross(wB, cross(wB, r)), q)\n bB= cross(wB, IB.dot(wB))-mB*cross(cross(wA, cross(wA, q))+cross(wB, cross(wB, r))+g*z, r)\n b= array([bA.dot(x), bA.dot(y), bA.dot(z), bB.dot(x), bB.dot(y), bB.dot(z)])\n \n px, py, pz= p; qx, qy, qz= q; rx, ry, rz= r\n A= zeros((6,6)) # A will be the coefficient matrix for [A] alphas = b\n A[0]= [-mA*(pz**2+py**2)-mB*qy**2-IA[0][0], mA*px*py+mB*qx*qy-IA[0][1], mA*px*pz+mB*qx*qz-IA[0][2], -mB*(rz*qz+ry*qy), mB*rx*qy, mB*rx*qz]\n A[1]= [mA*py*px+mB*qy*qx-IA[1][0], -mA*(px**2+pz**2)-mB*(qx**2+qz**2)-IA[1][1], mA*py*pz+mB*qy*qz-IA[1][2], mB*ry*qx, -mB*(rx*qx+rz*qz), mB*ry*qz]\n A[2]= [mA*pz*px+mB*qz*qx-IA[2][0], mA*pz*py+mB*qz*qy-IA[2][1], -mA*(py**2+px**2)-mB*(qy**2+qx**2)-IA[2][2], mB*rz*qx, mB*rz*qy, -mB*(ry*qy+rx*qx)]\n \n A[3]= [-mB*(qz*rz+qy*ry), mB*qx*ry, mB*qx*rz, -mB*(rz**2+ry**2)-IB[0][0], mB*rx*ry-IB[0][1], mB*rx*rz-IB[0][2]]\n A[4]= [mB*qy*rx, -mB*(qx*rx+qz*rz), mB*qy*rz, mB*ry*rx-IB[1][0], -mB*(rx**2+rz**2)-IB[1][1], mB*ry*rz-IB[1][2]]\n A[5]= [mB*qz*rx, mB*qz*ry, -mB*(qy*ry+qx*rx), mB*rz*rx-IB[2][0], mB*rz*ry-IB[2][1], -mB*(ry**2+rx**2)-IB[2][2]]\n \n alphas= linalg.solve(A, b)\n aA= alphas[:3] # alpha_A\n aB= alphas[3:]\n \n pdd= cross(aA, p) + cross(wA, cross(wA, p))\n qdd= cross(aA, q) + cross(wA, cross(wA, q))\n vAdd= cross(aA, vA) + cross(wA, cross(wA, vA))\n rdd= qdd + cross(aB, r) + cross(wB, cross(wB, r))\n vBdd= qdd + cross(aB, vB) + cross(wB, cross(wB, vB))\n \n return [pd, qd, rd, vAd, vBd, pdd, qdd, rdd, vAdd, vBdd, aA, aB]","title":""},{"docid":"5750ab4c9a8c8bcb4ab0b7b21e9c4d5f","score":"0.53902906","text":"def inv(self, state):\n return self.lmult_inv(state, np.eye(state.mom.shape[0]))","title":""},{"docid":"eb6c9ef2583b039f485472352eb1949b","score":"0.5387148","text":"def construct_HH_model(n: int, m: int, name: str = None):\n\n if n < 2 or m < 2:\n raise Exception()\n\n if name is None:\n name = f\"HH_{n}_{m}\"\n\n labels = []\n for i in range(n):\n for j in range(m):\n if i == 0:\n if j == 0:\n label = 'O'\n else:\n label = f\"C{j}\"\n elif j == 0:\n label = f\"I{i}\"\n else:\n label = f\"I{i}C{j}\"\n labels.append(label)\n\n mc = MarkovChain(name=name)\n\n for label in labels:\n if label == 'O':\n mc.add_state(label, open_state=True)\n else:\n mc.add_state(label)\n\n labels = np.array(labels, dtype=object).reshape((n, m))\n\n # Add inactivation transitions\n for i in range(n):\n for j in range(m):\n if i < n - 1:\n mc.add_both_transitions(labels[i, j], labels[i + 1, j], sp.sympify(f\"{n-i-1} * b_o\"),\n sp.sympify(f\"{i+1}*a_o\"))\n if j < m - 1:\n mc.add_both_transitions(labels[i, j], labels[i, j + 1], sp.sympify(f\"{m-j-1} * b_i\"),\n sp.sympify(f\"{j+1}*a_i\"))\n return mc","title":""},{"docid":"27f0cc2d130916f5b4ecfaf02fb090c6","score":"0.53861105","text":"def get_action(self, state):\n # starting point of algorithm\n if (self.q[state, :] == 0).all():\n return np.random.choice(self.actions)\n # Epsilon-Greedy\n if np.random.random() < self.epsilon:\n return np.random.choice(self.actions)\n else:\n return np.argmax(self.q[state, :])","title":""},{"docid":"92488b296c4f3842f0123702d96a0021","score":"0.53851795","text":"def random_HS_state(d):\n rho = np.random.normal(0, 1, size=(d, d)) + np.random.normal(0, 1, size=(d, d))*1j\n rho = rho @ rho.conj().T\n rho = rho / np.trace(rho)\n return rho","title":""}],"string":"[\n {\n \"docid\": \"97f2aec5ed08627489e932504160126d\",\n \"score\": \"0.7395203\",\n \"text\": \"def actHam(state, N, J, U):\\n t1, t2 = [], []\\n # First term\\n for i in range(len(state.vector)-1):\\n t1.append(state.create(i+1, N).destroy(i))\\n t1.append(state.create(i, N).destroy(i+1))\\n # Second term\\n for i in range(len(state.vector)):\\n PREFACTOR = state.num(i) * (state.num(i) - 1)\\n temp3 = deepcopy(state)\\n temp3.prefactor *= (PREFACTOR*U/2)\\n t2.append(temp3)\\n\\n for state in t1:\\n state.prefactor *= (-1 * J)\\n\\n return np.r_[t1, t2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e2bcfe0fdee658320bbefd516c24dd\",\n \"score\": \"0.6854056\",\n \"text\": \"def HMF(state, Delta, N):\\n\\n\\n\\t#kinetic term: sum_i(eps(i)*(n_i,up + n_i,down))\\n\\tkinetic_state = dict_list_sum(\\n\\t\\t[dict_prod(eps(i, N), dict_sum(number_op(state, i, 0, N), number_op(state, i, 1, N))) for i in range(N)])\\n\\n\\t#interaction term: sum_i( Delta c_iUP^dag c_iDOWN^dag + conj.(Delta) c_iDOWN c_iUP )\\n\\tinteraction_state = dict_list_sum(\\n\\t\\t[dict_sum(dict_prod(Delta, cr(cr(state, i, 1, N), i, 0, N)), dict_prod(np.conj(Delta), an(an(state, i, 0, N), i, 1, N))) for i in range(N)])\\n\\n\\treturn dict_sum(kinetic_state, interaction_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b219e360df4b439f5375946a9b7bea7\",\n \"score\": \"0.6688797\",\n \"text\": \"def make_Hamiltonian(p, state_table):\\n # dim = len(state_table)\\n # row = []\\n # col = []\\n # data = []\\n\\n # H = sparse.csr_matrix((data, (row, col)), shape=(dim, dim), dtype=complex)\\n H = transverse_field_matrix(p, state_table)\\n H += longitudinal_field_matrix(p, state_table)\\n H += ising_interaction_matrix(p, state_table)\\n\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efcd98dcfc3dca2bcb8789e628b922d\",\n \"score\": \"0.65604424\",\n \"text\": \"def Hexact(state, N):\\n\\n\\t#kinetic term: sum_i(eps(i)*(n_i,up + n_i,down))\\n\\tkinetic_state = dict_list_sum(\\n\\t\\t[dict_prod(eps(i, N), dict_sum(number_op(state, i, 0, N), number_op(state, i, 1, N))) for i in range(N)])\\n\\n\\t#interaction term: sum_ij(c_iUP^dag c_iDOWN^dag c_jDOWN c_jUP)\\n\\tinteraction_state = dict_list_sum([\\n\\t\\t\\t\\t\\t\\t\\tdict_list_sum([\\n\\t\\t\\t\\t\\t\\t\\t\\tcr(cr(an(an(state, j, 1, N), j, 0, N), i, 0, N), i, 1, N) for i in range(N)]) for j in range(N)])\\n\\t\\n\\tg=1/N\\n\\tinteraction_state = dict_prod(-g, interaction_state)\\n\\treturn dict_sum(kinetic_state, interaction_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0305491888b297f37caaeb325dcefbf6\",\n \"score\": \"0.6484014\",\n \"text\": \"def calc_h(self, a_state):\\n # this trivial version returns 0, a trivial estimate, but consistent and admissible\\n count = 0\\n for i in range(len(a_state.new)):\\n if a_state.new[i] != self._goal[i]:\\n count+=1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e281db8139379581670b477e5238c1\",\n \"score\": \"0.6339773\",\n \"text\": \"def getHamMatrix(N, M, J, U):\\n basis = getBasisStates(N, M)\\n ham_matrix = np.zeros((len(basis), len(basis)))\\n for state in basis:\\n # Act ham on each basis state\\n acted = actHam(state, N, J, U)\\n for x in acted:\\n for b in basis:\\n # Find the matrix location of the 'acted' state and enter into\\n # Hamiltonian matrix.\\n if(np.all(x.vector == b.vector)):\\n ham_matrix[state.idx][b.idx] += x.prefactor\\n # Return ham matrix and basis\\n return ham_matrix, basis\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8305c1a560f3afeb326b5427c2f1241\",\n \"score\": \"0.6338933\",\n \"text\": \"def Hsp_element(state,omega):\\n res= 0.0\\n for i in range(len(state)):\\n e = 1+i*(1-omega)\\n res=res+state[i]*e\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65b36235f3bf9b3a02b24c0e43b90cb\",\n \"score\": \"0.6199688\",\n \"text\": \"def ising_interaction_matrix(p, state_table):\\n if p['J'].shape[0] != p['N'] or p['J'].shape[1] != p['N']:\\n warnings.warn('J does NOT have dimensions LxL!')\\n if not np.array_equal(p['J'], p['J'].conj().T):\\n warnings.warn('J is NOT hermitian!')\\n\\n dim = len(state_table)\\n row = []\\n col = []\\n data = []\\n\\n for In in range(dim):\\n state = int_to_state(p, state_table[In])\\n\\n # eigenvalue of |0> is 1 and |1> is -1 so transform state (binary code) to spin basis\\n spin = 1 - np.multiply(2, state)\\n\\n # construct matrix which has all the ZiZj products as elements (i,j)\\n ZiZj = np.tensordot(spin, spin, axes=0)\\n\\n # get matrix element\\n matrixelement = -1.0 * np.sum(np.multiply(p['J'], ZiZj)) / 2\\n\\n # store matrix element (note hz is diagonal so Out = In)\\n row.append(In)\\n col.append(In)\\n data.append(matrixelement)\\n\\n del matrixelement\\n\\n ising_interaction = sparse.csr_matrix((data, (row, col)),\\n shape=(dim, dim), dtype=complex)\\n return ising_interaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ae9c4c8f13e19e763b1423af3ef7ee\",\n \"score\": \"0.6149006\",\n \"text\": \"def manhattan_heuristic(state):\\n goal = 0\\n mis = 0\\n\\n for y in state:\\n for x in y:\\n mis+= (abs(goal - x) //3) + (abs(abs(goal - x) % 3))\\n goal += 1\\n \\n return(mis) # replace this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24a4aeba011380cdc6b192337e00c463\",\n \"score\": \"0.6114106\",\n \"text\": \"def h(state: tuple):\\n return 6 - state[0] - state[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93419913bb220264de1c247a0db38e34\",\n \"score\": \"0.61005616\",\n \"text\": \"def simulate(self, input_state: ndarray) -> List[int]:\\n phi_0 = self.__prepare_initial_state(input_state)\\n evolved_state = dot(self.interferometer_matrix, phi_0)\\n probabilities = self.__calculate_probabilities(evolved_state)\\n return self.__calculate_approximation_of_boson_sampling_outcome(probabilities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca828b1d7a60c67bf048f70740ecb16\",\n \"score\": \"0.60028356\",\n \"text\": \"def make_trotter_Hamiltonian(p, state_table):\\n H_list = []\\n H_list.append(longitudinal_field_matrix(p, state_table) +\\n ising_interaction_matrix(p, state_table))\\n H_list.append(transverse_field_matrix(p, state_table))\\n return H_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"297ed502535f64ed50f9102b6fd39d2f\",\n \"score\": \"0.600123\",\n \"text\": \"def call(self, inputs, state):\\n\\n inputs_u, inputs_i = tf.split(value=inputs, num_or_size_splits=2, axis=1)\\n gate_inputs = math_ops.matmul(\\n array_ops.concat([inputs, state], 1), self._gate_kernel)\\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias)\\n\\n value = math_ops.sigmoid(gate_inputs)\\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\\n\\n r_state = r * state\\n\\n candidate = math_ops.matmul(\\n array_ops.concat([inputs, r_state], 1), self._candidate_kernel)\\n candidate = nn_ops.bias_add(candidate, self._candidate_bias)\\n\\n c = self._activation(candidate)\\n new_h = u * state + (1 - u) * c\\n return new_h, new_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f30d60bc9c67b7bedc9ccd4bd67323\",\n \"score\": \"0.59757584\",\n \"text\": \"def H(self, state):\\n\\t\\tH = state.EX + state.EV\\n\\t\\treturn H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14f67be23c2a4e9414e54490713d35c5\",\n \"score\": \"0.59659684\",\n \"text\": \"def state_obs_mat(ns,g_cords,state_obs):\\n state = sym.Matrix(np.zeros(2*len(g_cords)))\\n\\n for i in range(len(g_cords)):\\n state[i] = sym.symbols(g_cords[i], real = True)\\n state[i+len(g_cords)] = sym.symbols(g_cords[i] + '_dot', real = True)\\n\\n state_obs_s = sym.sympify(state_obs, locals = ns)\\n\\n Htilde_s = state_obs_s.jacobian(state)\\n\\n t = sym.symbols('t', real = True)\\n state_time[0] = t\\n for i in range(len(g_cords)):\\n state_time[i + 1] = sym.symbols(g_cords[i], real = True)\\n state_time[i + len(g_cords)+ 1] = sym.symbols(g_cords[i]+'_dot', real = True)\\n Htilde = sym.lambdify(state_time,Htilde_s, modules='numpy')\\n\\n return Htilde\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bb356fd591a18e8d4600fcbdce38b1\",\n \"score\": \"0.59569496\",\n \"text\": \"def call(self, inputs, state):\\n with vs.variable_scope(\\\"gates\\\"):\\n h = state\\n args = array_ops.concat([inputs, h], 1)\\n concat = self._linear(args, 2)\\n\\n z, r = array_ops.split(value=concat, num_or_size_splits=2, axis=1)\\n if self._layer_norm:\\n z = self._norm(z, \\\"update\\\")\\n r = self._norm(r, \\\"reset\\\")\\n\\n with vs.variable_scope(\\\"candidate\\\"):\\n args = array_ops.concat([inputs, math_ops.sigmoid(r) * h], 1)\\n new_c = self._linear(args, 1)\\n if self._layer_norm:\\n new_c = self._norm(new_c, \\\"state\\\")\\n new_h = self._activation(new_c) * math_ops.sigmoid(z) + \\\\\\n (1 - math_ops.sigmoid(z)) * h\\n return new_h, new_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecabccba7e3b7662aa5d2de5dc93c41\",\n \"score\": \"0.59503686\",\n \"text\": \"def call(self, inputs, state):\\n sigmoid = math_ops.sigmoid\\n one = constant_op.constant(1, dtype=dtypes.int32)\\n\\n # Parameters of gates are concatenated into one multiply for efficiency.\\n c, h, h_skip, h_cnt = state\\n n_skip = self._n_skip\\n if n_skip:\\n skip_bool = h_cnt % self._n_skip == 0\\n\\n gate_inputs = math_ops.matmul(\\n array_ops.concat([inputs, h], 1), self._kernel)\\n gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)\\n\\n # i = input_gate, j = new_input, f = forget_gate, o = output_gate\\n i, j, f, o = array_ops.split(\\n value=gate_inputs, num_or_size_splits=4, axis=one)\\n forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)\\n\\n # Note that using `add` and `multiply` instead of `+` and `*` gives a\\n # performance improvement. So using those at the cost of readability.\\n add = math_ops.add\\n multiply = math_ops.multiply\\n\\n first = multiply(c, sigmoid(add(f, forget_bias_tensor)))\\n new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))),\\n multiply(sigmoid(i), self._activation(j)))\\n if n_skip:\\n new_h = multiply(self._activation(new_c), sigmoid(o)) + skip_bool * 1 * h_skip\\n h_skip = h_skip * (1-skip_bool) + new_h * skip_bool\\n else:\\n new_h = multiply(self._activation(new_c), sigmoid(o)) \\n h_skip = new_h \\n\\n h_cnt += 1\\n new_state = SCLSTMStateTuple(new_h, new_c, h_skip, h_cnt)\\n return new_h, new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87868cbe1affefbb21921f94e9d7c122\",\n \"score\": \"0.59469837\",\n \"text\": \"def forward(self, state):\\n x = self.fc1(state)\\n x = self.act(x)\\n x = self.bn1(x)\\n x1 = self.fc2(x)\\n x1 = self.act(x1)\\n x1 = self.bn2(x1)\\n x1 = self.fc3(x1)\\n x1 = self.act(x1)\\n x1 = self.bn3(x1)\\n x = torch.add(x1,self.act(self.fc1_skip(x)))\\n x1 = self.fc4(x)\\n x1 = self.act(x1)\\n x1 = self.bn4(x1)\\n x1 = self.fc5(x1)\\n x1 = self.act(x1)\\n x1 = self.bn5(x1)\\n x = torch.add(x1,self.act(self.fc2_skip(x)))\\n x = self.fc6(x)\\n x = self.act(x)\\n x = self.bn6(x)\\n return torch.add(self.fc8_1(self.act(self.fc7_1(x))),self.fc8_2(self.act(self.fc7_2(x))) - torch.mean(self.fc8_2(self.act(self.fc7_2(x)))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef75533465394cff98e4f38ee8b87da1\",\n \"score\": \"0.59223974\",\n \"text\": \"def act(self, state):\\n action = np.zeros(self.action_size)\\n if np.random.rand() <= self.epsilon:\\n action[random.randrange(self.action_size)]=1\\n else:\\n state = state.reshape(1,self.state_size)\\n act_values = self.brain.predict(state)\\n action[np.argmax(act_values[0])] = 1\\n return action\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359cc813b316231a152502e83dc6f585\",\n \"score\": \"0.58734393\",\n \"text\": \"def act(self, state):\\n state = np.reshape(state, [-1, self.state_size])\\n action = self.actor_local.model.predict(state)[0]\\n actions = list(action + self.noise.sample()) # add some noise for exploration\\n #actions = list(action) # no noise for exploration\\n actions = np.maximum(actions, self.action_low)\\n actions = np.minimum(actions, self.action_high)\\n return actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cefa3e8e7750d39eaf74143397f1406\",\n \"score\": \"0.5870848\",\n \"text\": \"def hamiltonian(self):\\n return self.kineticmat() + self.potentialmat()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2684bd508eb3345dfcab6e3c76ab878\",\n \"score\": \"0.5867485\",\n \"text\": \"def process(state):\\n \\n ## normalized vector\\n return np.array([state[0]/(max(env.state_space)[0]), state[1]/(max(env.state_space)[1]), state[2]]).reshape((3,1))\\n \\n ## one-hot (dim = 280,1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100d19d84cea3e371fb5d96e6839cf35\",\n \"score\": \"0.58647317\",\n \"text\": \"def calc_eq_of_state(pyom, n):\\n density_args = (pyom, pyom.salt[..., n], pyom.temp[..., n], np.abs(pyom.zt))\\n\\n \\\"\\\"\\\"\\n calculate new density\\n \\\"\\\"\\\"\\n pyom.rho[..., n] = density.get_rho(*density_args) * pyom.maskT\\n\\n if pyom.enable_conserve_energy:\\n \\\"\\\"\\\"\\n calculate new dynamic enthalpy and derivatives\\n \\\"\\\"\\\"\\n pyom.Hd[..., n] = density.get_dyn_enthalpy(*density_args) * pyom.maskT\\n pyom.int_drhodT[..., n] = density.get_int_drhodT(*density_args)\\n pyom.int_drhodS[..., n] = density.get_int_drhodS(*density_args)\\n\\n \\\"\\\"\\\"\\n new stability frequency\\n \\\"\\\"\\\"\\n fxa = -pyom.grav / pyom.rho_0 / pyom.dzw[np.newaxis, np.newaxis, :-1] * pyom.maskW[:, :, :-1]\\n pyom.Nsqr[:, :, :-1, n] = fxa * (density.get_rho(pyom, pyom.salt[:,:,1:,n], pyom.temp[:,:,1:,n], np.abs(pyom.zt[:-1])) - pyom.rho[:,:,:-1,n])\\n pyom.Nsqr[:, :, -1, n] = pyom.Nsqr[:,:,-2,n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d2c0e6fcd90d2f4171a96ac0741f60\",\n \"score\": \"0.57848847\",\n \"text\": \"def test_state_dependent_exploration_grad():\\n n_states = 2\\n state_dim = 3\\n action_dim = 10\\n sigma_hat = th.ones(state_dim, action_dim, requires_grad=True)\\n # Reduce the number of parameters\\n # sigma_ = th.ones(state_dim, action_dim) * sigma_\\n # weights_dist = Normal(th.zeros_like(log_sigma), th.exp(log_sigma))\\n th.manual_seed(2)\\n weights_dist = Normal(th.zeros_like(sigma_hat), sigma_hat)\\n weights = weights_dist.rsample()\\n\\n state = th.rand(n_states, state_dim)\\n mu = th.ones(action_dim)\\n noise = th.mm(state, weights)\\n\\n action = mu + noise\\n\\n variance = th.mm(state**2, sigma_hat**2)\\n action_dist = Normal(mu, th.sqrt(variance))\\n\\n # Sum over the action dimension because we assume they are independent\\n loss = action_dist.log_prob(action.detach()).sum(dim=-1).mean()\\n loss.backward()\\n\\n # From Rueckstiess paper: check that the computed gradient\\n # correspond to the analytical form\\n grad = th.zeros_like(sigma_hat)\\n for j in range(action_dim):\\n # sigma_hat is the std of the gaussian distribution of the noise matrix weights\\n # sigma_j = sum_j(state_i **2 * sigma_hat_ij ** 2)\\n # sigma_j is the standard deviation of the policy gaussian distribution\\n sigma_j = th.sqrt(variance[:, j])\\n for i in range(state_dim):\\n # Derivative of the log probability of the jth component of the action\\n # w.r.t. the standard deviation sigma_j\\n d_log_policy_j = (noise[:, j] ** 2 - sigma_j**2) / sigma_j**3\\n # Derivative of sigma_j w.r.t. sigma_hat_ij\\n d_log_sigma_j = (state[:, i] ** 2 * sigma_hat[i, j]) / sigma_j\\n # Chain rule, average over the minibatch\\n grad[i, j] = (d_log_policy_j * d_log_sigma_j).mean()\\n\\n # sigma.grad should be equal to grad\\n assert sigma_hat.grad.allclose(grad)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b4036704b90211b08319d6f2ff190e\",\n \"score\": \"0.5756763\",\n \"text\": \"def inv_chol(self, state):\\n return self.lmult_inv_chol(state, np.eye(state.mom.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9925893a3fb16894a61d097ea3da8225\",\n \"score\": \"0.5751316\",\n \"text\": \"def state_symmetry_exploit(state):\\n player_id, board = np.split(state, [1])\\n assert bool(player_id)\\n return board*player_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e36e221f33fb516ebb392e5bc61ca1ab\",\n \"score\": \"0.5737062\",\n \"text\": \"def formulate_state(state):\\n\\n # Initalize all necessary infomration\\n arena = state['arena'].copy()\\n crates_arena = np.maximum(arena, 0)\\n for (cx, cy) in state['coins']:\\n crates_arena[cx, cy] = 2\\n crates_arena = crates_arena.T\\n\\n x, y, _, bombs_left, _ = state['self']\\n bombs = state['bombs']\\n others = [(xo, yo) for (xo, yo, _, _, _) in state['others']]\\n\\n # First Information: Direction to nearest coin\\n diglist = list()\\n if len(state['coins']) == 0:\\n diglist.append(0)\\n else:\\n closest_coin = sorted(state['coins'], key=lambda k: abs(k[0] - x) + abs(k[1] - y))[0]\\n best_orientation = np.argmin([(closest_coin[0] - mx)**2 + (closest_coin[1] - my)**2 for (mx, my) in\\n [(x, y-1), (x-1, y-1), (x-1, y), (x-1, y+1),\\n (x, y+1), (x+1, y+1), (x+1, y), (x+1, y-1)]]) + 1\\n diglist.append(best_orientation)\\n\\n # Second Information: Direction to more crates\\n if np.sum(crates_arena) == 0:\\n diglist.append(0)\\n else:\\n q1map = np.sum(crates_arena[1:6, 1:6])\\n q2map = np.sum(crates_arena[1:6, 6:11])\\n q3map = np.sum(crates_arena[1:6, 11:16])\\n q4map = np.sum(crates_arena[6:11, 1:6])\\n q5map = np.sum(crates_arena[6:11, 6:11])\\n q6map = np.sum(crates_arena[6:11, 11:16])\\n q7map = np.sum(crates_arena[11:16, 1:6])\\n q8map = np.sum(crates_arena[11:16, 6:11])\\n q9map = np.sum(crates_arena[11:16, 11:16])\\n diglist.append(np.argmax([q1map, q2map, q3map, q4map, q5map, q6map, q7map, q8map, q9map]) + 1)\\n\\n # Thrid Information: Direction to nearest opponent\\n if len(state['others']) == 0:\\n diglist.append(0)\\n else:\\n closest_p = sorted(state['others'], key=lambda k: abs(k[0] - x) + abs(k[1] - y))[0]\\n closest_orientation = np.argmin([abs(closest_p[0] - mx) + abs(closest_p[1] - my) for (mx, my) in\\n [(x, y - 1), (x - 1, y - 1), (x - 1, y), (x - 1, y + 1), (x, y + 1),\\n (x + 1, y + 1), (x + 1, y), (x + 1, y - 1)]]) + 1\\n diglist.append(closest_orientation)\\n\\n # Fourth Information: Number of bombs available\\n diglist.append(bombs_left)\\n\\n # 5-45th Information: Information in the 4-step vision\\n for (i, j) in [( 0, -4), (-1, -3), ( 0, -3), ( 1, -3),\\n (-2, -2), (-1, -2), ( 0, -2), ( 1, -2), ( 2, -2),\\n (-3, -1), (-2, -1), (-1, -1), ( 0, -1), ( 1, -1),\\n ( 2, -1), ( 3, -1), (-4, 0), (-3, 0), (-2, 0),\\n (-1, 0), (0 , 0), ( 1, 0), ( 2, 0), ( 3, 0),\\n ( 4, 0), (-3, 1), (-2, 1), (-1, 1), ( 0, 1),\\n ( 1, 1), ( 2, 1), ( 3, 1), (-2, 2), (-1, 2),\\n ( 0, 2), ( 1, 2), ( 2, 2), (-1, 3), ( 0, 3),\\n ( 1, 3), ( 0, 4)]:\\n\\n if (x + i) < 0 or (x + i) > 16 or (y + j) < 0 or (y + j) > 16:\\n diglist.append(0)\\n elif (x + i, y + j) in state['coins']:\\n diglist.append(300)\\n elif state['explosions'][x + i, y + j] == 1:\\n diglist.append(10)\\n elif state['explosions'][x + i, y + j] == 2:\\n diglist.append(9)\\n elif (x + i, y + j, 4) in bombs:\\n if (x + i, y + j) in others:\\n diglist.append(40)\\n else:\\n diglist.append(4)\\n elif (x + i, y + j, 3) in bombs:\\n if (x + i, y + j) in others:\\n diglist.append(50)\\n else:\\n diglist.append(5)\\n elif (x + i, y + j, 2) in bombs:\\n if (x + i, y + j) in others:\\n diglist.append(60)\\n else:\\n diglist.append(6)\\n elif (x + i, y + j, 1) in bombs:\\n if (x + i, y + j) in others:\\n diglist.append(70)\\n else:\\n diglist.append(7)\\n elif (x + i, y + j, 0) in bombs:\\n if (x + i, y + j) in others:\\n diglist.append(80)\\n else:\\n diglist.append(8)\\n elif (x + i, y + j) in others:\\n diglist.append(100)\\n else:\\n diglist.append(arena[x + i, y + j] + 1) # 0, 1, 2\\n\\n state = np.array(diglist)\\n return state.reshape((1, 45))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ce22e2f65d211f2d3efb91ab50f002\",\n \"score\": \"0.57361174\",\n \"text\": \"def construct_H(state_bra,state_ket):\\n states,coefs=Hint(state_ket)\\n res = 0\\n for i in range(len(states)):\\n if (state_bra==states[i]):\\n res = res+ coefs[i]\\n return 0.5*res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793ad6bfa6bc966e871e36ac947b51cc\",\n \"score\": \"0.5724917\",\n \"text\": \"def likelihood_and_decoding_hmm(*args,**kwargs):\\n num_of_states = kwargs['num_of_states']\\n time_grid = kwargs['time_grid']\\n num_of_steps = len(time_grid) # |W| - 1\\n log_alphas = np.zeros((num_of_steps,num_of_states),dtype=np.float)\\n log_viterbi = np.zeros((num_of_steps,num_of_states),dtype=np.float)\\n backpointers = np.zeros((num_of_steps),dtype=np.float)\\n init_probs = kwargs['init_probs'] # distribution over initial states\\n pi = kwargs['pi'] # state transition; \\\"parameters\\\" HMM (rows x cols ~ origin x destination)\\n e = kwargs['e'] # list of functions size (num_of_states); likelihood of obs from each state\\n O = kwargs['O'] # list of observations size (num_of_steps)\\n\\n # init forward pass\\n time_next = time_grid[0] # the first point in the time_grid, |W|, is _not_ zero in general.\\n # but it is zero currently.... not great.\\n for state_idx in range(num_of_states):\\n obs = [O[0,time_next],time_next]\\n log_alphas[0,state_idx] = np.ma.log([init_probs.l(state_idx)]).filled(-np.infty) +\\\\\\n np.ma.log([e(obs)[state_idx]]).filled(-np.infty)\\n log_viterbi[0,state_idx] = np.ma.log([init_probs.l(state_idx)]).filled(-np.infty) +\\\\\\n np.ma.log([e(obs)[state_idx]]).filled(-np.infty)\\n \\n \\\"\\\"\\\"\\n The augmented state space is actually the issue; we don't need to iterate over delta_w_enum\\n What we actually need is for the augmented state space to _include_ all the different\\n enumerations of delta_w. E.g. NOT [STATE,GRID] rather [STATE,DELTA_W]. \\n I do ~not~ think this should impact \\\"smjp_transition()\\\", but let's watch out.\\n \\\"\\\"\\\"\\n # run the forward\\n # print(\\\"---\\\")\\n index_range = range(1,len(time_grid))\\n print(len(index_range),len(time_grid[1:-1]),len(time_grid[0:-1]),len(time_grid))\\n for alpha_index,time in zip(index_range,time_grid[0:-1]): # {w_0,...,w_{|W|-1}}\\n # print(alpha_index)\\n time_n = time_grid[alpha_index] # update the next time.\\n delta_w = time_n - time \\n delta_w_l = [delta_w]\\n # delta_w_enumeration = create_delta_w_enumeration(time_n,time_grid)\\n for delta_w in delta_w_l: # todo: remove this loop\\n obs = [O[time,time_n],time_n]\\n for state_idx in range(num_of_states):\\n for state_prime_idx in range(num_of_states):\\n #print(pi(delta_w)[state_prime_idx,state_idx],e(obs)[state_idx])\\n # HMM: pi(t,t_next) = pi; does not depend on time difference\\n # sMJP: pi(t,t_next) =/= pi; depends on time difference\\n log_alpha = log_alphas[alpha_index-1,state_prime_idx]\\n log_transition = pi([time,time_n])[state_prime_idx,state_idx]\\n log_obs = np.ma.log([e(obs)[state_idx]]).filled(-np.infty)[0]\\n #print(log_alpha,log_transition,log_obs)\\n alpha_new = np.exp(log_alpha + log_transition + log_obs)\\n if alpha_index == index_range[-1] and state_prime_idx == 0 and state_idx in [0,1] and False:\\n print(log_alpha,log_transition,log_obs)\\n print(np.sum([log_alpha,log_transition,log_obs]))\\n\\n #print(alpha_new)\\n if state_prime_idx == 0 and state_idx == 1 and alpha_index > 2 and False:\\n print(\\\"[[state_prime_idx == 0 and state_idx == 1]]\\\")\\n print(log_alpha,log_transition,log_obs)\\n print(log_alphas[alpha_index-1,:])\\n print(log_alphas[alpha_index-1,state_prime_idx])\\n print('dw',delta_w,time,time_n)\\n print(pi.state_space[state_prime_idx])\\n print(pi.state_space[state_idx])\\n print(\\\"==========================\\\")\\n exit()\\n if do_we_thin(pi.state_space,state_prime_idx,state_idx,delta_w) and False:\\n print(pi.state_space)\\n print(alpha_index-1)\\n print(log_alphas[alpha_index-1,:])\\n print(state_prime_idx,state_idx)\\n print(log_alpha)\\n print(\\\"THN\\\")\\n v,l = pi.state_space[state_prime_idx]\\n vp,lp = pi.state_space[state_idx]\\n print(log_alphas[alpha_index-1,state_prime_idx])\\n print(delta_w)\\n print(pi.state_space[state_prime_idx])\\n print(pi.state_space[state_idx])\\n if np.any(np.isnan(alpha_new)):\\n print(\\\"found a nan\\\")\\n quit()\\n #print('alpha_new',alpha_new,log_alpha,state_idx)\\n log_alphas[alpha_index,state_idx] += alpha_new\\n log_alphas[alpha_index,:] = np.ma.log([log_alphas[alpha_index,:]]).filled(-np.infty)\\n # print(log_alphas)\\n # exit()\\n # print(alpha_index,time)\\n # print(time,alphas[time,:])\\n backpath = None # trace_backward_path(backpointers)\\n \\n # print(log_alphas)\\n # print(len(index_range),len(time_grid[1:-1]),len(time_grid[0:-1]),len(time_grid))\\n # print(time_grid)\\n # exit()\\n backpath = None\\n output_prob = np.sum(np.exp(log_alphas[-1,:]))\\n \\n return log_alphas,output_prob,log_viterbi,backpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e97c50e1486c399512fd98baac5ade\",\n \"score\": \"0.5722083\",\n \"text\": \"def produce_action_and_action_info(self, state):\\n actor_output = self.actor_local(state)\\n mean, log_std = actor_output[:, :self.action_size], actor_output[:, self.action_size:]\\n std = log_std.exp()\\n normal = Normal(mean, std)\\n x_t = normal.rsample() #rsample means it is sampled using reparameterisation trick\\n action = torch.tanh(x_t)\\n log_prob = normal.log_prob(x_t)\\n log_prob -= torch.log(1 - action.pow(2) + 1e-6)\\n log_prob = log_prob.sum(1, keepdim=True)\\n return action, log_prob, torch.tanh(mean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6516b51969a54d09829e27c15134f1ab\",\n \"score\": \"0.57211965\",\n \"text\": \"def hamiltonian(n):\\n N = 2**n\\n binaryFactor = np.array([1, 2, 4])\\n row = []\\n col = []\\n value = []\\n for j in range(n-1):\\n for i in range(N):\\n spin1 = (i % binaryFactor[1]) // binaryFactor[0]\\n spin2 = (i % binaryFactor[2]) // binaryFactor[1]\\n if spin1 != spin2:\\n row.append(i)\\n col.append(i)\\n value.append(-1)\\n \\n row.append(i)\\n value.append(2)\\n if spin1 == 1:\\n col.append((i + binaryFactor[0]) % N)\\n else:\\n col.append((i - binaryFactor[0]) % N)\\n else:\\n row.append(i)\\n col.append(i)\\n value.append(1)\\n binaryFactor *= 2\\n return scipy.sparse.coo_matrix((value, (row, col)), shape = (N, N), dtype = np.float64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bdcb0ad63e10f92e1349c816964aeb\",\n \"score\": \"0.5719273\",\n \"text\": \"def nextState(self):\\n newstate = np.zeros(self.N, dtype=int)\\n i = 0\\n for adj in self.adjmat:\\n input_ind = np.where(adj == 1)\\n inputs = [self.state[ind] for ind in input_ind[0]]\\n if np.random.rand() < self.LUT[tuple(inputs)]:\\n newstate[i] = 0\\n else:\\n newstate[i] = 1\\n i += 1\\n return newstate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f8eff0394c3192c08efb19daa74409\",\n \"score\": \"0.57086587\",\n \"text\": \"def call(self, state):\\n z = tf.cast(state, tf.float64)\\n h1 = tf.nn.leaky_relu(tf.matmul(z, self.w1) + self.b1)\\n h2 = tf.nn.relu(tf.matmul(h1, self.w2) + self.b2)\\n \\n h2 = tf.nn.relu(tf.matmul(h2, self.w21) + self.b21)\\n h2 = tf.nn.relu(tf.matmul(h2, self.w22) + self.b22)\\n \\n if self.deeper:\\n h2 = tf.nn.relu(tf.matmul(h2, self.w23) + self.b23)\\n h2 = tf.nn.relu(tf.matmul(h2, self.w24) + self.b24)\\n \\n out = (tf.matmul(h2, self.w3) + self.b3) #/10\\n # out = - tf.math.log(1.01 + tf.exp(out))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c62d308827bc5056be49da43a36ce1e\",\n \"score\": \"0.5694486\",\n \"text\": \"def forward(self, state):\\n x = state\\n x = self.hidden_activation(self.fc0(x))\\n state_value = self.hidden_activation(self.fc1_s(x))\\n state_value = self.fc2_s(state_value)\\n advantage_values = self.hidden_activation(self.fc1_a(x))\\n advantage_values = self.fc2_a(advantage_values)\\n q = state_value + advantage_values - advantage_values.mean(dim=1, keepdim=True)\\n\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d9d36d9a411ebe08b75567bee5c300\",\n \"score\": \"0.5693643\",\n \"text\": \"def call(self, inputs, state):\\n gate_inputs = tf.matmul(\\n tf.concat([inputs, state], 1), self._gate_kernel)\\n gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias)\\n\\n if self._layer_norm:\\n gate_inputs = common_layers.split_last_dim(gate_inputs, 3)\\n mean = tf.reduce_mean(gate_inputs, axis=[-1], keepdims=True)\\n variance = tf.reduce_mean(tf.square(gate_inputs - mean), axis=[-1], keepdims=True)\\n norm_x = (gate_inputs - mean) * tf.rsqrt(variance + self._ln_epsilon)\\n norm_x = common_layers.combine_last_two_dims(norm_x)\\n gate_inputs = norm_x * self._ln_scale + self._ln_bias\\n\\n value = tf.sigmoid(gate_inputs)\\n r, u, l = tf.split(value=value, num_or_size_splits=3, axis=1)\\n\\n r_state = r * state\\n\\n candidate = tf.matmul(\\n tf.concat([inputs, r_state], 1), self._candidate_kernel)\\n candidate = tf.nn.bias_add(candidate, self._candidate_bias)\\n\\n c = self._activation(candidate)\\n c += l * tf.matmul(inputs, self._linear_kernel)\\n if self._dropout_rate:\\n c = tf.nn.dropout(c, keep_prob=1-self._dropout_rate)\\n new_h = u * state + (1 - u) * c\\n return new_h, new_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966c4f025e93f8e5a8db02bea7be46c8\",\n \"score\": \"0.5692773\",\n \"text\": \"def hamiltonian(self, state, time, value, grad_value):\\n del time, value # unused\\n control = self.optimal_control(state, grad_value)\\n return grad_value @ self.dynamics(state, control)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61d7e95f8edaf1a53f2cbc094b743bc\",\n \"score\": \"0.5683809\",\n \"text\": \"def binary_mera_energy(hamiltonian, state, isometry, disentangler):\\n backend = \\\"jax\\\"\\n\\n out = []\\n for dirn in ('left', 'right'):\\n iso_c = tensornetwork.Node(isometry, backend=backend)\\n iso_r = tensornetwork.Node(isometry, backend=backend)\\n\\n iso_l_con = tensornetwork.linalg.node_linalg.conj(iso_l)\\n #iso_c_con = tensornetwork.linalg.node_linalg.conj(iso_c)\\n iso_r_con = tensornetwork.linalg.node_linalg.conj(iso_r)\\n\\n op = tensornetwork.Node(hamiltonian, backend=backend)\\n rho = tensornetwork.Node(state, backend=backend)\\n\\n un_l = tensornetwork.Node(disentangler, backend=backend)\\n un_l_con = tensornetwork.linalg.node_linalg.conj(un_l)\\n\\n un_r = tensornetwork.Node(disentangler, backend=backend)\\n un_r_con = tensornetwork.linalg.node_linalg.conj(un_r)\\n\\n tensornetwork.connect(iso_l[2], rho[0])\\n tensornetwork.connect(iso_c[2], rho[1])\\n tensornetwork.connect(iso_r[2], rho[2])\\n\\n tensornetwork.connect(iso_l[0], iso_l_con[0])\\n tensornetwork.connect(iso_l[1], un_l[2])\\n tensornetwork.connect(iso_c[0], un_l[3])\\n tensornetwork.connect(iso_c[1], un_r[2])\\n tensornetwork.connect(iso_r[0], un_r[3])\\n tensornetwork.connect(iso_r[1], iso_r_con[1])\\n\\n if dirn == 'right':\\n tensornetwork.connect(un_l[0], un_l_con[0])\\n tensornetwork.connect(un_l[1], op[3])\\n tensornetwork.connect(un_r[0], op[4])\\n tensornetwork.connect(un_r[1], op[5])\\n tensornetwork.connect(op[0], un_l_con[1])\\n tensornetwork.connect(op[1], un_r_con[0])\\n tensornetwork.connect(op[2], un_r_con[1])\\n elif dirn == 'left':\\n tensornetwork.connect(un_l[0], op[3])\\n tensornetwork.connect(un_l[1], op[4])\\n tensornetwork.connect(un_r[0], op[5])\\n tensornetwork.connect(un_r[1], un_r_con[1])\\n tensornetwork.connect(op[0], un_l_con[0])\\n tensornetwork.connect(op[1], un_l_con[1])\\n tensornetwork.connect(op[2], un_r_con[0])\\n\\n tensornetwork.connect(un_l_con[2], iso_l_con[1])\\n tensornetwork.connect(un_l_con[3], iso_c_con[0])\\n tensornetwork.connect(un_r_con[2], iso_c_con[1])\\n tensornetwork.connect(un_r_con[3], iso_r_con[0])\\n\\n #tensornetwork.connect(iso_l_con[2], rho[3])\\n tensornetwork.connect(iso_c_con[2], rho[4])\\n tensornetwork.connect(iso_r_con[2], rho[5])\\n\\n # FIXME: Check that this is giving us a good path!