25000: #change back to 25000\n \n split_perc = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\n split_perc_text = ['10%','20%','30%','40%','50%','60%', '70%', '80%', '90%', '100%']\n split_perc_tt = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\n split_perc_tt_total = []\n split_percent = []\n\n metric_results = []\n metric_name = []\n \n counter = 0\n \n for i in split_perc:\n \n progress.value += 1\n \n t0 = time.time()\n \n '''\n MONITOR UPDATE STARTS\n '''\n \n perc_text = split_perc_text[counter]\n monitor.iloc[1,1:] = 'Fitting Model on ' + perc_text + ' sample'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n\n '''\n MONITOR UPDATE ENDS\n '''\n \n X_, X__, y_, y__ = train_test_split(X, y, test_size=1-i, random_state=seed, shuffle=data_split_shuffle)\n X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n model.fit(X_train,y_train)\n pred_ = model.predict(X_test)\n \n r2 = metrics.r2_score(y_test,pred_)\n metric_results.append(r2)\n metric_name.append('R2')\n split_percent.append(i)\n \n t1 = time.time()\n \n '''\n Time calculation begins\n '''\n \n tt = t1 - t0\n total_tt = tt / i\n split_perc_tt.pop(0)\n \n for remain in split_perc_tt:\n ss = total_tt * remain\n split_perc_tt_total.append(ss)\n \n ttt = sum(split_perc_tt_total) / 60\n ttt = np.around(ttt, 2)\n \n if ttt < 1:\n ttt = str(np.around((ttt * 60), 2))\n ETC = ttt + ' Seconds Remaining'\n\n else:\n ttt = str (ttt)\n ETC = ttt + ' Minutes Remaining'\n \n monitor.iloc[2,1:] = ETC\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n \n \n '''\n Time calculation Ends\n '''\n \n split_perc_tt_total = []\n counter += 1\n\n model_results = pd.DataFrame({'Sample' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})\n fig = px.line(model_results, x='Sample', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])\n fig.update_layout(plot_bgcolor='rgb(245,245,245)')\n title= str(model_name) + ' Metric and Sample %'\n fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})\n fig.show()\n \n monitor.iloc[1,1:] = 'Waiting for input'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n \n \n print('Please Enter the sample % of data you would like to use for modeling. Example: Enter 0.3 for 30%.')\n print('Press Enter if you would like to use 100% of the data.')\n \n print(' ')\n \n sample_size = input(\"Sample Size: \")\n \n if sample_size == '' or sample_size == '1':\n \n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n\n else:\n \n sample_n = float(sample_size)\n X_selected, X_discard, y_selected, y_discard = train_test_split(X, y, test_size=1-sample_n, \n random_state=seed, shuffle=data_split_shuffle)\n \n X_train, X_test, y_train, y_test = train_test_split(X_selected, y_selected, test_size=1-train_size, \n random_state=seed, shuffle=data_split_shuffle)\n\n else:\n \n monitor.iloc[1,1:] = 'Splitting Data'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n progress.value += 1\n\n '''\n Final display Starts\n '''\n clear_output()\n if verbose:\n print(' ')\n if profile:\n print('Setup Succesfully Completed. Loading Profile Now... Please Wait!')\n else:\n if verbose:\n print('Setup Succesfully Completed.')\n functions = pd.DataFrame ( [ ['session_id', seed ],\n ['Transform Target ', transform_target],\n ['Transform Target Method', transform_target_method_grid],\n ['Original Data', data_before_preprocess.shape ],\n ['Missing Values ', missing_flag],\n ['Numeric Features ', str(float_type) ],\n ['Categorical Features ', str(cat_type) ],\n ['Ordinal Features ', ordinal_features_grid],\n ['High Cardinality Features ', high_cardinality_features_grid],\n ['High Cardinality Method ', high_cardinality_method_grid],\n ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], \n ['Transformed Train Set', X_train.shape ], \n ['Transformed Test Set',X_test.shape ],\n ['Numeric Imputer ', numeric_imputation],\n ['Categorical Imputer ', categorical_imputation],\n ['Normalize ', normalize ],\n ['Normalize Method ', normalize_grid ],\n ['Transformation ', transformation ],\n ['Transformation Method ', transformation_grid ],\n ['PCA ', pca],\n ['PCA Method ', pca_method_grid],\n ['PCA Components ', pca_components_grid],\n ['Ignore Low Variance ', ignore_low_variance],\n ['Combine Rare Levels ', combine_rare_levels],\n ['Rare Level Threshold ', rare_level_threshold_grid],\n ['Numeric Binning ', numeric_bin_grid],\n ['Remove Outliers ', remove_outliers],\n ['Outliers Threshold ', outliers_threshold_grid],\n ['Remove Multicollinearity ', remove_multicollinearity],\n ['Multicollinearity Threshold ', multicollinearity_threshold_grid],\n ['Clustering ', create_clusters],\n ['Clustering Iteration ', cluster_iter_grid],\n ['Polynomial Features ', polynomial_features],\n ['Polynomial Degree ', polynomial_degree_grid],\n ['Trignometry Features ', trigonometry_features],\n ['Polynomial Threshold ', polynomial_threshold_grid], \n ['Group Features ', group_features_grid],\n ['Feature Selection ', feature_selection],\n ['Features Selection Threshold ', feature_selection_threshold_grid],\n ['Feature Interaction ', feature_interaction],\n ['Feature Ratio ', feature_ratio],\n ['Interaction Threshold ', interaction_threshold_grid],\n ], columns = ['Description', 'Value'] )\n \n functions_ = functions.style.apply(highlight_max)\n if verbose:\n if html_param:\n display(functions_)\n else:\n print(functions_.data)\n \n if profile:\n try:\n import pandas_profiling\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\n clear_output()\n display(pf)\n except:\n print('Data Profiler Failed. No output to show, please continue with Modeling.')\n \n '''\n Final display Ends\n ''' \n \n #log into experiment\n experiment__.append(('Regression Setup Config', functions))\n experiment__.append(('X_training Set', X_train))\n experiment__.append(('y_training Set', y_train))\n experiment__.append(('X_test Set', X_test))\n experiment__.append(('y_test Set', y_test))\n experiment__.append(('Transformation Pipeline', prep_pipe))\n try:\n experiment__.append(('Target Inverse Transformer', target_inverse_transformer))\n except:\n pass\n \n #end runtime\n runtime_end = time.time()\n runtime = np.array(runtime_end - runtime_start).round(2)\n\n if logging_param:\n \n logger.info(\"Logging experiment in MLFlow\")\n \n import mlflow\n from pathlib import Path\n\n if experiment_name is None:\n exp_name_ = 'reg-default-name'\n else:\n exp_name_ = experiment_name\n\n URI = secrets.token_hex(nbytes=4) \n exp_name_log = exp_name_\n \n try:\n mlflow.create_experiment(exp_name_log)\n except:\n pass\n\n #mlflow logging\n mlflow.set_experiment(exp_name_log)\n\n run_name_ = 'Session Initialized ' + str(USI)\n with mlflow.start_run(run_name=run_name_) as run:\n\n # Get active run to log as tag\n RunID = mlflow.active_run().info.run_id\n \n k = functions.copy()\n k.set_index('Description',drop=True,inplace=True)\n kdict = k.to_dict()\n params = kdict.get('Value')\n mlflow.log_params(params)\n\n #set tag of compare_models\n mlflow.set_tag(\"Source\", \"setup\")\n \n import secrets\n URI = secrets.token_hex(nbytes=4)\n mlflow.set_tag(\"URI\", URI)\n mlflow.set_tag(\"USI\", USI) \n mlflow.set_tag(\"Run Time\", runtime)\n mlflow.set_tag(\"Run ID\", RunID)\n\n # Log the transformation pipeline\n logger.info(\"SubProcess save_model() called ==================================\")\n save_model(prep_pipe, 'Transformation Pipeline', verbose=False)\n logger.info(\"SubProcess save_model() end ==================================\")\n mlflow.log_artifact('Transformation Pipeline' + '.pkl')\n os.remove('Transformation Pipeline.pkl')\n\n # Log pandas profile\n if log_profile:\n import pandas_profiling\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\n pf.to_file(\"Data Profile.html\")\n mlflow.log_artifact(\"Data Profile.html\")\n os.remove(\"Data Profile.html\")\n clear_output()\n display(functions_)\n\n # Log training and testing set\n if log_data:\n X_train.join(y_train).to_csv('Train.csv')\n X_test.join(y_test).to_csv('Test.csv')\n mlflow.log_artifact(\"Train.csv\")\n mlflow.log_artifact(\"Test.csv\")\n os.remove('Train.csv')\n os.remove('Test.csv')\n\n logger.info(\"create_model_container: \" + str(len(create_model_container)))\n logger.info(\"master_model_container: \" + str(len(master_model_container)))\n logger.info(\"display_container: \" + str(len(display_container)))\n\n logger.info(\"setup() succesfully completed......................................\")\n\n return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\\\n experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\\\n master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI,\\\n data_before_preprocess, target_param","title":""}],"string":"[\n {\n \"docid\": \"8b71a66a734b075d1133dcee50313951\",\n \"score\": \"0.66847914\",\n \"text\": \"def train_model():\\r\\n x_train = []\\r\\n x_test = []\\r\\n x_valid = []\\r\\n\\r\\n y_train = []\\r\\n y_test = []\\r\\n y_valid = []\\r\\n\\r\\n # reading train\\r\\n read_images(fpath_train, x_train, y_train, FEMALE)\\r\\n read_images(mpath_train, x_train, y_train, MALE)\\r\\n\\r\\n # reading test\\r\\n read_images(fpath_test, x_test, y_test, FEMALE)\\r\\n read_images(mpath_test, x_test, y_test, MALE)\\r\\n\\r\\n # reading valid\\r\\n read_images(fpath_valid, x_valid, y_valid, FEMALE)\\r\\n read_images(mpath_valid, x_valid, y_valid, MALE)\\r\\n\\r\\n print('x_train - ', len(x_train))\\r\\n print('y_train - ', len(y_train))\\r\\n\\r\\n print('x_test - ', len(x_test))\\r\\n print('y_test - ', len(y_test))\\r\\n\\r\\n print('x_valid - ', len(x_valid))\\r\\n print('y_valid - ', len(y_valid))\\r\\n\\r\\n # x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size=0.10, random_state=42)\\r\\n x_train = np.asarray(x_train)\\r\\n x_train = x_train.astype('float32')\\r\\n x_train /= 255\\r\\n y_train = np.asarray(y_train)\\r\\n # x_train, y_train = shuffle_2_lists(x_train, y_train)\\r\\n\\r\\n x_test = np.asarray(x_test)\\r\\n x_test = x_test.astype('float32')\\r\\n x_test /= 255\\r\\n y_test = np.asarray(y_test)\\r\\n\\r\\n x_valid = np.asarray(x_valid)\\r\\n x_valid = x_valid.astype('float32')\\r\\n x_valid /= 255\\r\\n y_valid = np.asarray(y_valid)\\r\\n # x_valid, y_valid = shuffle_2_lists(x_valid, y_valid)\\r\\n\\r\\n # build model\\r\\n model = Sequential()\\r\\n\\r\\n model.add(Conv2D(32, (5, 5), strides=(1, 1), name = 'conv0', input_shape=(112, 112, 1)))\\r\\n\\r\\n model.add(BatchNormalization(axis=3, name = 'bn0'))\\r\\n model.add(Activation('relu'))\\r\\n\\r\\n model.add(MaxPooling2D((2, 2), name='max_pool'))\\r\\n model.add(Conv2D(64, (3, 3), strides=(1, 1), name=\\\"conv1\\\"))\\r\\n model.add(Activation('relu'))\\r\\n model.add(AveragePooling2D((3, 3), name='avg_pool'))\\r\\n\\r\\n model.add(GlobalAveragePooling2D())\\r\\n model.add(Dense(300, activation=\\\"relu\\\", name='rl'))\\r\\n model.add(Dropout(0.5))\\r\\n model.add(Dense(1, activation='sigmoid', name='sm'))\\r\\n\\r\\n model.compile(loss='binary_crossentropy',\\r\\n optimizer=Adam(lr=1e-4),\\r\\n metrics=['accuracy'])\\r\\n\\r\\n batch_size = 32\\r\\n nb_epoch = 98\\r\\n\\r\\n # Train model\\r\\n history = model.fit(x_train, y_train,\\r\\n batch_size=batch_size,\\r\\n epochs=nb_epoch,\\r\\n validation_data=(x_valid, y_valid),\\r\\n shuffle=True,\\r\\n )\\r\\n\\r\\n loss, accuracy = model.evaluate(x_test, y_test)\\r\\n print('loss = ', loss, 'accuracy = ', accuracy)\\r\\n save_model(model, model_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06bd04c7701270b855cad2d685903c15\",\n \"score\": \"0.6546097\",\n \"text\": \"def step_7_train_models(self):\\n title_line = [\\\"#\\\", \\\"accuracy\\\", \\\"MCC\\\", \\\"precision\\\", \\\"recall\\\", \\\"f1\\\", \\\"auc\\\", \\\"kappa\\\", \\\"prevalence\\\",\\n \\\"bias\\\", \\\"pickel-File\\\"]\\n self.csv_text = [title_line]\\n\\n TL_list = []\\n property_list_list = []\\n directory = os.getcwd().split(\\\"/\\\")[-2:]\\n dir_string = ';'.join(directory)\\n for cpd in self.sd_entries:\\n property_list = []\\n property_name_list = []\\n prop_name = cpd.GetPropNames()\\n for property in prop_name:\\n if property not in ['TL', 'value']:\\n try:\\n f = float(cpd.GetProp(property))\\n if math.isnan(f) or math.isinf(f):\\n print(\\\"invalid: %s\\\" % property)\\n\\n except ValueError:\\n print(\\\"valerror: %s\\\" % property)\\n continue\\n property_list.append(f)\\n property_name_list.append(property)\\n elif property == 'TL':\\n TL_list.append(int(cpd.GetProp(property)))\\n else:\\n print(property)\\n pass\\n property_list_list.append(property_list)\\n dataDescrs_array = np.asarray(property_list_list)\\n dataActs_array = np.array(TL_list)\\n\\n for randomseedcounter in range(1, 11):\\n if self.verbous:\\n print(\\\"################################\\\")\\n print(\\\"try to calculate seed %d\\\" % randomseedcounter)\\n X_train, X_test, y_train, y_test = cross_validation.train_test_split(\\n dataDescrs_array, dataActs_array, test_size=.4, random_state=randomseedcounter)\\n # try:\\n clf_RF = RandomForestClassifier(n_estimators=100, random_state=randomseedcounter)\\n clf_RF = clf_RF.fit(X_train, y_train)\\n\\n cv_counter = 5\\n\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\\n scoring='accuracy')\\n\\n accuracy_CV = round(scores.mean(), 3)\\n accuracy_std_CV = round(scores.std(), 3)\\n\\n calcMCC = make_scorer(metrics.matthews_corrcoef, greater_is_better=True,\\n needs_threshold=False)\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\\n scoring=calcMCC)\\n\\n MCC_CV = round(scores.mean(), 3)\\n MCC_std_CV = round(scores.std(), 3)\\n\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter, scoring='f1')\\n scores_rounded = [round(x, 3) for x in scores]\\n f1_CV = round(scores.mean(), 3)\\n f1_std_CV = round(scores.std(), 3)\\n\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\\n scoring='precision')\\n scores_rounded = [round(x, 3) for x in scores]\\n precision_CV = round(scores.mean(), 3)\\n precision_std_CV = round(scores.std(), 3)\\n\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\\n scoring='recall')\\n scores_rounded = [round(x, 3) for x in scores]\\n recall_CV = round(scores.mean(), 3)\\n recall_std_CV = round(scores.std(), 3)\\n\\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\\n scoring='roc_auc')\\n scores_rounded = [round(x, 3) for x in scores]\\n auc_CV = round(scores.mean(), 3)\\n auc_std_CV = round(scores.std(), 3)\\n\\n y_predict = clf_RF.predict(X_test)\\n conf_matrix = metrics.confusion_matrix(y_test, y_predict)\\n # coh_kappa = cohenskappa.cohens_kappa(conf_matrix)\\n coh_kappa = cohens_kappa(conf_matrix)\\n kappa = round(coh_kappa['kappa'], 3)\\n kappa_stdev = round(coh_kappa['std_kappa'], 3)\\n\\n tp = conf_matrix[0][0]\\n tn = conf_matrix[1][1]\\n fp = conf_matrix[1][0]\\n fn = conf_matrix[0][1]\\n n = tn + fp\\n p = tp + fn\\n kappa_prevalence = round(float(abs(tp - tn)) / float(n), 3)\\n kappa_bias = round(float(abs(fp - fn)) / float(n), 3)\\n\\n if self.verbous:\\n print(\\\"test:\\\")\\n print(\\\"\\\\tpos\\\\tneg\\\")\\n print(\\\"true\\\\t%d\\\\t%d\\\" % (tp, tn))\\n print(\\\"false\\\\t%d\\\\t%d\\\" % (fp, fn))\\n print(conf_matrix)\\n print(\\\"\\\\ntrain:\\\")\\n y_predict2 = clf_RF.predict(X_train)\\n conf_matrix2 = metrics.confusion_matrix(y_train, y_predict2)\\n tp2 = conf_matrix2[0][0]\\n tn2 = conf_matrix2[1][1]\\n fp2 = conf_matrix2[1][0]\\n fn2 = conf_matrix2[0][1]\\n print(\\\"\\\\tpos\\\\tneg\\\")\\n print(\\\"true\\\\t%d\\\\t%d\\\" % (tp2, tn2))\\n print(\\\"false\\\\t%d\\\\t%d\\\" % (fp2, fn2))\\n print(conf_matrix2)\\n\\n result_string_cut = [\\n randomseedcounter, str(accuracy_CV) + \\\"_\\\" + str(accuracy_std_CV),\\n str(MCC_CV) + \\\"_\\\" + str(MCC_std_CV), str(precision_CV) + \\\"_\\\" + str(precision_std_CV),\\n str(recall_CV) + \\\"_\\\" + str(recall_std_CV), str(f1_CV) + \\\"_\\\" + str(f1_std_CV),\\n str(auc_CV) + \\\"_\\\" + str(auc_std_CV), str(kappa) + \\\"_\\\" + str(kappa_stdev), kappa_prevalence,\\n kappa_bias, \\\"model_file.pkl\\\"\\n ]\\n\\n self.model.append(clf_RF)\\n self.csv_text.append(result_string_cut)\\n\\n# except Exception as e:\\n# print \\\"got %d models\\\" % len(self.model)\\n# print e\\n# sys.exit(-1)\\n# break\\n return True if len(self.model) > 0 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f857825a9e86686fd720c226f85e0674\",\n \"score\": \"0.6482086\",\n \"text\": \"def train():\\n # Build the data generators\\n train_generator, val_generator, eval_generator = build_data_pipelines()\\n # Get the classes mapping dictionnairy from the data generators\\n classes_dict = train_generator.class_indices\\n # Print the classes mapping\\n print(\\\"classes_dict : \\\\n \\\", classes_dict)\\n # Build the model to train\\n model = build_model(output_layer_dim=len(classes_dict))\\n # Prepare the callbacks\\n early_stopping, ckpt_saver = build_callbacks()\\n # Train the mdoel on the training data generator indicating the validation \\n # data generator to use at the end on each epochs.\\n model.fit(\\n train_generator,\\n steps_per_epoch=get_nb_step(DATA_DIR, 'training'),\\n validation_data=val_generator,\\n validation_steps=get_nb_step(DATA_DIR, 'validation'),\\n epochs=cfg.NB_EPOCHS,\\n callbacks=[early_stopping, ckpt_saver]\\n )\\n # Once the training is finished:\\n # Load best model\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n model.load_weights(os.path.join(OUTPUT_DIR, 'checkpoints'))\\n # Evaluate the model\\n print(\\\"[INFO] Evaluation phase...\\\")\\n # Make the predictions on the test images from the evaluation dataset\\n predictions = model.predict_generator(eval_generator)\\n predictions_idxs = np.argmax(predictions, axis=1)\\n # Build a classification report\\n my_classification_report = classification_report(eval_generator.classes,\\n predictions_idxs, target_names=eval_generator.class_indices.keys())\\n # Build a confusion matrix\\n my_confusion_matrix = confusion_matrix(eval_generator.classes,\\n predictions_idxs)\\n # Print the classifiyer performances\\n print(\\\"[INFO] Classification report : \\\")\\n print(my_classification_report)\\n\\n print(\\\"[INFO] Confusion matrix : \\\")\\n print(my_confusion_matrix)\\n\\n # Save the best model and it's results and config\\n model.save(os.path.join(OUTPUT_DIR, \\\"model.h5\\\"))\\n pickle.dump(my_classification_report, open(os.path.join(OUTPUT_DIR,\\n \\\"classification_report.pkl\\\"), \\\"wb\\\" ))\\n pickle.dump(my_confusion_matrix, open(os.path.join(OUTPUT_DIR,\\n \\\"confusion_matrix.pkl\\\"), \\\"wb\\\" ))\\n pickle.dump(classes_dict, open(os.path.join(OUTPUT_DIR,\\n \\\"classes_dict.pkl\\\"), \\\"wb\\\" ))\\n config_dict = cfg.get_config_dict() \\n pickle.dump(config_dict, open(os.path.join(OUTPUT_DIR,\\n \\\"config_dict.pkl\\\"), \\\"wb\\\" ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17da67f37e8ffd1adb60d71627c0ce49\",\n \"score\": \"0.6469292\",\n \"text\": \"def test_kaggle_classification_data():\\n torch.manual_seed(100)\\n n_episodes = 20\\n a_space = _algorithm_space()\\n for dataset_name in [\\n \\\"kaggle.homesite_quote_conversion\\\",\\n \\\"kaggle.santander_customer_satisfaction\\\",\\n \\\"kaggle.bnp_paribas_cardif_claims_management\\\",\\n \\\"kaggle.poker_rule_induction\\\",\\n \\\"kaggle.costa_rican_household_poverty_prediction\\\",\\n ]:\\n t_env = _task_environment(\\n env_sources=[\\\"KAGGLE\\\"],\\n target_types=[\\\"BINARY\\\", \\\"MULTICLASS\\\"],\\n dataset_names=[dataset_name],\\n n_samples=500)\\n controller = _metalearn_controller(a_space, t_env)\\n reinforce = _metalearn_reinforce(controller, t_env)\\n reinforce.fit(\\n n_episodes=n_episodes,\\n **_fit_kwargs())\\n history = pd.DataFrame(reinforce.history)\\n assert history.shape[0] == n_episodes\\n assert history[\\\"n_successful_mlfs\\\"].sum() > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d86f3ba0c1c9b84474430f78e7c1af7\",\n \"score\": \"0.64522827\",\n \"text\": \"def get_train_test_valid_dataloaders(data_path, test_data_path, seed, image_size, batch_size):\\n def build_data(data_path):\\n content_list = []\\n labels_list = []\\n\\n for image in tqdm(os.listdir(data_path)):\\n if \\\".jpg\\\" in image:\\n content = cv2.imread(data_path + image)\\n content_list.append(content)\\n elif \\\".txt\\\" in image:\\n with open(data_path + image, \\\"r\\\") as f:\\n labels = f.read()\\n labels = np.array(labels.split(\\\" \\\"), dtype=int)\\n labels[0] = 0 if labels[0] == 1 else 1\\n labels = np.roll(labels, -1)\\n labels_list.append(labels)\\n data = np.array([list(a) for a in zip(content_list, labels_list)])\\n\\n return data\\n\\n train_data = build_data(data_path=data_path)\\n test_data = build_data(data_path=test_data_path)\\n\\n train_data, valid_data = train_test_split(train_data, shuffle=True, test_size=0.1, random_state=seed)\\n\\n train_clf_labels = [a[-1] for a in train_data[:, 1]]\\n\\n transform = Compose(\\n [\\n Resize(width=image_size, height=image_size),\\n HorizontalFlip(p=0.4),\\n # ShiftScaleRotate(p=0.3),\\n MedianBlur(blur_limit=7, always_apply=False, p=0.3),\\n IAAAdditiveGaussianNoise(scale=(0, 0.15 * 255), p=0.5),\\n HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.4),\\n RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5),\\n # in this implementation imagenet normalization is used\\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\\n Cutout(p=0.4),\\n ToTensorV2(p=1.0),\\n ],\\n p=1.0,\\n bbox_params=A.BboxParams(format=\\\"pascal_voc\\\"),\\n )\\n\\n test_transform = Compose(\\n [\\n # only resize and normalization is used for testing\\n # no TTA is implemented in this solution\\n Resize(width=image_size, height=image_size),\\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\\n ToTensorV2(p=1.0),\\n ],\\n p=1.0,\\n bbox_params=A.BboxParams(format=\\\"pascal_voc\\\"),\\n )\\n\\n train_dataset = Dataset(train_data, transforms=transform)\\n valid_dataset = Dataset(valid_data, transforms=transform)\\n test_dataset = Dataset(test_data, transforms=test_transform)\\n\\n train_dataloader = DataLoader(\\n train_dataset,\\n # balanced sampler is used to minimize harmful effects of dataset not being fully balanced\\n sampler=BalanceClassSampler(labels=train_clf_labels, mode=\\\"upsampling\\\"),\\n batch_size=batch_size,\\n )\\n test_dataloader = DataLoader(test_dataset, sampler=SequentialSampler(test_dataset), batch_size=1)\\n valid_dataloader = DataLoader(valid_dataset, sampler=SequentialSampler(valid_dataset), batch_size=batch_size)\\n\\n return train_dataloader, test_dataloader, valid_dataloader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39bc838fe64492c50e98365a188c9f1\",\n \"score\": \"0.643714\",\n \"text\": \"def train_model(model):\\n # Add your code here\\n\\n #Preprocessing \\n # Data augmentation - creation of more images to train on\\n train_datagen = ImageDataGenerator(\\n rescale = 1./255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n width_shift_range=0.2,\\n\\t\\theight_shift_range=0.2,\\n rotation_range=20,\\n horizontal_flip=True)\\n\\n test_datagen = ImageDataGenerator(rescale = 1./255)\\n\\n training_set = train_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\\n '/Users/harryrodger/Desktop/data',\\n target_size=(64,64),\\n batch_size=32,\\n class_mode='categorical'\\n )\\n\\n test_set = test_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\\n '/Users/harryrodger/Desktop/ProjectCOMP309/ProjectCode/data/test',\\n target_size=(64,64),\\n batch_size=32,\\n class_mode='categorical'\\n )\\n\\n print('Data augmentation complete')\\n\\n model.fit_generator( \\n training_set,\\n steps_per_epoch=8000, \\n epochs = 50,\\n validation_data=test_set,\\n validation_steps = 15) \\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc1d590f7f0d9c927c217dc46001088\",\n \"score\": \"0.6421587\",\n \"text\": \"def test_nn_models(num_models, raw_data, preprocess_tech, save_path, eval_tech = 'k-fold', list_of_models= None):\\n model_hist = [] ; model_results = pd.DataFrame(columns=['Hidden', 'Activations', 'Learning rate', 'Train Accuracy', 'Test Accuracy', 'Train AUC', 'Test AUC']) # TODO: Add tensorboard and model saving capabilities to this function\\n result_index = 0\\n if eval_tech not in ['k-fold', 'boot']: # must be a valid evaluation technique\\n raise NameError\\n\\n for i in range(num_models):\\n if not list_of_models: # if a list of models to test isn't provided\\n model_info = generate_random_model() # randomly generate models\\n model_info[\\\"Preprocessing\\\"] = preprocess_tech\\n model_info[\\\"Input size\\\"] = raw_data.shape[1] - 1 # set input data size\\n else:\\n assert num_models == len(list_of_models) # if we provide models, ensure the num_models param lines up\\n model_info = list_of_models[i] # get the ith model's info\\n\\n model_info[\\\"Model type\\\"] = 'NN' # this is for k-fold cross validation, since k-fold is able to evaluate both neural nets and logistic regression models\\n model = build_nn(model_info) # build & compile NN\\n\\n if eval_tech == 'k-fold':\\n results = k_fold_cross_validation(model_info, raw_data, preprocess_tech, model=model) # do k-fold cross validation on model\\n else:\\n print(\\\"currently not supporting bootstrap evaluation due to computational constraints\\\")\\n\\n train_acc, test_acc, train_auc, test_auc = zip(*results) # separate results into lists\\n\\n model_info[\\\"Avg Test AUC\\\"] = round(np.mean(test_auc), 3) # compute average metrics for each fold of the evaluation\\n model_info[\\\"Avg Train AUC\\\"] = round(np.mean(train_auc), 3)\\n model_info[\\\"Avg Train Accuracy\\\"] = round(np.mean(train_acc), 3) # these are averages from k-fold cross validation\\n model_info[\\\"Avg Test Accuracy\\\"] = round(np.mean(test_acc), 3)\\n model_hist.append(model_info) # add this model's info to the list\\n\\n model_results.loc[result_index] = (model_info['Hidden layers'], model_info['Activations'],\\\\\\n model_info['Learning rate'], model_info['Avg Train Accuracy'],\\\\\\n model_info[\\\"Avg Test Accuracy\\\"], \\\\\\n model_info[\\\"Avg Train AUC\\\"], model_info[\\\"Avg Test AUC\\\"]) # write model results to df\\n result_index += 1\\n\\n model_results.to_csv(save_path + \\\"K-fold NN Results.csv\\\") # save results to disk\\n display_k_fold_results(model_hist, top_models=1) # display the top models' performance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665d6aca3d23932c887e6fb47be6e04d\",\n \"score\": \"0.6421472\",\n \"text\": \"def k_fold_cv(dataset, nbr_folds=5, m=10, alpha=0.0, feature_extraction=\\\"k-means distances\\\"):\\n # copy the data to avoid affecting reference\\n data = dataset.copy()\\n\\n # divide into k-folds\\n folds = assign_randomly(data, nbr_folds)\\n\\n accuracy_train, accuracy_test = [], []\\n MSE_train, MSE_test = [], []\\n\\n # train models for the folds\\n for i in range(nbr_folds):\\n\\n # distribute into train and test data\\n test_data, train_data = folds[i], []\\n for j in range(nbr_folds):\\n if i != j:\\n train_data.extend(folds[j])\\n\\n train_data = np.array(train_data)\\n test_data = np.array(test_data)\\n\\n # separate X (inputs) and Y (labels) for the training and testing data\\n x_train, y_train = train_data[:, :-TOTAL_DIGIT_CLASSES], train_data[:, -TOTAL_DIGIT_CLASSES:]\\n x_test, y_test = test_data[:, :-TOTAL_DIGIT_CLASSES], test_data[:, -TOTAL_DIGIT_CLASSES:]\\n\\n # Dimensionality reduction\\n if feature_extraction == \\\"k-means distances\\\":\\n clusters = k_means(x_train, m)\\n codebooks = get_codebooks(clusters)\\n x_train = select_features(x_train, codebooks)\\n x_test = select_features(x_test, codebooks)\\n elif feature_extraction == \\\"pca\\\":\\n components, x_bar = get_pca_components(x_train, m)\\n x_train = project_to_pca_comps(x_train, components)\\n x_test = project_to_pca_comps(x_test, components)\\n\\n # compute models for training data\\n model = compute_linreg(x_train, y_train, alpha)\\n\\n # prediction on training and testing data\\n pred_train = predict_hypothesis_matrix(model, x_train)\\n pred_test = predict_hypothesis_matrix(model, x_test)\\n\\n # assess accuracy\\n _acc_train = check_accuracy(pred_train, y_train)\\n _acc_test = check_accuracy(pred_test, y_test)\\n\\n accuracy_train.append(_acc_train)\\n accuracy_test.append(_acc_test)\\n\\n # assesss MSE\\n _MSE_train = check_MSE(pred_train, y_train)\\n _MSE_test = check_MSE(pred_test, y_test)\\n\\n MSE_train.append(_MSE_train)\\n MSE_test.append(_MSE_test)\\n\\n cv_accuracy_train = np.mean(accuracy_train)\\n cv_accuracy_test = np.mean(accuracy_test)\\n cv_MSE_train = np.mean(MSE_train)\\n cv_MSE_test = np.mean(MSE_test)\\n\\n return cv_accuracy_train, cv_accuracy_test, cv_MSE_train, cv_MSE_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abac45c5186b9632817485064a2cda0e\",\n \"score\": \"0.6410815\",\n \"text\": \"def random_labeled_data(data_size,randomness):\\n\\n with open('config.yml') as ymlfile:\\n cgf = yaml.load(ymlfile, Loader=yaml.SafeLoader);\\n n = cgf['DATASET_TRAIN']['arguments']['grid_size']\\n input_shape = (n,n,1)\\n output_shape = (1)\\t\\n\\n weights_path = \\\"model/7/keras_model_files.h5\\\"\\t\\n model = mb.build_model_fc2(input_shape, output_shape,cgf['MODEL']['arguments'])\\n model.load_weights(weights_path)\\n\\n optimizer = cgf['TRAIN']['optim']['type']\\n loss_type= cgf['TRAIN']['loss']['type']\\n metric_list = list(cgf['TRAIN']['metrics'].values())\\n\\n model.compile(optimizer=optimizer,\\n loss=loss_type,\\n metrics= metric_list)\\n \\n if randomness == \\\"gaussian\\\":\\n #Gaussian with total mean 96 \\n data = np.random.normal(0.09375, size=(data_size, n, n, 1))\\n elif randomness == \\\"uniform\\\":\\t\\t\\t\\t\\n #data = np.random.uniform(low=0, high=0.1875, size=(data_size, n, n, 1)) \\n data = np.random.uniform(low=-0.5, high=2, size=(data_size, n, n, 1)) \\n\\n elif randomness == \\\"stripes\\\":\\n data_loader_test = Data_loader_stripe_test(cgf['DATASET_TEST']['arguments'])\\n data, _ = data_loader_test.load_data() \\n\\n sum_pixels = [i.sum() for i in data[:]]\\n\\n labels = model.predict(data)\\n return data, labels, sum_pixels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5770646ac9e05493f3a0a76c930a5fd\",\n \"score\": \"0.6401645\",\n \"text\": \"def cross_validate(model, cv_set, cv_target, n=..., shuffle=..., n_jobs=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36652f9b73c547a618ff155e8f9161f\",\n \"score\": \"0.6391487\",\n \"text\": \"def main():\\n\\n configure_model_dir()\\n base = os.environ['DATA_ROOT']\\n\\n dp = dataset.DatasetProvider(\\n os.path.join(base, cfg.get('data', 'train')),\\n cfg.get('args', 'max_files'),\\n cfg.getint('args', 'max_cuis'),\\n cfg.getint('args', 'samples_per_doc'),\\n cfg.getint('bow', 'batch'),\\n cfg.getboolean('args', 'make_alphabet'),\\n cfg.getboolean('args', 'verbose'))\\n\\n max_cuis = int(cfg.get('args', 'max_cuis'))\\n model = get_model(max_cuis, max_cuis - 1)\\n optim = getattr(optimizers, cfg.get('bow', 'optimizer'))\\n\\n model.compile(\\n loss='binary_crossentropy',\\n optimizer=optim(lr=10**cfg.getint('bow', 'log10lr')),\\n metrics=['accuracy'])\\n\\n callback = ModelCheckpoint(\\n 'Model/model.h5',\\n verbose=1,\\n save_best_only=True)\\n\\n # load validation data\\n val_x, val_y = dp.load(os.path.join(base, cfg.get('data', 'dev')))\\n print('dev x, y shapes:', val_x.shape, val_y.shape)\\n\\n steps = math.ceil(dp.train_size / cfg.getint('bow', 'batch'))\\n print('steps per epoch:', steps)\\n\\n model.fit_generator(\\n dp.stream(),\\n\\t validation_data=(val_x, val_y),\\n epochs=cfg.getint('bow', 'epochs'),\\n steps_per_epoch=steps,\\n verbose=0,\\n callbacks=[callback])\\n\\n # save final model\\n model.save('Model/final.h5')\\n\\n # probability for each class; (test size, num of classes)\\n distribution = model.predict(val_x)\\n\\n # turn into an indicator matrix\\n distribution[distribution < 0.5] = 0\\n distribution[distribution >= 0.5] = 1\\n\\n f1 = f1_score(val_y, distribution, average='macro')\\n p = precision_score(val_y, distribution, average='macro')\\n r = recall_score(val_y, distribution, average='macro')\\n print(\\\"\\\\nmacro: p: %.3f - r: %.3f - f1: %.3f\\\" % (p, r, f1))\\n\\n f1 = f1_score(val_y, distribution, average='micro')\\n p = precision_score(val_y, distribution, average='micro')\\n r = recall_score(val_y, distribution, average='micro')\\n print(\\\"micro: p: %.3f - r: %.3f - f1: %.3f\\\" % (p, r, f1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b5a2e8ccc39473c9e6240035c8ee52\",\n \"score\": \"0.63597065\",\n \"text\": \"def test_train_models_with_best_params_CD5(self, real_genomic_data, real_labels_cat, real_idx):\\n\\t\\tdisease_id = disease_IDs[int(1-1)]\\n\\t\\tchrom = 5\\n\\n\\n\\n\\t\\t# Load data, hp & labels\\n\\t\\tdata = real_genomic_data(disease_id, chrom)\\n\\t\\tfm = char_matrix_to_featmat(data, '3d', real_pnorm_feature_scaling)\\n\\n\\t\\tlabels_cat = real_labels_cat(disease_id)\\n\\n\\t\\thp = pickle.load(open(os.path.join(FINAL_RESULTS_DIR, 'hyperparams', disease_id, 'chrom{}.p'.format(chrom)), 'rb'))\\n\\t\\tprint(hp)\\n\\n\\t\\thp['epochs'] = int(hp['epochs'])\\n\\t\\thp['n_snps'] = int(fm.shape[1])\\n\\t\\t#hp['epochs'] = 250\\n\\t\\t#hp['hidden_neurons'] = 6\\n\\t\\t#hp['lr'] = 1e-4\\n\\t\\t#hp['l1_reg'] = 1e-5 # TODO change me back\\n\\t\\t# Train\\n\\t\\tos.makedirs(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id), exist_ok=True)\\n\\n\\t\\tmodel = create_montaez_dense_model(hp)\\n\\t\\tmodel.fit(x=fm,\\n\\t\\t\\t\\t\\ty=labels_cat,\\n\\t\\t\\t\\t\\tepochs=hp['epochs'],\\n\\t\\t\\t\\t\\tcallbacks=[\\n\\t\\t\\t\\t\\t\\tCSVLogger(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id, '{}'.format(chrom)))\\n\\t\\t\\t\\t\\t],\\n\\t\\t\\t\\t\\tverbose=0)\\n\\t\\t\\t\\t\\t\\n\\t\\t# Calculate AUC from the best model\\n\\t\\ty_pred = model.predict(x=fm)\\n\\n\\t\\t#print(classification_report(np.argmax(Y, axis=-1), np.argmax(y_pred, axis=-1), output_dict=False))\\n\\n\\t\\tauc = roc_auc_score(labels_cat, y_pred, average='weighted')\\n\\t\\tprec = average_precision_score(labels_cat, y_pred, average='weighted')\\n\\t\\tprint('ROC AUC score for best model: %f' % auc)\\t\\n\\t\\tprint('Prec-Rec AUC score for best model: %f' % prec)\\t\\n\\t\\t\\n\\t\\tfilename = os.path.join(FINAL_RESULTS_DIR, 'trained_models', disease_id, 'model{}.h5'.format(chrom))\\n\\t\\tos.makedirs(os.path.dirname(filename), exist_ok=True)\\n\\t\\tmodel.save(filename)\\n\\t\\tK.clear_session()\\n\\t\\tdel data, fm, model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f5f13f17dc5afe9e9378e83539e768\",\n \"score\": \"0.63550985\",\n \"text\": \"def cvrand(model,\\n data,\\n traindatagen,\\n testdatagen,\\n itercol='subject',\\n n_iterations=3,\\n val_subjects=3,\\n epochs=1,\\n batch_size=32,\\n steps_per_epoch=None,\\n validation_steps=None,\\n target_size=(227, 227),\\n random_state=None,\\n min_delta=0,\\n patience=3):\\n\\n # Raise error if selected columns are numeric\\n if pd.api.types.is_numeric_dtype(data[itercol]):\\n raise TypeError('Columns must not be numeric')\\n\\n # set random seed\\n np.random.seed(random_state)\\n\\n # Create empty lists\\n sampledvalues = []\\n validation_accuracies = []\\n train_accuracies = []\\n\\n # Save initial default model weights\\n wsave = model.get_weights()\\n\\n # Designate model checkpoint and callbacks_list\\n checkpoint = ModelCheckpoint('weights.hdf5',\\n mode='max',\\n monitor='val_accuracy',\\n save_best_only=True)\\n\\n earlystop = EarlyStopping(monitor='val_accuracy', min_delta=min_delta, patience=patience)\\n\\n callbacks_list = [checkpoint, earlystop]\\n\\n # Initialize iteration counter\\n counter = 0\\n\\n # Pull unique values from itercol\\n valuelist = data[itercol].unique()\\n\\n for i in range(n_iterations):\\n sampledvalues.append(np.random.choice(valuelist, size=val_subjects, replace=False))\\n\\n # n_iteration for loop\\n for i in range(n_iterations):\\n # Substep counter\\n counter += 1\\n\\n # Print iteration and substep progress\\n print('CV iteration ' + str(counter) + ' of ' + str(n_iterations))\\n\\n # reset model states for fresh training\\n model.set_weights(wsave)\\n\\n # Sample the validation values\\n print('Validation subjects are ' + str(sampledvalues[i]))\\n\\n # Split train and test sets, iterating through each subject to be excluded from training\\n cvtest = data[data[itercol].isin(sampledvalues[i])]\\n cvtrain = data[~data[itercol].isin(sampledvalues[i])]\\n\\n # Split training data\\n train = traindatagen.flow_from_dataframe(cvtrain,\\n x_col='imgpath',\\n y_col='classname',\\n batch_size=batch_size,\\n target_size=target_size,\\n seed=random_state)\\n # Split validation data\\n val = testdatagen.flow_from_dataframe(cvtest,\\n x_col='imgpath',\\n y_col='classname',\\n target_size=target_size,\\n seed=random_state)\\n # Fit model\\n model.fit(train,\\n epochs=epochs,\\n steps_per_epoch=steps_per_epoch,\\n validation_data=val,\\n validation_steps=validation_steps,\\n callbacks=callbacks_list)\\n\\n # Append lists\\n valmax = max(model.history.history['val_accuracy'])\\n valmaxindex = model.history.history['val_accuracy'].index(valmax)\\n validation_accuracies.append(round(valmax, 3))\\n train_accuracies.append(round(model.history.history['accuracy'][valmaxindex], 3))\\n\\n # Fill dataframe with stats\\n dftemp = pd.DataFrame({'validation_subjects': sampledvalues,\\n 'train_accuracies': train_accuracies,\\n 'validation_accuracy': validation_accuracies})\\n\\n return dftemp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f029f3238380f5112a162693418c945\",\n \"score\": \"0.634898\",\n \"text\": \"def main():\\n ###!! Generate dataset at first.\\n generate_train_test_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93341f85a925dac3cb2a7d86eebd4847\",\n \"score\": \"0.6332978\",\n \"text\": \"def Classification_test(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size']\\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n msg_bot = config['msg_bot']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data\\n data = Data()\\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Split data (0% train, 100% test) and define path\\n splits = dict()\\n for ds_name, ds in data.datasets.items():\\n splits[ds_name] = split_dataset(ds, test_ratio=1.0, \\n val_ratio=0, nr_repetitions=config['nr_runs'], \\n cross_validation=config['cross_validation'])\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model, 'test_results')\\n if not os.path.exists(pathr):\\n os.makedirs(pathr)\\n else:\\n # Empty directory\\n shutil.rmtree(pathr)\\n os.makedirs(pathr)\\n\\n\\n # 4. Bring data to Pytorch format\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize'])\\n \\n # 6. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Load pretrained model\\n model = torch.load(os.path.join(model_result_path, 'models_', model_name, 'model.zip'))\\n model.eval()\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n \\n # 9. Test model\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, _, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 10. Save results\\n save_only_test_results(pathr, losses_test, accuracy_test, accuracy_det_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b4953eda7a0d519efa29d1a869444b\",\n \"score\": \"0.63083833\",\n \"text\": \"def train_validation_split(dataset_path, output_path, split_ratio, seed=120):\\n img_categories = os.listdir(dataset_path) # all the image categories\\n if os.path.exists(output_path):\\n print('Dataset already exists at the given path')\\n else:\\n os.makedirs(output_path)\\n os.mkdir(output_path + '/train')\\n os.mkdir(output_path + '/validation')\\n\\n # for every image category in the dataset build train and val folders with images in them a/c to split_ratio\\n print('Splitting dataset into train and validation sets: ')\\n for img_category in img_categories:\\n print('.', end='')\\n # list all the images for this category\\n imgs = os.listdir(dataset_path + '/' + img_category)\\n # sort and shuffle images randomly\\n imgs.sort()\\n random.seed(seed)\\n random.shuffle(imgs)\\n # split the imgs into two halves train and test\\n train_split = imgs[:int(split_ratio * len(imgs))]\\n test_split = imgs[int(split_ratio * len(imgs)):]\\n\\n # built the train set and copy images\\n if not os.path.exists(os.path.join(output_path, 'train', img_category)):\\n os.mkdir(os.path.join(output_path, 'train', img_category))\\n for img in train_split:\\n source = os.path.join(dataset_path, img_category, img)\\n dest = os.path.join(output_path, 'train', img_category, img)\\n shutil.copy(source, dest)\\n\\n # built the test set and copy images\\n if not os.path.exists(os.path.join(output_path, 'validation', img_category)):\\n os.mkdir(os.path.join(output_path, 'validation', img_category))\\n for img in test_split:\\n source = os.path.join(dataset_path, img_category, img)\\n dest = os.path.join(output_path, 'validation', img_category, img)\\n shutil.copy(source, dest)\\n print('\\\\nSuccess!!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6efe5146cb309dfdc58ab445a2fca0\",\n \"score\": \"0.6281842\",\n \"text\": \"def create_sets():\\n\\n X_training_1, Y_training_1, y_training_1 = LoadBatch('../../cifar-10-batches-py/data_batch_1')\\n X_training_2, Y_training_2, y_training_2 = LoadBatch('../../cifar-10-batches-py/data_batch_2')\\n X_training_3, Y_training_3, y_training_3 = LoadBatch('../../cifar-10-batches-py/data_batch_3')\\n X_training_4, Y_training_4, y_training_4 = LoadBatch('../../cifar-10-batches-py/data_batch_4')\\n X_training_5, Y_training_5, y_training_5 = LoadBatch('../../cifar-10-batches-py/data_batch_5')\\n\\n X_training = np.concatenate((X_training_1, X_training_3), axis=1)\\n X_training = np.copy(np.concatenate((X_training, X_training_4), axis=1))\\n X_training = np.copy(np.concatenate((X_training, X_training_5), axis=1))\\n\\n X_training = np.concatenate((X_training, X_training_2[:, :9000]), axis=1)\\n\\n Y_training = np.concatenate((Y_training_1, Y_training_3), axis=1)\\n Y_training = np.copy(np.concatenate((Y_training, Y_training_4), axis=1))\\n Y_training = np.copy(np.concatenate((Y_training, Y_training_5), axis=1))\\n\\n Y_training = np.concatenate((Y_training, Y_training_2[:, :9000]), axis=1)\\n\\n y_training = y_training_1 + y_training_3 + y_training_4 + y_training_5 + y_training_2[:9000]\\n\\n X_validation = np.copy(X_training_2[:, 9000:])\\n Y_validation = np.copy(Y_training_2[:, 9000:])\\n y_validation = y_training_2[9000:]\\n\\n X_test, _, y_test = LoadBatch('../../cifar-10-batches-py/test_batch')\\n\\n mean = np.mean(X_training)\\n X_training -= mean\\n X_validation -= mean\\n X_test -= mean\\n\\n return [X_training, Y_training, y_training], [X_validation, Y_validation, y_validation], [X_test, y_test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56f2c2b2a3f8242aba1a9f7544a3eb7\",\n \"score\": \"0.62642205\",\n \"text\": \"def train_test_original_dataset(norm, target_cat):\\n x_train, x_test, y_train, y_test = train_test_split(norm, target_cat, test_size=0.15)\\n\\n model = keras.Sequential()\\n model.add(keras.layers.Dense(10, input_dim=13, activation='relu'))\\n model.add(keras.layers.Dense(8, activation='relu'))\\n model.add(keras.layers.Dense(6, activation='relu'))\\n model.add(keras.layers.Dense(3, activation='softmax'))\\n\\n\\n model.compile(loss=\\\"categorical_crossentropy\\\", optimizer=\\\"adam\\\", metrics=['accuracy'])\\n\\n model.fit(x_train, y_train, batch_size=15, epochs=2000, verbose=0)\\n\\n test_loss, test_acc = model.evaluate(x_test, y_test)\\n\\n print('Original dataset - test accuracy:', test_acc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"236f3049bbff14ac691a0f3dff2befdb\",\n \"score\": \"0.62390447\",\n \"text\": \"def model_cv(make_model, data, featurizer, n_folds = 10, random_state = 1, getX = Features.getX, getY = Features.getY, stratified = False, **kwargs):\\n nobs = len(data)\\n cv_accuracies = []\\n if stratified:\\n folds = StratifiedKFold(getY(data), n_folds = n_folds, random_state = random_state, **kwargs)\\n else:\\n folds = KFold(n = nobs, n_folds= n_folds, random_state = random_state, **kwargs)\\n get_elems_at = lambda vals, indices: [vals[i] for i in indices]\\n for fold_id, (train_indices, test_indices) in enumerate(folds):\\n print \\\"Running fold %d\\\" % fold_id\\n train_data = get_elems_at(data, train_indices)\\n test_data = get_elems_at(data, test_indices)\\n # Featurize each time since our features can depend on training data\\n matrices = Features.make_experiment_matrices(train_data, test_data, featurizer, getX, getY)\\n # always make a new version of model...safer, not sure if model.fit would overwrite if re-trained\\n # as we want\\n model = make_model()\\n model.fit(matrices['train_X'], matrices['train_Y'])\\n accuracy = model.score(matrices['test_X'], matrices['test_Y'])\\n cv_accuracies.append(accuracy)\\n mean_accuracy = numpy.mean(cv_accuracies)\\n std_accuracy = numpy.std(cv_accuracies)\\n return (mean_accuracy, std_accuracy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad94c4de6b9d3187bd9e40b7ca5000ae\",\n \"score\": \"0.62292963\",\n \"text\": \"def compare_model_algorithms(data, Nrep=2, Nfolds=5):\\n\\t\\n\\t# set RNG\\n\\tnp.random.seed(0)\\n\\trandom.seed(0)\\n\\t\\n\\t# set KNN algorithm options\\n\\tuser_opt_cos = {\\\"name\\\":\\\"cosine\\\", \\\"user_based\\\":True}\\n\\titem_opt_cos = {\\\"name\\\":\\\"cosine\\\", \\\"user_based\\\":False}\\n\\t\\n\\t# The algorithms to cross-validate\\t\\n\\ts_SVD = SVD()\\n\\ts_SVDpp = SVDpp()\\n\\ts_NMF = NMF()\\n\\ts_SlopeOne = SlopeOne()\\n\\tu_KNNBasic = KNNBasic(sim_options=user_opt_cos)\\n\\tu_KNNWithMeans = KNNWithMeans(sim_options=user_opt_cos)\\n\\tu_KNNBaseline = KNNBaseline(sim_options=user_opt_cos)\\n\\ti_KNNBasic = KNNBasic(sim_options=item_opt_cos)\\n\\ti_KNNWithMeans = KNNWithMeans(sim_options=item_opt_cos)\\n\\ti_KNNBaseline = KNNBaseline(sim_options=item_opt_cos)\\n\\ts_CoClustering = CoClustering()\\n\\ts_BaselineOnly = BaselineOnly()\\n\\ts_NormalPredictor = NormalPredictor()\\n\\t\\n\\tclasses = [s_SVD, s_SVDpp, s_NMF, s_SlopeOne, u_KNNBasic, u_KNNWithMeans, \\n\\t\\t\\tu_KNNBaseline, i_KNNBasic, i_KNNWithMeans, i_KNNBaseline,\\n\\t\\t\\ts_CoClustering, s_BaselineOnly, s_NormalPredictor]\\n\\t\\n\\tclass_names = [\\\"SVD\\\", \\\"SVDpp\\\", \\\"NMF\\\", \\\"SlopeOne\\\", \\\"user-KNNBasic\\\", \\\"user-KNNWithMeans\\\", \\n\\t\\t\\t\\t \\\"user-KNNBaseline\\\", \\\"item-KNNBasic\\\", \\\"item-KNNWithMeans\\\", \\\"item-KNNBaseline\\\",\\n\\t\\t\\t\\t \\\"CoClustering\\\", \\\"BaselineOnly\\\", \\\"NormalPredictor\\\"]\\n\\t\\n\\t# repeat cross validation for different kfold splits for higher reliability\\n\\tperformance_list = []\\n\\theaders = ['RMSE', 'MAE', 'Time (min)']\\n\\tfor irep in range(0,Nrep):\\n\\t\\t\\n\\t\\t# cross validation folds will be the same for all algorithms. \\n\\t\\tkf = KFold(n_splits=Nfolds,random_state=0) \\n\\n\\t\\t# cross validate for each algorithm\\n\\t\\ttable = np.zeros((len(classes),len(headers)))\\n\\t\\tfor ik, klass in enumerate(classes):\\n\\t\\t start = time.time()\\n\\t\\t out = cross_validate(klass, data, ['rmse', 'mae'], kf)\\n\\t\\t cv_time = (time.time() - start) / 60\\n\\t\\t mean_rmse = np.mean(out['test_rmse'])\\n\\t\\t mean_mae = np.mean(out['test_mae'])\\n\\t\\t table[ik,:] = np.array([mean_rmse, mean_mae, cv_time])\\n\\t\\t\\t\\n\\t\\t# Accumulate results for each cross-validation\\t\\n\\t\\tperformance_list.append(table)\\n\\t\\n\\t# Show averaged results over cross validation iterations\\n\\tperformance = sum(performance_list)/len(performance_list)\\n\\tprint(tabulate(performance.tolist(), headers=headers, showindex=class_names))\\n\\n\\treturn performance_list, performance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ec9a46bb7b1e1cef72b0996fd24145\",\n \"score\": \"0.62239105\",\n \"text\": \"def split_data(n_folds,output, path_train=False, path_test=False):\\n \\n #define stratified K-fold\\n kf = StratifiedKFold(n_splits=n_folds,shuffle=True,random_state=57)\\n \\n #loads files\\n if path_train:\\n X_train,Y_train = read_corpus(path_train)\\n if path_test:\\n X_test,Y_test = read_corpus(path_test)\\n\\n #cross-validation process with train and test in different input files\\n if path_train and path_test:\\n i=0\\n for train_index, test_index in kf.split(X_train,Y_train):\\n X_train = X_train[test_index]\\n y_train = Y_train[test_index]\\n\\n print(i)\\n print(X_train.shape, y_train.shape)\\n print(\\\" \\\")\\n\\n save_corpus(output+\\\"train\\\"+str(i), y_train, X_train)\\n \\n i=i+1\\n\\n i=0\\n for train_index, test_index in kf.split(X_test,Y_test):\\n\\n x_test = X_test[test_index]\\n y_test = Y_test[test_index]\\n\\n print(i)\\n print(x_test.shape, y_test.shape)\\n print(\\\" \\\")\\n\\n save_corpus(output+\\\"test\\\"+str(i), y_test,x_test)\\n i=i+1\\n \\n #cross-validation process with train and test in the same file \\n if path_train and not path_test:\\n i=0\\n for train_index, test_index in kf.split(X_train,Y_train):\\n X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]\\n y_train_fold, y_test_fold = Y_train[train_index], Y_train[test_index]\\n \\n print(i)\\n print(X_train_fold.shape, y_train_fold.shape)\\n print(X_test_fold.shape, y_test_fold.shape)\\n print(\\\" \\\")\\n \\n save_corpus(output+\\\"train\\\"+str(i), train_index, y_train_fold,X_train_fold)\\n save_corpus(output+\\\"test\\\"+str(i), test_index, y_test_fold, X_test_fold)\\n save_corpus1(output+\\\"train\\\"+str(i)+'.csv', train_index, y_train_fold,X_train_fold)\\n save_corpus1(output+\\\"test\\\"+str(i)+'.csv', test_index, y_test_fold, X_test_fold)\\n i=i+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aafd6739f852cd594573eafc608366\",\n \"score\": \"0.6206666\",\n \"text\": \"def trainModel(self):\\n\\n # load data throught the provided dataset object\\n X, y = self.dataset.load()\\n \\n # count number of classes\\n unique, _ = np.unique(y, return_counts=True)\\n \\n # call buildDNN method\\n model = self.buildDNN(n_classes = len(unique))\\n \\n # preprocess data thorugh the dataset class\\n X_train = self.dataset.preProcessData(X)\\n y_train = self.dataset.labalEncoding(y, n_classes = len(unique))\\n \\n # define optimizer\\n optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\\n\\n # compile the model\\n model.compile(optimizer = optimizer , loss = \\\"categorical_crossentropy\\\", metrics= [\\\"accuracy\\\"])\\n\\n # set a learning rate annealer\\n learning_rate_reduction = ReduceLROnPlateau(monitor='acc', \\n patience=3, \\n verbose=1, \\n factor=0.5, \\n min_lr=0.00001)\\n \\n # data augmentation in order to improve model performance\\n datagen = ImageDataGenerator(\\n featurewise_center=False, # set input mean to 0 over the dataset\\n samplewise_center=False, # set each sample mean to 0\\n featurewise_std_normalization=False, # divide inputs by std of the dataset\\n samplewise_std_normalization=False, # divide each input by its std\\n zca_whitening=False, # apply ZCA whitening\\n rotation_range=10, # randomly rotate images\\n zoom_range = 0.1, # randomly zoom image \\n width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\\n height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\\n horizontal_flip=False, # randomly flip images\\n vertical_flip=False) # randomly flip images\\n\\n datagen.fit(X_train)\\n\\n # fit the model\\n history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=self.batch_size),\\n epochs = self.epochs, steps_per_epoch=X_train.shape[0] // self.batch_size\\n ,callbacks=[learning_rate_reduction])\\n\\n # save the model\\n if self.char:\\n model.save(os.path.join(self.dirBin, 'model_char.h5'))\\n model.save_weights(os.path.join(self.dirBin, 'model_char_weights.h5'))\\n else:\\n model.save(os.path.join(self.dirBin, 'model_digit.h5'))\\n model.save_weights(os.path.join(self.dirBin, 'model_digit_weights.h5'))\\n logging.info('Model saved in {}'.format(self.dirBin))\\n \\n if self.verbose:\\n self.plotHistory(history)\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fc7c74abfd563d4f5047f0410fa1b2\",\n \"score\": \"0.62013394\",\n \"text\": \"def cross_validation():\\n iris = datasets.load_iris()\\n X, y = iris.data, iris.target\\n\\n kf = KFold(n_splits=5, shuffle=True)\\n models = [NeuralNetwork([8, 2], alpha=1e-5), LogisticRegression(), GradientBoostingClassifier()]\\n predictions = [np.zeros(y.shape) for i in models]\\n for i, m in enumerate(models):\\n for train_index, test_index in kf.split(X):\\n m.fit(X[train_index], y[train_index])\\n predictions[i][test_index] = m.predict(X[test_index])\\n\\n return [f1_score(y, predict, average='micro') for predict in predictions]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb5b0d1989fd237fa72cd69d2ae8cda\",\n \"score\": \"0.62008345\",\n \"text\": \"def test_model(config, test_dataloader, device, model):\\n model = model.to(device)\\n model.eval()\\n config[\\\"logger\\\"].info(\\\"Testing Model...\\\")\\n correct_top1 = 0\\n correct_top5 = 0\\n total = len(test_dataloader.dataset)\\n for (x, y, extra) in test_dataloader:\\n if config[\\\"test\\\"][\\\"task\\\"] == \\\"CNN\\\":\\n x = x[\\\"image\\\"].to(device)\\n else:\\n x = x.to(device)\\n y = y.to(device)\\n if config[\\\"train\\\"][\\\"task\\\"] == \\\"BDNN\\\":\\n out = model(x)[0]\\n else:\\n out = model(x)\\n y_pred = F.softmax(out, dim=1)\\n y_pred = y_pred.cpu()\\n y = y.cpu()\\n # Calc Top-1\\n pred = y_pred.argmax(dim=1)\\n correct_top1 += torch.eq(pred, y).sum().float().item()\\n # Calc Top-5\\n maxk = max((1, 5))\\n y_resize = y.view(-1, 1)\\n _, pred = y_pred.topk(maxk, 1, True, True)\\n correct_top5 += torch.eq(pred, y_resize).sum().float().item()\\n correct_top1 /= total\\n correct_top5 /= total\\n config[\\\"logger\\\"].info(\\\"============Testing Results============\\\")\\n config[\\\"logger\\\"].info(\\\"Top-1 Acc. {} Top-5 Acc. {}\\\".format(correct_top1, correct_top5))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71666f94037f615546d1a382c18a5b1\",\n \"score\": \"0.6186508\",\n \"text\": \"def ex_2_1(X_train, y_train, X_test, y_test):\\n\\n randomSeed = np.random.randint(1, 100, 1)\\n\\n n_hidd = [100]\\n\\n score_train, score_test = [], []\\n\\n best_score = 0\\n\\n bestNetwork = MLPClassifier()\\n\\n classes = [\\\"T-shirt/top\\\", \\\"trousers/pants\\\", \\\"pullover shirt\\\", \\\"dress\\\", \\\"coat\\\", \\\"sandal\\\", \\\"shirt\\\", \\\"sneaker\\\", \\\"bag\\\", \\\"ankle boot\\\"]\\n\\n for n, n_h in enumerate(n_hidd):\\n for s, seed in enumerate(randomSeed):\\n nn = MLPClassifier(hidden_layer_sizes=(n_h,), activation='tanh', max_iter=50, random_state=seed)\\n\\n nn.fit(X_train, y_train)\\n\\n scoretrain = nn.score(X_train, y_train)\\n scoretest = nn.score(X_test, y_test)\\n\\n score_train.append(scoretrain)\\n score_test.append(scoretest)\\n\\n if scoretest > best_score:\\n bestNetwork = nn\\n best_score = scoretest\\n\\n print(100 / (len(n_hidd) * len(randomSeed)) * ((n*len(randomSeed)) + (s+1)), \\\"%\\\")\\n\\n \\n plot_boxplot(score_train, score_test)\\n\\n\\n prediction = bestNetwork.predict(X_test)\\n confusionMatrix = confusion_matrix(y_test, prediction)\\n\\n #confusion matrix\\n print(\\\"Confusion matrix:\\\")\\n print(classes)\\n\\n print(confusionMatrix)\\n\\n #Weight\\n print(len(bestNetwork.coefs_))\\n\\n plot_hidden_layer_weights(bestNetwork.coefs_[0])\\n\\n print(\\\"Misclassified Pictures\\\")\\n\\n falseList = prediction == y_test\\n\\n indexPosList = []\\n\\n for i, index in enumerate(falseList):\\n if index == False:\\n indexPosList.append(i)\\n\\n print(indexPosList)\\n\\n for i in range(5):\\n print(\\\"MLPClassifer think it is\\\", prediction[indexPosList[i]] + 1, \\\"but it is\\\", y_test[indexPosList[i]]+1)\\n plot_image(X_test[indexPosList[i]])\\n ## TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49fbd35b39d01fd5d6ab4711badb3868\",\n \"score\": \"0.6181956\",\n \"text\": \"def make_problem(graphs, labels, n_validation=50, seed=0):\\n if seed is not None:\\n np.random.seed(seed)\\n \\n # convert label to 1 hot encoding\\n y = convertToOneHot(labels)\\n\\n # Shuffle\\n indexes = np.random.permutation(len(graphs))\\n graphs_shuffle = np.array(graphs)[indexes]\\n y_shuffle = np.array(y)[indexes]\\n\\n dataset_train = Dataset(graphs_shuffle[:-n_validation], y_shuffle[:-n_validation])\\n dataset_validation = Dataset(graphs_shuffle[-n_validation:], y_shuffle[-n_validation:])\\n return dataset_train, dataset_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cf27be7827aae960dbc445f6eccda9\",\n \"score\": \"0.61763227\",\n \"text\": \"def test():\\n test_dataset, test_dataset_length = dataloader(args_opt.data_url, epoch=1, batch_size=1,\\n mode=args_opt.mode, shuffle=False)\\n predict = []\\n test_dataset_iter = test_dataset.create_dict_iterator()\\n print('Valid dataset length:', test_dataset_length)\\n deepid = get_network(args_opt, args_opt.num_class, True)\\n param_network = load_checkpoint(args_opt.ckpt_url)\\n load_param_into_net(deepid, param_network)\\n print('Start Testing!')\\n for data in test_dataset_iter:\\n img1 = \\\"img1\\\"\\n img2 = \\\"img2\\\"\\n img1_test = data['image1']\\n img2_test = data['image2']\\n label = data['label']\\n output1 = deepid(img1_test)\\n output2 = deepid(img2_test)\\n cosdistance = mindspore.ops.tensor_dot(output1, output2, (1, 1))\\n norm1 = P.norm(output1, dim=1)\\n norm2 = P.norm(output2, dim=1)\\n cosdistance = cosdistance / (norm1 * norm2 + 1e-5)\\n cosdistance = float(cosdistance.asnumpy())\\n predict.append('{}\\\\t{}\\\\t{}\\\\t{}\\\\n'.format(img1, img2, cosdistance, int(label.asnumpy())))\\n accuracy = []\\n thd = []\\n folds = KFold(n=3120, n_folds=10, shuffle=False)\\n thresholds = np.arange(-1.0, 1.0, 0.005)\\n ans = lambda line: line.strip('\\\\n').split()\\n predicts = np.array([ans(x) for x in predict])\\n for idx, (train, ktest) in enumerate(folds):\\n print(\\\"now the idx is %d\\\", idx)\\n best_thresh = find_best_threshold(thresholds, predicts[train])\\n accuracy.append(eval_acc(best_thresh, predicts[ktest]))\\n thd.append(best_thresh)\\n print('ACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b286e936ab6cb1f5e29a430cdc5b803\",\n \"score\": \"0.6162955\",\n \"text\": \"def train_model(model):\\n\\n # Pre-processing\\n print('preprocessing')\\n # Scale the image down to 90x90 & Apply random transform,\\n datagenerator = ImageDataGenerator(\\n # featurewise_center=True, featurewise_std_normalization=True,\\n rotation_range=360, shear_range=0.6,\\n horizontal_flip=True, vertical_flip=True,\\n zoom_range=0.6, rescale=1./255,\\n # preprocessing_function=preprocess_data\\n )\\n \\n train_path = '/content/drive/My Drive/Colab Notebooks/Data/Train_data'\\n test_path = '/content/drive/My Drive/Colab Notebooks/Data/Test_data'\\n \\n train_batches = datagenerator.flow_from_directory(train_path, target_size=scaled_img_dimensions, classes=['cherry', 'strawberry', 'tomato'], batch_size=32, class_mode='categorical')\\n \\n # Training\\n # model.fit(generated_imgs, train_labels[0], epochs=20, batch_size=2000)\\n model.fit_generator(train_batches, shuffle=True, epochs=2, steps_per_epoch=4500) \\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76296cbc051c6cd750cbcb7ec6e89219\",\n \"score\": \"0.6159247\",\n \"text\": \"def Classification_initialize_and_train(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size'] \\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n save_interval = config['save_interval']\\n msg_bot = config['msg_bot']\\n bot_msg_interval = config['bot_msg_interval']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data\\n data = Data()\\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\\n train_ds = (dataset_name, 'train')\\n val_ds = (dataset_name, 'val')\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Split data and define path\\n splits = dict()\\n for ds_name, ds in data.datasets.items():\\n splits[ds_name] = split_dataset(ds, test_ratio=config['test_ratio'], \\n val_ratio=config['val_ratio'], nr_repetitions=config['nr_runs'], \\n cross_validation=config['cross_validation'])\\n\\n # Include the model name, Alexnet, CNN, Resnet etc. what has been used\\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\\n if not os.path.exists(paths):\\n os.makedirs(paths)\\n else:\\n # Empty directory\\n shutil.rmtree(paths)\\n os.makedirs(paths)\\n if not os.path.exists(pathr):\\n os.makedirs(pathr)\\n else:\\n # Empty directory\\n shutil.rmtree(pathr)\\n os.makedirs(pathr)\\n\\n # Save split\\n if splits is not None:\\n lr.save_json(splits, path=paths, name='data_splits')\\n\\n\\n # 4. Create data splits for each repetition\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize']) #TODO: Test with resize=True and without approach from dataset_classification.py\\n\\n # 6. Build train dataloader, and visualize\\n dl = DataLoader(datasets[(train_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n dl_val = DataLoader(datasets[(val_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Initialize model\\n model = getattr(models, model_name)(output_features)\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\\n weight_decay=weight_decay)\\n\\n # 9. Train model\\n print('Training model in batches of {}..'.format(batch_size))\\n\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\\n dl_val, nr_epochs=config['nr_epochs'],\\n save_path=paths,\\n save_interval=save_interval, msg_bot=msg_bot,\\n bot_msg_interval=bot_msg_interval)\\n \\n # 10. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True)\\n \\n # 11. Test model\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 12. Save results\\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\\n accuracy_det_test, losses_cum_train, losses_cum_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e57cf40d2e063373cdc94bc0fc4667a8\",\n \"score\": \"0.6158664\",\n \"text\": \"def auth_train(client,random_class,train_samples):\\n T = 16\\n model = get_model()\\n model.load_weights('./weights/new_14.129-0.9992.h5')\\n print (len(model.layers))\\n model.pop()\\n frozen_layers = len(model.layers)-2\\n model.add(Dense(2, activation='sigmoid'))\\n for layer in model.layers[:frozen_layers]:\\n layer.trainable = False\\n model.summary()\\n\\n random_class[random_class.index(client)], random_class[0] = random_class[0], client\\n\\n rand_train_class = random_class[:50]\\n SGD = sgd(lr=0.01, momentum=0.9, decay=1e-3)\\n model.compile(loss='binary_crossentropy', optimizer=SGD, metrics=['accuracy'])\\n early_stop = EarlyStopping(patience=7, verbose=1, monitor='val_loss')\\n save_best = ModelCheckpoint('./weights/'+ str(client) + '.h5', monitor='val_loss', verbose=1,save_best_only=True)\\n train_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client,\\n height_shift_range=0.1,width_shift_range=0.1,\\n rotation_range=10,zoom_range=(0.9,1))\\n val_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client)\\n\\n model.fit_generator(\\n train_gen.flow_from_directory('../data/5sec10',sub_dir=train_samples[:3], sequence=T,\\n auth_rand_train=rand_train_class,\\n target_size=(100, 100),batch_size=32,authentication=True)\\n , steps_per_epoch=200, epochs=15, callbacks=[early_stop, save_best],\\n validation_data=val_gen.flow_from_directory('../data/5sec10', sub_dir=train_samples[3:],\\n sequence=T,auth_rand_train=rand_train_class,\\n target_size=(100, 100), batch_size=32,authentication=True)\\n , validation_steps=200, workers=4,use_multiprocessing=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc5e9ee025b64e904f4501a4eb8bb03\",\n \"score\": \"0.61550874\",\n \"text\": \"def train_on_test():\\n create_cbk()\\n train()\\n test(\\\"res186/\\\", \\\"models_file186.txt\\\", \\\"train186.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397e5482e4215a42a3729ca79e7a3d4e\",\n \"score\": \"0.61392045\",\n \"text\": \"def cross_validate_all(model, data, labels):\\n\\n print(\\\"Running Cross Validation\\\")\\n model = model\\n dum_unifrom = DummyClassifier(strategy='uniform') #uniform most_frequent stratified\\n dum_stratified = DummyClassifier(strategy='stratified')\\n dum_freq = DummyClassifier(strategy='most_frequent')\\n\\n scoring = ['accuracy', 'f1_micro', 'f1_macro']\\n\\n cv = KFold(n_splits=5, shuffle=True, random_state=42)\\n scores = cross_validate(model, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_uniform_scores = cross_validate(dum_unifrom, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_stratified_scores = cross_validate(dum_stratified, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_freq_scores = cross_validate(dum_freq, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n\\n acc_mean = np.around(scores['test_accuracy'].mean(), decimals=3)\\n acc_std = np.around(scores['test_accuracy'].std(), decimals=3)\\n\\n micro_mean = np.around(scores['test_f1_micro'].mean(), decimals=3)\\n micro_std = np.around(scores['test_f1_micro'].std(), decimals=3)\\n\\n macro_mean = np.around(scores['test_f1_macro'].mean(), decimals=3)\\n macro_std = np.around(scores['test_f1_macro'].std(), decimals=3)\\n\\n # calculate the lift over random score\\n dum_mean = dum_uniform_scores['test_f1_micro'].mean()\\n strat_mean = dum_stratified_scores['test_f1_micro'].mean()\\n freq_mean = dum_freq_scores['test_f1_micro'].mean()\\n\\n dum_lift_mean = np.around(((acc_mean-dum_mean)/dum_mean)*100, decimals=2)\\n strat_lift_mean = np.around(((acc_mean-strat_mean)/strat_mean)*100, decimals=2)\\n freq_lift_mean = np.around(((acc_mean-freq_mean)/freq_mean)*100, decimals=2)\\n\\n return acc_mean, acc_std, micro_mean, micro_std, macro_mean, macro_std, dum_lift_mean, strat_lift_mean, freq_lift_mean\\n\\n #acc = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='accuracy', n_jobs=-1)\\n #f1_macro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_macro', n_jobs=-1)\\n #f1_micro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_micro', n_jobs=-1)\\n\\n #return acc, f1_macro, f1_micro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce4e31380d3fe6a6391c7c869f47861\",\n \"score\": \"0.61378807\",\n \"text\": \"def train():\\n # Set the random seeds for reproducibility\\n # np.random.seed(42)\\n\\n onehot_input, y, _ = cross_entropy_input_to_onehot()\\n\\n LEARNING_RATE_DEFAULT = 3e-3\\n MAX_STEPS_DEFAULT = 4000000\\n\\n cnet_a = CrossNet2(onehot_input.shape[1])\\n script_directory = os.path.split(os.path.abspath(__file__))[0]\\n filepath = 'grubbyStarCE2.model'\\n model_to_train = os.path.join(script_directory, filepath)\\n\\n cnet_b = SimpleMLP(onehot_input.shape[1])\\n script_directory_b = os.path.split(os.path.abspath(__file__))[0]\\n filepath_b = 'grubbyStarCrossEntropy.model'\\n model_b = os.path.join(script_directory_b, filepath_b)\\n\\n print(cnet_a)\\n print(onehot_input.shape)\\n print(onehot_input.shape[1])\\n\\n optimizer = torch.optim.SGD(cnet_a.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\\n optimizer_b = torch.optim.SGD(cnet_b.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\\n\\n accuracies = []\\n losses = []\\n vag_losses = []\\n min_loss = 100\\n min_loss_b = 100\\n\\n vag_games = get_validation_ids()\\n vag_games = np.array(vag_games)\\n vag_ids = vag_games[-200:]\\n validation_games = 100\\n vag_input = onehot_input[vag_ids, :]\\n vag_targets = y[vag_ids]\\n\\n for epoch in range(1):\\n val_ids = [i for i in range(onehot_input.shape[0]-validation_games, onehot_input.shape[0])]\\n val_ids = np.append(val_ids, vag_ids)\\n val_ids = np.unique(val_ids)\\n val_ids = np.array(val_ids)\\n print(len(val_ids), \\\"val ids\\\")\\n print(val_ids)\\n\\n train_ids = [i for i in range(onehot_input.shape[0]) if i not in val_ids]\\n\\n X_train = onehot_input[train_ids, :]\\n print(X_train.shape)\\n y_train = y[train_ids]\\n\\n X_test = onehot_input[val_ids, :]\\n y_test = y[val_ids]\\n\\n print(\\\"epoch \\\" + str(epoch))\\n saturation = 1\\n p = 1\\n bce = True\\n ace = True\\n\\n for iteration in range(MAX_STEPS_DEFAULT):\\n BATCH_SIZE_DEFAULT = 8\\n cnet_a.train()\\n cnet_b.train()\\n if iteration % 20000 == 0:\\n # saturation *= 0.5\\n # saturation = max(0.5, saturation)\\n print(iteration)\\n print(saturation)\\n\\n ids = np.random.choice(X_train.shape[0], size=BATCH_SIZE_DEFAULT, replace=False)\\n\\n X_train_batch = X_train[ids, :]\\n y_train_batch = y_train[ids]\\n\\n X_train_batch = np.reshape(X_train_batch, (BATCH_SIZE_DEFAULT, -1))\\n X_train_batch = Variable(torch.FloatTensor(X_train_batch))\\n\\n output = cnet_a.forward(X_train_batch)\\n output_b = cnet_b.forward(X_train_batch)\\n\\n y_train_batch = np.reshape(y_train_batch, (BATCH_SIZE_DEFAULT, -1))\\n y_train_batch = Variable(torch.FloatTensor(y_train_batch))\\n\\n if iteration % 1 == 0:\\n loss = center_my_loss(output, y_train_batch, output_b, saturation)\\n else:\\n loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\\n\\n if True:\\n loss_b = center_my_loss(output_b, y_train_batch, output, saturation)\\n else:\\n loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\\n\\n ce_loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\\n ce_loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\\n\\n if iteration % EVAL_FREQ_DEFAULT == 0:\\n cnet_a.eval()\\n cnet_b.eval()\\n\\n ids = np.array(range(len(X_test)))\\n\\n x = X_test[ids, :]\\n targets = y_test[ids]\\n\\n x = np.reshape(x, (len(X_test), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred = cnet_a.forward(x)\\n pred_b = cnet_b.forward(x)\\n acc = accuracy(pred, targets)\\n\\n targets = np.reshape(targets, (len(X_test), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n calc_loss = torch.nn.functional.binary_cross_entropy(pred, targets)\\n calc_loss_b = torch.nn.functional.binary_cross_entropy(pred_b, targets)\\n\\n accuracies.append(acc)\\n\\n ###################\\n\\n if p*calc_loss.item()+(1-p)*ce_loss.item() < p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\\n cnet_b.train()\\n cnet_b.zero_grad()\\n loss_b.backward(retain_graph=True)\\n optimizer_b.step()\\n cnet_b.eval()\\n\\n if min_loss_b > calc_loss_b.item():\\n min_loss_b = calc_loss_b.item()\\n torch.save(cnet_b, model_b)\\n\\n ids = np.array(range(len(X_train)))\\n x = X_train[ids, :]\\n targets = y_train[ids]\\n\\n x = np.reshape(x, (len(X_train), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred_b = cnet_b.forward(x)\\n\\n train_acc = accuracy(pred_b, targets)\\n\\n targets = np.reshape(targets, (len(X_train), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n train_loss = torch.nn.functional.binary_cross_entropy(pred_b, targets)\\n losses.append(train_loss.item())\\n\\n print(\\\"iteration: \\\" + str(iteration) + \\\" train acc \\\" + str(train_acc) + \\\" val acc \\\" + str(\\n acc) + \\\" a \\\" + str(round(calc_loss.item()*1000)/1000) + \\\" b \\\" + str(\\n round(calc_loss_b.item() * 1000)/1000))\\n\\n if p*calc_loss.item()+(1-p)*ce_loss.item() > p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\\n cnet_a.train()\\n cnet_a.zero_grad()\\n loss.backward(retain_graph=True)\\n optimizer.step()\\n cnet_a.eval()\\n\\n if min_loss > calc_loss.item():\\n min_loss = calc_loss.item()\\n torch.save(cnet_a, model_to_train)\\n\\n ids = np.array(range(len(X_train)))\\n x = X_train[ids, :]\\n targets = y_train[ids]\\n\\n x = np.reshape(x, (len(X_train), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred = cnet_a.forward(x)\\n\\n train_acc = accuracy(pred, targets)\\n\\n targets = np.reshape(targets, (len(X_train), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n print(\\\"iteration: \\\" + str(iteration) + \\\" train acc \\\" + str(train_acc) + \\\" val acc \\\" + str(\\n acc) + \\\" a \\\" + str(round(calc_loss.item()*1000)/1000) + \\\" b \\\" + str(\\n round(calc_loss_b.item() * 1000)/1000))\\n\\n test_nn.test_all(model_to_train)\\n print(model_to_train)\\n\\n plt.plot(accuracies)\\n plt.ylabel('accuracies')\\n plt.show()\\n\\n plt.plot(vag_losses, 'r')\\n plt.plot(losses, 'b')\\n plt.ylabel('losses')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce0d687e29d6e3788e4836fe28ef733\",\n \"score\": \"0.6130701\",\n \"text\": \"def train():\\n net = model(load=False, shape=(100, 100, 3))\\n X, y = get_X_y('./data/t1_1/driving_log.csv')\\n net.fit_generator(_generator(256, X, y), samples_per_epoch=20224, nb_epoch=2)\\n net.save('checkpoints/short.h5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b79916dd2fd528c87eaac3cf7c4d4f1\",\n \"score\": \"0.61234725\",\n \"text\": \"def test_performance_cross_validation(dataset, classifier, label_col, n_folds, seed='46'):\\n rand_col = \\\"uid_rand\\\"\\n h = 1.0 / n_folds\\n df = dataset.select(\\\"*\\\", f.rand(seed).alias(rand_col))\\n\\n metrics_dict = {\\\"roc_auc\\\": [], # roc: y=tpr x=fpr\\n \\\"true_pos_rate\\\": [], # recall = true pos rate \\n \\\"false_pos_rate\\\": [],\\n \\\"precision\\\": [],\\n \\\"n_true_neg\\\": [],\\n \\\"n_false_neg\\\": [],\\n \\\"n_false_pos\\\": [],\\n \\\"n_true_pos\\\": [], }\\n\\n model = None\\n for i in range(n_folds):\\n\\n validate_lb = i * h # lower bound\\n validate_ub = (i + 1) * h # upper bound\\n condition = (df[rand_col] >= validate_lb) & (df[rand_col] < validate_ub)\\n validation = df.filter(condition)\\n train = df.filter(~condition)\\n \\n # train\\n model = classifier.fit(train)\\n \\n # predict\\n prediction = model.transform(validation)\\n \\n # assess performance metrics\\n prediction_and_labels = prediction.rdd.map(lambda x: (x['prediction'], x[label_col]))\\n print(prediction_and_labels)\\n metrics = MulticlassMetrics(prediction_and_labels)\\n metrics_areas = BinaryClassificationMetrics(prediction_and_labels) # gets roc and precRecall curves\\n metrics_dict['roc_auc'].append(metrics_areas.areaUnderROC)\\n # a bit slow, have to calc outside loop\\n cm = metrics.confusionMatrix().toArray()\\n n_true_neg = cm[0, 0]\\n n_false_neg = cm[1, 0]\\n n_true_pos = cm[1, 1]\\n n_false_pos = cm[0, 1]\\n #\\n metrics_dict['n_true_neg'].append(n_true_neg) \\n metrics_dict['n_false_neg'].append(n_false_neg)\\n metrics_dict['n_true_pos'].append(n_true_pos)\\n metrics_dict['n_false_pos'].append(n_false_pos) \\n metrics_dict['true_pos_rate'].append(n_true_pos / (n_true_pos+n_false_neg))\\n metrics_dict['false_pos_rate'].append(n_false_pos / (n_false_pos+n_true_neg))\\n metrics_dict['precision'].append(n_true_pos / (n_true_pos+n_false_pos))\\n\\n return model, metrics_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6f4a8324ee07037fa8904b56f6e4dc\",\n \"score\": \"0.610934\",\n \"text\": \"def prep_datasets(test_size, validation_size):\\n X, y = load_data(DATA_PATH)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\\n X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\\n\\n return X_train, X_test, X_validation, y_train, y_test, y_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be6173f0a97bc23d9661ba1438c9277\",\n \"score\": \"0.6109022\",\n \"text\": \"def few_label_classification(self, list_num_shots=[1, 10, 100, -1], num_runs=10, batch_size=1000, classifier='logistic'):\\n if not os.path.exists('results/few_label/'):\\n os.makedirs('results/few_label/')\\n results = {'Num_Shots': [], 'Mean': [], 'Std': []}\\n train_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=True, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_train, ys_train = self.encode_dataset(train_loader)\\n \\n test_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=False, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_test, ys_test = self.encode_dataset(test_loader)\\n \\n num_classes = len(np.unique(ys_train))\\n \\n for num_shots in tqdm(list_num_shots):\\n Accuracy = []\\n if num_shots == -1:\\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train, ys_train)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n else:\\n for i in tqdm(range(num_runs)):\\n # torch.cuda.empty_cache()\\n zs_train_selected = []\\n ys_train_selected = []\\n set_seed(i)\\n for k in range(num_classes):\\n indk = np.argwhere(ys_train == k)[:,0]\\n ind_of_indk = np.random.permutation(len(indk))[:num_shots]\\n indk_selected = indk[ind_of_indk]\\n zs_train_selected.append(zs_train[indk_selected])\\n ys_train_selected.append(ys_train[indk_selected])\\n zs_train_selected = np.concatenate(zs_train_selected, 0)\\n ys_train_selected = np.concatenate(ys_train_selected, 0)\\n\\n# print(indk_selected)\\n \\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train_selected, ys_train_selected)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n# return 0\\n Accuracy = np.concatenate(Accuracy)\\n results['Num_Shots'].append(num_shots)\\n results['Mean'].append(Accuracy.mean())\\n results['Std'].append(Accuracy.std())\\n pd.DataFrame.from_dict(results).to_csv('results/few_label/{}-{}-runs={}.csv'.format(self.model_name, self.data, num_runs), index=False)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4dbe68065116d05964931885b20db3b\",\n \"score\": \"0.61042625\",\n \"text\": \"def main():\\r\\n print \\\"running now\\\"\\r\\n print \\\"reading in data\\\"\\r\\n inputs_test = pandas.read_csv(\\\"random_baseline_test_inputs.csv\\\").values\\r\\n labels_test = pandas.read_csv(\\\"random_baseline_test_labels.csv\\\").values\\r\\n inputs_train = pandas.read_csv(\\\"two_layer_generated2_train_inputs.csv\\\").values\\r\\n labels_train = pandas.read_csv(\\\"two_layer_generated2_train_labels.csv\\\").values\\r\\n\\r\\n #Keeps track of the number of samples in each dataset.\\r\\n train_size = len(labels_train)\\r\\n test_size = len(labels_test)\\r\\n\\r\\n \\\"\\\"\\\"Batch size is how many samples to train/evaluate the model on at once.\\r\\n In my experience, this doesn't effect performance of the model but if it is\\r\\n too high, your machine can run out of memory and crash and if it is too low\\r\\n your training can take too long. 100 has worked well so far.\\\"\\\"\\\"\\r\\n batch_size=100\\r\\n\\r\\n \\\"\\\"\\\"The model will train for a number of epochs equal to num_epochs. One epoch\\r\\n passes when the model has looked at every point in the dataset for training.\\r\\n The more epochs, the better trained the model will be and the better performance\\r\\n it will have. More complex models tend to take more epochs to train properly. \\r\\n There will be a point of diminishing returns with the epochs. For this model\\r\\n improvements start to flatten out around 10 epochs\\\"\\\"\\\"\\r\\n num_epochs = 10 \\r\\n\\r\\n \\\"\\\"\\\"This is a small constant used in batch regularization calculations to prevent\\r\\n divide by zero errors. At present batch regularization code is not implemented\\r\\n but is commented out (more on that further down)\\\"\\\"\\\"\\r\\n delta = 10^-8 \\r\\n\\r\\n \\\"\\\"\\\"These are paramaters for dropout. Dropout is a way of preventing overfitting by\\r\\n temporarily removing neurons (by setting them to 0) during training. The idea is \\r\\n that you are forcing the rest of the neurons to compensate for the lost neuron and\\r\\n you are preventing neurons from \\\"conspiring\\\" to overfit. drop_input and drop_hidden\\r\\n are the proportions (out of 1) of the dropout inputs that are kept when dropout is \\r\\n called with those variables. Standard practice is to keep .8 of the input units and \\r\\n .5 of the units in each hidden layer. Here both keep probabilities are set to 1 meaning\\r\\n no dropout occurs. This is because overfitting has not yet become an issue. If it does\\r\\n (signified by a large difference between train and test errors), dropout can be \\r\\n implemented as a potential fix by changing these variables.\\\"\\\"\\\"\\r\\n drop_input = 1\\r\\n drop_hidden = 1\\r\\n\\r\\n\\r\\n print \\\"creating model\\\"\\r\\n \\\"\\\"\\\" x is a placeholder for the input data that is going to be passed in to the model.\\r\\n Later on when we tell the model to start training, we will tell it to look at the \\r\\n data we took from the CSVs to use as values for x. A value of [None, 5] in the shape \\r\\n argument means that there can be an arbitrary amount of length 5 vectors passed in\\\"\\\"\\\"\\r\\n x = tf.placeholder(tf.float64, [None, 5], name = \\\"input_placeholder\\\")\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"These are placeholders for the dropout keep probabilities. We want dropout to occur only during \\r\\n training and not testing so we make keep probabilities placeholders and pass in the value we want\\r\\n later when we know which (training/testing) we are doing.\\\"\\\"\\\"\\r\\n drop_input_placeholder = tf.placeholder(tf.float64, shape = (), name = \\\"drop_input_placeholder\\\")\\r\\n drop_hidden_placeholder = tf.placeholder(tf.float64, shape = (), name = \\\"drop_hidden_placeholder\\\")\\r\\n\\r\\n #Applies dropout to inputs\\r\\n x_drop = tf.nn.dropout(x, drop_input_placeholder)\\r\\n\\r\\n \\\"\\\"\\\"If reuse is set to false, weights and biases are initialized according to defaults. If true,\\r\\n it reads in the initial values of weights and biases from saved CSVs\\\"\\\"\\\"\\r\\n reuse = False\\r\\n\\r\\n \\\"\\\"\\\"This is the first layer of the neural network. This layer accepts [batch_size x 5] input vectors \\r\\n and multiplies them be a bias and adds a weight to create a [batch_size x 10] hidden vector. It then\\r\\n applies a ReLU activation function to force all elements of h_1 to be non-negative. This effectively\\r\\n either turns off each neuron or turns them on with an intensity linerly proportional to the value of\\r\\n the hidden unit. Subsequent layers are shaped similarly.\\\"\\\"\\\"\\r\\n\\r\\n if (reuse):\\r\\n W_1 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_1.csv\\\", header = None).values)\\r\\n b_1 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_1.csv\\\", header = None).values)\\r\\n else:\\r\\n W_1 = weight_variable([5,10])\\r\\n b_1 = bias_variable([10])\\r\\n h_1 = tf.nn.relu(tf.matmul(x_drop, W_1) + b_1)\\r\\n\\r\\n \\\"\\\"\\\"Batch normalization is a technique that has been shown in other cases to dramatically help convergence.\\r\\n The commented out lines here show the form that it would take in this implementation. If one wanted to \\r\\n apply batch normalization to this entire program, they would just have to uncomment the lines below and\\r\\n copy them in every step of the neural network. (e.g. change sig_1 to sig_2, mu_1 to mu_2 etc. and put the \\r\\n steps between the calculations of h_2 and h_2_drop.) Imperically this does not help the current model as it\\r\\n stands but it should be retried every once in a while if hte model is changing becuase it has been so \\r\\n effective in practice.\\r\\n mu_1 = tf.reduce_mean(h_1)\\r\\n sig_1 = tf.sqrt(tf.reduce_mean(tf.squared_difference(h_1, mu_1))+delta)\\r\\n h_1 = tf.divide((h_1-mu_1),sig_1)\\\"\\\"\\\"\\r\\n\\r\\n #Applies dropout to the first hidden layer.\\r\\n h_1_drop = tf.nn.dropout(h_1, drop_hidden_placeholder)\\r\\n\\r\\n #Second hidden layer, takes [batch_size x 10] input and gives [batch_size x 5] output\\r\\n if (reuse):\\r\\n W_2 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_2.csv\\\", header = None).values)\\r\\n b_2 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_2.csv\\\", header = None).values)\\r\\n else: \\r\\n W_2 = weight_variable([10,5])\\r\\n b_2 = bias_variable([5])\\r\\n h_2 = tf.nn.relu(tf.matmul(h_1_drop, W_2) + b_2)\\r\\n h_2_drop = tf.nn.dropout(h_2, drop_hidden_placeholder)\\r\\n\\r\\n\\r\\n #[batch_size x 5] -> [batch_size x 1]\\r\\n if (reuse):\\r\\n W_3 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_3.csv\\\", header = None).values)\\r\\n b_3 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_3.csv\\\", header = None).values)\\r\\n else: \\r\\n W_3 = weight_variable([5,1])\\r\\n b_3 = bias_variable([1])\\r\\n y = tf.matmul(h_2_drop, W_3) + b_3\\r\\n\\r\\n\\r\\n print \\\"making definitions\\\"\\r\\n\\r\\n\\r\\n # y_ is a placeholder for the actual costs. It will later be filled in with label data.\\r\\n y_ = tf.placeholder(tf.float64, [None,], name=\\\"label_placeholder\\\")\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"This is the cost for the neural network (not the pedestrian). Higher cost reflects\\r\\n predictions being further from reality.\\\"\\\"\\\"\\r\\n cost = tf.squared_difference(y, y_)\\r\\n\\r\\n #Accuracy is the average cost for the batch\\r\\n accuracy = tf.reduce_mean(cost) \\r\\n\\r\\n \\\"\\\"\\\"This is where the magic happens. Your optimizer will modify the weight and bias variables\\r\\n to minimize this cost as best it can\\\"\\\"\\\"\\r\\n train_step = tf.train.AdamOptimizer().minimize(cost)\\r\\n\\r\\n #Creates the session in which your model will run\\r\\n sess = tf.InteractiveSession()\\r\\n\\r\\n #In different versions of tensorflow, initialize_all_variables is used instead of global_variables_initializer\\r\\n tf.global_variables_initializer().run() \\r\\n \\r\\n print \\\"training\\\"\\r\\n\\r\\n #Calculating the number of batches you need based on number of samples and batch size\\r\\n num_batches = int(np.floor(train_size/batch_size))\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"The way I've set this up, the entire dataset doesn't get looked at, it can only look at up to a \\r\\n multiple of the batch size. There are ways to account for everything but it isn't super critical to\\r\\n the effectiveness of the model so I've let it slide for now. These printouts give you a sense of how \\r\\n much data you are ignoring\\\"\\\"\\\"\\r\\n print \\\"train size\\\"\\r\\n print train_size\\r\\n print \\\"actual size I'm sampling from\\\"\\r\\n print num_batches*batch_size\\r\\n\\r\\n for j in range (num_epochs): #This for loop iterated over epochs to train the model\\r\\n print \\\"epoch %d of %d\\\" % (j+1, num_epochs)\\r\\n\\r\\n \\\"\\\"\\\"In order to keep consecutive samples independent of each other and allow training to converge better,\\r\\n the dataset must be shuffled before being trained. The following lines shuffle the data once per epoch. \\r\\n It is important that inputs and labels are shuffled in the same order so that their rows still correspond\\r\\n to each other.\\\"\\\"\\\"\\r\\n permutation = np.random.permutation(len(labels_train))\\r\\n\\r\\n print \\\"shuffling\\\"\\r\\n shuffled_inputs_train = np.copy(inputs_train)\\r\\n shuffled_labels_train = np.copy(labels_train)\\r\\n\\r\\n for old_index, new_index in enumerate(permutation):\\r\\n shuffled_labels_train[new_index, 0] = np.copy(labels_train[old_index,0])\\r\\n shuffled_inputs_train[new_index, 0:5] = np.copy(inputs_train[old_index,0:5])\\r\\n \\r\\n print \\\"training\\\"\\r\\n\\r\\n #Iterates training over every batch \\r\\n batch_error_total = 0 #resets the error counter\\r\\n for i in range(num_batches): \\r\\n batch_xs = shuffled_inputs_train[i*batch_size:(i+1)*batch_size,0:5] #takes states and actions for the current batch\\r\\n batch_ys = shuffled_labels_train[i*batch_size:(i+1)*batch_size,0] #takes labels (costs) for the current batch\\r\\n #the line below initiates the training and keeps track of accuracy. The feed_dict shows where to look for placeholder values\\r\\n _, batch_error = sess.run([train_step, accuracy], feed_dict={x: batch_xs, y_: batch_ys, drop_hidden_placeholder: drop_hidden, drop_input_placeholder: drop_input})\\r\\n batch_error_total += float(batch_error)/num_batches #keeps track of error\\r\\n avg_train_error = batch_error_total\\r\\n print \\\"epoch %d has average training error of %d\\\" % (j+1, avg_train_error)\\r\\n \\r\\n\\r\\n print \\\"testing\\\"\\r\\n \\\"\\\"\\\"keeps track of the maximum and minimum predictions. This is useful to see how \\\"adventurous\\\" the model gets in\\r\\n its predictions. A small range means it isn't really making much different precitions every time while a larger range\\r\\n means it is picking up on patterns and is really able to discriminate between high likelihood and low likelihood crash\\r\\n scenarios\\\"\\\"\\\"\\r\\n y_max = tf.reduce_max(y)\\r\\n y_min = tf.reduce_min(y)\\r\\n\\r\\n\\r\\n #Similar to during training, our testing is batched\\r\\n num_batches = int(np.floor(test_size/batch_size))\\r\\n print \\\"test size\\\"\\r\\n print test_size\\r\\n print \\\"actual size I'm sampling from\\\"\\r\\n print num_batches*batch_size\\r\\n\\r\\n batch_error_total = 0\\r\\n ymax = 0\\r\\n ymin = 9999999999999999999999999999\\r\\n for i in range(num_batches): \\r\\n batch_xs = inputs_test[i*batch_size:(i+1)*batch_size,0:5] \\r\\n batch_ys = labels_test[i*batch_size:(i+1)*batch_size,0]\\r\\n batch_error, y_max_, y_min_ = sess.run([accuracy, y_max, y_min], feed_dict={x: batch_xs, y_: batch_ys, drop_input_placeholder: 1, drop_hidden_placeholder: 1})\\r\\n batch_error_total += float(batch_error)/num_batches\\r\\n #updates ymax and ymin from batch\\r\\n if (y_max_ > ymax):\\r\\n ymax = y_max_\\r\\n if (y_min_ < ymin):\\r\\n ymin = y_min_\\r\\n avg_test_error = batch_error_total\\r\\n\\r\\n \\r\\n print(\\\"Test Error: \\\", avg_test_error)\\r\\n print(\\\"Range of Predicted Outputs: \\\", ymin,\\\" - \\\", ymax)\\r\\n\\r\\n w1_ = W_1.eval()\\r\\n b1_ = b_1.eval()\\r\\n w2_ = W_2.eval()\\r\\n b2_ = b_2.eval()\\r\\n w3_ = W_3.eval()\\r\\n b3_ = b_3.eval()\\r\\n\\r\\n \\r\\n #save numpy arrays to CSV\\r\\n np.savetxt(\\\"two_layer_weights_1.csv\\\", w1_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_1.csv\\\", b1_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_weights_2.csv\\\", w2_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_2.csv\\\", b2_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_weights_3.csv\\\", w3_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_3.csv\\\", b3_, delimiter=\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd96f3ccb40454f64b27151c187fc1df\",\n \"score\": \"0.609836\",\n \"text\": \"def gen_imgs_1_to_5(samples, batch_size, crop_size,\\n folder_to_save=False, shuffle=True):\\n\\n num_samples = len(samples)\\n while 1:\\n if shuffle:\\n # if frac = 1 will reorganized list randomly\\n samples = samples.sample(frac=1)\\n\\n for offset in range(0, num_samples, batch_size):\\n batch_samples = samples.iloc[offset:offset+batch_size]\\n images = []\\n labels = []\\n for _, batch_sample in batch_samples.iterrows():\\n img = io.imread(batch_sample.patch_path)\\n img = img[:, :, :3]\\n image_name = osp.splitext(\\n osp.basename(batch_sample.patch_path))[0]\\n\\n img = patch_aug_flip_rotate_crop_1_to_5(\\n img, crop_size, image_name, folder_to_save)\\n # img = patch_aug_flip_rotate_crop(\\n # img, crop_size, image_name, folder_to_save)\\n label = batch_sample['is_tumor']\\n\\n # images.append(img)\\n images.extend(img)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n\\n X_train = np.array(images)\\n y_train = np.array(labels)\\n y_train = to_categorical(y_train, num_classes=2)\\n\\n yield X_train, y_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bd1848fb338eac7a886eda8b7ed8e8\",\n \"score\": \"0.60963017\",\n \"text\": \"def train_CNNs(datasets, input_size, num_of_classes, num_of_epochs, batch_size):\\n i = 0\\n for X_train, y_train, X_test, y_test in datasets:\\n X_train, y_train, X_test, y_test = prepare_data(X_train, y_train, X_test, y_test, input_size)\\n model = simple_cnn(input_size, num_of_classes)\\n model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=batch_size, validation_split=0.1, verbose=2)\\n # model.save('cifar-100_disjoint_dataset_models/cnn_model_%d.h5' % i)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7ca3b812c59bde2937561c954ce682a\",\n \"score\": \"0.6095634\",\n \"text\": \"def build_basic_model():\\n model = Sequential(name='Age_CNN')\\n\\n model.add(Conv2D(filters=64,kernel_size=(5,5),input_shape=(48, 48, 1),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_1'))\\n model.add(BatchNormalization(name='batchnorm_1'))\\n model.add(Conv2D(filters=64,kernel_size=(5,5),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_2'))\\n model.add(BatchNormalization(name='batchnorm_2'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_1'))\\n model.add(Dropout(0.4, name='dropout_1'))\\n\\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_3'))\\n model.add(BatchNormalization(name='batchnorm_3'))\\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_4'))\\n model.add(BatchNormalization(name='batchnorm_4'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_2'))\\n model.add(Dropout(0.4, name='dropout_2'))\\n\\n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_5'))\\n model.add(BatchNormalization(name='batchnorm_5'))\\n \\n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_6'))\\n model.add(BatchNormalization(name='batchnorm_6'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_3'))\\n model.add(Dropout(0.5, name='dropout_3'))\\n\\n model.add(Flatten(name='flatten')) \\n model.add(Dense(128,activation='elu',kernel_initializer='he_normal',name='dense_1'))\\n model.add(BatchNormalization(name='batchnorm_7'))\\n model.add(Dropout(0.6, name='dropout_4'))\\n \\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d2d4b98e214ff4bfdb3fd37ebc29f\",\n \"score\": \"0.608587\",\n \"text\": \"def cross_validate(self, folds):\\n\\t\\tdata_size = len(self.MH.all_dat)\\n\\n\\t\\tif folds == -1:\\n\\t\\t\\tfolds = data_size # Leave one out cross validation\\n\\n\\t\\tgroup_size = int(data_size/folds)\\n\\n\\t\\trand_indices = random.sample([x for x in range(data_size)], data_size)\\n\\n\\t\\tvalidations = {}\\n\\t\\tfor header in self.MH.output_headers:\\n\\t\\t\\tvalidations[header] = []\\n\\n\\t\\t# Go through each fold, train on everything else, and test it\\n\\t\\tfor i in range(folds):\\n\\t\\t\\ttrain_indices = [x for x in (rand_indices[:i*group_size] + rand_indices[(i+1)*group_size:])]\\n\\t\\t\\tself.MH.make_train_data(train_indices)\\n\\t\\t\\tself.forward_model = ForwardModel()\\n\\t\\t\\ttest_indices = rand_indices[i*group_size:(i+1)*group_size]\\n\\t\\t\\ttest_dat = [self.MH.all_dat[x] for x in test_indices]\\n\\t\\t\\tfor i,dat_point in enumerate(test_dat):\\n\\t\\t\\t\\tret_vals = self.validate_model(dat_point)\\n\\t\\t\\t\\tfor header in ret_vals:\\n\\t\\t\\t\\t\\tvalidations[header].append(ret_vals[header]) # Validations dict says how well we did for the point\\n\\n\\t\\t# Data for the cross validation is written out at all_preds_droplet_size.csv and all_preds_generation_rate.csv\\n\\t\\t# This data only has the shown file headers. For bulk statistics (like coefficient of determination), you will\\n\\t\\t# need to run the data through DAFD/model_data/disp_graphs.py. See that file for more information.