\\n out.append(\\n contractors.branch(tensornetwork.reachable(rho),\\n nbranch=2).get_tensor())\\n\\n return 0.5 * sum(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651ee5c4589e0381a6652cfc29f180ca\",\n \"score\": \"0.56658435\",\n \"text\": \"def forward(self, state):\\n \\n h_relu1 = F.relu(self.fc1(state))\\n h_relu2 = F.relu(self.fc2(h_relu1))\\n yhat = self.fc3(h_relu2)\\n return yhat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dff52b429a3171a910ecd3c9c529c69\",\n \"score\": \"0.5662271\",\n \"text\": \"def random_action(state):\\n cible = [int(random.uniform(0, w-1)), int(random.uniform(0, h-1))]\\n dist = Env.dist(state,cible)\\n if dist == 0 :\\n dist = 1\\n norm = min(100,dist)\\n return [int(norm/dist*(cible[0]-state[0])),int(norm/dist*(cible[1]-state[1]))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6903789fa0af81b1bc4d1dfc8d497914\",\n \"score\": \"0.56595314\",\n \"text\": \"def get_action_features(phi_state, action, n_actions):\\n if len(phi_state.shape) > 1:\\n assert phi_state.shape[0] == action.shape[0]\\n\\n phi = np.ones((phi_state.shape[0], n_actions * phi_state[0].size))\\n i = 0\\n for s, a in zip(phi_state, action):\\n start = s.size * int(a[0])\\n stop = start + s.size\\n\\n phi_sa = np.zeros(n_actions * s.size)\\n phi_sa[start:stop] = s\\n\\n phi[i] = phi_sa\\n\\n i += 1\\n else:\\n start = phi_state.size * action[0]\\n stop = start + phi_state.size\\n\\n phi = np.zeros(n_actions * phi_state.size)\\n phi[start:stop] = phi_state\\n\\n return phi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e40a7f569426fba704921e3ab3c635\",\n \"score\": \"0.5657275\",\n \"text\": \"def phi(self, state, action):\\n state_ = self._domain.states[state]\\n return np.array([self._feature_puddle(state_),\\n self._feature_goal_distance(state_)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83218e52feb5a7fbbe502c234abea26c\",\n \"score\": \"0.56513274\",\n \"text\": \"def act(self, state):\\n return self.brain.predict_ensemble(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"537b3d8dbada89e8c081ee8316d4ac87\",\n \"score\": \"0.5630806\",\n \"text\": \"def groundstate(H):\\n \\\"\\\"\\\"find the gs of a Hamiltonian matrix\\\"\\\"\\\"\\n Hinv = H.I\\n E, wf = sparse.linalg.eigsh(H, k = 1)\\n return 1/E, wf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1edd0af8ac092bb0c7bb7fb9573ae0c0\",\n \"score\": \"0.5627552\",\n \"text\": \"def act_target(self, state):\\n state = np.reshape(state, [-1, self.state_size])\\n action = self.actor_target.model.predict(state)[0]\\n #actions = list(action + self.noise.sample()) # add some noise for exploration\\n actions = list(action) # no noise for exploration\\n return actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893b7d3ead52d4d28f9514e003ec8c20\",\n \"score\": \"0.56271374\",\n \"text\": \"def act(self, state):\\n if np.random.rand() <= self.epsilon:\\n return np.random.choice(self.action_size)\\n act_values = self.model.predict(state)\\n\\n return np.argmax(act_values[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d19c1e930f0816aa24b6d4e78ac6fb7\",\n \"score\": \"0.56209517\",\n \"text\": \"def call(self, inputs, state):\\n # Using Attention Wrapper\\n print(\\\"self.input_depth:\\\",self.input_depth)\\n inputs, context = array_ops.split(value=inputs, num_or_size_splits=[int(self.input_depth), self._num_units], axis=-1)\\n #_, _, state = array_ops.split(value=state, num_or_size_splits=[int(self.input_depth), self._num_units, self._num_units], axis=-1)\\n \\n # First round\\n gate_inputs = math_ops.matmul(\\n array_ops.concat([inputs, state], 1), self._gate_kernel_1)\\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias_1)\\n\\n value = math_ops.sigmoid(gate_inputs)\\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\\n\\n r_state = r * state\\n\\n candidate = math_ops.matmul(\\n array_ops.concat([inputs, r_state], 1), self._candidate_kernel_1)\\n candidate = nn_ops.bias_add(candidate, self._candidate_bias_1)\\n\\n c = self._activation(candidate)\\n new_h_prime = u * state + (1 - u) * c\\n \\n # Final round\\n\\n gate_inputs = math_ops.matmul(\\n array_ops.concat([context, new_h_prime], 1), self._gate_kernel_2)\\n gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias_2)\\n\\n value = math_ops.sigmoid(gate_inputs)\\n r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)\\n\\n r_state = r * new_h_prime\\n\\n candidate = math_ops.matmul(\\n array_ops.concat([context, r_state], 1), self._candidate_kernel_2)\\n candidate = nn_ops.bias_add(candidate, self._candidate_bias_2)\\n\\n c = self._activation(candidate)\\n new_h = u * new_h_prime + (1 - u) * c\\n\\n return new_h, new_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38dfe1a26dc675e1c0401065753cb1c6\",\n \"score\": \"0.5617738\",\n \"text\": \"def call(self, inputs, state):\\n sigmoid = math_ops.sigmoid\\n add = math_ops.add\\n multiply = math_ops.multiply\\n\\n transformed_input = math_ops.matmul(inputs, self._kernel)\\n transformed_input = nn_ops.bias_add(transformed_input, self._bias)\\n in_all = array_ops.split(value=transformed_input, num_or_size_splits=self._k_depth, axis=1)\\n\\n h = state[0]\\n y = h\\n for idx in range(self._k_depth):\\n hy = math_ops.matmul(y, self._U_dict[idx])\\n y = sigmoid(in_all[idx] + hy)\\n\\n new_h = self._activation(add(h, y))\\n new_state = RRNStateTuple(new_h)\\n\\n return new_h, new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54439a91aa40e49339a02dc35813b2fc\",\n \"score\": \"0.5617707\",\n \"text\": \"def ComputeInteractionState(self, p_int, p_int_1, p_int_2):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54439a91aa40e49339a02dc35813b2fc\",\n \"score\": \"0.5617707\",\n \"text\": \"def ComputeInteractionState(self, p_int, p_int_1, p_int_2):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30cc5c9983124e06c8afce2afc2e92a\",\n \"score\": \"0.5616494\",\n \"text\": \"def Viterbi(all_possible_hidden_states,\\r\\n all_possible_observed_states,\\r\\n prior_distribution,\\r\\n transition_model,\\r\\n observation_model,\\r\\n observations):\\r\\n\\r\\n # TODO: Write your code here\\r\\n \\r\\n num_time_steps = len(observations)\\r\\n w = [None] * num_time_steps\\r\\n phi = [None] * (num_time_steps-1)\\r\\n estimated_hidden_states = [None] * num_time_steps\\r\\n \\r\\n # computing the transition matrix p(zn|zn-1)\\r\\n transition_matrix = rover.Distribution()\\r\\n \\r\\n # computing the observation matrix p(x,y | z)\\r\\n observation_matrix = rover.Distribution()\\r\\n \\r\\n for state in all_possible_hidden_states:\\r\\n transition_matrix[state] = transition_model(state)\\r\\n \\r\\n observation_matrix[state] = observation_model(state)\\r\\n\\r\\n # initial w(z1)\\r\\n initial_post = observation_matrix[ ( (observations[0]) + (\\\"stay\\\",) ) ]\\r\\n \\r\\n result = rover.Distribution()\\r\\n \\r\\n for state in initial_post:\\r\\n result[ (state) + (\\\"stay\\\",) ] = np.log(initial_post[state] * prior_distribution[state + (\\\"stay\\\",)])\\r\\n \\r\\n\\r\\n w[0] = result\\r\\n \\r\\n \\r\\n \\r\\n # loop\\r\\n for i in range(1, num_time_steps):\\r\\n \\r\\n w[i] = rover.Distribution()\\r\\n \\r\\n phi[i-1] = rover.Distribution()\\r\\n \\r\\n # loop through all states possible\\r\\n \\r\\n # i is zn and i-1 is zn-1\\r\\n \\r\\n # get the possible states of zn\\r\\n for zn in all_possible_hidden_states:\\r\\n \\r\\n # set to large negative number\\r\\n inner_term = -90000000\\r\\n \\r\\n # loop through zn-1 values\\r\\n for zn1 in w[i-1]:\\r\\n \\r\\n # probability p(z_n | z_n-1)\\r\\n probZZ = transition_matrix[zn1]\\r\\n \\r\\n # check if in the p(zn|zn-1)\\r\\n if zn not in probZZ:\\r\\n continue\\r\\n \\r\\n if w[i-1][zn1] == 0:\\r\\n continue\\r\\n\\r\\n \\r\\n # max term of forward message \\r\\n if (np.log(probZZ[zn]) + w[i-1][zn1] > inner_term):\\r\\n inner_term = np.log(probZZ[zn]) + w[i-1][zn1]\\r\\n phi[i-1][zn] = zn1\\r\\n \\r\\n \\r\\n # once gone through all values of z_n-1, multiply by p(x|z_n)\\r\\n post_xy = observation_model(zn)\\r\\n \\r\\n if (observations[i] == None):\\r\\n probxy = 1\\r\\n else:\\r\\n if observations[i] not in post_xy:\\r\\n continue\\r\\n probxy = post_xy[observations[i]]\\r\\n \\r\\n \\r\\n \\r\\n result = np.log(probxy) + inner_term\\r\\n \\r\\n if (inner_term != -90000000):\\r\\n w[i][zn] = result\\r\\n \\r\\n # find max z_n\\r\\n maximum = -90000\\r\\n arg_max = None\\r\\n \\r\\n estimated_hidden_states[-1] = rover.Distribution()\\r\\n \\r\\n for key in w[-1]:\\r\\n if w[-1][key] > maximum:\\r\\n arg_max = key\\r\\n maximum = w[-1][key]\\r\\n \\r\\n estimated_hidden_states[-1] = arg_max\\r\\n \\r\\n\\r\\n # backtrack\\r\\n for i in range(num_time_steps-2, -1, -1):\\r\\n estimated_hidden_states[i] = rover.Distribution()\\r\\n \\r\\n estimated_hidden_states[i] = phi[i][arg_max]\\r\\n \\r\\n # next term used to backtrack\\r\\n arg_max = phi[i][arg_max]\\r\\n \\r\\n \\r\\n return estimated_hidden_states\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1527e8d7df30709aee57f1fe8bfaf88\",\n \"score\": \"0.5615736\",\n \"text\": \"def act(self, state: np.ndarray): \\n state = tupleTensor(state)\\n \\n with torch.no_grad():\\n outputs,self.dqn_hidden_state,self.dqn_cell_state =\\\\\\n self.dqn(state,self.dqn_hidden_state,self.dqn_cell_state)\\n action = torch.argmax(outputs)\\n return action.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a564bc5a1453914d4e404d12321c31\",\n \"score\": \"0.5614487\",\n \"text\": \"def act(self, state):\\n\\n state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)\\n probs, embed = self.forward(state)\\n probs = probs.cpu()\\n embed = embed.cpu()\\n m = Categorical(probs)\\n action = m.sample()\\n return action.item(), m.log_prob(action), embed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02da9e841926f3df293097e7a5faf2d8\",\n \"score\": \"0.56090236\",\n \"text\": \"def transition(self, state):\\n #Find the label for the state\\n label = self.dict[state]\\n #For when I redfine the dictionary in the other part of the lab\\n if type(label) is int:\\n# print(self.mat[label])\\n# print(state)\\n result = np.random.multinomial(1, self.mat[:,label])\\n index = np.argmax(result)\\n for l, i in self.dict.items():\\n if index == i:\\n return l\\n \\n #For when I don't need to redefine the dictionary\\n for s in label:\\n #Transition\\n result = np.random.multinomial(1, self.mat[:,s])\\n index = np.argmax(result)\\n #Check where the transition took me\\n for l, i in self.dict.items():\\n if index in i:\\n return l\\n \\n \\n raise NotImplementedError(\\\"Problem 2 Incomplete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e77c78593475d34dbc7fd6b833fe62\",\n \"score\": \"0.559867\",\n \"text\": \"def _dense_ham_term(H):\\n h1, (h2L, h2R) = H\\n D = h1.shape[0]\\n dtype = h1.dtype\\n\\n E = backend.eye(D, dtype=dtype)\\n\\n h = backend.ncon([h1, E], [(-1, -3), (-2, -4)])\\n for (hl, hr) in zip(h2L, h2R):\\n h += backend.ncon([hl, hr], [(-1, -3), (-2, -4)])\\n\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c9f4ea47f07462371de3763b127fc4\",\n \"score\": \"0.5591284\",\n \"text\": \"def act(self, state):\\n if np.random.rand() < self.epsilon:\\n return self.action_space.sample() #sample() is a function of spaces.Discrete()\\n else:\\n state_interval = self.make_interval(state)\\n\\n q_values = self.q_network.predict(state_interval)\\n action = np.argmax(q_values[0])\\n return action\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c29856a7dc483236b4127bae92ae11\",\n \"score\": \"0.5582921\",\n \"text\": \"def actions(self, state):\\n sick = []\\n healthy = []\\n for i, line in enumerate(state):\\n for j, (value, _) in enumerate(line):\\n if value == 'S':\\n sick.append((Q_action, (i, j)))\\n elif value == 'H':\\n healthy.append((V_action, (i, j)))\\n\\n sick_permutations = list(itertools.combinations(sick, min(self.police, len(sick))))\\n healthy_permutations = list(itertools.combinations(healthy, min(self.medics, len(healthy))))\\n actions = [sick_perm + healthy_perm\\n for sick_perm in sick_permutations\\n for healthy_perm in healthy_permutations]\\n return actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed11b945f14fb39bcc7a2e7888525efc\",\n \"score\": \"0.5581947\",\n \"text\": \"def __call__(self, inputs, state, scope=None):\\n with tf.variable_scope(scope or type(self).__name__):\\n c, h = state\\n\\n # change bias argument to False since LN will add bias via shift\\n concat = tf.nn.rnn_cell._linear([inputs, h], 4 * self._num_units, False)\\n\\n i, j, f, o = tf.split(1, 4, concat)\\n\\n # add layer normalization to each gate\\n i = ln(i, scope = 'i/')\\n j = ln(j, scope = 'j/')\\n f = ln(f, scope = 'f/')\\n o = ln(o, scope = 'o/')\\n\\n new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) *\\n self._activation(j))\\n\\n # add layer_normalization in calculation of new hidden state\\n new_h = self._activation(ln(new_c, scope = 'new_h/')) * tf.nn.sigmoid(o)\\n new_state = tf.nn.rnn_cell.LSTMStateTuple(new_c, new_h)\\n\\n return new_h, new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85ff62fff976096c3a0516060ae9662\",\n \"score\": \"0.5581648\",\n \"text\": \"def ham_ising():\\n E = np.array([[1, 0], [0, 1]])\\n X = np.array([[0, 1], [1, 0]])\\n Z = np.array([[1, 0], [0, -1]])\\n # hmat = np.kron(X, np.kron(Z, X))\\n hmat -= 0.5 * (np.kron(np.kron(X, X), E) + np.kron(E, np.kron(X, X)))\\n return np.reshape(hmat, [2] * 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04116fb31389501f4d6b3b71b20f0eba\",\n \"score\": \"0.5579655\",\n \"text\": \"def lift_state(self):\\n init_state = self.x0\\n ex0 = np.array(self.x0)\\n for mu in self.centers:\\n init_state.append(logistic(np.linalg.norm(ex0 - mu)**2, self.alpha))\\n return np.hstack([np.array([1]), init_state])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbd4c21e63889886f3afd106d678c0a\",\n \"score\": \"0.55784917\",\n \"text\": \"def no_actions_first_and_second_clauses(states_encoding, t):\\n\\n if t != 1 and t != 2:\\n print(\\\"timestamp does not match clauses generation method\\\")\\n return\\n\\n formula = []\\n formula += unique_state_clauses(states_encoding, t)\\n H = states_encoding[\\\"H\\\"]\\n S = states_encoding[\\\"S\\\"]\\n U = states_encoding[\\\"U\\\"]\\n\\n X = H.shape[0]\\n Y = H.shape[1]\\n for x in range(X):\\n for y in range(Y):\\n\\n # unpopulated[t] ==> unpopulated[t-1]\\n # unpopulated[t-1] ==> unpopulated[t]\\n formula.append([-U[x, y, t], U[x, y, t-1]])\\n formula.append([-U[x, y, t-1], U[x, y, t]])\\n\\n # healthy[t] ==> healthy[t-1]\\n formula.append([-H[x, y, t], H[x, y, t-1]])\\n\\n # sick[t-1] ==> sick[t]\\n formula.append([-S[x, y, t-1], S[x, y, t]])\\n # sick[t] ==> sick[t-1] | healthy[t-1]\\n formula.append([-S[x, y, t], S[x, y, t-1], H[x, y, t-1]])\\n\\n adjacent_neighbors = neighbors((x, y), (X, Y))\\n # construct gradually the clause of (was_sick(t-1) OR one_of_neighbor_was_sick(t-1))\\n sick_neighbors = []\\n\\n for neighbor_x, neighbor_y in adjacent_neighbors:\\n # healthy[t] ==> ~sick_neighbor[t-1]\\n formula.append([-H[x, y, t], -S[neighbor_x, neighbor_y, t-1]])\\n # sick_neighbor[t-1] & healthy[t-1] ==> S[t]\\n formula.append([-S[neighbor_x, neighbor_y, t-1], -H[x, y, t-1], S[x, y, t]])\\n sick_neighbors.append(S[neighbor_x, neighbor_y, t-1])\\n\\n formula.append([-S[x, y, t], S[x, y, t-1]] + sick_neighbors)\\n # H[t-1] & ~S[x-1, y, t-1] & ~S[x+1, y, t-1] & ~ S[x, y+1, t-1] ... ==> H[t]\\n formula.append([-H[x, y, t-1], H[x, y, t]]+sick_neighbors)\\n\\n return formula\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b55e4efe62148cf2b7ec59db507e7a\",\n \"score\": \"0.5577992\",\n \"text\": \"def get_dyn_theory(env, state):\\n assert env.problem == OneMax\\n assert env.action_description == \\\"lbd\\\"\\n assert env.state_description == \\\"n, f(x)\\\"\\n assert len(env.state_functions) == 2\\n\\n return np.asarray([np.sqrt(env.x.n / (env.x.n - state[1]))], dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d5725a613f769b99f01d4b4469638ed\",\n \"score\": \"0.55727875\",\n \"text\": \"def getActionProbabilities(self, state: int) -> np.ndarray:\\n distribution = []\\n denominator = sum([np.exp(self._sigma * self._theta[state][i]) for i in range(self.numActions)])\\n for i in range(self.numActions):\\n numerator = np.exp(self._sigma * self._theta[state][i])\\n distribution.append(numerator / denominator)\\n return np.array(distribution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eba66fff22090b2f8011c4272d08818\",\n \"score\": \"0.5571411\",\n \"text\": \"def _heom_state_dictionaries(dims, excitations):\\n nstates = 0\\n state2idx = {}\\n idx2state = {}\\n\\n for state in state_number_enumerate(dims, excitations):\\n state2idx[state] = nstates\\n idx2state[nstates] = state\\n nstates += 1\\n\\n return nstates, state2idx, idx2state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef9f9492f2834e707b480176989d1be\",\n \"score\": \"0.55690783\",\n \"text\": \"def action_distribution(self, state):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4183db0d99516a080c915486f81dfc74\",\n \"score\": \"0.5557254\",\n \"text\": \"def reward(self, state):\\n test_pixels = self.get_underlying_pixels(state)\\n sim = self.similarity(self.agent[self.indices], test_pixels) #TODO scale this? \\n return sim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"160bcf209043a339d65ccfb731152936\",\n \"score\": \"0.5556321\",\n \"text\": \"def __call__(self, inputs, state, scope=None):\\n with tf.variable_scope(scope or type(self).__name__):\\n c, h = state\\n\\n # change bias argument to False since LN will add bias via shift\\n concat = tf.nn.rnn_cell._linear(\\n [inputs, h], 4 * self._num_units, False)\\n # ipdb.set_trace()\\n\\n i, j, f, o = tf.split(1, 4, concat)\\n\\n # add layer normalization to each gate\\n i = ln(i, scope='i/')\\n j = ln(j, scope='j/')\\n f = ln(f, scope='f/')\\n o = ln(o, scope='o/')\\n\\n new_c = (c * tf.nn.sigmoid(f + self._forget_bias) +\\n tf.nn.sigmoid(i) * self._activation(j))\\n\\n # add layer_normalization in calculation of new hidden state\\n new_h = self._activation(\\n ln(new_c, scope='new_h/')) * tf.nn.sigmoid(o)\\n new_state = tf.nn.rnn_cell.LSTMStateTuple(new_c, new_h)\\n return new_h, new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439a23fc1be390bc1b8f84b153324a71\",\n \"score\": \"0.5550651\",\n \"text\": \"def act(self, state):\\n if np.random.rand() < self.epsilon:\\n return random.randrange(self.action_size)\\n return np.argmax(self.predict(state)[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d2bc3d257b1d21c5e56b2b4979ea457\",\n \"score\": \"0.5548322\",\n \"text\": \"def run_baum_welch_algorithm(self, state_features, timeseries_observations, cycle_states):\\n\\n # create the initial transition matrix\\n transition_matrix = self.create_transition_matrix()\\n\\n # set cumulative probability, this will be used as a breaking criteria for the EM algorithm\\n cummulative_probability = np.inf\\n\\n for i in range(10):\\n # create the observation probabilities given the initial features\\n observation_probabilities = self.create_observation_probabilities(state_features, timeseries_observations, cycle_states)\\n\\n # run forward and backward pass through\\n forward_probabilities, forward_trellis = self.run_forward_pass(transition_matrix, observation_probabilities)\\n backward_probabilities, backward_trellis = self.run_backward_pass(transition_matrix,\\n observation_probabilities)\\n backward_trellis.reverse()\\n backward_probabilities.reverse()\\n\\n # update lambda parameter (probability of state i, time j)\\n numerator = np.multiply(np.array(forward_probabilities), np.array(backward_probabilities))\\n\\n denominator = sum(np.multiply(np.array(forward_probabilities), np.array(backward_probabilities)).T)\\n _lambda = []\\n for j in range(len(numerator)):\\n _lambda.append((numerator[j, :].T / denominator[j]).T)\\n\\n # update epsilon parameter (probability of moving for state i to state j)\\n numerator = np.multiply(forward_trellis[1:], backward_trellis[:-1])\\n epsilon = []\\n for g in range(len(numerator)):\\n denominator = np.sum(numerator[g, :, :])\\n epsilon.append((numerator[g, :, :].T / denominator).T)\\n\\n # Update the transition matrix and observation probabilities for the next iteration\\n transition_matrix = ((sum(epsilon) / sum(_lambda))).T / sum((sum(epsilon) / sum(_lambda)))\\n\\n # Update the state space parameters\\n observation_probabilities = _lambda\\n state_ind = 0\\n for state in state_features:\\n param_set = state_features[state]\\n state_weight = [0]*len(set(cycle_states))\\n state_var = [0]*len(set(cycle_states))\\n state_sum = [0]*len(set(cycle_states))\\n\\n for ind in range(len(timeseries_observations)):\\n cycle = cycle_states[ind]\\n state_weight[cycle] += _lambda[ind][state_ind]\\n state_sum[cycle] += timeseries_observations[ind] * _lambda[ind][state_ind]\\n state_var[cycle] += _lambda[ind][state_ind] * np.sqrt(\\n (timeseries_observations[ind] - param_set.loc[param_set['CYCLE']==cycle, 'SEASONALITY_MU'].item()) ** 2)\\n\\n state_mu_set = np.divide(state_sum, state_weight).tolist()\\n state_sigma_set = np.divide(state_var, state_weight).tolist()\\n cycle_ind = list(set(cycle_states))\\n cycle_ind.sort()\\n param_set_new = pd.DataFrame(columns=['CYCLE', 'SEASONALITY_MU', 'SEASONALITY_SIGMA'], data=\\n np.array([cycle_ind, state_mu_set, state_sigma_set]).T)\\n\\n state_features.update({state: param_set_new})\\n\\n state_ind += 1\\n\\n cummulative_probability_new = np.sum(_lambda)\\n pcnt_change = (cummulative_probability_new-cummulative_probability)/cummulative_probability\\n if pcnt_change < 0.01:\\n break\\n else:\\n cummulative_probability = cummulative_probability_new\\n\\n print('Fitted transition matrix: ')\\n print(transition_matrix)\\n print('Fitted state features: ')\\n print(state_features)\\n\\n # multiply the probabilities to get the overall probability. Convert to state using MAP\\n observation_probabilities = _lambda\\n\\n return transition_matrix, state_features, observation_probabilities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d35e4730c3dcdb9f69f6eb2563a4da\",\n \"score\": \"0.55421877\",\n \"text\": \"def compute_state(self):\\r\\n\\r\\n self.predicted_state[:, [0]] = self.vehicle.state\\r\\n u = []\\r\\n for idx in range(1, self.horizon+1):\\r\\n dstate, control = self.propagate(self.predicted_state[:, [0]])\\r\\n\\r\\n self.predicted_state[:, [idx]] = np.matmul(self.vehicle.system_matrix(), self.predicted_state[:, [idx-1]])\\\\\\r\\n + dstate\\r\\n u.append(control)\\r\\n self.update_time()\\r\\n print(\\\"----------------------------------\\\")\\r\\n print(\\\"Current initial state and horizon: \\\\n\\\")\\r\\n print(self.predicted_state[self.xidx, :], \\\"\\\\n\\\", self.predicted_state[self.yidx, :])\\r\\n print(\\\"----------------------------------\\\")\\r\\n current_initial = self.predicted_state[:self.num_states, [1]]\\r\\n\\r\\n self.vehicle.update(current_initial, u[0])\\r\\n return self.predicted_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10207053dd706df12c49568975759a62\",\n \"score\": \"0.5530769\",\n \"text\": \"def no_actions_clauses(states_encoding, t):\\n if t < 3:\\n print(\\\"timestamp does not match clauses generation method\\\")\\n return\\n\\n formula = []\\n formula += unique_state_clauses(states_encoding, t)\\n\\n H = states_encoding[\\\"H\\\"]\\n S = states_encoding[\\\"S\\\"]\\n U = states_encoding[\\\"U\\\"]\\n\\n X = H.shape[0]\\n Y = H.shape[1]\\n\\n for x in range(X):\\n for y in range(Y):\\n\\n # if unpopulated[t] ==> unpopulated[t-1]\\n formula.append([-U[x, y, t], U[x, y, t-1]])\\n # if unpopulated[t-1] ==> unpopulated[t]\\n formula.append([-U[x, y, t-1], U[x, y, t]])\\n\\n # S[t-1] & S[t-2] & S[t-3] ==> H[t]\\n formula.append([-S[x, y, t-1], -S[x, y, t-2], -S[x, y, t-3], H[x, y, t]])\\n\\n # S[t-1] & ~S[t-2] ==> S[t]\\n formula.append([-S[x, y, t-1], S[x, y, t-2], S[x, y, t]])\\n\\n adjacent_neighbors = neighbors((x, y), (X, Y))\\n\\n # H[x, y, t] ==> (S[t-1] & S[t-2] & S[t-3]) | (H[x, y, t-1] & ~S[x+1, y, t-1] & ~S[x-1, y, t-1]...)\\n healthy_condition_prefixes = [[-H[x, y, t], S[x, y, t-1]], [-H[x, y, t], S[x, y, t-2]],\\n [-H[x, y, t], S[x, y, t-3]]]\\n healthy_condition_suffixed = [[H[x, y, t-1]]]\\n sick_neighbors = []\\n for neighbor_x, neighbor_y in adjacent_neighbors:\\n healthy_condition_suffixed.append([-S[neighbor_x, neighbor_y, t-1]])\\n\\n # H[t-1] & sick_neighbor[t-1] ==> S[t]\\n formula.append([-H[x, y, t-1], -S[neighbor_x, neighbor_y, t-1], S[x, y, t]])\\n\\n # build or clause between sick neighbors sick conditions\\n sick_neighbors.append(S[neighbor_x, neighbor_y, t-1])\\n\\n formula += [prefix + suffix for prefix in healthy_condition_prefixes\\n for suffix in healthy_condition_suffixed]\\n\\n # S[t] ==> (S[t-1] & (~S[t-2] | ~ S[t-3]) | (H[t-1] & (S[x+1, y, t-1] | S[x-1, y, t-1]...))\\n sick_condition_prefixes = [[-S[x, y, t], S[x, y, t-1]], [-S[x, y, t], -S[x, y, t-2], -S[x, y, t-3]]]\\n sick_condition_suffixes = [[H[x, y, t-1]], sick_neighbors]\\n formula += [prefix + suffix for prefix in sick_condition_prefixes\\n for suffix in sick_condition_suffixes]\\n\\n return formula\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f44953b32b476eb41e4acc072e8c7d\",\n \"score\": \"0.5526509\",\n \"text\": \"def call(self, inputs, state):\\n\\n gate_inputs = tf.matmul(state, self._gate_kernel)\\n gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias)\\n\\n if self._layer_norm:\\n gate_inputs = common_layers.split_last_dim(gate_inputs, 2)\\n mean = tf.reduce_mean(gate_inputs, axis=[-1], keepdims=True)\\n variance = tf.reduce_mean(tf.square(gate_inputs - mean), axis=[-1], keepdims=True)\\n norm_x = (gate_inputs - mean) * tf.rsqrt(variance + self._ln_epsilon)\\n norm_x = common_layers.combine_last_two_dims(norm_x)\\n gate_inputs = norm_x * self._ln_scale + self._ln_bias\\n\\n value = tf.sigmoid(gate_inputs)\\n r, u = tf.split(value=value, num_or_size_splits=2, axis=1)\\n\\n r_state = r * state\\n\\n candidate = tf.matmul(r_state, self._candidate_kernel)\\n candidate = tf.nn.bias_add(candidate, self._candidate_bias)\\n\\n c = self._activation(candidate)\\n if self._dropout_rate:\\n c = tf.nn.dropout(c, keep_prob=1-self._dropout_rate)\\n new_h = u * state + (1 - u) * c\\n return new_h, new_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d666e7c4479fe5d688c66d2f7f8fe4\",\n \"score\": \"0.5525123\",\n \"text\": \"def actions(self, state):\\n # find all s and then insert the new ss to 'seakness_index_map'\\n\\n # make S's index list\\n comb_list_S = get_comb_list(self, 'S', state,2,self.zoc)\\n comb_list_S1 = get_comb_list(self, 'S', state, 1, self.zoc)\\n # make H's index list\\n comb_list_H = get_comb_list(self, 'H', state,1,self.zoc)\\n\\n # combined S an H\\n if comb_list_H==-1 or comb_list_S==-1:\\n combHS2=[]\\n else:\\n combHS2 = list(product(comb_list_S, comb_list_H))\\n if comb_list_H == -1 or comb_list_S1 == -1:\\n combHS1 = []\\n else:\\n combHS1 = list(product(comb_list_S1, comb_list_H))\\n if combHS1 != -1:\\n combHS2.extend(combHS1)\\n if comb_list_S != -1:\\n combHS2.extend(comb_list_S)\\n if comb_list_S != -1:\\n combHS2.extend(comb_list_S)\\n if comb_list_H!=-1:\\n combHS2.extend(comb_list_H)\\n return tuple(combHS2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d70d9aa325da8a50a773e69ebbf86b94\",\n \"score\": \"0.55226743\",\n \"text\": \"def IMU_3DOF(state_t0,state_t1,T):\\n # num_state = len(state_t0)\\n sigma_theta = 0.001\\n sigma_x = 0.01\\n sigma_y = 0.01\\n dtheta = state_t1[5] + sigma_theta*np.random.randn(1,1)\\n accel_x = (state_t1[3] - state_t0([3]))/T + sigma_x*np.random.randn(1,1)\\n accel_y = (state_t1[5] - state_t0([5]))/T + sigma_y*np.random.randn(1,1)\\n\\n return (dtheta,accel_x,accel_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aef5f47114586753cfc1bdfd2a3077f\",\n \"score\": \"0.55225915\",\n \"text\": \"def GetAction(state):\\n p = 1/actionSize\\n if len(state.shape)==3:\\n probs =np.full((1,actionSize),p)\\n else:\\n probs =np.full((state.shape[0],actionSize),p)\\n actions = np.array([np.random.choice(probs.shape[1], p=prob / sum(prob)) for prob in probs])\\n return actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4607d651d7e6712bc381744d33b876b0\",\n \"score\": \"0.55015206\",\n \"text\": \"def result(self, state, actions):\\n state = state_to_list(state)\\n\\n for action, (i, j) in actions:\\n if action == Q_action:\\n state[i][j] = ('Q', 3)\\n\\n elif action == V_action:\\n state[i][j] = ('I', 0)\\n\\n infect = []\\n for i, row in enumerate(state):\\n for j, value in enumerate(row):\\n if value[0] == 'S':\\n for k, l in self.get_neighbors(i, j):\\n if state[k][l][0] == 'H':\\n infect.append((k, l))\\n\\n for i, j in infect:\\n state[i][j] = ('S', 4) # 4 since this is going to be demoted immediately\\n\\n for i, row in enumerate(state):\\n for j, (value, days) in enumerate(row):\\n if value in ['S', 'Q']:\\n if days == 1:\\n state[i][j] = ('H', 0)\\n else:\\n state[i][j] = (value, days - 1)\\n\\n return state_to_tuple(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d2defb15a4080a1c22e131ad238d03\",\n \"score\": \"0.54844165\",\n \"text\": \"def calculate_gs(p):\\n state_table = generate_state_table(p)\\n H = make_Hamiltonian(p, state_table)\\n w, v = scipy.sparse.linalg.eigsh(H, k=1, which='SA')\\n\\n return w[0], v[:, 0], state_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90ab98973a5df59e1f8c07afdfbac95\",\n \"score\": \"0.5483582\",\n \"text\": \"def forward(observed_states):\\n L = len(observed_states[0])\\n matrix = [[] for i in range (0, L)]\\n for i in range (0, L):\\n for j in range(0, 4):\\n if j == 0: state = \\\"coding\\\"\\n if j == 1: state = \\\"regulatory\\\"\\n if j == 2: state = \\\"hetero\\\"\\n if j == 3: state = \\\"other\\\"\\n emission = math.log10(E1[state][observed_states[0][i]]) + math.log10(E2[state][observed_states[1][i]])\\n # emission = math.pow(10,emission)\\n if i == 0:\\n ## matrix[i][j] = P[state]\\n # matrix[i].insert(j, emission * P[state])\\n init = emission + math.log10(P[state])\\n init = math.pow(10, init)\\n matrix[i].insert(j, init)\\n else:\\n # print(\\\"first, \\\", E1[state][observed_states[0][i]], \\\"second, \\\", E2[state][observed_states[1][i]])\\n summation = 0\\n prevState = \\\"\\\"\\n for k in range(0, 4):\\n if k == 0: prevState = \\\"coding\\\"\\n if k == 1: prevState = \\\"regulatory\\\"\\n if k == 2: prevState = \\\"hetero\\\"\\n if k == 3: prevState = \\\"other\\\"\\n\\n # summation += matrix[i - 1][k] * A[prevState][state]\\n if matrix[i - 1][k] == 0 or A[prevState][state] == 0:\\n summation += 0\\n else:\\n product = math.log10(matrix[i - 1][k]) + math.log10(A[prevState][state])\\n product = math.pow(10, product)\\n summation += product\\n\\n # matrix[i].insert(j, emission * summation)\\n if summation == 0:\\n entry = 0\\n else:\\n entry = emission + math.log10(summation)\\n entry = math.pow(10, entry)\\n matrix[i].insert(j, entry)\\n\\n likelihood = 0\\n for i in range(0, 4):\\n likelihood += matrix[L - 1][i]\\n print(\\\"likelihood forward\\\", likelihood)\\n\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301646394b9e378532d8b3ecb1e2853a\",\n \"score\": \"0.5477483\",\n \"text\": \"def creation(i,state_in):\\n coef = np.sqrt(state_in[i]+1)\\n state_out=state_in.copy()\\n state_out[i] = state_out[i]+1\\n return state_out,coef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3580525e6888641ab93969a2a71b9259\",\n \"score\": \"0.5475777\",\n \"text\": \"def __call__(self, inputs, state, scope=None):\\n with vs.variable_scope(scope or type(self).__name__): # \\\"BasicLSTMCell\\\"\\n # Parameters of gates are concatenated into one multiply for efficiency.\\n if self._state_is_tuple:\\n # batch_size X num_cand * hidden_size, batch_size X num_cand * hidden_size\\n c, h = state\\n else:\\n c, h = array_ops.split(1, 2, state)\\n hidden_size = int(self._num_units / self._num_cand)\\n # batch_size X num_cand X hidden_size\\n h_flat = array_ops.reshape(h, [-1, self._num_cand, hidden_size])\\n # batch_size X hidden_size\\n h_sum = tf.reduce_mean(h_flat, reduction_indices=1)\\n # batch_size * num_cand X hidden_size\\n with vs.variable_scope('forget_gate'):\\n W_f = tf.get_variable(\\\"W_f\\\", [hidden_size, hidden_size])\\n U_f = tf.get_variable(\\\"U_f\\\", [hidden_size, hidden_size])\\n b_f = tf.get_variable(\\\"b_f\\\", [hidden_size],\\n initializer=tf.constant_initializer(1.0))\\n f_x = tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_f)\\n f_h = tf.reshape(tf.matmul(tf.reshape(h_flat,\\n [-1, hidden_size]),\\n U_f),\\n [-1, 1, self._num_cand, hidden_size])\\n f_x = tf.tile(tf.reshape(f_x,\\n [-1, self._num_cand, 1, hidden_size]),\\n [1, 1, self._num_cand, 1])\\n # batch_size * num_cand * num_cand * hidden_size\\n f_xh = sigmoid(f_x + f_h + b_f)\\n # f_x = _linear(array_ops.reshape(inputs, [-1, hidden_size]),\\n # hidden_size, True, 1.0, 'InputGate1')\\n # batch_size * num_cand X hidden_size\\n # f_h = array_ops.reshape(_linear(array_ops.reshape(h,\\n # [-1, hidden_size]),\\n # hidden_size, True, 1.0, 'InputGate2'),\\n # [-1, self._num_cand, hidden_size])\\n #\\n # f_x = array_ops.reshape(array_ops.tile(f_x, [1, self._num_cand]),\\n # [-1, self._num_cand, self._num_cand, hidden_size])\\n # f_h = array_ops.reshape(array_ops.tile(f_h, [1, self._num_cand, 1]),\\n # [-1, self._num_cand, self._num_cand, hidden_size])\\n # f_xh = array_ops.transpose(f_x + f_h, [1, 0, 2, 3])\\n\\n with vs.variable_scope('update'):\\n W_a = tf.get_variable(\\\"W_a\\\", [hidden_size, hidden_size * 3])\\n U_a = tf.get_variable(\\\"U_a\\\", [hidden_size, hidden_size * 3])\\n b_in = tf.get_variable(\\\"b_in\\\", [hidden_size])\\n b_o = tf.get_variable(\\\"b_o\\\", [hidden_size])\\n b_u = tf.get_variable(\\\"b_u\\\", [hidden_size])\\n a_x = tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_a)\\n a_h = tf.matmul(h_sum, U_a)\\n i_x, o_x, u_x = tf.split(1, 3, a_x)\\n i_h, o_h, u_h = tf.split(1, 3, a_h)\\n # batch_size X num_cand X hidden_size\\n i_xh = sigmoid(tf.reshape(i_x, [-1, self._num_cand, hidden_size]) \\\\\\n + tf.reshape(i_h, [-1, 1, hidden_size]) + b_in)\\n o_xh = sigmoid(tf.reshape(o_x, [-1, self._num_cand, hidden_size]) \\\\\\n + tf.reshape(o_h, [-1, 1, hidden_size]) + b_o)\\n u_xh = tanh(tf.reshape(u_x, [-1, self._num_cand, hidden_size]) \\\\\\n + tf.reshape(u_h, [-1, 1, hidden_size]) + b_u)\\n\\n # o_x = _linear(array_ops.reshape(inputs, [-1, hidden_size]), 3 * hidden_size, True)\\n # with vs.variable_scope('UpdateGates2'):\\n # o_h = _linear(array_ops.reshape(h_sum, [-1, hidden_size]), 3 * hidden_size, True)\\n # batch_size * num_cand * hidden_size\\n # o = array_ops.reshape(o_x, [-1, self._num_cand, 3 * hidden_size]) \\\\\\n # + array_ops.reshape(tf.tile(o_h, [1, self._num_cand]),\\n # [-1, self._num_cand, 3 * hidden_size])\\n # i_xh, o_xh, u_xh = array_ops.split(2, 3, o)\\n # i = input_gate, j = new_input, f = forget_gate, o = output_gate\\n new_c =tf.reduce_sum(\\n array_ops.reshape(c,[-1, 1, self._num_cand, hidden_size])\\n * f_xh, reduction_indices=2) + i_xh * u_xh\\n new_h = tanh(new_c) * o_xh\\n new_c = array_ops.reshape(new_c, [-1, hidden_size * self._num_cand])\\n new_h = array_ops.reshape(new_h, [-1, hidden_size * self._num_cand])\\n if self._state_is_tuple:\\n new_state = LSTMStateTuple(new_c, new_h)\\n else:\\n new_state = array_ops.concat(1, [new_c, new_h])\\n return new_h, new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f177517e3d49689d4bcbb9f45fa70695\",\n \"score\": \"0.54749775\",\n \"text\": \"def forward(self, state):\\n x = np.dot(state, self.w)\\n # softmax\\n x = np.exp(x)/sum(np.exp(x))\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ab2c7256a25ab59f166aee41eb5d66\",\n \"score\": \"0.5473587\",\n \"text\": \"def evolve(p, state_table, state, kind=\\\"list\\\", trotterised=False, xinit=False, xmeas=False):\\n if kind == \\\"ket\\\":\\n psi0 = state\\n elif kind == \\\"list\\\":\\n # if we parsed a product state, construct ket by identifying the\\n # corresponding number of the basis state and putting a 1 into the ket\\n psi0 = np.zeros((len(state_table), 1), dtype=complex)\\n psi0[state_table.index(state_to_int(p, state))] = 1.\\n elif kind == \\\"int\\\":\\n psi0 = np.zeros((len(state_table), 1), dtype=complex)\\n psi0[state_table.index(state)] = 1.\\n\\n if xinit:\\n H_matrix = local_H_matrix(p, state_table)\\n psi0 = H_matrix.dot(psi0)\\n\\n time = np.linspace(p['t_initial'], p['t_final'],\\n int(p['t_final'] / p['dt'] + 1))\\n\\n # make dictionary with measurement operators\\n meas = {}\\n for i in range(int(p['N'])):\\n meas['Zi' + ' Site ' + str(i)\\n ] = Zi_matrix(p, i, state_table)\\n meas['Yi' + ' Site ' + str(i)\\n ] = Yi_matrix(p, i, state_table)\\n meas['Xi' + ' Site ' + str(i)\\n ] = Xi_matrix(p, i, state_table)\\n\\n sim = {}\\n sim['Time'] = time\\n for key in meas.keys():\\n sim[key] = np.zeros(np.shape(time))\\n sim['Total Z'] = np.zeros(np.shape(time))\\n sim['Total Y'] = np.zeros(np.shape(time))\\n sim['Total X'] = np.zeros(np.shape(time))\\n\\n if trotterised:\\n H_list = make_trotter_Hamiltonian(p, state_table)\\n else:\\n H_list = [make_Hamiltonian(p, state_table)]\\n\\n # construct time-evolution operators for a single time-step\\n U_list = [LA.expm(-1.j * H.tocsc() * p['dt']) for H in H_list]\\n\\n # Time Evolution\\n for i in range(len(time)):\\n # define initial (t=0) state\\n if i == 0:\\n psi = psi0\\n\\n # measurements\\n for operator in meas.keys():\\n expct = expct_val(meas[operator], psi) # [0][0]\\n\\n if np.imag(expct) < 1e-12:\\n sim[operator][i] = np.real(expct)\\n else:\\n print(\\\"Imaginary Measurement %s\\\" % (operator))\\n\\n # apply U to current state psi to get psi(t+dt) = U * psi(t)\\n for U in U_list:\\n psi = U.dot(psi)\\n\\n for key in sim.keys():\\n if key[:2] == \\\"Zi\\\":\\n sim['Total Z'] += sim[key]\\n elif key[:2] == \\\"Yi\\\":\\n sim['Total Y'] += sim[key]\\n elif key[:2] == \\\"Xi\\\":\\n sim['Total X'] += sim[key]\\n\\n return sim, state_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"040bb8c1c8154722b89b7726987bed2b\",\n \"score\": \"0.5473407\",\n \"text\": \"def anihilation(i,state_in):\\n if not (state_in[i] == 0):\\n coef = np.sqrt(state_in[i])\\n state_out=state_in.copy()\\n state_out[i]=state_out[i]-1\\n stop = False\\n return state_out,coef,stop\\n else:\\n #print('This state cant be lowered at', i,'!', )\\n stop = True \\n state_out= []\\n coef=0\\n return state_out,coef,stop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12badc73be8c3bd67d63bf51657ec725\",\n \"score\": \"0.5472945\",\n \"text\": \"def H(state: SparseSim,\\n qubit: int) -> None:\\n\\n stabs = state.stabs\\n\\n # Change the sign appropriately\\n\\n # X and Z -> -1\\n # ----------\\n stabs.signs_minus ^= stabs.col_x[qubit] & stabs.col_z[qubit]\\n\\n # Update Paulis\\n # -------------------------------------------------------------------\\n for g in state.gens:\\n\\n # Swap X and Z for rows\\n xonly = g.col_x[qubit] - g.col_z[qubit]\\n\\n zonly = g.col_z[qubit] - g.col_x[qubit]\\n\\n for i in xonly:\\n g.row_x[i].discard(qubit)\\n g.row_z[i].add(qubit)\\n\\n for i in zonly:\\n g.row_z[i].discard(qubit)\\n g.row_x[i].add(qubit)\\n\\n # Swap X and Z for cols\\n g.col_x[qubit], g.col_z[qubit] = g.col_z[qubit], g.col_x[qubit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcea6487e36d1cecda3c5f71129f43eb\",\n \"score\": \"0.5468132\",\n \"text\": \"def getManhattanDistanceComponents(self, state):\\n\\t\\treshaped_state = np.reshape(state, self.outdim)\\n\\t\\t# np_state = np.array([[j for j in i.split('\\\\t')] for i in state.splitlines()])\\n\\t\\tavatar = 1\\n\\t\\t## Example: to find what ID a box would have, you'd just do ...index(\\\"box\\\"). This is the\\n\\t\\t## Schaul function for figuring the sprite IDs.\\n\\t\\tgoal = 2**(1+sorted(self._obstypes.keys())[::-1].index(\\\"goal\\\"))\\n\\t\\tavatar_loc = None\\n\\t\\tgoal_loc = None\\n\\t\\tnumRows, numCols = self.outdim\\n\\t\\tfor i in range(numRows): \\n\\t\\t\\tfor j in range(numCols):\\n\\t\\t\\t\\tif (reshaped_state[i,j] / goal) % 2 == 1:\\n\\t\\t\\t\\t\\tgoal_loc = (i,j)\\n\\n\\t\\t\\t\\tif (reshaped_state[i,j]/ avatar) % 2 == 1:\\n\\t\\t\\t\\t\\tavatar_loc = (i,j)\\n\\n\\t\\treturn avatar_loc[0]-goal_loc[0], avatar_loc[1] - goal_loc[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a44f0b3553645e8f860d46854bc194f\",\n \"score\": \"0.545734\",\n \"text\": \"def tclab_step(self, state, time, action, dist):\\n heater_pwm = action\\n sensor_temp, heater_temp = state\\n\\n dth = (self.surf_area * self.beta1 * (self.k_d * dist) ** self.beta2 * (\\n self.amb_temp - heater_temp) + self.emissivity * self.boltzmann\\n * self.surf_area * (self.amb_temp ** 4 - heater_temp ** 4)\\n + self.alpha * heater_pwm) / (self.mass * self.cp)\\n dtc = (-sensor_temp + heater_temp) / self.tau_hc\\n\\n new_state = np.zeros(2)\\n new_state[0] = dtc\\n new_state[1] = dth\\n return new_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e51e3c8410985c94b5d12315ff7fb4d\",\n \"score\": \"0.54566973\",\n \"text\": \"def act(self, state):\\n state = np.reshape(state, [-1, self.state_size])\\n action = self.actor_local.model.predict(state)[0]\\n # add some noise for exploration\\n return list(action + self.noise.sample())\\n return action\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ef0a1dda4a5f208c9530c289563c2b\",\n \"score\": \"0.54542744\",\n \"text\": \"def get_action(self, state:np.ndarray)->int:\\n x = self.basis.encode(state) # Computes the basis function representation of the state features\\n p = self.get_action_probabilities(x) # computes the probabilities of each action\\n a = int(np.random.choice(range(p.shape[0]), p=p, size=1)) # samples the action from p\\n\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27bb87e54eb058ab5f0a4061306398be\",\n \"score\": \"0.5453973\",\n \"text\": \"def state_transform(state):\\n return state.mean(2)[None, ...] / 256.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7428bdc2ef95a73f70b247fbf9a89c44\",\n \"score\": \"0.544846\",\n \"text\": \"def local_H_matrix(p, state_table):\\n single_H = np.array([[1., 1.], [1., -1.]]) / np.sqrt(2)\\n H_mat = np.eye(1)\\n for i in range(p['N']):\\n H_mat = np.kron(single_H, H_mat)\\n return H_mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa34013afda72da1e3fc9477101db97\",\n \"score\": \"0.54361\",\n \"text\": \"def h1(state: State):\\n delta = [state.board.end_state[i] - state.board.tiles[i] for i in state.board.tiles]\\n return sum(x != 0 for x in delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a41231b13a3b6866f87753f1e287da\",\n \"score\": \"0.54301536\",\n \"text\": \"def work(self):\\r\\n return self.m_act * self.eff_mech * (self.inlet.h - self.outlet.h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5002d9c33efb936bd11ed68682a96be\",\n \"score\": \"0.5418533\",\n \"text\": \"def act(self, state, sess):\\n probs = sess.run(self.a_prob, {self.states_St: np.expand_dims(state.flatten(), axis=0)})\\n a_t = 1 * (np.random.rand() < probs)\\n return a_t.squeeze()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d3a28d5d011def3ab71de89c1198fb\",\n \"score\": \"0.5418465\",\n \"text\": \"def get_action(self, state):\\n tens = torch.tensor(state, dtype = torch.float32).permute(2,0,1)\\n logits, _ = self.net(tens)\\n probs = torch.softmax(logits, dim=-1)\\n probs = probs.squeeze()\\n return np.random.choice(4, p = probs.detach().numpy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3e281d233f2a0acae83413984ffc6c\",\n \"score\": \"0.54177237\",\n \"text\": \"def airportMDP():\\n # States -------------------------------------------\\n # order of states goes: G_1, G_2, ... T_1, T_2,...