\\n\\t\\tfor header in validations:\\n\\t\\t\\tfile_headers = [\\\"actual_val\\\" ,\\\"pred_val\\\" ,\\\"deviation\\\", \\\"deviation_percent\\\",\\\"actual_regime\\\" ,\\\"pred_regime\\\" ,\\\"chip_number\\\"]\\n\\t\\t\\twith open(\\\"all_preds_\\\" + header + \\\".csv\\\" ,\\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(\\\",\\\".join(file_headers) + \\\"\\\\n\\\")\\n\\t\\t\\t\\tfor x in validations[header]:\\n\\t\\t\\t\\t\\tf.write(\\\",\\\".join([str(xi) for xi in x]) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1047f260623681f410855f4afdae04d8\",\n \"score\": \"0.6082897\",\n \"text\": \"def cross_validation_split(df, folds=10, is_shuffle=True):\\n dataset_split = []\\n df_copy = shuffle(df) if is_shuffle else df\\n fold_size = int(df_copy.shape[0] / folds)\\n training_dataset = []\\n testing_dataset = []\\n for i in range(folds):\\n fold = []\\n while len(fold) < fold_size:\\n r = randrange(df_copy.shape[0])\\n index = df_copy.index[r]\\n fold.append(df_copy.loc[[index]])\\n df_copy = df_copy.drop(index)\\n dataset_split.append(pd.concat(fold))\\n for i in range(folds):\\n r = list(range(folds))\\n r.pop(i)\\n for j in r :\\n if j == r[0]:\\n cv = dataset_split[j]\\n else: \\n cv=pd.concat([cv,dataset_split[j]])\\n training_data = cv.reset_index(drop=True)\\n testing_data = dataset_split[i].reset_index(drop=True)\\n training_dataset.append(training_data)\\n testing_dataset.append(testing_data)\\n return training_dataset, testing_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e1fba646891df795a989eba6ff3c8f\",\n \"score\": \"0.60722953\",\n \"text\": \"def main():\\n size = 200 #size of the images\\n imdim = size - 20 #strip 10 pixels buffer from each size\\n direct = \\\"../data/images\\\"+str(size)+\\\"/\\\" #directory containing the images\\n ld = listdir(direct) #contents of that directory\\n numEx = len(ld)\\n \\n \\n DUMP_WEIGHTS = True # will we dump the weights of conv layers for visualization\\n \\n shuffle(ld)\\n \\n trainTestSplit = 0.80\\n \\n with open(sys.argv[1]+\\\"testdata.csv\\\",'rb') as f:\\n testFs = [pn for pn in f.read().split(\\\"\\\\n\\\") if pn != '']\\n testL = len(testFs)\\n \\n print \\\"number of examples: \\\", numEx\\n print \\\"test examples : \\\", testL\\n \\n batch_size = 32\\n chunkSize = 2048\\n testChunkSize = testL\\n numTestEx = min(testL,testChunkSize)\\n \\n \\n ecfps = getECFPvecs()\\n atomlist = getAtomList()\\n \\n outsize = len(ecfps[ecfps.keys()[0]])\\n \\n testImages = np.zeros((testChunkSize,1,imdim,imdim),dtype=np.float)\\n testTargets = np.zeros((testChunkSize,outsize),dtype=np.float)\\n \\n \\n with open(sys.argv[1]+\\\"wholeModel.pickle\\\",'rb') as f:\\n model = cPickle.load(f)\\n \\n \\n numIterations = 1\\n for i in range(0,numIterations):\\n shuffle(testFs)\\n count = 0\\n cids = []\\n while count < testChunkSize: \\n for x in testFs: \\n if x.find(\\\".png\\\") > -1:\\n CID = x[:x.find(\\\".png\\\")]\\n cids.append(CID)\\n image = io.imread(direct+x,as_grey=True)[10:-10,10:-10] \\n #image = np.where(image > 0.1,1.0,0.0)\\n testImages[count,0,:,:] = image\\n testTargets[count] = ecfps[CID]\\n count +=1\\n \\n preds = model.predict(testImages)\\n RMSE = np.sqrt(mean_squared_error(testTargets, preds)) \\n ranks = [] \\n print RMSE\\n if RMSE < 300:\\n for ind1 in range(0,len(preds)):\\n p = [preds[ind1][ind2] for ind2 in range(0,len(preds[0]))]\\n t = [int(testTargets[ind1][ind2]) for ind2 in range(0,len(testTargets[0]))]\\n #printFormula(p,t,atomlist,cids[ind1])\\n ranks.append(getRank(cids[ind1],p,ecfps))\\n print ranks[ind1]\\n print np.mean(ranks)\\n with open(\\\"~/CHECKME.txt\\\",'wb') as f:\\n f.write('\\\\n'.join(ranks))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"281c22a40a9ccb649189afa665cbfa75\",\n \"score\": \"0.60718745\",\n \"text\": \"def preprocessing_test():\\n # create folders for test data\\n data_base_path = '../../../datasets/tiny-imagenet-200/'\\n val_path = data_base_path + 'val/'\\n test_path = data_base_path + 'test/'\\n print(val_path)\\n print(test_path)\\n with open(data_base_path + 'wnids.txt', 'r') as wnids:\\n i = 0\\n for line in wnids:\\n i += 1\\n label = line.split('\\\\n')[0]\\n print('%d. label: ' % i + label)\\n val_label_path = val_path + label + '/'\\n test_label_path = test_path + label + '/'\\n if os.path.exists(val_label_path):\\n if not os.path.exists(test_label_path):\\n os.mkdir(test_label_path)\\n # random choose images\\n path = os.listdir(val_label_path)\\n sample = random.sample(path, 25)\\n print(len(sample))\\n print(sample)\\n for name in sample:\\n shutil.move(val_label_path + name, test_label_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56056e5202e7ca56d503117d09dfcc0d\",\n \"score\": \"0.6071002\",\n \"text\": \"def train(batch, epochs, num_classes, size, weights, tclasses):\\n\\n train_generator, validation_generator, count1, count2 = generate(batch, size)\\n\\n train_generator = ImageDataGenerator(\\n rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n rotation_range=90,\\n width_shift_range=0.2,\\n height_shift_range=0.2,\\n brightness_range=(1, 1.3),\\n horizontal_flip=True)\\n \\n directory=\\\"/home/gnss/Desktop/garbage_train\\\"\\n train_generator = MixupImageDataGenerator(generator=train_generator,\\n directory=directory,\\n batch_size=32,\\n img_height=224,\\n img_width=224,\\n subset='training')\\n\\n if weights:\\n model = MobileNetv2((size, size, 3), tclasses)\\n model = fine_tune(num_classes, weights, model)\\n print(num_classes)\\n else:\\n model = MobileNetv2((size, size, 3), num_classes)\\n print(num_classes)\\n\\n opt = Adam(1e-2)\\n # earlystop = EarlyStopping(monitor='val_acc', patience=30, verbose=0, mode='auto')\\n tensorboard = TensorBoard('/home/gnss/Desktop/MobileNetV2/logs',write_images=True)\\n # reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5,patience=3, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)\\n\\n warmup_epoch = 5\\n \\n warm_up_lr = WarmUpCosineDecayScheduler(learning_rate_base=1e-2,\\n total_steps=count1 // batch)\\n\\n checkpointer = ModelCheckpoint(filepath='/home/gnss/Desktop/MobileNetV2/mobilenet.h5',verbose=1,save_best_only=True)\\n\\n model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\\n\\n \\n\\n # lr=XTensorBoard('/home/gnss/Desktop/MobileNetV2/logslr')\\n\\n hist = model.fit_generator(\\n train_generator,\\n validation_data=validation_generator,\\n steps_per_epoch=count1 // batch,\\n validation_steps=count2 // batch,\\n epochs=epochs,\\n callbacks=[warm_up_lr,tensorboard,checkpointer])\\n '''\\n learning_rate_base=1e-2\\n total_steps=count1 // batch\\n plt.plot(warm_up_lr.learning_rates)\\n plt.xlabel('Step', fontsize=20)\\n plt.ylabel('lr', fontsize=20)\\n plt.axis([0, total_steps, 0, learning_rate_base*1.1])\\n plt.xticks(np.arange(0, total_steps, 50))\\n plt.grid()\\n plt.title('Cosine decay with warmup', fontsize=20)\\n plt.show()\\n '''\\n if not os.path.exists('model'):\\n os.makedirs('model')\\n\\n df = pd.DataFrame.from_dict(hist.history)\\n df.to_csv('model/hist.csv', encoding='utf-8', index=False)\\n model.save_weights('model/weights.h5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabcd943cbdaf1e3270a11e84444f668\",\n \"score\": \"0.6066594\",\n \"text\": \"def _train_and_test_classifier(train_data, train_labels, test_data, test_labels, classifier_model):\\n if classifier_model == 'random_forest':\\n model = RandomForestClassifier()\\n model.fit(train_data, train_labels)\\n # out = codecs.open('/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/model', 'wb', 'utf-8')\\n # joblib.dump(model, '/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/test/model')\\n # out.close()\\n predicted_labels = model.predict(test_data)\\n print predicted_labels\\n predicted_probabilities = model.predict_proba(test_data)\\n # print predicted_labels[0:10]\\n # print predicted_probabilities[0:10]\\n elif classifier_model == 'knn':\\n k = 1\\n model = neighbors.KNeighborsClassifier(n_neighbors=k, weights='uniform')\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n predicted_probabilities = model.predict_proba(test_data)\\n elif classifier_model == 'manual_knn':\\n # this is not an scikit-learn model; does not support predicted_probabilities\\n k = 5\\n predicted_labels = list()\\n # print len(test_data)\\n for t in test_data:\\n scores_dict = dict()\\n for i in range(0, len(train_data)):\\n score = SimFunctions.SimFunctions.abs_dot_product_sim(train_data[i], t)\\n label = train_labels[i]\\n if score not in scores_dict:\\n scores_dict[score] = list()\\n scores_dict[score].append(label)\\n results = kNearestNeighbors._extract_top_k(scores_dict, k=k)\\n predicted_labels.append(TokenSupervised._compute_majority_label_in_vector(results))\\n predicted_labels = np.array(predicted_labels)\\n elif classifier_model == 'logistic_regression':\\n model = LogisticRegression()\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n predicted_probabilities = model.predict_proba(test_data)\\n elif classifier_model == 'linear_regression': # this is a regressor; be careful.\\n model = LinearRegression()\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n print 'AUC (Area Under Curve): ',\\n print roc_auc_score(test_labels, predicted_labels)\\n # precision, recall, thresholds = precision_recall_curve(test_labels, predicted_labels)\\n # plt.clf()\\n # plt.plot(recall, precision, label='precision-recall-curve')\\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('Precision-Recall curve')\\n # plt.savefig('/home/mayankkejriwal/Downloads/memex-cp4-october/tmp/fig.png')\\n if classifier_model not in ['linear_regression']:\\n print 'Accuracy: ',\\n print accuracy_score(test_labels, predicted_labels)\\n # print precision_score(test_labels, predicted_labels)\\n prf = ['Precision: ', 'Recall: ', 'F-score: ', 'Support: ']\\n print 'Class 0\\\\tClass 1'\\n k = precision_recall_fscore_support(test_labels, predicted_labels)\\n for i in range(0, len(k)):\\n print prf[i],\\n print k[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac78e1d8cb9eb12f562d133ce490d5c\",\n \"score\": \"0.6062178\",\n \"text\": \"def main():\\n\\n #I/O files\\n masterTrainingData = \\\"train_sample.csv\\\"\\n masterTestData = \\\"test.csv\\\"\\n sampleTrainingData = \\\"train_sample_10000.csv\\\"\\n submissionTemplate = \\\"sample_submission.csv\\\"\\n submissionOutput = \\\"mySubmission.csv\\\"\\n \\n # fix random seed for reproducibility\\n seed = 69\\n np.random.seed(seed)\\n\\n #load training data from csv\\n dataframe = pd.read_csv(masterTrainingData, header=0)\\n\\n # split into input (X) and output (Y) variables\\n x_train_master, y_train_master = preprocessTraining(dataframe);\\n\\n #print(x_train)\\n #print(len(x_train))\\n #print(y_train)\\n \\n #downsample to avoid unbalanced data\\n dataframe_train_neg = dataframe[(dataframe['is_attributed'] == 0)]\\n dataframe_train_pos = dataframe[(dataframe['is_attributed'] == 1)]\\n print(len(dataframe_train_neg))\\n print(len(dataframe_train_pos))\\n\\n dataframe_train_neg_sample = dataframe_train_neg.sample(n=4000)\\n dataframe_train_pos_sample = dataframe_train_pos.sample(n=227)\\n \\n print(len(dataframe_train_neg_sample))\\n print(len(dataframe_train_pos_sample))\\n\\n dataframe_train_comb = pd.concat([dataframe_train_neg_sample,dataframe_train_pos_sample])\\n x_train, y_train = preprocessTraining(dataframe_train_comb)\\n \\n #submission = pd.read_csv(submissionTemplate)\\n #submission['is_attributed'] = y_predss\\n #submission.to_csv(submissionOutput, index=False)\\n #print(submission.head())\\n\\n # create model\\n #model = Sequential()\\n #model.add(Dense(6, input_dim=9, kernel_initializer='normal', activation='relu'))\\n #model.add(Dense(3, kernel_initializer='normal', activation='relu'))\\n #model.add(Dense(1, kernel_initializer='normal', activation='tanh'))\\n # Compile model\\n #model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy', 'sparse_categorical_accuracy'])\\n #model.fit(x_train, y_train, epochs=8, batch_size=64)\\n \\n model = KerasRegressor(build_fn = create_model)\\n layers = [[5], [6,3], [6,5,4,3]]\\n activations = [relu, sigmoid]\\n param_grid = dict(layers = layers, activation = activations, batch_size = [32, 64], epochs = [4])\\n grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'neg_mean_squared_error')\\n\\n grid_result = grid.fit(x_train, y_train)\\n print(grid_result.best_score_)\\n print(grid_result.best_params_)\\n #[grid_result.best_score_, grid_result.best_params_]\\n\\n model.fit(x_train, y_train)\\n results = model.predict(x_train)\\n results = np.where(results > 0.5, 1, 0)\\n\\n negCount = 0\\n posCount = 0\\n for i in range(0, len(results)):\\n if results[i][0] == 1:\\n posCount += 1\\n else:\\n negCount += 1\\n print(negCount)\\n print(posCount)\\n \\n score = model.evaluate(x_train_master, y_train_master, batch_size=128)\\n print(score)\\n \\n #test on full training data\\n results = model.predict(x_train_master)\\n results = np.where(results > 0.5, 1, 0)\\n \\n negCount = 0\\n posCount = 0\\n for i in range(0, len(results)):\\n if results[i][0] == 1:\\n posCount += 1\\n else:\\n negCount += 1\\n print(negCount)\\n print(posCount)\\n false_positive_rate, recall, thresholds = roc_curve(y_train_master, results)\\n roc_auc = auc(false_positive_rate, recall)\\n print(roc_auc)\\n \\n #dataframe = pd.read_csv(masterTestData, header=0)\\n #x_test = preprocessTest(dataframe)\\n\\n #myPredictions = model.predict(x_test)\\n #myPredictions = np.where(myPredictions > 0.5, 1, 0)\\n\\n #output to a submission file\\n #mySubmission = pd.read_csv(submissionTemplate)\\n #mySubmission['is_attributed'] = myPredictions\\n #mySubmission.to_csv('mySubmission.csv', index=False)\\n #print(mySubmission.head())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcacfc998e9f4ca1dfd924c0507673c\",\n \"score\": \"0.60598314\",\n \"text\": \"def FIDbenchmarking(models, df_ori, dropdummies = None, num_vars = None):\\n \\n # Set X and y for original data\\n X_ori = df_ori.iloc[:,:-1]\\n y_ori = df_ori.iloc[:,-1]\\n\\n # Split data up into 5 folds to execute gridsearch on 5 different folds to check stability of classifiers\\n\\n # Split into 5 stratified folds, so 5 \\\"different\\\" original sets with corresponding holdout validation sets are created\\n skf = StratifiedKFold(n_splits=5, random_state=8, shuffle=True)\\n\\n # Save indices per split so separate dataframes can be saved later\\n val_idx = []\\n for train_index, test_index in skf.split(X_ori, y_ori):\\n val_idx.append(test_index)\\n\\n # Create the 5 folds by saving the 5 validation sets\\n fold_1 = df_ori.loc[val_idx[0]]\\n fold_2 = df_ori.loc[val_idx[1]]\\n fold_3 = df_ori.loc[val_idx[2]]\\n fold_4 = df_ori.loc[val_idx[3]]\\n fold_5 = df_ori.loc[val_idx[4]]\\n\\n folds = [fold_1, fold_2, fold_3, fold_4, fold_5]\\n\\n i=1\\n DTscores = []\\n LRscores = []\\n for fold in folds:\\n print(\\\"fold\\\", i)\\n DTscores.extend((\\\"fold\\\", i))\\n LRscores.extend(('fold', i))\\n\\n for model in models:\\n pipe = Pipeline([('classifier' , model)])\\n\\n # Drop dummy variables in case of regression\\n fold_copy = fold.copy()\\n if model.__class__.__name__ == 'LogisticRegression':\\n if dropdummies == None:\\n print(\\\"Warning: no dummy columns specified to drop while regression is applied!\\\")\\n else:\\n df_ori_copy = fold_copy.drop(columns=dropdummies);\\n print(\\\"Dummy columns dropped:\\\", dropdummies)\\n\\n # Scale numerical variables in case of regression\\n if model.__class__.__name__ == 'LogisticRegression':\\n if num_vars == None:\\n print(\\\"Warning: no numerical variables specified to scale while regression is applied!\\\")\\n else:\\n fold_copy[num_vars] = preprocessing.minmax_scale(fold_copy[num_vars].astype(np.float64))\\n print(\\\"Numerical variables scaled:\\\", num_vars)\\n\\n # Set X and y for the fold for this model\\n X = fold_copy.iloc[:,:-1]\\n y = fold_copy.iloc[:,-1]\\n\\n # Create param grid\\n if model.__class__.__name__ == 'LogisticRegression':\\n param_grid = [\\n {'classifier' : [model],\\n 'classifier__penalty' : ['l2'],\\n 'classifier__C' : [0.0001, .001, .01, .1, 1, 10, 100],\\n 'classifier__solver' : ['lbfgs'],\\n 'classifier__max_iter' : [4000]}\\n ]\\n elif model.__class__.__name__ == 'DecisionTreeClassifier':\\n param_grid = [\\n {'classifier' : [model],\\n 'classifier__criterion' : ['entropy'], # both (also gini) are considered, but after experimentation to speed up the process only entropy is kept\\n 'classifier__min_samples_leaf' : [0.005], # Avoid overfitting by minimizing the number of samples in leaf node\\n 'classifier__max_depth' : list(range(1,6))} # Avoid overfitting by limiting the depth\\n ]\\n else:\\n print(\\\"param grid not available for this model\\\")\\n\\n # Create grid search object with random state for cv folds so DT and LR are tested on same folds\\n clf = GridSearchCV(pipe, param_grid = param_grid, cv = StratifiedKFold(5, shuffle=True, random_state=2))\\n\\n print(\\\"model\\\", model)\\n\\n # Fit on data\\n best_clf_ori = clf.fit(X, y)\\n\\n best_mean = best_clf_ori.cv_results_['mean_test_score'][best_clf_ori.best_index_] \\n best_std = best_clf_ori.cv_results_['std_test_score'][best_clf_ori.best_index_] \\n\\n print(\\\"best parameters:\\\", best_clf_ori.best_estimator_.get_params()['classifier'])\\n print(\\\"mean\\\", best_mean)\\n print(\\\"std\\\", best_std)\\n \\n if model.__class__.__name__ == 'LogisticRegression':\\n LRscores.extend((\\\"mean\\\", best_mean, \\\"std\\\", best_std))\\n elif model.__class__.__name__ == 'DecisionTreeClassifier': \\n DTscores.extend((\\\"mean\\\", best_mean, \\\"std\\\", best_std))\\n i+=1\\n\\n return LRscores, DTscores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294bbf1213338bdd9c9735f086c160b5\",\n \"score\": \"0.60585856\",\n \"text\": \"def crossvalidation(input_data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e551fa8e33e7f8992c3da7730c1ec9d\",\n \"score\": \"0.6049004\",\n \"text\": \"def training():\\n # red color training images\\n for f in os.listdir('../data/training_dataset/red'):\\n color_histogram_of_training_image('../data/training_dataset/red/' + f)\\n\\n # yellow color training images\\n for f in os.listdir('../data/training_dataset/yellow'):\\n color_histogram_of_training_image('../data/training_dataset/yellow/' + f)\\n\\n # green color training images\\n for f in os.listdir('../data/training_dataset/green'):\\n color_histogram_of_training_image('../data/training_dataset/green/' + f)\\n\\n # orange color training images\\n for f in os.listdir('../data/training_dataset/orange'):\\n color_histogram_of_training_image('../data/training_dataset/orange/' + f)\\n\\n # white color training images\\n for f in os.listdir('../data/training_dataset/white'):\\n color_histogram_of_training_image('../data/training_dataset/white/' + f)\\n\\n # black color training images\\n for f in os.listdir('../data/training_dataset/black'):\\n color_histogram_of_training_image('../data/training_dataset/black/' + f)\\n\\n # blue color training images\\n for f in os.listdir('../data/training_dataset/blue'):\\n color_histogram_of_training_image('../data/training_dataset/blue/' + f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8306dae9d8431df121243b23398e11f9\",\n \"score\": \"0.60411876\",\n \"text\": \"def training(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30405d30ee3c4c458cb2f520effae2aa\",\n \"score\": \"0.6039188\",\n \"text\": \"def random_search(self, model, fold_n, _iter, scaled_X_train, fold_y_train, scaled_X_test, fold_y_test, params):\\n if model == \\\"NN\\\":\\n self.clfs[model] = keras.Sequential(\\n [\\n keras.layers.Dense(\\n units=params[\\\"NN\\\"][_iter][\\\"neurons1\\\"],\\n input_shape=(scaled_X_train.shape[1],),\\n activation=params[\\\"NN\\\"][_iter][\\\"activation\\\"],\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n ]\\n )\\n self.clfs[model].add(BatchNormalization())\\n self.clfs[model].add(Dropout(params[\\\"NN\\\"][_iter][\\\"dropout_rate\\\"]))\\n if params[\\\"NN\\\"][_iter][\\\"n_layers\\\"] == 2:\\n self.clfs[model].add(\\n layers.Dense(\\n units=params[\\\"NN\\\"][_iter][\\\"neurons2\\\"],\\n activation=params[\\\"NN\\\"][_iter][\\\"activation\\\"],\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n )\\n self.clfs[model].add(Dropout(params[\\\"NN\\\"][_iter][\\\"dropout_rate\\\"]))\\n self.clfs[model].add(\\n layers.Dense(\\n 1,\\n activation=\\\"sigmoid\\\",\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n )\\n\\n opt = adam_v2.Adam(\\n learning_rate=params[\\\"NN\\\"][_iter][\\\"learn_rate\\\"],\\n beta_1=params[\\\"NN\\\"][_iter][\\\"beta_1\\\"],\\n beta_2=params[\\\"NN\\\"][_iter][\\\"beta_1\\\"],\\n )\\n self.clfs[model].compile(loss=\\\"binary_crossentropy\\\", optimizer=opt, metrics=[keras.metrics.AUC()])\\n\\n # fit model\\n self.clfs[model].fit(\\n scaled_X_train,\\n fold_y_train,\\n batch_size=params[\\\"NN\\\"][_iter][\\\"batch_size\\\"],\\n epochs=params[\\\"NN\\\"][_iter][\\\"epochs\\\"],\\n shuffle=False,\\n verbose=False,\\n )\\n # predict\\n fold_pred = self.clfs[model].predict(scaled_X_test)\\n fold_pred = np.concatenate(fold_pred).ravel().tolist()\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n params[\\\"NN\\\"][_iter],\\n auc_score,\\n ]\\n elif model == \\\"XGBoost\\\":\\n self.clfs[model].set_params(**params[model][_iter])\\n # fit model\\n self.clfs[model].fit(scaled_X_train, fold_y_train, eval_metric=\\\"auc\\\")\\n # predict\\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[::, 1]\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n self.clfs[model].get_params(),\\n auc_score,\\n ]\\n else:\\n self.clfs[model].set_params(**params[model][_iter])\\n # fit model\\n self.clfs[model].fit(scaled_X_train, fold_y_train)\\n # predict\\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[:, 1]\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n self.clfs[model].get_params(),\\n auc_score,\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15825037f710339d5328e69419d7995b\",\n \"score\": \"0.60389423\",\n \"text\": \"def train(train_data, test_data, foldid: int = 0):\\n # Setup invariant repositories\\n # we'll take I many examples for each task with different answers for each fold\\n invariants = [select_invariants(train_data, i) for i in range(1, 5)] # T x (I, 1+L+1)\\n invariants = np.stack(invariants) # (T, I, 1+L+1)\\n # ---------------------------\\n # Setup model\\n model = UMLP(invariants)\\n cmodel = Classifier(model)\\n optimiser = C.optimizers.Adam(alpha=ARGS.learning_rate).setup(cmodel)\\n train_iter = C.iterators.SerialIterator(train_data, ARGS.batch_size)\\n updater = T.StandardUpdater(train_iter, optimiser, device=-1)\\n trainer = T.Trainer(updater, (2000, 'iteration'), out='results/umlp_result')\\n # ---------------------------\\n fname = (ARGS.name.format(foldid=foldid) if ARGS.name else '') or ('debug' if ARGS.debug else '') or str(uuid.uuid4())\\n # Setup trainer extensions\\n if ARGS.debug:\\n trainer.extend(print_vmap, trigger=(200, 'iteration'))\\n test_iter = C.iterators.SerialIterator(test_data, 128, repeat=False, shuffle=False)\\n trainer.extend(T.extensions.Evaluator(test_iter, cmodel, device=-1), name='test', trigger=(10, 'iteration'))\\n # trainer.extend(T.extensions.snapshot(filename=fname+'_latest.npz'), trigger=(100, 'iteration'))\\n trainer.extend(T.extensions.LogReport(log_name=fname+'_log.json', trigger=(10, 'iteration')))\\n trainer.extend(T.extensions.FailOnNonNumber())\\n train_keys = ['uloss', 'igloss', 'oloss', 'uacc', 'igacc', 'oacc', 'vloss']\\n test_keys = ['uloss', 'oloss', 'uacc', 'oacc']\\n trainer.extend(T.extensions.PrintReport(['iteration'] + ['main/'+k for k in train_keys] + ['test/main/'+k for k in test_keys] + ['elapsed_time']))\\n # ---------------------------\\n print(f\\\"---- FOLD {foldid} ----\\\")\\n try:\\n trainer.run()\\n except KeyboardInterrupt:\\n if not ARGS.debug:\\n return\\n # Save run parameters\\n params = ['length', 'symbols', 'invariants', 'embed', 'train_size', 'learning_rate', 'nouni', 'batch_size']\\n params = {k: vars(ARGS)[k] for k in params}\\n params['name'] = fname\\n params['foldid'] = foldid\\n with open(trainer.out + '/' + fname + '_params.json', 'w') as f:\\n json.dump(params, f)\\n # Save learned invariants\\n with open(trainer.out + '/' + fname + '.out', 'w') as f:\\n f.write(\\\"---- META ----\\\\n\\\")\\n train_data = np.stack(train_data)\\n test_data = np.stack(test_data)\\n meta = {'train': train_data.shape, 'train_tasks': np.unique(train_data[:,0], return_counts=True),\\n 'test': test_data.shape, 'test_tasks': np.unique(test_data[:,0], return_counts=True),\\n 'foldid': foldid}\\n f.write(str(meta))\\n f.write(\\\"\\\\n---- INVS ----\\\\n\\\")\\n f.write(str(model.inv_examples))\\n f.write(\\\"\\\\n--------\\\\n\\\")\\n f.write(str(model.log['vmap'][0].array))\\n for t in range(1, TASKS+1):\\n f.write(\\\"\\\\n---- SAMPLE ----\\\\n\\\")\\n test_data = np.stack(test_data) # (S, 1+L+1)\\n np.random.shuffle(test_data)\\n batch = test_data[test_data[:, 0] == t][:4] # (B, 1+L+1)\\n f.write(\\\"Input:\\\\n\\\")\\n f.write(str(batch))\\n out = model(batch) # (B, V)\\n f.write(\\\"\\\\nOutput:\\\\n\\\")\\n f.write(str(out.array))\\n f.write(\\\"\\\\nAtt:\\\\n\\\")\\n f.write(str(model.log['uniatt'][0].array))\\n f.write(\\\"\\\\n---- END ----\\\\n\\\")\\n if ARGS.debug:\\n print(batch)\\n import ipdb; ipdb.set_trace()\\n out = model(batch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b66734712b88ba72ea46c914b473c20b\",\n \"score\": \"0.6036707\",\n \"text\": \"def load_6_class_classifier():\\n\\n required_input_shape = (*IMAGE_DIMS, IMAGE_NUM_CHANNELS)\\n\\n model = Sequential()\\n model.add(Conv2D(32, (3, 3), padding='valid', input_shape=required_input_shape))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Conv2D(64, (3, 3)))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Conv2D(128, (3, 3)))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Dropout(0.5))\\n\\n model.add(Flatten())\\n \\n model.add(Dense(2048))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(Dropout(0.5))\\n\\n \\n model.add(Dense(256))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(Dropout(0.6))\\n \\n model.add(Dense(6))\\n model.add(Activation('softmax'))\\n model.summary()\\n\\n # load the model weights\\n model.load_weights(configurations.six_class_color_cnn_model_location)\\n \\n adam = optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\\n model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e133f36ecf91955fb752183002c422c1\",\n \"score\": \"0.6036274\",\n \"text\": \"def setup_dataset(self):\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n train_path = \\\"small_dataset/images/nir/\\\"\\r\\n val_path = \\\"small_dataset/images/nir/\\\"\\r\\n test_path = \\\"small_dataset/images/nir/\\\"\\r\\n\\r\\n train_labels_path = \\\"small_dataset/labels/\\\"\\r\\n val_labels_path = \\\"small_dataset/labels/\\\"\\r\\n test_labels_path = \\\"small_dataset/labels/\\\"\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n train_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n val_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n test_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n\\r\\n train_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n val_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n test_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n\\r\\n train = os.listdir(train_path)\\r\\n val = os.listdir(val_path)\\r\\n test = os.listdir(test_path)\\r\\n\\r\\n random.shuffle(train)\\r\\n random.shuffle(val)\\r\\n random.shuffle(test)\\r\\n\\r\\n train_img_names_index = train[:10000]\\r\\n val_img_names_index = val[:2000]\\r\\n test_img_names_index = test[:2000]\\r\\n\\r\\n labels_one_hot = {}\\r\\n k = self.cfg.classes # 8\\r\\n i=0\\r\\n for label in listdir_nohidden(train_labels_path):\\r\\n if label!=\\\"storm_damage\\\":\\r\\n labels_one_hot[label] = np.zeros((k,))\\r\\n labels_one_hot[label][i] = 1\\r\\n i+=1\\r\\n\\r\\n train_dataset = SegmentationDataset(\\\"train\\\", train_img_names_index, labels_one_hot, train_path, train_labels_path, use_cache=True)\\r\\n val_dataset = SegmentationDataset(\\\"validation\\\", val_img_names_index, labels_one_hot, val_path, val_labels_path, use_cache=True)\\r\\n # test_dataset = SegmentationDataset(\\\"test\\\", test_img_names_index, labels_one_hot, test_path, test_labels_path, use_cache=True)\\r\\n\\r\\n self.train_dataloader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\\r\\n self.val_dataloader = DataLoader(val_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\\r\\n # self.test_dataloader = DataLoader(test_dataset, batch_size=self.batch_size)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5702df2c52b31ca43f0ceeda852595\",\n \"score\": \"0.6029016\",\n \"text\": \"def testUnseenDES2017(model, neural_network, n_splits): # from last time everything worked\\n\\n # def testUnseenDES2017(model, neural_network, n_splits, input_shape):\\n\\n known_des2017_array = getUnseenDES2017()\\n\\n num = 47\\n unknown_array = getUnseenNegative(num)\\n\\n image_test, labels_test = loadImage(known_des2017_array, unknown_array)\\n print(\\\"Image test shape: \\\", str(image_test.shape))\\n image_test = image_test.reshape(image_test.shape[0], input_shape[0], input_shape[1], input_shape[2])\\n\\n encoder = LabelEncoder()\\n y_image_labels = encoder.fit_transform(labels_test)\\n\\n y_pred = model.predict(image_test)\\n\\n y_test_index = np.round(y_pred)\\n ones = np.count_nonzero(y_test_index == 1)\\n zeroes = np.count_nonzero(y_test_index == 0)\\n\\n print(\\\"Ones: %s / 47\\\" % ones)\\n print(\\\"Zeroes: %s / 47\\\" % zeroes)\\n\\n # Get Accuracy Score tests DES2017 on the mlpclassifier:\\n _, acc = model.evaluate(image_test, y_image_labels, verbose=0)\\n accuracy_score_47 = acc * 100\\n print(\\\"Accuracy Score_47: \\\" + str(accuracy_score_47))\\n\\n # get the k fold accuracy after k fold cross validation\\n scores = cross_val_score(neural_network, image_test, y_image_labels, scoring='accuracy', cv=n_splits)\\n scores_mean = scores.mean() * 100\\n print(\\\"kFold47 Scores Mean: \\\" + str(scores_mean))\\n k_fold_std_47 = scores.std()\\n print(\\\"kFold47 Scores Std: \\\" + str(k_fold_std_47))\\n k_fold_accuracy_47 = scores_mean\\n\\n return known_des2017_array, accuracy_score_47, k_fold_accuracy_47, k_fold_std_47\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdaa765ddb0e0fc7523a54313636171\",\n \"score\": \"0.6024136\",\n \"text\": \"def test_train_model(self):\\n opt = {\\n 'model': 'projects.self_feeding.self_feeding_agent:SelfFeedingAgent',\\n 'task': 'self_feeding:all',\\n 'max_train_time': 120,\\n 'dia_train': 'train_hh131k_hb60k.txt',\\n 'n_layers': 2,\\n 'n_heads': 2,\\n 'candidates': 'batch',\\n 'validation_metric': 'dia_acc',\\n 'optimizer': 'adamax',\\n 'learning_rate': 0.0025,\\n 'ffn_size': 32,\\n 'batchsize': 32,\\n 'embeddings_scale': False,\\n }\\n _, _, _ = testing_utils.train_model(opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de5956f3a979ffe84b3f90233b11164\",\n \"score\": \"0.60239935\",\n \"text\": \"def fit(data, n1, lr, bs, bayes_scope, model_num, args):\\n tf.set_random_seed(1234) # set tf seed to keep same weight initialization for each model\\n n_epochs = args.n_epochs # number of epochs\\n\\n # track losses and accuracies of each fold\\n loss = []\\n accuracy = []\\n test_list = []\\n # perform cross validation\\n with temp_seed(0):\\n for counter in range(len(data.y_train_list)):\\n bayes_scope += 1\\n with tf.Session() as sess:\\n x_train, y_train, x_test, y_test = data.return_cross_val(counter)\\n if args.noise:\\n x_train, y_train = data.training_augment(y_train, args.n_aug)\\n low_loss, low_accuracy = run(sess, x_train, y_train, x_test, y_test, n1, lr, bs, n_epochs, str(bayes_scope), counter, model_num)\\n loss.append(low_loss)\\n accuracy.append(low_accuracy)\\n sess.close()\\n tf.reset_default_graph()\\n test_list.append(y_test)\\n\\n # calculate mean and std of losses and accuracies across the folds\\n loss = np.array(loss)\\n accuracy = np.array(accuracy)\\n loss_mean = np.mean(loss)\\n loss_std = np.std(loss)\\n accuracy_mean = np.mean(accuracy)\\n accuracy_std = np.std(accuracy)\\n\\n return loss, accuracy, loss_mean, loss_std, accuracy_mean, accuracy_std, test_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea65cd17178461f0d9488bb540e9918\",\n \"score\": \"0.60236037\",\n \"text\": \"def prepare_train_test_split(self, test_pct, val_pct, seed):\\n\\n\\n if self.data is None:\\n raise Exception('Error: Must preporcess data first')\\n\\n features = list(self.data.columns)\\n features.remove(\\\"genre\\\")\\n\\n #split to features and labels\\n X = self.data[features].values\\n y = self.data[\\\"genre\\\"].values\\n\\n #split the dataset to train, validation and test datasets\\n X_train_val, self.X_test, y_train_val, self.y_test = train_test_split(X, y, test_size=test_pct, shuffle=True,\\n stratify=y, random_state=seed)\\n self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(X_train_val, y_train_val,\\n test_size=val_pct, shuffle=True,\\n stratify=y_train_val,random_state=seed)\\n\\n print(f\\\"Loaded train dataset containing: {len(self.X_train)} samples\\\")\\n print(f\\\"Loaded validation dataset containing: {len(self.X_val)} samples\\\")\\n print(f\\\"Loaded test dataset containing: {len(self.X_test)} samples\\\")\\n\\n images_files_df = pd.read_csv(os.path.join(self.image_source_path, self.source_images_list))\\n\\n\\n self.prepare_images_genres('images-train', self.X_train, self.y_train,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n self.prepare_images_genres('images-val', self.X_val, self.y_val,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n self.prepare_images_genres('images-test', self.X_test, self.y_test,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n\\n print(f\\\"All images prepared in folders\\\")\\n\\n #increase the dimention of labels to be 2 dim array\\n self.y_train = self.y_train[:, np.newaxis]\\n self.y_val = self.y_val[:, np.newaxis]\\n self.y_test = self.y_test[:, np.newaxis]\\n\\n from . import dump_datasets_to_pickle, load_datasets_from_pickle\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_train, self.y_train)), \\\"train.pkl\\\")\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_val, self.y_val)), \\\"val.pkl\\\")\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_test, self.y_test)), \\\"test.pkl\\\")\\n print(f\\\"Dataset splits saved as pickle files\\\")\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244f93f0465b452b71453a0937425bb8\",\n \"score\": \"0.6017135\",\n \"text\": \"def train_cnn_old(self, img_folder,check_folder=params.directories['cnn_checkpoint_weights'],pre_trained_weight_path=''):\\n if os.path.exists(check_folder)==0:\\n os.mkdir(check_folder) \\n img_file_t, lable_t,visit_file_t, img_file_v, label_v, visit_file_v=load_class_balanced_files(img_folder,max_samples=-100)\\n \\n nb_epoch=500\\n batch_size=round(128)\\n n_batch_per_epoch=len(img_file_t)//batch_size\\n\\n num_val_sample=len(img_file_v)\\n n_batch_per_epoch_val=num_val_sample//batch_size\\n # data_train, label_train=input_load_train_data(img_file_t,visit_file_t, lable_t)\\n # data_val, label_val=input_load_train_data(img_file_v,visit_file_v, label_v)\\n train_generator=input_generator(img_file_t, lable_t,visit_file_t, batch_size)\\n val_generator = input_generator(img_file_v,label_v,visit_file_v,batch_size)\\n\\n model = get_cnn_model(self.params)\\n #model = make_parallel(model, 4)\\n if len(pre_trained_weight_path)>1:\\n model.load_weights(pre_trained_weight_path,True)\\n print('use pre-trained weights: ',pre_trained_weight_path) \\n model.compile(optimizer=Adam(lr=self.params.cnn_adam_learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])\\n\\n print(\\\"training\\\")\\n filePath = os.path.join(check_folder, 'weights.{epoch:02d}.hdf5')\\n checkpoint = ModelCheckpoint(filepath=filePath, monitor='loss', verbose=0, save_best_only=False,\\n save_weights_only=False, mode='auto', period=5)\\n csv_logger = CSVLogger(os.path.join(check_folder,'train.csv'))\\n callbacks_list = [csv_logger,checkpoint]\\n\\n model.fit_generator(train_generator, steps_per_epoch=n_batch_per_epoch,\\n validation_data=val_generator, validation_steps=n_batch_per_epoch_val,\\n epochs=nb_epoch, callbacks=callbacks_list)\\n\\n #model.fit(data_train,label_train, batch_size=batch_size,validation_data=(data_val, label_val),\\n # epochs=nb_epoch, callbacks=callbacks_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25604d96783bc22ea17ba269d69f351c\",\n \"score\": \"0.601373\",\n \"text\": \"def do_kfold_validation(x, y, x_test, y_test):\\n # initializes kfold with 5 folds, including shuffling,\\n # using 9 as seed for the shuffling\\n kfold = KFold(n_splits=5, random_state=9, shuffle=True)\\n\\n # uses a Gaussian Naive Bayes classifier\\n model = GaussianNB()\\n\\n # splits datasets into folds and trains the model\\n for train_index, val_index in kfold.split(x):\\n x_train, x_val = x.loc[train_index], x.loc[val_index]\\n y_train, y_val = y.loc[train_index], y.loc[val_index]\\n # training of the model\\n model.fit(x_train, y_train)\\n\\n evaluate_accuracy(model, x_train, x_val, x_test, y_train, y_val, y_test)\\n # plot_correlation(x)\\n # plot_frequency(x, 'hesitations')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a45402288d6f35db88557c8e6796d2\",\n \"score\": \"0.60123587\",\n \"text\": \"def valid_model(model,device,cls_cri):\\n # test datasets\\n test_set = cubdataset.CubDataset(config.DATA_ROOT,training = False,resize = config.RESIZE,crop = config.CROP)\\n test_loader = DataLoader(test_set,1,shuffle = False,num_workers = 1)\\n\\n # valid run.\\n low_cls_loss = 0\\n mid_cls_loss = 0\\n hig_cls_loss = 0\\n coa_cls_loss = 0\\n com_cls_loss = 0\\n\\n low_acc = 0\\n mid_acc = 0\\n hig_acc = 0\\n coa_acc = 0\\n com_acc = 0\\n fine_acc = 0\\n ens_acc = 0\\n total_num = len(test_loader)\\n\\n # change to eval mode.\\n model.eval()\\n with torch.no_grad():\\n for data in tqdm.tqdm(test_loader,desc = \\\"valid\\\"):\\n # data\\n imgs,targets = data\\n imgs = imgs.to(device)\\n targets = targets.to(device)\\n\\n # forward\\n low_logits,mid_logits,hig_logits,coa_logits,com_logits = model(imgs)\\n low_loss,mid_loss,hig_loss,coa_loss,com_loss = \\\\\\n cls_cri(low_logits,targets),cls_cri(mid_logits,targets),\\\\\\n cls_cri(hig_logits,targets),cls_cri(coa_logits,targets),cls_cri(com_logits,targets)\\n\\n low_cls_loss += low_loss.item()\\n mid_cls_loss += mid_loss.item()\\n hig_cls_loss += hig_loss.item()\\n coa_cls_loss += coa_loss.item()\\n com_cls_loss += com_loss.item()\\n\\n # evaluation.\\n b = targets.size()[0]\\n fine_logits = coa_logits + com_logits\\n ens_logits = low_logits + mid_logits + hig_logits + coa_logits + com_logits\\n total_logits = torch.cat((low_logits,mid_logits,hig_logits,coa_logits,com_logits,fine_logits,ens_logits),dim = 0)\\n total_probs = torch.softmax(total_logits,dim = -1)\\n total_preds = torch.argmax(total_probs,dim = -1)\\n\\n low_preds,mid_preds,hig_preds,coa_preds,com_preds,fine_preds,ens_preds = torch.split(total_preds,[b,b,b,b,b,b,b],dim = 0)\\n low_acc += torch.sum(low_preds == targets).item()\\n mid_acc += torch.sum(mid_preds == targets).item()\\n hig_acc += torch.sum(hig_preds == targets).item()\\n coa_acc += torch.sum(coa_preds == targets).item()\\n com_acc += torch.sum(com_preds == targets).item()\\n fine_acc += torch.sum(fine_preds == targets).item()\\n ens_acc += torch.sum(ens_preds == targets).item()\\n\\n return low_cls_loss/total_num,mid_cls_loss/total_num,hig_cls_loss/total_num,\\\\\\n coa_cls_loss/total_num,com_cls_loss/total_num,\\\\\\n low_acc/total_num * 100,mid_acc/total_num * 100,hig_acc/total_num * 100,\\\\\\n coa_acc/total_num * 100,com_acc/total_num * 100,fine_acc/total_num * 100,ens_acc/total_num * 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ab4fdaf53fbfcf52709f5a3362d73b\",\n \"score\": \"0.6010357\",\n \"text\": \"def main():\\n augment_train_ds = transforms.Compose([\\n transforms.RandomCrop(64, padding=2),\\n transforms.RandomHorizontalFlip(),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\\n ])\\n \\\"\\\"\\\"Normalizing Test Dataset\\\"\\\"\\\"\\n augment_test_ds = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\\n ])\\n\\n \\\"\\\"\\\"Set seed.\\\"\\\"\\\"\\n np.random.seed(0)\\n random.seed(0)\\n torch.manual_seed(0)\\n torch.cuda.manual_seed(0)\\n\\n train_dir = '/u/training/tra287/scratch/tiny-imagenet-200/train'\\n train_ds = datasets.ImageFolder(train_dir, transform=augment_train_ds)\\n # print(train_ds.class_to_idx)\\n train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\\n\\n val_dir = '/u/training/tra287/scratch/tiny-imagenet-200/val/'\\n\\n # print(\\\"Now working on Val Dir\\\")\\n if 'val_' in os.listdir(val_dir+'images/')[0]:\\n # print(\\\"Calling create_val_dir() with val_dir: \\\", val_dir)\\n create_val_folder(val_dir)\\n val_dir = val_dir + 'images/'\\n # print(\\\"changed val_dir to : \\\", val_dir)\\n else:\\n # print(\\\"Didnt call create_val_dir\\\")\\n val_dir = val_dir + 'images/'\\n #train_ds = torchvision.datasets.ImageNet(train_dir, split='train', download=False, transform=augment_train_ds)\\n # train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\\n # test_ds = torchvision.datasets.ImageNet(val_dir, split='val', download=False, transform=augment_test_ds)\\n # test_ds_loader = data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\\n\\n test_ds = datasets.ImageFolder(val_dir, transform=augment_test_ds)\\n #print(test_ds.class_to_idx)\\n test_ds_loader = torch.utils.data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\\n\\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\\n print(\\\"Initializing Model\\\")\\n basic_block = BasicBlock\\n res_net = ResNet(basic_block=basic_block, num_basic_blocks_list=[2, 4, 4, 2], num_classes=200,\\n linear_layer_num_input=2304, max_pool_stride=2)\\n res_net = res_net.to(device)\\n start_epoch = 0\\n\\n if load_chkpt:\\n print(\\\"Saved Model is being loaded\\\")\\n chkpt = torch.load('./Checkpoint/model_state.pt')\\n res_net.load_state_dict(chkpt['res_net_model'])\\n start_epoch = chkpt['epoch']\\n\\n \\\"\\\"\\\"If multiple GPUs are available then use asynchronous training \\\"\\\"\\\"\\n if device == 'cuda':\\n res_net = torch.nn.DataParallel(res_net)\\n cudnn.benchmark = True\\n\\n \\\"\\\"\\\"___________ Training ___________\\\"\\\"\\\"\\n\\n print(\\\"Starting Training\\\")\\n\\n \\\"\\\"\\\"Criterion Function: Softmax + Log-Likelihood\\\"\\\"\\\"\\n loss_fn = nn.CrossEntropyLoss()\\n \\\"\\\"\\\"Adam Optimizer (as it takes advantage of both RMSDrop and Momentum\\\"\\\"\\\"\\n optimizer = optim.Adam(res_net.parameters(), lr=learning_rate)\\n\\n test_acc_list = []\\n epochs_list = [x for x in range(epochs)]\\n\\n for epoch in range(start_epoch, epochs):\\n\\n cur_loss = 0.0\\n total_correct = 0\\n total_samples = 0\\n\\n \\\"\\\"\\\" Overflow error in the optimizer if the step size is not reset.\\\"\\\"\\\"\\n if epoch > 8:\\n for group in optimizer.param_groups:\\n for p in group['params']:\\n state = optimizer.state[p]\\n if state['step'] >= 1024:\\n state['step'] = 1000\\n\\n for i, (inputs, labels) in enumerate(train_ds_loader):\\n\\n \\\"\\\"\\\"Transfer inputs and labels to CUDA if available\\\"\\\"\\\"\\n inputs = Variable(inputs).to(device)\\n labels = Variable(labels).to(device)\\n\\n \\\"\\\"\\\"Loss function requires the inputs to be wrapped in variables\\\"\\\"\\\"\\n # inputs = Variable(inputs)\\n\\n \\\"\\\"\\\"Torch tends to take cumulative gradients which is not required so setting it to zero after each batch\\\"\\\"\\\"\\n optimizer.zero_grad()\\n\\n outputs = res_net(inputs)\\n loss = loss_fn(outputs, labels)\\n loss.backward()\\n optimizer.step()\\n\\n cur_loss += loss.item()\\n avg_loss = cur_loss / (i + 1)\\n\\n _, predicted_label = torch.max(outputs, 1)\\n # print(predicted_label.shape, labels.shape)\\n total_samples += labels.shape[0]\\n # arr = (predicted_label == labels).numpy()\\n # print(np.sum(arr))\\n \\\"\\\"\\\"can not use numpy as the tensors are in CUDA\\\"\\\"\\\"\\n total_correct += predicted_label.eq(labels.long()).float().sum().item()\\n accuracy = total_correct / total_samples\\n\\n if i % 100 == 0:\\n print('Training [epoch: %d, batch: %d] loss: %.3f, accuracy: %.5f' %\\n (epoch + 1, i + 1, avg_loss, accuracy))\\n\\n test_acc_list.append(test(device, loss_fn, res_net, test_ds_loader))\\n\\n \\\"\\\"\\\"Saving model after every 5 epochs\\\"\\\"\\\"\\n if (epoch + 1) % 5 == 0:\\n print('==> Saving model ...')\\n state = {\\n 'res_net_model': res_net.state_dict(),\\n 'epoch': epoch,\\n }\\n if not os.path.isdir('./Checkpoint'):\\n os.mkdir('Checkpoint')\\n torch.save(state, './Checkpoint/model_state.pt')\\n\\n print(\\\"Training Completed!\\\")\\n\\n \\\"\\\"\\\"___________ Testing ____________\\\"\\\"\\\"\\n print(\\\"Testing Started\\\")\\n \\\"\\\"\\\"Puts model in testing state\\\"\\\"\\\"\\n res_net.eval()\\n\\n accuracy = test(device, loss_fn, res_net, test_ds_loader)\\n\\n print(\\\"Testing Completed with accuracy:\\\" + str(accuracy))\\n\\n with open('graph_resnet_tinyimagenet.csv', 'w') as result_file:\\n wr = csv.writer(result_file, dialect='excel')\\n wr.writerow(test_acc_list)\\n\\n print(\\\"Saved Test Accuracy list for graph\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232173d16e2d32eed36666657126e8f9\",\n \"score\": \"0.6008869\",\n \"text\": \"def test_DCAKNN(noise_dataset):\\n X = noise_dataset\\n model = DCAKNN(d=1, T=10)\\n model.fit(X)\\n model.transform(X)\\n model.fit_transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d1416b2cbec5bfd4f15e5b20dd166b\",\n \"score\": \"0.6007508\",\n \"text\": \"def toy5():\\r\\n\\tX_train = torch.stack((torch.arange(2.0).repeat_interleave(10), torch.arange(0.1, 1.1, 0.1).repeat(2))).t()\\r\\n\\tY_train = torch.tensor([0] * 2 + [1] * 8 + [0] * 8 + [1] * 2)\\r\\n\\r\\n\\tX_train_min = [0.0, 0.0]\\r\\n\\tX_train_max = [1.0, 1.0]\\r\\n\\treturn {\\\"dataset_name\\\": \\\"toy5\\\", \\\"X_train\\\": X_train, \\\"Y_train\\\": Y_train, \\\"X_train_min\\\": X_train_min, \\\"X_train_max\\\": X_train_max}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8787c50ad17cac715273ab2ce03320\",\n \"score\": \"0.60015285\",\n \"text\": \"def _run_training(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f3dba3af1538f8dde86449fc01e48cf\",\n \"score\": \"0.5996881\",\n \"text\": \"def initialisedataset():\\r\\n import initialdefs\\r\\n import math\\r\\n\\r\\n task_file, alltasks = initialdefs.starup()\\r\\n\\r\\n X = np.array([[np.zeros([32, 32]), np.zeros([32, 32])]])\\r\\n Y = [0]\\r\\n\\r\\n # make prelim Y's - labels for which problems are patterns. Prelim because we'll make more samples from each problem\\r\\n # so we'll only use these to inform us what label we should use\\r\\n Yprelim = [0] * 400\\r\\n\\r\\n # from manually going through and seeing what tasks were filling in repeating patterns / mosaics\\r\\n for i in [16, 60, 73, 109, 241, 286, 304, 312, 350, 399]:\\r\\n Yprelim[i] = 1\\r\\n\\r\\n for taskno in range(len(alltasks)):\\r\\n print(taskno)\\r\\n task = alltasks[taskno]\\r\\n train = task['train']\\r\\n\\r\\n # check the input & output are the same size: if not, don't use (too different, would cause too many problems)\\r\\n check = train[0]\\r\\n checkinput = np.array(check['input'])\\r\\n checkoutput = np.array(check['output'])\\r\\n\\r\\n # if they are the same, we can use as sample for the model.