\\n # gate ordered from concourse A/B/C/D satellite N/S \\n # -------------------------------------------\\n S = 6 + 77;\\n \\n # Actions -------------------------------------------\\n # actions: going within each concourse/satellite, \\n # going to neighbouring concourse/satellite\\n # action order: first action is to stay in concourse\\n # subsequent actions are to move to neighbouring state\\n # -------------------------------------------\\n A = np.zeros(S);\\n # concourse A\\n A[0] = 3;\\n # concourse B\\n A[1] = 4;\\n # concourse C\\n A[2] = 4;\\n # concourse D\\n A[3] = 3;\\n # satellite N\\n A[4] = 3;\\n # satellite S\\n A[5] = 3;\\n # the gate states T_i\\n A[6:] = 1;\\n# print (A)\\n actionSize = int( max(A));\\n # corresponding terminal states\\n Terminals = np.array([14,12,12, 11,14,14]);\\n # neighbouring states \\n Neighbours = [[1,5], # A is connected to (B, S)\\n [0,2,5], # B is connected to (A, C, S)\\n [1,3,4], # C is connected to (B, D, N),\\n [2,4], # D is connected to (C, N)\\n [2,3], # N is connected to (C, D)\\n [0,1]]; # S is connected to (A, B)\\n \\n # probability kernel -------------------------------------------\\n P = np.zeros((S,S,actionSize));\\n for s in range(S):\\n if s <= 5: # state is a concourse state\\n # action 0: state in concourse state\\n P[s,s,0] = 0.5;\\n termStart = int(6 + sum(Terminals[0:s]));\\n termEnd = termStart + Terminals[s];\\n P[termStart:termEnd, s, 0] = 0.5/Terminals[s];\\n# print (\\\"state \\\", s );\\n# print (P[:,s,0])\\n # other actions: going to neighbouring concourses\\n neighbourAction = 1;\\n neighbours = len(Neighbours[s]) - 1;\\n for desti in Neighbours[s]:\\n P[desti, s, neighbourAction] = 0.9;\\n for otherNeighB in Neighbours[s]:\\n if otherNeighB != desti:\\n P[otherNeighB, s, neighbourAction] = 0.1/neighbours;\\n neighbourAction += 1;\\n else: # state is a gate state\\n P[s,s,0] = 1;\\n \\n # cost -------------------------------------------\\n # l(y) = Cy + D, C: S x A, D : S x A\\n C = np.zeros((S,actionSize));\\n D = np.zeros((S,actionSize));\\n # cost of non-existent actions is infinity \\n for s in range(S):\\n action = 0;\\n # cost of feasible action\\n while action < A[s]:\\n if action == 0:\\n if s <= 5: # cost of staying in concourse s\\n C[s, action]= 50;\\n else: # cost of staying in gate s\\n C[s, action] = 0;\\n D[s, action] = 1;\\n else:\\n # going to neighbouring states depends on how \\n # many other planes are traversing the road\\n C[s, action] = 1; \\n # gas money of going else where\\n D[s, action] = 3;\\n action += 1;\\n # cost of non-existent actions is infinity\\n someInf = 10000; # apparently np.inf doesn't work\\n while action < actionSize: \\n C[s, action] = 0;\\n D[s, action] = someInf;\\n action +=1; \\n return P, C, D, S, actionSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d36c2cd1d7924bb1a36c987ca0628d94\",\n \"score\": \"0.5408316\",\n \"text\": \"def get_action(self, state):\\n\\t\\tq_vals = self._get_q_vals(state)\\n\\t\\tnum = np.random.uniform()\\n\\n\\t\\tif num < self._epsilon:\\n\\t\\t\\treturn np.random.choice(self.k)\\n\\t\\telse:\\n\\t\\t\\treturn np.argmax(q_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567e3d4ae89622d60a370fdd7c747826\",\n \"score\": \"0.5407528\",\n \"text\": \"def solve(t, state):\\n \\n p= state[0]; q= state[1]; r= state[2]; vA= state[3]; vB= state[4]\\n pd= state[5]; qd= state[6]; rd= state[7]; vAd= state[8]; vBd= state[9]\\n wA= state[10]; wB= state[11] \\n \\n bA= cross(wA, IA.dot(wA))-mA*cross(g*z+cross(wA, cross(wA, p)), p)-mB*cross(g*z+cross(wA, cross(wA, q))+cross(wB, cross(wB, r)), q)\\n bB= cross(wB, IB.dot(wB))-mB*cross(cross(wA, cross(wA, q))+cross(wB, cross(wB, r))+g*z, r)\\n b= array([bA.dot(x), bA.dot(y), bA.dot(z), bB.dot(x), bB.dot(y), bB.dot(z)])\\n \\n px, py, pz= p; qx, qy, qz= q; rx, ry, rz= r\\n A= zeros((6,6)) # A will be the coefficient matrix for [A] alphas = b\\n A[0]= [-mA*(pz**2+py**2)-mB*qy**2-IA[0][0], mA*px*py+mB*qx*qy-IA[0][1], mA*px*pz+mB*qx*qz-IA[0][2], -mB*(rz*qz+ry*qy), mB*rx*qy, mB*rx*qz]\\n A[1]= [mA*py*px+mB*qy*qx-IA[1][0], -mA*(px**2+pz**2)-mB*(qx**2+qz**2)-IA[1][1], mA*py*pz+mB*qy*qz-IA[1][2], mB*ry*qx, -mB*(rx*qx+rz*qz), mB*ry*qz]\\n A[2]= [mA*pz*px+mB*qz*qx-IA[2][0], mA*pz*py+mB*qz*qy-IA[2][1], -mA*(py**2+px**2)-mB*(qy**2+qx**2)-IA[2][2], mB*rz*qx, mB*rz*qy, -mB*(ry*qy+rx*qx)]\\n \\n A[3]= [-mB*(qz*rz+qy*ry), mB*qx*ry, mB*qx*rz, -mB*(rz**2+ry**2)-IB[0][0], mB*rx*ry-IB[0][1], mB*rx*rz-IB[0][2]]\\n A[4]= [mB*qy*rx, -mB*(qx*rx+qz*rz), mB*qy*rz, mB*ry*rx-IB[1][0], -mB*(rx**2+rz**2)-IB[1][1], mB*ry*rz-IB[1][2]]\\n A[5]= [mB*qz*rx, mB*qz*ry, -mB*(qy*ry+qx*rx), mB*rz*rx-IB[2][0], mB*rz*ry-IB[2][1], -mB*(ry**2+rx**2)-IB[2][2]]\\n \\n alphas= linalg.solve(A, b)\\n aA= alphas[:3] # alpha_A\\n aB= alphas[3:]\\n \\n pdd= cross(aA, p) + cross(wA, cross(wA, p))\\n qdd= cross(aA, q) + cross(wA, cross(wA, q))\\n vAdd= cross(aA, vA) + cross(wA, cross(wA, vA))\\n rdd= qdd + cross(aB, r) + cross(wB, cross(wB, r))\\n vBdd= qdd + cross(aB, vB) + cross(wB, cross(wB, vB))\\n \\n return [pd, qd, rd, vAd, vBd, pdd, qdd, rdd, vAdd, vBdd, aA, aB]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5750ab4c9a8c8bcb4ab0b7b21e9c4d5f\",\n \"score\": \"0.53902906\",\n \"text\": \"def inv(self, state):\\n return self.lmult_inv(state, np.eye(state.mom.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb6c9ef2583b039f485472352eb1949b\",\n \"score\": \"0.5387148\",\n \"text\": \"def construct_HH_model(n: int, m: int, name: str = None):\\n\\n if n < 2 or m < 2:\\n raise Exception()\\n\\n if name is None:\\n name = f\\\"HH_{n}_{m}\\\"\\n\\n labels = []\\n for i in range(n):\\n for j in range(m):\\n if i == 0:\\n if j == 0:\\n label = 'O'\\n else:\\n label = f\\\"C{j}\\\"\\n elif j == 0:\\n label = f\\\"I{i}\\\"\\n else:\\n label = f\\\"I{i}C{j}\\\"\\n labels.append(label)\\n\\n mc = MarkovChain(name=name)\\n\\n for label in labels:\\n if label == 'O':\\n mc.add_state(label, open_state=True)\\n else:\\n mc.add_state(label)\\n\\n labels = np.array(labels, dtype=object).reshape((n, m))\\n\\n # Add inactivation transitions\\n for i in range(n):\\n for j in range(m):\\n if i < n - 1:\\n mc.add_both_transitions(labels[i, j], labels[i + 1, j], sp.sympify(f\\\"{n-i-1} * b_o\\\"),\\n sp.sympify(f\\\"{i+1}*a_o\\\"))\\n if j < m - 1:\\n mc.add_both_transitions(labels[i, j], labels[i, j + 1], sp.sympify(f\\\"{m-j-1} * b_i\\\"),\\n sp.sympify(f\\\"{j+1}*a_i\\\"))\\n return mc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27f0cc2d130916f5b4ecfaf02fb090c6\",\n \"score\": \"0.53861105\",\n \"text\": \"def get_action(self, state):\\n # starting point of algorithm\\n if (self.q[state, :] == 0).all():\\n return np.random.choice(self.actions)\\n # Epsilon-Greedy\\n if np.random.random() < self.epsilon:\\n return np.random.choice(self.actions)\\n else:\\n return np.argmax(self.q[state, :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92488b296c4f3842f0123702d96a0021\",\n \"score\": \"0.53851795\",\n \"text\": \"def random_HS_state(d):\\n rho = np.random.normal(0, 1, size=(d, d)) + np.random.normal(0, 1, size=(d, d))*1j\\n rho = rho @ rho.conj().T\\n rho = rho / np.trace(rho)\\n return rho\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993998,"cells":{"query_id":{"kind":"string","value":"ab02758ba0c11476d13c5cbb34b4f7ee"},"query":{"kind":"string","value":"Update the mapping (nodal2global and elements2global)"},"positive_passages":{"kind":"list like","value":[{"docid":"63e31e5664150100ebe332549c1fd359","score":"0.6652489","text":"def update_mapping(self, fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs):\n self._set_standard_mapping(fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs)","title":""}],"string":"[\n {\n \"docid\": \"63e31e5664150100ebe332549c1fd359\",\n \"score\": \"0.6652489\",\n \"text\": \"def update_mapping(self, fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs):\\n self._set_standard_mapping(fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c51c89163158e8315eca9b877ad3d659","score":"0.6116114","text":"def _update_global_vars(self):\n\n pass","title":""},{"docid":"88cec46d1e0d250965becc0faf411415","score":"0.60464275","text":"def _update_mappings(self):\n self.existing_cost_entry_map.update(self.processed_report.cost_entries)\n self.existing_product_map.update(self.processed_report.products)\n self.existing_pricing_map.update(self.processed_report.pricing)\n self.existing_reservation_map.update(self.processed_report.reservations)\n\n self.processed_report.remove_processed_rows()","title":""},{"docid":"887980315c5f8b7230278e395e40877e","score":"0.58705133","text":"def _set_standard_mapping(self, fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs):\n raise NotImplementedError('The _set_standard_mapping method must be implemented in subclasses')","title":""},{"docid":"2fb4c3b4b6babce1477387971704cf90","score":"0.58472997","text":"def setChampIdMap():\n\tglobal champIdMap\n\tchampIdMap = getChampMap()\n\tprint 'map updated'","title":""},{"docid":"3a8db93bc1a0d7e458dc1f1eace9b2a9","score":"0.5842898","text":"def set_global_attributes(self):\n\n for key, value in sorted(self._attributes['global'].items()):\n self._nc.setncattr(key, value)","title":""},{"docid":"846dea85c48ad26d54fcea24ee8b60de","score":"0.57833976","text":"def update_global(data):\n for key, value in data.items():\n setattr(g, key, value)","title":""},{"docid":"f9c06b2ac88442709ad6f5f9227b1144","score":"0.5764773","text":"def global_update(self):\n pass","title":""},{"docid":"43f25ee6e442b9bef343c0315349fc4e","score":"0.5733424","text":"def transform_mappings(self):\n self.entity2idx = {v: k for k, v in enumerate(self.all_entities)}\n self.idx2entity = {v: k for k, v in self.entity2idx.items()}\n self.relation2idx = {v: k for k, v in enumerate(self.all_relations)}\n self.idx2relation = {v: k for k, v in self.relation2idx.items()}","title":""},{"docid":"78292a087bbb8b537e09cc2a7669695c","score":"0.56884396","text":"def update(self, mapping: dict):\n self.__d.update(mapping)","title":""},{"docid":"2aba0deaf061ccb789440dde516ca413","score":"0.5644098","text":"def make_map(self):","title":""},{"docid":"d3fc7375f9195c3e686d7864f61b8109","score":"0.56298625","text":"def update2(self, ir, can, nheav):\n self.ncan = len(self.cans)\n if can not in self.cans:\n #print '++', can #, '\\n\\n'\n self.maps.append( [ir, self.ncan, 0] )\n #self.iokgs.append( T )\n self.cans.append( can )\n self.nsheav.append( nheav )\n self.ncan += 1\n else:\n ican = self.cans.index( can )\n entry = [ir, ican, 0]\n if entry not in self.maps:\n self.maps.append( entry )\n #self.iokgs.append( T )\n #print(' -- maps = ', self.maps)","title":""},{"docid":"c8159952b3a5e728d4b04e2337280cab","score":"0.562656","text":"def add_to_map(self):\n pass","title":""},{"docid":"c8159952b3a5e728d4b04e2337280cab","score":"0.562656","text":"def add_to_map(self):\n pass","title":""},{"docid":"3f27649842dce2a80494688cd03d0570","score":"0.5603346","text":"def update_discovery_map(self) -> int:\n raise NotImplementedError() # pragma no cover","title":""},{"docid":"525f1b847e4f220709f7716eaacd4922","score":"0.56023455","text":"def _clearNodeMap(self):\r\n \r\n self._py_nodes = {}","title":""},{"docid":"fa8c3505ebf715c0d25393cca664a280","score":"0.5580438","text":"def reload_mapping():\r\n mod = __import__(module_name)\r\n mapping = getattr(mod, mapping_name, None)\r\n if mapping:\r\n self.fvars = mod.__dict__\r\n self.mapping = mapping","title":""},{"docid":"d36ee12d1838e6f9672c04a6e5c52c11","score":"0.55775356","text":"def update_map(self, map_form):\n pass","title":""},{"docid":"6725b8ec2df9a1ed6551f7b4663809ab","score":"0.5571848","text":"def static_information_update(microgrid, info, logger):\n\tmicrogrid = deepcopy(microgrid)\n\tmicrogrid[\"AREA\"] = info.AREA\n\tmicrogrid[\"TIME_STAMP\"] = info.TIME_STAMP\n\t# Update the utility grid group\n\tfor i in updated_attributes_static_ac_generator:\n\t\tif i in microgrid[\"UG\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.dg[default_sequence[\"UG\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"UG\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"UG\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of UG has been changed!\")\n\n\tfor i in updated_attributes_static_ac_generator:\n\t\tif i in microgrid[\"DG\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.dg[default_sequence[\"DG\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"DG\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"DG\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of DG has been changed!\")\n\t# Update the energy storage system group\n\tfor i in updated_attributes_static_ess:\n\t\tif i in microgrid[\"ESS\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.ess[0], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"ESS\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"ESS\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of ESS has been changed!\")\n\n\t# Update the photovoltaic generator grid group\n\tfor i in updated_attributes_static_res_generator:\n\t\tif i in microgrid[\"PV\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.pv[0], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"PV\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"PV\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of PV has been changed!\")\n\n\tfor i in updated_attributes_static_res_generator:\n\t\tif i in microgrid[\"WP\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.wp[0], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"WP\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"WP\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of WP has been changed!\")\n\n\t# Update the critical AC load group\n\tfor i in updated_attributes_static_ac_load:\n\t\tif i in microgrid[\"Load_ac\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.load_ac[default_sequence[\"CRI\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"Load_ac\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"Load_ac\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of Load_ac has been changed!\")\n\n\t# Update the non-critical AC load group\n\tfor i in updated_attributes_static_ac_load:\n\t\tif i in microgrid[\"Load_nac\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.load_ac[default_sequence[\"NON_CRI\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"Load_nac\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"Load_nac\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of Load_nac has been changed!\")\n\n\t# Update the critical DC load group\n\tfor i in updated_attributes_static_dc_load:\n\t\tif i in microgrid[\"Load_dc\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.load_dc[default_sequence[\"CRI\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"Load_dc\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"Load_dc\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of Load_dc has been changed!\")\n\n\t# Update the non-critical DC load group\n\tfor i in updated_attributes_static_dc_load:\n\t\tif i in microgrid[\"Load_nac\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.load_dc[default_sequence[\"NON_CRI\"]], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"Load_nac\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"Load_nac\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of Load_nac has been changed!\")\n\n\t# Update the bi-directional convertor group\n\tfor i in updated_attributes_static_bic:\n\t\tif i in microgrid[\"BIC\"]: # Update the attribute value of given attributes list\n\t\t\ttemp = getattr(info.bic[0], i, 0)\n\t\t\tif type(temp) is float or type(temp) is int:\n\t\t\t\tmicrogrid[\"BIC\"][i] = temp\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tmicrogrid[\"BIC\"][i] = temp._values\n\t\t\t\texcept:\n\t\t\t\t\tlogger.warning(\"The protocol buffer model of BIC has been changed!\")\n\t# Return result\n\treturn microgrid","title":""},{"docid":"3771ab371f6ff2cbc260a23006f622fb","score":"0.55485487","text":"def global_update(nodes, rank, network, weights_list):\n\n for j, parameter in enumerate(network.get_parameters()):\n if rank != 0:\n dist.gather(tensor=network.get_parameters()[parameter].data, gather_list=[], dst=0, group=nodes)\n else:\n dist.gather(tensor=network.get_parameters()[parameter].data, gather_list=weights_list[j], dst=0, group=nodes)\n network.get_parameters()[parameter].data = torch.mean(torch.stack(weights_list[j][1:]), dim=0)\n dist.broadcast(network.get_parameters()[parameter], 0, group=nodes)","title":""},{"docid":"dbb69a5a9de7b9737a8138e82ff47d96","score":"0.55434436","text":"def initialize_map(self):\n with open(self.mapping_file, 'r') as mapping_f:\n for line in mapping_f:\n freebase_type, figer_type = line.rstrip('\\n').split('\\t')\n freebase_type = freebase_type.replace('/', '', 1).replace('/', '.')\n figer_type = figer_type.replace('/', '', 1).replace('/', '.')\n self.freebase2figer_map[freebase_type] = figer_type","title":""},{"docid":"2d35445a2d9758f01dea1f947037a5bc","score":"0.55419","text":"def _assign_variables(self) -> None:\n mdg = self.mdg\n for g, d in mdg.subdomains(return_data=True):\n d[pp.PRIMARY_VARIABLES] = {self.displacement_variable: {\"cells\": self.nd}}","title":""},{"docid":"5d53dc62ae65b8deb84b568b1e5ded94","score":"0.55303204","text":"def _update_map(self, xi, yi, resid):\n # Do nothing if doesn't satisfy condition\n if abs(resid) > self.error_term:\n return\n \n # Decide whether to store in map\n store_in_map = False\n if (xi not in self.xi2yi) and (yi not in self.yi2xi):\n # no competition, store in map\n store_in_map = True\n elif (xi in self.xi2yi) and (yi not in self.yi2xi):\n if abs(resid) < abs(self.xi2resid[xi]):\n # This is a better fit than the stored one\n # Break the stored link\n self.yi2resid.pop(self.xi2yi[xi])\n self.yi2xi.pop(self.xi2yi[xi])\n store_in_map = True\n elif (xi not in self.xi2yi) and (yi in self.yi2xi):\n if abs(resid) < abs(self.yi2resid[yi]):\n # This is a better fit than the stored one\n # Break the stored link\n self.xi2resid.pop(self.yi2xi[yi])\n self.xi2yi.pop(self.yi2xi[yi])\n store_in_map = True\n elif self.xi2yi[xi] == yi and self.yi2xi[yi] == xi:\n # this exact link is already stored\n pass\n else:\n # Both are already in the map, and they are out of sync\n raise ValueError(\"map out of sync\")\n \n # Now actually update the dicts\n if store_in_map:\n self.xi2yi[xi] = yi\n self.xi2resid[xi] = resid \n self.yi2xi[yi] = xi\n self.yi2resid[yi] = resid","title":""},{"docid":"661f6c4300039aa8eda144bd5e151fa8","score":"0.55198646","text":"def _assemble_global_K(self):\n elements = self._model.elements\n # Generate Node Freedom Al[location Table and total number of\n # active DOFs of the system\n self.__generate_node_freedom_allocation_dict__()\n # Generate Node Freedom Map dictionary\n self.__generate_node_freedom_map_dict__()\n # number of dof per node\n n_dof = self.n_active_dof\n # Initialize the global stiffness matrix\n K = np.zeros([n_dof, n_dof], dtype=np.float64)\n # Fill the global stiffness matrix\n for n_elem, element in elements.items():\n # Get nodes of the respective element\n nodes_elem = element._nodal_connectivity\n # Assemble element stiffness matrix\n element.assemble_Ke()\n # List to store the global system indices\n g_i = []\n # List of indices of used element DOFs\n g_e = []\n # For each node in the current element\n for n_node_e, node in nodes_elem.items():\n # Get Element Freedom Signature\n efs = element.efs[n_node_e]\n # Number of active DOF in the node of the element\n active_dof = np.sum((efs >= 1))\n if active_dof > 0:\n # Get value of th Node Freedom Assign Table for the\n # current node\n nfat_node = self.nfmt[node.number]\n # Get NFS of the node in the element\n enfs_node = element.enfmt[n_node_e]\n # for the total of used DOF in the node\n # FIXME!!\n index_base = element.get_node_active_dof(n_node_e)\n active_nodes = nfat_node + index_base\n # Extend the list\n g_i.extend(active_nodes)\n #\n index_base_e = element.get_element_active_dof(n_node_e)\n active_nodes_e = enfs_node + index_base_e\n g_e.extend(active_nodes_e)\n\n # Convert list to numpy array in order to broadcast more\n # easily to the global stiffness matrix\n g_i = np.array(g_i)\n g_e = np.array(g_e)\n # Add the contributions to the respective DOFs in global system\n K[g_i[:, None], g_i] += element._Ke[g_e[:, None], g_e]\n\n # Generate sparse matrix\n K_s = sparse.csr_matrix(K)\n\n return K_s","title":""},{"docid":"fdcc207c0f1fd714e5789ca4b0911882","score":"0.55162865","text":"def after_map(self, map):","title":""},{"docid":"8f3c39c304d71f3689fd04d5279f1067","score":"0.5508919","text":"def save_mappings(self):\n self.mappings0 = self.mappings.copy()","title":""},{"docid":"d85815918c97312e8e00330959ed4817","score":"0.549593","text":"def _map_personalities(self):\n self.personality_map = {p: n for n in self.graph.nodes() for p in self.personalities[n]}","title":""},{"docid":"90b6bfd0d29be0248cec16c04c6d857e","score":"0.54929286","text":"def merge_info(self):\n global global_path\n global global_known_p\n global_path = global_path + (list(set(self.path) - set(global_path))) # removes duplicates\n self.path = self.path + (list(set(global_path) - set(self.path))) # removes duplicates\n\n # haalt uit global_known_p eerst weg wat in self.known_p_removed zit\n l3 = [x for x in global_known_p if\n x not in self.known_p_removed]\n global_known_p = l3\n # merge dan alles\n global_known_p = global_known_p + list(set(self.known_p) - set(global_known_p))\n self.known_p = global_known_p","title":""},{"docid":"322572407dc2b24401d5df388e98444f","score":"0.5489253","text":"def update(self, other):\n self._map.update(other._map)","title":""},{"docid":"4572dbb10ca1ad112faaaea4b4287478","score":"0.5486843","text":"def put_map(self):\n self._check(pn_data_put_map(self._data))","title":""},{"docid":"705b5c810ad096d8f1ede78987610aaf","score":"0.54607624","text":"def _set_netcdf_grid_mapping_variable(root, grid_mapping):\n name = grid_mapping.pop(\"name\")\n var = root.createVariable(name, \"S1\", dimensions=())\n for attr in grid_mapping.keys():\n setattr(var, attr, grid_mapping[attr])","title":""},{"docid":"d7b711d9f1af1d606fedb2315d4e5084","score":"0.54587203","text":"def update(self, mapping):\r\n self.frames[0].update(mapping)","title":""},{"docid":"d7b711d9f1af1d606fedb2315d4e5084","score":"0.54587203","text":"def update(self, mapping):\r\n self.frames[0].update(mapping)","title":""},{"docid":"6bdc724b59cce3567d8b30e7fee1a1b6","score":"0.54581684","text":"def mapFromGlobal(self, obj, subdev=None):\n tr = self.inverseGlobalTransform(subdev)\n if tr is not None:\n return self._mapTransform(obj, tr)\n \n ## If our transformation is nonlinear, then the local mapping step must be done separately.\n subdev = self._subdevDict(subdev)\n parent = self.parentDevice()\n if parent is None:\n obj = parent.mapFromGlobal(obj, subdev)\n return self.mapFromParent(obj, subdev)","title":""},{"docid":"0ed3d6a8f3da4fa828a696e4ec58f0a2","score":"0.5453379","text":"def update_discovery_map(self) -> int:\n ret = self.generate_discovery_map()\n if ret != 0:\n return ret\n if self.is_generated_map_different():\n ret = self.commit_discovery_map()\n return ret","title":""},{"docid":"a25a11ca0c74964ec3c3ee9738ba5d0d","score":"0.54403746","text":"def new_feature_map(self, device):\n raise NotImplementedError()","title":""},{"docid":"9eabc2c0e2a4b903d413c0145a101e6f","score":"0.54342556","text":"def _updateKnowledge(self):\n \n # updating known locations\n locations = set(self.locations)\n for loc in self.conn.keys():\n locations.add(loc)\n locations.union(self.conn[loc])\n self.locations = list(locations)\n \n \n # updating hawkweed info at locs\n for loc in self.locations:\n if loc not in self.hawkweed.keys():\n self.hawkweed[loc] = 0.0\n \n # updating _conn to reflect bi-directional paths\n temp = dict(self.conn)\n for loc in self.conn.keys():\n for node in self.conn[loc]:\n if node not in self.conn.keys():\n temp[node] = set()\n temp[node].add(loc)\n self.conn = dict(temp)","title":""},{"docid":"9f5cbefcad8555ef27748045d9677b80","score":"0.54269683","text":"def _update_index_mapper(self):\n\n x = self.x\n x2 = self.x2\n y = self.y\n y2 = self.y2\n\n if \"left\" in self.origin:\n x_low = x\n x_high = x2\n else:\n x_low = x2\n x_high = x\n\n if \"bottom\" in self.origin:\n y_low = y\n y_high = y2\n else:\n y_low = y2\n y_high = y\n\n if self.index_mapper is not None:\n if self.orientation == 'h':\n self.index_mapper.screen_bounds = (x_low, x_high, y_low, y_high)\n else:\n self.index_mapper.screen_bounds = (y_low, y_high, x_low, x_high)\n self.index_mapper_changed = True\n self.invalidate_draw()","title":""},{"docid":"08e87c978cd446bc374cf9c46632f1c7","score":"0.54263204","text":"def _update_proximity(self):\n pass","title":""},{"docid":"ad5ba8913105520e210e98b12d64e6fd","score":"0.5416478","text":"def update_persistent_graph():\n from goldstone.cinder.utils import update_nodes as update_cinder_nodes\n from goldstone.glance.utils import update_nodes as update_glance_nodes\n from goldstone.keystone.utils import update_nodes as update_keystone_nodes\n from goldstone.nova.utils import update_nodes as update_nova_nodes\n\n update_cinder_nodes()\n update_glance_nodes()\n update_keystone_nodes()\n update_nova_nodes()","title":""},{"docid":"199ae760ba6cc1f3ee02b038c85933b3","score":"0.541002","text":"def UpdateMapLabel(self):\n\n vector = self.instruction.FindInstructionByType('vector')\n if vector:\n vectorId = vector.id \n else:\n vectorId = None\n\n raster = self.instruction.FindInstructionByType('raster')\n if raster:\n rasterId = raster.id \n else:\n rasterId = None\n\n rasterName = 'None'\n if rasterId:\n rasterName = self.instruction[rasterId]['raster'].split('@')[0]\n \n self.itemLabels['map'] = self.itemLabels['map'][0:1]\n self.itemLabels['map'].append(\"raster: \" + rasterName)\n if vectorId: \n for map in self.instruction[vectorId]['list']:\n self.itemLabels['map'].append('vector: ' + map[0].split('@')[0])","title":""},{"docid":"b2a0681e33180385a2cec2e16ed3d2f0","score":"0.5409486","text":"def global_update(self):\n grads = []\n for uid in range(self.client_num):\n w = self.weight[uid] / sum(self.weight)\n grads.append(self.client_update(uid) * w)\n torch.cuda.empty_cache()\n\n grads = sum(grads)\n self.server_model.update(grads)\n self.broad_cast()","title":""},{"docid":"78a21b9e451b5095790bca1e2c82c065","score":"0.5400456","text":"def read_mappings(self):\n self.entity2idx = {v: k for k, v in enumerate(self.read_entities())} ##\n self.idx2entity = {v: k for k, v in self.entity2idx.items()}\n self.relation2idx = {v: k for k, v in enumerate(self.read_relations())} ##\n self.idx2relation = {v: k for k, v in self.relation2idx.items()}","title":""},{"docid":"f04a54897a2283bad6bea1a4289bb02d","score":"0.5397055","text":"def _reset_map(self):\n # reset obstacle msg id\n self.last_id = -1\n # first reset map\n if self.manager is not None:\n # create clean slate\n self.manager.map = deepcopy(self.manager.original_map)\n self.manager.neighbourhood = deepcopy(self.manager.original_neighbourhood)\n self.manager.message_by_lanelet = deepcopy(self.manager.original_message_by_lanelet)","title":""},{"docid":"9edd11197f00cf60ac1aa9654896454a","score":"0.53909904","text":"def update_map_info(self, map):\n possible_lanes = ray_localization(self.heading, self.spawn_place, self.engine, return_all_result=True)\n possible_lane_indexes = [lane_index for lane, lane_index, dist in possible_lanes]\n try:\n idx = possible_lane_indexes.index(self.config[\"spawn_lane_index\"])\n except ValueError:\n lane, new_l_index = possible_lanes[0][:-1]\n else:\n lane, new_l_index = possible_lanes[idx][:-1]\n dest = self.config[\"destination_node\"]\n self.navigation.update(\n map,\n current_lane_index=new_l_index,\n final_road_node=dest if dest is not None else None,\n random_seed=self.engine.global_random_seed\n )\n assert lane is not None, \"spawn place is not on road!\"\n self.navigation.update_localization(self)\n self.lane_index = new_l_index\n self.lane = lane","title":""},{"docid":"e9205b466dcb1a23c7c5198375ed36fa","score":"0.5385583","text":"def redraw_map_cmd(self):\n FillMapWithNodes(self).redraw_map()","title":""},{"docid":"0ecff58947fe052db9e4f711f4423fb7","score":"0.5373781","text":"def _update_geospatial_global_attributes(self):\n \n lat_var_name = self.sensor_def_exists('drv_interp_m_gps_lat')\n lon_var_name = self.sensor_def_exists('drv_interp_m_gps_lon')\n if not lat_var_name or not lon_var_name:\n self._logger.warning('Skipping set global geospatial_lat/lon attributes')\n return\n else:\n \n if lat_var_name in self._nc.variables and lon_var_name in self._nc.variables:\n min_lat = self._nc.variables[lat_var_name][:].min()\n max_lat = self._nc.variables[lat_var_name][:].max()\n min_lon = self._nc.variables[lon_var_name][:].min()\n max_lon = self._nc.variables[lon_var_name][:].max()\n \n # Create polygon WKT and set geospatial_bounds\n coords = ((max_lat, min_lon),\n (max_lat, max_lon),\n (min_lat, max_lon),\n (min_lat, min_lon),\n (max_lat, min_lon))\n polygon = Polygon(coords)\n polygon_wkt = polygon.wkt\n else:\n min_lat = np.nan\n max_lat = np.nan\n min_lon = np.nan\n max_lon = np.nan\n polygon_wkt = u'POLYGON EMPTY'\n \n # Set the global attributes\n self._nc.setncattr('geospatial_lat_min', min_lat)\n self._nc.setncattr('geospatial_lat_max', max_lat)\n self._nc.setncattr('geospatial_lon_min', min_lon)\n self._nc.setncattr('geospatial_lon_max', max_lon)\n self._nc.setncattr('geospatial_bounds', polygon_wkt)\n \n depth_var_name = self.sensor_def_exists('drv_depth')\n if not depth_var_name:\n self._logger.warning('Skipping set global geospatial_vertical attributes')\n else:\n if depth_var_name in self._nc.variables:\n min_depth = self._nc.variables[depth_var_name][:].min()\n max_depth = self._nc.variables[depth_var_name][:].max()\n else:\n min_depth = np.nan\n max_depth = np.nan\n \n self._nc.setncattr('geospatial_vertical_min', min_depth)\n self._nc.setncattr('geospatial_vertical_max', max_depth)","title":""},{"docid":"4b3cf55810f540625233cf7cdb28493f","score":"0.53670615","text":"def recreate_map(self):\n self.create_map()\n for item in self.saved_positions.items():\n print(item[1][-1])\n self.update_position(item[1][-1])\n self.draw_historic_path(device_id=item[1][-1]['device_id'],last=20)\n m.draw_map()","title":""},{"docid":"6caa37af69f31dd753f3a30e8d31947f","score":"0.5366982","text":"def before_map(self, map):","title":""},{"docid":"6331a53da197340c1ef29e48dcbdc1a8","score":"0.53653723","text":"def register_for_changed_maps(self):\n pass","title":""},{"docid":"db25b8c4877968567f636861d0781a55","score":"0.5341022","text":"def svf_piomap(self, mapping):","title":""},{"docid":"2375f9a15a5b9effe719648bc7f1b938","score":"0.5339042","text":"def reset_idxes(G):\n mapping = {}\n for new_idx, old_idx in enumerate(G.nodes()):\n mapping[old_idx] = new_idx\n new_G = nx.relabel_nodes(G, mapping, copy=True)\n return new_G, mapping","title":""},{"docid":"c3a2dd5c3327330e96610f0451f0f9f0","score":"0.5338325","text":"def update(self, other):\n return self.__map.update(other)","title":""},{"docid":"74deb448e370242c1cd9caefc50a7b14","score":"0.5315969","text":"def _resort_mapper( self ):\n for s in self._symbol_mapper[ \"node\" ]:\n for k, l in self._symbol_mapper[ \"node\" ][ s ].iteritems( ):\n self._symbol_mapper[ \"node\" ][ s ][ k ] = sorted( set( l ) )","title":""},{"docid":"1a21597549e88e25210badea5acdaa13","score":"0.5310865","text":"def set_map(self, M):\n self._reset(self)\n self.start = None\n self.goal = None\n # TODO: Set map to new value.\n self.map=M\n\n def set_start(self, start):\n \"\"\"Method used to set start attribute \"\"\"\n self._reset(self)\n # TODO: Set start value. Remember to remove goal, closedSet, openSet, cameFrom, gScore, fScore,\n # and path attributes' values.\n self.start=start\n\n\n def set_goal(self, goal):\n \"\"\"Method used to set goal attribute \"\"\"\n self._reset(self)\n # TODO: Set goal value.\n self.goal=goal\n\n def get_current_node(self):\n \"\"\" Returns the node in the open set with the lowest value of f(node).\"\"\"\n # TODO: Return the node in the open set with the lowest value of f(node).\n cur={}\n for i in self.openSet:\n cur[i]=self.fScore[i]\n Min=min(cur.items(), key = lambda x: x[1])\n return Min[0]\n\n\n def get_neighbors(self, node):\n \"\"\"Returns the neighbors of a node\"\"\"\n # TODO: Return the neighbors of a node\n return self.map.roads[node]\n\n def get_gScore(self, node):\n \"\"\"Returns the g Score of a node\"\"\"\n # TODO: Return the g Score of a node\n return self.gScore[node]\n\n\n def get_tenative_gScore(self, current, neighbor):\n \"\"\"Returns the tenative g Score of a node\"\"\"\n # TODO: Return the g Score of the current node\n # plus distance from the current node to it's neighbors\n return self.get_gScore(current) + self.distance(current,neighbor)\n\n def is_open_empty(self):\n \"\"\"returns True if the open set is empty. False otherwise. \"\"\"\n # TODO: Return True if the open set is empty. False otherwise.\n if not self.openSet:\n return True\n\n def distance(self, node_1, node_2):\n \"\"\" Computes the Euclidean L2 Distance\"\"\"\n # TODO: Compute and return the Euclidean L2 Distance\n n1_x=self.map.intersections[node_1][0]\n n1_y=self.map.intersections[node_1][1]\n n2_x=self.map.intersections[node_2][0]\n n2_y=self.map.intersections[node_1][1]\n return math.sqrt(pow(n1_x - n2_x, 2)+pow(n1_y - n2_y, 2))\n\n def heuristic_cost_estimate(self, node):\n \"\"\" Returns the heuristic cost estimate of a node \"\"\"\n # TODO: Return the heuristic cost estimate of a node\n end = self.goal\n return self.distance(end, node)\n\n def calculate_fscore(self, node):\n \"\"\"Calculate the f score of a node. \"\"\"\n # TODO: Calculate and returns the f score of a node.\n # REMEMBER F = G + H\n return self.get_gScore(node)+self.heuristic_cost_estimate(node)\n\n\n def record_best_path_to(self, current, neighbor):\n \"\"\"Record the best path to a node \"\"\"\n # TODO: Record the best path to a node, by updating cameFrom, gScore, and fScore\n self.cameFrom[neighbor]=current\n self.gScore[neighbor]=self.get_tenative_gScore(current,neighbor)\n self.fScore[neighbor]=self.calculate_fscore(neighbor)\n\n PathPlanner.create_closedSet = create_closedSet","title":""},{"docid":"373194bc83c8ad88ff7682bffeccd00a","score":"0.5299723","text":"def _update_weighted_matrix(self) -> None:\n self.weighted_map = deepcopy(self.map)\n for connection in self.weighted_map:\n connections = self.weighted_map[connection]\n connections_count = sum(list(connections.values()))\n for key in self.weighted_map[connection]:\n self.weighted_map[connection][key] /= connections_count","title":""},{"docid":"09d03ebab194d5e392b9b65be3050d3d","score":"0.5297442","text":"def redraw_map(self):\n for node_tag_item in tag_list:\n self.parent.canvas.delete(node_tag_item)\n ReadCfg().read_cfg()\n FillMapWithNodes.run(self)","title":""},{"docid":"3d78b9612c1bb464cd9e766ee7e57617","score":"0.5295578","text":"def get_map_2d(self):\n return super().get_map_2d()","title":""},{"docid":"4d60fd830db3b01ba47724008fcfb462","score":"0.5293758","text":"def update(self, prob_map, position):\r\n raise NotImplementedError()","title":""},{"docid":"91ed8834258d013b7f5e107050bcd3ac","score":"0.5287312","text":"def infomap(self):\n cur = self.con.cursor()\n with open('/home/ubuntu/insight/network_update.pickle', 'rb') as f:\n network = pickle.load(f)\n edge_weight_query = \"\"\"SELECT \"Weights\" FROM network_update;\"\"\"\n cur.execute(edge_weight_query)\n weights = cur.fetchall()\n\n mod_weights = []\n for w in weights:\n mod_weights.append(w[0])\n #clear memory of weights\n weights = None\n\n info_map = network.community_infomap(edge_weights = mod_weights)\n\n with open('/home/ubuntu/insight/communities_updated.pickle', 'wb') as f:\n pickle.dump(info_map, f)","title":""},{"docid":"df3f0ec6de0c3daeb3e28abf6ac39e62","score":"0.52838075","text":"def set_global2local(self, name, global2local=None):\n assert len(name) > 0, 'name cannot be empty.'\n\n if global2local is not None: # Create shared-tensor\n if isinstance(global2local, list):\n global2local = F.tensor(global2local)\n assert 'int64' == get_type_str(F.dtype(global2local)), 'global2local must be int64 type.'\n shared_data = empty_shared_mem(name+'-g2l-', True, global2local.shape, 'int64')\n dlpack = shared_data.to_dlpack()\n self._data_store[name+'-g2l-'] = F.zerocopy_from_dlpack(dlpack)\n self._data_store[name+'-g2l-'][:] = global2local[:]\n # write data information to temp file that can be read by other processes\n self._write_data_shape_type(name+'-g2l-shape-'+str(self._machine_id), global2local)\n self._open_file_list.append(name+'-g2l-shape-'+str(self._machine_id))\n else: # Read shared-tensor\n while True:\n if (os.path.exists(name+'-g2l-shape-'+str(self._machine_id))):\n time.sleep(2) # wait writing finish\n break\n else:\n time.sleep(2) # wait until the file been created\n data_shape, data_type = self._read_data_shape_type(name+'-g2l-shape-'+str(self._machine_id))\n assert data_type == 'int64'\n shared_data = empty_shared_mem(name+'-g2l-', False, data_shape, 'int64')\n dlpack = shared_data.to_dlpack()\n self._data_store[name+'-g2l-'] = F.zerocopy_from_dlpack(dlpack)\n\n self._has_data.add(name+'-g2l-')","title":""},{"docid":"6d0c02cb97b9f765259d90fe059b73ae","score":"0.5277513","text":"def register_for_new_maps(self):\n pass","title":""},{"docid":"8591a855c7377c26fedc622490936f2b","score":"0.5275509","text":"def __load_id_maps(self):\n\n self.id_map.update(dict(zip(self.par_ids, self.names)))\n self.id_map.update(dict(zip(self.names, self.par_ids)))\n\n rdx_names = []\n for keyname in self.names:\n name_ = ''.join(s if s.isalpha() else ' ' for s in keyname).casefold()\n rdx_names.append(name_)\n\n self.__norm_name_map = dict(zip(rdx_names, self.names))","title":""},{"docid":"966aa86de86f8b8a1b41ab0d57ba356a","score":"0.5272972","text":"def _update(self, columns):\n for c in columns:\n self._map[c.key] = c\n self._map[c.id] = c\n if c.iskey:\n setattr(self.key, c.key, c)\n else:\n setattr(self.value, c.key, c)","title":""},{"docid":"9b46ee5795f3f23805fbe7966857ed43","score":"0.5271868","text":"def mapToGlobal(self, obj, subdev=None):\n tr = self.globalTransform(subdev)\n if tr is not None:\n mapped = self._mapTransform(obj, tr)\n return mapped\n\n ## If our transformation is nonlinear, then the local mapping step must be done separately.