\\r\\n if checkoutput.shape == checkinput.shape:\\r\\n for trainno in range(len(train)):\\r\\n # dim0: samples dim1: channels (2: input, out), dim3: x dim4: y\\r\\n imagepair = train[trainno]\\r\\n imageinput = imagepair['input']\\r\\n imageoutput = imagepair['output']\\r\\n sz0l = math.floor((32 - np.size(imageinput, 0))/2) # padding for the left of dimension 0\\r\\n sz0r = math.ceil((32 - np.size(imageinput, 0))/2) # padding for the right of dimension 0\\r\\n sz1l = math.floor((32 - np.size(imageinput, 1))/2) # padding for the left of dimension 1\\r\\n sz1r = math.ceil((32 - np.size(imageinput, 1))/2) # padding for the right of dimension 1\\r\\n ippad = np.pad(imageinput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\\r\\n oppad = np.pad(imageoutput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n # create more images from the rotated versions\\r\\n for i in range(3):\\r\\n ippad = np.rot90(ippad)\\r\\n oppad = np.rot90(oppad)\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n # create more images from the transposed & rotated versions\\r\\n ippad = ippad.T\\r\\n oppad = oppad.T\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n for i in range(3):\\r\\n ippad = np.rot90(ippad)\\r\\n oppad = np.rot90(oppad)\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n X = np.delete(X, 0, axis=0)\\r\\n Y.__delitem__(0)\\r\\n\\r\\n # make channel the last dim\\r\\n X = np.moveaxis(X, 1, -1)\\r\\n\\r\\n return X, Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595a5ffcf5e2ea58fea3e0e6cce83d4c\",\n \"score\": \"0.59949315\",\n \"text\": \"def test_cv_shuffled(self):\\n _, y = data_iris()\\n X = np.array([[i] for i in range(100)])\\n pred = solution.test_cv(DummyShuffleLearner(), X, y, k=4)\\n self.assertIsNotNone(pred)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb25dde381ce47ffc7f7e85fbed0bc4\",\n \"score\": \"0.5986747\",\n \"text\": \"def train(\\n model, img_width, img_height, train_data_path,\\n validation_data_path, no_of_epochs):\\n nb_train_samples = sum(len(files)\\n for _, _, files in os.walk(train_data_path))\\n nb_validation_samples = sum(len(files)\\n for _, _, files in os.walk(\\n validation_data_path))\\n\\n epochs = no_of_epochs\\n batch_size = 3\\n checkpoint = ModelCheckpoint(\\n filepath=\\\"checkpoint_srkw-{epoch:02d}-{val_accuracy:.2f}.h5\\\",\\n monitor=\\\"val_accuracy\\\", verbose=0, save_best_only=True)\\n\\n reduce_lr = ReduceLROnPlateau(monitor=\\\"val_loss\\\", factor=0.1,\\n patience=100, min_lr=1e-8)\\n\\n train_datagen = ImageDataGenerator(rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2)\\n\\n # only rescaling\\n test_datagen = ImageDataGenerator(rescale=1. / 255)\\n\\n train_batchsize = 1\\n val_batchsize = 1\\n\\n train_generator = train_datagen.flow_from_directory(\\n train_data_path,\\n target_size=(img_width, img_height),\\n batch_size=train_batchsize,\\n class_mode=\\\"binary\\\",\\n shuffle=True)\\n\\n validation_generator = test_datagen.flow_from_directory(\\n validation_data_path,\\n target_size=(img_width, img_height),\\n batch_size=val_batchsize,\\n class_mode=\\\"binary\\\",\\n shuffle=False)\\n\\n model.fit_generator(\\n train_generator,\\n steps_per_epoch=nb_train_samples // batch_size,\\n epochs=epochs,\\n validation_data=validation_generator,\\n validation_steps=nb_validation_samples // batch_size,\\n callbacks=[checkpoint, reduce_lr])\\n\\n model.save(\\\"srkw_cnn.h5\\\")\\n\\n logger.info(\\\"Detection Model saved\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f184aa90c03c358209230f835e0f86\",\n \"score\": \"0.5976398\",\n \"text\": \"def train_model(train: Train, ds:Dataset(\\\"catsdogs\\\"), pf: Featureset(\\\"cat_and_dog_features\\\")) -> Any:\\n # train: Train, df:Dataset(\\\"cats-and-dogs-classification\\\"), pf: Featureset(\\\"animal_features\\\")\\n df = ds.to_pandas().merge(pf.to_pandas(), on='id')\\n training_set = df[(df['path'] == 'training_set/dogs') | (df['path'] == 'training_set/cats')]\\n testing_set = df[(df['path'] == 'test_set/dogs') | (df['path'] == 'test_set/cats')]\\n X_train = np.stack(training_set['content'].map(load_process_images))\\n X_test = np.stack(testing_set['content'].map(load_process_images))\\n train.register_input(X_train)\\n train.register_output(df['category'])\\n train_datagen = ImageDataGenerator(rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n horizontal_flip=True,\\n width_shift_range=0.1,\\n height_shift_range=0.1\\n )\\n train_datagen.fit(X_train)\\n training_data = train_datagen.flow(X_train, training_set['category'], batch_size=32)\\n validation_gen = ImageDataGenerator(rescale=1. / 255)\\n testing_data = validation_gen.flow(X_test, testing_set['category'], batch_size=32)\\n\\n model = Sequential([\\n Conv2D(filters=32, kernel_size=(3, 3), input_shape=(224, 224, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n\\n Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n Dropout(0.25),\\n\\n Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n Dropout(0.25),\\n\\n Flatten(),\\n Dense(128, activation='relu'),\\n Dropout(0.25),\\n Dense(1, activation='sigmoid')\\n ])\\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\\n callbacks = [\\n EarlyStopping(patience=2),\\n\\n ]\\n epochs = 10\\n model.fit(\\n training_data,\\n epochs=epochs,\\n validation_data=testing_data,\\n callbacks=callbacks\\n )\\n test_loss, test_accuracy = model.evaluate(testing_data)\\n train_loss, train_accuracy = model.evaluate(training_data)\\n\\n train.log_metric(\\\"Testing Accuracy\\\", test_accuracy)\\n train.log_metric(\\\"Testing Loss\\\", test_loss)\\n\\n train.log_metric(\\\"Training Accuracy\\\", train_accuracy)\\n train.log_metric(\\\"Training Loss\\\", train_loss)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef953872049cc8735a5f3564ce27a78\",\n \"score\": \"0.5974803\",\n \"text\": \"def classify(DD, CD):\\n\\n # do K-fold cross validation\\n all_data = np.concatenate((DD,CD))\\n all_class = np.vstack((np.ones((DD.shape[0], 1)), np.zeros((CD.shape[0], 1))))\\n N = all_data.shape[0]\\n K = 5\\n accuracies = np.zeros((K, 1))\\n sensitivities = np.zeros((K, 1))\\n specificities = np.zeros((K, 1))\\n randIndices = np.random.permutation(range(0, N))\\n\\n for fold in range(0, K) :\\n i_test = randIndices[fold * (N // K) : (fold + 1) * (N // K)]\\n i_train = [val for val in randIndices if val not in i_test]\\n c_train = all_class[i_train]\\n c_test = all_class[i_test] \\n\\n # train the model using features all_data[i_train,:] and classes c_train \\n # TODO\\n \\n\\n # compute the % errors using all_data[i_test,:], c_test, and the model you trained\\n # TODO\\n \\n\\n # report result mean and variance, over all folds, of each of accuracy, specificity, and sensitivity\\n # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f9cdb59bf6e995c9d2b296fac83e37\",\n \"score\": \"0.59640414\",\n \"text\": \"def train_models(models=model_list, file_name=pr.np_save, in_dims=input_dims, lrs=learning_rates, epochs=epoch_counts,\\r\\n n_splits=splits, saveFile=save_file):\\r\\n X, Y = shape_data(file_name)\\r\\n X = X.reshape((8732, in_dims[0], in_dims[1], in_dims[2]))\\r\\n kfold = KFold(n_splits=n_splits, shuffle=True)\\r\\n with open(saveFile, 'w') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(['Model Number', 'Learning Rate', 'Number of epochs', 'Accuracy', 'STD', 'Loss'])\\r\\n for lr in lrs:\\r\\n opt = opts.Adam(learning_rate=lr)\\r\\n for batch_size in batch_sizes:\\r\\n for epoch_count in epochs:\\r\\n for model_num in range(len(models)):\\r\\n fold_no = 0\\r\\n model = models[model_num]\\r\\n acc_per_fold = []\\r\\n loss_per_fold = []\\r\\n for train, test in kfold.split(X):\\r\\n fold_no += 1\\r\\n model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\\r\\n model.fit(X[train], Y[train], epochs=epoch_count, batch_size=batch_size,\\r\\n validation_data=(X[test], Y[test]))\\r\\n model.summary()\\r\\n scores = model.evaluate(X[test], Y[test])\\r\\n print(\\r\\n f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; '\\r\\n f'{model.metrics_names[1]} of {scores[1] * 100}%')\\r\\n acc_per_fold.append(scores[1] * 100)\\r\\n loss_per_fold.append(scores[0])\\r\\n if fold_no == 1:\\r\\n predictions = model.predict(X[test])\\r\\n preds = np.argmax(predictions, axis=1)\\r\\n conf_matrix = confusion_matrix(np.argmax(Y[test], axis=1), preds)\\r\\n print('Confusion Matrix for fold 1')\\r\\n print(conf_matrix)\\r\\n print('------------------------------------------------------------------------')\\r\\n print('Score per fold')\\r\\n for i in range(0, len(acc_per_fold)):\\r\\n print('------------------------------------------------------------------------')\\r\\n print(f'> Fold {i + 1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\\r\\n print('------------------------------------------------------------------------')\\r\\n print('Average scores for all folds:')\\r\\n print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\\r\\n print(f'> Loss: {np.mean(loss_per_fold)}')\\r\\n print('------------------------------------------------------------------------')\\r\\n with open(saveFile, 'a') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(\\r\\n [model_num + 1, lr, epoch_count, np.mean(acc_per_fold), np.std(acc_per_fold),\\r\\n np.mean(loss_per_fold)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91769a32f38873175c4167a220486de4\",\n \"score\": \"0.59445524\",\n \"text\": \"def cross_validate(model, X, Y, k_fold=10, verbose=False, plot=False):\\r\\n global rand_gen, np_rand_gen\\r\\n\\r\\n n_samples = X.shape[1]\\r\\n n_samples_validation = n_samples / k_fold\\r\\n n_samples_train = n_samples - n_samples_validation\\r\\n fold_stats = []\\r\\n X, Y = model.shuffle(X, Y)\\r\\n\\r\\n for fold in range(k_fold):\\r\\n rand_gen = lib.random.RandomState(seed=random_generator_seed)\\r\\n np_rand_gen = numpy.random.RandomState(seed=random_generator_seed)\\r\\n\\r\\n _model = MultiClassNeuralNetwork(\\r\\n data_dim=model._data_dim,\\r\\n activation_func=model._activation_func,\\r\\n l1_hidden_size=model._l1_hidden_size,\\r\\n l2_hidden_size=model._l2_hidden_size,\\r\\n num_of_classes=model._num_of_classes,\\r\\n lr=model._lr,\\r\\n initial_lr=model._initial_lr,\\r\\n epochs=model._epochs,\\r\\n batch_size=model._batch_size,\\r\\n reg=model._reg,\\r\\n input_noise_p=model._input_noise_p,\\r\\n dropout_p=model._dropout_p,\\r\\n early_stop_max_epochs=model._early_stop_max_epochs,\\r\\n input_z_score_normalization=model._input_z_score_normalization,\\r\\n init_weights_mu=model._init_weights_mu,\\r\\n init_weights_sigma=model._init_weights_sigma\\r\\n )\\r\\n\\r\\n sliding_window_left = fold * n_samples_validation\\r\\n sliding_window_right = (fold + 1) * n_samples_validation\\r\\n\\r\\n X_validation = X[:, sliding_window_left:sliding_window_right]\\r\\n Y_validation = Y[:, sliding_window_left:sliding_window_right]\\r\\n\\r\\n X_train_left = X[:, 0:sliding_window_left]\\r\\n X_train_right = X[:, sliding_window_right:n_samples]\\r\\n Y_train_left = Y[:, 0:sliding_window_left]\\r\\n Y_train_right = Y[:, sliding_window_right:n_samples]\\r\\n\\r\\n X_train = lib.concatenate((X_train_left, X_train_right), axis=1)\\r\\n Y_train = lib.concatenate((Y_train_left, Y_train_right), axis=1)\\r\\n\\r\\n performance = _model.fit(\\r\\n X_train, Y_train, X_validation, Y_validation, verbose)\\r\\n predictions = _model.predict(X_validation)\\r\\n accuracy = _model.evaluate(predictions, Y_validation)\\r\\n fold_stats.append(accuracy)\\r\\n\\r\\n if plot is True:\\r\\n print(\\\"\\\")\\r\\n _model.plot(performance)\\r\\n print(\\\"\\\")\\r\\n\\r\\n print(\\\"\\\\nFold ({fold}) test accuracy: {accuracy} %\\\\n\\\".format(\\r\\n fold=fold + 1, accuracy=accuracy))\\r\\n\\r\\n total_avg_accuracy = round(float(lib.average(fold_stats)), 2)\\r\\n\\r\\n return total_avg_accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8363ed89ffcb5af9c876b7a99e9e4a7\",\n \"score\": \"0.5938936\",\n \"text\": \"def main():\\r\\n \\r\\n cudnn.enabled = True\\r\\n cudnn.benchmark = True \\r\\n os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus\\r\\n \\r\\n ############# Create mask-guided classification network.\\r\\n model = DSI_Net(config, K = args.K)\\r\\n optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE, weight_decay =config.WEIGHT_DECAY)\\r\\n model.cuda()\\r\\n if config.FP16 is True:\\r\\n model, optimizer = amp.initialize(model, optimizer, opt_level=\\\"O1\\\")\\r\\n model = torch.nn.DataParallel(model)\\r\\n \\r\\n ############# Load pretrained weights\\r\\n pretrained_dict = torch.load(model_urls['deeplabv3plus_xception'])\\r\\n net_dict = model.state_dict()\\r\\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in net_dict) and (v.shape == net_dict[k].shape)}\\r\\n net_dict.update(pretrained_dict)\\r\\n model.load_state_dict(net_dict)\\r\\n print(len(net_dict))\\r\\n print(len(pretrained_dict))\\r\\n model.train()\\r\\n model.float()\\r\\n ce_loss = nn.CrossEntropyLoss()\\r\\n dice_loss = Dice_Loss()\\r\\n task_interaction_loss = Task_Interaction_Loss()\\r\\n \\r\\n ############# Load training and validation data\\r\\n trainloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list, config.SIZE, data_type='train', mode = 'train'), batch_size=config.BATCH_SIZE, shuffle=True, \\r\\n num_workers=config.NUM_WORKERS, pin_memory=True, drop_last = config.DROP_LAST)\\r\\n testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'test', mode='test'), \\r\\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True)\\r\\n train_testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'train', mode = 'test'), \\r\\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True) \\r\\n \\r\\n \\r\\n if not os.path.isdir(config.SAVE_PATH):\\r\\n os.mkdir(config.SAVE_PATH)\\r\\n if not os.path.isdir(config.SAVE_PATH+'Seg_results/'):\\r\\n os.mkdir(config.SAVE_PATH+'Seg_results/')\\r\\n if not os.path.isdir(config.LOG_PATH):\\r\\n os.mkdir(config.LOG_PATH) \\r\\n \\r\\n f_path = config.LOG_PATH + 'training_output.log'\\r\\n logfile = open(f_path, 'a')\\r\\n \\r\\n print_f(os.getcwd(), f=logfile)\\r\\n print_f('Device: {}'.format(args.gpus), f=logfile)\\r\\n print_f('==={}==='.format(time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime())), f=logfile)\\r\\n print_f('===Setting===', f=logfile)\\r\\n print_f(' Data_list: {}'.format(args.image_list), f=logfile)\\r\\n print_f(' K: {}'.format(args.K), f=logfile)\\r\\n print_f(' Lost_weight: {}'.format(args.alpha), f=logfile)\\r\\n print_f(' LR: {}'.format(config.LEARNING_RATE), f=logfile) \\r\\n\\r\\n OA_bulk_train = []\\r\\n CK_bulk_train = []\\r\\n DI_bulk_train = []\\r\\n JA_bulk_train = []\\r\\n SE_bulk_train = []\\r\\n\\r\\n OA_bulk_test = []\\r\\n CK_bulk_test = []\\r\\n DI_bulk_test = []\\r\\n JA_bulk_test = []\\r\\n SE_bulk_test = [] \\r\\n \\r\\n \\r\\n for epoch in range(config.EPOCH): \\r\\n #cls\\r\\n cls_train_loss = []\\r\\n seg_train_loss = []\\r\\n train_inter_loss = [] \\r\\n ############# Start the training\\r\\n for i_iter, batch in enumerate(trainloader):\\r\\n step = (config.TRAIN_NUM/config.BATCH_SIZE)*epoch+i_iter \\r\\n images, masks, labels, name = batch\\r\\n images = images.cuda()\\r\\n labels = labels.cuda().long()\\r\\n masks = masks.cuda().squeeze(1)\\r\\n optimizer.zero_grad()\\r\\n lr = adjust_learning_rate(optimizer, step)\\r\\n model.train()\\r\\n preds_seg_coarse, preds_seg_fine, preds_cls = model(images)\\r\\n cls_loss = ce_loss(preds_cls, labels) \\r\\n seg_loss_fine = dice_loss(preds_seg_fine, masks)\\r\\n seg_loss_coarse = dice_loss(preds_seg_coarse, masks) \\r\\n inter_loss = task_interaction_loss(preds_cls, preds_seg_fine, labels)\\r\\n loss = cls_loss + seg_loss_fine + seg_loss_coarse + args.alpha * inter_loss\\r\\n \\r\\n if config.FP16 is True:\\r\\n with amp.scale_loss(loss, optimizer) as scaled_loss:\\r\\n scaled_loss.backward()\\r\\n else:\\r\\n loss.backward()\\r\\n optimizer.step()\\r\\n #cls\\r\\n cls_train_loss.append(cls_loss.cpu().data.numpy()) \\r\\n seg_train_loss.append(seg_loss_fine.cpu().data.numpy())\\r\\n train_inter_loss.append(inter_loss.cpu().data.numpy()) \\r\\n \\r\\n ############ train log\\r\\n line = \\\"Train-Epoch [%d/%d] [All]: Seg_loss = %.6f, Class_loss = %.6f, Inter_loss = %.6f, LR = %0.9f\\\\n\\\" % (epoch, config.EPOCH, np.nanmean(seg_train_loss), np.nanmean(cls_train_loss), np.nanmean(train_inter_loss), lr)\\r\\n print_f(line, f=logfile) \\r\\n \\r\\n result = test(train_testloader, model, epoch, verbose=False)\\r\\n #cls\\r\\n [CK, OA, EREC] = result['cls']\\r\\n OA_bulk_train.append(OA)\\r\\n CK_bulk_train.append(CK)\\r\\n \\r\\n # seg \\r\\n [AC, DI, SE, SP, JA] = result['seg'] \\r\\n JA_bulk_train.append(np.nanmean(JA)) \\r\\n DI_bulk_train.append(np.nanmean(DI))\\r\\n SE_bulk_train.append(np.nanmean(SE)) \\r\\n \\r\\n ############# Start the test\\r\\n result = test(testloader, model, epoch, config.SAVE_PATH+'Seg_results/' , verbose = config.VERBOSE)\\r\\n #cls\\r\\n [CK, OA, EREC] = result['cls']\\r\\n line = \\\"Test -Epoch [%d/%d] [Cls]: CK-Score = %f, OA = %f, Rec-N = %f, Rec-V = %f, Rec-I=%f \\\\n\\\" % (epoch, config.EPOCH, CK, OA, EREC[0],EREC[1],EREC[2] )\\r\\n print_f(line, f=logfile)\\r\\n OA_bulk_test.append(OA)\\r\\n CK_bulk_test.append(CK)\\r\\n \\r\\n # seg \\r\\n [AC, DI, SE, SP, JA] = result['seg'] \\r\\n line = \\\"Test -Epoch [%d/%d] [Seg]: AC = %f, DI = %f, SE = %f, SP = %f, JA = %f \\\\n\\\" % (epoch, config.EPOCH, np.nanmean(AC), np.nanmean(DI), np.nanmean(SE), np.nanmean(SP), np.nanmean(JA))\\r\\n print_f(line, f=logfile)\\r\\n \\r\\n JA_bulk_test.append(np.nanmean(JA)) \\r\\n DI_bulk_test.append(np.nanmean(DI))\\r\\n SE_bulk_test.append(np.nanmean(SE))\\r\\n \\r\\n ############# Plot val curve\\r\\n filename = os.path.join(config.LOG_PATH, 'cls_curves.png')\\r\\n data_list = [OA_bulk_train, OA_bulk_test, CK_bulk_train, CK_bulk_test]\\r\\n label_list = ['OA_train','OA_test','CK_train','CK_test']\\r\\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:4], filename = filename)\\r\\n filename = os.path.join(config.LOG_PATH, 'seg_curves.png')\\r\\n data_list = [JA_bulk_train, JA_bulk_test, DI_bulk_train, DI_bulk_test, SE_bulk_train, SE_bulk_test]\\r\\n label_list = ['JA_train','JA_test','DI_train','DI_test', 'SE_train','SE_test'] \\r\\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:6], filename = filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0796727cc4b78bb5a1a5ead43d7e85aa\",\n \"score\": \"0.59347063\",\n \"text\": \"def ms_classification(\\n self,\\n data_dir = \\\"PATDATA\\\",\\n patient_file_path = \\\"PATSTAT\\\",\\n model_path = \\\"MODELPATH\\\",\\n model_name = \\\"model_test\\\",\\n num_layers = 8,\\n lr = 0.0005,\\n drop_rate = 0.2,\\n num_folds = 1,\\n num_splits = 1,\\n flip = False,\\n random_seed = None,\\n test_fraction = 0.1,\\n filter_age = None,\\n batch_size = 32,\\n early_stopping = True,\\n epochs = 100,\\n log_dir = \\\"./logs/fit/\\\",\\n use_spectrogram = False,\\n ms_only = False,\\n use_lstm = False,\\n verbose = True\\n ):\\n job_id = str(uuid.uuid4())\\n logger.info(f\\\"Job ID for this run: {job_id}\\\")\\n \\n # Create the paths to the directories \\n data_dir = os.environ[data_dir]\\n patient_file_path = os.environ[patient_file_path]\\n model_path = os.environ[model_path]\\n traces = data_manipulation.readin_traces(\\n data_dir, patient_file_path, start_index=100\\n )\\n\\n if filter_age is not None:\\n traces = [t for t in traces if int(t.sub_age.item()) < filter_age]\\n \\n if verbose:\\n print(f\\\"Number of patient traces: {len(traces)}\\\")\\n\\n # Convert the images to Eye trace features \\n if ms_only:\\n X, y, subject_ids = pre_processing.build_using_edss(traces)\\n\\n # Augment Severe EDSS indices to get rid of data imbalance\\n severe_indices = np.where(y == 1)\\n X_aug, y_aug, sub_id_aug = pre_processing.flip_augment(X, y, \\n subject_ids,\\n severe_indices)\\n # Append these Augmented samples \\n print(subject_ids[severe_indices].shape)\\n X = np.concatenate((X, X_aug))\\n y = np.concatenate((y, y_aug))\\n subject_ids = np.concatenate((subject_ids, sub_id_aug))\\n \\n else:\\n X, y, subject_ids = pre_processing.traces_to_feature(traces, \\n mean_center=False, scale_std=False)\\n\\n # Use only distance and test it out \\n # X = pre_processing.use_dist(X)\\n\\n # Prepare the Data. Augment the data. \\n X = pre_processing.downsample_traces(X, 128)\\n \\n # Use spectrograms if use_spectrogram is set to True\\n if use_spectrogram:\\n X = pre_processing.spec_build(X)\\n freq_bins = X.shape[2]\\n time_sets = X.shape[3]\\n X = np.reshape(X, newshape=(-1, 2, freq_bins * time_sets))\\n\\n if verbose:\\n print(f\\\"Number of samples with Label 0:{y[y == False].shape[0]}\\\")\\n print(f\\\"Number of samples with Label 1:{y[y == True].shape[0]}\\\")\\n print(f\\\"Number of features used from Eye trace: {X.shape[1]}\\\")\\n \\n # Create the test train split here \\n (\\n X_train,\\n X_test,\\n y_train,\\n y_test,\\n subject_ids_train,\\n subject_ids_test,\\n ) = grouped_train_test_split(\\n X, y, subject_ids, test_fraction, random_seed=random_seed\\n )\\n\\n if verbose:\\n print(f\\\"Shape of the Input is : {X_train.shape}\\\")\\n \\n # Double the dataset by including the flipped version of the inputs\\n if flip:\\n X_train, y_train, subject_ids_train = data_manipulation.flip_y_traces(\\n X_train, y_train, subject_ids_train\\n )\\n\\n X_train = data_manipulation.channel_last(X_train)\\n X_test = data_manipulation.channel_last(X_test)\\n y_train, y_test = (\\n y_train.astype(np.int32).squeeze(),\\n y_test.astype(np.int32).squeeze(),\\n )\\n \\n if verbose:\\n print(f\\\"Shape of the Input after setting channel last is {X_train.shape}\\\")\\n \\n input_shape = tuple(list(X_train.shape)[1:])\\n print(f\\\"Shape of the input is: {input_shape}\\\") \\n \\n if use_lstm:\\n model = LSTM_Model(input_shape) \\n \\n if verbose:\\n model.model.summary()\\n \\n model.run_model(X_train, y_train, \\n lr = lr, epochs = epochs,\\n batch_size = batch_size)\\n # Save the model.\\n model.save_model(model_path, model_name)\\n\\n if verbose:\\n print(f\\\"Model has been saved to {os.path.join(model_path, model_name) + '.h5'}\\\")\\n else:\\n # Build the Model \\n # Any new model should have this Keras Classifier wrapper.\\n log_dir = log_dir + datetime.datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\")\\n model = KerasClassifier(\\n resnet_build_fn_closure(input_shape, log_dir=log_dir,\\n drop_rate=drop_rate), \\n batch_size=batch_size, \\n epochs=epochs)\\n \\n # How many input channels \\n in_channel = X_train.shape[2]\\n if verbose:\\n print(f\\\"Number of Input Channels: {in_channel}\\\")\\n print(f\\\"Shape of X : {X.shape}\\\")\\n print(f\\\"Shape of Y: {y.shape}\\\")\\n # Parameters that are used for the model \\n param_grid = {\\n \\\"num_layers\\\": [num_layers],\\n \\\"n_feature_maps\\\": [in_channel],\\n \\\"lr\\\": [lr],\\n \\\"early_stopping\\\": [early_stopping],\\n \\\"kernel_multiple\\\": [1],\\n }\\n\\n cv_results = manual_grid_search_with_validation(\\n X_train, y_train, subject_ids_train, \\n num_folds, model, param_grid,\\n model_path, model_name)\\n \\n save_path = Path(\\\"~\\\").expanduser() / \\\"cv_runs\\\"\\n # Create the directory if it doesn't exist\\n save_path.mkdir(parents=True, exist_ok=True)\\n save_file = save_path / f\\\"cv_run_{job_id}.csv\\\"\\n cv_results.to_csv(save_file)\\n \\n # Use the X_test set to calculate some metrics \\n y_pred = model.predict_proba(X_test)\\n test_accuracy = accuracy_score(y_test, np.argmax(y_pred, axis=1))\\n\\n # Test Accuracy Calculation\\n print(f\\\"Test Accuracy of the model is: {test_accuracy:.2f} when tested on {X_test.shape[0]} samples.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c36c1cd92a24e9acb50b85e9ed76c5\",\n \"score\": \"0.5934338\",\n \"text\": \"def train_model(model_params):\\n cars = glob.glob('util_images/vehicles/**/*.png')\\n not_cars = glob.glob('util_images/non-vehicles/**/*.png')\\n sample_size = model_params[\\\"sample_size\\\"]\\n cars = cars[0:sample_size]\\n not_cars = not_cars[0:sample_size]\\n\\n color_item = model_params[\\\"color_item\\\"]\\n orient = model_params[\\\"orient\\\"]\\n pix_per_cell = model_params[\\\"pix_per_cell\\\"]\\n cells_per_block = 2\\n hog_channels = model_params[\\\"hog_channel\\\"]\\n\\n\\n print_parameters(color_item, orient, pix_per_cell, cells_per_block, hog_channels)\\n\\n car_features = object_detection_utils.extract_features(cars, color_space=color_item, orient=orient,\\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\\n hog_channel=hog_channels)\\n notcar_features = object_detection_utils.extract_features(not_cars, color_space=color_item, orient=orient,\\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\\n hog_channel=hog_channels)\\n # Create an array stack of feature vectors\\n X = np.vstack((car_features, notcar_features)).astype(np.float64)\\n # Define the labels vector\\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\\n # Split up data into randomized training and test sets\\n rand_state = np.random.randint(0, 100)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand_state)\\n # Fit a per-column scaler\\n X_scaler = StandardScaler().fit(X_train)\\n # Apply the scaler to X\\n X_train = X_scaler.transform(X_train)\\n X_test = X_scaler.transform(X_test)\\n # Use a linear SVC\\n svc = LinearSVC()\\n # Check the training time for the SVC\\n svc.fit(X_train, y_train)\\n # Check the score of the SVC\\n accuracy = round(svc.score(X_test, y_test), 4)\\n model = dict()\\n model[\\\"linearSVC\\\"] = svc\\n model[\\\"X_scaler\\\"] = X_scaler\\n model[\\\"color_item\\\"] = model_params[\\\"color_item\\\"]\\n model[\\\"orient\\\"] = model_params[\\\"orient\\\"]\\n model[\\\"pix_per_cell\\\"] = model_params[\\\"pix_per_cell\\\"]\\n model[\\\"cells_per_block\\\"] = 2\\n model[\\\"hog_channel\\\"] = model_params[\\\"hog_channel\\\"]\\n model[\\\"sample_size\\\"] = model_params[\\\"sample_size\\\"]\\n model[\\\"spatial_feat\\\"] = True\\n model[\\\"hist_feat\\\"] = True\\n model[\\\"hog_feat\\\"] = True\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33ed3dd79e174c6177e1851c8ce0b24\",\n \"score\": \"0.5933601\",\n \"text\": \"def generate_attack_inputs(sess,model,x_test,y_test,class_num,nb_imgs, load_imgs = False,load_robust = True,file_path = 'local_info/'):\\n if not load_imgs:\\n # if load_robust:\\n # original_predict = sess.run(model.pre_softmax,feed_dict = {model.x_input:x_test.reshape(-1,784)})\\n # else:\\n original_predict = model.predict_prob(x_test)\\n original_class = np.argmax(original_predict, axis = 1)\\n true_class = np.argmax(y_test, axis = 1)\\n mask = true_class == original_class\\n corr_idx = np.where(true_class == original_class)[0]\\n print(np.sum(mask), \\\"out of\\\", mask.size, \\\"are correctly labeled,\\\", len(x_test[mask])) \\n\\n # generate targeted labels, choose least likely class, untargeted attack does not require\\n orig_class_vec = range(class_num)\\n target_ys_one_hot = []\\n target_ys = []\\n orig_images = []\\n orig_labels = []\\n all_true_ids = []\\n trans_test_images = []\\n for orig_class in orig_class_vec:\\n # guarantees that same sets of images are selected...\\n np.random.seed(1234)\\n cls_idx = np.where(true_class == orig_class)[0]\\n corr_cls_idx = np.intersect1d(cls_idx,corr_idx)\\n #random sample, this is doubled because I want to set aside a test set which is to measure transferability\\n corr_cls_idx = np.random.choice(corr_cls_idx, size=nb_imgs*2, replace=False)\\n # print(\\\"selected img ids:\\\",corr_cls_idx)\\n x_sel = x_test[corr_cls_idx]\\n orig_labels_tmp = y_test[corr_cls_idx]\\n y_sel = model.predict_prob(x_sel)\\n cls_count = np.bincount(np.argmin(y_sel,axis=1))# count number of occurence\\n tar_class = np.argmax(cls_count)\\n print(\\\"Orig: {}, Tar: {}\\\".format(orig_class,tar_class))\\n # store the adversarial samples related files\\n target_ys.extend([tar_class]*int(len(corr_cls_idx)/2))\\n target_ys_one_hot.extend([np.eye(class_num)[tar_class]]*int(len(corr_cls_idx)/2))\\n orig_images.extend(instance for instance in x_sel[:int(len(x_sel)/2)])\\n orig_labels.extend(lab for lab in orig_labels_tmp[:int(len(orig_labels_tmp)/2)])\\n all_true_ids.extend(idx for idx in corr_cls_idx[:int(len(corr_cls_idx)/2)])\\n # store the transferability test related files\\n trans_test_images.extend(instance for instance in x_sel[int(len(x_sel)/2):])\\n\\n target_ys_one_hot = np.array(target_ys_one_hot)\\n orig_images = np.array(orig_images)\\n target_ys = np.array(target_ys)\\n orig_labels = np.array(orig_labels)\\n all_true_ids = np.array(all_true_ids)\\n trans_test_images = np.array(trans_test_images)\\n\\n # can uncomment below if not needed\\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,target_ys_one_hot)\\n fname = '/orig_images_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,orig_images)\\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,orig_labels)\\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,all_true_ids)\\n fname = '/target_ys_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,target_ys)\\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,trans_test_images)\\n else:\\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\\n target_ys_one_hot = np.load(file_path+fname)\\n fname = '/orig_images_{}.npy'.format(nb_imgs)\\n orig_images = np.load(file_path+fname)\\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\\n orig_labels = np.load(file_path+fname)\\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\\n all_true_ids = np.load(file_path+fname)\\n fname = '/target_ys_{}.npy'.format(nb_imgs)\\n target_ys = np.load(file_path+fname)\\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\\n trans_test_images = np.load(file_path+fname)\\n return target_ys_one_hot,orig_images,target_ys,orig_labels,all_true_ids, trans_test_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e73201314edf20d5183214b6e3104c5\",\n \"score\": \"0.5933446\",\n \"text\": \"def learn_test(self):\\r\\n self.net.train_step()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32d8d6f2faf543f1badf64caac4a023c\",\n \"score\": \"0.59314454\",\n \"text\": \"def train(self):\\n steps_for_validation = self.data.validation_data.n//self.data.validation_data.batch_size\\n steps_for_train = self.data.training_data.n//self.data.training_data.batch_size\\n\\n # Compilation process\\n self.model.compile(\\n optimizer=\\\"rmsprop\\\", loss=\\\"categorical_crossentropy\\\", metrics=[\\\"accuracy\\\"])\\n\\n # Training process\\n self.model.fit_generator(self.data.training_data, epochs=5, steps_per_epoch=steps_for_train,\\n validation_data=self.data.validation_data, validation_steps=steps_for_validation)\\n\\n # Evaluation process\\n evaluation = self.model.evaluate_generator(\\n generator=self.data.validation_data, steps=steps_for_validation)\\n print(\\n f\\\"EVALUATION:\\\\n Loss {evaluation[0]}\\\\nAccuracy: {evaluation[1]}\\\\n\\\")\\n\\n self.model.save(\\\"magichands_model.h5\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ac53ff48da1bb7ec2ed1361735ecbc\",\n \"score\": \"0.59222907\",\n \"text\": \"def _generate_train_batch(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad73916bbe322fa35bd7d7f8483a5cb\",\n \"score\": \"0.59176\",\n \"text\": \"def create_validation(args):\\n \\n dataset_dir = args.dataset_dir\\n workspace = args.workspace\\n folds_num = 3\\n \\n rs = np.random.RandomState(0)\\n \\n filenames = ['BirdVoxDCASE20k_csvpublic.csv', \\n 'ff1010bird_metadata_2018.csv', \\n 'warblrb10k_public_metadata_2018.csv']\\n \\n # Read dataframe\\n dataframes = []\\n \\n for (n, filename) in enumerate(filenames):\\n \\n filepath = os.path.join(dataset_dir, filename)\\n \\n df = pd.read_csv(filepath)\\n df = pd.DataFrame(df)\\n df['fold'] = n + 1\\n \\n dataframes.append(df)\\n \\n dataframes = pd.concat(dataframes)\\n \\n # Write out to csv\\n out_path = os.path.join(workspace, 'validation.csv')\\n dataframes.to_csv(out_path)\\n print(\\\"Write out to {}\\\".format(out_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e58975c6c39ba9ad4c420830817f5c\",\n \"score\": \"0.5915359\",\n \"text\": \"def __init__(self, Examples, Labels, n_folds=10, epochs=50):\\r\\n \\r\\n # Set up an n-fold test and call train_and_evaluate_model\\r\\n # for each fold. Keep statistics and report summary\\r\\n # information of mean and variance.\\r\\n \\r\\n # Randmize examples within each fold\\r\\n kfold = StratifiedKFold(n_folds, shuffle=True)\\r\\n one_hot_labels = np_utils.to_categorical(Labels)\\r\\n # Generate indices that can be used to split into training\\r\\n # and test data, e.g. examples[train_idx]\\r\\n accuracy = []\\r\\n for (train_idx, test_idx) in kfold.split(Examples, Labels):\\r\\n # normally, we would gather results about each fold\\r\\n accuracy.append(self.train_and_evaluate__model(Examples, one_hot_labels, train_idx, test_idx, 100, epochs))\\r\\n accuracy = np.array(accuracy)\\r\\n mu, sd = np.mean(accuracy), np.std(accuracy)\\r\\n print()\\r\\n print('mean accuracy {} and standard deviation {}'.format(mu,sd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8704d8db46bfdc6fb2723045f09b85dd\",\n \"score\": \"0.5912254\",\n \"text\": \"def main():\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n split_train_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15329cda45e293524f75eab42e1b7b0\",\n \"score\": \"0.591102\",\n \"text\": \"def train_model(self,valid_dicom_npy_lst, boolean_mask_lst):\\n train_data = []\\n train_target = []\\n\\n # Using the saved numpy information files from the DICOM images and contour files,\\n for dicom_npy_file in valid_dicom_npy_lst:\\n train_data.append(np.load(dicom_npy_file))\\n\\n for mask_npy_file in boolean_mask_lst:\\n train_target.append(np.load(mask_npy_file))\\n\\n # Forming the single numpy array for the entire data set\\n train_data = np.array(train_data, dtype=np.float32)\\n train_target = np.array(train_target, dtype=np.float32)\\n\\n for i in range(self.nb_epoch) :\\n print('Epoch {} of {}'.format(i, self.nb_epoch))\\n # get the single batch of train data consists of one numpy array for images & one numpy array for targets.\\n mini_random_batches = self.get_random_mini_batches(train_data, train_target)\\n print 'Random Mini Batch Length : {} out of {} entire samples'.format(mini_random_batches.__len__(),train_data.__len__())\\n for random_batch in mini_random_batches:\\n train_data_batch = random_batch[0]\\n train_target_batch = random_batch[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d7e22c90369d59220d5e7ea08937f5\",\n \"score\": \"0.59054047\",\n \"text\": \"def __init_dataset(self):\\r\\n\\r\\n if self.__mode == 'test':\\r\\n self.__test_data = pd.read_csv(self.__label_root)\\r\\n self.__get_column_name(self.__test_data)\\r\\n self.__test_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n self.__test_data[self.__img_column_name].values]\\r\\n else:\\r\\n self.__train_csv_data = pd.read_csv(self.__label_root)\\r\\n self.__get_column_name(self.__train_csv_data)\\r\\n # 全数据训练\\r\\n if config.train_proportion == 1.0:\\r\\n\\r\\n if self.__mode == 'train':\\r\\n self.__train_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n self.__train_csv_data[self.__img_column_name].values]\\r\\n self.__train_label_list = self.__train_csv_data[self.__label_column_name].values\\r\\n if self.__mode == 'val':\\r\\n val_data = self.__train_csv_data[round(0.8*len(self.__train_csv_data)):]\\r\\n self.__val_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n val_data[self.__img_column_name].values]\\r\\n self.__val_label_list = val_data[self.__label_column_name].values\\r\\n\\r\\n\\r\\n else:\\r\\n # random split\\r\\n train_img, train_label, val_img, val_label = train_test_split(\\r\\n self.__train_csv_data[self.__img_column_name].values,\\r\\n self.__train_csv_data[self.__label_column_name].values,\\r\\n train_size=config.train_proportion,\\r\\n test_size=1-config.train_proportion\\r\\n )\\r\\n # data for training\\r\\n if self.__mode == 'train':\\r\\n self.__train_img_list = train_img\\r\\n self.__train_label_list = train_label\\r\\n # data for validating\\r\\n if self.__mode == 'val':\\r\\n self.__val_img_list = val_img\\r\\n self.__val_label_list = val_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f12b9848d19a13693b83ac4081c97df\",\n \"score\": \"0.5903171\",\n \"text\": \"def prepare_data_cross_validation(pos_data, neg_data, ADD_FEATURE=False):\\n\\n _pos_cross_valid_all = get_valid_data(pos_data)\\n _neg_cross_valid_all = get_valid_data(neg_data)\\n\\n for i in range(5):\\n [_pos_4_train, _pos_4_test] = _pos_cross_valid_all[i]\\n [_neg_4_train, _neg_4_test] = _neg_cross_valid_all[i]\\n _neg_4_train = random.sample(_neg_4_train, len(_pos_4_train))\\n test_data = _pos_4_test + _neg_4_test[:len(_pos_4_test)]\\n train_data = _pos_4_train + _neg_4_train\\n print '---------------------------------'\\n print 'cross_validation id:' + str(i)\\n print 'pos for training:' + len(_pos_4_train).__str__()\\n print 'neg for training:' + len(_neg_4_train).__str__()\\n print 'pos for testing:' + len(_pos_4_test).__str__()\\n print 'neg for testing:' + len(_neg_4_test[:len(_pos_4_test)]).__str__()\\n print '---------------------------------'\\n vec_train, label_train, trigger_train, aux_train = get_data_and_lable_trigger(train_data)\\n vec_test, label_test, trigger_test, aux_test = get_data_and_lable_trigger(test_data)\\n datas = [(np.array(vec_train), np.array(label_train),\\n trigger_train, aux_train, np.array(vec_test),\\n np.array(label_test), trigger_test, aux_test)]\\n _path_to_save_datas = '5-cross_data/prepared_datas_with_feature_{}'.format(i) if ADD_FEATURE else '5-cross_data/prepared_datas_{}'.format(i)\\n with open(_path_to_save_datas, 'w') as f:\\n pickle.dump(datas, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5c3099dfe5c5a1aaf838214a3540e8\",\n \"score\": \"0.5902428\",\n \"text\": \"def cv_split_classification(self):\\n # Define base data size and size of validation\\n data_size = len(self.transformed_data)\\n validation_size = int(data_size / 10)\\n\\n # Check and set the random seed\\n if self.random_state:\\n np.random.seed(self.random_state)\\n\\n # Sample for validation\\n validation_splitter = []\\n\\n # Randomize a number between 0 and 10 and multiply by the index to randomly pick observations over data set\\n for index in range(validation_size):\\n validation_splitter.append(np.random.choice(a=10) + (10 * index))\\n self.tune_data = self.transformed_data.iloc[validation_splitter]\\n\\n # Determine the remaining index that weren't picked for validation\\n remainder = list(set(self.transformed_data.index) - set(validation_splitter))\\n remainder_df = pd.DataFrame(self.transformed_data.iloc[remainder]['Class'])\\n\\n # Assign a random number\\n remainder_df['Random_Number'] = np.random.randint(0, len(remainder), remainder_df.shape[0])\\n\\n # Sort over class and the random number\\n remainder_df.sort_values(by=['Class', 'Random_Number'], inplace=True)\\n remainder_df.reset_index(inplace=True)\\n\\n # Sample for CV\\n for index in range(5):\\n # Mod the index by 5 and there will be 5 remainder groups for the CV split\\n splitter = remainder_df.loc[remainder_df.index % 5 == index]['index']\\n\\n # Update our attribute with the dictionary for this index\\n self.test_split.update({\\n index: self.transformed_data.iloc[splitter]\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ef1af4360bbaa010b3774d4a46929d\",\n \"score\": \"0.58991945\",\n \"text\": \"def test_if_the_name_is_correct(model_name):\\n \\n possible_datasets = ['IconArt_v1','RMN','RASTA','Paintings']\\n possible_lr = ['_big0001_modif','_small001_modif','_big001_modif','_small01_modif','_big01_modif']\\n possible_llt = ['_GAP','_GMP']\\n # For the last layer transformation !!! GlobalAveragePooling2D\\n possible_opt = ['_adam','_Adadelta','_RMSprop']\\n possible_freeze= ['_unfreeze50','_unfreeze84','_unfreeze44','_unfreeze20','_unfreeze8']\\n # _unfreeze50 for InceptionV1 to train starting at mixed4a_3x3_bottleneck_pre_relu\\n # but _unfreeze84 for InceptionV1_slim to train at \\n # Mixed_4b_Branch_1_a_1x1_conv : because the name of the layer are not the same !\\n possible_loss= ['_cosineloss']\\n possibleInit = ['_RandInit','_RandForUnfreezed']\\n possible_crop = ['_randomCrop']\\n possible_Sup = ['_deepSupervision']\\n possible_Aug = ['_dataAug','_SmallDataAug','_MediumDataAug']\\n possible_epochs = ['_ep120','_ep200','_ep1']\\n possible_clipnorm = ['_cn1','_cn10']\\n possible_LRSched = ['_LRschedG','_RedLROnPlat'] # For LR scheduler\\n possible_dropout = ['_dropout04','_dropout070704'] # For LR scheduler\\n # For the parameters based on : https://www.analyticsvidhya.com/blog/2018/10/understanding-inception-network-from-scratch/\\n # Use the learning rate at 0.01 and the list dropout\\n possible_lastEpochs = ['_LastEpoch']\\n correct_name = False\\n for dataset in possible_datasets:\\n #print(dataset,model_name)\\n if dataset in model_name:\\n correct_name = True\\n model_name_new = model_name.replace(dataset,'')\\n break\\n if not(correct_name):\\n if not(model_name in [None,'RandForUnfreezed','imagenet']):\\n print('Dataset is missing in',model_name)\\n return(False)\\n #raise(ValueError('Dataset is missing'))\\n correct_name = False\\n for lr in possible_lr:\\n if lr in model_name:\\n correct_name = True\\n model_name_new = model_name_new.replace(lr,'')\\n break\\n if not(correct_name):\\n print('lr is missing in ',model_name)\\n return(False)\\n #raise(ValueError('lr is missing'))\\n\\n for llt in possible_llt:\\n model_name_new = model_name_new.replace(llt,'')\\n \\n for opt in possible_opt:\\n model_name_new = model_name_new.replace(opt,'')\\n for f in possible_freeze:\\n model_name_new = model_name_new.replace(f,'')\\n for loss in possible_loss:\\n model_name_new = model_name_new.replace(loss,'')\\n for init in possibleInit:\\n model_name_new = model_name_new.replace(init,'')\\n for crop in possible_crop:\\n model_name_new = model_name_new.replace(crop,'')\\n for sup in possible_Sup:\\n model_name_new = model_name_new.replace(sup,'')\\n for aug in possible_Aug:\\n model_name_new = model_name_new.replace(aug,'')\\n for ep in possible_epochs:\\n model_name_new = model_name_new.replace(ep,'')\\n for le in possible_lastEpochs:\\n model_name_new = model_name_new.replace(le,'')\\n for c in possible_clipnorm:\\n model_name_new = model_name_new.replace(c,'')\\n for ls in possible_LRSched:\\n model_name_new = model_name_new.replace(ls,'')\\n for dp in possible_dropout:\\n model_name_new = model_name_new.replace(dp,'')\\n \\n if not(model_name_new==''):\\n print(model_name_new + ' parameter is unknonw sorry.')\\n return(False)\\n #raise(ValueError(model_name_new + 'is unknonw sorry.'))