\n subdev = self._subdevDict(subdev)\n o2 = self.mapToParentDevice(obj, subdev)\n parent = self.parentDevice()\n if parent is None:\n return o2\n else:\n return parent.mapToGlobal(o2, subdev)","title":""},{"docid":"83072c0c410f0ef44e1ed2052e0d6899","score":"0.5265351","text":"def update_type_and_attribute_ids(self):\n\n self.get_type_name_map()\n\n if len(self.input_network.types)>0:\n # If the network has type\n self.input_network.types = [self.network_template_type]\n\n #map the name of the nodes, links and groups to its negative ID\n for n_j in self.input_network.nodes:\n self.name_maps['NODE'][n_j.name] = n_j.id\n self.update_type_and_attribute(n_j)\n\n for l_j in self.input_network.links:\n self.name_maps['LINK'][l_j.name] = l_j.id\n self.update_type_and_attribute(l_j)\n\n for g_j in self.input_network.resourcegroups:\n self.name_maps['GROUP'][g_j.name] = g_j.id\n self.update_type_and_attribute(g_j)","title":""},{"docid":"3d4ed00f8918f1ca806b65d160ee092e","score":"0.5257187","text":"def update_func_world(mech, world):\n globals().update(world)","title":""},{"docid":"b13d7ec9e51858045a707b88cf300de5","score":"0.52528906","text":"def remap(self,states):\n raise NotImplementedError()","title":""},{"docid":"9e0270eaa18a07f6905370c8420fbec6","score":"0.5237808","text":"def update_feed_dict(self):\n gp = self.gaussian_process\n feed_dict = self.feed_dict\n\n gp.update_feed_dict(gp.get_feed_dict_keys(), feed_dict)\n feed_dict[self.hyperparameters[0]] = gp.get_free_state()","title":""},{"docid":"e52d9d722798b948ec0c7bf52676717b","score":"0.5237184","text":"def map_graphs(self):\n\n for k, v in list(self.graphmap.items()):\n if k == v: continue\n self.ruleset[v] = self.ruleset[k]\n del self.ruleset[k]\n for g in self.ruleset:\n for r in self.ruleset[g]:\n r.grapheme = re.sub(k, v, r.grapheme)\n r.leftcontext = re.sub(k, v, r.leftcontext)\n r.rightcontext = re.sub(k, v, r.rightcontext)\n if self.gnulls:\n for gk, gv in list(self.gnulls.items()):\n if (k in gk) or (k in gv):\n del self.gnulls[gk]\n gk = re.sub(k, v, gk)\n gv = re.sub(k, v, gv)\n self.gnulls.update({gk: gv})","title":""},{"docid":"957535cbc732876b71a88720ef3fbbd6","score":"0.5214665","text":"def update_labelid_geo(self):\n local_sql = MysqlConnecttion(\"local\")\n booth_sql = MysqlConnecttion(\"booth\")\n label_map = {}#{\"word\":labelid}\n query = r\"select Word, Labelid from labels WHERE Geogr = 1 and Vert = 0 and Mktseg = 0\"\n rows = local_sql.excute_with_result(query)\n for row in rows:\n label_map[row[0]] = row[1]\n query = r'''(SELECT mqid, docid, labelid, mq_title_vector_short FROM magic_quadrants where removed = 0)'''\n mq_vector_map = {}#{\"mqid\":\"word vector (short)\"}\n label_tmap = {}\n docid_map ={}\n rows = local_sql.excute_with_result(query)\n for row in rows:\n mq_vector_map[row[0]] = row[3]\n docid_map[row[0]] = row[1]\n label_map[row[0]] = row[2]\n query = r'''(SELECT title_short, docid FROM doc_deatail_vector)'''\n cool_map ={}\n rows = local_sql.excute_with_result(query)\n for row in rows:\n cool_map[row[1]] = row[0]\n for mq_id in mq_vector_map:\n json_word_set = mq_vector_map[mq_id]\n if json_word_set == None or json_word_set == \"\":\n json_word_set = \"{}\"\n word_map = json.loads(json_word_set)\n label_list = []\n for word in word_map:\n if word in label_map:\n label_list.append(str(label_map[word]))\n json_word_set = cool_map[docid_map[mq_id]]\n if json_word_set == None or json_word_set == \"\":\n json_word_set = \"{}\"\n word_map = json.loads(json_word_set)\n for word in word_map:\n if word in label_map:\n label_list.append(str(label_map[word]))\n label_list = list(set(label_list))\n length = len(label_list)\n labels = \";\".join(label_list)\n query = r\"update new_magic_quadrants set Geo_label = '%s' where MQID = '%s'\"%(labels, mq_id)\n local_sql.excute(query)\n booth_sql.excute(query)","title":""},{"docid":"15bede240fabcfa504f6c6cf1ca89d59","score":"0.5214411","text":"def reload_mappings(self):\n with open(self.mappings_path, 'rb') as f:\n mappings = cPickle.load(f)\n self.id_to_word = mappings['id_to_word']\n self.id_to_char = mappings['id_to_char']\n self.id_to_tag = mappings['id_to_tag']","title":""},{"docid":"1dc37a233ace5af412a0f91cc0b9398e","score":"0.5208248","text":"def update_global_view_status (self, status):\n log.debug(\"Update Global view (DoV) mapping status with: %s\" % status)\n NFFGToolBox.update_status_info(nffg=self.__dov.get_resource_info(),\n status=status, log=log)","title":""},{"docid":"3416196f9db65217bcc5b02a4a66228a","score":"0.5205673","text":"def _lu_swap(self, r1: int, r2: int) -> Dict[int, str]:\n\n for i in range(self.M.nrows() + min(r1, r2)):\n self.M[r1, i], self.M[r2, i] = self.M[r2, i], self.M[r1, i]\n\n return {r1: fr'\\leftarrow r_{r2+1} \\setminus 0', r2: fr'\\leftarrow r_{r1+1} \\setminus 0'}","title":""},{"docid":"55c75732aae55391b5c0c236db2c88e2","score":"0.5204835","text":"def _update_pointers(self):\n pass","title":""},{"docid":"b51be8ac8c620c4dcb4b889951b7f1ad","score":"0.51973325","text":"def updateTransform(self):\n\n coordCalibrationsOrigin, coordCalibrationsDestiny, namesCalibration = self.getLandmarksByType(PointType.calibrated)\n\n total_calibration_points = len(coordCalibrationsOrigin)\n if (total_calibration_points < 2):\n self.logger.info(\"From \" + str(self.map_id) + \":Not enough points to update.\")\n return\n\n self.logger.info(\"From \"+str(self.map_id)+\":Updating transform with \" + str(total_calibration_points) + \" reference points\")\n origin = np.zeros((total_calibration_points, 2), dtype=np.float32)\n destiny = np.zeros((total_calibration_points, 2), dtype=np.float32)\n for i in range(total_calibration_points):\n origin[i, 0] = coordCalibrationsOrigin[i][0]\n origin[i, 1] = coordCalibrationsOrigin[i][1]\n destiny[i, 0] = coordCalibrationsDestiny[i][0]\n destiny[i, 1] = coordCalibrationsDestiny[i][1]\n\n\n self.CalibratedPtp.updateGlobal(origin,destiny,namesCalibration)\n\n coordT, _, namesTarget = self.getLandmarksByType(PointType.target)\n self.processLocalArea(coordT,namesTarget)\n\n coordNC, _, namesNonCal = self.getLandmarksByType(PointType.non_calibrated)\n self.processLocalArea(coordNC, namesNonCal)\n\n _, coordACQ, namesAcq = self.getLandmarksByType(PointType.acquired)\n self.processLocalArea(coordACQ, namesAcq)\n\n namesAll = self.getLandmarkIds()\n originAll = self.getCoordsFromLandmarks(namesAll,1)\n destinyAll = self.getCoordsFromLandmarks(namesAll,2)\n self.GlobalPtp.updateGlobal(originAll, destinyAll, namesAll)","title":""},{"docid":"4c537836bb19de309e946cbcd4510e93","score":"0.519472","text":"def setAttrMap(self, attr):\n\t\t\n\t\tattrName = attr.getAttribute(\"name\")\n\t\tattrValue = attr.getAttribute(\"value\")\n\t\t\t\t\t\n\t\tif self.mods:\n\t\t\tself.pmSetAttr(self.objWithNS, attrName, float(attrValue), self.mult)\n\t\telse:\n\t\t\tcmds.setAttr(self.objWithNS + \".\" + attrName, float(attrValue))","title":""},{"docid":"da8555175f15d5005b801230f05deb95","score":"0.51908076","text":"def update_objects(self):\n\t\tself.update_projectiles()","title":""},{"docid":"f5d72fc4851081af7f890e41d4f6cced","score":"0.5188522","text":"def SetKey2(self, *args):\n return _TopTools.TopTools_IndexedMapNodeOfIndexedMapOfShape_SetKey2(self, *args)","title":""},{"docid":"34ecf36406dea43ce27cd68365feb032","score":"0.5180778","text":"def request_rebuild_midi_map(self):\n pass","title":""},{"docid":"8c4b1ae5ca27414da51a4be37a7d47d1","score":"0.51782566","text":"def adjustPCmap(self,transition):\r\n poly_map = {}\r\n\tpc_list = sort(self.pc_list)\r\n\tfor i in range(shape(transition)[1]):\r\n\t poly_map[pc_list[i]] = i\r\n\tfor key in self.pc_map.keys():\r\n\t self.pc_map[key] = poly_map[self.pc_map[key]]","title":""},{"docid":"e2956e1113e300458bfaafaab409d074","score":"0.5170163","text":"def _reassign_ids(self, old_new: Dict[int, int]) -> None:\n # update id_fw\n new_id_fw = {}\n for fwid, fws in self.id_fw.items():\n new_id_fw[old_new.get(fwid, fwid)] = fws\n self.id_fw = new_id_fw\n\n # update the Links\n new_l = {}\n for parent, children in self.links.items():\n new_l[old_new.get(parent, parent)] = [old_new.get(child, child) for child in children]\n self.links = Workflow.Links(new_l)\n\n # update the states\n new_fw_states = {}\n for fwid, fw_state in self.fw_states.items():\n new_fw_states[old_new.get(fwid, fwid)] = fw_state\n self.fw_states = new_fw_states","title":""},{"docid":"f3b0b375f86d20e87cb83a906297188e","score":"0.5165266","text":"def request_rebuild_midi_map(self):\r\n return","title":""},{"docid":"e52f41542f0d6796c146e2d4720525ee","score":"0.51637876","text":"def update_map(self, grid_map, pose, scan):\n\n # Current yaw of the robot\n robot_yaw = self.get_yaw(pose.pose.orientation)\n # The origin of the map [m, m, rad]. This is the real-world pose of the\n # cell (0,0) in the map.\n origin = grid_map.get_origin()\n # The map resolution [m/cell]\n resolution = grid_map.get_resolution()\n\n\n \"\"\"\n Fill in your solution here\n \"\"\"\n map_x =[]\n map_y = []\n ind_x = []\n ind_y = [] \n robot_x = []\n robot_y = []\n px=pose.pose.position.x\n py=pose.pose.position.y\n \n \n #For occupied space\n i=0\n for j in range(len( (scan.ranges))):\n \n if scan.range_min >= (scan.ranges[j]) or (scan.ranges[j]) >= scan.range_max:\n continue\n\n else:\n b_angle = scan.angle_min + (j * scan.angle_increment)\n \n map_x.append(px + (scan.ranges[j]) * cos(robot_yaw + b_angle))\n map_y.append(py + (scan.ranges[j]) * sin(robot_yaw + b_angle))\n\n ind_x.append(int((map_x[i]- origin.position.x) / resolution))\n ind_y.append(int((map_y[i]- origin.position.y) / resolution))\n\n # grid index of the robot\n robot_x.append(int((px - origin.position.x) / resolution) )\n robot_y.append(int((py - origin.position.y) / resolution)) \n start =(robot_x[i],robot_y[i])\n end = (ind_x[i],ind_y[i])\n \n trace=self.raytrace(start,end)\n i=i+1\n\n #Filling the free space\n for l in trace:\n self.add_to_map(grid_map, l[0], l[1], self.free_space) \n\n #Filling the occupied space \n for k in range(len(ind_x)):\n self.add_to_map(grid_map, ind_x[k], ind_y[k], self.occupied_space)\n\n\n \"\"\"\n For C only!\n Fill in the update correctly below.\n \"\"\"\n # Only get the part that has been updated\n update = OccupancyGridUpdate()\n # The minimum x index in 'grid_map' that has been updated\n update.x = np.amin(ind_x)\n # The minimum y index in 'grid_map' that has been updated\n update.y = np.amin(ind_y)\n # Maximum x index - minimum x index + 1\n update.w = np.amax(ind_x) - np.amin(ind_x) + 1\n # Maximum y index - minimum y index + 1\n update.h = np.amax(ind_y) - np.amin(ind_y) + 1\n # The map data inside the rectangle, in row-major order.\n update.data = []\n for y_u in range(np.amin(ind_y), np.amax(ind_y) + 1):\n for x_u in range(np.amin(ind_x), np.amax(ind_x) + 1):\n updated_value = grid_map[x_u,y_u]\n update.data.append(updated_value)\n\n # Return the updated map together with only the\n # part of the map that has been updated\n return grid_map, update","title":""},{"docid":"ad8e01e73a8667290a5bbb31fad6287b","score":"0.51632774","text":"def updateMapKey(initkey, key, map,value = 1):\n\tif initkey not in map:\n\t\tmap[initkey] = {}\n\tif key not in map[initkey]:\n\t\tmap[initkey][key] = 0\n\tmap[initkey][key] = map[initkey][key] +value","title":""},{"docid":"a39cce74753d252e93ec0cdebbf41697","score":"0.5159968","text":"def perform_mapping(\n self, fs: typing.List[AnyFunction], map: PointType, inverse_map: PointType, tdim: int\n ) -> typing.List[AnyFunction]:\n raise mappings.MappingNotImplemented()","title":""},{"docid":"a39cce74753d252e93ec0cdebbf41697","score":"0.5159968","text":"def perform_mapping(\n self, fs: typing.List[AnyFunction], map: PointType, inverse_map: PointType, tdim: int\n ) -> typing.List[AnyFunction]:\n raise mappings.MappingNotImplemented()","title":""},{"docid":"0801832ae89c00440c55d6e04dc14e58","score":"0.51568234","text":"def _pad_static_map(self):\n global_costmap = np.array(self.static_map.data, dtype=np.int8).reshape(self.static_map.info.height, -1)\n\n # Get index of occupied cells\n occupied_index = np.where(global_costmap == 100)\n\n # Loop over occupied cells and pad them\n for coord in zip(occupied_index[0], occupied_index[1]):\n self._pad_point(global_costmap, coord)\n \n # Show messages for debugging\n if self.debug_mode == True:\n # Receive an image of the global_costmap\n cv2.imwrite('map_padded.jpg', global_costmap.astype(np.uint8))\n print(np.unique(global_costmap))\n\n self.static_map.data = global_costmap.ravel()","title":""},{"docid":"60ff5edb4617790d1566bc05d5b5e006","score":"0.51456535","text":"def reinit(self):\n self.current_value_global = copy_dict(self.varGlobalInit)\n for mod in self.modules:\n mod.reinit()","title":""},{"docid":"20cf72991c6f600c1c90b3d4c6386e50","score":"0.5143412","text":"def updateConstantsFromMapping(self, mapping):\n for key, val in mapping.iteritems():\n if isinstance(val, Constant):\n if key in self.constants:\n log.warn(\"Constant with name %s is already declared, overriding\", key)\n self.constants[key] = (val.value, val.type)","title":""},{"docid":"c9002293ef606c513567d1176729c260","score":"0.51360947","text":"def assign_simple_node_features(ndata, g, ntypes, assign_id=False):\n for ntype in ntypes:\n for col in g.nodes[ntype].data.keys():\n if not assign_id and col == dgl.NID:\n continue\n induced_nodes = ndata[dgl.NID][ntype]\n ndata[col] = {ntype: g.nodes[ntype].data[col][induced_nodes]}","title":""},{"docid":"a5a96233a922d24cbc576114b67200ad","score":"0.51353544","text":"def test_incremental_fcache_buildup_and_global_access(self):\n # map with empty\n self.upapa_class.purge_fasta_info(\"Test.fasta\")\n maps = self.upapa_class.map_peptide(peptide=\"KLEINER\", fasta_name=\"Test.fasta\")\n # print(maps)\n self.assertEqual(len(maps), 0)\n\n # parse one fasta via a unode ..\n self.upapa_class.build_lookup(fasta_name=\"Test.fasta\", fasta_stream=TEST_FASTA)\n # map with one parsed fasta\n maps = self.upapa_class.map_peptide(peptide=\"KLEINER\", fasta_name=\"Test.fasta\")\n self.assertEqual(len(maps), 1)\n # parse another fasta via a different unode ..\n self.upapa_class.build_lookup(\n fasta_name=\"Test.fasta\", fasta_stream=TEST_FASTA_TWO\n )\n # map with two parsed fastas\n\n maps = self.upapa_class.map_peptide(peptide=\"KLEINER\", fasta_name=\"Test.fasta\")\n print(maps)\n self.assertEqual(len(maps), 2)","title":""},{"docid":"8a37e733648f3b986c55cf9db5553f95","score":"0.5135315","text":"def update_nni(nodes, links):\n for link in links:\n ports = link[\"ports\"]\n nni_a, nni_b = ports[0], ports[1]\n node_a = nni_a.split(\":\")[4]\n port_a = nni_a.split(\":\")[5]\n node_b = nni_b.split(\":\")[4]\n port_b = nni_b.split(\":\")[5]\n for node in nodes:\n if node[\"name\"] == node_a:\n for port in node[\"ports\"]:\n if port_a == port[\"id\"].split(\":\")[5]:\n port[\"nni\"] = nni_b\n elif node[\"name\"] == node_b:\n for port in node[\"ports\"]:\n if port_b == port[\"id\"].split(\":\")[5]:\n port[\"nni\"] = nni_a","title":""},{"docid":"37e120373fa89087f76b7651b1315eb6","score":"0.513131","text":"def merge_info(self):\n global global_path\n global global_known_p\n global_path = global_path + (list(set(self.path) - set(global_path))) # removes duplicates\n self.path = self.path + (list(set(global_path) - set(self.path))) # removes duplicates\n\n # haalt uit global_known_p eerst weg wat in self.known_p_removed zit\n l3 = [x for x in global_known_p if x not in self.known_p_removed]\n global_known_p = l3\n # merge dan alles\n global_known_p = global_known_p + list(set(self.known_p) - set(global_known_p))\n self.known_p = global_known_p\n self.sort_known_patients()\n l3 = []","title":""},{"docid":"3afdeee6f9b19b30e8ad8412d3772e2a","score":"0.5125499","text":"def update_idx_id_mapping(self):\n if self.dpd is not None and self.dpd.shape[0] > 0:\n # sort by id link ascending\n self.dpd = self.dpd[self.dpd[:, 1].argsort()]\n\n # update dpd_idx to make the index to ID pairing bidirectional again\n for idx, dpd_pair in enumerate(self.dpd):\n self.reps[dpd_pair[1]].dpd_idx = idx","title":""},{"docid":"6ff897a6151f85183d9da06575ac678b","score":"0.51248366","text":"def map_variable(gdb_path, mukey_path, variable, dst):\n\n # Expand user path\n mukey_path = os.path.expanduser(mukey_path)\n gdb_path = os.path.expanduser(gdb_path)\n dst = os.path.expanduser(dst)\n\n # Get the Map Unit Aggregated Attribute Table\n mukey = xr.open_rasterio(mukey_path, chunks=(1, 5000, 5000))\n umukeys = np.unique(mukey[:]).astype(str)\n muaggatt = gpd.read_file(gdb_path, layer=\"muaggatt\")\n chorizon = gpd.read_file(gdb_path, layer=\"chorizon\")\n cogeomordesc = gpd.read_file(gdb_path, layer=\"cogeomordesc\")\n components = gpd.read_file(gdb_path, layer=\"component\")\n components = pd.merge(chorizon, components, on=\"cokey\")\n components = pd.merge(components, muaggatt, on=\"mukey\")\n\n # Put the keys in front\n keys = [c for c in components.columns if \"key\" in c]\n others = [c for c in components.columns if \"key\" not in c]\n new_order = keys + others\n components = components[new_order]\n\n # Get the Horizon Table\n munits = muaggatt[[\"mukey\", \"muname\"]]\n variable_df = pd.merge(variable_df, munits, on=\"mukey\")\n variable_df = components[[\"mukey\", \"chkey\", \"muname\", \"hzname\", \"geomdesc\",\n \"desgnmaster\", \"hzdept_r\", \"hzdepb_r\", \"hzthk_r\",\n \"sandtotal_r\", \"silttotal_r\", \"claytotal_r\", \n variable]]\n\n # Now, whats the best way to map these values\n val_dict = dict(zip(variable_df[\"mukey\"].astype(int),\n variable_df[variable]))\n mv = Map_Values(val_dict, err_val=-9999)\n mv.map_file(mukey_path, dst)","title":""},{"docid":"965d1c87eb8723c7aa692e6b73d897bf","score":"0.5122539","text":"def map_update(self, m):\n dists = np.zeros((self.num_points,))\n for idx, point in enumerate(self.coords):\n dists[idx] = m.nearest_lane_dist(point)\n if np.percentile(dists, 5) > 15:\n # Don't bother incorporating weights if the 95%+ of particles are\n # more than 15m from a road. In that case, we can safely assume\n # that we're off the road.\n return\n factors = 1.0 / ((1 + dists ** 2) ** 1.1)\n self.weights *= factors","title":""},{"docid":"3de2192a9ab603c96997a048cbd4c709","score":"0.5116645","text":"def __reset_node_ids (info, binding):\n log.debug(\"Reset NF paths...\")\n for attr in (getattr(info, e) for e in info._sorted_children):\n rewrite = []\n for element in attr:\n if hasattr(element, \"object\"):\n old_path = element.object.get_value()\n bb, nf = get_bb_nf_from_path(path=old_path)\n if bb not in binding:\n log.warning(\"Missing binding for node: %s\" % bb)\n continue\n new_bb = binding.get(bb)\n log.debug(\"Find BiSBiS node remapping: %s --> %s\" % (bb, new_bb))\n old_bb, new_bb = \"/node[id=%s]\" % bb, \"/node[id=%s]\" % new_bb\n new_path = str(old_path).replace(old_bb, new_bb)\n rewrite.append((element, new_path))\n # Tricky override because object is key in yang -> del and re-add\n for e, p in rewrite:\n attr.remove(e)\n e.object.set_value(p)\n attr.add(e)\n log.debug(\"Overrided new path for NF --> %s\" % e.object.get_value())\n log.log(VERBOSE, info.xml())\n return info","title":""},{"docid":"97354a1a33c34f4428dda61ed1f460df","score":"0.51147324","text":"def updatekernel(self,kernel):\n self.GPkernel = kernel","title":""},{"docid":"71a3d64111258b46fa8a2a867a71cd1e","score":"0.51105374","text":"def recalc_info_dict(self):\n self._info_dict = self.__create_info_dict()\n map(self.add_s2_info_dict, self.slist)","title":""},{"docid":"aa1e5029ae5d3a96acd4f03baf72ffa1","score":"0.51089907","text":"def _post_setattr_formulas(self, old, new):\n self.database_entries = {key: 1.0 for key in new}","title":""}],"string":"[\n {\n \"docid\": \"c51c89163158e8315eca9b877ad3d659\",\n \"score\": \"0.6116114\",\n \"text\": \"def _update_global_vars(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cec46d1e0d250965becc0faf411415\",\n \"score\": \"0.60464275\",\n \"text\": \"def _update_mappings(self):\\n self.existing_cost_entry_map.update(self.processed_report.cost_entries)\\n self.existing_product_map.update(self.processed_report.products)\\n self.existing_pricing_map.update(self.processed_report.pricing)\\n self.existing_reservation_map.update(self.processed_report.reservations)\\n\\n self.processed_report.remove_processed_rows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"887980315c5f8b7230278e395e40877e\",\n \"score\": \"0.58705133\",\n \"text\": \"def _set_standard_mapping(self, fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs):\\n raise NotImplementedError('The _set_standard_mapping method must be implemented in subclasses')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb4c3b4b6babce1477387971704cf90\",\n \"score\": \"0.58472997\",\n \"text\": \"def setChampIdMap():\\n\\tglobal champIdMap\\n\\tchampIdMap = getChampMap()\\n\\tprint 'map updated'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8db93bc1a0d7e458dc1f1eace9b2a9\",\n \"score\": \"0.5842898\",\n \"text\": \"def set_global_attributes(self):\\n\\n for key, value in sorted(self._attributes['global'].items()):\\n self._nc.setncattr(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846dea85c48ad26d54fcea24ee8b60de\",\n \"score\": \"0.57833976\",\n \"text\": \"def update_global(data):\\n for key, value in data.items():\\n setattr(g, key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c06b2ac88442709ad6f5f9227b1144\",\n \"score\": \"0.5764773\",\n \"text\": \"def global_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f25ee6e442b9bef343c0315349fc4e\",\n \"score\": \"0.5733424\",\n \"text\": \"def transform_mappings(self):\\n self.entity2idx = {v: k for k, v in enumerate(self.all_entities)}\\n self.idx2entity = {v: k for k, v in self.entity2idx.items()}\\n self.relation2idx = {v: k for k, v in enumerate(self.all_relations)}\\n self.idx2relation = {v: k for k, v in self.relation2idx.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78292a087bbb8b537e09cc2a7669695c\",\n \"score\": \"0.56884396\",\n \"text\": \"def update(self, mapping: dict):\\n self.__d.update(mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aba0deaf061ccb789440dde516ca413\",\n \"score\": \"0.5644098\",\n \"text\": \"def make_map(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3fc7375f9195c3e686d7864f61b8109\",\n \"score\": \"0.56298625\",\n \"text\": \"def update2(self, ir, can, nheav):\\n self.ncan = len(self.cans)\\n if can not in self.cans:\\n #print '++', can #, '\\\\n\\\\n'\\n self.maps.append( [ir, self.ncan, 0] )\\n #self.iokgs.append( T )\\n self.cans.append( can )\\n self.nsheav.append( nheav )\\n self.ncan += 1\\n else:\\n ican = self.cans.index( can )\\n entry = [ir, ican, 0]\\n if entry not in self.maps:\\n self.maps.append( entry )\\n #self.iokgs.append( T )\\n #print(' -- maps = ', self.maps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8159952b3a5e728d4b04e2337280cab\",\n \"score\": \"0.562656\",\n \"text\": \"def add_to_map(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8159952b3a5e728d4b04e2337280cab\",\n \"score\": \"0.562656\",\n \"text\": \"def add_to_map(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f27649842dce2a80494688cd03d0570\",\n \"score\": \"0.5603346\",\n \"text\": \"def update_discovery_map(self) -> int:\\n raise NotImplementedError() # pragma no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525f1b847e4f220709f7716eaacd4922\",\n \"score\": \"0.56023455\",\n \"text\": \"def _clearNodeMap(self):\\r\\n \\r\\n self._py_nodes = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8c3505ebf715c0d25393cca664a280\",\n \"score\": \"0.5580438\",\n \"text\": \"def reload_mapping():\\r\\n mod = __import__(module_name)\\r\\n mapping = getattr(mod, mapping_name, None)\\r\\n if mapping:\\r\\n self.fvars = mod.__dict__\\r\\n self.mapping = mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d36ee12d1838e6f9672c04a6e5c52c11\",\n \"score\": \"0.55775356\",\n \"text\": \"def update_map(self, map_form):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6725b8ec2df9a1ed6551f7b4663809ab\",\n \"score\": \"0.5571848\",\n \"text\": \"def static_information_update(microgrid, info, logger):\\n\\tmicrogrid = deepcopy(microgrid)\\n\\tmicrogrid[\\\"AREA\\\"] = info.AREA\\n\\tmicrogrid[\\\"TIME_STAMP\\\"] = info.TIME_STAMP\\n\\t# Update the utility grid group\\n\\tfor i in updated_attributes_static_ac_generator:\\n\\t\\tif i in microgrid[\\\"UG\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.dg[default_sequence[\\\"UG\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"UG\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"UG\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of UG has been changed!\\\")\\n\\n\\tfor i in updated_attributes_static_ac_generator:\\n\\t\\tif i in microgrid[\\\"DG\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.dg[default_sequence[\\\"DG\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"DG\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"DG\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of DG has been changed!\\\")\\n\\t# Update the energy storage system group\\n\\tfor i in updated_attributes_static_ess:\\n\\t\\tif i in microgrid[\\\"ESS\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.ess[0], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"ESS\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"ESS\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of ESS has been changed!\\\")\\n\\n\\t# Update the photovoltaic generator grid group\\n\\tfor i in updated_attributes_static_res_generator:\\n\\t\\tif i in microgrid[\\\"PV\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.pv[0], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"PV\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"PV\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of PV has been changed!\\\")\\n\\n\\tfor i in updated_attributes_static_res_generator:\\n\\t\\tif i in microgrid[\\\"WP\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.wp[0], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"WP\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"WP\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of WP has been changed!\\\")\\n\\n\\t# Update the critical AC load group\\n\\tfor i in updated_attributes_static_ac_load:\\n\\t\\tif i in microgrid[\\\"Load_ac\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.load_ac[default_sequence[\\\"CRI\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"Load_ac\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"Load_ac\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of Load_ac has been changed!\\\")\\n\\n\\t# Update the non-critical AC load group\\n\\tfor i in updated_attributes_static_ac_load:\\n\\t\\tif i in microgrid[\\\"Load_nac\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.load_ac[default_sequence[\\\"NON_CRI\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"Load_nac\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"Load_nac\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of Load_nac has been changed!\\\")\\n\\n\\t# Update the critical DC load group\\n\\tfor i in updated_attributes_static_dc_load:\\n\\t\\tif i in microgrid[\\\"Load_dc\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.load_dc[default_sequence[\\\"CRI\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"Load_dc\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"Load_dc\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of Load_dc has been changed!\\\")\\n\\n\\t# Update the non-critical DC load group\\n\\tfor i in updated_attributes_static_dc_load:\\n\\t\\tif i in microgrid[\\\"Load_nac\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.load_dc[default_sequence[\\\"NON_CRI\\\"]], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"Load_nac\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"Load_nac\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of Load_nac has been changed!\\\")\\n\\n\\t# Update the bi-directional convertor group\\n\\tfor i in updated_attributes_static_bic:\\n\\t\\tif i in microgrid[\\\"BIC\\\"]: # Update the attribute value of given attributes list\\n\\t\\t\\ttemp = getattr(info.bic[0], i, 0)\\n\\t\\t\\tif type(temp) is float or type(temp) is int:\\n\\t\\t\\t\\tmicrogrid[\\\"BIC\\\"][i] = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tmicrogrid[\\\"BIC\\\"][i] = temp._values\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tlogger.warning(\\\"The protocol buffer model of BIC has been changed!\\\")\\n\\t# Return result\\n\\treturn microgrid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3771ab371f6ff2cbc260a23006f622fb\",\n \"score\": \"0.55485487\",\n \"text\": \"def global_update(nodes, rank, network, weights_list):\\n\\n for j, parameter in enumerate(network.get_parameters()):\\n if rank != 0:\\n dist.gather(tensor=network.get_parameters()[parameter].data, gather_list=[], dst=0, group=nodes)\\n else:\\n dist.gather(tensor=network.get_parameters()[parameter].data, gather_list=weights_list[j], dst=0, group=nodes)\\n network.get_parameters()[parameter].data = torch.mean(torch.stack(weights_list[j][1:]), dim=0)\\n dist.broadcast(network.get_parameters()[parameter], 0, group=nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb69a5a9de7b9737a8138e82ff47d96\",\n \"score\": \"0.55434436\",\n \"text\": \"def initialize_map(self):\\n with open(self.mapping_file, 'r') as mapping_f:\\n for line in mapping_f:\\n freebase_type, figer_type = line.rstrip('\\\\n').split('\\\\t')\\n freebase_type = freebase_type.replace('/', '', 1).replace('/', '.')\\n figer_type = figer_type.replace('/', '', 1).replace('/', '.')\\n self.freebase2figer_map[freebase_type] = figer_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d35445a2d9758f01dea1f947037a5bc\",\n \"score\": \"0.55419\",\n \"text\": \"def _assign_variables(self) -> None:\\n mdg = self.mdg\\n for g, d in mdg.subdomains(return_data=True):\\n d[pp.PRIMARY_VARIABLES] = {self.displacement_variable: {\\\"cells\\\": self.nd}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d53dc62ae65b8deb84b568b1e5ded94\",\n \"score\": \"0.55303204\",\n \"text\": \"def _update_map(self, xi, yi, resid):\\n # Do nothing if doesn't satisfy condition\\n if abs(resid) > self.error_term:\\n return\\n \\n # Decide whether to store in map\\n store_in_map = False\\n if (xi not in self.xi2yi) and (yi not in self.yi2xi):\\n # no competition, store in map\\n store_in_map = True\\n elif (xi in self.xi2yi) and (yi not in self.yi2xi):\\n if abs(resid) < abs(self.xi2resid[xi]):\\n # This is a better fit than the stored one\\n # Break the stored link\\n self.yi2resid.pop(self.xi2yi[xi])\\n self.yi2xi.pop(self.xi2yi[xi])\\n store_in_map = True\\n elif (xi not in self.xi2yi) and (yi in self.yi2xi):\\n if abs(resid) < abs(self.yi2resid[yi]):\\n # This is a better fit than the stored one\\n # Break the stored link\\n self.xi2resid.pop(self.yi2xi[yi])\\n self.xi2yi.pop(self.yi2xi[yi])\\n store_in_map = True\\n elif self.xi2yi[xi] == yi and self.yi2xi[yi] == xi:\\n # this exact link is already stored\\n pass\\n else:\\n # Both are already in the map, and they are out of sync\\n raise ValueError(\\\"map out of sync\\\")\\n \\n # Now actually update the dicts\\n if store_in_map:\\n self.xi2yi[xi] = yi\\n self.xi2resid[xi] = resid \\n self.yi2xi[yi] = xi\\n self.yi2resid[yi] = resid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661f6c4300039aa8eda144bd5e151fa8\",\n \"score\": \"0.55198646\",\n \"text\": \"def _assemble_global_K(self):\\n elements = self._model.elements\\n # Generate Node Freedom Al[location Table and total number of\\n # active DOFs of the system\\n self.__generate_node_freedom_allocation_dict__()\\n # Generate Node Freedom Map dictionary\\n self.__generate_node_freedom_map_dict__()\\n # number of dof per node\\n n_dof = self.n_active_dof\\n # Initialize the global stiffness matrix\\n K = np.zeros([n_dof, n_dof], dtype=np.float64)\\n # Fill the global stiffness matrix\\n for n_elem, element in elements.items():\\n # Get nodes of the respective element\\n nodes_elem = element._nodal_connectivity\\n # Assemble element stiffness matrix\\n element.assemble_Ke()\\n # List to store the global system indices\\n g_i = []\\n # List of indices of used element DOFs\\n g_e = []\\n # For each node in the current element\\n for n_node_e, node in nodes_elem.items():\\n # Get Element Freedom Signature\\n efs = element.efs[n_node_e]\\n # Number of active DOF in the node of the element\\n active_dof = np.sum((efs >= 1))\\n if active_dof > 0:\\n # Get value of th Node Freedom Assign Table for the\\n # current node\\n nfat_node = self.nfmt[node.number]\\n # Get NFS of the node in the element\\n enfs_node = element.enfmt[n_node_e]\\n # for the total of used DOF in the node\\n # FIXME!!\\n index_base = element.get_node_active_dof(n_node_e)\\n active_nodes = nfat_node + index_base\\n # Extend the list\\n g_i.extend(active_nodes)\\n #\\n index_base_e = element.get_element_active_dof(n_node_e)\\n active_nodes_e = enfs_node + index_base_e\\n g_e.extend(active_nodes_e)\\n\\n # Convert list to numpy array in order to broadcast more\\n # easily to the global stiffness matrix\\n g_i = np.array(g_i)\\n g_e = np.array(g_e)\\n # Add the contributions to the respective DOFs in global system\\n K[g_i[:, None], g_i] += element._Ke[g_e[:, None], g_e]\\n\\n # Generate sparse matrix\\n K_s = sparse.csr_matrix(K)\\n\\n return K_s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdcc207c0f1fd714e5789ca4b0911882\",\n \"score\": \"0.55162865\",\n \"text\": \"def after_map(self, map):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3c39c304d71f3689fd04d5279f1067\",\n \"score\": \"0.5508919\",\n \"text\": \"def save_mappings(self):\\n self.mappings0 = self.mappings.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85815918c97312e8e00330959ed4817\",\n \"score\": \"0.549593\",\n \"text\": \"def _map_personalities(self):\\n self.personality_map = {p: n for n in self.graph.nodes() for p in self.personalities[n]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b6bfd0d29be0248cec16c04c6d857e\",\n \"score\": \"0.54929286\",\n \"text\": \"def merge_info(self):\\n global global_path\\n global global_known_p\\n global_path = global_path + (list(set(self.path) - set(global_path))) # removes duplicates\\n self.path = self.path + (list(set(global_path) - set(self.path))) # removes duplicates\\n\\n # haalt uit global_known_p eerst weg wat in self.known_p_removed zit\\n l3 = [x for x in global_known_p if\\n x not in self.known_p_removed]\\n global_known_p = l3\\n # merge dan alles\\n global_known_p = global_known_p + list(set(self.known_p) - set(global_known_p))\\n self.known_p = global_known_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322572407dc2b24401d5df388e98444f\",\n \"score\": \"0.5489253\",\n \"text\": \"def update(self, other):\\n self._map.update(other._map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4572dbb10ca1ad112faaaea4b4287478\",\n \"score\": \"0.5486843\",\n \"text\": \"def put_map(self):\\n self._check(pn_data_put_map(self._data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705b5c810ad096d8f1ede78987610aaf\",\n \"score\": \"0.54607624\",\n \"text\": \"def _set_netcdf_grid_mapping_variable(root, grid_mapping):\\n name = grid_mapping.pop(\\\"name\\\")\\n var = root.createVariable(name, \\\"S1\\\", dimensions=())\\n for attr in grid_mapping.keys():\\n setattr(var, attr, grid_mapping[attr])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b711d9f1af1d606fedb2315d4e5084\",\n \"score\": \"0.54587203\",\n \"text\": \"def update(self, mapping):\\r\\n self.frames[0].update(mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b711d9f1af1d606fedb2315d4e5084\",\n \"score\": \"0.54587203\",\n \"text\": \"def update(self, mapping):\\r\\n self.frames[0].update(mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bdc724b59cce3567d8b30e7fee1a1b6\",\n \"score\": \"0.54581684\",\n \"text\": \"def mapFromGlobal(self, obj, subdev=None):\\n tr = self.inverseGlobalTransform(subdev)\\n if tr is not None:\\n return self._mapTransform(obj, tr)\\n \\n ## If our transformation is nonlinear, then the local mapping step must be done separately.\\n subdev = self._subdevDict(subdev)\\n parent = self.parentDevice()\\n if parent is None:\\n obj = parent.mapFromGlobal(obj, subdev)\\n return self.mapFromParent(obj, subdev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ed3d6a8f3da4fa828a696e4ec58f0a2\",\n \"score\": \"0.5453379\",\n \"text\": \"def update_discovery_map(self) -> int:\\n ret = self.generate_discovery_map()\\n if ret != 0:\\n return ret\\n if self.is_generated_map_different():\\n ret = self.commit_discovery_map()\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25a11ca0c74964ec3c3ee9738ba5d0d\",\n \"score\": \"0.54403746\",\n \"text\": \"def new_feature_map(self, device):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eabc2c0e2a4b903d413c0145a101e6f\",\n \"score\": \"0.54342556\",\n \"text\": \"def _updateKnowledge(self):\\n \\n # updating known locations\\n locations = set(self.locations)\\n for loc in self.conn.keys():\\n locations.add(loc)\\n locations.union(self.conn[loc])\\n self.locations = list(locations)\\n \\n \\n # updating hawkweed info at locs\\n for loc in self.locations:\\n if loc not in self.hawkweed.keys():\\n self.hawkweed[loc] = 0.0\\n \\n # updating _conn to reflect bi-directional paths\\n temp = dict(self.conn)\\n for loc in self.conn.keys():\\n for node in self.conn[loc]:\\n if node not in self.conn.keys():\\n temp[node] = set()\\n temp[node].add(loc)\\n self.conn = dict(temp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5cbefcad8555ef27748045d9677b80\",\n \"score\": \"0.54269683\",\n \"text\": \"def _update_index_mapper(self):\\n\\n x = self.x\\n x2 = self.x2\\n y = self.y\\n y2 = self.y2\\n\\n if \\\"left\\\" in self.origin:\\n x_low = x\\n x_high = x2\\n else:\\n x_low = x2\\n x_high = x\\n\\n if \\\"bottom\\\" in self.origin:\\n y_low = y\\n y_high = y2\\n else:\\n y_low = y2\\n y_high = y\\n\\n if self.index_mapper is not None:\\n if self.orientation == 'h':\\n self.index_mapper.screen_bounds = (x_low, x_high, y_low, y_high)\\n else:\\n self.index_mapper.screen_bounds = (y_low, y_high, x_low, x_high)\\n self.index_mapper_changed = True\\n self.invalidate_draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e87c978cd446bc374cf9c46632f1c7\",\n \"score\": \"0.54263204\",\n \"text\": \"def _update_proximity(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5ba8913105520e210e98b12d64e6fd\",\n \"score\": \"0.5416478\",\n \"text\": \"def update_persistent_graph():\\n from goldstone.cinder.utils import update_nodes as update_cinder_nodes\\n from goldstone.glance.utils import update_nodes as update_glance_nodes\\n from goldstone.keystone.utils import update_nodes as update_keystone_nodes\\n from goldstone.nova.utils import update_nodes as update_nova_nodes\\n\\n update_cinder_nodes()\\n update_glance_nodes()\\n update_keystone_nodes()\\n update_nova_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199ae760ba6cc1f3ee02b038c85933b3\",\n \"score\": \"0.541002\",\n \"text\": \"def UpdateMapLabel(self):\\n\\n vector = self.instruction.FindInstructionByType('vector')\\n if vector:\\n vectorId = vector.id \\n else:\\n vectorId = None\\n\\n raster = self.instruction.FindInstructionByType('raster')\\n if raster:\\n rasterId = raster.id \\n else:\\n rasterId = None\\n\\n rasterName = 'None'\\n if rasterId:\\n rasterName = self.instruction[rasterId]['raster'].split('@')[0]\\n \\n self.itemLabels['map'] = self.itemLabels['map'][0:1]\\n self.itemLabels['map'].append(\\\"raster: \\\" + rasterName)\\n if vectorId: \\n for map in self.instruction[vectorId]['list']:\\n self.itemLabels['map'].append('vector: ' + map[0].split('@')[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a0681e33180385a2cec2e16ed3d2f0\",\n \"score\": \"0.5409486\",\n \"text\": \"def global_update(self):\\n grads = []\\n for uid in range(self.client_num):\\n w = self.weight[uid] / sum(self.weight)\\n grads.append(self.client_update(uid) * w)\\n torch.cuda.empty_cache()\\n\\n grads = sum(grads)\\n self.server_model.update(grads)\\n self.broad_cast()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78a21b9e451b5095790bca1e2c82c065\",\n \"score\": \"0.5400456\",\n \"text\": \"def read_mappings(self):\\n self.entity2idx = {v: k for k, v in enumerate(self.read_entities())} ##\\n self.idx2entity = {v: k for k, v in self.entity2idx.items()}\\n self.relation2idx = {v: k for k, v in enumerate(self.read_relations())} ##\\n self.idx2relation = {v: k for k, v in self.relation2idx.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f04a54897a2283bad6bea1a4289bb02d\",\n \"score\": \"0.5397055\",\n \"text\": \"def _reset_map(self):\\n # reset obstacle msg id\\n self.last_id = -1\\n # first reset map\\n if self.manager is not None:\\n # create clean slate\\n self.manager.map = deepcopy(self.manager.original_map)\\n self.manager.neighbourhood = deepcopy(self.manager.original_neighbourhood)\\n self.manager.message_by_lanelet = deepcopy(self.manager.original_message_by_lanelet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9edd11197f00cf60ac1aa9654896454a\",\n \"score\": \"0.53909904\",\n \"text\": \"def update_map_info(self, map):\\n possible_lanes = ray_localization(self.heading, self.spawn_place, self.engine, return_all_result=True)\\n possible_lane_indexes = [lane_index for lane, lane_index, dist in possible_lanes]\\n try:\\n idx = possible_lane_indexes.index(self.config[\\\"spawn_lane_index\\\"])\\n except ValueError:\\n lane, new_l_index = possible_lanes[0][:-1]\\n else:\\n lane, new_l_index = possible_lanes[idx][:-1]\\n dest = self.config[\\\"destination_node\\\"]\\n self.navigation.update(\\n map,\\n current_lane_index=new_l_index,\\n final_road_node=dest if dest is not None else None,\\n random_seed=self.engine.global_random_seed\\n )\\n assert lane is not None, \\\"spawn place is not on road!\\\"\\n self.navigation.update_localization(self)\\n self.lane_index = new_l_index\\n self.lane = lane\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9205b466dcb1a23c7c5198375ed36fa\",\n \"score\": \"0.5385583\",\n \"text\": \"def redraw_map_cmd(self):\\n FillMapWithNodes(self).redraw_map()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecff58947fe052db9e4f711f4423fb7\",\n \"score\": \"0.5373781\",\n \"text\": \"def _update_geospatial_global_attributes(self):\\n \\n lat_var_name = self.sensor_def_exists('drv_interp_m_gps_lat')\\n lon_var_name = self.sensor_def_exists('drv_interp_m_gps_lon')\\n if not lat_var_name or not lon_var_name:\\n self._logger.warning('Skipping set global geospatial_lat/lon attributes')\\n return\\n else:\\n \\n if lat_var_name in self._nc.variables and lon_var_name in self._nc.variables:\\n min_lat = self._nc.variables[lat_var_name][:].min()\\n max_lat = self._nc.variables[lat_var_name][:].max()\\n min_lon = self._nc.variables[lon_var_name][:].min()\\n max_lon = self._nc.variables[lon_var_name][:].max()\\n \\n # Create polygon WKT and set geospatial_bounds\\n coords = ((max_lat, min_lon),\\n (max_lat, max_lon),\\n (min_lat, max_lon),\\n (min_lat, min_lon),\\n (max_lat, min_lon))\\n polygon = Polygon(coords)\\n polygon_wkt = polygon.wkt\\n else:\\n min_lat = np.nan\\n max_lat = np.nan\\n min_lon = np.nan\\n max_lon = np.nan\\n polygon_wkt = u'POLYGON EMPTY'\\n \\n # Set the global attributes\\n self._nc.setncattr('geospatial_lat_min', min_lat)\\n self._nc.setncattr('geospatial_lat_max', max_lat)\\n self._nc.setncattr('geospatial_lon_min', min_lon)\\n self._nc.setncattr('geospatial_lon_max', max_lon)\\n self._nc.setncattr('geospatial_bounds', polygon_wkt)\\n \\n depth_var_name = self.sensor_def_exists('drv_depth')\\n if not depth_var_name:\\n self._logger.warning('Skipping set global geospatial_vertical attributes')\\n else:\\n if depth_var_name in self._nc.variables:\\n min_depth = self._nc.variables[depth_var_name][:].min()\\n max_depth = self._nc.variables[depth_var_name][:].max()\\n else:\\n min_depth = np.nan\\n max_depth = np.nan\\n \\n self._nc.setncattr('geospatial_vertical_min', min_depth)\\n self._nc.setncattr('geospatial_vertical_max', max_depth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3cf55810f540625233cf7cdb28493f\",\n \"score\": \"0.53670615\",\n \"text\": \"def recreate_map(self):\\n self.create_map()\\n for item in self.saved_positions.items():\\n print(item[1][-1])\\n self.update_position(item[1][-1])\\n self.draw_historic_path(device_id=item[1][-1]['device_id'],last=20)\\n m.draw_map()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6caa37af69f31dd753f3a30e8d31947f\",\n \"score\": \"0.5366982\",\n \"text\": \"def before_map(self, map):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6331a53da197340c1ef29e48dcbdc1a8\",\n \"score\": \"0.53653723\",\n \"text\": \"def register_for_changed_maps(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db25b8c4877968567f636861d0781a55\",\n \"score\": \"0.5341022\",\n \"text\": \"def svf_piomap(self, mapping):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2375f9a15a5b9effe719648bc7f1b938\",\n \"score\": \"0.5339042\",\n \"text\": \"def reset_idxes(G):\\n mapping = {}\\n for new_idx, old_idx in enumerate(G.nodes()):\\n mapping[old_idx] = new_idx\\n new_G = nx.relabel_nodes(G, mapping, copy=True)\\n return new_G, mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a2dd5c3327330e96610f0451f0f9f0\",\n \"score\": \"0.5338325\",\n \"text\": \"def update(self, other):\\n return self.__map.update(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74deb448e370242c1cd9caefc50a7b14\",\n \"score\": \"0.5315969\",\n \"text\": \"def _resort_mapper( self ):\\n for s in self._symbol_mapper[ \\\"node\\\" ]:\\n for k, l in self._symbol_mapper[ \\\"node\\\" ][ s ].iteritems( ):\\n self._symbol_mapper[ \\\"node\\\" ][ s ][ k ] = sorted( set( l ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a21597549e88e25210badea5acdaa13\",\n \"score\": \"0.5310865\",\n \"text\": \"def set_map(self, M):\\n self._reset(self)\\n self.start = None\\n self.goal = None\\n # TODO: Set map to new value.\\n self.map=M\\n\\n def set_start(self, start):\\n \\\"\\\"\\\"Method used to set start attribute \\\"\\\"\\\"\\n self._reset(self)\\n # TODO: Set start value. Remember to remove goal, closedSet, openSet, cameFrom, gScore, fScore,\\n # and path attributes' values.\\n self.start=start\\n\\n\\n def set_goal(self, goal):\\n \\\"\\\"\\\"Method used to set goal attribute \\\"\\\"\\\"\\n self._reset(self)\\n # TODO: Set goal value.\\n self.goal=goal\\n\\n def get_current_node(self):\\n \\\"\\\"\\\" Returns the node in the open set with the lowest value of f(node).\\\"\\\"\\\"\\n # TODO: Return the node in the open set with the lowest value of f(node).\\n cur={}\\n for i in self.openSet:\\n cur[i]=self.fScore[i]\\n Min=min(cur.items(), key = lambda x: x[1])\\n return Min[0]\\n\\n\\n def get_neighbors(self, node):\\n \\\"\\\"\\\"Returns the neighbors of a node\\\"\\\"\\\"\\n # TODO: Return the neighbors of a node\\n return self.map.roads[node]\\n\\n def get_gScore(self, node):\\n \\\"\\\"\\\"Returns the g Score of a node\\\"\\\"\\\"\\n # TODO: Return the g Score of a node\\n return self.gScore[node]\\n\\n\\n def get_tenative_gScore(self, current, neighbor):\\n \\\"\\\"\\\"Returns the tenative g Score of a node\\\"\\\"\\\"\\n # TODO: Return the g Score of the current node\\n # plus distance from the current node to it's neighbors\\n return self.get_gScore(current) + self.distance(current,neighbor)\\n\\n def is_open_empty(self):\\n \\\"\\\"\\\"returns True if the open set is empty. False otherwise. \\\"\\\"\\\"\\n # TODO: Return True if the open set is empty. False otherwise.\\n if not self.openSet:\\n return True\\n\\n def distance(self, node_1, node_2):\\n \\\"\\\"\\\" Computes the Euclidean L2 Distance\\\"\\\"\\\"\\n # TODO: Compute and return the Euclidean L2 Distance\\n n1_x=self.map.intersections[node_1][0]\\n n1_y=self.map.intersections[node_1][1]\\n n2_x=self.map.intersections[node_2][0]\\n n2_y=self.map.intersections[node_1][1]\\n return math.sqrt(pow(n1_x - n2_x, 2)+pow(n1_y - n2_y, 2))\\n\\n def heuristic_cost_estimate(self, node):\\n \\\"\\\"\\\" Returns the heuristic cost estimate of a node \\\"\\\"\\\"\\n # TODO: Return the heuristic cost estimate of a node\\n end = self.goal\\n return self.distance(end, node)\\n\\n def calculate_fscore(self, node):\\n \\\"\\\"\\\"Calculate the f score of a node. \\\"\\\"\\\"\\n # TODO: Calculate and returns the f score of a node.\\n # REMEMBER F = G + H\\n return self.get_gScore(node)+self.heuristic_cost_estimate(node)\\n\\n\\n def record_best_path_to(self, current, neighbor):\\n \\\"\\\"\\\"Record the best path to a node \\\"\\\"\\\"\\n # TODO: Record the best path to a node, by updating cameFrom, gScore, and fScore\\n self.cameFrom[neighbor]=current\\n self.gScore[neighbor]=self.get_tenative_gScore(current,neighbor)\\n self.fScore[neighbor]=self.calculate_fscore(neighbor)\\n\\n PathPlanner.create_closedSet = create_closedSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373194bc83c8ad88ff7682bffeccd00a\",\n \"score\": \"0.5299723\",\n \"text\": \"def _update_weighted_matrix(self) -> None:\\n self.weighted_map = deepcopy(self.map)\\n for connection in self.weighted_map:\\n connections = self.weighted_map[connection]\\n connections_count = sum(list(connections.values()))\\n for key in self.weighted_map[connection]:\\n self.weighted_map[connection][key] /= connections_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d03ebab194d5e392b9b65be3050d3d\",\n \"score\": \"0.5297442\",\n \"text\": \"def redraw_map(self):\\n for node_tag_item in tag_list:\\n self.parent.canvas.delete(node_tag_item)\\n ReadCfg().read_cfg()\\n FillMapWithNodes.run(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d78b9612c1bb464cd9e766ee7e57617\",\n \"score\": \"0.5295578\",\n \"text\": \"def get_map_2d(self):\\n return super().get_map_2d()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d60fd830db3b01ba47724008fcfb462\",\n \"score\": \"0.5293758\",\n \"text\": \"def update(self, prob_map, position):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ed8834258d013b7f5e107050bcd3ac\",\n \"score\": \"0.5287312\",\n \"text\": \"def infomap(self):\\n cur = self.con.cursor()\\n with open('/home/ubuntu/insight/network_update.pickle', 'rb') as f:\\n network = pickle.load(f)\\n edge_weight_query = \\\"\\\"\\\"SELECT \\\"Weights\\\" FROM network_update;\\\"\\\"\\\"\\n cur.execute(edge_weight_query)\\n weights = cur.fetchall()\\n\\n mod_weights = []\\n for w in weights:\\n mod_weights.append(w[0])\\n #clear memory of weights\\n weights = None\\n\\n info_map = network.community_infomap(edge_weights = mod_weights)\\n\\n with open('/home/ubuntu/insight/communities_updated.pickle', 'wb') as f:\\n pickle.dump(info_map, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df3f0ec6de0c3daeb3e28abf6ac39e62\",\n \"score\": \"0.52838075\",\n \"text\": \"def set_global2local(self, name, global2local=None):\\n assert len(name) > 0, 'name cannot be empty.'\\n\\n if global2local is not None: # Create shared-tensor\\n if isinstance(global2local, list):\\n global2local = F.tensor(global2local)\\n assert 'int64' == get_type_str(F.dtype(global2local)), 'global2local must be int64 type.'\\n shared_data = empty_shared_mem(name+'-g2l-', True, global2local.shape, 'int64')\\n dlpack = shared_data.to_dlpack()\\n self._data_store[name+'-g2l-'] = F.zerocopy_from_dlpack(dlpack)\\n self._data_store[name+'-g2l-'][:] = global2local[:]\\n # write data information to temp file that can be read by other processes\\n self._write_data_shape_type(name+'-g2l-shape-'+str(self._machine_id), global2local)\\n self._open_file_list.append(name+'-g2l-shape-'+str(self._machine_id))\\n else: # Read shared-tensor\\n while True:\\n if (os.path.exists(name+'-g2l-shape-'+str(self._machine_id))):\\n time.sleep(2) # wait writing finish\\n break\\n else:\\n time.sleep(2) # wait until the file been created\\n data_shape, data_type = self._read_data_shape_type(name+'-g2l-shape-'+str(self._machine_id))\\n assert data_type == 'int64'\\n shared_data = empty_shared_mem(name+'-g2l-', False, data_shape, 'int64')\\n dlpack = shared_data.to_dlpack()\\n self._data_store[name+'-g2l-'] = F.zerocopy_from_dlpack(dlpack)\\n\\n self._has_data.add(name+'-g2l-')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0c02cb97b9f765259d90fe059b73ae\",\n \"score\": \"0.5277513\",\n \"text\": \"def register_for_new_maps(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8591a855c7377c26fedc622490936f2b\",\n \"score\": \"0.5275509\",\n \"text\": \"def __load_id_maps(self):\\n\\n self.id_map.update(dict(zip(self.par_ids, self.names)))\\n self.id_map.update(dict(zip(self.names, self.par_ids)))\\n\\n rdx_names = []\\n for keyname in self.names:\\n name_ = ''.join(s if s.isalpha() else ' ' for s in keyname).casefold()\\n rdx_names.append(name_)\\n\\n self.__norm_name_map = dict(zip(rdx_names, self.names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966aa86de86f8b8a1b41ab0d57ba356a\",\n \"score\": \"0.5272972\",\n \"text\": \"def _update(self, columns):\\n for c in columns:\\n self._map[c.key] = c\\n self._map[c.id] = c\\n if c.iskey:\\n setattr(self.key, c.key, c)\\n else:\\n setattr(self.value, c.key, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b46ee5795f3f23805fbe7966857ed43\",\n \"score\": \"0.5271868\",\n \"text\": \"def mapToGlobal(self, obj, subdev=None):\\n tr = self.globalTransform(subdev)\\n if tr is not None:\\n mapped = self._mapTransform(obj, tr)\\n return mapped\\n\\n ## If our transformation is nonlinear, then the local mapping step must be done separately.\\n subdev = self._subdevDict(subdev)\\n o2 = self.mapToParentDevice(obj, subdev)\\n parent = self.parentDevice()\\n if parent is None:\\n return o2\\n else:\\n return parent.mapToGlobal(o2, subdev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83072c0c410f0ef44e1ed2052e0d6899\",\n \"score\": \"0.5265351\",\n \"text\": \"def update_type_and_attribute_ids(self):\\n\\n self.get_type_name_map()\\n\\n if len(self.input_network.types)>0:\\n # If the network has type\\n self.input_network.types = [self.network_template_type]\\n\\n #map the name of the nodes, links and groups to its negative ID\\n for n_j in self.input_network.nodes:\\n self.name_maps['NODE'][n_j.name] = n_j.id\\n self.update_type_and_attribute(n_j)\\n\\n for l_j in self.input_network.links:\\n self.name_maps['LINK'][l_j.name] = l_j.id\\n self.update_type_and_attribute(l_j)\\n\\n for g_j in self.input_network.resourcegroups:\\n self.name_maps['GROUP'][g_j.name] = g_j.id\\n self.update_type_and_attribute(g_j)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d4ed00f8918f1ca806b65d160ee092e\",\n \"score\": \"0.5257187\",\n \"text\": \"def update_func_world(mech, world):\\n globals().update(world)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b13d7ec9e51858045a707b88cf300de5\",\n \"score\": \"0.52528906\",\n \"text\": \"def remap(self,states):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e0270eaa18a07f6905370c8420fbec6\",\n \"score\": \"0.5237808\",\n \"text\": \"def update_feed_dict(self):\\n gp = self.gaussian_process\\n feed_dict = self.feed_dict\\n\\n gp.update_feed_dict(gp.get_feed_dict_keys(), feed_dict)\\n feed_dict[self.hyperparameters[0]] = gp.get_free_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e52d9d722798b948ec0c7bf52676717b\",\n \"score\": \"0.5237184\",\n \"text\": \"def map_graphs(self):\\n\\n for k, v in list(self.graphmap.items()):\\n if k == v: continue\\n self.ruleset[v] = self.ruleset[k]\\n del self.ruleset[k]\\n for g in self.ruleset:\\n for r in self.ruleset[g]:\\n r.grapheme = re.sub(k, v, r.grapheme)\\n r.leftcontext = re.sub(k, v, r.leftcontext)\\n r.rightcontext = re.sub(k, v, r.rightcontext)\\n if self.gnulls:\\n for gk, gv in list(self.gnulls.items()):\\n if (k in gk) or (k in gv):\\n del self.gnulls[gk]\\n gk = re.sub(k, v, gk)\\n gv = re.sub(k, v, gv)\\n self.gnulls.update({gk: gv})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957535cbc732876b71a88720ef3fbbd6\",\n \"score\": \"0.5214665\",\n \"text\": \"def update_labelid_geo(self):\\n local_sql = MysqlConnecttion(\\\"local\\\")\\n booth_sql = MysqlConnecttion(\\\"booth\\\")\\n label_map = {}#{\\\"word\\\":labelid}\\n query = r\\\"select Word, Labelid from labels WHERE Geogr = 1 and Vert = 0 and Mktseg = 0\\\"\\n rows = local_sql.excute_with_result(query)\\n for row in rows:\\n label_map[row[0]] = row[1]\\n query = r'''(SELECT mqid, docid, labelid, mq_title_vector_short FROM magic_quadrants where removed = 0)'''\\n mq_vector_map = {}#{\\\"mqid\\\":\\\"word vector (short)\\\"}\\n label_tmap = {}\\n docid_map ={}\\n rows = local_sql.excute_with_result(query)\\n for row in rows:\\n mq_vector_map[row[0]] = row[3]\\n docid_map[row[0]] = row[1]\\n label_map[row[0]] = row[2]\\n query = r'''(SELECT title_short, docid FROM doc_deatail_vector)'''\\n cool_map ={}\\n rows = local_sql.excute_with_result(query)\\n for row in rows:\\n cool_map[row[1]] = row[0]\\n for mq_id in mq_vector_map:\\n json_word_set = mq_vector_map[mq_id]\\n if json_word_set == None or json_word_set == \\\"\\\":\\n json_word_set = \\\"{}\\\"\\n word_map = json.loads(json_word_set)\\n label_list = []\\n for word in word_map:\\n if word in label_map:\\n label_list.append(str(label_map[word]))\\n json_word_set = cool_map[docid_map[mq_id]]\\n if json_word_set == None or json_word_set == \\\"\\\":\\n json_word_set = \\\"{}\\\"\\n word_map = json.loads(json_word_set)\\n for word in word_map:\\n if word in label_map:\\n label_list.append(str(label_map[word]))\\n label_list = list(set(label_list))\\n length = len(label_list)\\n labels = \\\";\\\".join(label_list)\\n query = r\\\"update new_magic_quadrants set Geo_label = '%s' where MQID = '%s'\\\"%(labels, mq_id)\\n local_sql.excute(query)\\n booth_sql.excute(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15bede240fabcfa504f6c6cf1ca89d59\",\n \"score\": \"0.5214411\",\n \"text\": \"def reload_mappings(self):\\n with open(self.mappings_path, 'rb') as f:\\n mappings = cPickle.load(f)\\n self.id_to_word = mappings['id_to_word']\\n self.id_to_char = mappings['id_to_char']\\n self.id_to_tag = mappings['id_to_tag']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc37a233ace5af412a0f91cc0b9398e\",\n \"score\": \"0.5208248\",\n \"text\": \"def update_global_view_status (self, status):\\n log.debug(\\\"Update Global view (DoV) mapping status with: %s\\\" % status)\\n NFFGToolBox.update_status_info(nffg=self.__dov.get_resource_info(),\\n status=status, log=log)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3416196f9db65217bcc5b02a4a66228a\",\n \"score\": \"0.5205673\",\n \"text\": \"def _lu_swap(self, r1: int, r2: int) -> Dict[int, str]:\\n\\n for i in range(self.M.nrows() + min(r1, r2)):\\n self.M[r1, i], self.M[r2, i] = self.M[r2, i], self.M[r1, i]\\n\\n return {r1: fr'\\\\leftarrow r_{r2+1} \\\\setminus 0', r2: fr'\\\\leftarrow r_{r1+1} \\\\setminus 0'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c75732aae55391b5c0c236db2c88e2\",\n \"score\": \"0.5204835\",\n \"text\": \"def _update_pointers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51be8ac8c620c4dcb4b889951b7f1ad\",\n \"score\": \"0.51973325\",\n \"text\": \"def updateTransform(self):\\n\\n coordCalibrationsOrigin, coordCalibrationsDestiny, namesCalibration = self.getLandmarksByType(PointType.calibrated)\\n\\n total_calibration_points = len(coordCalibrationsOrigin)\\n if (total_calibration_points < 2):\\n self.logger.info(\\\"From \\\" + str(self.map_id) + \\\":Not enough points to update.\\\")\\n return\\n\\n self.logger.info(\\\"From \\\"+str(self.map_id)+\\\":Updating transform with \\\" + str(total_calibration_points) + \\\" reference points\\\")\\n origin = np.zeros((total_calibration_points, 2), dtype=np.float32)\\n destiny = np.zeros((total_calibration_points, 2), dtype=np.float32)\\n for i in range(total_calibration_points):\\n origin[i, 0] = coordCalibrationsOrigin[i][0]\\n origin[i, 1] = coordCalibrationsOrigin[i][1]\\n destiny[i, 0] = coordCalibrationsDestiny[i][0]\\n destiny[i, 1] = coordCalibrationsDestiny[i][1]\\n\\n\\n self.CalibratedPtp.updateGlobal(origin,destiny,namesCalibration)\\n\\n coordT, _, namesTarget = self.getLandmarksByType(PointType.target)\\n self.processLocalArea(coordT,namesTarget)\\n\\n coordNC, _, namesNonCal = self.getLandmarksByType(PointType.non_calibrated)\\n self.processLocalArea(coordNC, namesNonCal)\\n\\n _, coordACQ, namesAcq = self.getLandmarksByType(PointType.acquired)\\n self.processLocalArea(coordACQ, namesAcq)\\n\\n namesAll = self.getLandmarkIds()\\n originAll = self.getCoordsFromLandmarks(namesAll,1)\\n destinyAll = self.getCoordsFromLandmarks(namesAll,2)\\n self.GlobalPtp.updateGlobal(originAll, destinyAll, namesAll)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c537836bb19de309e946cbcd4510e93\",\n \"score\": \"0.519472\",\n \"text\": \"def setAttrMap(self, attr):\\n\\t\\t\\n\\t\\tattrName = attr.getAttribute(\\\"name\\\")\\n\\t\\tattrValue = attr.getAttribute(\\\"value\\\")\\n\\t\\t\\t\\t\\t\\n\\t\\tif self.mods:\\n\\t\\t\\tself.pmSetAttr(self.objWithNS, attrName, float(attrValue), self.mult)\\n\\t\\telse:\\n\\t\\t\\tcmds.setAttr(self.objWithNS + \\\".\\\" + attrName, float(attrValue))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8555175f15d5005b801230f05deb95\",\n \"score\": \"0.51908076\",\n \"text\": \"def update_objects(self):\\n\\t\\tself.update_projectiles()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d72fc4851081af7f890e41d4f6cced\",\n \"score\": \"0.5188522\",\n \"text\": \"def SetKey2(self, *args):\\n return _TopTools.TopTools_IndexedMapNodeOfIndexedMapOfShape_SetKey2(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ecf36406dea43ce27cd68365feb032\",\n \"score\": \"0.5180778\",\n \"text\": \"def request_rebuild_midi_map(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c4b1ae5ca27414da51a4be37a7d47d1\",\n \"score\": \"0.51782566\",\n \"text\": \"def adjustPCmap(self,transition):\\r\\n poly_map = {}\\r\\n\\tpc_list = sort(self.pc_list)\\r\\n\\tfor i in range(shape(transition)[1]):\\r\\n\\t poly_map[pc_list[i]] = i\\r\\n\\tfor key in self.pc_map.keys():\\r\\n\\t self.pc_map[key] = poly_map[self.pc_map[key]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2956e1113e300458bfaafaab409d074\",\n \"score\": \"0.5170163\",\n \"text\": \"def _reassign_ids(self, old_new: Dict[int, int]) -> None:\\n # update id_fw\\n new_id_fw = {}\\n for fwid, fws in self.id_fw.items():\\n new_id_fw[old_new.get(fwid, fwid)] = fws\\n self.id_fw = new_id_fw\\n\\n # update the Links\\n new_l = {}\\n for parent, children in self.links.items():\\n new_l[old_new.get(parent, parent)] = [old_new.get(child, child) for child in children]\\n self.links = Workflow.Links(new_l)\\n\\n # update the states\\n new_fw_states = {}\\n for fwid, fw_state in self.fw_states.items():\\n new_fw_states[old_new.get(fwid, fwid)] = fw_state\\n self.fw_states = new_fw_states\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b0b375f86d20e87cb83a906297188e\",\n \"score\": \"0.5165266\",\n \"text\": \"def request_rebuild_midi_map(self):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e52f41542f0d6796c146e2d4720525ee\",\n \"score\": \"0.51637876\",\n \"text\": \"def update_map(self, grid_map, pose, scan):\\n\\n # Current yaw of the robot\\n robot_yaw = self.get_yaw(pose.pose.orientation)\\n # The origin of the map [m, m, rad]. This is the real-world pose of the\\n # cell (0,0) in the map.\\n origin = grid_map.get_origin()\\n # The map resolution [m/cell]\\n resolution = grid_map.get_resolution()\\n\\n\\n \\\"\\\"\\\"\\n Fill in your solution here\\n \\\"\\\"\\\"\\n map_x =[]\\n map_y = []\\n ind_x = []\\n ind_y = [] \\n robot_x = []\\n robot_y = []\\n px=pose.pose.position.x\\n py=pose.pose.position.y\\n \\n \\n #For occupied space\\n i=0\\n for j in range(len( (scan.ranges))):\\n \\n if scan.range_min >= (scan.ranges[j]) or (scan.ranges[j]) >= scan.range_max:\\n continue\\n\\n else:\\n b_angle = scan.angle_min + (j * scan.angle_increment)\\n \\n map_x.append(px + (scan.ranges[j]) * cos(robot_yaw + b_angle))\\n map_y.append(py + (scan.ranges[j]) * sin(robot_yaw + b_angle))\\n\\n ind_x.append(int((map_x[i]- origin.position.x) / resolution))\\n ind_y.append(int((map_y[i]- origin.position.y) / resolution))\\n\\n # grid index of the robot\\n robot_x.append(int((px - origin.position.x) / resolution) )\\n robot_y.append(int((py - origin.position.y) / resolution)) \\n start =(robot_x[i],robot_y[i])\\n end = (ind_x[i],ind_y[i])\\n \\n trace=self.raytrace(start,end)\\n i=i+1\\n\\n #Filling the free space\\n for l in trace:\\n self.add_to_map(grid_map, l[0], l[1], self.free_space) \\n\\n #Filling the occupied space \\n for k in range(len(ind_x)):\\n self.add_to_map(grid_map, ind_x[k], ind_y[k], self.occupied_space)\\n\\n\\n \\\"\\\"\\\"\\n For C only!\\n Fill in the update correctly below.\\n \\\"\\\"\\\"\\n # Only get the part that has been updated\\n update = OccupancyGridUpdate()\\n # The minimum x index in 'grid_map' that has been updated\\n update.x = np.amin(ind_x)\\n # The minimum y index in 'grid_map' that has been updated\\n update.y = np.amin(ind_y)\\n # Maximum x index - minimum x index + 1\\n update.w = np.amax(ind_x) - np.amin(ind_x) + 1\\n # Maximum y index - minimum y index + 1\\n update.h = np.amax(ind_y) - np.amin(ind_y) + 1\\n # The map data inside the rectangle, in row-major order.\\n update.data = []\\n for y_u in range(np.amin(ind_y), np.amax(ind_y) + 1):\\n for x_u in range(np.amin(ind_x), np.amax(ind_x) + 1):\\n updated_value = grid_map[x_u,y_u]\\n update.data.append(updated_value)\\n\\n # Return the updated map together with only the\\n # part of the map that has been updated\\n return grid_map, update\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8e01e73a8667290a5bbb31fad6287b\",\n \"score\": \"0.51632774\",\n \"text\": \"def updateMapKey(initkey, key, map,value = 1):\\n\\tif initkey not in map:\\n\\t\\tmap[initkey] = {}\\n\\tif key not in map[initkey]:\\n\\t\\tmap[initkey][key] = 0\\n\\tmap[initkey][key] = map[initkey][key] +value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39cce74753d252e93ec0cdebbf41697\",\n \"score\": \"0.5159968\",\n \"text\": \"def perform_mapping(\\n self, fs: typing.List[AnyFunction], map: PointType, inverse_map: PointType, tdim: int\\n ) -> typing.List[AnyFunction]:\\n raise mappings.MappingNotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39cce74753d252e93ec0cdebbf41697\",\n \"score\": \"0.5159968\",\n \"text\": \"def perform_mapping(\\n self, fs: typing.List[AnyFunction], map: PointType, inverse_map: PointType, tdim: int\\n ) -> typing.List[AnyFunction]:\\n raise mappings.MappingNotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0801832ae89c00440c55d6e04dc14e58\",\n \"score\": \"0.51568234\",\n \"text\": \"def _pad_static_map(self):\\n global_costmap = np.array(self.static_map.data, dtype=np.int8).reshape(self.static_map.info.height, -1)\\n\\n # Get index of occupied cells\\n occupied_index = np.where(global_costmap == 100)\\n\\n # Loop over occupied cells and pad them\\n for coord in zip(occupied_index[0], occupied_index[1]):\\n self._pad_point(global_costmap, coord)\\n \\n # Show messages for debugging\\n if self.debug_mode == True:\\n # Receive an image of the global_costmap\\n cv2.imwrite('map_padded.jpg', global_costmap.astype(np.uint8))\\n print(np.unique(global_costmap))\\n\\n self.static_map.data = global_costmap.ravel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ff5edb4617790d1566bc05d5b5e006\",\n \"score\": \"0.51456535\",\n \"text\": \"def reinit(self):\\n self.current_value_global = copy_dict(self.varGlobalInit)\\n for mod in self.modules:\\n mod.reinit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cf72991c6f600c1c90b3d4c6386e50\",\n \"score\": \"0.5143412\",\n \"text\": \"def updateConstantsFromMapping(self, mapping):\\n for key, val in mapping.iteritems():\\n if isinstance(val, Constant):\\n if key in self.constants:\\n log.warn(\\\"Constant with name %s is already declared, overriding\\\", key)\\n self.constants[key] = (val.value, val.type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9002293ef606c513567d1176729c260\",\n \"score\": \"0.51360947\",\n \"text\": \"def assign_simple_node_features(ndata, g, ntypes, assign_id=False):\\n for ntype in ntypes:\\n for col in g.nodes[ntype].data.keys():\\n if not assign_id and col == dgl.NID:\\n continue\\n induced_nodes = ndata[dgl.NID][ntype]\\n ndata[col] = {ntype: g.nodes[ntype].data[col][induced_nodes]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a96233a922d24cbc576114b67200ad\",\n \"score\": \"0.51353544\",\n \"text\": \"def test_incremental_fcache_buildup_and_global_access(self):\\n # map with empty\\n self.upapa_class.purge_fasta_info(\\\"Test.fasta\\\")\\n maps = self.upapa_class.map_peptide(peptide=\\\"KLEINER\\\", fasta_name=\\\"Test.fasta\\\")\\n # print(maps)\\n self.assertEqual(len(maps), 0)\\n\\n # parse one fasta via a unode ..\\n self.upapa_class.build_lookup(fasta_name=\\\"Test.fasta\\\", fasta_stream=TEST_FASTA)\\n # map with one parsed fasta\\n maps = self.upapa_class.map_peptide(peptide=\\\"KLEINER\\\", fasta_name=\\\"Test.fasta\\\")\\n self.assertEqual(len(maps), 1)\\n # parse another fasta via a different unode ..\\n self.upapa_class.build_lookup(\\n fasta_name=\\\"Test.fasta\\\", fasta_stream=TEST_FASTA_TWO\\n )\\n # map with two parsed fastas\\n\\n maps = self.upapa_class.map_peptide(peptide=\\\"KLEINER\\\", fasta_name=\\\"Test.fasta\\\")\\n print(maps)\\n self.assertEqual(len(maps), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a37e733648f3b986c55cf9db5553f95\",\n \"score\": \"0.5135315\",\n \"text\": \"def update_nni(nodes, links):\\n for link in links:\\n ports = link[\\\"ports\\\"]\\n nni_a, nni_b = ports[0], ports[1]\\n node_a = nni_a.split(\\\":\\\")[4]\\n port_a = nni_a.split(\\\":\\\")[5]\\n node_b = nni_b.split(\\\":\\\")[4]\\n port_b = nni_b.split(\\\":\\\")[5]\\n for node in nodes:\\n if node[\\\"name\\\"] == node_a:\\n for port in node[\\\"ports\\\"]:\\n if port_a == port[\\\"id\\\"].split(\\\":\\\")[5]:\\n port[\\\"nni\\\"] = nni_b\\n elif node[\\\"name\\\"] == node_b:\\n for port in node[\\\"ports\\\"]:\\n if port_b == port[\\\"id\\\"].split(\\\":\\\")[5]:\\n port[\\\"nni\\\"] = nni_a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e120373fa89087f76b7651b1315eb6\",\n \"score\": \"0.513131\",\n \"text\": \"def merge_info(self):\\n global global_path\\n global global_known_p\\n global_path = global_path + (list(set(self.path) - set(global_path))) # removes duplicates\\n self.path = self.path + (list(set(global_path) - set(self.path))) # removes duplicates\\n\\n # haalt uit global_known_p eerst weg wat in self.known_p_removed zit\\n l3 = [x for x in global_known_p if x not in self.known_p_removed]\\n global_known_p = l3\\n # merge dan alles\\n global_known_p = global_known_p + list(set(self.known_p) - set(global_known_p))\\n self.known_p = global_known_p\\n self.sort_known_patients()\\n l3 = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afdeee6f9b19b30e8ad8412d3772e2a\",\n \"score\": \"0.5125499\",\n \"text\": \"def update_idx_id_mapping(self):\\n if self.dpd is not None and self.dpd.shape[0] > 0:\\n # sort by id link ascending\\n self.dpd = self.dpd[self.dpd[:, 1].argsort()]\\n\\n # update dpd_idx to make the index to ID pairing bidirectional again\\n for idx, dpd_pair in enumerate(self.dpd):\\n self.reps[dpd_pair[1]].dpd_idx = idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff897a6151f85183d9da06575ac678b\",\n \"score\": \"0.51248366\",\n \"text\": \"def map_variable(gdb_path, mukey_path, variable, dst):\\n\\n # Expand user path\\n mukey_path = os.path.expanduser(mukey_path)\\n gdb_path = os.path.expanduser(gdb_path)\\n dst = os.path.expanduser(dst)\\n\\n # Get the Map Unit Aggregated Attribute Table\\n mukey = xr.open_rasterio(mukey_path, chunks=(1, 5000, 5000))\\n umukeys = np.unique(mukey[:]).astype(str)\\n muaggatt = gpd.read_file(gdb_path, layer=\\\"muaggatt\\\")\\n chorizon = gpd.read_file(gdb_path, layer=\\\"chorizon\\\")\\n cogeomordesc = gpd.read_file(gdb_path, layer=\\\"cogeomordesc\\\")\\n components = gpd.read_file(gdb_path, layer=\\\"component\\\")\\n components = pd.merge(chorizon, components, on=\\\"cokey\\\")\\n components = pd.merge(components, muaggatt, on=\\\"mukey\\\")\\n\\n # Put the keys in front\\n keys = [c for c in components.columns if \\\"key\\\" in c]\\n others = [c for c in components.columns if \\\"key\\\" not in c]\\n new_order = keys + others\\n components = components[new_order]\\n\\n # Get the Horizon Table\\n munits = muaggatt[[\\\"mukey\\\", \\\"muname\\\"]]\\n variable_df = pd.merge(variable_df, munits, on=\\\"mukey\\\")\\n variable_df = components[[\\\"mukey\\\", \\\"chkey\\\", \\\"muname\\\", \\\"hzname\\\", \\\"geomdesc\\\",\\n \\\"desgnmaster\\\", \\\"hzdept_r\\\", \\\"hzdepb_r\\\", \\\"hzthk_r\\\",\\n \\\"sandtotal_r\\\", \\\"silttotal_r\\\", \\\"claytotal_r\\\", \\n variable]]\\n\\n # Now, whats the best way to map these values\\n val_dict = dict(zip(variable_df[\\\"mukey\\\"].astype(int),\\n variable_df[variable]))\\n mv = Map_Values(val_dict, err_val=-9999)\\n mv.map_file(mukey_path, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965d1c87eb8723c7aa692e6b73d897bf\",\n \"score\": \"0.5122539\",\n \"text\": \"def map_update(self, m):\\n dists = np.zeros((self.num_points,))\\n for idx, point in enumerate(self.coords):\\n dists[idx] = m.nearest_lane_dist(point)\\n if np.percentile(dists, 5) > 15:\\n # Don't bother incorporating weights if the 95%+ of particles are\\n # more than 15m from a road. In that case, we can safely assume\\n # that we're off the road.\\n return\\n factors = 1.0 / ((1 + dists ** 2) ** 1.1)\\n self.weights *= factors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de2192a9ab603c96997a048cbd4c709\",\n \"score\": \"0.5116645\",\n \"text\": \"def __reset_node_ids (info, binding):\\n log.debug(\\\"Reset NF paths...\\\")\\n for attr in (getattr(info, e) for e in info._sorted_children):\\n rewrite = []\\n for element in attr:\\n if hasattr(element, \\\"object\\\"):\\n old_path = element.object.get_value()\\n bb, nf = get_bb_nf_from_path(path=old_path)\\n if bb not in binding:\\n log.warning(\\\"Missing binding for node: %s\\\" % bb)\\n continue\\n new_bb = binding.get(bb)\\n log.debug(\\\"Find BiSBiS node remapping: %s --> %s\\\" % (bb, new_bb))\\n old_bb, new_bb = \\\"/node[id=%s]\\\" % bb, \\\"/node[id=%s]\\\" % new_bb\\n new_path = str(old_path).replace(old_bb, new_bb)\\n rewrite.append((element, new_path))\\n # Tricky override because object is key in yang -> del and re-add\\n for e, p in rewrite:\\n attr.remove(e)\\n e.object.set_value(p)\\n attr.add(e)\\n log.debug(\\\"Overrided new path for NF --> %s\\\" % e.object.get_value())\\n log.log(VERBOSE, info.xml())\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97354a1a33c34f4428dda61ed1f460df\",\n \"score\": \"0.51147324\",\n \"text\": \"def updatekernel(self,kernel):\\n self.GPkernel = kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a3d64111258b46fa8a2a867a71cd1e\",\n \"score\": \"0.51105374\",\n \"text\": \"def recalc_info_dict(self):\\n self._info_dict = self.__create_info_dict()\\n map(self.add_s2_info_dict, self.slist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1e5029ae5d3a96acd4f03baf72ffa1\",\n \"score\": \"0.51089907\",\n \"text\": \"def _post_setattr_formulas(self, old, new):\\n self.database_entries = {key: 1.0 for key in new}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993999,"cells":{"query_id":{"kind":"string","value":"4d39bb1519a7f62a7cf3caeafcd634a3"},"query":{"kind":"string","value":"GET request to retrieve a specific product from database."},"positive_passages":{"kind":"list like","value":[{"docid":"4ad297d0b00a0f6bc43b64684bf5bab8","score":"0.7202844","text":"def get_specific_product(product_id):\n try:\n # Query database for all products\n selection = Products.query.order_by(Products.id).filter(\n Products.id == product_id).one_or_none()\n\n return jsonify({\n 'success': True,\n 'products': selection.info()\n })\n\n except Exception as e:\n # Print exception error as well as abort 404\n print(f'Exception \"{e}\" in get_specific_product()')\n abort(404, {'message':\n f'Product ID: {product_id} does not exist.'})","title":""}],"string":"[\n {\n \"docid\": \"4ad297d0b00a0f6bc43b64684bf5bab8\",\n \"score\": \"0.7202844\",\n \"text\": \"def get_specific_product(product_id):\\n try:\\n # Query database for all products\\n selection = Products.query.order_by(Products.id).filter(\\n Products.id == product_id).one_or_none()\\n\\n return jsonify({\\n 'success': True,\\n 'products': selection.info()\\n })\\n\\n except Exception as e:\\n # Print exception error as well as abort 404\\n print(f'Exception \\\"{e}\\\" in get_specific_product()')\\n abort(404, {'message':\\n f'Product ID: {product_id} does not exist.'})\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0dec387335a7d688dff232ff44946424","score":"0.77556497","text":"def retrieve(self, request, pk=None):\n try:\n product = ProductModel.objects.get(pk=pk)\n serializer = ProductSerializer(product, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"f62e413f620cacede006454b0a085f3b","score":"0.7739515","text":"def get(self):\n\n identifier = self.get_query_argument(\"id\", None)\n if not identifier:\n self.generic_resp(404)\n return\n\n direct = self.get_query_argument(\"direct\", False)\n\n try:\n direct = bool(int(direct))\n except:\n direct = False\n try:\n\n res = self.get_product(identifier, direct)\n if not res:\n self.generic_resp(404)\n return\n\n resp = dict()\n resp[\"product\"] = res\n resp[\"status\"] = 200\n resp[\"mesasage\"] = \"OK\"\n\n self.write(json.dumps(resp))\n self.set_status(200)\n return\n except Exception as e:\n self.generic_resp(500, str(e))\n self.finish()","title":""},{"docid":"0322cefc47631d684b43847f320b8966","score":"0.7682345","text":"def retrieve(self, request, pk=None):\n try:\n product = Product.objects.get(pk=pk)\n serializer = ProductsSerializer(\n product, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"2426a33dd1b9aafdf872c20dd4ab99c5","score":"0.7679826","text":"def get_product(self, productId):\n\n method = \"GET\"\n endpoint = \"products/{}\".format(productId)\n\n return self.request(method, endpoint)","title":""},{"docid":"99b547f0d2dcc5bcaecf0a3fcc89f125","score":"0.7581064","text":"def get(self, product_id):\n app.logger.info('Request for product with id: %s', product_id)\n product = Product.find(product_id)\n if not product:\n api.abort(status.HTTP_404_NOT_FOUND,\n \"Product with id '{}' was not found.