\\n \\n return(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85239ab57ee0ec63f4f87cf0827597a0\",\n \"score\": \"0.5899114\",\n \"text\": \"def test(self, sample_every=30, verbose=True):\\n print('start testing the cnn')\\n start = time.time()\\n\\n # visualize the test model\\n merged = tf.summary.merge_all()\\n writter = tf.summary.FileWriter('graphs/{0}/test'.format(self.name), self.sess.graph) \\n\\n # restore saved model\\n saver = tf.train.Saver()\\n save_path = \\\"model/{0}.ckpt\\\".format(self.name)\\n saver.restore(self.sess, save_path)\\n\\n # total prediction in each class {Normal, AF, Other, Noise}\\n total = {0: 0, 1: 0, 2: 0, 3: 0}\\n # total correct prediction in each class {Normal, AF, Other, Noise}\\n corrects = {0: 0, 1: 0, 2: 0, 3: 0}\\n\\n # run through every single data in test set\\n for i in range(self.ecg.ntests):\\n # run single forward pass\\n X_test, Y_test = self.ecg.get_test(i)\\n\\n # no drop out in testing\\n loss, logit, summary = self.sess.run([self.loss, self.logits, merged], feed_dict={self.X: X_test, self.Y: Y_test, self.keep_prob: 1})\\n\\n # get the prediction\\n writter.add_summary(summary, i)\\n probs = self.sess.run(tf.nn.softmax(logit))\\n pred = self.sess.run(tf.argmax(probs, 1))[0]\\n\\n correct = np.argmax(Y_test)\\n\\n total[pred] += 1\\n if pred == correct:\\n corrects[pred] += 1\\n\\n if verbose:\\n # plot(X_test)\\n print('True label is {0}'.format(self.id_to_class_name[correct]), '- The model predicts', self.id_to_class_name[pred])\\n\\n # calculate the accuracy, base Scoring part at https://physionet.org/challenge/2017/#preparing\\n fn = 2.0 * corrects[0] / (total[0] + self.ecg.N)\\n fa = 2.0 * corrects[1] / (total[1] + self.ecg.A)\\n fo = 2.0 * corrects[2] / (total[2] + self.ecg.O)\\n fp = 2.0 * corrects[3] / (total[3] + self.ecg.P)\\n f = (fn + fa + fo + fp) / 4.0\\n print('Accuracy in the validation set is {0}'.format(f))\\n print('Testing time {0}'.format(time.time() - start))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70077f7954fa6195bfbf2ddacb50c2d8\",\n \"score\": \"0.58966506\",\n \"text\": \"def KNN_test_objectnet_shuffle(val_loader, model, args, train_list, path_visualization_results=None):\\n # crucial for use when loading a model with bn, if we don't run eval, its very bad\\n model.eval()\\n\\n if len(train_list)==2:\\n emb_list, label_list = train_list\\n else:\\n emb_list, label_list, output_list = train_list\\n\\n try:\\n emb_list = torch.from_numpy(emb_list)\\n output_list = torch.from_numpy(output_list)\\n except:\\n pass\\n\\n cnt=0\\n top1_cnt = 0\\n top1_cnt_old = 0\\n top5_cnt = 0\\n\\n test_hidden=True\\n \\n if args.visualize:\\n labels_path = os.getenv(\\\"HOME\\\") + '/.torch/models/imagenet_class_index.json'\\n with open(labels_path) as json_data:\\n idx_to_labels = json.load(json_data)\\n\\n\\n with torch.no_grad():\\n for i, example in enumerate(val_loader):\\n images = example['images']\\n target = example['labels']\\n path = example['path']\\n if args.gpu is not None:\\n images = images.cuda(args.gpu, non_blocking=True)\\n # target = target.cuda(args.gpu, non_blocking=True)\\n\\n\\n # print(\\\"target\\\", target)\\n # exit(0)\\n\\n # print('img', images.size())\\n output, fea, norm = model(images)\\n # fea, _ = normalize_l2(fea)\\n # fea = MLP(fea)\\n\\n if test_hidden:\\n score_mat = torch.mm(fea.cpu(), emb_list.t()) # test_num * train_num\\n else:\\n score_mat = torch.mm(output.cpu(), output_list.t()) # test_num * train_num\\n # print('topk', TOPK_NN)\\n score, pred = torch.topk(score_mat, k=TOPK_NN, dim=1, largest=True, sorted=False)\\n # print(\\\"done\\\")\\n pred_np = pred.data.cpu().numpy()\\n # print(\\\"sim\\\", score_mat)\\n # print(\\\"pred\\\", pred)\\n\\n # print(\\\"Getting scores: \\\", score, pred)\\n\\n # print('target b4', target)\\n # The way to organize is that, i-th sublist in the target, contains the i-th label for all test examples\\n target_num = len(target)\\n target = [each.numpy() for each in target]\\n # print('target after', target)\\n\\n pred_label = []\\n\\n for each in range(pred_np.shape[0]): # for each test images\\n # label_gt_all = target[each]\\n label_gt_list = []\\n # for mogu in label_gt_all:\\n # label_gt_list.append(mogu.numpy()[0])\\n # print('label gt', label_gt)\\n for kk in range(target_num):\\n label_gt_list.append(target[kk][each]) # Since target size is: \\\"the multilabel dim, number of test examples\\\"\\n\\n cnt += 1\\n\\n index = pred[each]\\n retrieved_labels = label_list[index.cpu().numpy()]\\n\\n retrieved_labels = np.sort(retrieved_labels)\\n # print(\\\"retrieved labels\\\", len(retrieved_labels))\\n\\n label_num_dict = {}\\n last = None\\n num = None\\n\\n for kk in range(retrieved_labels.shape[0]):\\n if kk==0:\\n last = retrieved_labels[0]\\n num=1\\n\\n else:\\n if retrieved_labels[kk] == last:\\n num += 1\\n else:\\n label_num_dict[retrieved_labels[kk-1]] = num\\n num = 1\\n last = retrieved_labels[kk]\\n\\n if kk == retrieved_labels.shape[0] - 1:\\n label_num_dict[retrieved_labels[kk]] = num\\n # print(label_num_dict)\\n\\n # print(\\\"label dict num\\\", label_num_dict)\\n # Sort dict from most frequent to least frequent\\n sort_topk_neighbor = sorted(label_num_dict.items(), key=lambda kv: (-kv[1], kv[0]))\\n\\n # print(\\\"sort topk neighbor\\\", sort_topk_neighbor, len(label_gt_list), label_gt_list,)\\n\\n\\n # Most frequent label for this test image is in sort_topk_neighbour[0][0], and gt is label_gt_list, so check if our top prediction is among\\n if args.visualize:\\n #path_viz = os.path.join(path_visualization_results,'knn' )\\n #path_viz = os.path.join(path_visualization_results,<category_objnet>/knn_prediction/<basename> )\\n \\n\\n path_image = path[each]\\n list_path_split = path_image.split('/')\\n objnet_category = list_path_split[-2]\\n image_basename = list_path_split[-1]\\n path_to_append = objnet_category + '/' + 'knn_output' + '/' + image_basename\\n path_destination_image = os.path.join(path_visualization_results, path_to_append)\\n # print(\\\"__path__\\\",path[each])\\n # path_dest_visualization_img = os.path.join(path_viz, 'dfd')\\n \\n true_label = objnet_category\\n predicted_labels_id = [kv[0] for kv in sort_topk_neighbor[:5]]\\n predicted_labels = [idx_to_labels[str(id)][1] for id in predicted_labels_id]\\n predicted_label = objnet_category \\n \\n temp = images[each].detach().numpy()\\n temp = np.moveaxis(temp, 0, 2)\\n mean = np.array([0.485, 0.456, 0.406])\\n std = np.array([0.229, 0.224, 0.225])\\n temp = np.clip((temp*std)+mean,0,1)\\n f,ax = plt.subplots()\\n ax.imshow(temp)\\n str_title = 'Truth Objnet category: {} \\\\n Predicted labels: {} '.format(true_label,predicted_labels)\\n f.suptitle(str_title, fontsize=12)\\n print(\\\"savign at : \\\", path_destination_image)\\n if i%500 ==0:\\n print(i, 'done') \\n dirname = os.path.dirname(path_destination_image)\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\\n f.savefig(path_destination_image,bbox_inches='tight')\\n plt.close()\\n\\n top1_cnt_old=top1_cnt\\n try:\\n for label_gt in label_gt_list:\\n if sort_topk_neighbor[0][0] == label_gt:\\n top1_cnt += 1\\n break\\n except:\\n print(sort_topk_neighbor, label_gt_list, top1_cnt)\\n\\n if top1_cnt-top1_cnt_old > 1:\\n print(top1_cnt-top1_cnt_old, label_gt_list)\\n\\n # Make sure if see one category correct, then stop loop\\n flag = True\\n for nnn in range(5):\\n try:\\n if flag == False:\\n break\\n for label_gt in label_gt_list:\\n if sort_topk_neighbor[nnn][0] == label_gt:\\n top5_cnt += 1\\n flag = False\\n break\\n except:\\n # print(\\\"top 5 but number less than 5\\\")\\n break\\n\\n print('{} Overlapping ImgNet'.format(args.dataset), i, \\\"/%d top 1: %.5f, top 5: %.5f\\\" %\\n (len(val_loader), top1_cnt*100.0/cnt, top5_cnt*100.0/cnt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c777305f27dcd901af5ad15a5837cf\",\n \"score\": \"0.58955616\",\n \"text\": \"def main():\\n # Data loaders\\n if check_for_augmented_data(\\\"./data\\\"):\\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\\n task=\\\"target\\\", batch_size=config(\\\"cnn.batch_size\\\"), augment=True\\n )\\n else:\\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\\n task=\\\"target\\\",\\n batch_size=config(\\\"cnn.batch_size\\\"),\\n )\\n # Model\\n model = Target()\\n\\n # TODO: define loss function, and optimizer\\n criterion = torch.nn.CrossEntropyLoss()\\n optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\\n #\\n\\n print(\\\"Number of float-valued parameters:\\\", count_parameters(model))\\n\\n # Attempts to restore the latest checkpoint if exists\\n print(\\\"Loading cnn...\\\")\\n model, start_epoch, stats = restore_checkpoint(model, config(\\\"cnn.checkpoint\\\"))\\n\\n axes = utils.make_training_plot()\\n\\n # Evaluate the randomly initialized model\\n evaluate_epoch(\\n axes, tr_loader, va_loader, te_loader, model, criterion, start_epoch, stats\\n )\\n\\n # initial val loss for early stopping\\n prev_val_loss = stats[0][1]\\n\\n # TODO: define patience for early stopping\\n patience = 5\\n curr_patience = 0\\n #\\n\\n # Loop over the entire dataset multiple times\\n # for epoch in range(start_epoch, config('cnn.num_epochs')):\\n epoch = start_epoch\\n while curr_patience < patience:\\n # Train model\\n train_epoch(tr_loader, model, criterion, optimizer)\\n\\n # Evaluate model\\n evaluate_epoch(\\n axes, tr_loader, va_loader, te_loader, model, criterion, epoch + 1, stats\\n )\\n\\n # Save model parameters\\n save_checkpoint(model, epoch + 1, config(\\\"cnn.checkpoint\\\"), stats)\\n\\n # update early stopping parameters\\n curr_patience, prev_val_loss = early_stopping(\\n stats, curr_patience, prev_val_loss\\n )\\n\\n epoch += 1\\n print(\\\"Finished Training\\\")\\n # Save figure and keep plot open\\n utils.save_cnn_training_plot()\\n utils.hold_training_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbe1449e57be9c34d14dfcbde67c564\",\n \"score\": \"0.5892119\",\n \"text\": \"def Classification_restore_and_train(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size'] \\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n save_interval = config['save_interval']\\n msg_bot = config['msg_bot']\\n bot_msg_interval = config['bot_msg_interval']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data to restore dataset\\n data = Data()\\n data.add_dataset(Cholec80Restored())\\n train_ds = (dataset_name, 'train')\\n val_ds = (dataset_name, 'val')\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Restore and define path\\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\\n splits = lr.load_json(path=paths, name='data_splits')\\n print('Restored existing splits')\\n\\n # 4. Create data splits for each repetition\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize'])\\n\\n # 6. Build train dataloader, and visualize\\n dl = DataLoader(datasets[(train_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=0)\\n dl_val = DataLoader(datasets[(val_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Initialize model\\n model = getattr(models, model_name)(output_features)\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\\n weight_decay=weight_decay)\\n\\n # 9. Train model\\n state_names = [name for name in os.listdir(paths) if '.' not in name]\\n state_name = state_names[0].split('_')[0]\\n for idx, state in enumerate(state_names):\\n state_names[idx] = int(state.split('_')[-1])\\n state_names.sort()\\n state_name += '_' + str(state_names[-1])\\n\\n print('Restore last saved model from epoch {}..'.format(state_name.split('_')[-1]))\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n restored, restored_results = agent.restore_state(paths, state_name, optimizer=optimizer)\\n if not restored:\\n print(\\\"Desired state could not be recovered. --> Error!\\\")\\n raise FileNotFoundError\\n\\n losses_train_r, losses_cum_train_r, losses_val_r, losses_cum_val_r, accuracy_train_r,\\\\\\n accuracy_det_train_r, accuracy_val_r, accuracy_det_val_r = restored_results\\n\\n print('Training model in batches of {}..'.format(batch_size))\\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\\n dl_val, nr_epochs=config['nr_epochs'],\\n start_epoch=int(state_name.split('_')[-1]),\\n save_path=paths, losses=losses_train_r.tolist(),\\n losses_cum=losses_cum_train_r.tolist(),\\n losses_val=losses_val_r.tolist(),\\n losses_cum_val=losses_cum_val_r.tolist(),\\n accuracy=accuracy_train_r.tolist(),\\n accuracy_detailed=accuracy_det_train_r.tolist(),\\n accuracy_val=accuracy_val_r.tolist(),\\n accuracy_val_detailed=accuracy_det_val_r.tolist(),\\n save_interval=save_interval, msg_bot=msg_bot,\\n bot_msg_interval=bot_msg_interval)\\n\\n # 10. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True)\\n \\n # 11. Test model\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 12. Save results\\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\\n accuracy_det_test, losses_cum_train, losses_cum_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a2ed71f70a2f7851513e0fa81e36e1\",\n \"score\": \"0.58881956\",\n \"text\": \"def main(feature_pkl='C:\\\\\\\\Users\\\\Cory\\\\\\\\Documents\\\\\\\\DataScienceWorkshop\\\\\\\\avito_kaggle\\\\\\\\new-feat-full\\\\\\\\train_data.pkl', model=SGDClassifier(loss='log',penalty='l2',alpha=1e-4,class_weight='auto'), KFOLD=10):\\n # DEFAULT MODEL:\\n # Stochastic Gradient Descent (online learning)\\n # loss (cost) = log ~ Logistic Regression\\n # L2 norm used for cost, alpha ~ Regularization\\n # class_weight = auto\\n\\n # Wrapper function may pre-load these large variables and pass as tuple instead of doing this step iteratively.\\n if type(feature_pkl) is tuple: \\n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = feature_pkl\\n else:\\n print 'Loading .pkl data for fitting/cross-validation...'\\n if feature_pkl.find('new-feat')>-1:\\n # Benchmark code did not save column names (featureIndex)\\n trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\\n else:\\n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\\n if type(model) is str:\\n model = eval(model)\\n KFOLD = int(KFOLD)\\n \\n font = {'family' : 'normal',\\n 'weight' : 'bold',\\n 'size' : 22}\\n matplotlib.rc('font', **font)\\n \\n # convert features to CSR for row-slicing\\n trainFeatures = trainFeatures.tocsr()\\n\\n #Cross validation split into 10 folds for cross-validation\\n kf_total = cross_validation.KFold(len(trainItemIds),n_folds=KFOLD,shuffle=True,indices=True)\\n \\n #conversion of targets to numpy \\n trainTargets = np.asarray(trainTargets)\\n count = 0\\n total_conf=np.zeros(shape=(2,2))\\n mean_tpr = 0\\n mean_fpr = np.linspace(0, 1, 100)\\n \\n #Iterate through the folds of the dataset\\n for train_indices, test_indices in kf_total:\\n #Calculation of the confusion matrix values for each fold \\n model.fit(trainFeatures[train_indices], trainTargets[train_indices])\\n predicted = model.predict(trainFeatures[test_indices])\\n conf_arr = metrics.confusion_matrix(trainTargets[test_indices],predicted)\\n norm_conf = [] \\n for i in conf_arr:\\n a = 0\\n tmp_arr = []\\n a = sum(i, 0)\\n for j in i:\\n tmp_arr.append(float(j)/float(a))\\n norm_conf.append(tmp_arr)\\n total_conf += norm_conf\\n #Calculation of the ROC/AUC for each fold\\n prob = model_predicted_prob(model,trainFeatures[test_indices])\\n fpr, tpr, thresholds = metrics.roc_curve(trainTargets[test_indices],prob)\\n mean_tpr += interp(mean_fpr, fpr, tpr)\\n mean_tpr[0] = 0.0\\n print \\\"Finished with fold number \\\" + str(count+1)\\n count += 1\\n \\n #Calculate mean values and plot the results\\n mean_tpr /= KFOLD\\n mean_tpr[-1] = 1.0\\n total_conf /= KFOLD\\n \\n #Plot the confusion matrix\\n labels = ['not blocked','blocked']\\n fig = plt.figure(figsize=(10,8))\\n plt.clf()\\n ax = fig.add_subplot(111)\\n cax = ax.matshow(np.array(norm_conf), cmap=plt.cm.jet, \\n interpolation='nearest')\\n plt.title('Confusion matrix')\\n fig.colorbar(cax)\\n ax.set_xticklabels([''] + labels)\\n ax.set_yticklabels([''] + labels)\\n plt.xlabel('Predicted')\\n plt.ylabel('True')\\n \\n #Add confusion matrix values to the graph\\n width = len(norm_conf)\\n height = len(norm_conf[0])\\n for x in xrange(width):\\n for y in xrange(height):\\n ax.annotate('%.4f' % norm_conf[x][y], xy=(y, x), \\n horizontalalignment='center',\\n verticalalignment='center')\\n print \\\"Confusion Matrix \\\\n\\\" + str(total_conf)\\n for ext in ['.png','.pdf','.jpg']:\\n fig.savefig('confusion'+ext)\\n \\n #Plot the ROC\\n fig = plt.figure(figsize=(10,8))\\n plt.plot(mean_fpr,mean_tpr)\\n plt.xlim([-0.05, 1.05])\\n plt.ylim([-0.05, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n for ext in ['.png','.pdf','.jpg']:\\n fig.savefig('roc'+ext)\\n \\n auc_score = metrics.auc(mean_fpr,mean_tpr)\\n print \\\"AUC score\\\\n\\\" + str(auc_score)\\n \\n logging.info(\\\"Done with cross-validation\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4903e00d13c6d1a5d4967e817001609\",\n \"score\": \"0.58879054\",\n \"text\": \"def dataset_setup(self):\\n settings = self.settings\\n if settings.crowd_dataset == 'World Expo':\\n train_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\\n data.RandomHorizontalFlip(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n validation_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n dataset_path = '../World Expo/'\\n with open(os.path.join(dataset_path, 'viable_with_validation_and_random_test.json')) as json_file:\\n cameras_dict = json.load(json_file)\\n self.train_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\\n number_of_cameras=settings.number_of_cameras,\\n number_of_images_per_camera=settings.number_of_images_per_camera,\\n transform=train_transform, seed=settings.labeled_dataset_seed)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n # self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\\n # transform=train_transform, unlabeled=True,\\n # seed=100)\\n self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\\n number_of_cameras=settings.number_of_cameras,\\n transform=train_transform, unlabeled=True,\\n seed=settings.labeled_dataset_seed)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.validation_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\\n transform=validation_transform, seed=101)\\n elif settings.crowd_dataset == 'ShanghaiTech':\\n train_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\\n data.RandomHorizontalFlip(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n validation_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n self.train_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.unlabeled_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed,\\n unlabeled=True)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.validation_dataset = ShanghaiTechDataset(dataset='test', transform=validation_transform, seed=101)\\n else:\\n raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914e7b701c70b5ff67c1331a07c46010\",\n \"score\": \"0.588063\",\n \"text\": \"def initiate_training(augmentation, backbone, batch_size, dataset_path, epochs, model, random_occlusions,\\n snapshot_base_name, snapshot_path, steps_per_epoch, tensorboard_dir, validation_set,\\n validation_split, counting_model, balance_datasets=False):\\n if augmentation:\\n transform_generator = random_transform_generator(\\n min_rotation=-0.1,\\n max_rotation=0.1,\\n min_translation=(-0.1, -0.1),\\n max_translation=(0.1, 0.1),\\n min_shear=-0.1,\\n max_shear=0.1,\\n min_scaling=(0.9, 0.9),\\n max_scaling=(1.1, 1.1),\\n flip_x_chance=0.5,\\n flip_y_chance=0.5)\\n else:\\n transform_generator = random_transform_generator(flip_x_chance=0.5)\\n\\n dataset = CarsDataset(dataset_path, validation_split=validation_split, validation_set=validation_set,\\n balance_datasets=balance_datasets)\\n\\n val_generator = None\\n validation_steps = None\\n train_generator = CarsGenerator(dataset.train,\\n preprocess_image=backbone.get_preprocess_image(),\\n counting_model=counting_model,\\n transform_generator=transform_generator,\\n batch_size=batch_size,\\n image_min_side=720,\\n image_max_side=1280,\\n perform_random_occlusions=random_occlusions)\\n\\n if dataset.validation:\\n val_generator = CarsGenerator(dataset.validation,\\n preprocess_image=backbone.get_preprocess_image(),\\n counting_model=counting_model,\\n batch_size=batch_size,\\n image_min_side=720,\\n image_max_side=1280,\\n perform_random_occlusions=False)\\n validation_steps = len(val_generator)\\n os.makedirs(snapshot_path, exist_ok=True)\\n with open('{}/validation.txt'.format(snapshot_path), \\\"wt\\\") as f:\\n for img_path in dataset.validation.keys():\\n print(img_path, file=f)\\n\\n callbacks = create_callbacks(model,\\n batch_size=batch_size,\\n tensorboard_dir=tensorboard_dir,\\n snapshot_path=snapshot_path,\\n snapshot_name_base=snapshot_base_name)\\n if steps_per_epoch is None:\\n steps_per_epoch = len(train_generator)\\n\\n model.fit_generator(\\n generator=train_generator,\\n steps_per_epoch=steps_per_epoch,\\n callbacks=callbacks,\\n epochs=epochs,\\n validation_data=val_generator,\\n validation_steps=validation_steps,\\n verbose=1,\\n workers=1,\\n use_multiprocessing=True,\\n max_queue_size=10\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703a4c5a748da4a3e27519a47d5fd13e\",\n \"score\": \"0.587915\",\n \"text\": \"def evaluate_lenet5(learning_rate=0.1, n_epochs=200,\\n dataset='mnist.pkl.gz',\\n nkerns=[20,50],batch_size=500):\\n \\n rng = numpy.random.RandomState(1234)\\n print('Loading Data'+'.'*20)\\n datasets = load_data(dataset)\\n \\n trainSetX, trainSetY = datasets[0]\\n validSetX, validSetY = datasets[1]\\n testSetX, testSetY = datasets[2]\\n \\n n_train_batches = trainSetX.get_value(borrow=True).shape[0] // batch_size\\n n_valid_batches = validSetX.get_value(borrow=True).shape[0] // batch_size\\n n_test_batches = testSetX.get_value(borrow=True).shape[0] // batch_size\\n \\n print('Building Data'+'.'*20)\\n \\n index = T.lscalar('index')\\n x = T.matrix('x')\\n y = T.ivector('y')\\n \\n # Reshape matrix of rasterized images of shape (batch_size, 28*28)\\n # to a 4D tensor, compatible with our LeNetConvPoolLayer\\n layer0_input = x.reshape((batch_size,1,28,28))\\n \\n # construct the first convolutional pooling layer\\n # filtering reduces the image size to (28-5+1,28-5+1) = (24, 24)\\n # maxpooling reduces this further to (24/2, 24/2) = (12, 12)\\n # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)\\n layer0 = LeNetConvPoolLayer(\\n rng=rng,\\n input=layer0_input,\\n image_shape=(batch_size,1,28,28),\\n filter_shape=(nkerns[0],1,5,5),\\n poolsize=(2,2)\\n )\\n \\n # construct the second convolutional pooling layer\\n # filtering reduces the image size to (12-5+1,12-5+1) = (8, 8)\\n # maxpooling reduces this further to (8/2, 8/2) = (4, 4)\\n # 4D output tensor is thus of shape (batch_size,nkerns[1],4,4)\\n layer1 = LeNetConvPoolLayer(\\n rng=rng,\\n input=layer0.output,\\n image_shape=(batch_size,nkerns[0],12,12),\\n filter_shape=(nkerns[1],nkerns[0],5,5),\\n poolsize=(2,2)\\n )\\n \\n layer2_input = layer1.output.flatten(2)\\n layer2 = HiddenLayer(\\n rng=rng,\\n input=layer2_input,\\n n_in=nkerns[1]*4*4,\\n n_out=500,\\n activation=T.tanh\\n )\\n \\n layer3 = LogisticRegression(input=layer2.output,n_in=500,n_out=10)\\n \\n testModel = theano.function(\\n inputs=[index],\\n outputs=layer3.errors(y),\\n givens={\\n x:testSetX[index*batch_size:(index+1)*batch_size], \\n y:testSetY[index*batch_size:(index+1)*batch_size] \\n } \\n )\\n validModel = theano.function(\\n inputs=[index],\\n outputs=layer3.errors(y),\\n givens={\\n x:validSetX[index*batch_size:(index+1)*batch_size], \\n y:validSetY[index*batch_size:(index+1)*batch_size] \\n }\\n )\\n \\n params = layer3.params+layer2.params+layer1.params+layer0.params\\n cost = layer3.negative_log_likelihood(y)\\n grads = T.grad(cost,params)\\n \\n updates= [(param, param - learning_rate*grad)\\n for param,grad in zip(params,grads)\\n ]\\n \\n trainModel = theano.function(\\n inputs=[index],\\n outputs=cost,\\n updates=updates,\\n givens={\\n x:trainSetX[index*batch_size:(index+1)*batch_size], \\n y:trainSetY[index*batch_size:(index+1)*batch_size] \\n } \\n )\\n \\n print('Training'+'.'*20)\\n \\n patience = 10000\\n patience_increase = 2\\n improvement_threshold = 2\\n validation_frequence = min(n_train_batches, patience/2)\\n \\n best_validation_loss = numpy.inf\\n best_iter = 0\\n test_score =0.\\n start_time = timeit.default_timer()\\n \\n epoch = 0\\n done_looping = False\\n \\n while (epoch < n_epochs) and (not done_looping):\\n epoch += 1\\n \\n for mini_batch_index in range(n_train_batches):\\n iter = (epoch - 1) * n_train_batches + mini_batch_index\\n if iter % 100 == 0:\\n print('training @ iter = ' , iter)\\n \\n cost_ij = trainModel(mini_batch_index)\\n if (iter + 1) % validation_frequence ==0:\\n validation_losses = [validModel(i) for i \\n in range(n_valid_batches)]\\n this_validation_losses = numpy.mean(validation_losses)\\n print('epoch %i, minibatch %i/%i, validation error %f %%' %\\n (epoch, mini_batch_index+1, n_train_batches,\\n this_validation_losses*100)\\n )\\n if this_validation_losses < best_validation_loss:\\n best_validation_loss = this_validation_losses\\n best_iter = iter\\n if this_validation_losses < best_validation_loss * \\\\\\n improvement_threshold:\\n patience = max(patience, patience*patience_increase)\\n \\n test_losses = [testModel(i) for i in range(n_test_batches)]\\n test_score = numpy.mean(test_losses)\\n \\n print(' epoch %i, minibatch %i/%i, test error of'\\n 'best model %f %%'%\\n (epoch, mini_batch_index+1, n_train_batches,\\n this_validation_losses*100)\\n )\\n if patience <= iter:\\n done_looping = True\\n break\\n endtime = timeit.default_timer()\\n print('Optimization complete.')\\n print('Best validation score of %f %% obtained at iteration %i, '\\n ' with test performance %f %%' %\\n (best_validation_loss*100, best_iter+1, test_score*100.)\\n )\\n \\n print('The code for file ' + os.path.split(__file__)[1]+\\n ' ran for %.2fm' % (endtime - start_time)/60. \\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf981ff48b6e7565b993d0c026bd7c2e\",\n \"score\": \"0.58766633\",\n \"text\": \"def ModelTraindata():\\n data = request.get_json()\\n with open(app.config[\\\"DATA_PATH\\\"] + \\\"/config/params.json\\\") as json_file:\\n default_configs = json.load(json_file)\\n default_configs[\\\"experiment\\\"][\\\"model\\\"] = data[\\\"model\\\"]\\n default_configs[\\\"experiment\\\"][\\\"batch_size\\\"] = int(data[\\\"batch_size\\\"])\\n default_configs[\\\"experiment\\\"][\\\"epochs\\\"] = int(data[\\\"epochs\\\"])\\n default_configs[\\\"experiment\\\"][\\\"learning_rate\\\"] = float(\\n data[\\\"learning_rate\\\"])\\n default_configs[\\\"experiment\\\"][\\\"class_ids\\\"] = list(\\n range(0, 43)) + (np.array(data[\\\"class_ids\\\"]) + 43).tolist()\\n default_configs[\\\"experiment\\\"][\\\"num_classes\\\"] = int(\\n data[\\\"num_classes\\\"]) + 43\\n\\n max_train = -1\\n for file in os.listdir(app.config[\\\"DATA_PATH\\\"] + \\\"checkpoints/logs/\\\"):\\n if file.startswith(\\\"temp\\\"):\\n number = int(file.split(\\\"_\\\")[-1])\\n max_train = max(max_train, number)\\n\\n max_train+=1\\n next_config = \\\"temp_config_\\\"+ str(max_train)\\n with open(app.config[\\\"DATA_PATH\\\"] + \\\"config/\\\" + next_config + \\\".json\\\", \\\"w\\\") as outfile:\\n json.dump(default_configs, outfile, indent=4)\\n\\n try:\\n shutil.move(os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA\\\"),\\n os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA_copy\\\"))\\n remake_EXTRA_folder(\\n 0, 0, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n remake_EXTRA_folder(\\n 0, 1, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n remake_EXTRA_folder(\\n 1, 0, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n \\n \\n t1 = threading.Thread(target=train.train, args=[next_config])\\n t1.start()\\n while t1.is_alive():\\n continue\\n\\n except KeyboardInterrupt:\\n \\\"Stopped Abruptly\\\"\\n finally:\\n print(\\\"finally\\\")\\n f = open(os.path.join(\\\"data/traffic_sign_interiit/dataset/\\\", \\\"TrainInfo.txt\\\"), \\\"w+\\\")\\n f.close()\\n shutil.rmtree(app.config[\\\"DATA_PATH\\\"] + \\\"dataset/EXTRA\\\")\\n shutil.move(os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA_copy\\\"),\\n os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA\\\"))\\n\\n check = True\\n if check:\\n return make_response(jsonify({\\\"message\\\": \\\"Model Trained Successfuly! Saved as: \\\"+ next_config +\\\".pt. Reload to see it in test\\\"}), 200)\\n else:\\n return make_response(jsonify({\\\"error\\\": \\\"Something is Wrong. Try Again!\\\"}), 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e135a1d790910c67e738f3c6dfc769e\",\n \"score\": \"0.5874946\",\n \"text\": \"def train_coxnet_model_for_baseline(cancer_data_path, folds_data_path,\\r\\n test_fold_index, max_iter, top_k_variance_limit,\\r\\n selected_features_path, selected_features_count, present_output=True,\\r\\n model_output_path=\\\"\\\"):\\r\\n\\r\\n # create train and test sets\\r\\n X_train, y_train = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path,\\r\\n [i for i in range(5) if i != test_fold_index], selected_features_path,\\r\\n selected_features_count)\\r\\n X_test, y_test = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path, [test_fold_index],\\r\\n selected_features_path, selected_features_count)\\r\\n\\r\\n # train a simple coxnet model to get a range of possible alphas\\r\\n cox_net = CoxnetSurvivalAnalysis(max_iter=max_iter)\\r\\n cox_net.fit(X_train, y_train)\\r\\n initial_alphas = cox_net.alphas_\\r\\n\\r\\n params = {\\\"alphas\\\": [[v] for i, v in enumerate(initial_alphas) if i % 5 == 0] + [[v * 10] for i, v in\\r\\n enumerate(initial_alphas) if\\r\\n i % 5 == 0]\\r\\n , \\\"l1_ratio\\\": [0.00101, 0.1, 0.5, 0.7, 1]}\\r\\n\\r\\n gcv_results = get_grid_search_results(params, X_train, y_train, max_iter)\\r\\n gcv_results_table = pd.DataFrame(gcv_results.cv_results_)\\r\\n best_model = gcv_results.best_estimator_\\r\\n alphas = gcv_results_table.param_alphas.map(lambda x: x[0])\\r\\n\\r\\n best_l1_ratio = best_model.l1_ratio\\r\\n best_coefs = pd.DataFrame(\\r\\n best_model.coef_,\\r\\n index=X_train.columns,\\r\\n columns=[\\\"coefficient\\\"]\\r\\n )\\r\\n\\r\\n best_model_cv = get_ci_score(best_model, X_test, y_test)\\r\\n\\r\\n if present_output:\\r\\n # present results\\r\\n print(\\\"Best model C.I for test set on fold #{} is: {}\\\".format(test_fold_index, best_model_cv))\\r\\n print(\\\"Best alpha: {}\\\".format(best_model.alphas_))\\r\\n print(\\\"Best l1 ratio: {}\\\".format(best_l1_ratio))\\r\\n visualize_alpha_values(best_model, best_coefs, alphas)\\r\\n visualize_ci_for_alpha_values(gcv_results_table, gcv_results, alphas)\\r\\n print(\\\"Internal CV scores while training: \\\")\\r\\n print(cross_val_score(best_model, X_train, y_train,\\r\\n cv=5)) # check what to actually cross validate on - holdout or all?\\r\\n\\r\\n # save model\\r\\n if model_output_path:\\r\\n pickle.dump(best_model, open(model_output_path, \\\"wb\\\"))\\r\\n return best_model, best_model_cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f59f72f592409cc0bd15f81fb5b465\",\n \"score\": \"0.5869422\",\n \"text\": \"def setup(data, \\n target, \\n train_size = 0.7,\\n sampling = True,\\n sample_estimator = None,\\n categorical_features = None,\\n categorical_imputation = 'constant',\\n ordinal_features = None,\\n high_cardinality_features = None, \\n high_cardinality_method = 'frequency', \\n numeric_features = None,\\n numeric_imputation = 'mean',\\n date_features = None,\\n ignore_features = None,\\n normalize = False,\\n normalize_method = 'zscore',\\n transformation = False,\\n transformation_method = 'yeo-johnson',\\n handle_unknown_categorical = True, \\n unknown_categorical_method = 'least_frequent',\\n pca = False,\\n pca_method = 'linear',\\n pca_components = None, \\n ignore_low_variance = False, \\n combine_rare_levels = False,\\n rare_level_threshold = 0.10,\\n bin_numeric_features = None, \\n remove_outliers = False,\\n outliers_threshold = 0.05,\\n remove_multicollinearity = False,\\n multicollinearity_threshold = 0.9,\\n remove_perfect_collinearity = False, #added in pycaret==2.0.0\\n create_clusters = False,\\n cluster_iter = 20,\\n polynomial_features = False, \\n polynomial_degree = 2,\\n trigonometry_features = False,\\n polynomial_threshold = 0.1,\\n group_features = None,\\n group_names = None,\\n feature_selection = False,\\n feature_selection_threshold = 0.8,\\n feature_interaction = False,\\n feature_ratio = False,\\n interaction_threshold = 0.01, \\n transform_target = False,\\n transform_target_method = 'box-cox',\\n data_split_shuffle = True, #added in pycaret==2.0.0\\n folds_shuffle = False, #added in pycaret==2.0.0\\n n_jobs = -1, #added in pycaret==2.0.0\\n use_gpu = False, #added in pycaret==2.1\\n html = True, #added in pycaret==2.0.0\\n session_id = None,\\n log_experiment = False, #added in pycaret==2.0.0\\n experiment_name = None, #added in pycaret==2.0.0\\n log_plots = False, #added in pycaret==2.0.0\\n log_profile = False, #added in pycaret==2.0.0\\n log_data = False, #added in pycaret==2.0.0\\n silent = False,\\n verbose = True, #added in pycaret==2.0.0\\n profile = False):\\n \\n #exception checking \\n import sys\\n \\n from pycaret.utils import __version__\\n ver = __version__()\\n\\n import logging\\n\\n # create logger\\n global logger\\n\\n logger = logging.getLogger('logs')\\n logger.setLevel(logging.DEBUG)\\n \\n # create console handler and set level to debug\\n\\n if logger.hasHandlers():\\n logger.handlers.clear()\\n \\n ch = logging.FileHandler('logs.log')\\n ch.setLevel(logging.DEBUG)\\n\\n # create formatter\\n formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')\\n\\n # add formatter to ch\\n ch.setFormatter(formatter)\\n\\n # add ch to logger\\n logger.addHandler(ch)\\n\\n logger.info(\\\"PyCaret Regression Module\\\")\\n logger.info('version ' + str(ver))\\n logger.info(\\\"Initializing setup()\\\")\\n\\n #generate USI for mlflow tracking\\n import secrets\\n global USI\\n USI = secrets.token_hex(nbytes=2)\\n logger.info('USI: ' + str(USI))\\n\\n logger.info(\\\"\\\"\\\"setup(data={}, target={}, train_size={}, sampling={}, sample_estimator={}, categorical_features={}, categorical_imputation={}, ordinal_features={},\\n high_cardinality_features={}, high_cardinality_method={}, numeric_features={}, numeric_imputation={}, date_features={}, ignore_features={}, normalize={},\\n normalize_method={}, transformation={}, transformation_method={}, handle_unknown_categorical={}, unknown_categorical_method={}, pca={}, pca_method={},\\n pca_components={}, ignore_low_variance={}, combine_rare_levels={}, rare_level_threshold={}, bin_numeric_features={}, remove_outliers={}, outliers_threshold={},\\n remove_multicollinearity={}, multicollinearity_threshold={}, remove_perfect_collinearity={}, create_clusters={}, cluster_iter={},\\n polynomial_features={}, polynomial_degree={}, trigonometry_features={}, polynomial_threshold={}, group_features={},\\n group_names={}, feature_selection={}, feature_selection_threshold={}, feature_interaction={}, feature_ratio={}, interaction_threshold={}, transform_target={},\\n transform_target_method={}, data_split_shuffle={}, folds_shuffle={}, n_jobs={}, html={}, session_id={}, log_experiment={},\\n experiment_name={}, log_plots={}, log_profile={}, log_data={}, silent={}, verbose={}, profile={})\\\"\\\"\\\".format(\\\\\\n str(data.shape), str(target), str(train_size), str(sampling), str(sample_estimator), str(categorical_features), str(categorical_imputation), str(ordinal_features),\\\\\\n str(high_cardinality_features), str(high_cardinality_method), str(numeric_features), str(numeric_imputation), str(date_features), str(ignore_features),\\\\\\n str(normalize), str(normalize_method), str(transformation), str(transformation_method), str(handle_unknown_categorical), str(unknown_categorical_method), str(pca),\\\\\\n str(pca_method), str(pca_components), str(ignore_low_variance), str(combine_rare_levels), str(rare_level_threshold), str(bin_numeric_features), str(remove_outliers),\\\\\\n str(outliers_threshold), str(remove_multicollinearity), str(multicollinearity_threshold), str(remove_perfect_collinearity), str(create_clusters), str(cluster_iter),\\\\\\n str(polynomial_features), str(polynomial_degree), str(trigonometry_features), str(polynomial_threshold), str(group_features), str(group_names),\\\\\\n str(feature_selection), str(feature_selection_threshold), str(feature_interaction), str(feature_ratio), str(interaction_threshold), str(transform_target),\\\\\\n str(transform_target_method), str(data_split_shuffle), str(folds_shuffle), str(n_jobs), str(html), str(session_id),\\\\\\n str(log_experiment), str(experiment_name), str(log_plots), str(log_profile), str(log_data), str(silent), str(verbose), str(profile)))\\n\\n #logging environment and libraries\\n logger.info(\\\"Checking environment\\\")\\n \\n from platform import python_version, platform, python_build, machine\\n\\n try:\\n logger.info(\\\"python_version: \\\" + str(python_version()))\\n except:\\n logger.warning(\\\"cannot find platform.python_version\\\")\\n\\n try:\\n logger.info(\\\"python_build: \\\" + str(python_build()))\\n except:\\n logger.warning(\\\"cannot find platform.python_build\\\")\\n\\n try:\\n logger.info(\\\"machine: \\\" + str(machine()))\\n except:\\n logger.warning(\\\"cannot find platform.machine\\\")\\n\\n try:\\n logger.info(\\\"platform: \\\" + str(platform()))\\n except:\\n logger.warning(\\\"cannot find platform.platform\\\")\\n\\n try:\\n import psutil\\n logger.info(\\\"Memory: \\\" + str(psutil.virtual_memory()))\\n logger.info(\\\"Physical Core: \\\" + str(psutil.cpu_count(logical=False)))\\n logger.info(\\\"Logical Core: \\\" + str(psutil.cpu_count(logical=True)))\\n except:\\n logger.warning(\\\"cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging. \\\")\\n \\n logger.info(\\\"Checking libraries\\\")\\n\\n try:\\n from pandas import __version__\\n logger.info(\\\"pd==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"pandas not found\\\")\\n\\n try:\\n from numpy import __version__\\n logger.info(\\\"numpy==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"numpy not found\\\")\\n\\n try:\\n from sklearn import __version__\\n logger.info(\\\"sklearn==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"sklearn not found\\\")\\n\\n try:\\n from xgboost import __version__\\n logger.info(\\\"xgboost==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"xgboost not found\\\")\\n\\n try:\\n from lightgbm import __version__\\n logger.info(\\\"lightgbm==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"lightgbm not found\\\")\\n\\n try:\\n from catboost import __version__\\n logger.info(\\\"catboost==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"catboost not found\\\")\\n\\n try:\\n from mlflow.version import VERSION\\n import warnings\\n warnings.filterwarnings('ignore') \\n logger.info(\\\"mlflow==\\\" + str(VERSION))\\n except:\\n logger.warning(\\\"mlflow not found\\\")\\n\\n #run_time\\n import datetime, time\\n runtime_start = time.time()\\n\\n logger.info(\\\"Checking Exceptions\\\")\\n\\n #checking data type\\n if hasattr(data,'shape') is False:\\n sys.exit('(Type Error): data passed must be of type pandas.DataFrame')\\n\\n #checking train size parameter\\n if type(train_size) is not float:\\n sys.exit('(Type Error): train_size parameter only accepts float value.')\\n \\n #checking sampling parameter\\n if type(sampling) is not bool:\\n sys.exit('(Type Error): sampling parameter only accepts True or False.')\\n \\n #checking sampling parameter\\n if target not in data.columns:\\n sys.exit('(Value Error): Target parameter doesnt exist in the data provided.') \\n\\n #checking session_id\\n if session_id is not None:\\n if type(session_id) is not int:\\n sys.exit('(Type Error): session_id parameter must be an integer.') \\n \\n #checking sampling parameter\\n if type(profile) is not bool:\\n sys.exit('(Type Error): profile parameter only accepts True or False.')\\n \\n #checking normalize parameter\\n if type(normalize) is not bool:\\n sys.exit('(Type Error): normalize parameter only accepts True or False.')\\n \\n #checking transformation parameter\\n if type(transformation) is not bool:\\n sys.exit('(Type Error): transformation parameter only accepts True or False.')\\n \\n #checking categorical imputation\\n allowed_categorical_imputation = ['constant', 'mode']\\n if categorical_imputation not in allowed_categorical_imputation:\\n sys.exit(\\\"(Value Error): categorical_imputation param only accepts 'constant' or 'mode' \\\")\\n \\n #ordinal_features\\n if ordinal_features is not None:\\n if type(ordinal_features) is not dict:\\n sys.exit(\\\"(Type Error): ordinal_features must be of type dictionary with column name as key and ordered values as list. \\\")\\n \\n #ordinal features check\\n if ordinal_features is not None:\\n data_cols = data.columns\\n data_cols = data_cols.drop(target)\\n ord_keys = ordinal_features.keys()\\n \\n for i in ord_keys:\\n if i not in data_cols:\\n sys.exit(\\\"(Value Error) Column name passed as a key in ordinal_features param doesnt exist. \\\")\\n \\n for k in ord_keys:\\n if data[k].nunique() != len(ordinal_features.get(k)):\\n sys.exit(\\\"(Value Error) Levels passed in ordinal_features param doesnt match with levels in data. \\\")\\n\\n for i in ord_keys:\\n value_in_keys = ordinal_features.get(i)\\n value_in_data = list(data[i].unique().astype(str))\\n for j in value_in_keys:\\n if j not in value_in_data:\\n text = \\\"Column name '\\\" + str(i) + \\\"' doesnt contain any level named '\\\" + str(j) + \\\"'.\\\"\\n sys.exit(text)\\n \\n #high_cardinality_features\\n if high_cardinality_features is not None:\\n if type(high_cardinality_features) is not list:\\n sys.exit(\\\"(Type Error): high_cardinality_features param only accepts name of columns as a list. \\\")\\n \\n if high_cardinality_features is not None:\\n data_cols = data.columns\\n data_cols = data_cols.drop(target)\\n for i in high_cardinality_features:\\n if i not in data_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #checking numeric imputation\\n allowed_numeric_imputation = ['mean', 'median']\\n if numeric_imputation not in allowed_numeric_imputation:\\n sys.exit(\\\"(Value Error): numeric_imputation param only accepts 'mean' or 'median' \\\")\\n \\n #checking normalize method\\n allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']\\n if normalize_method not in allowed_normalize_method:\\n sys.exit(\\\"(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. \\\") \\n \\n #checking transformation method\\n allowed_transformation_method = ['yeo-johnson', 'quantile']\\n if transformation_method not in allowed_transformation_method:\\n sys.exit(\\\"(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile' \\\") \\n \\n #handle unknown categorical\\n if type(handle_unknown_categorical) is not bool:\\n sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')\\n \\n #unknown categorical method\\n unknown_categorical_method_available = ['least_frequent', 'most_frequent']\\n \\n if unknown_categorical_method not in unknown_categorical_method_available:\\n sys.exit(\\\"(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.\\\")\\n \\n #check pca\\n if type(pca) is not bool:\\n sys.exit('(Type Error): PCA parameter only accepts True or False.')\\n \\n #pca method check\\n allowed_pca_methods = ['linear', 'kernel', 'incremental',]\\n if pca_method not in allowed_pca_methods:\\n sys.exit(\\\"(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. \\\") \\n \\n #pca components check\\n if pca is True:\\n if pca_method != 'linear':\\n if pca_components is not None:\\n if(type(pca_components)) is not int:\\n sys.exit(\\\"(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. \\\")\\n\\n #pca components check 2\\n if pca is True:\\n if pca_method != 'linear':\\n if pca_components is not None:\\n if pca_components > len(data.columns)-1:\\n sys.exit(\\\"(Type Error): pca_components parameter cannot be greater than original features space.\\\") \\n \\n #pca components check 3\\n if pca is True:\\n if pca_method == 'linear':\\n if pca_components is not None:\\n if type(pca_components) is not float:\\n if pca_components > len(data.columns)-1: \\n sys.exit(\\\"(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.\\\") \\n \\n #check ignore_low_variance\\n if type(ignore_low_variance) is not bool:\\n sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')\\n \\n #check ignore_low_variance\\n if type(combine_rare_levels) is not bool:\\n sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')\\n \\n #check rare_level_threshold\\n if type(rare_level_threshold) is not float:\\n sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')\\n \\n #bin numeric features\\n if bin_numeric_features is not None:\\n all_cols = list(data.columns)\\n all_cols.remove(target)\\n \\n for i in bin_numeric_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #check transform_target\\n if type(transform_target) is not bool:\\n sys.exit('(Type Error): transform_target parameter only accepts True or False.')\\n \\n #transform_target_method\\n allowed_transform_target_method = ['box-cox', 'yeo-johnson']\\n if transform_target_method not in allowed_transform_target_method:\\n sys.exit(\\\"(Value Error): transform_target_method param only accepts 'box-cox' or 'yeo-johnson'. \\\") \\n \\n #remove_outliers\\n if type(remove_outliers) is not bool:\\n sys.exit('(Type Error): remove_outliers parameter only accepts True or False.') \\n \\n #outliers_threshold\\n if type(outliers_threshold) is not float:\\n sys.exit('(Type Error): outliers_threshold must be a float between 0 and 1. ') \\n \\n #remove_multicollinearity\\n if type(remove_multicollinearity) is not bool:\\n sys.exit('(Type Error): remove_multicollinearity parameter only accepts True or False.')\\n \\n #multicollinearity_threshold\\n if type(multicollinearity_threshold) is not float:\\n sys.exit('(Type Error): multicollinearity_threshold must be a float between 0 and 1. ') \\n \\n #create_clusters\\n if type(create_clusters) is not bool:\\n sys.exit('(Type Error): create_clusters parameter only accepts True or False.')\\n \\n #cluster_iter\\n if type(cluster_iter) is not int:\\n sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ') \\n \\n #polynomial_features\\n if type(polynomial_features) is not bool:\\n sys.exit('(Type Error): polynomial_features only accepts True or False. ') \\n \\n #polynomial_degree\\n if type(polynomial_degree) is not int:\\n sys.exit('(Type Error): polynomial_degree must be an integer. ')\\n \\n #polynomial_features\\n if type(trigonometry_features) is not bool:\\n sys.exit('(Type Error): trigonometry_features only accepts True or False. ') \\n \\n #polynomial threshold\\n if type(polynomial_threshold) is not float:\\n sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ') \\n \\n #group features\\n if group_features is not None:\\n if type(group_features) is not list:\\n sys.exit('(Type Error): group_features must be of type list. ') \\n \\n if group_names is not None:\\n if type(group_names) is not list:\\n sys.exit('(Type Error): group_names must be of type list. ') \\n \\n #cannot drop target\\n if ignore_features is not None:\\n if target in ignore_features:\\n sys.exit(\\\"(Value Error): cannot drop target column. \\\") \\n \\n #feature_selection\\n if type(feature_selection) is not bool:\\n sys.exit('(Type Error): feature_selection only accepts True or False. ') \\n \\n #feature_selection_threshold\\n if type(feature_selection_threshold) is not float:\\n sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ') \\n \\n #feature_interaction\\n if type(feature_interaction) is not bool:\\n sys.exit('(Type Error): feature_interaction only accepts True or False. ') \\n \\n #feature_ratio\\n if type(feature_ratio) is not bool:\\n sys.exit('(Type Error): feature_ratio only accepts True or False. ') \\n \\n #interaction_threshold\\n if type(interaction_threshold) is not float:\\n sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ') \\n\\n #cannot drop target\\n if ignore_features is not None:\\n if target in ignore_features:\\n sys.exit(\\\"(Value Error): cannot drop target column. \\\") \\n \\n #forced type check\\n all_cols = list(data.columns)\\n all_cols.remove(target)\\n \\n #categorical\\n if categorical_features is not None:\\n for i in categorical_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #numeric\\n if numeric_features is not None:\\n for i in numeric_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\") \\n \\n #date features\\n if date_features is not None:\\n for i in date_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\") \\n \\n #drop features\\n if ignore_features is not None:\\n for i in ignore_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Feature ignored is either target column or doesn't exist in the dataset.\\\") \\n \\n #silent\\n if type(silent) is not bool:\\n sys.exit(\\\"(Type Error): silent parameter only accepts True or False. \\\")\\n\\n #remove_perfect_collinearity\\n if type(remove_perfect_collinearity) is not bool:\\n sys.exit('(Type Error): remove_perfect_collinearity parameter only accepts True or False.')\\n \\n #html\\n if type(html) is not bool:\\n sys.exit('(Type Error): html parameter only accepts True or False.')\\n\\n #folds_shuffle\\n if type(folds_shuffle) is not bool:\\n sys.exit('(Type Error): folds_shuffle parameter only accepts True or False.')\\n\\n #data_split_shuffle\\n if type(data_split_shuffle) is not bool:\\n sys.exit('(Type Error): data_split_shuffle parameter only accepts True or False.')