\".format(product_id))\n return product.serialize(), status.HTTP_200_OK","title":""},{"docid":"23f2e401da392bf4e22abf6999862c32","score":"0.74065155","text":"def fetch(cls, product_id):\n return cls().requests.get(f\"product/{product_id}\")","title":""},{"docid":"784fd520ee573308a251ee7fbcfc47dc","score":"0.7405221","text":"def get(self, id):\n product = ProductsModel()\n product_to_get = product.get_item('products', product_id=id)\n message = \"Product with id {} does not exist\".format(id)\n if product_to_get:\n return product_to_get\n return {\"message\": message}, 404","title":""},{"docid":"d0f3090a37ffba66fcfcb0a65fabfd20","score":"0.7219862","text":"def get(self, p_id):\n for i in range(len(products)):\n if products[i]['id'] == int(p_id):\n product = products[i]\n return make_response(jsonify(product)), 200\n else:\n response = {\n 'status': 'Failed',\n 'message': 'Product Not Found'\n }\n return make_response(jsonify(response)), 404","title":""},{"docid":"760a97e4491587f09585195ca3ae075a","score":"0.7085526","text":"def get(self):\n\n\t\tparams = self.parseParams()\n\t\tself.doProductSearch(params)","title":""},{"docid":"673d0e8331196d40be8a8c2056f6993e","score":"0.7048351","text":"def product(db, id):\n session.get_or_create_session(db)\n\n product = model.product_get(db, id)\n\n if product:\n #this product exists, to get its info.\n info = {\n 'product': model.product_get(db, id)\n }\n return template('product', info)\n else:\n #no product exists, return 404\n return HTTPError(404, \"No such product.\")","title":""},{"docid":"cbe4fab1337913dbd17ce2a516d022ae","score":"0.6942659","text":"def get_products(self, **kwargs):\n\n method = \"GET\"\n endpoint = self._create_endpoint(\"products\", kwargs)\n\n return self.request(method, endpoint)","title":""},{"docid":"d6c80405be2161d99baa3de01cf076fe","score":"0.6934231","text":"def get(self, request, slug):\n product = get_object_or_404(Product, slug=slug)\n return render(request, \"niunius/product.html\", {\"product\": product})","title":""},{"docid":"b02cd35abaff4812804577ab349f600f","score":"0.68860555","text":"def retrieve(self, request, pk=None):\n try:\n category = Product_Category.objects.get(pk=pk)\n serializer = Product_Category_Serializer(category, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"00ef0aeeabd0e901cbb9668073a7809b","score":"0.6879762","text":"def get_product(product_id):\n\n response = _get('products/' + product_id)\n if response.status_code != 200:\n error_msg = response.json()['data']\n logger.error('get_product() failed with \"%s\"' % error_msg)\n raise Exception('Product not found')\n product_data = response.json()[u'data']\n return api.Product(product_data)","title":""},{"docid":"aabf3b82af7f2f14cf25c3b05c2284d2","score":"0.6842081","text":"def api_product_detail(request, product_id):\n try:\n product = Product.objects.get(pk=product_id)\n except Product.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = ProductSerializer(product)\n return Response(serializer.data)\n\n elif request.method == 'PUT':\n serializer = ProductSerializer(product, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n elif request.method == 'DELETE':\n product.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)","title":""},{"docid":"7dff2cfa8ecd20f574949d680574f015","score":"0.68408215","text":"def retrieve(self, request, pk=None): \n return Response({\"http_method\":\"GET\"})","title":""},{"docid":"6ce5d12791a21f4ade0dc69e340c54c7","score":"0.6810289","text":"def get(self, request, id):\n \n reviews = Review.objects.filter(user__id=id)\n\n if request.GET.get('product'):\n reviews.filter(product__slug=request.GET.get('product'))\n\n if not reviews:\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n serializer = ReviewSerializer(reviews, many=True)\n return Response(serializer.data)","title":""},{"docid":"5119df8009633f439a7db35afbfff853","score":"0.67971087","text":"def get(self):\n app.logger.info('Request for product list')\n products = []\n category = request.args.get('category')\n name = request.args.get('name')\n price = request.args.get('price')\n if category:\n products = Product.find_by_category(category)\n elif name:\n products = Product.find_by_name(name)\n elif price and int(price) > 0 and int(price) < 4: # query price by range\n if int(price) == 1:\n products = Product.find_by_price(0, 25)\n elif int(price) == 2:\n products = Product.find_by_price(25, 50)\n else:\n products = Product.find_by_price(50, 75)\n else:\n products = Product.all()\n results = [product.serialize() for product in products]\n return results, status.HTTP_200_OK","title":""},{"docid":"3068d4bdc78abbda5b162006ba10bffe","score":"0.67844826","text":"def test_product_get(self):\n\n product = self.products['Yellow Wool Jumper']\n result = model.product_get(self.db, product['id'])\n\n # check a few fields\n self.assertEqual(product['id'], result['id'])\n self.assertEqual(product['name'], result['name'])","title":""},{"docid":"cd18e1d24022b6ca0d4d8d8371b1340c","score":"0.67698896","text":"def test_valid_retrieve_product(self):\n response = self.client.get(\n r('products:product-detail', pk=self.valid_pk), format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"2874e3d87fe3d73f814b517502d9fe1f","score":"0.67581475","text":"def get(self, request):\n\n reviews = Review.objects.filter(user=request.user)\n\n if request.GET.get('product'):\n reviews.filter(product__slug=request.GET.get('product'))\n\n if not reviews:\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n serializer = ReviewSerializer(reviews, many=True)\n return Response(serializer.data)","title":""},{"docid":"e2ff35075bf78741ac3178e754f5dad7","score":"0.6736622","text":"def retrieve(self, request, pk=None):\n\n return Response({'http_method':'GET'})","title":""},{"docid":"c32086c67fd1b4a68e4543ea546f9fcb","score":"0.67365336","text":"def retrieve(self,request,pk=None):\n\t\treturn Response({'message':'GET'})","title":""},{"docid":"c91c02f60eee46f2721f2ced623f7a72","score":"0.673558","text":"def get(self):\n parser = reqparse.RequestParser()\n parser.add_argument(\"category_id\", type=int, default=None, required=True)\n args = parser.parse_args()\n return c.get_product_list(args)","title":""},{"docid":"769be5c4ac2cbcbe8d32e1512d0792fa","score":"0.6734989","text":"def retrieve(self, request, pk=None):\n\n return Response({'http_method': 'GET'})","title":""},{"docid":"769be5c4ac2cbcbe8d32e1512d0792fa","score":"0.6734989","text":"def retrieve(self, request, pk=None):\n\n return Response({'http_method': 'GET'})","title":""},{"docid":"769be5c4ac2cbcbe8d32e1512d0792fa","score":"0.6734989","text":"def retrieve(self, request, pk=None):\n\n return Response({'http_method': 'GET'})","title":""},{"docid":"769be5c4ac2cbcbe8d32e1512d0792fa","score":"0.6734989","text":"def retrieve(self, request, pk=None):\n\n return Response({'http_method': 'GET'})","title":""},{"docid":"eedbf37c7f41126c7de2a3f3b3a0d8c0","score":"0.67247903","text":"def retrieve(self, request, pk = None):\n return Response({'http_method':'GET'})","title":""},{"docid":"9244b8d6d941f7277b1bbac525f0c588","score":"0.6713015","text":"def get_products():\n return requests.get(API_URL + \"products\").json()","title":""},{"docid":"27386e599b9c1629d1187a2bc27759d3","score":"0.6705706","text":"def retrieve(self,request,pk=None):\n return Response({'http_method':'GET'})","title":""},{"docid":"27386e599b9c1629d1187a2bc27759d3","score":"0.6705706","text":"def retrieve(self,request,pk=None):\n return Response({'http_method':'GET'})","title":""},{"docid":"21b2575288ea732e121e598f883bfa8b","score":"0.6705331","text":"def get_product_info():\n iid = request.json[\"idProducto\"] # item id\n uid = request.json.get(\"idSocio\", None) # user id\n return safe_return(db.get_product_info, iid, uid)","title":""},{"docid":"0efe1b664315de5e20c4c6d8d38c06a3","score":"0.67022336","text":"def retrieve(self, request, pk=None):\n return Response({'method': 'Retrieve(GET)'})","title":""},{"docid":"96c4a3c54f55648259279c6e6655a850","score":"0.6679882","text":"def product(request, product_id):\n\n product = get_object_or_404(Product, pk=product_id)\n\n context = {\n 'product': product,\n }\n\n return render(request, 'products/product.html', context)","title":""},{"docid":"caaf9a0ec911620fe9837cf4783862f9","score":"0.6678344","text":"def get_product(self, id_):\n for _product in self.products:\n if _product.id == id_:\n return _product\n raise LookupError(\"Product Not in DB\")","title":""},{"docid":"bdcc0a0c7fb38ca8de3875271a3188e7","score":"0.66685796","text":"def retrieve(self,request,pk= None):\n return Response({\"http_method\":'GET'})","title":""},{"docid":"561cf5fe187abb4c4aaaeb31d0e7d438","score":"0.6626251","text":"def get_specific_product(self,id):\n return self.records.get_data(\"id\",id)","title":""},{"docid":"8f9533d0a88a17a5584f4e5a9b4c1f15","score":"0.66077554","text":"def get(self):\n \"\"\"\n @api {get} /product/:productId Get a seller\n @apiVersion 1.0.0\n @apiGroup Product\n\n @apiUrlParam {Integer} productId\n \"\"\"\n return self","title":""},{"docid":"ca6ce37c09ee029fc78589d0d68df51e","score":"0.6592273","text":"def get(self):\n products = self.user.get_all_products()\n # check_product = [product for product in products if product[\"product_name\"]==product_name]\n if products:\n return make_response(jsonify({\"products\":products}),200)\n else:\n return make_response(jsonify({\"message\":\"No products available\"}))\n # return make_response(jsonify({\"message\":\"No products available\"}),204)","title":""},{"docid":"57d907064e96f15a9dcea227f448679a","score":"0.65551853","text":"def product_detail(request, product_id):\n\n product = get_object_or_404(Product, pk=product_id)\n\n # product singular to return on product with that id.\n context = {\n 'product': product,\n }\n\n return render(request, 'products/product_detail.html', context)","title":""},{"docid":"a6042c939de14b349349b6becf95a5e6","score":"0.6554168","text":"def view_product():\n try:\n db = get_db()\n products = db.execute(\"SELECT product_id, product_name FROM Product\")\n return render_template(\"product/view_product.html\", result=products)\n except sqlite3.Error as error:\n print(error)\n return render_template(\"error_occured.html\")","title":""},{"docid":"855b856751eb0f7522ce525dadd6712c","score":"0.65521985","text":"def test_retrieve_product(self):\n serializer = ProductSerializer(self.product, many=False)\n response = self.client.get(\n r('products:product-detail', pk=self.valid_pk), format='json')\n self.assertEqual(response.data, serializer.data)","title":""},{"docid":"940df445847be49979a91a2c3dcb9508","score":"0.6545566","text":"def get_product(self, product_name=False, product_id=False):\n if not product_name and not product_id:\n self.error(\"Can't look for a PRODUCT without NAME or ID\")\n return False\n self.debug('Getting PRODUCT \"{}\" from database'\n ''.format(product_name or product_id))\n #TODO: get PRODUCT from database using psycopg2\n prod_obj = _MODEL_PRODUCT(self_id=1, name='demo', stock=0, recipe_id=1)\n return prod_obj","title":""},{"docid":"182bf005f5859cb8a643954ae5b75d64","score":"0.6534951","text":"def product_detail(request, pk):\n product = get_object_or_404(Product, pk=pk)\n return render(request, \"product_detail.html\", {'product': product})","title":""},{"docid":"2fe4c94fb5ff849a5efc1ed97dc52730","score":"0.6522919","text":"def retrieve(self, request, pk=None):\n return Response({'message': 'get methode'})","title":""},{"docid":"68be0141094391e73c9f58e719df9b9e","score":"0.65121883","text":"def get_request(id ,url): \n\n http = urllib3.PoolManager()\n r = http.request('GET', f'http://localhost:5000/product/{id}',\n headers={'Content-Type': 'application/json'})\n\n new_product = json.loads(r.data)\n #print(y['name'])\n return product(new_product['name'], new_product['description'], new_product['price'], new_product['qty'])","title":""},{"docid":"bd3bc2787ec997b08418eb6e90c85000","score":"0.6511772","text":"def getSpecific(self, **kwargs):\n\n allParams = ['productId', 'id']\n\n params = locals()\n for (key, val) in params['kwargs'].iteritems():\n if key not in allParams:\n raise TypeError(\"Got an unexpected keyword argument '%s' to method getSpecific\" % key)\n params[key] = val\n del params['kwargs']\n\n resourcePath = '/products/{productId}/product-versions/{id}'\n resourcePath = resourcePath.replace('{format}', 'json')\n method = 'GET'\n\n queryParams = {}\n headerParams = {}\n formParams = {}\n files = {}\n bodyParam = None\n\n \n\n \n\n \n if ('productId' in params):\n replacement = str(self.apiClient.toPathValue(params['productId']))\n replacement = urllib.quote(replacement)\n resourcePath = resourcePath.replace('{' + 'productId' + '}',\n replacement)\n \n if ('id' in params):\n replacement = str(self.apiClient.toPathValue(params['id']))\n replacement = urllib.quote(replacement)\n resourcePath = resourcePath.replace('{' + 'id' + '}',\n replacement)\n \n\n \n\n \n\n postData = (formParams if formParams else bodyParam)\n\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\n postData, headerParams, files=files)\n\treturn response","title":""},{"docid":"37b3e5266320fd2aa3ab8e8f4de8c889","score":"0.64975417","text":"def get_product(request):\r\n try:\r\n token = request.META.get(\"HTTP_AUTHORIZATION\")\r\n payload = jwt.decode(token, \"Temage\")\r\n identity = payload['id']\r\n product_id = json.loads(request.body.decode('utf-8'))['productID']\r\n product = Product.objects.get(id=product_id)\r\n content = {}\r\n user_info = {}\r\n user_info['username'] = product.creator.user.username\r\n user_info['id'] = product.creator.user.id\r\n user_info['avator'] = settings.MEDIA_PATH + str(product.creator.avator)\r\n if product.creator.user.id == identity:\r\n content['can_be_delete'] = 1\r\n else:\r\n content['can_be_delete'] = 0\r\n user = Profile.objects.get(user__id=identity)\r\n collect = user.collection\r\n been_owned = collect.cards.filter(product_id = product_id)\r\n if been_owned:\r\n content['has_been_colleted'] = 1\r\n else:\r\n content['has_been_collected'] = 0\r\n content['id'] = product_id\r\n content['text'] = product.html\r\n content['creator'] = user_info\r\n content['title'] = product.title\r\n themes = product.theme.all()\r\n themelist = []\r\n for theme in themes:\r\n themelist.append(theme.name)\r\n content['style'] = themelist\r\n return HttpResponse(json.dumps(content), status=200, content_type=\"application/json\")\r\n except:\r\n return HttpResponse(json.dumps(\"something wrong\"), status=400, content_type=\"application/json\")","title":""},{"docid":"a4d9a81b83c9da8416970bbfd96d3465","score":"0.6488897","text":"def retrieve(self, request, pk=None):\n try:\n vendor = Vendor.objects.get(pk=pk)\n serializer = VendorSerializer(vendor, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"b9fd4cd315bd3d7721ea969ec5b510db","score":"0.64838034","text":"def test_invalid_retrieve_product(self):\n response = self.client.get(\n r('products:product-detail', pk=self.invalid_pk), format='json')\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)","title":""},{"docid":"058b089eb965087edb035db4799bb162","score":"0.6476916","text":"def retrieve(self,request,pk=None):\n return Response({'method':'RETRIEVE'})","title":""},{"docid":"7be9860444f6d9c205074cbdbf7d1f15","score":"0.64646006","text":"def show(self, req, id):\n # FIXME(nmg): should catch exception if any\n query = self.db.get_veg(id)\n LOG.info(query)\n\n if not query:\n return Fault(webob.exc.HTTPNotFound())\n\n item = {\n 'id': str(query['_id']),\n 'name': query['name'],\n 'photo': query['photo'],\n 'price': query['price'],\n 'mprice': query['mprice'],\n 'size': query['size'],\n 'origin': query['origin'],\n 'desc': query['desc']\n }\n\n return HttpResponse(item)","title":""},{"docid":"51af46ab163f08d6d67313e04d7dbb11","score":"0.64368194","text":"def find_by_id(\n self,\n product_id: str,\n ) -> Product:\n pass","title":""},{"docid":"700bf702cee983647e8ed68bfae83f32","score":"0.639829","text":"def getProductByID(id):\n product = session.query(ProductItem)\\\n .filter(ProductItem.product_id == id).first()\n return product","title":""},{"docid":"c20ee3a68049df9c66b85e0ef462bfd4","score":"0.6396573","text":"def test_get(self):\n response = self.client.get(r('products:product-list'), format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"4bd64a9ae5f2156ae8c459dda8841cfc","score":"0.6395936","text":"def get(self):\n if not product_list:\n return {\"message\": \"no product saved\"}, 404\n return product_list","title":""},{"docid":"2a199e998efbb92ab8af29cf28e2c7a8","score":"0.637678","text":"def fetch_products():\n products = product_controller.get_all_products()\n if products:\n return jsonify({\"Products\": products}), 200\n return jsonify({\"message\": \"No products available\"}), 404","title":""},{"docid":"5f37ddb2ab6e6caa130e1927becae96f","score":"0.6373812","text":"def get(self, request, id, format=None):\n cat_obj = Products.objects.filter(id=id, is_active=True).all()\n product_list = []\n for each in cat_obj:\n product_list.append({each.id: each.name})\n return Response(product_list, status=status.HTTP_200_OK)","title":""},{"docid":"344e290cfa669d45955b8d5e4a6be784","score":"0.63417745","text":"def get_a_product(self, product_id):\n\n return self.database.Product.get_by_id(product_id)","title":""},{"docid":"4339f026fdc33069673871a31200ca4a","score":"0.6329463","text":"def get_product_details(product_id,access_token):\r\n product_url = \"https://api.kroger.com/v1/products/\" + product_id\r\n headers = {\r\n \"Accept\": \"application/json\",\r\n \"Authorization\": \"Bearer \" + access_token,\r\n }\r\n response = requests.get(product_url,headers=headers)\r\n #print(response.status_code)\r\n if(response.status_code == requests.codes.ok):\r\n return json.loads(response.text) # Return the product details as python dictionary\r\n\r\n print(\"ERROR:Could not get product detail for product_id:\"+product_id)\r\n return None","title":""},{"docid":"2498f142d6532e2afa9ae1abd68c6ffd","score":"0.6306155","text":"def get(self):\n try:\n vend_controller = Vend_Controller()\n products = vend_controller.view_all_products()\n return { 'message': 'Success', \"data\" : products } , 200\n except Exception as e:\n return self.error_controller.handle(e)","title":""},{"docid":"350d562d32073ceb8cf34070fce7d52e","score":"0.6297312","text":"def test_get_product_by_id(self):\n pass","title":""},{"docid":"078e17c5bf92fb90461545a1b1d3cc44","score":"0.6295105","text":"def get(self, request, format=None):\n cat_obj = Products.objects.filter(is_active=True).all()\n product_list = []\n for each in cat_obj:\n product_list.append({each.id: each.name})\n return Response(product_list, status=status.HTTP_200_OK)","title":""},{"docid":"a9acc997f3826cb78d328600174deecc","score":"0.6254769","text":"def test_get_product_by_id(test_app, test_database, add_product):\n test_database.session.query(Product).delete()\n product = add_product(test_product)\n client = test_app.test_client()\n response = client.get(f\"/products/{product.id}\")\n data = response.json\n assert response.status_code == 200\n for key in test_product.keys():\n assert data[key] == test_product[key]","title":""},{"docid":"48be21e1cf00226d249a83aaa11103c0","score":"0.61984587","text":"def retrieve(self, request, pk=None):\n try:\n resource = Resource.objects.get(pk=pk)\n serializer = ResourceSerializer(resource, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"c5aa106a5897872920a36a1381945912","score":"0.61776114","text":"def retrieve(self,request):\n return Response({'http_method':'GET'})","title":""},{"docid":"24b67ebe96af43fdcb7295eca2c383ec","score":"0.6152506","text":"def get(self, uri, query=None, **kwargs):\n return self.fetch('get', uri, query, **kwargs)","title":""},{"docid":"68915964a24d032a0a39d6863fc5dad0","score":"0.61452615","text":"def products(self, request):\n user_id = int(request.query_params.get('user_id'))\n\n products = Products.objects.all_products(user_id)\n\n return Response(products)","title":""},{"docid":"508720d89bd31393b7e0f30545f12af7","score":"0.61174357","text":"def get(self, **kwargs):\n return self._make_request('get', kwargs)","title":""},{"docid":"886eb8f3c7ccf2f781f221cb28a0402b","score":"0.61054057","text":"def am_get_product_info():\n return get_single_product(external_facing=False)","title":""},{"docid":"a5b7d52cfdc90998a5698a663e80697c","score":"0.6091989","text":"def get_product_detail(\n self, collection_id: str, user: Dict[str, Any] = None\n ) -> dict:\n try:\n LOGGER.info(\"%s product requested\", collection_id)\n\n product_record = None\n if settings.IS_CSW_SERVER and collection_id in settings.WHITELIST:\n product_record = self.csw_session.get_product(collection_id)\n elif settings.IS_CSW_SERVER_DC and collection_id in settings.WHITELIST_DC:\n product_record = self.csw_session_dc.get_product(collection_id)\n elif settings.IS_HDA_WEKEO and collection_id in settings.WHITELIST_WEKEO:\n product_record = self.hda_session.get_product(collection_id)\n if not product_record:\n return ServiceException(400, self._get_user_id(user),\n f\"The requested collection {collection_id} does not exist on the backend.\",\n internal=False, links=[]).to_dict()\n\n # Check user permission\n # TODO: implement better logic for checking user permissions\n # Unauthorized\n if collection_id in ('TUW_SIG0_S1') and not user:\n return ServiceException(401, self._get_user_id(user), \"This collection is not publicly accessible.\",\n internal=False, links=[]).to_dict()\n # Forbidden (does not have permissions)\n elif collection_id == 'TUW_SIG0_S1' and user \\\n and self.csw_session_dc.data_access not in user[\"profile\"][\"data_access\"]:\n return ServiceException(403, self._get_user_id(user),\n \"User is not authorized to access this collection.\", internal=False,\n links=[]).to_dict()\n\n response = CollectionSchema().dump(product_record)\n LOGGER.debug(\"response:\\n%s\", pformat(response))\n return {\"status\": \"success\", \"code\": 200, \"data\": response}\n except Exception as exp:\n return ServiceException(500, self._get_user_id(user), str(exp)).to_dict()","title":""},{"docid":"d1b7531f23d3919546a9340648ecd7a0","score":"0.6088593","text":"def test_detail_get(self) -> None:\n client: Client = Client()\n Product.objects.create(**Config.DATABASE_EXPECTED)\n code_product: dict = Config.DATABASE_EXPECTED[\"code\"]\n response: HttpResponse = client.get(f\"/products/{code_product}/\")\n assert response.status_code == 200","title":""},{"docid":"7d029d2a4363dd960a60911766825f22","score":"0.6081455","text":"def retrieve(self, request, pk=None):\n try:\n # `pk` is a parameter to this function, and\n # Django parses it from the URL route parameter\n # http://localhost:8000/categories/2\n #\n # The `2` at the end of the route becomes `pk`\n category = Category.objects.get(pk=pk)\n serializer = CategorySerializer(category, context={'request': request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"56a363bf1bd29c611a7df21953b51ebe","score":"0.6070192","text":"def get_product(product_id):\n current_time = datetime.utcnow()\n yesterday = current_time - timedelta(days=1)\n products = models.storage.get_session().query(Product).filter(Product.product_id == product_id).filter(\n Product.created_at > yesterday).all()\n if len(products) > 0:\n return products[0]\n else:\n return None","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""},{"docid":"14e1f88bf29271be5f145fd195e69553","score":"0.6059371","text":"def get(self, request, *args, **kwargs):\r\n return self.retrieve(request, *args, **kwargs)","title":""}],"string":"[\n {\n \"docid\": \"0dec387335a7d688dff232ff44946424\",\n \"score\": \"0.77556497\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n product = ProductModel.objects.get(pk=pk)\\n serializer = ProductSerializer(product, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62e413f620cacede006454b0a085f3b\",\n \"score\": \"0.7739515\",\n \"text\": \"def get(self):\\n\\n identifier = self.get_query_argument(\\\"id\\\", None)\\n if not identifier:\\n self.generic_resp(404)\\n return\\n\\n direct = self.get_query_argument(\\\"direct\\\", False)\\n\\n try:\\n direct = bool(int(direct))\\n except:\\n direct = False\\n try:\\n\\n res = self.get_product(identifier, direct)\\n if not res:\\n self.generic_resp(404)\\n return\\n\\n resp = dict()\\n resp[\\\"product\\\"] = res\\n resp[\\\"status\\\"] = 200\\n resp[\\\"mesasage\\\"] = \\\"OK\\\"\\n\\n self.write(json.dumps(resp))\\n self.set_status(200)\\n return\\n except Exception as e:\\n self.generic_resp(500, str(e))\\n self.finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0322cefc47631d684b43847f320b8966\",\n \"score\": \"0.7682345\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n product = Product.objects.get(pk=pk)\\n serializer = ProductsSerializer(\\n product, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2426a33dd1b9aafdf872c20dd4ab99c5\",\n \"score\": \"0.7679826\",\n \"text\": \"def get_product(self, productId):\\n\\n method = \\\"GET\\\"\\n endpoint = \\\"products/{}\\\".format(productId)\\n\\n return self.request(method, endpoint)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b547f0d2dcc5bcaecf0a3fcc89f125\",\n \"score\": \"0.7581064\",\n \"text\": \"def get(self, product_id):\\n app.logger.info('Request for product with id: %s', product_id)\\n product = Product.find(product_id)\\n if not product:\\n api.abort(status.HTTP_404_NOT_FOUND,\\n \\\"Product with id '{}' was not found.\\\".format(product_id))\\n return product.serialize(), status.HTTP_200_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23f2e401da392bf4e22abf6999862c32\",\n \"score\": \"0.74065155\",\n \"text\": \"def fetch(cls, product_id):\\n return cls().requests.get(f\\\"product/{product_id}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784fd520ee573308a251ee7fbcfc47dc\",\n \"score\": \"0.7405221\",\n \"text\": \"def get(self, id):\\n product = ProductsModel()\\n product_to_get = product.get_item('products', product_id=id)\\n message = \\\"Product with id {} does not exist\\\".format(id)\\n if product_to_get:\\n return product_to_get\\n return {\\\"message\\\": message}, 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f3090a37ffba66fcfcb0a65fabfd20\",\n \"score\": \"0.7219862\",\n \"text\": \"def get(self, p_id):\\n for i in range(len(products)):\\n if products[i]['id'] == int(p_id):\\n product = products[i]\\n return make_response(jsonify(product)), 200\\n else:\\n response = {\\n 'status': 'Failed',\\n 'message': 'Product Not Found'\\n }\\n return make_response(jsonify(response)), 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760a97e4491587f09585195ca3ae075a\",\n \"score\": \"0.7085526\",\n \"text\": \"def get(self):\\n\\n\\t\\tparams = self.parseParams()\\n\\t\\tself.doProductSearch(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"673d0e8331196d40be8a8c2056f6993e\",\n \"score\": \"0.7048351\",\n \"text\": \"def product(db, id):\\n session.get_or_create_session(db)\\n\\n product = model.product_get(db, id)\\n\\n if product:\\n #this product exists, to get its info.\\n info = {\\n 'product': model.product_get(db, id)\\n }\\n return template('product', info)\\n else:\\n #no product exists, return 404\\n return HTTPError(404, \\\"No such product.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe4fab1337913dbd17ce2a516d022ae\",\n \"score\": \"0.6942659\",\n \"text\": \"def get_products(self, **kwargs):\\n\\n method = \\\"GET\\\"\\n endpoint = self._create_endpoint(\\\"products\\\", kwargs)\\n\\n return self.request(method, endpoint)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c80405be2161d99baa3de01cf076fe\",\n \"score\": \"0.6934231\",\n \"text\": \"def get(self, request, slug):\\n product = get_object_or_404(Product, slug=slug)\\n return render(request, \\\"niunius/product.html\\\", {\\\"product\\\": product})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02cd35abaff4812804577ab349f600f\",\n \"score\": \"0.68860555\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n category = Product_Category.objects.get(pk=pk)\\n serializer = Product_Category_Serializer(category, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ef0aeeabd0e901cbb9668073a7809b\",\n \"score\": \"0.6879762\",\n \"text\": \"def get_product(product_id):\\n\\n response = _get('products/' + product_id)\\n if response.status_code != 200:\\n error_msg = response.json()['data']\\n logger.error('get_product() failed with \\\"%s\\\"' % error_msg)\\n raise Exception('Product not found')\\n product_data = response.json()[u'data']\\n return api.Product(product_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aabf3b82af7f2f14cf25c3b05c2284d2\",\n \"score\": \"0.6842081\",\n \"text\": \"def api_product_detail(request, product_id):\\n try:\\n product = Product.objects.get(pk=product_id)\\n except Product.DoesNotExist:\\n return Response(status=status.HTTP_404_NOT_FOUND)\\n\\n if request.method == 'GET':\\n serializer = ProductSerializer(product)\\n return Response(serializer.data)\\n\\n elif request.method == 'PUT':\\n serializer = ProductSerializer(product, data=request.data)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data)\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\\n\\n elif request.method == 'DELETE':\\n product.delete()\\n return Response(status=status.HTTP_204_NO_CONTENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dff2cfa8ecd20f574949d680574f015\",\n \"score\": \"0.68408215\",\n \"text\": \"def retrieve(self, request, pk=None): \\n return Response({\\\"http_method\\\":\\\"GET\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce5d12791a21f4ade0dc69e340c54c7\",\n \"score\": \"0.6810289\",\n \"text\": \"def get(self, request, id):\\n \\n reviews = Review.objects.filter(user__id=id)\\n\\n if request.GET.get('product'):\\n reviews.filter(product__slug=request.GET.get('product'))\\n\\n if not reviews:\\n return Response(status=status.HTTP_204_NO_CONTENT)\\n\\n serializer = ReviewSerializer(reviews, many=True)\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5119df8009633f439a7db35afbfff853\",\n \"score\": \"0.67971087\",\n \"text\": \"def get(self):\\n app.logger.info('Request for product list')\\n products = []\\n category = request.args.get('category')\\n name = request.args.get('name')\\n price = request.args.get('price')\\n if category:\\n products = Product.find_by_category(category)\\n elif name:\\n products = Product.find_by_name(name)\\n elif price and int(price) > 0 and int(price) < 4: # query price by range\\n if int(price) == 1:\\n products = Product.find_by_price(0, 25)\\n elif int(price) == 2:\\n products = Product.find_by_price(25, 50)\\n else:\\n products = Product.find_by_price(50, 75)\\n else:\\n products = Product.all()\\n results = [product.serialize() for product in products]\\n return results, status.HTTP_200_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3068d4bdc78abbda5b162006ba10bffe\",\n \"score\": \"0.67844826\",\n \"text\": \"def test_product_get(self):\\n\\n product = self.products['Yellow Wool Jumper']\\n result = model.product_get(self.db, product['id'])\\n\\n # check a few fields\\n self.assertEqual(product['id'], result['id'])\\n self.assertEqual(product['name'], result['name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd18e1d24022b6ca0d4d8d8371b1340c\",\n \"score\": \"0.67698896\",\n \"text\": \"def test_valid_retrieve_product(self):\\n response = self.client.get(\\n r('products:product-detail', pk=self.valid_pk), format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2874e3d87fe3d73f814b517502d9fe1f\",\n \"score\": \"0.67581475\",\n \"text\": \"def get(self, request):\\n\\n reviews = Review.objects.filter(user=request.user)\\n\\n if request.GET.get('product'):\\n reviews.filter(product__slug=request.GET.get('product'))\\n\\n if not reviews:\\n return Response(status=status.HTTP_204_NO_CONTENT)\\n\\n serializer = ReviewSerializer(reviews, many=True)\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ff35075bf78741ac3178e754f5dad7\",\n \"score\": \"0.6736622\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n return Response({'http_method':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32086c67fd1b4a68e4543ea546f9fcb\",\n \"score\": \"0.67365336\",\n \"text\": \"def retrieve(self,request,pk=None):\\n\\t\\treturn Response({'message':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91c02f60eee46f2721f2ced623f7a72\",\n \"score\": \"0.673558\",\n \"text\": \"def get(self):\\n parser = reqparse.RequestParser()\\n parser.add_argument(\\\"category_id\\\", type=int, default=None, required=True)\\n args = parser.parse_args()\\n return c.get_product_list(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769be5c4ac2cbcbe8d32e1512d0792fa\",\n \"score\": \"0.6734989\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n return Response({'http_method': 'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769be5c4ac2cbcbe8d32e1512d0792fa\",\n \"score\": \"0.6734989\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n return Response({'http_method': 'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769be5c4ac2cbcbe8d32e1512d0792fa\",\n \"score\": \"0.6734989\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n return Response({'http_method': 'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769be5c4ac2cbcbe8d32e1512d0792fa\",\n \"score\": \"0.6734989\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n return Response({'http_method': 'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedbf37c7f41126c7de2a3f3b3a0d8c0\",\n \"score\": \"0.67247903\",\n \"text\": \"def retrieve(self, request, pk = None):\\n return Response({'http_method':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9244b8d6d941f7277b1bbac525f0c588\",\n \"score\": \"0.6713015\",\n \"text\": \"def get_products():\\n return requests.get(API_URL + \\\"products\\\").json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27386e599b9c1629d1187a2bc27759d3\",\n \"score\": \"0.6705706\",\n \"text\": \"def retrieve(self,request,pk=None):\\n return Response({'http_method':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27386e599b9c1629d1187a2bc27759d3\",\n \"score\": \"0.6705706\",\n \"text\": \"def retrieve(self,request,pk=None):\\n return Response({'http_method':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b2575288ea732e121e598f883bfa8b\",\n \"score\": \"0.6705331\",\n \"text\": \"def get_product_info():\\n iid = request.json[\\\"idProducto\\\"] # item id\\n uid = request.json.get(\\\"idSocio\\\", None) # user id\\n return safe_return(db.get_product_info, iid, uid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0efe1b664315de5e20c4c6d8d38c06a3\",\n \"score\": \"0.67022336\",\n \"text\": \"def retrieve(self, request, pk=None):\\n return Response({'method': 'Retrieve(GET)'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c4a3c54f55648259279c6e6655a850\",\n \"score\": \"0.6679882\",\n \"text\": \"def product(request, product_id):\\n\\n product = get_object_or_404(Product, pk=product_id)\\n\\n context = {\\n 'product': product,\\n }\\n\\n return render(request, 'products/product.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caaf9a0ec911620fe9837cf4783862f9\",\n \"score\": \"0.6678344\",\n \"text\": \"def get_product(self, id_):\\n for _product in self.products:\\n if _product.id == id_:\\n return _product\\n raise LookupError(\\\"Product Not in DB\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdcc0a0c7fb38ca8de3875271a3188e7\",\n \"score\": \"0.66685796\",\n \"text\": \"def retrieve(self,request,pk= None):\\n return Response({\\\"http_method\\\":'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561cf5fe187abb4c4aaaeb31d0e7d438\",\n \"score\": \"0.6626251\",\n \"text\": \"def get_specific_product(self,id):\\n return self.records.get_data(\\\"id\\\",id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9533d0a88a17a5584f4e5a9b4c1f15\",\n \"score\": \"0.66077554\",\n \"text\": \"def get(self):\\n \\\"\\\"\\\"\\n @api {get} /product/:productId Get a seller\\n @apiVersion 1.0.0\\n @apiGroup Product\\n\\n @apiUrlParam {Integer} productId\\n \\\"\\\"\\\"\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6ce37c09ee029fc78589d0d68df51e\",\n \"score\": \"0.6592273\",\n \"text\": \"def get(self):\\n products = self.user.get_all_products()\\n # check_product = [product for product in products if product[\\\"product_name\\\"]==product_name]\\n if products:\\n return make_response(jsonify({\\\"products\\\":products}),200)\\n else:\\n return make_response(jsonify({\\\"message\\\":\\\"No products available\\\"}))\\n # return make_response(jsonify({\\\"message\\\":\\\"No products available\\\"}),204)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57d907064e96f15a9dcea227f448679a\",\n \"score\": \"0.65551853\",\n \"text\": \"def product_detail(request, product_id):\\n\\n product = get_object_or_404(Product, pk=product_id)\\n\\n # product singular to return on product with that id.\\n context = {\\n 'product': product,\\n }\\n\\n return render(request, 'products/product_detail.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6042c939de14b349349b6becf95a5e6\",\n \"score\": \"0.6554168\",\n \"text\": \"def view_product():\\n try:\\n db = get_db()\\n products = db.execute(\\\"SELECT product_id, product_name FROM Product\\\")\\n return render_template(\\\"product/view_product.html\\\", result=products)\\n except sqlite3.Error as error:\\n print(error)\\n return render_template(\\\"error_occured.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855b856751eb0f7522ce525dadd6712c\",\n \"score\": \"0.65521985\",\n \"text\": \"def test_retrieve_product(self):\\n serializer = ProductSerializer(self.product, many=False)\\n response = self.client.get(\\n r('products:product-detail', pk=self.valid_pk), format='json')\\n self.assertEqual(response.data, serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940df445847be49979a91a2c3dcb9508\",\n \"score\": \"0.6545566\",\n \"text\": \"def get_product(self, product_name=False, product_id=False):\\n if not product_name and not product_id:\\n self.error(\\\"Can't look for a PRODUCT without NAME or ID\\\")\\n return False\\n self.debug('Getting PRODUCT \\\"{}\\\" from database'\\n ''.format(product_name or product_id))\\n #TODO: get PRODUCT from database using psycopg2\\n prod_obj = _MODEL_PRODUCT(self_id=1, name='demo', stock=0, recipe_id=1)\\n return prod_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182bf005f5859cb8a643954ae5b75d64\",\n \"score\": \"0.6534951\",\n \"text\": \"def product_detail(request, pk):\\n product = get_object_or_404(Product, pk=pk)\\n return render(request, \\\"product_detail.html\\\", {'product': product})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe4c94fb5ff849a5efc1ed97dc52730\",\n \"score\": \"0.6522919\",\n \"text\": \"def retrieve(self, request, pk=None):\\n return Response({'message': 'get methode'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68be0141094391e73c9f58e719df9b9e\",\n \"score\": \"0.65121883\",\n \"text\": \"def get_request(id ,url): \\n\\n http = urllib3.PoolManager()\\n r = http.request('GET', f'http://localhost:5000/product/{id}',\\n headers={'Content-Type': 'application/json'})\\n\\n new_product = json.loads(r.data)\\n #print(y['name'])\\n return product(new_product['name'], new_product['description'], new_product['price'], new_product['qty'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3bc2787ec997b08418eb6e90c85000\",\n \"score\": \"0.6511772\",\n \"text\": \"def getSpecific(self, **kwargs):\\n\\n allParams = ['productId', 'id']\\n\\n params = locals()\\n for (key, val) in params['kwargs'].iteritems():\\n if key not in allParams:\\n raise TypeError(\\\"Got an unexpected keyword argument '%s' to method getSpecific\\\" % key)\\n params[key] = val\\n del params['kwargs']\\n\\n resourcePath = '/products/{productId}/product-versions/{id}'\\n resourcePath = resourcePath.replace('{format}', 'json')\\n method = 'GET'\\n\\n queryParams = {}\\n headerParams = {}\\n formParams = {}\\n files = {}\\n bodyParam = None\\n\\n \\n\\n \\n\\n \\n if ('productId' in params):\\n replacement = str(self.apiClient.toPathValue(params['productId']))\\n replacement = urllib.quote(replacement)\\n resourcePath = resourcePath.replace('{' + 'productId' + '}',\\n replacement)\\n \\n if ('id' in params):\\n replacement = str(self.apiClient.toPathValue(params['id']))\\n replacement = urllib.quote(replacement)\\n resourcePath = resourcePath.replace('{' + 'id' + '}',\\n replacement)\\n \\n\\n \\n\\n \\n\\n postData = (formParams if formParams else bodyParam)\\n\\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\\n postData, headerParams, files=files)\\n\\treturn response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b3e5266320fd2aa3ab8e8f4de8c889\",\n \"score\": \"0.64975417\",\n \"text\": \"def get_product(request):\\r\\n try:\\r\\n token = request.META.get(\\\"HTTP_AUTHORIZATION\\\")\\r\\n payload = jwt.decode(token, \\\"Temage\\\")\\r\\n identity = payload['id']\\r\\n product_id = json.loads(request.body.decode('utf-8'))['productID']\\r\\n product = Product.objects.get(id=product_id)\\r\\n content = {}\\r\\n user_info = {}\\r\\n user_info['username'] = product.creator.user.username\\r\\n user_info['id'] = product.creator.user.id\\r\\n user_info['avator'] = settings.MEDIA_PATH + str(product.creator.avator)\\r\\n if product.creator.user.id == identity:\\r\\n content['can_be_delete'] = 1\\r\\n else:\\r\\n content['can_be_delete'] = 0\\r\\n user = Profile.objects.get(user__id=identity)\\r\\n collect = user.collection\\r\\n been_owned = collect.cards.filter(product_id = product_id)\\r\\n if been_owned:\\r\\n content['has_been_colleted'] = 1\\r\\n else:\\r\\n content['has_been_collected'] = 0\\r\\n content['id'] = product_id\\r\\n content['text'] = product.html\\r\\n content['creator'] = user_info\\r\\n content['title'] = product.title\\r\\n themes = product.theme.all()\\r\\n themelist = []\\r\\n for theme in themes:\\r\\n themelist.append(theme.name)\\r\\n content['style'] = themelist\\r\\n return HttpResponse(json.dumps(content), status=200, content_type=\\\"application/json\\\")\\r\\n except:\\r\\n return HttpResponse(json.dumps(\\\"something wrong\\\"), status=400, content_type=\\\"application/json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d9a81b83c9da8416970bbfd96d3465\",\n \"score\": \"0.6488897\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n vendor = Vendor.objects.get(pk=pk)\\n serializer = VendorSerializer(vendor, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9fd4cd315bd3d7721ea969ec5b510db\",\n \"score\": \"0.64838034\",\n \"text\": \"def test_invalid_retrieve_product(self):\\n response = self.client.get(\\n r('products:product-detail', pk=self.invalid_pk), format='json')\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058b089eb965087edb035db4799bb162\",\n \"score\": \"0.6476916\",\n \"text\": \"def retrieve(self,request,pk=None):\\n return Response({'method':'RETRIEVE'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be9860444f6d9c205074cbdbf7d1f15\",\n \"score\": \"0.64646006\",\n \"text\": \"def show(self, req, id):\\n # FIXME(nmg): should catch exception if any\\n query = self.db.get_veg(id)\\n LOG.info(query)\\n\\n if not query:\\n return Fault(webob.exc.HTTPNotFound())\\n\\n item = {\\n 'id': str(query['_id']),\\n 'name': query['name'],\\n 'photo': query['photo'],\\n 'price': query['price'],\\n 'mprice': query['mprice'],\\n 'size': query['size'],\\n 'origin': query['origin'],\\n 'desc': query['desc']\\n }\\n\\n return HttpResponse(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51af46ab163f08d6d67313e04d7dbb11\",\n \"score\": \"0.64368194\",\n \"text\": \"def find_by_id(\\n self,\\n product_id: str,\\n ) -> Product:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700bf702cee983647e8ed68bfae83f32\",\n \"score\": \"0.639829\",\n \"text\": \"def getProductByID(id):\\n product = session.query(ProductItem)\\\\\\n .filter(ProductItem.product_id == id).first()\\n return product\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20ee3a68049df9c66b85e0ef462bfd4\",\n \"score\": \"0.6396573\",\n \"text\": \"def test_get(self):\\n response = self.client.get(r('products:product-list'), format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd64a9ae5f2156ae8c459dda8841cfc\",\n \"score\": \"0.6395936\",\n \"text\": \"def get(self):\\n if not product_list:\\n return {\\\"message\\\": \\\"no product saved\\\"}, 404\\n return product_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a199e998efbb92ab8af29cf28e2c7a8\",\n \"score\": \"0.637678\",\n \"text\": \"def fetch_products():\\n products = product_controller.get_all_products()\\n if products:\\n return jsonify({\\\"Products\\\": products}), 200\\n return jsonify({\\\"message\\\": \\\"No products available\\\"}), 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f37ddb2ab6e6caa130e1927becae96f\",\n \"score\": \"0.6373812\",\n \"text\": \"def get(self, request, id, format=None):\\n cat_obj = Products.objects.filter(id=id, is_active=True).all()\\n product_list = []\\n for each in cat_obj:\\n product_list.append({each.id: each.name})\\n return Response(product_list, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344e290cfa669d45955b8d5e4a6be784\",\n \"score\": \"0.63417745\",\n \"text\": \"def get_a_product(self, product_id):\\n\\n return self.database.Product.get_by_id(product_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4339f026fdc33069673871a31200ca4a\",\n \"score\": \"0.6329463\",\n \"text\": \"def get_product_details(product_id,access_token):\\r\\n product_url = \\\"https://api.kroger.com/v1/products/\\\" + product_id\\r\\n headers = {\\r\\n \\\"Accept\\\": \\\"application/json\\\",\\r\\n \\\"Authorization\\\": \\\"Bearer \\\" + access_token,\\r\\n }\\r\\n response = requests.get(product_url,headers=headers)\\r\\n #print(response.status_code)\\r\\n if(response.status_code == requests.codes.ok):\\r\\n return json.loads(response.text) # Return the product details as python dictionary\\r\\n\\r\\n print(\\\"ERROR:Could not get product detail for product_id:\\\"+product_id)\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2498f142d6532e2afa9ae1abd68c6ffd\",\n \"score\": \"0.6306155\",\n \"text\": \"def get(self):\\n try:\\n vend_controller = Vend_Controller()\\n products = vend_controller.view_all_products()\\n return { 'message': 'Success', \\\"data\\\" : products } , 200\\n except Exception as e:\\n return self.error_controller.handle(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350d562d32073ceb8cf34070fce7d52e\",\n \"score\": \"0.6297312\",\n \"text\": \"def test_get_product_by_id(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078e17c5bf92fb90461545a1b1d3cc44\",\n \"score\": \"0.6295105\",\n \"text\": \"def get(self, request, format=None):\\n cat_obj = Products.objects.filter(is_active=True).all()\\n product_list = []\\n for each in cat_obj:\\n product_list.append({each.id: each.name})\\n return Response(product_list, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9acc997f3826cb78d328600174deecc\",\n \"score\": \"0.6254769\",\n \"text\": \"def test_get_product_by_id(test_app, test_database, add_product):\\n test_database.session.query(Product).delete()\\n product = add_product(test_product)\\n client = test_app.test_client()\\n response = client.get(f\\\"/products/{product.id}\\\")\\n data = response.json\\n assert response.status_code == 200\\n for key in test_product.keys():\\n assert data[key] == test_product[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48be21e1cf00226d249a83aaa11103c0\",\n \"score\": \"0.61984587\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n resource = Resource.objects.get(pk=pk)\\n serializer = ResourceSerializer(resource, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5aa106a5897872920a36a1381945912\",\n \"score\": \"0.61776114\",\n \"text\": \"def retrieve(self,request):\\n return Response({'http_method':'GET'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b67ebe96af43fdcb7295eca2c383ec\",\n \"score\": \"0.6152506\",\n \"text\": \"def get(self, uri, query=None, **kwargs):\\n return self.fetch('get', uri, query, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68915964a24d032a0a39d6863fc5dad0\",\n \"score\": \"0.61452615\",\n \"text\": \"def products(self, request):\\n user_id = int(request.query_params.get('user_id'))\\n\\n products = Products.objects.all_products(user_id)\\n\\n return Response(products)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508720d89bd31393b7e0f30545f12af7\",\n \"score\": \"0.61174357\",\n \"text\": \"def get(self, **kwargs):\\n return self._make_request('get', kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886eb8f3c7ccf2f781f221cb28a0402b\",\n \"score\": \"0.61054057\",\n \"text\": \"def am_get_product_info():\\n return get_single_product(external_facing=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b7d52cfdc90998a5698a663e80697c\",\n \"score\": \"0.6091989\",\n \"text\": \"def get_product_detail(\\n self, collection_id: str, user: Dict[str, Any] = None\\n ) -> dict:\\n try:\\n LOGGER.info(\\\"%s product requested\\\", collection_id)\\n\\n product_record = None\\n if settings.IS_CSW_SERVER and collection_id in settings.WHITELIST:\\n product_record = self.csw_session.get_product(collection_id)\\n elif settings.IS_CSW_SERVER_DC and collection_id in settings.WHITELIST_DC:\\n product_record = self.csw_session_dc.get_product(collection_id)\\n elif settings.IS_HDA_WEKEO and collection_id in settings.WHITELIST_WEKEO:\\n product_record = self.hda_session.get_product(collection_id)\\n if not product_record:\\n return ServiceException(400, self._get_user_id(user),\\n f\\\"The requested collection {collection_id} does not exist on the backend.\\\",\\n internal=False, links=[]).to_dict()\\n\\n # Check user permission\\n # TODO: implement better logic for checking user permissions\\n # Unauthorized\\n if collection_id in ('TUW_SIG0_S1') and not user:\\n return ServiceException(401, self._get_user_id(user), \\\"This collection is not publicly accessible.\\\",\\n internal=False, links=[]).to_dict()\\n # Forbidden (does not have permissions)\\n elif collection_id == 'TUW_SIG0_S1' and user \\\\\\n and self.csw_session_dc.data_access not in user[\\\"profile\\\"][\\\"data_access\\\"]:\\n return ServiceException(403, self._get_user_id(user),\\n \\\"User is not authorized to access this collection.\\\", internal=False,\\n links=[]).to_dict()\\n\\n response = CollectionSchema().dump(product_record)\\n LOGGER.debug(\\\"response:\\\\n%s\\\", pformat(response))\\n return {\\\"status\\\": \\\"success\\\", \\\"code\\\": 200, \\\"data\\\": response}\\n except Exception as exp:\\n return ServiceException(500, self._get_user_id(user), str(exp)).to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b7531f23d3919546a9340648ecd7a0\",\n \"score\": \"0.6088593\",\n \"text\": \"def test_detail_get(self) -> None:\\n client: Client = Client()\\n Product.objects.create(**Config.DATABASE_EXPECTED)\\n code_product: dict = Config.DATABASE_EXPECTED[\\\"code\\\"]\\n response: HttpResponse = client.get(f\\\"/products/{code_product}/\\\")\\n assert response.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d029d2a4363dd960a60911766825f22\",\n \"score\": \"0.6081455\",\n \"text\": \"def retrieve(self, request, pk=None):\\n try:\\n # `pk` is a parameter to this function, and\\n # Django parses it from the URL route parameter\\n # http://localhost:8000/categories/2\\n #\\n # The `2` at the end of the route becomes `pk`\\n category = Category.objects.get(pk=pk)\\n serializer = CategorySerializer(category, context={'request': request})\\n return Response(serializer.data)\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56a363bf1bd29c611a7df21953b51ebe\",\n \"score\": \"0.6070192\",\n \"text\": \"def get_product(product_id):\\n current_time = datetime.utcnow()\\n yesterday = current_time - timedelta(days=1)\\n products = models.storage.get_session().query(Product).filter(Product.product_id == product_id).filter(\\n Product.created_at > yesterday).all()\\n if len(products) > 0:\\n return products[0]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e1f88bf29271be5f145fd195e69553\",\n \"score\": \"0.6059371\",\n \"text\": \"def get(self, request, *args, **kwargs):\\r\\n return self.retrieve(request, *args, 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
95eb8785976f83f94f082197f7eb07b6 | Base scoring of a misere contract round. | [
{
"docid": "3c27f353c5304059c0ee539b1c8b74c9",
"score": "0.0",
"text": "def score_misere(self, player, tricks):\n # Successful misere, player receives value of contract into the pool\n if tricks == 0:\n self.inc_pool(player, self.CONTRACT_VALUE[MISERE])\n # Failed misere, player receives v... | [
{
"docid": "b64b6377b3fb44c0722b7c52a8961ab4",
"score": "0.64262015",
"text": "def score(self, round_number: int = 0) -> int:\n # We are assuming 1-indexed round numbers\n if round_number:\n if round_number > len(self.hand):\n return 0\n return self.han... |
58f4d7bbff54905795fc687c6a761f53 | Bulk set variables from arguments. Intended for internal use by the Set and From methods. | [
{
"docid": "f05fda4f6a55f882a4c7fa9334414708",
"score": "0.0",
"text": "def Apply(self, args, kwargs):\n if len(args) == 1:\n arg = args[0]\n if isinstance(arg, dict):\n self.ApplyMap(arg)\n else:\n self.ApplyMap(dict(arg))\n elif len(args) > 1:\n self.ApplyMap(di... | [
{
"docid": "98838b7ea7102453dc9d6be12a0d2909",
"score": "0.6670922",
"text": "def set(self, **kwargs):\r\n setAllArgs(self, kwargs)",
"title": ""
},
{
"docid": "98ec79dba3f8112d1c67e2200a55b1fc",
"score": "0.656707",
"text": "def __setFourParameters(self, args):\n self.setM... |
89f7327ed06d85dd9d8429a9a2ef528d | Shell sort. O(n^(3/2)) algorithm. | [
{
"docid": "f42e521c6f005785f2a76c2ca39d9999",
"score": "0.75712293",
"text": "def shell_sort(array):\n # Gap between adjacent elements\n gap = int(len(array) / 2)\n while gap > 0:\n for position in range(gap, len(array)):\n # Insert element at position in its sub-array\n ... | [
{
"docid": "7b265fc09557b6bbe24693d4f7fb382f",
"score": "0.75653803",
"text": "def shell_sort(a_list):\n start = time.time()\n list_count = len(a_list) // 2\n while list_count > 0:\n for start_position in range(list_count):\n gap_insertion_sort(a_list, start_position, list_cou... |
6fb552c67c4bda0ffa2529c12ddc20a1 | Run shell command with args and keywords | [
{
"docid": "7076452a671592094e1121a1ae0d679d",
"score": "0.6708656",
"text": "def cmd(self, shellcmd, *args, **kwargs):\n _cmd = shellcmd.format(*args, **kwargs)\n self.log.info(_cmd)\n os.system(_cmd)",
"title": ""
}
] | [
{
"docid": "dfba1f779d4fa088eb710c699712bfde",
"score": "0.7237709",
"text": "def do_shell(args):\n os.system(args)",
"title": ""
},
{
"docid": "7e5155bf0d386e91f282e3d483ecd603",
"score": "0.7085518",
"text": "def do_shell(self, arg):\n run(arg.split())",
"title": ... |
ea0e1657d25fa363957a6c8004a0f67e | Allow header advertises REPORT | [
{
"docid": "13b96c906996f5e22de31346f0ef2ac6",
"score": "0.60892195",
"text": "def test_allow_header_deltav(self):\n def do_test(response):\n response = IResponse(response)\n\n allow = response.headers.getHeader(\"allow\")\n if not allow:\n self.fai... | [
{
"docid": "8d2003e1f6a54240820966697c8de90e",
"score": "0.62994033",
"text": "def report_only(self) -> None:\n self.header = \"Content-Security-Policy-Report-Only\"",
"title": ""
},
{
"docid": "997e22d61331e7d9a7eff0758254bbbb",
"score": "0.6258528",
"text": "def report_heade... |
605c3d2d364735d62b5ed1a9a8991a37 | Get map of var_name > set(input var names) for the model | [
{
"docid": "9e6522c73a1371b171b810c30a983e33",
"score": "0.6106772",
"text": "def make_compute_graph(self):\n input_map = {}\n for var_name in self.var_names:\n input_map[var_name] = self.get_parents(self.model[var_name])\n # add in constants\n input_map[va... | [
{
"docid": "432c08e5e8607e48a63c1e3f3d993946",
"score": "0.7184372",
"text": "def variables(model: Model) -> AbstractSet[str]:\n assert is_model(model)\n return model.keys()",
"title": ""
},
{
"docid": "9790cb9ad8fbc2913d8a9a64c0c38c45",
"score": "0.69497913",
"text": "def inpu... |
596e3ab4ff641e249fde2c3008184dfb | Align post title to the Jekyll post name requirements. | [
{
"docid": "5a92ae4ebde3914fad22ce99722836f8",
"score": "0.0",
"text": "def sanitize(str):\n res = str.lower()\n return res.replace(' ', '-')",
"title": ""
}
] | [
{
"docid": "33b47465d97f487c74fc8226c22da8d1",
"score": "0.62691516",
"text": "def get_title(self, entry):\n title = _('%(title)s (%(word_count)i words)') % \\\n {'title': entry.title, 'word_count': entry.word_count}\n return title",
"title": ""
},
{
"docid": "9a... |
55aaa4dd8d5d0246f24c4c30303c69ca | Return True if entity is available. | [
{
"docid": "9834f7b54461beea8e74ee1e5e1e64fe",
"score": "0.0",
"text": "def available(self) -> bool:\n return (\n super().available\n and self.serial_number in self.coordinator.data[\"climate\"]\n and \"temperature\" in self.coordinator.data[\"climate\"][self.seri... | [
{
"docid": "1c89ccd3cdc881324348895730d33496",
"score": "0.7533917",
"text": "def available(self):\n return self.fetch.data is not None",
"title": ""
},
{
"docid": "68b280265c7e1a24bcec0f5e12030eca",
"score": "0.7303804",
"text": "def available(self) -> bool:\n return s... |
b1a1f78c1400329b156016f857d80ba4 | MarkovNetwork.test_update_input_states() with invalid input | [
{
"docid": "6f2c1d384b9725b8379bfd720da2b0eb",
"score": "0.8879466",
"text": "def test_update_input_states_invalid_input():\n np.random.seed(98342)\n test_mn = MarkovNetwork(2, 4, 2)\n try:\n test_mn.update_input_states([1, 1, 0])\n except Exception as e:\n assert type(e) is Va... | [
{
"docid": "417db7ac04437880cc0294f26acee2ef",
"score": "0.89389145",
"text": "def test_update_input_states_bad_input():\n np.random.seed(98342)\n test_mn = MarkovNetwork(2, 4, 2)\n test_mn.update_input_states([-7, 2])\n assert np.all(test_mn.states[:2] == np.array([1, 1]))",
"title": ""... |
2889e4d95e1a2afe4725a0fa1246c57e | Ingest nodes capture data | [
{
"docid": "91cea2ea0f95cb8ff3af87f82893d91c",
"score": "0.0",
"text": "async def capture_done(self):\n return {\"status\": \"capture_done\"}",
"title": ""
}
] | [
{
"docid": "39747bb26a7f7002dc443639e32f9fb3",
"score": "0.66707087",
"text": "def execute_ingest(self):",
"title": ""
},
{
"docid": "77c68669678699f652f264717b388401",
"score": "0.6277186",
"text": "def ingest(self, **kwargs) -> None:\n pass",
"title": ""
},
{
"do... |
9d5cf97fdf742bcc546ec4fe25d1aa88 | M3VTEF creates the M3VTEF database and associated tables | [
{
"docid": "e2ff9e012ab00969a943fb20e1aa7044",
"score": "0.71232563",
"text": "def run_M3VTEF_DB(db_connection, vegtype_ef_out, ClassID, Jrating):\n # Queries need to be executed in specific order\n # due to dependency of tables generated from previous queries\n print(\"\\n BEGINNING M3VTEF DB ... | [
{
"docid": "f719ae0ee9f79c9006012525b8888d3e",
"score": "0.7367714",
"text": "def make_M3VTEF_tables(conn, csv_input_dir, Description_Vegetation, DB_SLA, DB_emissions):\n\n print(\"Creating M3VTEF DB tables from: %s\\n\" % csv_input_dir)\n csv_Canopy_Position_adjust = pd.read_csv(csv_input_dir + '... |
195e6e8f38cc9fb6d6dc260e659ea85e | This function sends a register service command to the manager. THe manager then logs the event and add the local endpoint as the specified service (service_name). | [
{
"docid": "84212171bf1d9a2ecd0ec53ab0d2bf8a",
"score": "0.7933257",
"text": "async def request_register_service(self):\n register_service = RegisterService(\n service=self._service_name,\n local_endpoint=self._local_endpoint,\n pid=self.pid,\n depth=se... | [
{
"docid": "248ba0e97e0bdabed00c3d08d6622eaf",
"score": "0.73495746",
"text": "def _register_with_service_manager(self):\n process_id = os.getpid()\n routed_message = \\\n service_manager_messages.SvcMgrRegisterLocalServiceRoutedMessage(\n send_to='ServiceManager_... |
b005e7c1fdfde1419f05fee06fbc5412 | __init__ Function Initializes Constant Attributes for BitTimeConv Class | [
{
"docid": "71881f03bec44c26ffe3d8dc64338714",
"score": "0.58839476",
"text": "def __init__(self):\r\n\r\n # Initialize Class Attributes\r\n self.START = timedelta(hours=3)\r\n self.END = timedelta(hours=23)\r\n self.T_UNIT = 15*60\r\n self.SPM = 60\r\n s... | [
{
"docid": "a2ad54557cf04feb72ac398d2f684fa5",
"score": "0.742722",
"text": "def __init__(self):\n ## Convolution time per layer\n self.t_convolution = []\n ## Downsampling time per layer\n self.t_downsample_activation = []\n self.t_non_conv_layers = 0\n self.t_... |
4b8718f5c1e2aa91801e74fabf31a1e9 | Recursively convert Bunches in `d` to a regular dicts. | [
{
"docid": "577f53696729e516eac0e979c595e16b",
"score": "0.7504093",
"text": "def unbunchify(d):\n return _convert(d, dict)",
"title": ""
}
] | [
{
"docid": "250961978e717ef452175ddb37a456f1",
"score": "0.67221117",
"text": "def recursive_asdict(d):\n out = {}\n for k, v in asdict(d).iteritems():\n if hasattr(v, \"__keylist__\"):\n out[k] = recursive_asdict(v)\n elif isinstance(v, list):\n out[k] = []\n ... |
9eb6839d03e64232b9bb7c334225d01a | Get the bcurve size with more options | [
{
"docid": "0032ab02905825b65d578747246c6ddb",
"score": "0.5603801",
"text": "def IGetBCurveParamsSize3(self, WantCubicIn=defaultNamedNotOptArg, WantNRational=defaultNamedNotOptArg, ForceNonPeriodic=defaultNamedNotOptArg):\n\t\treturn self._oleobj_.InvokeTypes(61, LCID, 1, (3, 0), ((11, 1), (11, 1), (11... | [
{
"docid": "11706e10d76bacfd435dc896fd9ade88",
"score": "0.7180612",
"text": "def IGetBCurveParamsSize(self, WantCubicIn=defaultNamedNotOptArg):\n\t\treturn self._oleobj_.InvokeTypes(16, LCID, 1, (3, 0), ((11, 1),),WantCubicIn\n\t\t\t)",
"title": ""
},
{
"docid": "07a2610952d94e5bee604d2c9c9... |
e369648db1ac23f96685962920b8f75e | Load metadata from multiple datasets into a single table with unique utterance ids for every row. | [
{
"docid": "15f414d2309c786d99f101a1bbb42e9f",
"score": "0.0",
"text": "def load_all(corpus_dir, langs, usecols=USE_COLUMNS, num_processes=os.cpu_count()):\n if num_processes > 0:\n with multiprocessing.Pool(processes=num_processes) as pool:\n lang_dfs = pool.starmap(load, ((corpus_... | [
{
"docid": "3a708ea54b609df8d7d514f6a89b6e5b",
"score": "0.63951355",
"text": "def load_metadata_adaptive_all(**kwargs):\n kwargs['cohort'] = 'both'\n metadata_emerson = load_metadata_emerson(**kwargs)\n metadata_lindau = load_metadata_lindau(**kwargs)\n metadata = pd.concat([metadata_emerso... |
c28aee430da2c76ae2914d6aeca5a13d | Gets the SharesCollectionPage in async | [
{
"docid": "0e1bf656fcc455cff94a91681137bf7c",
"score": "0.63819665",
"text": "def get_async(self):\n future = self._client._loop.run_in_executor(None,\n self.get)\n collection_page = yield from future\n return collection_page",
... | [
{
"docid": "47a58e4fad06da48e8ef633f63ef95ff",
"score": "0.73378843",
"text": "def collection_page(self):\n if self._collection_page:\n self._collection_page._prop_list = self._prop_dict[\"value\"]\n else:\n self._collection_page = SharesCollectionPage(self._prop_dict... |
93362519e55e546b3e963fd8b86ee02a | read excel file for target | [
{
"docid": "68c86ecf7a8fdfb2368874eb0d6b6e87",
"score": "0.7322174",
"text": "def read_excel(target):\n \n bpath = '/home/sam/Dropbox/HIGP/Crater_Lakes/Dmitri_Sam/data/{0}/{0}'.format(target)\n fpath = '{0}_satellite.xlsx'.format(bpath)\n df = pd.read_excel(fpath)\n\n # set datetime as index\n df ... | [
{
"docid": "de8846ccf04c7035ebfb08f8da3a26b5",
"score": "0.71546036",
"text": "def _read_excel(self):\n self.workbook = xlrd.open_workbook(self.handler.output_file_path)\n self.worksheet = self.workbook.sheet_by_index(0)",
"title": ""
},
{
"docid": "c3aff5b016160299da8a4b585640... |
398be6fb10a69c0a785eb240f174c075 | Does a POST request to /public/clusters/cloudEdition. Sends a request to create a new Cloud Edition Cohesity Cluster and returns the IDs, name, and software version of the new cluster. Also returns the status of each node. | [
{
"docid": "be7e214a76a89f11be93995025f6cc40",
"score": "0.7171899",
"text": "def create_cloud_cluster(self, body):\n try:\n self.logger.info('create_cloud_cluster called.')\n\n # Validate required parameters\n self.logger.info(\n 'Validating requir... | [
{
"docid": "985f340a66c978a5cae7d74f141e616b",
"score": "0.72254884",
"text": "def create_expand_cloud_cluster(self, body):\n try:\n self.logger.info('create_expand_cloud_cluster called.')\n\n # Validate required parameters\n self.logger.info(\n 'Va... |
649087d6ced5dca822efab808bee8372 | Custom premake hook for building libjpeg. | [
{
"docid": "3f4fd333dbfc50897df7f0c581deac67",
"score": "0.47270572",
"text": "def pre_configure(options, buildout):\n os.system(\"sh autogen.sh\")",
"title": ""
}
] | [
{
"docid": "3df4074d540a4efc12734b5d27c2b239",
"score": "0.55957216",
"text": "def _jpgProcess(sourceFile):\n if utils.isToolPresent('jpegtran'):\n args = ['-optimize',\n '-progressive',\n '-copy', 'none',\n ]\n return utils.pipeRun('jpegtran... |
3262375d1f6c329c5b91d05fbc3cd507 | Build a random vector with random indexes Thiago F Pappacena integer size The vector size float min The minimum random number float max The maximum random number | [
{
"docid": "3159f2a650547ec677599fac03d5b500",
"score": "0.7505865",
"text": "def makeRandomVector(size, min = 0.0, max = 1.0):\n import random\n return tuple([min + (random.random() * (max-min)) for i in range(size)])",
"title": ""
}
] | [
{
"docid": "0399617dbc736d7223479c5119f263e2",
"score": "0.720517",
"text": "def gen_vector(size):\n ret = []\n for _ in range(size):\n ret.append(random.randint(0, 20))\n \n return(ret)",
"title": ""
},
{
"docid": "c58773bfd20fbb308bce725a48a9e39e",
"score": "0.719733... |
a06723196544353c4f3557cc9541c3d1 | `without_role_check` returns `False` if `Context.author` has unwanted role. | [
{
"docid": "c75b617f8fa485e3043cce60070c8a5d",
"score": "0.7814249",
"text": "def test_without_role_check_returns_false_with_unwanted_role(self):\n role_id = 42\n self.ctx.author.roles.append(MockRole(id=role_id))\n self.assertFalse(checks.without_role_check(self.ctx, role_id))",
... | [
{
"docid": "00cd2fe15e15fb6a12b5a5d2315858b8",
"score": "0.7967805",
"text": "def test_with_role_check_without_required_roles(self):\n self.ctx.author.roles = []\n self.assertFalse(checks.with_role_check(self.ctx))",
"title": ""
},
{
"docid": "13990780036da3b2db8f1e73dc9e80d5",... |
c732de38d48d22cb8ff99906815730e3 | Returns argument at given index, else none. | [
{
"docid": "5723224e2b2c339c3814707f91f0d21e",
"score": "0.0",
"text": "def get(self, x):\r\n try:\r\n return self.all[x]\r\n except IndexError:\r\n return None",
"title": ""
}
] | [
{
"docid": "1b435c63ddb1ed3e184f9ee53599ada4",
"score": "0.72445065",
"text": "def arg(self, index):\n\n if self.current_node.is_a(lpl.CallExpr):\n return self._with_current_node(\n self.current_node.f_suffix[index].f_expr\n )\n elif self.current_node.i... |
b5d32bc7d4481a83810dc500fab61f8f | Render the output into the proper format. | [
{
"docid": "75e6d2cf5d65171f1b0fe0b14321f787",
"score": "0.0",
"text": "def process_result(results, output_format, **kwargs):\n module_name = 'monitorstack.common.formatters'\n method_name = 'write_{}'.format(output_format.replace('-', '_'))\n output_formatter = getattr(\n importlib.impo... | [
{
"docid": "c25af8348f8522a3cff527bba3ff3fa4",
"score": "0.72547436",
"text": "def render():",
"title": ""
},
{
"docid": "5ef62b7811a31f1f104641cb0374764d",
"score": "0.68567944",
"text": "def render(self):\n pass",
"title": ""
},
{
"docid": "5ef62b7811a31f1f104641... |
2d51276f3ae132f7a728c654f3f18324 | Allows you to retry opening the file if theres an IOError so that 2 hour processing run isn't wasted. | [
{
"docid": "55ab7254fefe31bfd1a63a5dbfd1a2be",
"score": "0.630632",
"text": "def retry_open(fl, *args, **kwargs):\n while True:\n try:\n return codecs.open(fl, *args, **kwargs)\n except IOError as ex:\n sys.stdout.write(\"{0} when opening {1}\\n\".format(ex, fl))\n... | [
{
"docid": "ea3e0d12da9b85a41269447fc1a7a0fd",
"score": "0.6223054",
"text": "def test_lock_file_error(self):\n with tempfile.TemporaryFile() as fp:\n fp.close()\n with pytest.raises(ValueError):\n with lock_file(fp):\n pass",
"title": "... |
d80eaebe061dba2fec374effb1258b7b | Gets the query interface for a parameter. | [
{
"docid": "40b48b8c6bd432aa87c4cb285b04c1ec",
"score": "0.7126727",
"text": "def get_parameter_query(self):\n return # osid.configuration.ParameterQuery",
"title": ""
}
] | [
{
"docid": "4b7819f700261f5e24fb35c743438eb6",
"score": "0.6674734",
"text": "def query_parameter(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"query_parameter\")",
"title": ""
},
{
"docid": "27054f178c9db5c9c41275d118edd515",
"score": "0.62531286",
"text":... |
3f54b1b2a4c1f7c4ea0c9dee34adadc7 | kk is a vector of k values, zval is a scalar. Returns P(kk,zval) | [
{
"docid": "3a3e93bff869cb81a5562e778ece9ada",
"score": "0.7042401",
"text": "def interpolate(self,kk,zval):\n pofz = np.dot(self.pktable,self.lagrange_spam(zval))\n pk = np.interp(kk,self.kk,pofz)\n return(pk)\n #",
"title": ""
}
] | [
{
"docid": "f767fef19dc479ea256fa605aad67d44",
"score": "0.6809561",
"text": "def __call__(self,z=1.0):\n pofz = np.dot(self.pktable,self.lagrange_spam(z))\n return((self.kk,pofz))\n #",
"title": ""
},
{
"docid": "f767fef19dc479ea256fa605aad67d44",
"score": "0.680956... |
3d6427bc986034793e2e2844a5d522cb | Get sensor temperature value from dictionary list. | [
{
"docid": "186790b4769487ff6bb59a48265a5e30",
"score": "0.0",
"text": "def read():\n\n # Check if temperature list is empty\n if not global_dict['temperature']:\n temp = {}\n # Get first element from temperature list, and then pop it from list\n else:\n temp = [t for t in glob... | [
{
"docid": "e08185ab808b9ad7dbaecaeb1dc17c9e",
"score": "0.6676416",
"text": "def get_temperature(self):\n return self.result['current_conditions']['temp_c']",
"title": ""
},
{
"docid": "c17f85daa53c33a28dcecaf9d7f69ea7",
"score": "0.6675716",
"text": "def get_sensor_temperature(s... |
9a47bf42baf67f0fb9006cccbb52312e | Method to find the comfort zone for the robot to begin measure ambience | [
{
"docid": "6a104ff13eeccbb52661329a21970444",
"score": "0.57630306",
"text": "def comfortZone():\n # If the robot is too close with a wall in front, or if it is touching the wall\n if robot.readDistance() < DISTANCE_TO_WALL or robot.readTouch()[0] == 1:\n robot.backward(SPEED_MOVEMENT, 0.5... | [
{
"docid": "30982d4ec599f57101bd074c18227ad4",
"score": "0.57420546",
"text": "def antenna():",
"title": ""
},
{
"docid": "7e0c4028b64b6011945b4d82211ea695",
"score": "0.5630881",
"text": "def atgoal(self):\n\n return np.sqrt(np.sum((self.position[:, self.t] - self.platform_lo... |
d92a22a0968fefe94ea8f346ec69e7e8 | Returns artifacts with spans in [start_span, end_span] inclusive. This resolver function is based on the spanversion semantics, which only considers the latest version of each span. If you want to keep all versions, then set keep_all_versions=True. Input artifacts must have both "span" int property and "version" int pr... | [
{
"docid": "375e6a0514b8a2cbb01e2ba741ae8d7f",
"score": "0.79851294",
"text": "def static_range(artifacts,\n *,\n start_span_number: int = -1,\n end_span_number: int = -1,\n keep_all_versions: bool = False,\n exclude_span_nu... | [
{
"docid": "db98839d51840100dbe276ad0430e33e",
"score": "0.6690784",
"text": "def rolling_range(artifacts,\n *,\n start_span_number: int = 0,\n num_spans: int = 1,\n skip_num_recent_spans: int = 0,\n keep_all_versions: ... |
99f78cd801828c47c468dde22fed4952 | plots the final result of the trained model on the input data | [
{
"docid": "39189d822a479cbe3249845e807a280d",
"score": "0.0",
"text": "def plot_3d_mlp(epoch=100, nand=False):\n if not nand:\n inputs, labels = dataset(2, 200)\n else:\n inputs, labels = dataset_nand(2, 200)\n\n mlp = MLP(2, 2, epoch)\n mlp.train(inputs, labels)\n x_1 = in... | [
{
"docid": "6dd78742d784ef5ffeae68abe49fe372",
"score": "0.7612694",
"text": "def visualize_model():\n sns.set_style(\"darkgrid\")\n plt.scatter(x=y_test, y=y_predict, alpha=0.4)\n\n plt.xlabel(\"Actual prices\")\n plt.ylabel(\"Predicted prices\")\n plt.title(\"Actual vs Predicted prices\... |
13aa2d7782d9b5810a055a80e3e9e114 | Find application and its corresponding versions | [
{
"docid": "d47acf5dcd33af52c89b45a996862281",
"score": "0.0",
"text": "def write_to_csv_check_application_versions():\n process_list = backdoor.check_application_version()\n\n if process_list:\n # Write to CSV file\n final_file_path = read_configure_file('file_location', value='appl... | [
{
"docid": "9aecd4f08ea2caedf7097609f00b2eb9",
"score": "0.7185892",
"text": "def versions_for_app(app_path):\n assert(os.path.isdir(app_path))\n\n versions = set()\n\n for potential_version in os.listdir(app_path):\n # Skip invalid entries\n if potential_version.startswith(\".\")... |
88b60b38933845833091e1930c7fc728 | Testing if the ``DocumentManager`` retrieves the correct objects. | [
{
"docid": "667e1b8a43b0683139b26a934093703d",
"score": "0.6640915",
"text": "def test_manager(self):\n request = Mock(LANGUAGE_CODE='de')\n self.assertEqual(\n models.Document.objects.published(request).count(), 1, msg=(\n 'In German, there should be one publishe... | [
{
"docid": "f9c9d1bd862ae9183edf36403f90dc66",
"score": "0.71074057",
"text": "def test_client_document_retrieve(self):\n pass",
"title": ""
},
{
"docid": "ceeba713fa4c7f6491558110e856064b",
"score": "0.69682175",
"text": "def test_documents_for(self):\n # Test the defa... |
34ddb702a86b35078394c58921e0456b | Get get general document preferences from sublime preferences. | [
{
"docid": "7b669abd88a28de68f020e93affca0b6",
"score": "0.0",
"text": "def setup(self, **kwargs):\n\n eh_settings = sublime.load_settings(PACKAGE_SETTINGS)\n settings = self.view.settings()\n alternate_font_size = eh_settings.get(\"alternate_font_size\", False)\n alternate_f... | [
{
"docid": "a89d575bf649c9bcb65bc1478dafa92c",
"score": "0.680794",
"text": "def Settings(name, default):\n view = sublime.active_window().active_view()\n project_config = view.settings().get('sublimeautopep8', {}) if view else {}\n global_config = sublime.load_settings(common.USER_CONFIG_NAME)... |
17d845a7945a76ac53fb4f92dd7a46a9 | Set ajax class attribute on self.form. | [
{
"docid": "f61ae54a88b35a3fc5718e4c77ffd992",
"score": "0.77012664",
"text": "def prepare_ajax(self):\n if not self.ajax:\n return\n if self.form.attrs.get('class_add') \\\n and self.form.attrs['class_add'].find('ajax') == -1:\n self.form.attrs['class_... | [
{
"docid": "eb4dc8607e9397d0bc9155df9e3371eb",
"score": "0.80698156",
"text": "def prepare_ajax(self):\n if not self.ajax:\n return\n if self.form.attrs.get('class') \\\n and self.form.attrs['class'].find('ajax') == -1:\n self.form.attrs['class'] += ' ajax'\n... |
721a58bb79187b76f3a34dcc2612caf2 | Return true if start date is earlier and end date is later than the test_date | [
{
"docid": "c107b508ffe07da8f4f2d9b9f6b5ee32",
"score": "0.69243836",
"text": "def is_active_at_date(self, test_date):\n return ((self.start_date <= test_date) and \n (self.end_date >= test_date))",
"title": ""
}
] | [
{
"docid": "444b5e2562051fb7f34e87db70361b05",
"score": "0.7660612",
"text": "def is_end_date_before_start_date(start_date: dt, end_date: dt) -> bool:\n return start_date > end_date",
"title": ""
},
{
"docid": "edf3eea0c955dda09f6c509efcd79be7",
"score": "0.7135487",
"text": "def ... |
e07c802ed5a452456a5cd07f2507b689 | Get the OLS registry. | [
{
"docid": "16a4e70d850b4acf0171f0266bd14e8b",
"score": "0.0",
"text": "def get_ols(force_download: bool = False):\n if PROCESSED_PATH.exists() and not force_download:\n with PROCESSED_PATH.open() as file:\n return json.load(file)\n\n data = requests.get(URL).json()\n data[\"_... | [
{
"docid": "8ab3e6a6e433509d323ce714abcf5c90",
"score": "0.7038571",
"text": "def registry(self):\n\n return self._registry",
"title": ""
},
{
"docid": "4bf38d3a7b168f4437572774e204ad74",
"score": "0.6848575",
"text": "def get_registry(self):\n return copy.deepcopy(self... |
677a083435b83c78c455b1699bbae99c | Calculate the square values. | [
{
"docid": "1a0de8957cc2d81a01a9a3c917a67237",
"score": "0.6400062",
"text": "def square(value: float) -> float:\n value = value ** 2\n return value",
"title": ""
}
] | [
{
"docid": "a91879b1138e927693b42d7acb85182a",
"score": "0.70643616",
"text": "def sum_sqr_vals(self):\n\treturn numpy.sum(numpy.square(self.data))",
"title": ""
},
{
"docid": "5c289eea3dd34a8eb2241d184e8ef879",
"score": "0.7055393",
"text": "def get_square(self):\n return num... |
d886c01c4e667f8b25ead077c50aa4ac | Transform a bytes sequence to a token compliant to the pattern. | [
{
"docid": "07b377e2b88a307927be13499f4c07bf",
"score": "0.0",
"text": "def token(self, key):\n if len(key) < 1:\n raise PatternException(_('Password length must be at least 2'))\n\n n = int(key.hex(), base=16)\n d = len(self.gliphs)\n p = []\n\n p.append(se... | [
{
"docid": "7f762237daede125fb9a62581f3b76ab",
"score": "0.5276037",
"text": "def bytes_transform(byte_str, start_idx, stop_idx, fction):\n return bytes_replace(byte_str, start_idx, stop_idx, fction(byte_str[start_idx:stop_idx]))",
"title": ""
},
{
"docid": "e40b07b6af3046d5e25e441af64f92... |
28aaba9269dc7fcd05970ee089833be2 | A fake refresh method for oauth2client service account credentails. | [
{
"docid": "2b427e6162671612f5b4ab4d5b58f1d0",
"score": "0.7772881",
"text": "def _FakeRefreshOauth2clientServiceAccountCredentials(self, http):\n self.access_token = 'REFRESHED-ACCESS-TOKEN'\n self.token_expiry = _MakeFakeCredentialsRefreshExpiry()",
"title": ""
}
] | [
{
"docid": "7e22844d1cbc6d74f370b67bf76f203a",
"score": "0.768099",
"text": "def testRefreshServiceAccountId(self):\n response = httplib2.Response({'status': httplib.OK})\n content = '{\"id_token\": \"old-id-token\"}'.encode()\n self.request_mock.return_value = response, content\n properties... |