\\n\\n #log_experiment\\n if type(log_experiment) is not bool:\\n sys.exit('(Type Error): log_experiment parameter only accepts True or False.')\\n\\n #log_plots\\n if type(log_plots) is not bool:\\n sys.exit('(Type Error): log_plots parameter only accepts True or False.')\\n\\n #log_data\\n if type(log_data) is not bool:\\n sys.exit('(Type Error): log_data parameter only accepts True or False.')\\n\\n #log_profile\\n if type(log_profile) is not bool:\\n sys.exit('(Type Error): log_profile parameter only accepts True or False.')\\n\\n logger.info(\\\"Preloading libraries\\\")\\n\\n #pre-load libraries\\n import pandas as pd\\n import ipywidgets as ipw\\n from IPython.display import display, HTML, clear_output, update_display\\n import datetime, time\\n import os\\n\\n #pandas option\\n pd.set_option('display.max_columns', 500)\\n pd.set_option('display.max_rows', 500)\\n \\n #global html_param\\n global html_param\\n \\n #create html_param\\n html_param = html\\n\\n #silent parameter to also set sampling to False\\n if silent:\\n sampling = False\\n\\n logger.info(\\\"Preparing display monitor\\\")\\n\\n #progress bar\\n if sampling:\\n max = 10 + 3\\n else:\\n max = 3\\n \\n progress = ipw.IntProgress(value=0, min=0, max=max, step=1 , description='Processing: ')\\n if verbose:\\n if html_param:\\n display(progress)\\n \\n timestampStr = datetime.datetime.now().strftime(\\\"%H:%M:%S\\\")\\n monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], \\n ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],\\n ['ETC' , '. . . . . . . . . . . . . . . . . .', 'Calculating ETC'] ],\\n columns=['', ' ', ' ']).set_index('')\\n \\n if verbose:\\n if html_param:\\n display(monitor, display_id = 'monitor')\\n \\n logger.info(\\\"Importing libraries\\\")\\n\\n #general dependencies\\n import numpy as np\\n from sklearn.linear_model import LinearRegression\\n from sklearn.model_selection import train_test_split\\n from sklearn import metrics\\n import random\\n import seaborn as sns\\n import matplotlib.pyplot as plt\\n import plotly.express as px\\n \\n #setting sklearn config to print all parameters including default\\n import sklearn\\n sklearn.set_config(print_changed_only=False)\\n \\n #define highlight function for function grid to display\\n def highlight_max(s):\\n is_max = s == True\\n return ['background-color: yellow' if v else '' for v in is_max]\\n \\n #cufflinks\\n import cufflinks as cf\\n cf.go_offline()\\n cf.set_config_file(offline=False, world_readable=True)\\n \\n #ignore warnings\\n import warnings\\n warnings.filterwarnings('ignore') \\n \\n\\n logger.info(\\\"Declaring global variables\\\")\\n\\n #declaring global variables to be accessed by other functions\\n global X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__,\\\\\\n preprocess, folds_shuffle_param, n_jobs_param, create_model_container, master_model_container,\\\\\\n display_container, exp_name_log, logging_param, log_plots_param, data_before_preprocess, target_param,\\\\\\n gpu_param\\n\\n logger.info(\\\"Copying data for preprocessing\\\")\\n #copy original data for pandas profiler\\n data_before_preprocess = data.copy()\\n \\n #generate seed to be used globally\\n if session_id is None:\\n seed = random.randint(150,9000)\\n else:\\n seed = session_id\\n \\n\\n \\\"\\\"\\\"\\n preprocessing starts here\\n \\\"\\\"\\\"\\n \\n monitor.iloc[1,1:] = 'Preparing Data for Modeling'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n #define parameters for preprocessor\\n \\n logger.info(\\\"Declaring preprocessing parameters\\\")\\n\\n #categorical features\\n if categorical_features is None:\\n cat_features_pass = []\\n else:\\n cat_features_pass = categorical_features\\n \\n #numeric features\\n if numeric_features is None:\\n numeric_features_pass = []\\n else:\\n numeric_features_pass = numeric_features\\n \\n #drop features\\n if ignore_features is None:\\n ignore_features_pass = []\\n else:\\n ignore_features_pass = ignore_features\\n \\n #date features\\n if date_features is None:\\n date_features_pass = []\\n else:\\n date_features_pass = date_features\\n \\n #categorical imputation strategy\\n if categorical_imputation == 'constant':\\n categorical_imputation_pass = 'not_available'\\n elif categorical_imputation == 'mode':\\n categorical_imputation_pass = 'most frequent'\\n \\n #transformation method strategy\\n if transformation_method == 'yeo-johnson':\\n trans_method_pass = 'yj'\\n elif transformation_method == 'quantile':\\n trans_method_pass = 'quantile'\\n \\n #pass method\\n if pca_method == 'linear':\\n pca_method_pass = 'pca_liner'\\n \\n elif pca_method == 'kernel':\\n pca_method_pass = 'pca_kernal'\\n \\n elif pca_method == 'incremental':\\n pca_method_pass = 'incremental'\\n \\n elif pca_method == 'pls':\\n pca_method_pass = 'pls'\\n \\n #pca components\\n if pca is True:\\n if pca_components is None:\\n if pca_method == 'linear':\\n pca_components_pass = 0.99\\n else:\\n pca_components_pass = int((len(data.columns)-1)*0.5)\\n \\n else:\\n pca_components_pass = pca_components\\n \\n else:\\n pca_components_pass = 0.99\\n \\n if bin_numeric_features is None:\\n apply_binning_pass = False\\n features_to_bin_pass = []\\n \\n else:\\n apply_binning_pass = True\\n features_to_bin_pass = bin_numeric_features\\n \\n #trignometry\\n if trigonometry_features is False:\\n trigonometry_features_pass = []\\n else:\\n trigonometry_features_pass = ['sin', 'cos', 'tan']\\n \\n #group features\\n #=============#\\n \\n #apply grouping\\n if group_features is not None:\\n apply_grouping_pass = True\\n else:\\n apply_grouping_pass = False\\n \\n #group features listing\\n if apply_grouping_pass is True:\\n \\n if type(group_features[0]) is str:\\n group_features_pass = []\\n group_features_pass.append(group_features)\\n else:\\n group_features_pass = group_features\\n \\n else:\\n \\n group_features_pass = [[]]\\n \\n #group names\\n if apply_grouping_pass is True:\\n\\n if (group_names is None) or (len(group_names) != len(group_features_pass)):\\n group_names_pass = list(np.arange(len(group_features_pass)))\\n group_names_pass = ['group_' + str(i) for i in group_names_pass]\\n\\n else:\\n group_names_pass = group_names\\n \\n else:\\n group_names_pass = []\\n \\n #feature interactions\\n \\n if feature_interaction or feature_ratio:\\n apply_feature_interactions_pass = True\\n else:\\n apply_feature_interactions_pass = False\\n \\n interactions_to_apply_pass = []\\n \\n if feature_interaction:\\n interactions_to_apply_pass.append('multiply')\\n \\n if feature_ratio:\\n interactions_to_apply_pass.append('divide')\\n \\n #unknown categorical\\n if unknown_categorical_method == 'least_frequent':\\n unknown_categorical_method_pass = 'least frequent'\\n elif unknown_categorical_method == 'most_frequent':\\n unknown_categorical_method_pass = 'most frequent'\\n\\n #ordinal_features\\n if ordinal_features is not None:\\n apply_ordinal_encoding_pass = True\\n else:\\n apply_ordinal_encoding_pass = False\\n \\n if apply_ordinal_encoding_pass is True:\\n ordinal_columns_and_categories_pass = ordinal_features\\n else:\\n ordinal_columns_and_categories_pass = {}\\n \\n if high_cardinality_features is not None:\\n apply_cardinality_reduction_pass = True\\n else:\\n apply_cardinality_reduction_pass = False\\n \\n if high_cardinality_method == 'frequency':\\n cardinal_method_pass = 'count'\\n elif high_cardinality_method == 'clustering':\\n cardinal_method_pass = 'cluster'\\n \\n if apply_cardinality_reduction_pass:\\n cardinal_features_pass = high_cardinality_features\\n else:\\n cardinal_features_pass = []\\n \\n if silent:\\n display_dtypes_pass = False\\n else:\\n display_dtypes_pass = True\\n \\n #transform target method\\n if transform_target_method == 'box-cox':\\n transform_target_method_pass = 'bc'\\n elif transform_target_method == 'yeo-johnson':\\n transform_target_method_pass = 'yj'\\n\\n logger.info(\\\"Importing preprocessing module\\\")\\n \\n #import library\\n import pycaret.preprocess as preprocess\\n \\n logger.info(\\\"Creating preprocessing pipeline\\\")\\n\\n data = preprocess.Preprocess_Path_One(train_data = data, \\n target_variable = target,\\n categorical_features = cat_features_pass,\\n apply_ordinal_encoding = apply_ordinal_encoding_pass, \\n ordinal_columns_and_categories = ordinal_columns_and_categories_pass, \\n apply_cardinality_reduction = apply_cardinality_reduction_pass,\\n cardinal_method = cardinal_method_pass, \\n cardinal_features = cardinal_features_pass,\\n numerical_features = numeric_features_pass,\\n time_features = date_features_pass,\\n features_todrop = ignore_features_pass,\\n numeric_imputation_strategy = numeric_imputation,\\n categorical_imputation_strategy = categorical_imputation_pass,\\n scale_data = normalize,\\n scaling_method = normalize_method,\\n Power_transform_data = transformation,\\n Power_transform_method = trans_method_pass,\\n apply_untrained_levels_treatment= handle_unknown_categorical,\\n untrained_levels_treatment_method = unknown_categorical_method_pass, \\n apply_pca = pca, \\n pca_method = pca_method_pass, \\n pca_variance_retained_or_number_of_components = pca_components_pass, \\n apply_zero_nearZero_variance = ignore_low_variance,\\n club_rare_levels = combine_rare_levels,\\n rara_level_threshold_percentage = rare_level_threshold,\\n apply_binning = apply_binning_pass,\\n features_to_binn = features_to_bin_pass,\\n remove_outliers = remove_outliers,\\n outlier_contamination_percentage = outliers_threshold,\\n outlier_methods = ['pca'], #pca hardcoded\\n remove_multicollinearity = remove_multicollinearity,\\n maximum_correlation_between_features = multicollinearity_threshold,\\n remove_perfect_collinearity = remove_perfect_collinearity, \\n cluster_entire_data = create_clusters, \\n range_of_clusters_to_try = cluster_iter, \\n apply_polynomial_trigonometry_features = polynomial_features, \\n max_polynomial = polynomial_degree, \\n trigonometry_calculations = trigonometry_features_pass, \\n top_poly_trig_features_to_select_percentage = polynomial_threshold, \\n apply_grouping = apply_grouping_pass, \\n features_to_group_ListofList = group_features_pass, \\n group_name = group_names_pass, \\n apply_feature_selection = feature_selection, \\n feature_selection_top_features_percentage = feature_selection_threshold, \\n apply_feature_interactions = apply_feature_interactions_pass, \\n feature_interactions_to_apply = interactions_to_apply_pass, \\n feature_interactions_top_features_to_select_percentage=interaction_threshold, \\n display_types = display_dtypes_pass, \\n target_transformation = transform_target, \\n target_transformation_method = transform_target_method_pass, \\n random_state = seed)\\n\\n progress.value += 1\\n logger.info(\\\"Preprocessing pipeline created successfully\\\")\\n \\n if hasattr(preprocess.dtypes, 'replacement'):\\n label_encoded = preprocess.dtypes.replacement\\n label_encoded = str(label_encoded).replace(\\\"'\\\", '')\\n label_encoded = str(label_encoded).replace(\\\"{\\\", '')\\n label_encoded = str(label_encoded).replace(\\\"}\\\", '')\\n\\n else:\\n label_encoded = 'None'\\n\\n try:\\n res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']\\n res = preprocess.dtypes.response\\n if res in res_type:\\n sys.exit(\\\"(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.\\\" )\\n except:\\n pass\\n \\n #save prep pipe\\n prep_pipe = preprocess.pipe\\n \\n\\n #save target inverse transformer\\n try:\\n target_inverse_transformer = preprocess.pt_target.p_transform_target\\n except:\\n target_inverse_transformer = None\\n logger.info(\\\"No inverse transformer found\\\")\\n\\n \\n logger.info(\\\"Creating grid variables\\\")\\n\\n #generate values for grid show\\n missing_values = data_before_preprocess.isna().sum().sum()\\n if missing_values > 0:\\n missing_flag = True\\n else:\\n missing_flag = False\\n \\n if normalize is True:\\n normalize_grid = normalize_method\\n else:\\n normalize_grid = 'None'\\n \\n if transformation is True:\\n transformation_grid = transformation_method\\n else:\\n transformation_grid = 'None'\\n \\n if pca is True:\\n pca_method_grid = pca_method\\n else:\\n pca_method_grid = 'None'\\n \\n if pca is True:\\n pca_components_grid = pca_components_pass\\n else:\\n pca_components_grid = 'None'\\n \\n if combine_rare_levels:\\n rare_level_threshold_grid = rare_level_threshold\\n else:\\n rare_level_threshold_grid = 'None'\\n \\n if bin_numeric_features is None:\\n numeric_bin_grid = False\\n else:\\n numeric_bin_grid = True\\n \\n if remove_outliers is False:\\n outliers_threshold_grid = None\\n else:\\n outliers_threshold_grid = outliers_threshold\\n \\n if remove_multicollinearity is False:\\n multicollinearity_threshold_grid = None\\n else:\\n multicollinearity_threshold_grid = multicollinearity_threshold\\n \\n if create_clusters is False:\\n cluster_iter_grid = None\\n else:\\n cluster_iter_grid = cluster_iter\\n \\n if polynomial_features:\\n polynomial_degree_grid = polynomial_degree\\n else:\\n polynomial_degree_grid = None\\n \\n if polynomial_features or trigonometry_features:\\n polynomial_threshold_grid = polynomial_threshold\\n else:\\n polynomial_threshold_grid = None\\n \\n if feature_selection:\\n feature_selection_threshold_grid = feature_selection_threshold\\n else:\\n feature_selection_threshold_grid = None\\n \\n if feature_interaction or feature_ratio:\\n interaction_threshold_grid = interaction_threshold\\n else:\\n interaction_threshold_grid = None\\n \\n if ordinal_features is not None:\\n ordinal_features_grid = True\\n else:\\n ordinal_features_grid = False\\n \\n if handle_unknown_categorical:\\n unknown_categorical_method_grid = unknown_categorical_method\\n else:\\n unknown_categorical_method_grid = None\\n \\n if group_features is not None:\\n group_features_grid = True\\n else:\\n group_features_grid = False\\n \\n if high_cardinality_features is not None:\\n high_cardinality_features_grid = True\\n else:\\n high_cardinality_features_grid = False\\n \\n if high_cardinality_features_grid:\\n high_cardinality_method_grid = high_cardinality_method\\n else:\\n high_cardinality_method_grid = None\\n \\n learned_types = preprocess.dtypes.learent_dtypes\\n learned_types.drop(target, inplace=True)\\n\\n float_type = 0 \\n cat_type = 0\\n\\n for i in preprocess.dtypes.learent_dtypes:\\n if 'float' in str(i):\\n float_type += 1\\n elif 'object' in str(i):\\n cat_type += 1\\n elif 'int' in str(i):\\n float_type += 1\\n \\n #target transformation method\\n if transform_target is False:\\n transform_target_method_grid = None\\n else:\\n transform_target_method_grid = preprocess.pt_target.function_to_apply\\n \\n \\\"\\\"\\\"\\n preprocessing ends here\\n \\\"\\\"\\\"\\n \\n #reset pandas option\\n pd.reset_option(\\\"display.max_rows\\\")\\n pd.reset_option(\\\"display.max_columns\\\")\\n \\n logger.info(\\\"Creating global containers\\\")\\n\\n #create an empty list for pickling later.\\n experiment__ = []\\n \\n #create folds_shuffle_param\\n folds_shuffle_param = folds_shuffle\\n\\n #create n_jobs_param\\n n_jobs_param = n_jobs\\n\\n #create create_model_container\\n create_model_container = []\\n\\n #create master_model_container\\n master_model_container = []\\n\\n #create display container\\n display_container = []\\n\\n #create logging parameter\\n logging_param = log_experiment\\n\\n #create exp_name_log param incase logging is False\\n exp_name_log = 'no_logging'\\n\\n #create an empty log_plots_param\\n if log_plots:\\n log_plots_param = True\\n else:\\n log_plots_param = False\\n\\n # create target param\\n target_param = target\\n\\n # create gpu param\\n gpu_param = use_gpu\\n\\n #sample estimator\\n if sample_estimator is None:\\n model = LinearRegression(n_jobs=n_jobs_param)\\n else:\\n model = sample_estimator\\n \\n model_name = str(model).split(\\\"(\\\")[0]\\n \\n if 'CatBoostRegressor' in model_name:\\n model_name = 'CatBoostRegressor'\\n \\n #creating variables to be used later in the function\\n X = data.drop(target,axis=1)\\n y = data[target]\\n \\n progress.value += 1\\n \\n if sampling is True and data.shape[0] > 25000: #change back to 25000\\n \\n split_perc = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\\n split_perc_text = ['10%','20%','30%','40%','50%','60%', '70%', '80%', '90%', '100%']\\n split_perc_tt = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\\n split_perc_tt_total = []\\n split_percent = []\\n\\n metric_results = []\\n metric_name = []\\n \\n counter = 0\\n \\n for i in split_perc:\\n \\n progress.value += 1\\n \\n t0 = time.time()\\n \\n '''\\n MONITOR UPDATE STARTS\\n '''\\n \\n perc_text = split_perc_text[counter]\\n monitor.iloc[1,1:] = 'Fitting Model on ' + perc_text + ' sample'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n\\n '''\\n MONITOR UPDATE ENDS\\n '''\\n \\n X_, X__, y_, y__ = train_test_split(X, y, test_size=1-i, random_state=seed, shuffle=data_split_shuffle)\\n X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n model.fit(X_train,y_train)\\n pred_ = model.predict(X_test)\\n \\n r2 = metrics.r2_score(y_test,pred_)\\n metric_results.append(r2)\\n metric_name.append('R2')\\n split_percent.append(i)\\n \\n t1 = time.time()\\n \\n '''\\n Time calculation begins\\n '''\\n \\n tt = t1 - t0\\n total_tt = tt / i\\n split_perc_tt.pop(0)\\n \\n for remain in split_perc_tt:\\n ss = total_tt * remain\\n split_perc_tt_total.append(ss)\\n \\n ttt = sum(split_perc_tt_total) / 60\\n ttt = np.around(ttt, 2)\\n \\n if ttt < 1:\\n ttt = str(np.around((ttt * 60), 2))\\n ETC = ttt + ' Seconds Remaining'\\n\\n else:\\n ttt = str (ttt)\\n ETC = ttt + ' Minutes Remaining'\\n \\n monitor.iloc[2,1:] = ETC\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n \\n '''\\n Time calculation Ends\\n '''\\n \\n split_perc_tt_total = []\\n counter += 1\\n\\n model_results = pd.DataFrame({'Sample' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})\\n fig = px.line(model_results, x='Sample', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])\\n fig.update_layout(plot_bgcolor='rgb(245,245,245)')\\n title= str(model_name) + ' Metric and Sample %'\\n fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})\\n fig.show()\\n \\n monitor.iloc[1,1:] = 'Waiting for input'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n \\n print('Please Enter the sample % of data you would like to use for modeling. Example: Enter 0.3 for 30%.')\\n print('Press Enter if you would like to use 100% of the data.')\\n \\n print(' ')\\n \\n sample_size = input(\\\"Sample Size: \\\")\\n \\n if sample_size == '' or sample_size == '1':\\n \\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n\\n else:\\n \\n sample_n = float(sample_size)\\n X_selected, X_discard, y_selected, y_discard = train_test_split(X, y, test_size=1-sample_n, \\n random_state=seed, shuffle=data_split_shuffle)\\n \\n X_train, X_test, y_train, y_test = train_test_split(X_selected, y_selected, test_size=1-train_size, \\n random_state=seed, shuffle=data_split_shuffle)\\n\\n else:\\n \\n monitor.iloc[1,1:] = 'Splitting Data'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n progress.value += 1\\n\\n '''\\n Final display Starts\\n '''\\n clear_output()\\n if verbose:\\n print(' ')\\n if profile:\\n print('Setup Succesfully Completed. Loading Profile Now... Please Wait!')\\n else:\\n if verbose:\\n print('Setup Succesfully Completed.')\\n functions = pd.DataFrame ( [ ['session_id', seed ],\\n ['Transform Target ', transform_target],\\n ['Transform Target Method', transform_target_method_grid],\\n ['Original Data', data_before_preprocess.shape ],\\n ['Missing Values ', missing_flag],\\n ['Numeric Features ', str(float_type) ],\\n ['Categorical Features ', str(cat_type) ],\\n ['Ordinal Features ', ordinal_features_grid],\\n ['High Cardinality Features ', high_cardinality_features_grid],\\n ['High Cardinality Method ', high_cardinality_method_grid],\\n ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], \\n ['Transformed Train Set', X_train.shape ], \\n ['Transformed Test Set',X_test.shape ],\\n ['Numeric Imputer ', numeric_imputation],\\n ['Categorical Imputer ', categorical_imputation],\\n ['Normalize ', normalize ],\\n ['Normalize Method ', normalize_grid ],\\n ['Transformation ', transformation ],\\n ['Transformation Method ', transformation_grid ],\\n ['PCA ', pca],\\n ['PCA Method ', pca_method_grid],\\n ['PCA Components ', pca_components_grid],\\n ['Ignore Low Variance ', ignore_low_variance],\\n ['Combine Rare Levels ', combine_rare_levels],\\n ['Rare Level Threshold ', rare_level_threshold_grid],\\n ['Numeric Binning ', numeric_bin_grid],\\n ['Remove Outliers ', remove_outliers],\\n ['Outliers Threshold ', outliers_threshold_grid],\\n ['Remove Multicollinearity ', remove_multicollinearity],\\n ['Multicollinearity Threshold ', multicollinearity_threshold_grid],\\n ['Clustering ', create_clusters],\\n ['Clustering Iteration ', cluster_iter_grid],\\n ['Polynomial Features ', polynomial_features],\\n ['Polynomial Degree ', polynomial_degree_grid],\\n ['Trignometry Features ', trigonometry_features],\\n ['Polynomial Threshold ', polynomial_threshold_grid], \\n ['Group Features ', group_features_grid],\\n ['Feature Selection ', feature_selection],\\n ['Features Selection Threshold ', feature_selection_threshold_grid],\\n ['Feature Interaction ', feature_interaction],\\n ['Feature Ratio ', feature_ratio],\\n ['Interaction Threshold ', interaction_threshold_grid],\\n ], columns = ['Description', 'Value'] )\\n \\n functions_ = functions.style.apply(highlight_max)\\n if verbose:\\n if html_param:\\n display(functions_)\\n else:\\n print(functions_.data)\\n \\n if profile:\\n try:\\n import pandas_profiling\\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\\n clear_output()\\n display(pf)\\n except:\\n print('Data Profiler Failed. No output to show, please continue with Modeling.')\\n \\n '''\\n Final display Ends\\n ''' \\n \\n #log into experiment\\n experiment__.append(('Regression Setup Config', functions))\\n experiment__.append(('X_training Set', X_train))\\n experiment__.append(('y_training Set', y_train))\\n experiment__.append(('X_test Set', X_test))\\n experiment__.append(('y_test Set', y_test))\\n experiment__.append(('Transformation Pipeline', prep_pipe))\\n try:\\n experiment__.append(('Target Inverse Transformer', target_inverse_transformer))\\n except:\\n pass\\n \\n #end runtime\\n runtime_end = time.time()\\n runtime = np.array(runtime_end - runtime_start).round(2)\\n\\n if logging_param:\\n \\n logger.info(\\\"Logging experiment in MLFlow\\\")\\n \\n import mlflow\\n from pathlib import Path\\n\\n if experiment_name is None:\\n exp_name_ = 'reg-default-name'\\n else:\\n exp_name_ = experiment_name\\n\\n URI = secrets.token_hex(nbytes=4) \\n exp_name_log = exp_name_\\n \\n try:\\n mlflow.create_experiment(exp_name_log)\\n except:\\n pass\\n\\n #mlflow logging\\n mlflow.set_experiment(exp_name_log)\\n\\n run_name_ = 'Session Initialized ' + str(USI)\\n with mlflow.start_run(run_name=run_name_) as run:\\n\\n # Get active run to log as tag\\n RunID = mlflow.active_run().info.run_id\\n \\n k = functions.copy()\\n k.set_index('Description',drop=True,inplace=True)\\n kdict = k.to_dict()\\n params = kdict.get('Value')\\n mlflow.log_params(params)\\n\\n #set tag of compare_models\\n mlflow.set_tag(\\\"Source\\\", \\\"setup\\\")\\n \\n import secrets\\n URI = secrets.token_hex(nbytes=4)\\n mlflow.set_tag(\\\"URI\\\", URI)\\n mlflow.set_tag(\\\"USI\\\", USI) \\n mlflow.set_tag(\\\"Run Time\\\", runtime)\\n mlflow.set_tag(\\\"Run ID\\\", RunID)\\n\\n # Log the transformation pipeline\\n logger.info(\\\"SubProcess save_model() called ==================================\\\")\\n save_model(prep_pipe, 'Transformation Pipeline', verbose=False)\\n logger.info(\\\"SubProcess save_model() end ==================================\\\")\\n mlflow.log_artifact('Transformation Pipeline' + '.pkl')\\n os.remove('Transformation Pipeline.pkl')\\n\\n # Log pandas profile\\n if log_profile:\\n import pandas_profiling\\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\\n pf.to_file(\\\"Data Profile.html\\\")\\n mlflow.log_artifact(\\\"Data Profile.html\\\")\\n os.remove(\\\"Data Profile.html\\\")\\n clear_output()\\n display(functions_)\\n\\n # Log training and testing set\\n if log_data:\\n X_train.join(y_train).to_csv('Train.csv')\\n X_test.join(y_test).to_csv('Test.csv')\\n mlflow.log_artifact(\\\"Train.csv\\\")\\n mlflow.log_artifact(\\\"Test.csv\\\")\\n os.remove('Train.csv')\\n os.remove('Test.csv')\\n\\n logger.info(\\\"create_model_container: \\\" + str(len(create_model_container)))\\n logger.info(\\\"master_model_container: \\\" + str(len(master_model_container)))\\n logger.info(\\\"display_container: \\\" + str(len(display_container)))\\n\\n logger.info(\\\"setup() succesfully completed......................................\\\")\\n\\n return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\\\\\\n experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\\\\\\n master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI,\\\\\\n data_before_preprocess, target_param\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":787,"cells":{"query_id":{"kind":"string","value":"012e9d167c4fb65431c816f0d2bcbe37"},"query":{"kind":"string","value":"The name of the category."},"positive_passages":{"kind":"list like","value":[{"docid":"e9c3dee8d88435fba7afa382d2833fa5","score":"0.0","text":"def name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"name\")","title":""}],"string":"[\n {\n \"docid\": \"e9c3dee8d88435fba7afa382d2833fa5\",\n \"score\": \"0.0\",\n \"text\": \"def name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"name\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"89d5292e894ed33a2743b5ac6e40c96c","score":"0.9356435","text":"def category_name(self) -> str:\n return pulumi.get(self, \"category_name\")","title":""},{"docid":"0708909944d1765b981bfd898b8dc4cb","score":"0.93421","text":"def category_name(self):\n return self.category.name","title":""},{"docid":"837b2e5849f05c1d3a5c0c8c54b14d6d","score":"0.8533845","text":"def display_category(self):\n return self.category.name","title":""},{"docid":"847ddfc9482fb33002fac12dfe2a54ac","score":"0.8499749","text":"def category_name(self):\r\n for short, name in Log.CATEGORIES:\r\n if short == self.category:\r\n return name","title":""},{"docid":"d64baca15ee050ea116217e1713805d9","score":"0.8397046","text":"def category_string(self):\n return self.category.category","title":""},{"docid":"6fbfa1cd29b03bac59ef2d15304c2d49","score":"0.8353404","text":"def category(self) -> str:\n return pulumi.get(self, \"category\")","title":""},{"docid":"6fbfa1cd29b03bac59ef2d15304c2d49","score":"0.8353404","text":"def category(self) -> str:\n return pulumi.get(self, \"category\")","title":""},{"docid":"cc084d9b86ab30a8c39027b34a50ae84","score":"0.82416683","text":"def category(self) -> str:\n return self.__category","title":""},{"docid":"784e11c23c379d212eda73f408b01c39","score":"0.8241514","text":"def category(self) -> str:\n return self._category","title":""},{"docid":"784e11c23c379d212eda73f408b01c39","score":"0.8241514","text":"def category(self) -> str:\n return self._category","title":""},{"docid":"bb9fa73c2381cb09ce34ed6335171b31","score":"0.8165807","text":"def get_category(self) -> str:\n return self.category","title":""},{"docid":"bb9fa73c2381cb09ce34ed6335171b31","score":"0.8165807","text":"def get_category(self) -> str:\n return self.category","title":""},{"docid":"1dd37b8c9e970c52b2838189a550ddd5","score":"0.8058707","text":"def __str__(self):\n return self.category_name","title":""},{"docid":"0b193e584f63e1639bfb75e19c6f946c","score":"0.79800016","text":"def __str__(self):\n return f'{self.category_name}'","title":""},{"docid":"0dc710ad2f02611012cdcbc487823c8b","score":"0.79722446","text":"def category(self):\n return self.name.rsplit('/', 1)[0] + '/'","title":""},{"docid":"06374a70d4477972bd07ac41a314264f","score":"0.7918446","text":"def category() -> str:","title":""},{"docid":"3c3f8f7b4b813ea0bddad61c6bf90c67","score":"0.7842589","text":"def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"3c3f8f7b4b813ea0bddad61c6bf90c67","score":"0.7842589","text":"def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"77ff1a6143a22e370cd3a6a6f7e521fd","score":"0.77653986","text":"def name(self):\r\n return self.cat_constructor.Name","title":""},{"docid":"95a247d99194548d29a1d23c029c3dcd","score":"0.7652714","text":"def __str__(self):\r\n return 'Category %s - %s' % (self.uuid, self.name)","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"a57544f9261000503d64e99de99db7e4","score":"0.7472379","text":"def category(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"4a855dd9f24799bbcfbed783efb17b92","score":"0.7465466","text":"def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])","title":""},{"docid":"4a855dd9f24799bbcfbed783efb17b92","score":"0.7465466","text":"def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])","title":""},{"docid":"7a1ae1c7692d99ade070e5b08bd50d0b","score":"0.74586797","text":"def category(self):\n return self._spec.get(\"category\", \"cat1\")","title":""},{"docid":"72ac3527e07c63658a0e557a89bec324","score":"0.74584407","text":"def display_category_names(self):\n print(\"Categories in '{}':\".format(self.name))\n for x in self.categories:\n print(\" {}\".format(x.get_category()))","title":""},{"docid":"1657d74940af32240709239acb26e1d6","score":"0.7458107","text":"def atomCatName(self):\n return \"/\".join([self.category, self.package])","title":""},{"docid":"21af89cdf7e410fa22fad75d418ec611","score":"0.74382997","text":"def category(self):\r\n\t\treturn self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"1a0d0a787f626a0f90d29e2250f74fd8","score":"0.7415323","text":"def __unicode__(self):\n return '%s' % (self.category_name)","title":""},{"docid":"d2660afd93fca59c30089d0744754271","score":"0.7400903","text":"def display_category(self):\n return \", \".join(category.name for category in self.category.all()[:3])","title":""},{"docid":"dd5bee67836f1b8eca6d998ee562dee9","score":"0.7382513","text":"def category(self):\n return self.data.get('category', None)","title":""},{"docid":"d74a87d0efd15837adfe8fbcbeef56fa","score":"0.73162085","text":"def getcategory(self):\n return self._category","title":""},{"docid":"d906b9603a9910e2aa620469645f0078","score":"0.7305942","text":"def __str__(self):\n return \"{}_{}\".format(self.naics_code, self.category)","title":""},{"docid":"abb0f496a90b8fa3b807169ea66e4710","score":"0.73001766","text":"def __repr__(self):\n return \"<Category: {}>\".format(self.name)","title":""},{"docid":"dc3ea140787dcb7fa956825f78c1cdcf","score":"0.7292876","text":"def get_category(self):\n return self.category","title":""},{"docid":"8a469419a3fb137705f86403cfe1dec9","score":"0.7285824","text":"def category_control_name(v):\n return \"%s_category\" % (str(v.key()))","title":""},{"docid":"d60f3e48b3561d736891b1aa8476c110","score":"0.72473085","text":"def name_subcategory(self):\n name_cat = sc.select()\n return name_cat","title":""},{"docid":"307206fa4cddf4e8180af82b538bca12","score":"0.7234101","text":"def full_name(self):\n #Todo category.name to str\n names = [unicode(category.name) for category in self.get_ancestors_and_self()]\n return self._full_name_separator.join(names)","title":""},{"docid":"6a0dca4c22be27656fb9052f77fd4393","score":"0.72217125","text":"def getCategory(self):\n\t\treturn self.category","title":""},{"docid":"70ec38ab3039627e38d4f3bc3dd6b110","score":"0.7205014","text":"def getCategory(self):\n return self.category","title":""},{"docid":"00ad380e04978509eb45ba207d3b8189","score":"0.7178186","text":"def get_formatted_category(self):\n if (self.category == \"dinner\"):\n return \"Dinner\"\n elif (self.category == \"pregame\"):\n return \"Pregame\"\n elif (self.category == \"mainEvent\"):\n return \"Main Event\"","title":""},{"docid":"deb598c830491d4dd73290aa76ee3871","score":"0.71753734","text":"def getCategory(self):\r\n return self._category","title":""},{"docid":"fc37e7c612504a7bc7a075be43fcc727","score":"0.71447045","text":"def __repr__(self):\n return f\"Category('{self.name}', '{self.link}')\"","title":""},{"docid":"cabedc836d9d0b6efe6a18c61f0c911e","score":"0.7112214","text":"def category(self):\n first_slash = self._category.find('/')\n return self._category[first_slash+1:]","title":""},{"docid":"980cc4ca2d2bfed62c4c539e2f29acac","score":"0.7083927","text":"def __str__(self):\n return \"{}_{}\".format(self.o_net_soc_code, self.category_name)","title":""},{"docid":"d0077bcc85160eb5ee87403a2706536c","score":"0.7082638","text":"def category(self) -> int:\n return pulumi.get(self, \"category\")","title":""},{"docid":"b36ea215313f96dddf7b2ae0a7bf4281","score":"0.7078961","text":"def get_cat_name(self, catIds):\n coco_cat = self.coco.loadCats(self.coco.getCatIds())\n cat_id_list = [cat['id'] for cat in coco_cat]\n if catIds in cat_id_list:\n idx = cat_id_list.index(catIds)\n return coco_cat[idx]['name']\n else:\n print('Category ID: {} is not found'.format(catIds))","title":""},{"docid":"e61c3f38f95d7548bb62d8f86bbc1c5b","score":"0.7058682","text":"def get_category_name(self, val):\n for x,y in self.categories.iteritems():\n if val == y:\n return x","title":""},{"docid":"db58e08706bfa0189036feac713d888b","score":"0.70486426","text":"def category(self):\r\n return self.family.category","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"e81567a46738762e670c313b26e83e92","score":"0.7003943","text":"def __unicode__(self):\n return '%s' % (self.category)","title":""},{"docid":"04e61fdcad64605a82bffa55e70e636d","score":"0.68851626","text":"def __repr__(self):\n return (\n f\"Category=(id={self.id},category_name={self.category_name}\"\n f\",category_slug={self.category_slug})\"\n )","title":""},{"docid":"132385416c41c8a88f2d74b077a54a78","score":"0.68466485","text":"def category(self, session) -> str:\n return None","title":""},{"docid":"132385416c41c8a88f2d74b077a54a78","score":"0.68466485","text":"def category(self, session) -> str:\n return None","title":""},{"docid":"09f79861b611cd9716d7a0dd4e6ff761","score":"0.6819605","text":"def get_category_name_from_file(self, file_path):\n\t\treturn os.path.basename(file_path).split(\"_\")[0].lower()","title":""},{"docid":"78426cf0ec4c27103dc9c5c81f641b79","score":"0.67999417","text":"def getCat(self):\n return self.__cat","title":""},{"docid":"0f4baac6e9e2ee62778ab614918eac22","score":"0.67872024","text":"def name_get(self):\n if self._context.get('base_category_display') == 'short':\n return super(BaseCategory, self).name_get()\n\n res = []\n for cat in self:\n names = []\n current = cat\n while current:\n names.append(current.name)\n current = current.parent_id\n res.append((cat.id, ' / '.join(reversed(names))))\n return res","title":""},{"docid":"ec4fa44ae9c32427f82d1f86209f8cae","score":"0.67868376","text":"def _getCategory(self):\n return self._category","title":""},{"docid":"58c2687b3486d37c06537a719680373a","score":"0.6763114","text":"def category_for_sidebar(self):\n if self.category == '/':\n return self.name + '/'\n else:\n return self.category","title":""},{"docid":"5481b7a5b087a23d40cf673ab900756a","score":"0.6749293","text":"def get_category(self, item_name):\n\t\treturn self._get_inner(item_name, \"category\")","title":""},{"docid":"131ebf67d44098cb9345aa71c2fa0484","score":"0.6714853","text":"def category(self):\r\n if len(self.name) > 0:\r\n return \"{0} Commands\".format(self.name)\r\n else:\r\n return \"Commands\"","title":""},{"docid":"3e9e273c04faa5f31a9d8ecc2837dc05","score":"0.6709203","text":"def __repr__(self):\r\n\r\n return \"<Category category_id=%s name=%s>\" % (self.category_id,\r\n self.name)","title":""},{"docid":"d3c8382079a50d9066a6b6b7eb36702f","score":"0.6706454","text":"def category_names(self):\n return self._category_names","title":""},{"docid":"f3abc7d54f964dd2a02568ca92aa6288","score":"0.6704538","text":"def category_name(value):\n return {\n 0: \"idle\",\n 1: \"unassigned\",\n 2: \"work\",\n 3: \"private\",\n 4: \"break\",\n }.get(value, \"?\")","title":""},{"docid":"7a7b61604dda3071777b0ac773df792d","score":"0.6700998","text":"def name(self) -> str:\n return self.mcls_data[\"name\"]","title":""},{"docid":"72131489f884be4ee198b3f775d36bc6","score":"0.66930056","text":"def add_category(self, name):\n pass","title":""},{"docid":"d4ab0a3b54bb0838df83bb4f7a2e44cf","score":"0.6680661","text":"def category(self) -> str:\n return self._match_json[\"rule\"][\"category\"][\"id\"]","title":""},{"docid":"e0845856b3b4613543378128ee2696b5","score":"0.66691476","text":"def description(self):\r\n return \"List of {0}\".format(self.category)","title":""},{"docid":"1ce8cbd5fcad52b41ad30ed17c7d600a","score":"0.66662973","text":"def category(self) -> str:\n return self._match_json[\"sentence\"]","title":""},{"docid":"859cedc6a3acc81eee9bfe33949e594c","score":"0.66522664","text":"def _classifier_name(self):\n pass","title":""},{"docid":"45b3e084e12e07d059d13bc9c39c85ae","score":"0.66405225","text":"def category(self):\r\n return None","title":""},{"docid":"0f98736dac789271f2092ab254ca1f78","score":"0.6631525","text":"def __unicode__(self):\n return '{cat} ({name})'.format(cat=self.cat, name=self.name)","title":""},{"docid":"1256cc671eed3e9c05ac7098aa629bc3","score":"0.6622872","text":"def ccle_name(self) -> str:\n return self._ccle_name","title":""},{"docid":"a7c7b7feeeb4368fd49ec71bd21a782c","score":"0.6616305","text":"def get_name(self):\n return self.nacp.title[0].get_name()","title":""},{"docid":"4e0676a63812c08b59a27a79805cd8d3","score":"0.6608744","text":"def name(self):\n return self._channel.display_name","title":""},{"docid":"2942a49de69b38fe0787f4947804c86b","score":"0.65840465","text":"def name(self):\n return self.get_name()","title":""},{"docid":"2942a49de69b38fe0787f4947804c86b","score":"0.65840465","text":"def name(self):\n return self.get_name()","title":""},{"docid":"2d6fffbfd28ae4bbf8b3adfe2759111f","score":"0.6575162","text":"def name(self):\n return self.title","title":""},{"docid":"8662746c1da3997d5994d94cb6a21e60","score":"0.6558912","text":"def name(self):\n\n return self.get_name()","title":""},{"docid":"55d93fe321b4c0aae22e789ba10cb083","score":"0.65544754","text":"def name(self, value):\r\n self.cat_constructor.Name = value","title":""},{"docid":"30537caf198aed0fefa485c28010e25c","score":"0.65399724","text":"def get_category_by_name(self, category_name):\n category = self.st.category.find(filters=\"name=%s\" % category_name)\n return category","title":""},{"docid":"2e5a1670aa466fd329bb723522948fea","score":"0.6515061","text":"def primary_category(self) -> str:\n assert self.primary_classification is not None\n return str(self.primary_classification.category)","title":""},{"docid":"cfedbc43ed83e949155c875195d82c3f","score":"0.649954","text":"def display_categories(self):\n return \", \".join([category.title for category in self.categories.all()])","title":""},{"docid":"18f03ba6408b62ee86f7f67aa07c8d55","score":"0.6494066","text":"def label(self) -> str:\n return self._str_representation_for(\"name\")","title":""},{"docid":"961ff47bd328452e856e4e249ea6ff51","score":"0.6491235","text":"def get_name(self):\n return","title":""},{"docid":"bf02a874f1168c6a3c8ab1b8ea4513e5","score":"0.64903516","text":"def name(self):\r\n return self.name_","title":""},{"docid":"3c617e4b1cdbe7f4d6973fcc7c3a40bc","score":"0.6483566","text":"def __repr__(self):\n\n return f\"\"\"< Resource Category: code = {self.code}, \n name = {self.name} >\"\"\"","title":""},{"docid":"d09b4e6cb66b2ab70cf087407fcbb8e5","score":"0.64835054","text":"def get_name(self):\r\n\r\n return self.get_title()","title":""},{"docid":"d09b4e6cb66b2ab70cf087407fcbb8e5","score":"0.64835054","text":"def get_name(self):\r\n\r\n return self.get_title()","title":""},{"docid":"db0061ef3cf4c041256576c24e2ad0ed","score":"0.6476098","text":"def get_name(self) -> str:\r\n\r\n return self.name","title":""},{"docid":"c99c902a9a1c269a9dea7384547d60da","score":"0.6466613","text":"def GetName(self):\n return _lldb.SBTypeCategory_GetName(self)","title":""}],"string":"[\n {\n \"docid\": \"89d5292e894ed33a2743b5ac6e40c96c\",\n \"score\": \"0.9356435\",\n \"text\": \"def category_name(self) -> str:\\n return pulumi.get(self, \\\"category_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0708909944d1765b981bfd898b8dc4cb\",\n \"score\": \"0.93421\",\n \"text\": \"def category_name(self):\\n return self.category.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837b2e5849f05c1d3a5c0c8c54b14d6d\",\n \"score\": \"0.8533845\",\n \"text\": \"def display_category(self):\\n return self.category.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ddfc9482fb33002fac12dfe2a54ac\",\n \"score\": \"0.8499749\",\n \"text\": \"def category_name(self):\\r\\n for short, name in Log.CATEGORIES:\\r\\n if short == self.category:\\r\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64baca15ee050ea116217e1713805d9\",\n \"score\": \"0.8397046\",\n \"text\": \"def category_string(self):\\n return self.category.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbfa1cd29b03bac59ef2d15304c2d49\",\n \"score\": \"0.8353404\",\n \"text\": \"def category(self) -> str:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbfa1cd29b03bac59ef2d15304c2d49\",\n \"score\": \"0.8353404\",\n \"text\": \"def category(self) -> str:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc084d9b86ab30a8c39027b34a50ae84\",\n \"score\": \"0.82416683\",\n \"text\": \"def category(self) -> str:\\n return self.__category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e11c23c379d212eda73f408b01c39\",\n \"score\": \"0.8241514\",\n \"text\": \"def category(self) -> str:\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e11c23c379d212eda73f408b01c39\",\n \"score\": \"0.8241514\",\n \"text\": \"def category(self) -> str:\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fa73c2381cb09ce34ed6335171b31\",\n \"score\": \"0.8165807\",\n \"text\": \"def get_category(self) -> str:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fa73c2381cb09ce34ed6335171b31\",\n \"score\": \"0.8165807\",\n \"text\": \"def get_category(self) -> str:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd37b8c9e970c52b2838189a550ddd5\",\n \"score\": \"0.8058707\",\n \"text\": \"def __str__(self):\\n return self.category_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b193e584f63e1639bfb75e19c6f946c\",\n \"score\": \"0.79800016\",\n \"text\": \"def __str__(self):\\n return f'{self.category_name}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc710ad2f02611012cdcbc487823c8b\",\n \"score\": \"0.79722446\",\n \"text\": \"def category(self):\\n return self.name.rsplit('/', 1)[0] + '/'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06374a70d4477972bd07ac41a314264f\",\n \"score\": \"0.7918446\",\n \"text\": \"def category() -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3f8f7b4b813ea0bddad61c6bf90c67\",\n \"score\": \"0.7842589\",\n \"text\": \"def category(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3f8f7b4b813ea0bddad61c6bf90c67\",\n \"score\": \"0.7842589\",\n \"text\": \"def category(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ff1a6143a22e370cd3a6a6f7e521fd\",\n \"score\": \"0.77653986\",\n \"text\": \"def name(self):\\r\\n return self.cat_constructor.Name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a247d99194548d29a1d23c029c3dcd\",\n \"score\": \"0.7652714\",\n \"text\": \"def __str__(self):\\r\\n return 'Category %s - %s' % (self.uuid, self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57544f9261000503d64e99de99db7e4\",\n \"score\": \"0.7472379\",\n \"text\": \"def category(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a855dd9f24799bbcfbed783efb17b92\",\n \"score\": \"0.7465466\",\n \"text\": \"def display_category(self):\\n return ', '.join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a855dd9f24799bbcfbed783efb17b92\",\n \"score\": \"0.7465466\",\n \"text\": \"def display_category(self):\\n return ', '.join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1ae1c7692d99ade070e5b08bd50d0b\",\n \"score\": \"0.74586797\",\n \"text\": \"def category(self):\\n return self._spec.get(\\\"category\\\", \\\"cat1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ac3527e07c63658a0e557a89bec324\",\n \"score\": \"0.74584407\",\n \"text\": \"def display_category_names(self):\\n print(\\\"Categories in '{}':\\\".format(self.name))\\n for x in self.categories:\\n print(\\\" {}\\\".format(x.get_category()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1657d74940af32240709239acb26e1d6\",\n \"score\": \"0.7458107\",\n \"text\": \"def atomCatName(self):\\n return \\\"/\\\".join([self.category, self.package])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21af89cdf7e410fa22fad75d418ec611\",\n \"score\": \"0.74382997\",\n \"text\": \"def category(self):\\r\\n\\t\\treturn self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0d0a787f626a0f90d29e2250f74fd8\",\n \"score\": \"0.7415323\",\n \"text\": \"def __unicode__(self):\\n return '%s' % (self.category_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2660afd93fca59c30089d0744754271\",\n \"score\": \"0.7400903\",\n \"text\": \"def display_category(self):\\n return \\\", \\\".join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5bee67836f1b8eca6d998ee562dee9\",\n \"score\": \"0.7382513\",\n \"text\": \"def category(self):\\n return self.data.get('category', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74a87d0efd15837adfe8fbcbeef56fa\",\n \"score\": \"0.73162085\",\n \"text\": \"def getcategory(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d906b9603a9910e2aa620469645f0078\",\n \"score\": \"0.7305942\",\n \"text\": \"def __str__(self):\\n return \\\"{}_{}\\\".format(self.naics_code, self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb0f496a90b8fa3b807169ea66e4710\",\n \"score\": \"0.73001766\",\n \"text\": \"def __repr__(self):\\n return \\\"<Category: {}>\\\".format(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3ea140787dcb7fa956825f78c1cdcf\",\n \"score\": \"0.7292876\",\n \"text\": \"def get_category(self):\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a469419a3fb137705f86403cfe1dec9\",\n \"score\": \"0.7285824\",\n \"text\": \"def category_control_name(v):\\n return \\\"%s_category\\\" % (str(v.key()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d60f3e48b3561d736891b1aa8476c110\",\n \"score\": \"0.72473085\",\n \"text\": \"def name_subcategory(self):\\n name_cat = sc.select()\\n return name_cat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307206fa4cddf4e8180af82b538bca12\",\n \"score\": \"0.7234101\",\n \"text\": \"def full_name(self):\\n #Todo category.name to str\\n names = [unicode(category.name) for category in self.get_ancestors_and_self()]\\n return self._full_name_separator.join(names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0dca4c22be27656fb9052f77fd4393\",\n \"score\": \"0.72217125\",\n \"text\": \"def getCategory(self):\\n\\t\\treturn self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ec38ab3039627e38d4f3bc3dd6b110\",\n \"score\": \"0.7205014\",\n \"text\": \"def getCategory(self):\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ad380e04978509eb45ba207d3b8189\",\n \"score\": \"0.7178186\",\n \"text\": \"def get_formatted_category(self):\\n if (self.category == \\\"dinner\\\"):\\n return \\\"Dinner\\\"\\n elif (self.category == \\\"pregame\\\"):\\n return \\\"Pregame\\\"\\n elif (self.category == \\\"mainEvent\\\"):\\n return \\\"Main Event\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb598c830491d4dd73290aa76ee3871\",\n \"score\": \"0.71753734\",\n \"text\": \"def getCategory(self):\\r\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc37e7c612504a7bc7a075be43fcc727\",\n \"score\": \"0.71447045\",\n \"text\": \"def __repr__(self):\\n return f\\\"Category('{self.name}', '{self.link}')\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabedc836d9d0b6efe6a18c61f0c911e\",\n \"score\": \"0.7112214\",\n \"text\": \"def category(self):\\n first_slash = self._category.find('/')\\n return self._category[first_slash+1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980cc4ca2d2bfed62c4c539e2f29acac\",\n \"score\": \"0.7083927\",\n \"text\": \"def __str__(self):\\n return \\\"{}_{}\\\".format(self.o_net_soc_code, self.category_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0077bcc85160eb5ee87403a2706536c\",\n \"score\": \"0.7082638\",\n \"text\": \"def category(self) -> int:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36ea215313f96dddf7b2ae0a7bf4281\",\n \"score\": \"0.7078961\",\n \"text\": \"def get_cat_name(self, catIds):\\n coco_cat = self.coco.loadCats(self.coco.getCatIds())\\n cat_id_list = [cat['id'] for cat in coco_cat]\\n if catIds in cat_id_list:\\n idx = cat_id_list.index(catIds)\\n return coco_cat[idx]['name']\\n else:\\n print('Category ID: {} is not found'.format(catIds))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61c3f38f95d7548bb62d8f86bbc1c5b\",\n \"score\": \"0.7058682\",\n \"text\": \"def get_category_name(self, val):\\n for x,y in self.categories.iteritems():\\n if val == y:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db58e08706bfa0189036feac713d888b\",\n \"score\": \"0.70486426\",\n \"text\": \"def category(self):\\r\\n return self.family.