5800a54734ba51004c4c8adbe35d22cf | Returns the current version and patchnotes | [
{
"docid": "34adb4005a421509bf0a2a8267d35ec0",
"score": "0.6681273",
"text": "async def info(self):\n \n message = \"Current cog version: **\" + self.version + \"**\\n\"\n message += \"Patchnotes:\"\n message += self.patchnote\n\n await self.bot.say(message)",
"tit... | [
{
"docid": "98ea3a806b99445d84baf0bb8c298e0a",
"score": "0.67388994",
"text": "def get_version_info() -> Tuple[Text, Text]: # type: ignore[empty-body]",
"title": ""
},
{
"docid": "ce3ec83e553abfdc4d44f0757e49b8be",
"score": "0.6537433",
"text": "def get_version():",
"title": ""
... |
807735cb0993beeba81fc77d92f40936 | Status of the parent submission is updated based on modifications made to related objects | [
{
"docid": "b189d60b3cc419d09c33fe0a1e146477",
"score": "0.55623835",
"text": "def test_changing_moderation_changes_status(self):\n target = \"https://google.com/1234\"\n submission: Submission = Submission.objects.create(\n target_url=target,\n description=\"this is ... | [
{
"docid": "a1d86117ba2222d5eefdb15a0ed6e6bb",
"score": "0.60847354",
"text": "def parent_changed(self, old, new):\n pass",
"title": ""
},
{
"docid": "8212d204dbd53fef809491f07b850dd1",
"score": "0.6048187",
"text": "def update_submission(self, submission: DbSubmission) -> boo... |
ad8880ee8f17bd629d02f1eaf372a9e2 | Returns only the __texts of the __results. | [
{
"docid": "279b0b24a1a59b79b6b13b762926587f",
"score": "0.6544478",
"text": "def gettext(self):\n return self.__texts",
"title": ""
}
] | [
{
"docid": "6adc5f023979fa1cf5effeeff7709520",
"score": "0.7373752",
"text": "def get_texts(self):\n return self._texts",
"title": ""
},
{
"docid": "8bd59f4ce3036abe9c663188ba6b656d",
"score": "0.70294625",
"text": "def all_text(self):\n return tweet_text.get_all_text(s... |
41aaff270011b323101da95b59ee3a60 | Calculate the great circle distance between two points on the earth (specified in decimal degrees) | [
{
"docid": "6aae576098c963fcb21eed1748290320",
"score": "0.697009",
"text": "def calcDistance(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1... | [
{
"docid": "2d03a8f9ad790b3cb18e7f3be6b64e59",
"score": "0.78439695",
"text": "def great_circle_distance(latlong_a, latlong_b):\n lat1, lon1 = latlong_a\n lat2, lon2 = latlong_b\n\n dLat = math.radians(lat2 - lat1)\n dLon = math.radians(lon2 - lon1)\n a = (math.sin(dLat / 2) * math.sin(dL... |
2b5045417a633adf0914209eda41fb5e | Get the syntax form of the input word | [
{
"docid": "31665e9ae2a14b12285329bd0c45f988",
"score": "0.6982608",
"text": "def getSyntax(self,):\n\t\treturn self.syntax;",
"title": ""
}
] | [
{
"docid": "016773b9384a7fcb9df1f714ccdf9034",
"score": "0.67101586",
"text": "def get_syntax(self,nameoroid):\n at_oid = self.getoid(AttributeType,nameoroid)\n try:\n at_obj = self.get_inheritedobj(AttributeType,at_oid)\n except KeyError:\n return None\n else:\n return at_obj... |
6288e1caf3d3aa219d6224c3baa4a33b | With no arguments, return a dictionary of all configuration variables relevant for the current platform. Generally this includes everything needed to build extensions and install both pure modules and extensions. On Unix, this means every variable defined in Python's installed Makefile; on Windows and Mac OS it's a muc... | [
{
"docid": "fe9e9fe913211e621b097a69685c6b68",
"score": "0.7583598",
"text": "def get_config_vars(*args):\n global _config_vars\n if _config_vars is None:\n _config_vars = {}\n\n # Normalized versions of prefix and exec_prefix are handy to have;\n # in fact, these are the stan... | [
{
"docid": "7db8c915ff717bc2263e113c79603dee",
"score": "0.63694656",
"text": "def get_host_package_vars():\n # linux, mac or windows.\n platform_variant = {\n 'darwin': 'mac',\n 'linux2': 'linux',\n 'win32': 'windows',\n }.get(sys.platform)\n if not platform_variant:\n raise ValueError(... |
69100a1fba92af3c16f4219ba47b26c6 | Converts a HTTP response to an object (from headers) | [
{
"docid": "b9f0299e6dd8fba27481c3cf95ae8ed5",
"score": "0.64695716",
"text": "def _response_to_object(self, object_name, container, response):\n\n headers = response.headers\n size = int(headers[\"content-length\"])\n etag = headers[\"etag\"]\n scheme = \"https\" if self.sec... | [
{
"docid": "4b5a69218c07cd43d00ca956e4e1a294",
"score": "0.68540525",
"text": "def parse_response(self, response):\n return ParseHTTPResponse(response)",
"title": ""
},
{
"docid": "5a6a3c44eb55189438ac0995ee619fb9",
"score": "0.68098414",
"text": "def parse_response(response):\n ... |
6663362abf37edf3b58f7dace091d973 | Pick from 20 subgraphs randomly some subgraphs. If n_subgraphs_requested = 1 use labeling_rate only else use n_subgraphs_requested directly return train_idx_random_selected | [
{
"docid": "2f5250ef9f606bd59319b56e1b2f4a83",
"score": "0.83010703",
"text": "def sample_subgraphs_from_ppi(n_subgraphs_requested, seed=None):\n all_train_idx = np.arange(1, 21)\n set_seed(seed)\n random_train_idx = np.random.permutation(all_train_idx)\n train_idx_random_selected = random_train_idx... | [
{
"docid": "cf1e7da4ab1b4dacc0000d6abfa8ddd5",
"score": "0.75647837",
"text": "def get_subsampled_train_idx(graph_id, n_subgraphs_used, seed=None):\n train_indices_all = np.arange(1, 21)\n set_seed(seed)\n random_train_subgraph_idx = np.random.permutation(train_indices_all)\n selected_subgraphs = ra... |
855c0dc61c20b3eea86003e65a55f2b9 | This function returns a list of document ids that are in the indicated project(s) (and of all children projects if cascade specified) | [
{
"docid": "177f5f28c9e2b91a10429b2f13e6d247",
"score": "0.7152817",
"text": "def get_projs_docs(self, proj_ids, cascade = False):\n # Some initial checks/tweaks\n if not isinstance(proj_ids, list):\n proj_ids = [proj_ids]\n\n # Add any children projects if specified\n ... | [
{
"docid": "a1ffa0e9bd95603c45f2bfd83bfa0f87",
"score": "0.68752444",
"text": "def get_projects_ids():\n\n projects = load_data('projects.json')\n\n project_ids = []\n for project in projects:\n project_ids.append(project['id'])\n\n return project_ids",
"title": ""
},
{
"d... |
540d53c03187eb8362cbe679ac1e875b | Fill Ns in maf file mafdict[ind][chrom].append([block_key, coord, "N"]) | [
{
"docid": "d33a0c3a1c9dd0b1727b67b67654e050",
"score": "0.6722032",
"text": "def fillMaf(mafdict, mafFile):\n f = open(\"{}.fill\".format(mafFile), 'w')\n with open(mafFile, 'r') as maf:\n for line in maf:\n if line.startswith(\"a\"):\n f.write(\"a\\n\")\n ... | [
{
"docid": "a5d44e0fcb3494ad959182eeab084c42",
"score": "0.5201526",
"text": "def add_block(n_block,maze,inplace=False):\n if inplace:\n for position in available_path(maze)[:n_block]:\n maze[position[0]][position[1]] = 4\n return maze\n else:\n maze_copy = maze.cop... |
c515892961cd913beb16a6649562f427 | Resnet 101 Based Network | [
{
"docid": "360979935b5340307c01fc3d0a6ed7bd",
"score": "0.0",
"text": "def DeepR101V3PlusD_HANet(args, num_classes, criterion, criterion_aux):\n print(\"Model : DeepLabv3+, Backbone : ResNet-101\")\n return DeepV3PlusHANet(num_classes, trunk='resnet-101', criterion=criterion, criterion_aux=criter... | [
{
"docid": "b6179eed08cd0abdb9df6a4ee2d08bba",
"score": "0.72976834",
"text": "def resnet101(**kwargs):\n model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)\n return model",
"title": ""
},
{
"docid": "b6179eed08cd0abdb9df6a4ee2d08bba",
"score": "0.72976834",
"text": "def resne... |
68bc2820b2451fe9fecd4b7b9ba6ffd3 | Tests if the input configuration can be used to successfully connect to the integration | [
{
"docid": "be09aa3e96c01fdd9c2eb90dd94e1ad2",
"score": "0.57417065",
"text": "def check(self, logger: logging.Logger, config: ConfigContainer) -> AirbyteConnectionStatus:\n return self.check_config(logger, config.config_path, config)",
"title": ""
}
] | [
{
"docid": "9f6d45e4a3e6db7ba445ddc1392439a1",
"score": "0.7090428",
"text": "def check_configuration(self):",
"title": ""
},
{
"docid": "1683f2254f9c347148cf6a0d8ddbbe6e",
"score": "0.70189494",
"text": "def verify_config(self):\n pass",
"title": ""
},
{
"docid": ... |
8bd8e980cf04d35e6b38ac315a1baa71 | Return ``True`` if the Pixel Data should to be converted from YCbCr to RGB. This affects JPEG transfer syntaxes. | [
{
"docid": "b014c23b4d338cb8ecd2e7884bb03548",
"score": "0.6915101",
"text": "def needs_to_convert_to_RGB(ds: \"Dataset\") -> bool:\n return False",
"title": ""
}
] | [
{
"docid": "8ec1d586b528ba2539eb81241dc48b12",
"score": "0.6485655",
"text": "def should_change_PhotometricInterpretation_to_RGB(ds: \"Dataset\") -> bool:\n return False",
"title": ""
},
{
"docid": "e669456d6b3dd517d5162c581e892192",
"score": "0.64236915",
"text": "def rgb(self) -... |
7a227b3036fd3185a0211dd6cc939054 | Description of the minimization problem | [
{
"docid": "6f296f455eb5d3f4b2aa91d2d01d97cb",
"score": "0.0",
"text": "def initialize(self, func, nvar, bounds, vdx=None):\n # Test the arguments\n error = validate(func, nvar, bounds, vdx, self.verbose)\n if error:\n if self.verbose:\n print( '--> newop E... | [
{
"docid": "243623a1f048649b2fdb67811aa09a8b",
"score": "0.6914253",
"text": "def minimax():",
"title": ""
},
{
"docid": "8184c2d3bb1b25cb043721112ca6771e",
"score": "0.6489799",
"text": "def _minimize(self):\n return 0",
"title": ""
},
{
"docid": "09d46dc6dda570c2... |
478f5f982f16fc0e76a4eadbb5c01864 | The rotation matrix of the image Relates to the exterior orientation | [
{
"docid": "86dfb7458a143ce1a28d5e19a7d03974",
"score": "0.77758867",
"text": "def rotationMatrix(self):\n\n R = Compute3DRotationMatrix(self.exteriorOrientationParameters[3], self.exteriorOrientationParameters[4],\n self.exteriorOrientationParameters[5])\n\n ... | [
{
"docid": "c5785e1d4b134c7c43ef3846c184d908",
"score": "0.80493206",
"text": "def getRotationMatrix( self):",
"title": ""
},
{
"docid": "9ea3c54f501c0ff5106c01d4919e0798",
"score": "0.7800133",
"text": "def rotationMatrix(self):\n\n # R = Compute3DRotationMatrix(self.exterior... |
c475eb5c714dda23a8b0070cdd4abed4 | Test getting contract ABI by invalid address | [
{
"docid": "c505aa8a0c7dd1373097fb10f11df59a",
"score": "0.75141275",
"text": "def test_get_wrong_contract_abi(self):\n response = _get_contracts_abi_sync({\"wrong\": \"0x0\"})\n self.assertSequenceEqual(response, {\"wrong\": []})",
"title": ""
}
] | [
{
"docid": "1258b3b3c659b69492be70df72559956",
"score": "0.7237029",
"text": "def test_get_contract_abi(self):\n response = _get_contracts_abi_sync({1: TEST_CONTRACT_ADDRESS})\n self.assertSequenceEqual(response, {1: TEST_CONTRACT_ABI})",
"title": ""
},
{
"docid": "1f99a05372ff... |
4e54b02e96fdf43d7e8535acedfb9e84 | r"""Propogate input through the layer. | [
{
"docid": "9e56e01818320d237d6344cdff3025f9",
"score": "0.0",
"text": "def forward(self, input, hidden, ctx):\n def recurrence(input, hidden, ctx):\n \"\"\"Recurrence helper.\"\"\"\n input_gate = self.input_weights(input)\n hidden_gate = self.hidden_weights(hidde... | [
{
"docid": "489e2b175bc8d9e68705e661f28b9b03",
"score": "0.6702976",
"text": "def applyInputParams(self):\n pass",
"title": ""
},
{
"docid": "0ade0f639b80566a9895265ca9c30796",
"score": "0.65563494",
"text": "def propagate_input(self, features):\n for layer in self.laye... |
f79fb3e13ed95b711bb5b8b2fc67ea2b | Calculates the entropy gain from the two splits | [
{
"docid": "d1781cea78284312ae6e922254bb6b89",
"score": "0.7582539",
"text": "def get_gain(self, labels, split_1, split_2):\n orig_ent = self.get_entropy(labels)\n after_ent = (self.get_entropy(labels[split_1]) * (sum(split_1) / len(labels)) +\n self.get_entropy(labels[... | [
{
"docid": "22ff3aa3a3476c44f70c41e19489e4fd",
"score": "0.6996975",
"text": "def information_gain(before_split: np.array,\n splits: list) -> float:\n\n assert len(before_split) == sum([len(split) for split in splits]), f\"splits must add up to length of original{len(original)}/{s... |
35637ad93933de3599450fbc0a74977a | Use self learning algo to reply () | [
{
"docid": "98cadbd8dc765fbaa7b650b5e78c562b",
"score": "0.0",
"text": "def quote_lsd(self, msg, status, args):\n #TODO: Shouldn't crash if structures aren't build, just an empty response\n #not sure needs to be tested\n speech = \" \".join(args[1:])\n response = lsdQuote(spe... | [
{
"docid": "223ea358153fa91ca62105b9bbff6062",
"score": "0.676823",
"text": "def learning(self):\n pass",
"title": ""
},
{
"docid": "482b797911d143e4a165ce58963e975d",
"score": "0.6712703",
"text": "def learn(self, reward, observation):",
"title": ""
},
{
"docid": ... |
91dfb5d67afddc9f13ee0c6955ced692 | Tests the calling of get_allsubscription with a valid parameters | [
{
"docid": "c3f1137d959c4e90e600973d908f0ca8",
"score": "0.82672656",
"text": "def test_get_allsubscription(self): \r\n statuslink = self.ms.get_allsubscription()\r\n self.assertTrue(len(statuslink)>=0)",
"title": ""
}
] | [
{
"docid": "0d22f0f217a77cd95853c97cbd6efbcf",
"score": "0.8271291",
"text": "def test_get_all_subscriptions(self):\n pass",
"title": ""
},
{
"docid": "90565508ed55dcce2008b07c50c6efc8",
"score": "0.715819",
"text": "def test_get_available_credit_subscription(self):\n p... |
f6116501c576aef1766a2b274ce6c2b5 | Execute the class method. | [
{
"docid": "ed0051e6e4d520d78d368835e5a53e16",
"score": "0.0",
"text": "async def execute(self, sleep: float):\n assert not self.run.locked()\n async with self.run:\n await asyncio.sleep(sleep)",
"title": ""
}
] | [
{
"docid": "d5c5692e0ae74989a1da18ba58c1b49d",
"score": "0.7394682",
"text": "def execute(cls):\n pass",
"title": ""
},
{
"docid": "c93d9f0cf6afbe6353f4f3399cc31b0e",
"score": "0.71576184",
"text": "def execute(self):\n raise NotImplementedError(\"Subclasses should over... |
eba952d294cab4cbe3a8d5753a2b9925 | Parse command line arguments. | [
{
"docid": "0f93e885f731bbea666de4f1e7d81e15",
"score": "0.0",
"text": "def parse_args():\n parser = argparse.ArgumentParser('train.py')\n add_arg = parser.add_argument\n add_arg('config', nargs='?', default='configs/test.yaml')\n add_arg('-d', '--distributed', action='store_true')\n add_... | [
{
"docid": "fbefb0e59061fbe6716504a6e1a8d663",
"score": "0.7420028",
"text": "def parse_args():\n parser = ArgumentParser()\n return parser.parse_args()",
"title": ""
},
{
"docid": "b62f6792f0eee8860859d47ebb64f450",
"score": "0.73423314",
"text": "def parse_args():\n\t# Help M... |
c4aac080fcd65a538f62b7b02d5a6a96 | Initialize your data structure here. | [
{
"docid": "15ef55cc94a785758caa65df6411d27f",
"score": "0.0",
"text": "def __init__(self, nestedList):\n self.flater_list=[]\n for n_e in nestedList:\n if n_e.isInteger():\n self.flater_list.append(n_e.getInteger())\n else:\n n_e=n_e.get... | [
{
"docid": "9ef4ed4212f822f4b78823c94a4a9635",
"score": "0.78362477",
"text": "def init_data(self):",
"title": ""
},
{
"docid": "b4c2dddb27354091df8041bff75f9898",
"score": "0.7647474",
"text": "def __init__(self):\n self._data = []",
"title": ""
},
{
"docid": "b4c... |
25489050411a6b76b1f37963f5bc8817 | przerwanie 21H, funkcja 2Ah (42), Get System Date AL Day of the week (0 6; 0 = Sunday) CX Year (1980 2099) DH Month (1 12) DL Day (1 31) | [
{
"docid": "46e0399dfa82a16044c375263c9d4297",
"score": "0.5788917",
"text": "def int_21H_42(self):\r\n date_now = datetime.datetime.now()\r\n\r\n year = date_now.year\r\n month = date_now.month\r\n day = date_now.day\r\n weekday = (datetime.datetime.today().weekday())... | [
{
"docid": "07396f146082972a84c69ee1041cfb53",
"score": "0.67846864",
"text": "def wkday_on_first(yr, mon): # returns day of week of first of month of the given year (1/1/2016)\r\n TotalDays = 0\r\n for x in range(1754, yr):\r\n YearNum = yeardays(x)\r\n TotalDays += YearNum\r\n ... |
a8fda660651dd2cbe8fc3835c2402716 | Function that finds a root using Newton's iteration for a given function f(x) with known derivative f'(x). The function finds the root of f(x) with a predefined absolute accuracy epsilon. The function excepts a starting point x0 that belongs to an interval [a,b] in which is known that f has a root.If the function f has... | [
{
"docid": "5357d96546a3b61900b293c024e68def",
"score": "0.7213952",
"text": "def newton_raphson(f, fprime, x0, eps=5e-6, max_iterations=50):\n\n if f(x0) == 0: # check if x0 is root of f\n return x0, 0\n\n # initializations\n current_x = x0 - f(x0) / fprime(x0)\n previous_x = x0\n ... | [
{
"docid": "88fffe03bfe4cfc189651c7be2b9de99",
"score": "0.7853638",
"text": "def NewtonRoot(f, x, tol=1e-10, iters=2000, der_shift=1):\n\n\tif isinstance(x, list):\n\t\t# Number of variables\n\t\tm = len(x)\n\n\t\t# Convert to da.Var type\n\t\tif m > 1:\n\t\t\tfor i in range(m):\n\t\t\t\tif not isinsta... |
eff73b351cf5214d1ceb7090ea18fe4c | Serve main content (global storage). | [
{
"docid": "ff7847cffa5235339a19a9d2bcafc62b",
"score": "0.0",
"text": "def images(filepath: str):\n return common_static('images', filepath)",
"title": ""
}
] | [
{
"docid": "2da4a5b350705097a8f83ba3c33e9942",
"score": "0.7041915",
"text": "def _serve_main(self):\n\t\t\tready.wait()\n\n\t\t\tenv = Environment(loader=FileSystemLoader(\"templates\"))\n\t\t\ttemplate = env.get_template(\"sensorapp.html\")\n\n\t\t\tself.send_response(200, 'OK')\n\t\t\tself.send_heade... |
064a32ad81b2745b5c1843928ca9c783 | Call the same parent constructor, then call setup() if we have a session. | [
{
"docid": "088d2b82a919c55c17a2323888421224",
"score": "0.6701795",
"text": "def __init__(self, session=None):\n self.experiment_repeats = 5\n super().__init__(session)\n if session:\n self.setup()\n\n self.log('initialize')",
"title": ""
}
] | [
{
"docid": "9e2fb0a70c9e84897bc65ae3e343a870",
"score": "0.73652965",
"text": "def init_session(self):\n pass",
"title": ""
},
{
"docid": "4468a722ea66ce2f0668eb1f8f0f6db7",
"score": "0.73297703",
"text": "def initialize_session(self):\n pass",
"title": ""
},
{
... |
45f0c8a71b490493867c7e63cdc3ffd2 | Compares this card to other, first by suit, then rank. | [
{
"docid": "489036d087d5ef07476810a38ca87f7b",
"score": "0.6491065",
"text": "def __lt__(self, other):\n t1 = self.suit, self.rank\n t2 = other.suit, other.rank\n return t1 < t2",
"title": ""
}
] | [
{
"docid": "8f35e44eef683a4c5596f634f120d6d9",
"score": "0.7621892",
"text": "def compare_rank(card1, card2):\n if card1.get_rank < card2.get_rank:\n return -1\n elif card1.get_rank > card2.get_rank:\n return 1\n else:\n return 0",
"title": ""
... |
e7ae463295786e541cce1d9d4fbfad94 | Instantiate and return the `Template` object based on the given class and parameters. This function is intended for subclasses to override if they need to implement special template instantiation logic. Code that just uses the `TemplateLoader` should use the `load` method instead. | [
{
"docid": "c2616ca00d446526281eb29b98fdb4fd",
"score": "0.5182011",
"text": "def _instantiate(self, cls, fileobj, filepath, filename, encoding=None):\n if encoding is None:\n encoding = self.default_encoding\n return cls(fileobj, filepath=filepath, filename=filename, loader=sel... | [
{
"docid": "f73f3be43cc0013896e468aadf48fa68",
"score": "0.67032987",
"text": "def instantiate_template(cls, data, raw_data, origin, provider, parameters,\n field_offset_map):\n # This assertion is a low-cost trick to ensure that we override this\n # method in a... |
c68d0c17a1f37935b39f27ae28d10bff | Save out a transaction to a .yumtx file to be loaded later. | [
{
"docid": "a486151aa6e79a7510a7e34a80b1ea7e",
"score": "0.71357524",
"text": "def save_ts(self, filename=None, auto=False):\n if self.tsInfo._unresolvedMembers:\n if auto:\n self.logger.critical(_(\"Dependencies not solved. Will not save unresolved transaction.\"))\n ... | [
{
"docid": "74208d2e2824fc5e8aa565282ea71402",
"score": "0.6902627",
"text": "def _save_transaction_data(self, path='/data/info'):\n base_dir = os.getcwd() + path\n # Save transactions data record\n with open(f'{base_dir}/transactions', 'w+') as f:\n for tx in self._trans... |
d0a19727f1c069c671bc757f00f28501 | Select a random move from those available | [
{
"docid": "e5b2a018ca1af217d1d81b9c4eee3e7b",
"score": "0.75547254",
"text": "def randomMove():\n move = self.pickRandomAvailableSquareToEat() # positive int or -1\n return move",
"title": ""
}
] | [
{
"docid": "cc5e061c85fc578cb98aa857aea6a2ff",
"score": "0.8201249",
"text": "def _random_move(self, grid):\n return random.choice(list_actions(grid))",
"title": ""
},
{
"docid": "d9ad96550adadc59cb5e2aabb391a920",
"score": "0.81967247",
"text": "def make_random_move(self):\n ... |
8b6981e00c6948c86907bb36a43d4c0e | Find a link on this or a child workflow that has target as a sink. | [
{
"docid": "894cb00f1d12c3da0715d4897f28378f",
"score": "0.6926945",
"text": "def find_link_by_target(self, target: Union[CommandInput, CommandParameter]):\n for link in self.links:\n if target in link.sinks:\n yield link, self\n\n for plan in self.plans:\n ... | [
{
"docid": "025d34998e9a38ef9dc2c46dc588dc98",
"score": "0.5772282",
"text": "def get_target(self, share):",
"title": ""
},
{
"docid": "832a11e7a60e6d1681fdfb8c43111497",
"score": "0.57031846",
"text": "def target(self):\n target_id = self.target_id\n if target_id:\n ... |
d642fa0d04315f8d72b138360099416d | Asigna un int como el identificado del factor sobre el que se obtiene la lectura. | [
{
"docid": "c8ab05fe650e115bb39a9924cfeb4937",
"score": "0.59797025",
"text": "def set_id_factor(self, value):\n self.__idFactor = value",
"title": ""
}
] | [
{
"docid": "6da0bbb2aad6b8aec5ab23be9e14d65b",
"score": "0.60904473",
"text": "def __int__(self):\n return int(self.zaehler / self.nenner)",
"title": ""
},
{
"docid": "a83745d51bda29d42e1dd44a1cd65756",
"score": "0.60733753",
"text": "def factor_carga(self):\n return se... |
85a30106f5afbb277c96bbd4beff562b | Generate a map of known licenses based on `nixpkgs`. | [
{
"docid": "3ae921fecdfa86d5f5c8bdf8b5f02ba7",
"score": "0.7784949",
"text": "def get_nix_licenses():\n global _nix_licenses\n\n if _nix_licenses is None:\n nix_licenses_json = check_output([\n 'nix-instantiate', '--eval', '--expr',\n 'with import <nixpkgs> { }; builti... | [
{
"docid": "73fc30d86229d5f0c76a60b8fdfe497d",
"score": "0.6401995",
"text": "def get_licenses_from_pkginfo(self):\n licenses = set()\n data = \"\"\n try:\n try:\n data = self.pip_req.get_dist().get_metadata('PKG-INFO')\n except (FileNotFoundErro... |
73821787ab984ccb56d5e67dd6fc439e | The configuration settings of the storage of the tokens if a file system is used. | [
{
"docid": "d551f4f3b9e27b3856657dd11566cc54",
"score": "0.5914945",
"text": "def file_system(self) -> Optional[pulumi.Input['FileSystemTokenStoreArgs']]:\n return pulumi.get(self, \"file_system\")",
"title": ""
}
] | [
{
"docid": "37b3738304a27691132542a36ac34039",
"score": "0.6504488",
"text": "def configuration(self):\n return os.path.expanduser(self.settings.get('configuration'))",
"title": ""
},
{
"docid": "6ff621098f65684905dd57e654a377fb",
"score": "0.6473083",
"text": "def _getConfig(... |
990f4eac6cf19f38c7d60b80c2a4c885 | Get Oxalis Minecraft server status | [
{
"docid": "510f636dfab6d2497112d3425b4c00af",
"score": "0.6496605",
"text": "async def status(ctx: commands.Context):\n await ctx.send(str(subprocess.run(\"papermc status | grep Status\", stdout=subprocess.PIPE, shell=True).stdout, \"utf-8\"))",
"title": ""
}
] | [
{
"docid": "4a82e7890222cbf56e7e2e23bcf81c3c",
"score": "0.8241819",
"text": "def server_status(self):\n\t\treq_id = self._send(b'SHOW_STATUS')\n\t\treturn jsonapi.loads(self._recv(req_id).content[1])",
"title": ""
},
{
"docid": "7857be212d49cd8e7daa0f2971e9f170",
"score": "0.78942055",
... |
fcd071579d4fd2aeabf01752e31d76e3 | Run jobs from a sql table. | [
{
"docid": "931353192b9fdd9a479504f52d6fc813",
"score": "0.5596132",
"text": "def runner_sql(options, dbdescr, exproot):\n if options.modules:\n modules = options.modules.split(',')\n else:\n modules = []\n for module in modules:\n __import__(module, fromlist=[])\n\n db ... | [
{
"docid": "f6c1fab4be007de17dfb374158154517",
"score": "0.67062944",
"text": "def runSQL(self, sql, label):\n startTime = time.time()\n verbose(label + '...')\n if type(sql) == type(''):\n results = db.sql(sql.split(self.SQLSEPARATOR), 'auto')\n else:\n ... |
5c29a638617b95da4741672f55fd84e8 | Sets the item of this Inventory. | [
{
"docid": "cb1e7ade7cef844cadf4e257577eba8e",
"score": "0.7469803",
"text": "def item(self, item):\n\n self._item = item",
"title": ""
}
] | [
{
"docid": "0e77ca0fb4bafc81575720198fee9e0f",
"score": "0.81508094",
"text": "def set_item(self, item):\n self._item = item",
"title": ""
},
{
"docid": "23f601c64be51554bb0bc647f09f9282",
"score": "0.7558917",
"text": "def set_item(self, new_item):\n self.item = new_it... |
2b2c2dadef91f92a188bf766ad37a1cd | Constructor @ In, kwargs, dict, arguments @ Out, None | [
{
"docid": "1872d722f5cdcfd96c697a5e0dfc3b98",
"score": "0.0",
"text": "def __init__(self, **kwargs):\n Interaction.__init__(self, **kwargs)\n self._demands = None # the resource demanded by this interaction\n self._penalty = None # how to penalize for not meeting demand NOT IMPLEMENTED",
... | [
{
"docid": "6c0e35b9d4d7795af375ec34db478ceb",
"score": "0.828617",
"text": "def __init__(self, *args, **kwargs):\n self.args = args\n self.kwargs = kwargs",
"title": ""
},
{
"docid": "cce888fc35e8c8577470cab46fb38476",
"score": "0.81837237",
"text": "def __init__(self,... |
af8d11fe0f568e83bd7293056c820f72 | Function computes a 2D distance field Distance at member of entity_queue is zero Shortest paths avoid obstacles and use distance_type distances | [
{
"docid": "04b882c9bfb1c8ba1a73ead8f86fe83c",
"score": "0.6837322",
"text": "def compute_distance_field(self, entity_type):\n visited = [[EMPTY for dummy_col in range(self._grid_width)]\n for dummy_row in range(self._grid_height)]\n distance_field = [[self._grid_widt... | [
{
"docid": "15e8aa11b62ca2778ac69d66394d1324",
"score": "0.72263473",
"text": "def compute_distance_field(self, entity_type):\n grid_height = self.get_grid_height()\n grid_width = self.get_grid_width()\n #Create a 2D list distance_field of the same size as the grid and initialize ea... |
995892ac6f0346f57b21b0bd45e216a1 | When getting the URL of an ImageCacheFile, the storage shouldn't be checked. | [
{
"docid": "112b8f8898a4d1badcf03acc13621b82",
"score": "0.75797176",
"text": "def test_no_io_on_url():\n file = get_image_cache_file()\n file.url\n assert not file.storage.exists.called\n assert not file.storage.open.called",
"title": ""
}
] | [
{
"docid": "cf151055813356df2adfaec8dce1fc64",
"score": "0.6698855",
"text": "def get_url(url: str) -> Optional[str]:\n try:\n parsed = urlparse(url)\n except ValueError:\n return None\n\n if parsed.scheme in (\"file\", \"\"):\n return unquote(parsed.path)\n elif parsed.... |
9a8974939db3cf419a2799d18e500be1 | The ID of an Azure Active Directory server application of type \\"Web app/API\\". This application represents the managed cluster's apiserver (Server application) (string) | [
{
"docid": "53d534dd54140f88c1bbaf2d0e60f8e4",
"score": "0.73392534",
"text": "def add_server_app_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"add_server_app_id\")",
"title": ""
}
] | [
{
"docid": "134caedbe6b529fe9ce366e221179202",
"score": "0.79774123",
"text": "def server_app_id(self) -> Optional[str]:\n return pulumi.get(self, \"server_app_id\")",
"title": ""
},
{
"docid": "aac51cc07f1e0d6be0a043f58d9e1552",
"score": "0.76388377",
"text": "def app_id(self... |
bfc90003e943be34c028aa78b08084bc | Evaluate a model on a given CV split | [
{
"docid": "59ccda8a4a49b6a84deb090dab951621",
"score": "0.6733805",
"text": "def compute_evaluation(model, cv_split_filename, params=None,\r\n train_size=1.0, mmap_mode='r',\r\n scoring=None, dump_model=False,\r\n dump_predictions=False,... | [
{
"docid": "35ea1019c0b7a7f5741d8cba7b8458c8",
"score": "0.68407726",
"text": "def evaluate_model(model_name, model, x, y):\n print('%s:' % model_name)\n model.fit(x, y.values.ravel())\n print('CV f1_micro (not reusing data): %s' % np.mean(cross_val_score(model,\n x, y.values.ravel(), ... |
1015f0d475a10360dd0e41ccacad1fc6 | Create a site configuration from a Volt config file. | [
{
"docid": "2cca93d684692315c46caed80f111696",
"score": "0.0",
"text": "def from_file_name(\n cls,\n invoc_dir: Path,\n project_dir: Path,\n config_file_name: str,\n **kwargs: Any,\n ) -> Self:\n config_path = project_dir / config_file_name\n with conf... | [
{
"docid": "8c9a1a385e5301c630e9e1751cbf39e6",
"score": "0.713112",
"text": "def load_from_file(self, config_file):\n with open(config_file, 'r') as f:\n parser = ConfigParser()\n parser.readfp(f)\n if parser.has_option('site', 'outdir'):\n self.out... |
ac797dc1950168e935fd4e6788e034a4 | Check that a TableDumpV2 JSON document from mabo is correctly abstracted. | [
{
"docid": "30c2a70372eb4dab434bc2014251637c",
"score": "0.5781272",
"text": "def test_TableDumpV2(self):\n\n # Get the abstract BGP message\n abstracted = MaboTableDumpV2Document(\"collector\", json_TableDumpV2)\n\n # Check each element\n elements = abstracted.elements()\n assert len(ele... | [
{
"docid": "c8ac963d2472571f1d37e326483a25e1",
"score": "0.62551606",
"text": "def test_parse_biom_table(self):\r\n # This is a TSV as a list of lines\r\n t = parse_biom_table(self.classic_otu_table1_no_tax)\r\n\r\n # Test TSV as a list of lines\r\n t_tsv_str = t.to_tsv()\r\n... |
890b2bfc4b102965155938e2ca874bb1 | Sets the residence of this Criminal. | [
{
"docid": "ef8fd305f836631f8748820b1c0b4118",
"score": "0.8034765",
"text": "def residence(self, residence):\n\n self._residence = residence",
"title": ""
}
] | [
{
"docid": "43fd1c6070ff4412d49071a8e6ed45bb",
"score": "0.6250316",
"text": "def residence(self):\n return self._residence",
"title": ""
},
{
"docid": "44706b55c15938a2eec0f688e775491e",
"score": "0.57946104",
"text": "def set_evidence(self, evidence):\n self.evidence ... |
4be294a3daaf1f9dd3c0be1dd7890a7c | Test the result preserves input dimension order when the coordinate to integrate is not the first dimension (eg there's a leading realization coordinate) | [
{
"docid": "98dbb8c89a9297a91af2488f6b5d6a1d",
"score": "0.5471508",
"text": "def test_dimension_preservation(self):\n cube = set_up_variable_cube(280 * np.ones((3, 3, 3), dtype=np.float32))\n cube = add_coordinate(\n cube, np.array([5.0, 10.0, 20.0]), \"height\", coord_units=\"... | [
{
"docid": "f41ba7a1922d8fd415d35d28ac8c8643",
"score": "0.5874654",
"text": "def test_integrate_column_coordinates(self):\n x = np.linspace(0, 1, 5)\n t = math.integrate_column(np.arange(5), x)\n\n assert(np.isclose(t, 2))",
"title": ""
},
{
"docid": "2b120bfe091aa6100e... |
2fe086c3e5a427da90d6431edd2ef5c7 | Browse the file system and update the control instance accordingly. If a valid direcorty has already been entered the dialogue will automatically point to this folder, otherwise the current working directory is used. | [
{
"docid": "897bc328a97b5b014ceb9ef88458331d",
"score": "0.779447",
"text": "def onBrowseClicked(control_instance):\n # Get the current directory\n current_control_value = os.path.join(os.getcwd(), os.pardir)\n if DirectoryControlWidget.is_valid(control_instance):\n curre... | [
{
"docid": "5a030732cf12ef84300d8a20743a3891",
"score": "0.8512092",
"text": "def _browse(self):\n\n initial_dir = self._variable.get()\n initial_dir = os.path.abspath(os.path.expanduser(initial_dir))\n\n self.options['initialdir'] = initial_dir\n dlg = filedialog.Directory(s... |
8d872fb7864e42a7495f4b21e3bbb084 | Parses a media_file path into its component segments. | [
{
"docid": "a8a29dc123502596e6c9c7d23ad6beec",
"score": "0.65137494",
"text": "def parse_media_file_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^customers/(?P<customer_id>.+?)/mediaFiles/(?P<media_file_id>.+?)$\",\n path,\n )\n return m.groupdict() ... | [
{
"docid": "9f7445ca524d3ea5bd135aa47a25c27f",
"score": "0.5895364",
"text": "def split_path(full_path):\n parsed_list = full_path.split('/')[-2:] # directtory, file_name\n \n return parsed_list",
"title": ""
},
{
"docid": "3405777c166d80d631ed05388b7710ab",
"score": "0.58513534... |
7cdb93f03454b8ff65070a1611850f65 | Distance from a proposed point to its nearest neighbour (proposed point) | [
{
"docid": "2367061b483d48ffb9ef0488832fb63c",
"score": "0.6574492",
"text": "def ppn2_distance_to_nearest_neighbour(self):\n # FIXME vectorize loop\n distances = []\n for point in self.im_proposals:\n distances.append(np.partition(np.sum(np.power(point - self.im_proposal... | [
{
"docid": "f3a2e04fa10b7f690c79eb3d0e02849a",
"score": "0.80314505",
"text": "def distance_to_point(self, point):",
"title": ""
},
{
"docid": "2ee7ca52fefe409ffc2a9594fb14a392",
"score": "0.7305967",
"text": "def distance(self, p):\n return ((self.x - p.x) ** 2 + (self.y - p.... |
099e314d87cb3a8f39b796e97ab050a8 | fixed value comparator test | [
{
"docid": "6e63a52c489faa303fcf223b6647ddee",
"score": "0.6537535",
"text": "def test_fixed_value_comparator(self, num_state_qubits, value, geq):\n # initialize weighted sum operator factory\n comp = Comparator(num_state_qubits, value, geq)\n\n # initialize circuit\n q = Qua... | [
{
"docid": "a5bff1832536e4806c5a098b17b040b7",
"score": "0.65169156",
"text": "def comparator_converter(self, val):\r\n return val",
"title": ""
},
{
"docid": "b5bc04f0cc6c2a68b4b7b8a19a45f85c",
"score": "0.6449908",
"text": "def __cmp__(self, p):\n val = self[0].__cmp_... |
c1ef3fb3fca50659a61d3135daea5bf1 | Subscribe to the model update request stream. | [
{
"docid": "f64491422975b5dd6dc00f57e0bff34d",
"score": "0.7139607",
"text": "def __listen_to_model_update_stream(self):\n r = alliance.ClientAvailableMessage()\n\n whoami(r.sender, self)\n\n for request in self.orchestrator.ModelUpdateStream(r):\n # A client sent a model... | [
{
"docid": "8c39a358475085d8a1c88a34ea6e2c7c",
"score": "0.66264987",
"text": "def ModelUpdateRequestStream(self, response, context):\n\n client = response.sender\n metadata = context.invocation_metadata()\n if metadata:\n print(\"\\n\\n\\nGOT METADATA: {}\\n\\n\\n\".form... |
e936c006f2b60cb1447f0caf61b07198 | r""" Return the orientation of the crossings of the link diagram of ``self``. | [
{
"docid": "eb391244b5bc7f6d2cc3e43b0e0fc07c",
"score": "0.6693827",
"text": "def orientation(self):\n directions = self._directions_of_edges()[0]\n orientation = []\n for C in self.pd_code():\n if C[0] == C[1] or C[2] == C[3]:\n orientation.append(-1)\n ... | [
{
"docid": "f1ce0f0417423532c5526f5a36e613ce",
"score": "0.6612555",
"text": "def get_orientation(self, visited):\n #print(visited)\n if visited:\n rot = mathutils.Quaternion(self.helical_axisParam, self.get_angle(2))\n a = self.positio... |
05d347d75016454d195ce03d8236430f | Handles input file browsing and selection. | [
{
"docid": "49171b4d5fc7d7816a1b2a758f052185",
"score": "0.0",
"text": "def get_input_file_name(self):\r\n\r\n\t\tentry = self.input_file_E\r\n\t\tentry.delete(0, \"end\")\r\n\t\t\r\n\t\troot.update()\r\n\t\tself.master_input = filedialog.askopenfilename(multiple = True)\r\n\t\troot.update()\r\n\t\t\r\n... | [
{
"docid": "32ea7dad953fd98a57afaf65664098bb",
"score": "0.68089163",
"text": "def input_pick(self):\n file_name = QtWidgets.QFileDialog.getOpenFileName(self, 'Open file', self.vid_file)\n if file_name[0]:\n self.input_load(file_name[0])",
"title": ""
},
{
"docid": "... |
abc3539d6a4d16fc36b94d707e583008 | Return triangle area given its vertices' coordinates. | [
{
"docid": "0add7d83ad7bf33b1cf10765399b27f0",
"score": "0.70234346",
"text": "def get_triangle_area(point1: Point, point2: Point, point3: Point):\n\t\tprod1 = (point1[0] - point3[0]) * (point2[1] - point1[1])\n\t\tprod2 = (point1[0] - point2[0]) * (point3[1] - point1[1])\n\n\t\ttriangle_area = 1/2 * ab... | [
{
"docid": "ce87c52ea5d3ae5d6cadd5a7d6f1c8a6",
"score": "0.8274563",
"text": "def area_of_triangle(*args):\n if len(args) != 1:\n print('ERROR: one argument expected, got {}.'.format(len(args)))\n return None\n vertexes = args[0]\n if not isinstance(vertexes, list):\n print... |
4128657e337d1d76f50983275de1d03c | Method to convert assessor label into a dictionary | [
{
"docid": "08d533d9dece6896af31f5254a0b5073",
"score": "0.7269525",
"text": "def get_assessor_dict(assessor_label):\n assessor_dict = dict()\n labels = assessor_label.split('-x-')\n if len(labels) == 1:\n print'ERROR: WRONG PROCESS LABEL: the assessor label can not be set (ERROR no \"-x... | [
{
"docid": "807e2a0238177386f84ae6d97f5c6d81",
"score": "0.61845165",
"text": "def get_labels(self):\n\t\tld = {}\n\t\tfor label in self.labels:\n\t\t\ttokens = label.split(\":\")\n\t\t\tld[tokens[0]] = tokens[1]\n\t\treturn ld",
"title": ""
},
{
"docid": "c791b0778493c3bc4f7b391e1407d05c",
... |
7ce80f9f8797379e4a6aa6862d2a224d | Update the cell with new input x_t = the new input | [
{
"docid": "181e455170603e85e323fcba7a64b0ba",
"score": "0.78416604",
"text": "def update(self, x_t):\n # the output of the previous update\n h_t_0 = self.h_t\n \n # the previous cell content \n C_t_0 = self.C_t\n\n # step 1: Decide what information to forget\n ... | [
{
"docid": "9b44555f42e0d42190d1746033231b46",
"score": "0.70777196",
"text": "def Updatev(Cell,dt):",
"title": ""
},
{
"docid": "c6a57999dc4ecee8ddd36a67f5fc584f",
"score": "0.680216",
"text": "def recurrent_cell(self, st, t):\n\n\t\tz = self.z[t-par['latency'],..., cp.newaxis]\n\t\... |
08221d73799e78906b30f1464d647dc0 | Computes how the interaction hamiltonian acts over a given state | [
{
"docid": "a69fab8c6a837f99f24123569f0e0dad",
"score": "0.55249983",
"text": "def Hint(state_ini):\n states = []\n coefs = []\n for k in range(len(state_ini)):\n for l in range(len(state_ini)):\n for p in range(len(state_ini)):\n for q in range(len(state_ini)):... | [
{
"docid": "97f2aec5ed08627489e932504160126d",
"score": "0.7395203",
"text": "def actHam(state, N, J, U):\n t1, t2 = [], []\n # First term\n for i in range(len(state.vector)-1):\n t1.append(state.create(i+1, N).destroy(i))\n t1.append(state.create(i, N).destroy(i+1))\n # Second... |
ab02758ba0c11476d13c5cbb34b4f7ee | Update the mapping (nodal2global and elements2global) | [
{
"docid": "63e31e5664150100ebe332549c1fd359",
"score": "0.6652489",
"text": "def update_mapping(self, fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs):\n self._set_standard_mapping(fields, nodeids, connectivity, dofs_by_element, callbacks, callbackargs, **kwargs... | [
{
"docid": "c51c89163158e8315eca9b877ad3d659",
"score": "0.6116114",
"text": "def _update_global_vars(self):\n\n pass",
"title": ""
},
{
"docid": "88cec46d1e0d250965becc0faf411415",
"score": "0.60464275",
"text": "def _update_mappings(self):\n self.existing_cost_entry_m... |
4d39bb1519a7f62a7cf3caeafcd634a3 | GET request to retrieve a specific product from database. | [
{
"docid": "4ad297d0b00a0f6bc43b64684bf5bab8",
"score": "0.7202844",
"text": "def get_specific_product(product_id):\n try:\n # Query database for all products\n selection = Products.query.order_by(Products.id).filter(\n Products.id == product_id).one_or_none()... | [
{
"docid": "0dec387335a7d688dff232ff44946424",
"score": "0.77556497",
"text": "def retrieve(self, request, pk=None):\n try:\n product = ProductModel.objects.get(pk=pk)\n serializer = ProductSerializer(product, context={'request': request})\n return Response(serial... |