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81567a46738762e670c313b26e83e92\",\n \"score\": \"0.7003943\",\n \"text\": \"def __unicode__(self):\\n return '%s' % (self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04e61fdcad64605a82bffa55e70e636d\",\n \"score\": \"0.68851626\",\n \"text\": \"def __repr__(self):\\n return (\\n f\\\"Category=(id={self.id},category_name={self.category_name}\\\"\\n f\\\",category_slug={self.category_slug})\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132385416c41c8a88f2d74b077a54a78\",\n \"score\": \"0.68466485\",\n \"text\": \"def category(self, session) -> str:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132385416c41c8a88f2d74b077a54a78\",\n \"score\": \"0.68466485\",\n \"text\": \"def category(self, session) -> str:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f79861b611cd9716d7a0dd4e6ff761\",\n \"score\": \"0.6819605\",\n \"text\": \"def get_category_name_from_file(self, file_path):\\n\\t\\treturn os.path.basename(file_path).split(\\\"_\\\")[0].lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78426cf0ec4c27103dc9c5c81f641b79\",\n \"score\": \"0.67999417\",\n \"text\": \"def getCat(self):\\n return self.__cat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4baac6e9e2ee62778ab614918eac22\",\n \"score\": \"0.67872024\",\n \"text\": \"def name_get(self):\\n if self._context.get('base_category_display') == 'short':\\n return super(BaseCategory, self).name_get()\\n\\n res = []\\n for cat in self:\\n names = []\\n current = cat\\n while current:\\n names.append(current.name)\\n current = current.parent_id\\n res.append((cat.id, ' / '.join(reversed(names))))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4fa44ae9c32427f82d1f86209f8cae\",\n \"score\": \"0.67868376\",\n \"text\": \"def _getCategory(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c2687b3486d37c06537a719680373a\",\n \"score\": \"0.6763114\",\n \"text\": \"def category_for_sidebar(self):\\n if self.category == '/':\\n return self.name + '/'\\n else:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5481b7a5b087a23d40cf673ab900756a\",\n \"score\": \"0.6749293\",\n \"text\": \"def get_category(self, item_name):\\n\\t\\treturn self._get_inner(item_name, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131ebf67d44098cb9345aa71c2fa0484\",\n \"score\": \"0.6714853\",\n \"text\": \"def category(self):\\r\\n if len(self.name) > 0:\\r\\n return \\\"{0} Commands\\\".format(self.name)\\r\\n else:\\r\\n return \\\"Commands\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9e273c04faa5f31a9d8ecc2837dc05\",\n \"score\": \"0.6709203\",\n \"text\": \"def __repr__(self):\\r\\n\\r\\n return \\\"<Category category_id=%s name=%s>\\\" % (self.category_id,\\r\\n self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c8382079a50d9066a6b6b7eb36702f\",\n \"score\": \"0.6706454\",\n \"text\": \"def category_names(self):\\n return self._category_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3abc7d54f964dd2a02568ca92aa6288\",\n \"score\": \"0.6704538\",\n \"text\": \"def category_name(value):\\n return {\\n 0: \\\"idle\\\",\\n 1: \\\"unassigned\\\",\\n 2: \\\"work\\\",\\n 3: \\\"private\\\",\\n 4: \\\"break\\\",\\n }.get(value, \\\"?\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7b61604dda3071777b0ac773df792d\",\n \"score\": \"0.6700998\",\n \"text\": \"def name(self) -> str:\\n return self.mcls_data[\\\"name\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72131489f884be4ee198b3f775d36bc6\",\n \"score\": \"0.66930056\",\n \"text\": \"def add_category(self, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ab0a3b54bb0838df83bb4f7a2e44cf\",\n \"score\": \"0.6680661\",\n \"text\": \"def category(self) -> str:\\n return self._match_json[\\\"rule\\\"][\\\"category\\\"][\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0845856b3b4613543378128ee2696b5\",\n \"score\": \"0.66691476\",\n \"text\": \"def description(self):\\r\\n return \\\"List of {0}\\\".format(self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce8cbd5fcad52b41ad30ed17c7d600a\",\n \"score\": \"0.66662973\",\n \"text\": \"def category(self) -> str:\\n return self._match_json[\\\"sentence\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"859cedc6a3acc81eee9bfe33949e594c\",\n \"score\": \"0.66522664\",\n \"text\": \"def _classifier_name(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b3e084e12e07d059d13bc9c39c85ae\",\n \"score\": \"0.66405225\",\n \"text\": \"def category(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f98736dac789271f2092ab254ca1f78\",\n \"score\": \"0.6631525\",\n \"text\": \"def __unicode__(self):\\n return '{cat} ({name})'.format(cat=self.cat, name=self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1256cc671eed3e9c05ac7098aa629bc3\",\n \"score\": \"0.6622872\",\n \"text\": \"def ccle_name(self) -> str:\\n return self._ccle_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c7b7feeeb4368fd49ec71bd21a782c\",\n \"score\": \"0.6616305\",\n \"text\": \"def get_name(self):\\n return self.nacp.title[0].get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0676a63812c08b59a27a79805cd8d3\",\n \"score\": \"0.6608744\",\n \"text\": \"def name(self):\\n return self._channel.display_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2942a49de69b38fe0787f4947804c86b\",\n \"score\": \"0.65840465\",\n \"text\": \"def name(self):\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2942a49de69b38fe0787f4947804c86b\",\n \"score\": \"0.65840465\",\n \"text\": \"def name(self):\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d6fffbfd28ae4bbf8b3adfe2759111f\",\n \"score\": \"0.6575162\",\n \"text\": \"def name(self):\\n return self.title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8662746c1da3997d5994d94cb6a21e60\",\n \"score\": \"0.6558912\",\n \"text\": \"def name(self):\\n\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d93fe321b4c0aae22e789ba10cb083\",\n \"score\": \"0.65544754\",\n \"text\": \"def name(self, value):\\r\\n self.cat_constructor.Name = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30537caf198aed0fefa485c28010e25c\",\n \"score\": \"0.65399724\",\n \"text\": \"def get_category_by_name(self, category_name):\\n category = self.st.category.find(filters=\\\"name=%s\\\" % category_name)\\n return category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5a1670aa466fd329bb723522948fea\",\n \"score\": \"0.6515061\",\n \"text\": \"def primary_category(self) -> str:\\n assert self.primary_classification is not None\\n return str(self.primary_classification.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfedbc43ed83e949155c875195d82c3f\",\n \"score\": \"0.649954\",\n \"text\": \"def display_categories(self):\\n return \\\", \\\".join([category.title for category in self.categories.all()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f03ba6408b62ee86f7f67aa07c8d55\",\n \"score\": \"0.6494066\",\n \"text\": \"def label(self) -> str:\\n return self._str_representation_for(\\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961ff47bd328452e856e4e249ea6ff51\",\n \"score\": \"0.6491235\",\n \"text\": \"def get_name(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf02a874f1168c6a3c8ab1b8ea4513e5\",\n \"score\": \"0.64903516\",\n \"text\": \"def name(self):\\r\\n return self.name_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c617e4b1cdbe7f4d6973fcc7c3a40bc\",\n \"score\": \"0.6483566\",\n \"text\": \"def __repr__(self):\\n\\n return f\\\"\\\"\\\"< Resource Category: code = {self.code}, \\n name = {self.name} >\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09b4e6cb66b2ab70cf087407fcbb8e5\",\n \"score\": \"0.64835054\",\n \"text\": \"def get_name(self):\\r\\n\\r\\n return self.get_title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09b4e6cb66b2ab70cf087407fcbb8e5\",\n \"score\": \"0.64835054\",\n \"text\": \"def get_name(self):\\r\\n\\r\\n return self.get_title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0061ef3cf4c041256576c24e2ad0ed\",\n \"score\": \"0.6476098\",\n \"text\": \"def get_name(self) -> str:\\r\\n\\r\\n return self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c99c902a9a1c269a9dea7384547d60da\",\n \"score\": \"0.6466613\",\n \"text\": \"def GetName(self):\\n return _lldb.SBTypeCategory_GetName(self)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":788,"cells":{"query_id":{"kind":"string","value":"25b75ea7bf5cb76a12789e7989129fb6"},"query":{"kind":"string","value":"Returns a ApplicationCache Object to interact with the browser app cache."},"positive_passages":{"kind":"list like","value":[{"docid":"30b841e126a587fbf9af547ebf5dc15d","score":"0.83621204","text":"def get_application_cache(self) -> ApplicationCache:\n return self._selenium_web_driver().application_cache","title":""}],"string":"[\n {\n \"docid\": \"30b841e126a587fbf9af547ebf5dc15d\",\n \"score\": \"0.83621204\",\n \"text\": \"def get_application_cache(self) -> ApplicationCache:\\n return self._selenium_web_driver().application_cache\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ee705e4bfe2f7971b08deb8b63d3f503","score":"0.6836842","text":"def create_cache():\n class _App(object):\n config = {}\n\n cache_config = {\n 'CACHE_TYPE': 'filesystem',\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\n 'CACHE_THRESHOLD': 100,\n 'CACHE_DIR': CACHE_DIR\n }\n return Cache(app=_App(), with_jinja2_ext=False, config=cache_config)","title":""},{"docid":"334a688276c7862ca0bb5905d0a49fd0","score":"0.6681711","text":"def register_cache(app):\n cache.init_app(app, config=app.config['FLASK_CACHING'])\n return cache","title":""},{"docid":"7642dc511277a83d32862396659c17f1","score":"0.6571262","text":"def get_app(self, app_name: str) -> 'Application':\n return self._cache.get(Application, app_name)","title":""},{"docid":"869dfebcc5223afdd7f3a0efe886514c","score":"0.6241348","text":"def request_bingo_cache(self):\n if self.is_requesting_archives():\n return BingoCache.load_from_datastore(archives=True)\n else:\n return BingoCache.get()","title":""},{"docid":"67664eccbc30c0e7d178394b6d8b3ad6","score":"0.6231686","text":"def cache(self):\n\t\tif self.is_cache_outdated():\n\t\t\tself.build_cache()\n\t\treturn self._cache","title":""},{"docid":"153b33b20666b9c4cedce670d4310f24","score":"0.61012405","text":"def GetCache(self, *args, **kwargs):\n pass","title":""},{"docid":"289d4072202e1d90878dcb9292403b8d","score":"0.60995215","text":"def get_cache() -> dict:\n return cache_data","title":""},{"docid":"de836b5da6ef2795c54a70be15ae3ff8","score":"0.59789747","text":"def __init__(self, app, config=None, environ_key='beaker.cache', **kwargs):\r\n self.app = app\r\n config = config or {}\r\n \r\n self.options = {}\r\n \r\n # Update the options with the parsed config\r\n self.options.update(parse_cache_config_options(config))\r\n \r\n # Add any options from kwargs, but leave out the defaults this\r\n # time\r\n self.options.update(\r\n parse_cache_config_options(kwargs, include_defaults=False))\r\n \r\n # Assume all keys are intended for cache if none are prefixed with\r\n # 'cache.'\r\n if not self.options and config:\r\n self.options = config\r\n \r\n self.options.update(kwargs)\r\n self.cache_manager = CacheManager(**self.options)\r\n self.environ_key = environ_key","title":""},{"docid":"c476ea367ecbb6929bd81bc289a10d6d","score":"0.5942076","text":"def _cache(self):\r\n if getattr(self, '_client', None) is None:\r\n self._client = self._lib.Client(self._servers)\r\n\r\n return self._client","title":""},{"docid":"bca59bca7ecf59bdae92c0021209b061","score":"0.5933238","text":"def cache(self):\n return self.__cache","title":""},{"docid":"3d301b63ac638716d047a343058c1758","score":"0.5932765","text":"def getApplication(self, request):\n return self.app","title":""},{"docid":"a5b049f6b924c0d60adde586af89db49","score":"0.59322166","text":"def _cache(self) -> Any:\n return caches[self.cache_alias]","title":""},{"docid":"28af4f8e54bd26c7cd73a2cf5ea2fdf1","score":"0.5918129","text":"def get_cache_item(cls, app_name, name, project=None):\n cls._check_app_name(app_name)\n query_string = {'app_name': app_name, 'name': name}\n if project:\n query_string['project'] = project\n return JSONCacheItem.objects.filter(**query_string).first()","title":""},{"docid":"05dd9a0157a7106df30e4a315653f530","score":"0.5873467","text":"def cache(self):\n return self._cache","title":""},{"docid":"19c0bbeba4aae70fe2b037660d1f5e44","score":"0.58454627","text":"def __get_application(self):\n if self.request.method in ['GET', 'DELETE']:\n access_token = self.request.get('accessToken')\n else:\n try:\n access_token = loads(self.request.body).get('accessToken')\n except ValueError:\n access_token = None\n if access_token is None:\n return None\n application_key = get_application_key(access_token)\n if not application_key:\n return None\n return Application.get_by_id(application_key)","title":""},{"docid":"baf7ac30be61fcda29e2c1467fe74286","score":"0.57707256","text":"def _cache(self):\n if hasattr(self, '_c_cache'):\n return self._c_cache","title":""},{"docid":"8871d716d8b9fc95d49422015115f9d1","score":"0.5753878","text":"def __init__(self):\n self.cache = Cache.Cache()","title":""},{"docid":"f0dba87b29c1fb9cdab9b96416208122","score":"0.5749011","text":"def get_system_cache_configuration(self):\n return SystemCacheConfiguration.from_json(self.service_client.GET('configurations/cache'))","title":""},{"docid":"5e06a73f248ad6fa75d9c193c09452ec","score":"0.5745871","text":"async def get_application(self):\n self.api_server = RestApi()\n return self.api_server.app","title":""},{"docid":"02b22474b0390b3b51772482ab1e97ae","score":"0.57296216","text":"async def get_application(self):\n app = web.Application()\n setup_routes(app)\n await read_json(app, NEWS_FILENAME, COMMENTS_FILENAME)\n return app","title":""},{"docid":"cf9facd2e37038a2167d32b13ef1af14","score":"0.57231057","text":"def get_cache_backend():\n cache_alias = getattr(settings, 'METATAGS_CACHE_ALIAS', DEFAULT_CACHE_ALIAS)\n return caches[cache_alias]","title":""},{"docid":"9fe2fbaf73204d994e5343659fa9a5f4","score":"0.5628217","text":"def load(self, app: OrmCacheApp) -> _T:\n\n session = app.session_manager.session()\n\n # before accessing any cached values we need to make sure that all\n # pending changes are properly flushed -> this leads to some extra cpu\n # cycles spent but eliminates the chance of accessing a stale entry\n # after a change\n if session.dirty:\n session.flush()\n\n # we use a secondary request cache for even more lookup speed and to\n # make sure that inside a request we always get the exact same instance\n # (otherwise we don't see changes reflected)\n if self.cache_key in app.request_cache:\n\n # it is possible for objects in the request cache to become\n # detached - in this case we need to merge them again\n # (the merge function only does this if necessary)\n return self.merge(session, app.request_cache[self.cache_key])\n\n else:\n obj = app.cache.get_or_create(\n key=self.cache_key,\n creator=lambda: self.create(app)\n )\n\n # named tuples\n if isinstance(obj, tuple) and hasattr(obj.__class__, '_make'):\n obj = obj._make(self.merge(session, o) for o in obj) # type:ignore\n\n # lists (we can save some memory here)\n elif isinstance(obj, list):\n for ix, o in enumerate(obj):\n obj[ix] = self.merge(session, o)\n\n # generic iterables\n elif isinstance(obj, (tuple, set)):\n obj = obj.__class__(self.merge(session, o) for o in obj)\n\n # generic objects\n else:\n obj = self.merge(session, obj)\n\n app.request_cache[self.cache_key] = obj\n\n return obj","title":""},{"docid":"db4887c5a66ab5ae0f5986923dfe4261","score":"0.5611781","text":"def GetApplication():\r\n global applicationinstance\r\n if applicationinstance is None:\r\n applicationinstance = Application()\r\n return applicationinstance","title":""},{"docid":"5a17577055d30528e507006564db0a89","score":"0.5601422","text":"def get_backend(self):\n try:\n cache = caches[machina_settings.ATTACHMENT_CACHE_NAME]\n except InvalidCacheBackendError:\n raise ImproperlyConfigured(\n 'The attachment cache backend ({}) is not configured'.format(\n machina_settings.ATTACHMENT_CACHE_NAME,\n ),\n )\n return cache","title":""},{"docid":"3cfb9d05acb64afffe315c5561f7c5ed","score":"0.5581193","text":"async def get_application(self):\n\n return self.api_server.app","title":""},{"docid":"b70c4becdaada755059aa07437d4c524","score":"0.5558913","text":"def cache_controller_in_ram():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"66f96f0b609062c43880b76822d679dd","score":"0.5556888","text":"def db_cache() -> DbCache:\n return DbCache()","title":""},{"docid":"ace87eb211e4148baa97d2cf1c61bab1","score":"0.55439925","text":"def repopulate_app_cache(self):\r\n cache._populate()","title":""},{"docid":"3832c8bc07725352eed7d22b1008df38","score":"0.54964185","text":"def getApplication(self):\n return FacebookApplication(self.base.get(\"application\", []))","title":""},{"docid":"13cb7837c3f4c82e42eb920acc825d86","score":"0.5493108","text":"def setup_cache(self):\n if config.get('redis.host'):\n if 'redis.timeout' in config:\n default_timeout = asint(config['redis.timeout'])\n else:\n default_timeout = None\n cache = RedisCache(\n host=config['redis.host'],\n port=asint(config.get('redis.port', 6379)),\n db=asint(config.get('redis.db', 0)),\n prefix=config.get('redis.prefix', ''),\n default_timeout=default_timeout\n )\n else:\n cache = None\n\n config['pylons.app_globals'].cache = cache","title":""},{"docid":"ca1e444b3ac520643889a7a48a17951c","score":"0.5489794","text":"def get_cache(cache_class, namespace, max_entries=1000,\n redis_host='localhost', redis_port=6379, redis_protocol=None):\n\n def got_connection(client):\n return cache_class(namespace, max_entries=max_entries,\n client=client)\n\n d = get_redis_client(redis_host, redis_port, redis_protocol)\n d.addCallback(got_connection)\n return d","title":""},{"docid":"d04ba07ed214d1d987240b5b6a464de4","score":"0.54842114","text":"def get_object_cache(self, key, **kwargs):\n if key not in self._object_caches:\n return self.create_object_cache(key, **kwargs)\n return self._object_caches[key]['cache']","title":""},{"docid":"ee9dce7b8e65156d08727068dcb8d6b4","score":"0.5458705","text":"def registry(self):\n if webapp2.get_app().registry:\n return webapp2.get_app().registry\n else:\n return AppCachedProperty.GLOBAL_CACHE","title":""},{"docid":"42af6e00cbfc49d384648dc212af1b3c","score":"0.5456047","text":"def init_cache(app):\r\n if not hasattr(app.env, 'programoutput_cache'):\r\n app.env.programoutput_cache = ProgramOutputCache()","title":""},{"docid":"3a4b88fb5fb29ec511ce0e004d4edcca","score":"0.5446816","text":"def _get_form_cache(self):\n\t\tcache = FormCache.get_form_cache(cache_name=self.cache_name)\n\t\tif cache is None:\n\t\t\tcache = self.default_cache\n\t\t\tFormCache.set_form_cache(\n\t\t\t\tcache_name=self.cache_name,\n\t\t\t\tvalues=cache,\n\t\t\t)\n\t\treturn cache","title":""},{"docid":"e73b8c3756d0a2bb5d74d0a08ddd2e9a","score":"0.5431631","text":"def application(self):\n return self.__instance__.application","title":""},{"docid":"91441c5ce0580d43fe81d264c119a49d","score":"0.5430361","text":"def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached","title":""},{"docid":"91441c5ce0580d43fe81d264c119a49d","score":"0.5430361","text":"def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached","title":""},{"docid":"b1517e2db769d7f9eb9d90956f0bb1fd","score":"0.54203856","text":"def cache(self) -> typing.Optional[hikari.api.Cache]:","title":""},{"docid":"b1517e2db769d7f9eb9d90956f0bb1fd","score":"0.54203856","text":"def cache(self) -> typing.Optional[hikari.api.Cache]:","title":""},{"docid":"0796dc1f4c91c06c9a14153e5aaa4e93","score":"0.5419042","text":"async def get_application(self):\n parser, args = httpproxy.parse_args(self.get_args())\n return httpproxy.get_app(args)","title":""},{"docid":"6696ca05a9b63e58dcf94a1e9366ece7","score":"0.5381189","text":"def application(self):\r\n return Live.Application.get_application()","title":""},{"docid":"1f4e7f91068174b1f596a5964a4c1ea0","score":"0.5379811","text":"def cache(self):\n self.is_cached = True\n self.persist(StorageLevel.MEMORY_ONLY_SER)\n return self","title":""},{"docid":"e0c06854fdff64f5801b9ceb5e7f3389","score":"0.5374801","text":"def app_cache_restorer():\n state = _app_cache_deepcopy(apps.__dict__)\n try:\n yield state\n finally:\n with apps_lock():\n apps.__dict__ = state\n # Rebind the app registry models cache to\n # individual app config ones.\n for app_conf in apps.get_app_configs():\n app_conf.models = apps.all_models[app_conf.label]\n apps.clear_cache()","title":""},{"docid":"6010386b54023d11ab2b9e1343cbeb01","score":"0.53698903","text":"def get_application(routes):\n cookie_secret = None\n if not on_dev_server():\n cookie_secret = get_project_metadata('cookie_secret')\n else:\n cookie_secret = get_dev_secrets()\n\n config = {}\n config['webapp2_extras.sessions'] = {\n 'secret_key': str(cookie_secret),\n }\n\n if cookie_secret:\n return webapp2.WSGIApplication(routes=routes, config=config)\n else:\n return error_app(get_cookie_secret_error())","title":""},{"docid":"8c455cac6227c2052174516c27f9a823","score":"0.53665507","text":"def __init__(self, simplecache=None):\n if not simplecache:\n from simplecache import SimpleCache\n self.cache = SimpleCache()\n else:\n self.cache = simplecache","title":""},{"docid":"59c4f863d36bcaa265d22d8af732f9ba","score":"0.5362422","text":"def cache(self, *args, **kwargs):\n return self.get_query_set().cache(*args, **kwargs)","title":""},{"docid":"c0c2da69bbe01bfedc16759813f47b0c","score":"0.5360249","text":"def __call__(self, *args, **kwargs):\n return self._cache_wrapper(None, *args, **kwargs)","title":""},{"docid":"152df095861d66b1e00b698bae74a35d","score":"0.5358146","text":"def add_get_application_json_to_weak_caching(context):\n registry = getUtility(IRegistry)\n try:\n from plone.app.caching.interfaces import IPloneCacheSettings\n except ImportError:\n # plone.app.caching is optional.\n return\n\n try:\n settings = registry.forInterface(IPloneCacheSettings)\n except KeyError:\n # It is available, but not activated. Nothing to do.\n return\n mapping = settings.templateRulesetMapping\n key = \"GET_application_json_\"\n if key in mapping:\n # already set, do not change\n return\n mapping[key] = \"plone.content.folderView\"\n # Note: if we edit templateRulesetMapping, our change will not be persisted,\n # because it is a simple dict. We have to set the entire mapping.\n settings.templateRulesetMapping = mapping","title":""},{"docid":"9d71866de901d0a4f6fe621ae055dfa8","score":"0.53514236","text":"def cache():\n pass","title":""},{"docid":"19bdd5a6c663d5975005537d1ddad3cb","score":"0.5350763","text":"def cache(self, cache = None):\n try:\n currentCache = self.__request[\"cache\"]\n except Exception:\n currentCache = None\n if type(cache) is bool:\n self.__request[\"cache\"] = cache\n elif not (cache is None):\n raise Exception(\"cache must be of type bool\") \n return(currentCache)","title":""},{"docid":"09cc63cffc95a64464f289ff60a972a0","score":"0.5331978","text":"def get(self):\n return pickle.loads(self.db.get(self.cache_key))","title":""},{"docid":"143320186be55d43f35a0cbec3bb1a6c","score":"0.5324998","text":"def get_project_cache(cls, project, data_type='json'):\n cls._check_data_type(data_type)\n return JSONCacheItem.objects.filter(project=project)","title":""},{"docid":"4e63ad3426854737590231ef1f5a14a2","score":"0.5324482","text":"def cache(self):\n self.rdd.cache()\n return self","title":""},{"docid":"b9cb2ff8bf3587177d0a21c392d0a3aa","score":"0.5307121","text":"def __init__(self, cache, cache_key=None):\n self.cache = cache\n self.cache_key = cache_key","title":""},{"docid":"7cda2a535c305eb27ff3312122683ee2","score":"0.5298131","text":"async def get_application(self):\n return await servinit()","title":""},{"docid":"223ac8ecb438e90a6a126a73aa19c711","score":"0.52853745","text":"def _get_activity(_cache=[]):\n if _cache:\n return _cache[0]\n # See MainActivity.onCreate() for initialization of .singletonThis:\n # https://github.com/beeware/briefcase-android-gradle-template/blob/3.7/%7B%7B%20cookiecutter.formal_name%20%7D%7D/app/src/main/java/org/beeware/android/MainActivity.java\n # This can't be tested because if it isn't set, nothing else will work.\n if not MainActivity.singletonThis: # pragma: no cover\n raise ValueError(\n \"Unable to find MainActivity.singletonThis from Python. This is typically set by \"\n \"org.beeware.android.MainActivity.onCreate().\"\n )\n _cache.append(MainActivity.singletonThis.__global__())\n return _cache[0]","title":""},{"docid":"117a4865f41357039d3e9afaa14aadbf","score":"0.52616775","text":"def create_app(config_name):\n\n # instantiate an app where config will be in an instance\n app = Flask(__name__, instance_relative_config=True)\n api = Api(app)\n cor_app = CORS(app)\n\n # Set the config parameters\n app.config.from_object(config[config_name])\n app.config.from_pyfile('config.py')\n \n # Initialize the cache using the config params\n cache = Cache(\n app.config['NUMBER_OF_SLOTS'], \n app.config['TIME_TO_LIVE'], \n EvictionStrategies(app.config['EVICTION_POLICY'])\n )\n CacheApi.initialize_cache(cache)\n\n # Set the routes\n \n api.add_resource(CacheApi, '/object/<string:key>')\n\n return app","title":""},{"docid":"2150e2dc9809741e315158ab94b40be0","score":"0.524874","text":"def _init_cache(self):\n self.cache = Cache()","title":""},{"docid":"1d53deeaee79dd16889659342055f392","score":"0.52353615","text":"def cache_in_ram():\r\n\r\n t = cache.ram('time', lambda: time.ctime(), time_expire=5)\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"4cce37b55384c1355090c31bcabae321","score":"0.5233077","text":"def cache_controller_on_disk():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"1d9e34cb1536560b88113c751ed5d066","score":"0.5232964","text":"def cache(context):\n pass","title":""},{"docid":"21781c1219feb51c4c32df6a3bdfc805","score":"0.5228117","text":"def copy(self) -> \"LRUCache\":\n rv = self.__class__(self.capacity)\n rv._mapping.update(self._mapping)\n rv._queue.extend(self._queue)\n return rv","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"c2637b40860ccde8ff311f2db227d1a9","score":"0.51805556","text":"def synchronize_apps_cache():\n sync_id = start_synchronization()\n try:\n cached_requests = get_cached_session()\n synced_apps = _sync_golab_translations(cached_requests, force_reload = False)\n _sync_regular_apps(cached_requests, synced_apps, force_reload = False)\n finally:\n end_synchronization(sync_id)","title":""},{"docid":"e881a491de4108eb2309b0abc0489a0e","score":"0.51803803","text":"def appinit(self):\n return Appinit(self)","title":""},{"docid":"0955005ce38d9fb33f9c61ea62849cf5","score":"0.5178926","text":"def load(self):\n return app","title":""},{"docid":"aa5be44577fef68e21bd261cbb8eb343","score":"0.5176882","text":"def cache_object(*args, **kwargs):\n global __loop__\n if __loop__ is None:\n __loop__ = DefaultEventLoop(GLOBAL_NAME)\n\n __loop__.cache_object(*args, **kwargs)","title":""},{"docid":"826ff4c5dfa46ce200d716f8c979c71f","score":"0.51712286","text":"def cache(self) -> Path:\n return self._impl.get_cache_path()","title":""},{"docid":"ba34225b33d8ee6d77a88f36f6da0eb1","score":"0.5167296","text":"def create_app(self):\n # Create APP and set configuration\n app = APP\n config = Config()\n\n app.config['TESTING'] = True\n app.config['LIVESERVER_PORT'] = config.agent_api_ip_bind_port()\n os.environ['FLASK_ENV'] = 'development'\n\n # Clear the flask cache\n cache = Cache(config={'CACHE_TYPE': 'null'})\n cache.init_app(app)\n\n # Return\n return app","title":""},{"docid":"ea095adf9d10118bfac112c4d08bab27","score":"0.5156244","text":"def __get__(self, instance: RedisCache, owner: Any) -> RedisCache:\n if self.bot:\n return self\n\n if self._namespace is None:\n error_message = \"RedisCache must be a class attribute.\"\n log.error(error_message)\n raise NoNamespaceError(error_message)\n\n if instance is None:\n error_message = (\n \"You must access the RedisCache instance through the cog instance \"\n \"before accessing it using the cog's class object.\"\n )\n log.error(error_message)\n raise NoParentInstanceError(error_message)\n\n for attribute in vars(instance).values():\n if isinstance(attribute, Bot):\n self.bot = attribute\n self._redis = self.bot.redis_session\n return self\n else:\n error_message = (\n \"Critical error: RedisCache has no `Bot` instance. \"\n \"This happens when the class RedisCache was created in doesn't \"\n \"have a Bot instance. Please make sure that you're instantiating \"\n \"the RedisCache inside a class that has a Bot instance attribute.\"\n )\n log.error(error_message)\n raise NoBotInstanceError(error_message)","title":""},{"docid":"ecdcf580e6dadac9b79940b89047d257","score":"0.5144671","text":"def get_app(self):\n return app","title":""},{"docid":"ecdcf580e6dadac9b79940b89047d257","score":"0.5144671","text":"def get_app(self):\n return app","title":""},{"docid":"6376a6e74ef6bcf425e908c4432620fd","score":"0.5133707","text":"def __install_cache(self) -> None:\n if USE_CACHE:\n CACHE_PATH.mkdir(exist_ok=True)\n requests_cache.install_cache(\n str(CACHE_PATH.joinpath(self.get_class_name()))\n )","title":""},{"docid":"5da1adaa6297fe43915b98a452f62088","score":"0.51205796","text":"def static_config(self):\n\n return CacheConfig(1024, None)","title":""},{"docid":"b16be2098fbca620e32869fc48ffd6ac","score":"0.51037395","text":"def __call__(self, environ, start_response):\n ctx = ndb.get_context()\n ctx.set_cache_policy(False)\n\n response = self.application(environ, start_response)\n\n ctx.clear_cache()\n return response","title":""},{"docid":"b5977509f45af093701976e2ade8bac2","score":"0.5083971","text":"def get_metadata_cache_manager():\n global _METADATA_CACHE_MANAGER\n return _METADATA_CACHE_MANAGER","title":""},{"docid":"7fc4e69a79bbf77e3395e0aaa6245980","score":"0.5079018","text":"def get_full_cache(self):\n self.update_time()\n return self._cache","title":""},{"docid":"25af86466512750d1c59fda9de5954c4","score":"0.50715613","text":"def _get_cache_obj(self, type, id):\n cache = self._resolve_cache(type)\n return cache.get(id, None)","title":""},{"docid":"773e0199280cb8d13f98978e26d54757","score":"0.5059492","text":"def get(cls) -> MemCacheHandlerBase:\n if not hasattr(cls, \"instance\"):\n cls_name = cls.__class__.__name__\n raise MemCacheHandlerError(f\"Before calling {cls_name}.get(), you should call {cls_name}.create() first.\")\n\n return cls.instance","title":""},{"docid":"03c3efd180677017fc9a9577570c04f2","score":"0.5054254","text":"def get_app(self):\n if self.app is None:\n self._register_app()\n return self.app","title":""},{"docid":"153fed25f31f98f5702716cb7a2be054","score":"0.5037384","text":"def get_app(self):\n return self.app","title":""},{"docid":"e44ac8b2c1de1c94cd56bd19fe5aa44a","score":"0.50314593","text":"def __init__(self, app):\n self._initialize_app(app)\n self._initialize_cache(SortedDict(key=lambda p: p.date, reverse=True))","title":""},{"docid":"54cc4030140cae06ac04f930aa3ab44a","score":"0.5023157","text":"def app(self):\n return self._app","title":""},{"docid":"38c7a5050d751a7deed2a178db436be1","score":"0.5017547","text":"def load_cache(cache_file):\n fd = open(cache_file)\n cache_data = load(fd)\n fd.close()\n return cache_data","title":""},{"docid":"d1e74660c616410ec8202c90cda4c051","score":"0.50134623","text":"def get(self, url):\n session = self.sessionmaker()\n try:\n cache_result = session.query(HTTPCacheContent) \\\n .filter(HTTPCacheContent.url == url) \\\n .one_or_none()\n\n # if expiration is enabled then don't return anything that is expired\n if cache_result is not None and \\\n not self.dont_expire and \\\n cache_result.expire_on_dt is not None and \\\n cache_result.expire_on_dt < datetime.utcnow():\n print(\n f\"URL '{url}' found in cache, but set for expiration in the past at \"\n f\"{cache_result.expire_on_dt}, so not returned.\"\n )\n cache_result = None\n finally:\n session.close()\n\n if cache_result is None:\n return None\n elif cache_result.content is not None:\n return cache_result.content\n else:\n assert cache_result.content_bzip2 is not None\n return bz2.decompress(cache_result.content_bzip2)","title":""},{"docid":"abb9359623ada198ed4ffdf06225b3fc","score":"0.500541","text":"def api_with_cache(epirr_id):\n if epirr_id not in cache:\n cache[epirr_id] = api_call_epirr(epirr_id)\n return cache[epirr_id]","title":""},{"docid":"5cd670ae42f6218ae3950ce5d1b15fac","score":"0.50006336","text":"def _get_cache(self, param, namespace = None):\n\n return self._get_cache_map([param], namespace).get(param, None)","title":""},{"docid":"6db3e2c53e4f23305597543bddd7e1f0","score":"0.49994013","text":"def get_route_cache(cls):\n if hasattr(cls.thread_locals, 'request'):\n request = cls.thread_locals.request\n if not hasattr(request, 'route_cache'):\n request.route_cache = {}\n return request.route_cache\n return {}","title":""},{"docid":"5d085c0843cd6a2fcaecbd641b4c7cb7","score":"0.4996278","text":"def get_session(cls, app: object = None) -> 'SessionStore':\n config = get_application_config(app)\n host = config.get('REDIS_HOST', 'localhost')\n port = int(config.get('REDIS_PORT', '7000'))\n db = int(config.get('REDIS_DATABASE', '0'))\n token = config.get('REDIS_TOKEN', None)\n cluster = config.get('REDIS_CLUSTER', '1') == '1'\n secret = config['JWT_SECRET']\n duration = int(config.get('SESSION_DURATION', '7200'))\n fake = config.get('REDIS_FAKE', False)\n return cls(host, port, db, secret, duration, token=token,\n cluster=cluster, fake=fake)","title":""},{"docid":"92662aad18d3d08d9394e5a41bd4354b","score":"0.4986594","text":"def cache_data(self):\n # Initialize key variables\n result = self.data['cache_data']\n return result","title":""},{"docid":"0ba0b7031e90c85452d1a720f6a03a9e","score":"0.49527672","text":"def get_app_singleton():\n return _singleton","title":""},{"docid":"1d671bb769197d57a80411b7f2fe1661","score":"0.4948296","text":"def get_cache(self, queue_name: str) -> dict:\n\n # Check queue name. Raise exception if not exists\n self.check_queue_name(queue_name)\n\n # Get cache from queue\n cache = self.queues[queue_name].get_cache()\n return cache","title":""},{"docid":"b35e91efbb19a6fb8e708214fffa593b","score":"0.49419135","text":"def get_default_cache_ctrl():\n\n if settings.get(\"DEFAULT_CACHE\") is None: # missing or None\n return CacheCtrl([])\n\n return make_cache_ctrl(settings[\"DEFAULT_CACHE\"])","title":""},{"docid":"679b837e1b0576c33d4c25c0bed3086d","score":"0.4934507","text":"def _create_session(self):\n kwargs = {\"connection\": self.memcached, \"ip_address\": self.request.remote_ip}\n new_session = MemcachedSession(**kwargs)\n new_session.save()\n return new_session","title":""},{"docid":"d7e28d69713a7047707a1f602dceba8d","score":"0.49208707","text":"def save_in_cache(self):\n memcache.set(self.code(), self)","title":""},{"docid":"ede37ea1f20c4e14732e6445d4722ac0","score":"0.49168393","text":"def get_application():\n description = '''Turbinia API server'''\n _app = FastAPI(\n title='Turbinia API Server', description=description, version='1.0.0',\n license_info={\n 'name': 'Apache License 2.0',\n 'url': 'https://www.apache.org/licenses/LICENSE-2.0.html'\n }, routes=router.routes)\n return _app","title":""}],"string":"[\n {\n \"docid\": \"ee705e4bfe2f7971b08deb8b63d3f503\",\n \"score\": \"0.6836842\",\n \"text\": \"def create_cache():\\n class _App(object):\\n config = {}\\n\\n cache_config = {\\n 'CACHE_TYPE': 'filesystem',\\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\\n 'CACHE_THRESHOLD': 100,\\n 'CACHE_DIR': CACHE_DIR\\n }\\n return Cache(app=_App(), with_jinja2_ext=False, config=cache_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334a688276c7862ca0bb5905d0a49fd0\",\n \"score\": \"0.6681711\",\n \"text\": \"def register_cache(app):\\n cache.init_app(app, config=app.config['FLASK_CACHING'])\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7642dc511277a83d32862396659c17f1\",\n \"score\": \"0.6571262\",\n \"text\": \"def get_app(self, app_name: str) -> 'Application':\\n return self._cache.get(Application, app_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869dfebcc5223afdd7f3a0efe886514c\",\n \"score\": \"0.6241348\",\n \"text\": \"def request_bingo_cache(self):\\n if self.is_requesting_archives():\\n return BingoCache.load_from_datastore(archives=True)\\n else:\\n return BingoCache.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67664eccbc30c0e7d178394b6d8b3ad6\",\n \"score\": \"0.6231686\",\n \"text\": \"def cache(self):\\n\\t\\tif self.is_cache_outdated():\\n\\t\\t\\tself.build_cache()\\n\\t\\treturn self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153b33b20666b9c4cedce670d4310f24\",\n \"score\": \"0.61012405\",\n \"text\": \"def GetCache(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289d4072202e1d90878dcb9292403b8d\",\n \"score\": \"0.60995215\",\n \"text\": \"def get_cache() -> dict:\\n return cache_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de836b5da6ef2795c54a70be15ae3ff8\",\n \"score\": \"0.59789747\",\n \"text\": \"def __init__(self, app, config=None, environ_key='beaker.cache', **kwargs):\\r\\n self.app = app\\r\\n config = config or {}\\r\\n \\r\\n self.options = {}\\r\\n \\r\\n # Update the options with the parsed config\\r\\n self.options.update(parse_cache_config_options(config))\\r\\n \\r\\n # Add any options from kwargs, but leave out the defaults this\\r\\n # time\\r\\n self.options.update(\\r\\n parse_cache_config_options(kwargs, include_defaults=False))\\r\\n \\r\\n # Assume all keys are intended for cache if none are prefixed with\\r\\n # 'cache.'\\r\\n if not self.options and config:\\r\\n self.options = config\\r\\n \\r\\n self.options.update(kwargs)\\r\\n self.cache_manager = CacheManager(**self.options)\\r\\n self.environ_key = environ_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c476ea367ecbb6929bd81bc289a10d6d\",\n \"score\": \"0.5942076\",\n \"text\": \"def _cache(self):\\r\\n if getattr(self, '_client', None) is None:\\r\\n self._client = self._lib.Client(self._servers)\\r\\n\\r\\n return self._client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca59bca7ecf59bdae92c0021209b061\",\n \"score\": \"0.5933238\",\n \"text\": \"def cache(self):\\n return self.__cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d301b63ac638716d047a343058c1758\",\n \"score\": \"0.5932765\",\n \"text\": \"def getApplication(self, request):\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b049f6b924c0d60adde586af89db49\",\n \"score\": \"0.59322166\",\n \"text\": \"def _cache(self) -> Any:\\n return caches[self.cache_alias]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28af4f8e54bd26c7cd73a2cf5ea2fdf1\",\n \"score\": \"0.5918129\",\n \"text\": \"def get_cache_item(cls, app_name, name, project=None):\\n cls._check_app_name(app_name)\\n query_string = {'app_name': app_name, 'name': name}\\n if project:\\n query_string['project'] = project\\n return JSONCacheItem.objects.filter(**query_string).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dd9a0157a7106df30e4a315653f530\",\n \"score\": \"0.5873467\",\n \"text\": \"def cache(self):\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c0bbeba4aae70fe2b037660d1f5e44\",\n \"score\": \"0.58454627\",\n \"text\": \"def __get_application(self):\\n if self.request.method in ['GET', 'DELETE']:\\n access_token = self.request.get('accessToken')\\n else:\\n try:\\n access_token = loads(self.request.body).get('accessToken')\\n except ValueError:\\n access_token = None\\n if access_token is None:\\n return None\\n application_key = get_application_key(access_token)\\n if not application_key:\\n return None\\n return Application.get_by_id(application_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf7ac30be61fcda29e2c1467fe74286\",\n \"score\": \"0.57707256\",\n \"text\": \"def _cache(self):\\n if hasattr(self, '_c_cache'):\\n return self._c_cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8871d716d8b9fc95d49422015115f9d1\",\n \"score\": \"0.5753878\",\n \"text\": \"def __init__(self):\\n self.cache = Cache.Cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0dba87b29c1fb9cdab9b96416208122\",\n \"score\": \"0.5749011\",\n \"text\": \"def get_system_cache_configuration(self):\\n return SystemCacheConfiguration.from_json(self.service_client.GET('configurations/cache'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e06a73f248ad6fa75d9c193c09452ec\",\n \"score\": \"0.5745871\",\n \"text\": \"async def get_application(self):\\n self.api_server = RestApi()\\n return self.api_server.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b22474b0390b3b51772482ab1e97ae\",\n \"score\": \"0.57296216\",\n \"text\": \"async def get_application(self):\\n app = web.Application()\\n setup_routes(app)\\n await read_json(app, NEWS_FILENAME, COMMENTS_FILENAME)\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9facd2e37038a2167d32b13ef1af14\",\n \"score\": \"0.57231057\",\n \"text\": \"def get_cache_backend():\\n cache_alias = getattr(settings, 'METATAGS_CACHE_ALIAS', DEFAULT_CACHE_ALIAS)\\n return caches[cache_alias]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe2fbaf73204d994e5343659fa9a5f4\",\n \"score\": \"0.5628217\",\n \"text\": \"def load(self, app: OrmCacheApp) -> _T:\\n\\n session = app.session_manager.session()\\n\\n # before accessing any cached values we need to make sure that all\\n # pending changes are properly flushed -> this leads to some extra cpu\\n # cycles spent but eliminates the chance of accessing a stale entry\\n # after a change\\n if session.dirty:\\n session.flush()\\n\\n # we use a secondary request cache for even more lookup speed and to\\n # make sure that inside a request we always get the exact same instance\\n # (otherwise we don't see changes reflected)\\n if self.cache_key in app.request_cache:\\n\\n # it is possible for objects in the request cache to become\\n # detached - in this case we need to merge them again\\n # (the merge function only does this if necessary)\\n return self.merge(session, app.request_cache[self.cache_key])\\n\\n else:\\n obj = app.cache.get_or_create(\\n key=self.cache_key,\\n creator=lambda: self.create(app)\\n )\\n\\n # named tuples\\n if isinstance(obj, tuple) and hasattr(obj.__class__, '_make'):\\n obj = obj._make(self.merge(session, o) for o in obj) # type:ignore\\n\\n # lists (we can save some memory here)\\n elif isinstance(obj, list):\\n for ix, o in enumerate(obj):\\n obj[ix] = self.merge(session, o)\\n\\n # generic iterables\\n elif isinstance(obj, (tuple, set)):\\n obj = obj.__class__(self.merge(session, o) for o in obj)\\n\\n # generic objects\\n else:\\n obj = self.merge(session, obj)\\n\\n app.request_cache[self.cache_key] = obj\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db4887c5a66ab5ae0f5986923dfe4261\",\n \"score\": \"0.5611781\",\n \"text\": \"def GetApplication():\\r\\n global applicationinstance\\r\\n if applicationinstance is None:\\r\\n applicationinstance = Application()\\r\\n return applicationinstance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a17577055d30528e507006564db0a89\",\n \"score\": \"0.5601422\",\n \"text\": \"def get_backend(self):\\n try:\\n cache = caches[machina_settings.ATTACHMENT_CACHE_NAME]\\n except InvalidCacheBackendError:\\n raise ImproperlyConfigured(\\n 'The attachment cache backend ({}) is not configured'.format(\\n machina_settings.ATTACHMENT_CACHE_NAME,\\n ),\\n )\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfb9d05acb64afffe315c5561f7c5ed\",\n \"score\": \"0.5581193\",\n \"text\": \"async def get_application(self):\\n\\n return self.api_server.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70c4becdaada755059aa07437d4c524\",\n \"score\": \"0.5558913\",\n \"text\": \"def cache_controller_in_ram():\\r\\n\\r\\n t = time.ctime()\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f96f0b609062c43880b76822d679dd\",\n \"score\": \"0.5556888\",\n \"text\": \"def db_cache() -> DbCache:\\n return DbCache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace87eb211e4148baa97d2cf1c61bab1\",\n \"score\": \"0.55439925\",\n \"text\": \"def repopulate_app_cache(self):\\r\\n cache._populate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3832c8bc07725352eed7d22b1008df38\",\n \"score\": \"0.54964185\",\n \"text\": \"def getApplication(self):\\n return FacebookApplication(self.base.get(\\\"application\\\", []))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cb7837c3f4c82e42eb920acc825d86\",\n \"score\": \"0.5493108\",\n \"text\": \"def setup_cache(self):\\n if config.get('redis.host'):\\n if 'redis.timeout' in config:\\n default_timeout = asint(config['redis.timeout'])\\n else:\\n default_timeout = None\\n cache = RedisCache(\\n host=config['redis.host'],\\n port=asint(config.get('redis.port', 6379)),\\n db=asint(config.get('redis.db', 0)),\\n prefix=config.get('redis.prefix', ''),\\n default_timeout=default_timeout\\n )\\n else:\\n cache = None\\n\\n config['pylons.app_globals'].cache = cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1e444b3ac520643889a7a48a17951c\",\n \"score\": \"0.5489794\",\n \"text\": \"def get_cache(cache_class, namespace, max_entries=1000,\\n redis_host='localhost', redis_port=6379, redis_protocol=None):\\n\\n def got_connection(client):\\n return cache_class(namespace, max_entries=max_entries,\\n client=client)\\n\\n d = get_redis_client(redis_host, redis_port, redis_protocol)\\n d.addCallback(got_connection)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04ba07ed214d1d987240b5b6a464de4\",\n \"score\": \"0.54842114\",\n \"text\": \"def get_object_cache(self, key, **kwargs):\\n if key not in self._object_caches:\\n return self.create_object_cache(key, **kwargs)\\n return self._object_caches[key]['cache']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9dce7b8e65156d08727068dcb8d6b4\",\n \"score\": \"0.5458705\",\n \"text\": \"def registry(self):\\n if webapp2.get_app().registry:\\n return webapp2.get_app().registry\\n else:\\n return AppCachedProperty.GLOBAL_CACHE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42af6e00cbfc49d384648dc212af1b3c\",\n \"score\": \"0.5456047\",\n \"text\": \"def init_cache(app):\\r\\n if not hasattr(app.env, 'programoutput_cache'):\\r\\n app.env.programoutput_cache = ProgramOutputCache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4b88fb5fb29ec511ce0e004d4edcca\",\n \"score\": \"0.5446816\",\n \"text\": \"def _get_form_cache(self):\\n\\t\\tcache = FormCache.get_form_cache(cache_name=self.cache_name)\\n\\t\\tif cache is None:\\n\\t\\t\\tcache = self.default_cache\\n\\t\\t\\tFormCache.set_form_cache(\\n\\t\\t\\t\\tcache_name=self.cache_name,\\n\\t\\t\\t\\tvalues=cache,\\n\\t\\t\\t)\\n\\t\\treturn cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73b8c3756d0a2bb5d74d0a08ddd2e9a\",\n \"score\": \"0.5431631\",\n \"text\": \"def application(self):\\n return self.__instance__.application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91441c5ce0580d43fe81d264c119a49d\",\n \"score\": \"0.5430361\",\n \"text\": \"def memcached(self):\\n if self._memcached is None:\\n self._memcached = MemcachedConnect()\\n return self._memcached\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91441c5ce0580d43fe81d264c119a49d\",\n \"score\": \"0.5430361\",\n \"text\": \"def memcached(self):\\n if self._memcached is None:\\n self._memcached = MemcachedConnect()\\n return self._memcached\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1517e2db769d7f9eb9d90956f0bb1fd\",\n \"score\": \"0.54203856\",\n \"text\": \"def cache(self) -> typing.Optional[hikari.api.Cache]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1517e2db769d7f9eb9d90956f0bb1fd\",\n \"score\": \"0.54203856\",\n \"text\": \"def cache(self) -> typing.Optional[hikari.api.Cache]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0796dc1f4c91c06c9a14153e5aaa4e93\",\n \"score\": \"0.5419042\",\n \"text\": \"async def get_application(self):\\n parser, args = httpproxy.parse_args(self.get_args())\\n return httpproxy.get_app(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6696ca05a9b63e58dcf94a1e9366ece7\",\n \"score\": \"0.5381189\",\n \"text\": \"def application(self):\\r\\n return Live.Application.get_application()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4e7f91068174b1f596a5964a4c1ea0\",\n \"score\": \"0.5379811\",\n \"text\": \"def cache(self):\\n self.is_cached = True\\n self.persist(StorageLevel.MEMORY_ONLY_SER)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c06854fdff64f5801b9ceb5e7f3389\",\n \"score\": \"0.5374801\",\n \"text\": \"def app_cache_restorer():\\n state = _app_cache_deepcopy(apps.__dict__)\\n try:\\n yield state\\n finally:\\n with apps_lock():\\n apps.__dict__ = state\\n # Rebind the app registry models cache to\\n # individual app config ones.\\n for app_conf in apps.get_app_configs():\\n app_conf.models = apps.all_models[app_conf.label]\\n apps.clear_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6010386b54023d11ab2b9e1343cbeb01\",\n \"score\": \"0.53698903\",\n \"text\": \"def get_application(routes):\\n cookie_secret = None\\n if not on_dev_server():\\n cookie_secret = get_project_metadata('cookie_secret')\\n else:\\n cookie_secret = get_dev_secrets()\\n\\n config = {}\\n config['webapp2_extras.sessions'] = {\\n 'secret_key': str(cookie_secret),\\n }\\n\\n if cookie_secret:\\n return webapp2.WSGIApplication(routes=routes, config=config)\\n else:\\n return error_app(get_cookie_secret_error())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c455cac6227c2052174516c27f9a823\",\n \"score\": \"0.53665507\",\n \"text\": \"def __init__(self, simplecache=None):\\n if not simplecache:\\n from simplecache import SimpleCache\\n self.cache = SimpleCache()\\n else:\\n self.cache = simplecache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c4f863d36bcaa265d22d8af732f9ba\",\n \"score\": \"0.5362422\",\n \"text\": \"def cache(self, *args, **kwargs):\\n return self.get_query_set().cache(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c2da69bbe01bfedc16759813f47b0c\",\n \"score\": \"0.5360249\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n return self._cache_wrapper(None, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152df095861d66b1e00b698bae74a35d\",\n \"score\": \"0.5358146\",\n \"text\": \"def add_get_application_json_to_weak_caching(context):\\n registry = getUtility(IRegistry)\\n try:\\n from plone.app.caching.interfaces import IPloneCacheSettings\\n except ImportError:\\n # plone.app.caching is optional.\\n return\\n\\n try:\\n settings = registry.forInterface(IPloneCacheSettings)\\n except KeyError:\\n # It is available, but not activated. Nothing to do.\\n return\\n mapping = settings.templateRulesetMapping\\n key = \\\"GET_application_json_\\\"\\n if key in mapping:\\n # already set, do not change\\n return\\n mapping[key] = \\\"plone.content.folderView\\\"\\n # Note: if we edit templateRulesetMapping, our change will not be persisted,\\n # because it is a simple dict. We have to set the entire mapping.\\n settings.templateRulesetMapping = mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d71866de901d0a4f6fe621ae055dfa8\",\n \"score\": \"0.53514236\",\n \"text\": \"def cache():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bdd5a6c663d5975005537d1ddad3cb\",\n \"score\": \"0.5350763\",\n \"text\": \"def cache(self, cache = None):\\n try:\\n currentCache = self.__request[\\\"cache\\\"]\\n except Exception:\\n currentCache = None\\n if type(cache) is bool:\\n self.__request[\\\"cache\\\"] = cache\\n elif not (cache is None):\\n raise Exception(\\\"cache must be of type bool\\\") \\n return(currentCache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09cc63cffc95a64464f289ff60a972a0\",\n \"score\": \"0.5331978\",\n \"text\": \"def get(self):\\n return pickle.loads(self.db.get(self.cache_key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"143320186be55d43f35a0cbec3bb1a6c\",\n \"score\": \"0.5324998\",\n \"text\": \"def get_project_cache(cls, project, data_type='json'):\\n cls._check_data_type(data_type)\\n return JSONCacheItem.objects.filter(project=project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e63ad3426854737590231ef1f5a14a2\",\n \"score\": \"0.5324482\",\n \"text\": \"def cache(self):\\n self.rdd.cache()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cb2ff8bf3587177d0a21c392d0a3aa\",\n \"score\": \"0.5307121\",\n \"text\": \"def __init__(self, cache, cache_key=None):\\n self.cache = cache\\n self.cache_key = cache_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cda2a535c305eb27ff3312122683ee2\",\n \"score\": \"0.5298131\",\n \"text\": \"async def get_application(self):\\n return await servinit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223ac8ecb438e90a6a126a73aa19c711\",\n \"score\": \"0.52853745\",\n \"text\": \"def _get_activity(_cache=[]):\\n if _cache:\\n return _cache[0]\\n # See MainActivity.onCreate() for initialization of .singletonThis:\\n # https://github.com/beeware/briefcase-android-gradle-template/blob/3.7/%7B%7B%20cookiecutter.formal_name%20%7D%7D/app/src/main/java/org/beeware/android/MainActivity.java\\n # This can't be tested because if it isn't set, nothing else will work.\\n if not MainActivity.singletonThis: # pragma: no cover\\n raise ValueError(\\n \\\"Unable to find MainActivity.singletonThis from Python. This is typically set by \\\"\\n \\\"org.beeware.android.MainActivity.onCreate().\\\"\\n )\\n _cache.append(MainActivity.singletonThis.__global__())\\n return _cache[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117a4865f41357039d3e9afaa14aadbf\",\n \"score\": \"0.52616775\",\n \"text\": \"def create_app(config_name):\\n\\n # instantiate an app where config will be in an instance\\n app = Flask(__name__, instance_relative_config=True)\\n api = Api(app)\\n cor_app = CORS(app)\\n\\n # Set the config parameters\\n app.config.from_object(config[config_name])\\n app.config.from_pyfile('config.py')\\n \\n # Initialize the cache using the config params\\n cache = Cache(\\n app.config['NUMBER_OF_SLOTS'], \\n app.config['TIME_TO_LIVE'], \\n EvictionStrategies(app.config['EVICTION_POLICY'])\\n )\\n CacheApi.initialize_cache(cache)\\n\\n # Set the routes\\n \\n api.add_resource(CacheApi, '/object/<string:key>')\\n\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2150e2dc9809741e315158ab94b40be0\",\n \"score\": \"0.524874\",\n \"text\": \"def _init_cache(self):\\n self.cache = Cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d53deeaee79dd16889659342055f392\",\n \"score\": \"0.52353615\",\n \"text\": \"def cache_in_ram():\\r\\n\\r\\n t = cache.ram('time', lambda: time.ctime(), time_expire=5)\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cce37b55384c1355090c31bcabae321\",\n \"score\": \"0.5233077\",\n \"text\": \"def cache_controller_on_disk():\\r\\n\\r\\n t = time.ctime()\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9e34cb1536560b88113c751ed5d066\",\n \"score\": \"0.5232964\",\n \"text\": \"def cache(context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21781c1219feb51c4c32df6a3bdfc805\",\n \"score\": \"0.5228117\",\n \"text\": \"def copy(self) -> \\\"LRUCache\\\":\\n rv = self.__class__(self.capacity)\\n rv._mapping.update(self._mapping)\\n rv._queue.extend(self._queue)\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2637b40860ccde8ff311f2db227d1a9\",\n \"score\": \"0.51805556\",\n \"text\": \"def synchronize_apps_cache():\\n sync_id = start_synchronization()\\n try:\\n cached_requests = get_cached_session()\\n synced_apps = _sync_golab_translations(cached_requests, force_reload = False)\\n _sync_regular_apps(cached_requests, synced_apps, force_reload = False)\\n finally:\\n end_synchronization(sync_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e881a491de4108eb2309b0abc0489a0e\",\n \"score\": \"0.51803803\",\n \"text\": \"def appinit(self):\\n return Appinit(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0955005ce38d9fb33f9c61ea62849cf5\",\n \"score\": \"0.5178926\",\n \"text\": \"def load(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5be44577fef68e21bd261cbb8eb343\",\n \"score\": \"0.5176882\",\n \"text\": \"def cache_object(*args, **kwargs):\\n global __loop__\\n if __loop__ is None:\\n __loop__ = DefaultEventLoop(GLOBAL_NAME)\\n\\n __loop__.cache_object(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826ff4c5dfa46ce200d716f8c979c71f\",\n \"score\": \"0.51712286\",\n \"text\": \"def cache(self) -> Path:\\n return self._impl.get_cache_path()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba34225b33d8ee6d77a88f36f6da0eb1\",\n \"score\": \"0.5167296\",\n \"text\": \"def create_app(self):\\n # Create APP and set configuration\\n app = APP\\n config = Config()\\n\\n app.config['TESTING'] = True\\n app.config['LIVESERVER_PORT'] = config.agent_api_ip_bind_port()\\n os.environ['FLASK_ENV'] = 'development'\\n\\n # Clear the flask cache\\n cache = Cache(config={'CACHE_TYPE': 'null'})\\n cache.init_app(app)\\n\\n # Return\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea095adf9d10118bfac112c4d08bab27\",\n \"score\": \"0.5156244\",\n \"text\": \"def __get__(self, instance: RedisCache, owner: Any) -> RedisCache:\\n if self.bot:\\n return self\\n\\n if self._namespace is None:\\n error_message = \\\"RedisCache must be a class attribute.\\\"\\n log.error(error_message)\\n raise NoNamespaceError(error_message)\\n\\n if instance is None:\\n error_message = (\\n \\\"You must access the RedisCache instance through the cog instance \\\"\\n \\\"before accessing it using the cog's class object.\\\"\\n )\\n log.error(error_message)\\n raise NoParentInstanceError(error_message)\\n\\n for attribute in vars(instance).values():\\n if isinstance(attribute, Bot):\\n self.bot = attribute\\n self._redis = self.bot.redis_session\\n return self\\n else:\\n error_message = (\\n \\\"Critical error: RedisCache has no `Bot` instance. \\\"\\n \\\"This happens when the class RedisCache was created in doesn't \\\"\\n \\\"have a Bot instance. Please make sure that you're instantiating \\\"\\n \\\"the RedisCache inside a class that has a Bot instance attribute.\\\"\\n )\\n log.error(error_message)\\n raise NoBotInstanceError(error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdcf580e6dadac9b79940b89047d257\",\n \"score\": \"0.5144671\",\n \"text\": \"def get_app(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdcf580e6dadac9b79940b89047d257\",\n \"score\": \"0.5144671\",\n \"text\": \"def get_app(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6376a6e74ef6bcf425e908c4432620fd\",\n \"score\": \"0.5133707\",\n \"text\": \"def __install_cache(self) -> None:\\n if USE_CACHE:\\n CACHE_PATH.mkdir(exist_ok=True)\\n requests_cache.install_cache(\\n str(CACHE_PATH.joinpath(self.get_class_name()))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da1adaa6297fe43915b98a452f62088\",\n \"score\": \"0.51205796\",\n \"text\": \"def static_config(self):\\n\\n return CacheConfig(1024, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16be2098fbca620e32869fc48ffd6ac\",\n \"score\": \"0.51037395\",\n \"text\": \"def __call__(self, environ, start_response):\\n ctx = ndb.get_context()\\n ctx.set_cache_policy(False)\\n\\n response = self.application(environ, start_response)\\n\\n ctx.clear_cache()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5977509f45af093701976e2ade8bac2\",\n \"score\": \"0.5083971\",\n \"text\": \"def get_metadata_cache_manager():\\n global _METADATA_CACHE_MANAGER\\n return _METADATA_CACHE_MANAGER\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc4e69a79bbf77e3395e0aaa6245980\",\n \"score\": \"0.5079018\",\n \"text\": \"def get_full_cache(self):\\n self.update_time()\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25af86466512750d1c59fda9de5954c4\",\n \"score\": \"0.50715613\",\n \"text\": \"def _get_cache_obj(self, type, id):\\n cache = self._resolve_cache(type)\\n return cache.get(id, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773e0199280cb8d13f98978e26d54757\",\n \"score\": \"0.5059492\",\n \"text\": \"def get(cls) -> MemCacheHandlerBase:\\n if not hasattr(cls, \\\"instance\\\"):\\n cls_name = cls.__class__.__name__\\n raise MemCacheHandlerError(f\\\"Before calling {cls_name}.get(), you should call {cls_name}.create() first.\\\")\\n\\n return cls.instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c3efd180677017fc9a9577570c04f2\",\n \"score\": \"0.5054254\",\n \"text\": \"def get_app(self):\\n if self.app is None:\\n self._register_app()\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153fed25f31f98f5702716cb7a2be054\",\n \"score\": \"0.5037384\",\n \"text\": \"def get_app(self):\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44ac8b2c1de1c94cd56bd19fe5aa44a\",\n \"score\": \"0.50314593\",\n \"text\": \"def __init__(self, app):\\n self._initialize_app(app)\\n self._initialize_cache(SortedDict(key=lambda p: p.date, reverse=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cc4030140cae06ac04f930aa3ab44a\",\n \"score\": \"0.5023157\",\n \"text\": \"def app(self):\\n return self._app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c7a5050d751a7deed2a178db436be1\",\n \"score\": \"0.5017547\",\n \"text\": \"def load_cache(cache_file):\\n fd = open(cache_file)\\n cache_data = load(fd)\\n fd.close()\\n return cache_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e74660c616410ec8202c90cda4c051\",\n \"score\": \"0.50134623\",\n \"text\": \"def get(self, url):\\n session = self.sessionmaker()\\n try:\\n cache_result = session.query(HTTPCacheContent) \\\\\\n .filter(HTTPCacheContent.url == url) \\\\\\n .one_or_none()\\n\\n # if expiration is enabled then don't return anything that is expired\\n if cache_result is not None and \\\\\\n not self.dont_expire and \\\\\\n cache_result.expire_on_dt is not None and \\\\\\n cache_result.expire_on_dt < datetime.utcnow():\\n print(\\n f\\\"URL '{url}' found in cache, but set for expiration in the past at \\\"\\n f\\\"{cache_result.expire_on_dt}, so not returned.\\\"\\n )\\n cache_result = None\\n finally:\\n session.close()\\n\\n if cache_result is None:\\n return None\\n elif cache_result.content is not None:\\n return cache_result.content\\n else:\\n assert cache_result.content_bzip2 is not None\\n return bz2.decompress(cache_result.content_bzip2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb9359623ada198ed4ffdf06225b3fc\",\n \"score\": \"0.500541\",\n \"text\": \"def api_with_cache(epirr_id):\\n if epirr_id not in cache:\\n cache[epirr_id] = api_call_epirr(epirr_id)\\n return cache[epirr_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd670ae42f6218ae3950ce5d1b15fac\",\n \"score\": \"0.50006336\",\n \"text\": \"def _get_cache(self, param, namespace = None):\\n\\n return self._get_cache_map([param], namespace).get(param, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db3e2c53e4f23305597543bddd7e1f0\",\n \"score\": \"0.49994013\",\n \"text\": \"def get_route_cache(cls):\\n if hasattr(cls.thread_locals, 'request'):\\n request = cls.thread_locals.request\\n if not hasattr(request, 'route_cache'):\\n request.route_cache = {}\\n return request.route_cache\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d085c0843cd6a2fcaecbd641b4c7cb7\",\n \"score\": \"0.4996278\",\n \"text\": \"def get_session(cls, app: object = None) -> 'SessionStore':\\n config = get_application_config(app)\\n host = config.get('REDIS_HOST', 'localhost')\\n port = int(config.get('REDIS_PORT', '7000'))\\n db = int(config.get('REDIS_DATABASE', '0'))\\n token = config.get('REDIS_TOKEN', None)\\n cluster = config.get('REDIS_CLUSTER', '1') == '1'\\n secret = config['JWT_SECRET']\\n duration = int(config.get('SESSION_DURATION', '7200'))\\n fake = config.get('REDIS_FAKE', False)\\n return cls(host, port, db, secret, duration, token=token,\\n cluster=cluster, fake=fake)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92662aad18d3d08d9394e5a41bd4354b\",\n \"score\": \"0.4986594\",\n \"text\": \"def cache_data(self):\\n # Initialize key variables\\n result = self.data['cache_data']\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba0b7031e90c85452d1a720f6a03a9e\",\n \"score\": \"0.49527672\",\n \"text\": \"def get_app_singleton():\\n return _singleton\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d671bb769197d57a80411b7f2fe1661\",\n \"score\": \"0.4948296\",\n \"text\": \"def get_cache(self, queue_name: str) -> dict:\\n\\n # Check queue name. Raise exception if not exists\\n self.check_queue_name(queue_name)\\n\\n # Get cache from queue\\n cache = self.queues[queue_name].get_cache()\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b35e91efbb19a6fb8e708214fffa593b\",\n \"score\": \"0.49419135\",\n \"text\": \"def get_default_cache_ctrl():\\n\\n if settings.get(\\\"DEFAULT_CACHE\\\") is None: # missing or None\\n return CacheCtrl([])\\n\\n return make_cache_ctrl(settings[\\\"DEFAULT_CACHE\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679b837e1b0576c33d4c25c0bed3086d\",\n \"score\": \"0.4934507\",\n \"text\": \"def _create_session(self):\\n kwargs = {\\\"connection\\\": self.memcached, \\\"ip_address\\\": self.request.remote_ip}\\n new_session = MemcachedSession(**kwargs)\\n new_session.save()\\n return new_session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7e28d69713a7047707a1f602dceba8d\",\n \"score\": \"0.49208707\",\n \"text\": \"def save_in_cache(self):\\n memcache.set(self.code(), self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede37ea1f20c4e14732e6445d4722ac0\",\n \"score\": \"0.49168393\",\n \"text\": \"def get_application():\\n description = '''Turbinia API server'''\\n _app = FastAPI(\\n title='Turbinia API Server', description=description, version='1.0.0',\\n license_info={\\n 'name': 'Apache License 2.0',\\n 'url': 'https://www.apache.org/licenses/LICENSE-2.0.html'\\n }, routes=router.routes)\\n return _app\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":789,"cells":{"query_id":{"kind":"string","value":"e6be7d743ec3c3a768eef75172fd2f8f"},"query":{"kind":"string","value":"find users with their username that start with letter that user has entered"},"positive_passages":{"kind":"list like","value":[{"docid":"2c1b08376ea3c1ba98ed3949f10267b2","score":"0.83855855","text":"def users(letter):\n return User.objects.filter(username__istartswith=letter)","title":""}],"string":"[\n {\n \"docid\": \"2c1b08376ea3c1ba98ed3949f10267b2\",\n \"score\": \"0.83855855\",\n \"text\": \"def users(letter):\\n return User.objects.filter(username__istartswith=letter)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d8e566029b9c771acb958a39552c0017","score":"0.85593385","text":"def getUsersStartingWith(letters):","title":""},{"docid":"8664fa1d80739b81e26240163caa7420","score":"0.6641168","text":"def check_username(self, username):\n match = re.findall(r\"^[a-zA-Z]+\\w+$\", username)\n if match and username == match[0] and len(username) >= 4:\n if not settings.first_run:\n query = f\"SELECT user FROM {settings.users_table}\"\n users = database.get_record(query)\n if username in users[0]:\n return None, \"Your username is taken\\n\"\n return username, \"\"\n else:\n return None, \"Username is incorrect, check the tip in the input field\\n\"","title":""},{"docid":"b0d07a24adf7d116bc2430e9a8b3be28","score":"0.6440513","text":"def search_users_byusername(self, uid):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE username LIKE %s'), ('%%%s%%' % uid, ))\n return self.cur.fetchall()","title":""},{"docid":"53d6cdc39eb7021e73e9ba4c4996a088","score":"0.64390105","text":"def searchname(partialname, maxresults=32):\n people = Person.select().where( \\\n Person.name.contains(partialname) | \\\n Person.username.contains(partialname)) \\\n .order_by(Person.username).limit(maxresults)\n return [p.username for p in people]","title":""},{"docid":"afc18538883cccf5725b2dce2f8c60a0","score":"0.6423979","text":"def usernames_in(message):\n # Don't parse usernames in the commands\n if _command_re.match(message.split(\" \", 1)[0]):\n message = message.split(\" \", 1)[1]\n\n # Strip email addresses from the message, in order to avoid matching the\n # user's domain. Also strip URLs, in order to avoid usernames in them.\n message = strip_urls(message)\n\n results = []\n for result in _username_re.finditer(message):\n if result.group(1):\n results.append(result.group(1))\n\n return results","title":""},{"docid":"fc4f8574b1f5698bd6006dda181c5d0b","score":"0.6417107","text":"def get_username(self):\n is_valid_username = False\n while not is_valid_username:\n print(\"Username: \", end=\"\")\n username = input().strip()\n user_regex = re.compile(r\"^[A-z0-9]+$\")\n is_valid_username = user_regex.match(username) is not None\n if not is_valid_username:\n print(\"{} is not a valid username\".format(username))\n # check if username already exists in the system\n if self.db.is_username_exists(username):\n is_valid_username = False\n print(\"The username {} already exists\".format(username))\n return username","title":""},{"docid":"e43cb84cbc70ae2360760c291046ee1c","score":"0.63649565","text":"def usernames(request):\n term = request.GET.get('term', '')\n query = request.GET.get('query', '')\n pre = term or query\n\n if not pre:\n return []\n if not request.user.is_authenticated():\n return []\n with statsd.timer('users.api.usernames.search'):\n profiles = (\n Profile.objects.filter(Q(name__istartswith=pre))\n .values_list('user_id', flat=True))\n users = (\n User.objects.filter(\n Q(username__istartswith=pre) | Q(id__in=profiles))\n .extra(select={'length': 'Length(username)'})\n .order_by('length').select_related('profile'))\n\n if not waffle.switch_is_active('users-dont-limit-by-login'):\n last_login = datetime.now() - timedelta(weeks=12)\n users = users.filter(last_login__gte=last_login)\n\n return [{'username': u.username,\n 'display_name': display_name_or_none(u)}\n for u in users[:10]]","title":""},{"docid":"08a2c22b67b0f2a978e7780ae7c0336d","score":"0.6349212","text":"def username_condition(environ, match):\n map_user = match.get('map_username')\n if not map_user:\n return False \n if len(map_user) < 4:\n return False\n if map_user in CONFIG.BANNED_USERNAMES:\n return False\n\n return True","title":""},{"docid":"2a13f74a9806a22a6449e0636d9524fe","score":"0.6297197","text":"def findUsers(conn, username):\n curs = dbi.dict_cursor(conn)\n curs.execute('''select * from user_accounts where username like %s ''', \n ['%' + username + '%'])\n return curs.fetchall()","title":""},{"docid":"594beefe0a7b7fdf252067fc0a2c3576","score":"0.6248869","text":"def get_usernames(keyword):\n con = get_binded_connection()\n usernames = [result[1][\"uid\"][0] for result in\\\n con.search_s(\"%s,%s\" % (settings.USERS_OU, settings.DC),\n ldap.SCOPE_SUBTREE, \"(uid=*)\", ['uid'])]\n con.unbind_s()\n return filter(lambda username: re.search(keyword, username, re.IGNORECASE), usernames)","title":""},{"docid":"2a8f38999dd1d2fd8616deec79bde372","score":"0.6243985","text":"def valid_name(username):\n return username and re.match(r'[a-zA-Z][a-zA-Z0-9_]{2,}$', username)","title":""},{"docid":"edbdd034511c2b18b1c0215116f2b0f0","score":"0.62313473","text":"def matching_user(self, name):","title":""},{"docid":"dccf8958baf7b0b08b5e164ed7102675","score":"0.6210032","text":"def check_username(cls, username):\n return cls.db.keys('user:' + username)","title":""},{"docid":"bb3730a4ec076f9fc8d1347cddd7de83","score":"0.6201446","text":"def check_username(username):\n\n usernames = db.execute(\"SELECT * FROM users WHERE username = :username\", username=username)\n\n return usernames","title":""},{"docid":"c5acc83ec17386263af6a5d412e0fd45","score":"0.616268","text":"def search_users_byname(self, name):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n return self.search_users('name ILIKE', name)","title":""},{"docid":"cc1ef7ea82d61048aa09dd3ed3e44c15","score":"0.6128579","text":"def search_usernames(self, query):\n\t\tresults = self.t.users.search(q=query)\n\t\tusernames = [(user['name'], user['screen_name']) for user in results]\n\t\treturn usernames","title":""},{"docid":"df9359dba65ecb00cd096116cbc81acd","score":"0.60037506","text":"def test_user_is_a_johnny(self):\n one_user = User.objects.first()\n self.assertTrue(one_user.username.startswith('Johnny'))","title":""},{"docid":"c06d376bfd8ed8b7d44e8d44ecd462c9","score":"0.5999096","text":"def validate_username(self, username_field):\n if re.match(r'[A-Z]', username_field.data):\n raise ValidationError(\"you only can using lowercase for username\")\n \n if User.query.filter_by(username=username_field.data).first():\n raise ValidationError(\"username already registered, try another one\")","title":""},{"docid":"0d3ad88fbc1aaab9ad1becb48ae653c5","score":"0.5974542","text":"def search_users(self, where, var):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE ') + where + ' %s', ('%%%s%%' % var, ))\n return self.cur.fetchall()","title":""},{"docid":"4b384e75efcc1d67949f0ef8c80505b6","score":"0.5971448","text":"def search_for_users(self, query: str) -> list:","title":""},{"docid":"a02b78a0e0147084bb2f8d657650deb0","score":"0.59693974","text":"def search(request):\n global unknow_user_list\n unknow_user_list = []\n user_name = request.POST['res_user']\n first = user_name.split(' ')[0]\n second = user_name.split(' ')[1]\n for i in SCUser.objects.filter(first_name=first,last_name=second)[:]:\n unknow_user_list.append(i)","title":""},{"docid":"ffd486a999e8dc1924a47854a645327c","score":"0.59549856","text":"def valid_username(username):\n return username and USER_RE.match(username)","title":""},{"docid":"00be54a5087127aa2f0dc169f7128251","score":"0.5947252","text":"def userPrefix(self):\n return self.userName[0].lower()","title":""},{"docid":"368ecd47b076d6bda76aab3a613cba06","score":"0.5938804","text":"def username(Username):\n\treturn Username.strip().lower().replace(\"_\", \"\").replace(\" \", \"-\")","title":""},{"docid":"717f9b0b7330473e2adc7cf854da0e6e","score":"0.59299797","text":"def user_searcher(self, bot, name: str, max_users=5) -> List[any]:\n username, discriminator = self.username_parser(name)\n if discriminator:\n users = [\n x\n for x in bot.get_all_members()\n if x.name.lower() == username.lower()\n and x.discriminator == discriminator\n ]\n else:\n users = [\n x for x in bot.get_all_members() if x.name.lower() == username.lower()\n ]\n if not users:\n name = re.escape(name)\n users = [\n x\n for x in bot.get_all_members()\n if re.search(name.lower(), x.display_name.lower())\n ]\n if len(users) > max_users:\n raise ValueError(\"Too many users returned.\")\n return users","title":""},{"docid":"49e9d832ff89507a1c9d784f031875b5","score":"0.5920381","text":"def find_account(cls,username):\n for account in cls.users:\n if account.firstName == username:\n return account","title":""},{"docid":"cf9af93bf0b1284fc4bd295ec2b5eef8","score":"0.5917308","text":"def _findUserNameCLEARCHAT(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split(':')[2])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"","title":""},{"docid":"cb6ee67c968d2081cd7cd8f23dba9b5f","score":"0.59135354","text":"def clean_username(self):\n if not ALNUM_REGEXP.search(self.cleaned_data['username']):\n raise forms.ValidationError(_(u'Uživatelské jméno může obsahovat pouze písmena, číslice a podtržítko'))\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(u'Toto uživatelské jméno už je zabráno'))","title":""},{"docid":"45977a361e20948d23517a0937d6ba6b","score":"0.5911364","text":"def test_contains_lowercase(self):\n assert re.search(\"[a-z]\", PasswordGenerator().create())","title":""},{"docid":"1747b598c0bd18602f0a68ac2c4399ca","score":"0.58801764","text":"def userfilter(text):\n return stringutil.shortuser(text)","title":""},{"docid":"df0d9d1c946f81589b520c6d0eb77721","score":"0.5878371","text":"def get_user_input():\r\n valid_inputs = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\r\n 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\r\n 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' ']\r\n username = input()\r\n if len(username) < 1:\r\n print(\"Name must be at least one letter. Please enter another name:\")\r\n username = get_user_input()\r\n for letter in username:\r\n if letter not in valid_inputs:\r\n print(\"Username can only contain letters. Please enter another name:\")\r\n username = get_user_input()\r\n return username","title":""},{"docid":"009a78a17b2eecd6200119ecaf2d1859","score":"0.58749855","text":"def validate_username(self, username):\n regex = re.compile(r'[@_!#$%^&*()<>?/\\|}{~:]')\n check_username = User.objects.filter(username=username)\n if check_username.exists():\n raise serializers.ValidationError(\"Provided username already exist, please provide a different one\")\n elif regex.search(username):\n raise serializers.ValidationError(\"should not contain special characters @_!#$%^&*()<>?/\\|}{~:\")\n elif len(username.split()) > 1:\n raise serializers.ValidationError(\n \"Username should not contain spaces\"\n )","title":""},{"docid":"dfebcbddfded50c6d9ec60be675c99bd","score":"0.587404","text":"def clean_username(self):\n username = self.cleaned_data[\"username\"]\n\n if Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError('Username non disponibile!')\n else:\n return username","title":""},{"docid":"31b35357c4c9938c12a2a94fd81842d0","score":"0.5864838","text":"def find_user(self):\n users = []\n place = -1\n not_found = True\n # until a user is chosen\n while not_found:\n name = raw_input(\"Name: \")\n users = self.searchForUsers(name)\n # Printout 4 best matches\n for i, user in enumerate(users[:4], 1):\n print(f'#{i} {user.name}')\n place = int(input(\"Enter number, in case the user is not listed, press enter and try their name again:\"))\n if 0 <= place <= 3:\n not_found = False\n return users[place - 1]","title":""},{"docid":"3d070a3ee437c821903d36715620acb2","score":"0.58620286","text":"def username_validator(form, field):\n username = field.data\n if len(username) < 3:\n raise ValidationError(_('Username must be at least 3 characters long'))\n valid_chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._'\n chars = list(username)\n for char in chars:\n if char not in valid_chars:\n raise ValidationError(_(\"Username may only contain letters, numbers, '-', '.' and '_'.\"))","title":""},{"docid":"6480d21680c8dd6952e8aea6c8929864","score":"0.5858849","text":"def clean_username(self):\r\n existing = User.objects.filter(username__iexact=self.cleaned_data['username'])\r\n if existing.exists():\r\n raise forms.ValidationError(_(\"O email já se encontra registado.\"))\r\n else:\r\n return self.cleaned_data['username']","title":""},{"docid":"bb509753ebe1a402b4655be04aca08b9","score":"0.585632","text":"def check():\n userNames = db.execute(\"SELECT username FROM users\")\n userName = request.args.get(\"username\")\n search = {'username' : userName}\n if search in userNames:\n print(\"user name found\")\n return jsonify(\"false\")\n return jsonify(\"TODO\")","title":""},{"docid":"c50cb820f73822e2b0f5cc90572b7b68","score":"0.5852539","text":"def valid_username(self, username):\n return self.USER_RE.match(username)","title":""},{"docid":"4695547fd1518cdc497f17e06805a09c","score":"0.58498293","text":"def valid_username(self, username):\n return username and USER_RE.match(username)","title":""},{"docid":"97828fd3b7f52be6efdb1d914cf15d1b","score":"0.584555","text":"def get_username(infile):\n username = 0\n myfile = open(infile, mode=\"r\", encoding=\"latin_1\")\n what_to_look = 'Uploaded by '\n for line_username in myfile:\n if what_to_look in line_username:\n username_length = line_username.find('</font>')\n username = line_username[58:username_length]\n print(\"Username: \" + username.strip())\n return username.strip()\n myfile.close()","title":""},{"docid":"8b086f5cca7f18c70d9deeb692a32faf","score":"0.58317703","text":"def process_username(self, value) -> str:","title":""},{"docid":"0cde03d4228e8685827660495aa1a6b1","score":"0.58283216","text":"def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tif not re.search(r'^\\w+$', username):\n\t\t\traise forms.ValidationError ('Username can only contain '\n\t\t\t'alphanumeric characters and the underscore.')\n\t\ttry:\n\t\t\tUser.objects.get(username=username)\n\t\texcept User.DoesNotExist:\n\t\t\treturn username\n\t\traise forms.ValidationError('Username already taken.')","title":""},{"docid":"886622e20a72c5ffc26f7711c383fbfc","score":"0.5823204","text":"def clean_username(self):\n existing = Users.objects.filter(username=self.cleaned_data['username'])\n if existing.exists():\n raise forms.ValidationError(_('A user with that username already exists.'))\n else:\n return self.cleaned_data['username']","title":""},{"docid":"e27cb3f8ef14d1b9c8665d9cd4f0f134","score":"0.5819259","text":"def handle_search(request):\n\n query = request.GET.get('query')\n if query:\n users = User.objects.filter(username__icontains=query\n ).values_list('username', flat=True\n ).order_by('profile__score')\n else:\n users = User.objects.order_by('profile__score').values_list('username', flat=True)\n\n users = list(users[:20])\n # Return list of users matching username\n return ajax_success(users=users, msg=\"Username searched\")","title":""},{"docid":"402f6542db3a31a58cd1343b3fc307c8","score":"0.5805711","text":"def validate_username(cls, username, skip_uniqueness_check=False):\n un = username.lower()\n\n if (un in Config['blocked_usernames']\n or any(fragment in un for fragment in Config['blocked_username_fragments'])\n or any(fragment in un for fragment in Config['autoflag_words'])):\n return \"Sorry, this username is not allowed.\"\n\n if not un:\n return \"Please enter a username.\"\n elif len(un) < 3:\n return \"Username must be 3 or more characters.\"\n elif len(un) > 16:\n return \"Username must be 16 characters or less.\"\n\n alphabet = string.lowercase + string.uppercase + string.digits + '_'\n if not all(char in alphabet for char in username):\n return \"Usernames can only contain letters, digits and underscores.\"\n\n if not skip_uniqueness_check:\n if cls.objects.filter(username__iexact=username):\n return \"Sorry! This username is taken. Please pick a different username.\"","title":""},{"docid":"ccf82e5c207b54640641b69971396aec","score":"0.5800986","text":"def clean_username(self):\n username = self.cleaned_data['username'].lower()\n if (User.objects.filter(username__iexact=username).exists() or\n username in settings.ACCOUNT_USERNAME_BLACKLIST):\n raise ValidationError(_('A user with that username already exists.'))\n return username","title":""},{"docid":"8b2719c2a52bb1843d98111dca318d8e","score":"0.5790296","text":"def get_users_like(self, name):\n name_length = len(name)\n if name_length > USER_ALL_NAME_LENGTH_MAX_WITH_DISCRIMINATOR:\n return []\n \n users = self.users\n \n # name with discriminator\n \n name_with_discriminator = _parse_name_with_discriminator(name)\n if (name_with_discriminator is not None):\n for user in users.values():\n if _is_user_matching_name_with_discriminator(user, name_with_discriminator):\n return [user]\n \n if name_length > USER_ALL_NAME_LENGTH_MAX:\n return []\n \n user_name_pattern = re_compile('.*?'.join(re_escape(char) for char in name), re_ignore_case)\n matches = []\n guild_id = self.id\n \n for user in users.values():\n # name\n \n parsed = user_name_pattern.search(user.name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # display_name\n \n user_display_name = user.display_name\n if (user_display_name is not None):\n parsed = user_name_pattern.search(user_display_name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_DISPLAY_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # nick\n \n try:\n guild_profile = user.guild_profiles[guild_id]\n except KeyError:\n pass\n else:\n user_nick = guild_profile.nick\n if (user_nick is not None):\n parsed = user_name_pattern.search(user_nick)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NICK, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n \n return [item[0] for item in sorted(matches, key = _user_match_sort_key)]","title":""},{"docid":"b9555fdcc9e54fd5f5f4380bc80d68d4","score":"0.57825446","text":"def validate_username_field(value):\n msg = []\n if value:\n if not value.isalnum() and \"_\" not in value:\n msg.append(\n \"field mustn't contain any special character other than _\")\n if not ((re.search(\"[a-z]\", value))or(re.search(\"[A-Z]\", value))):\n msg.append(\"field must contain atleast one character\")\n if len(msg) > 0:\n raise serializers.ValidationError(msg)","title":""},{"docid":"a35b0a5cfdaf998533757ab81ef80b7a","score":"0.5759538","text":"def validate_username(username: str) -> bool:\n return True if re.match(\"^([a-zA-Z0-9]|\\.){6,32}$\", username) else False","title":""},{"docid":"bccfcb1a4faa69fafb35985b1726fcc8","score":"0.5750044","text":"def sanity_check_username(name):\n VALID_CHARACTERS = string.ascii_letters+string.digits+\"_-.\"\n rules = [ \n len(name) > 3, # User name is longer than 3 characters\n all([k in VALID_CHARACTERS for k in list(name)]) # Username is made of valid characters\n ]\n return all(rules)","title":""},{"docid":"73419325271abbbdc5c616ca0cd524bf","score":"0.574605","text":"def usersname_length(username):\n try:\n username = str(username)\n # if username.length() > 3 and < 21:\n if (len(username) >= 3) and (len(username) < 21):\n return True\n except ValueError:\n return False","title":""},{"docid":"901627988b4967b6959f5399749d0ce6","score":"0.57419056","text":"def is_valid_username(u_name):\n if not re.match(r\"^[A-Za-z\\.\\+_-]*$\", u_name) != None:\n return False\n return True","title":""},{"docid":"921e0723b5599cc772717ee38636de85","score":"0.57408136","text":"def findUsersByName(conn, name):\n query = \"SELECT * FROM users WHERE name LIKE %s\"\n params = [\"%\" + name + \"%\"]\n return getSqlQuery(conn, query, params)","title":""},{"docid":"11dd260e48c8b537315631ad539f4f7a","score":"0.5739077","text":"def clean_username(self):\n username = self.cleaned_data[\"username\"]\n if not Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError(\"Username non registrato\")\n else:\n return username","title":""},{"docid":"10457fa52cefb6a629aa87718148e306","score":"0.5726845","text":"def check_user_name(self, username):\n query = \"SELECT username FROM users WHERE username = '%s'\" % (username)\n result = self.fetch_single_data_row(query)\n if result is None:\n return False\n return True","title":""},{"docid":"e0f0e453b2520b68c9cb1c756d008987","score":"0.57148904","text":"def check_valid_username(self, username : str):\n\t\treturn True","title":""},{"docid":"c9c54c91fd6a80bbb5edc552561ef5b2","score":"0.57078695","text":"def security_check_username(self, bot, update):\n\n full_name = (update.message.from_user.first_name + \" \"\n + update.message.from_user.last_name)\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(full_name.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n logger.info(notify_user_id,\"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(update.message.from_user.username.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)","title":""},{"docid":"60497a226d11be3cafe143f5f4baf5a3","score":"0.56966424","text":"def clean_username(self):\r\n username = self.cleaned_data[\"username\"]\r\n if '@' in username:\r\n # The user entered an email, so try to log them in by e-mail\r\n emails = ContactValue.objects.filter(value=username,\r\n field__field_type='email',\r\n contact__trash=False,\r\n contact__related_user__isnull=False)\r\n users = [email.contact.related_user.user for email in emails]\r\n else:\r\n users = User.objects.filter(user__username=username)\r\n if len(users) == 0:\r\n raise forms.ValidationError(\r\n _(\"Sorry, we don't know that user or e-mail.\"))\r\n else:\r\n username = users[0]\r\n return username","title":""},{"docid":"c1353c764d75f75d8f8558f24aaa1ab2","score":"0.5692759","text":"def clean_username(self):\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(\"A user with that username already exists.\"))","title":""},{"docid":"c3de59944fb73806d9a0d76fdef129df","score":"0.5677581","text":"def auto_complete_model(request):\n if request.is_ajax():\n searchString=request.POST[\"search\"]\n search_qs = User.objects.filter(username__startswith=searchString)\n print search_qs\n results = []\n for r in search_qs:\n results.append(r.username)\n print results\n return HttpResponse(json.dumps(results), content_type=\"application/json\", status=200)","title":""},{"docid":"a5e0005f4d22a3f09122843f9e940b11","score":"0.5675287","text":"def is_letter_in_pattern(user_input, word):\r\n for letter in list(word):\r\n if user_input == letter:\r\n return True\r\n return False","title":""},{"docid":"7de1e6e4e7af2ec958fd08cc5adad7c4","score":"0.56643456","text":"def get_userName():\n userName = input(\"Tapez Votre nom d'utilisateur: \")\n\n userName = userName.capitalize() # 1st letter in cap\n\n if not userName.isalnum() or len(userName)<4:\n\n print(\"Nom invalide\")\n return get_userName()\n\n else:\n return userName","title":""},{"docid":"11bb76360b33e6a116242866fab5edd2","score":"0.5660173","text":"def _check_username(self, username):\n # Username cannot contain spaces\n if ' ' in username:\n return False\n else:\n return True","title":""},{"docid":"dc451def748fc8dda4ebad1e3f2dd02c","score":"0.5657971","text":"def clean_username(self):\n username = self.cleaned_data.get('username')\n if len(username) < 3:\n raise forms.ValidationError(\"001,Your username must be longer than 3 words.\")\n elif len(username) > 20:\n raise forms.ValidationError(\"002,Your username must be shorter than 20 words.\")\n else:\n\n #To check whether the username already exists\n \n checkresult = User_Info.objects.filter(username=username)\n if len(checkresult) > 0:\n raise forms.ValidationError(\"003,Your username already exists.\")\n return username","title":""},{"docid":"0411422ddb7c66213d92669fbfea4606","score":"0.5654673","text":"def find(word, letter, start):\n\n index = start\n hits = []\n\n while index < len(word):\n if word[index].lower() == letter.lower():\n hits.append(index)\n index += 1\n\n return(hits)","title":""},{"docid":"fe06db84edc8d4419919a5d393e0d0da","score":"0.5648181","text":"def user_name():\n while 1:\n username = input(\"enter username, please: \")\n if not len(username) == 0:\n return username","title":""},{"docid":"9c82479f2897275c5c78a4cb5bfe4b88","score":"0.56426215","text":"def clean_username(self, username):\n return username","title":""},{"docid":"58c8af63dd87814589e29192c85d4b4c","score":"0.5637166","text":"def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Este correo electronico ya existe')\n return username","title":""},{"docid":"1e7833e36e5978bb94e34dd72ed08206","score":"0.5629253","text":"def _contains_lowercase_name(self, name):\n return name in (x.lower() for x in self._distinct())","title":""},{"docid":"4746a4438fcd4d437bbb5a83cd66e336","score":"0.56258893","text":"def userSearch(self, username, pwd=\"abc\", usneed=False):\n try:\n self.cur.execute('''\n SELECT * FROM \"Student\"\n WHERE \"UserName\" = ?''', (username,))\n\n rows = self.cur.fetchall()\n # print(rows)\n if usneed:\n return rows[0][1]\n elif len(rows) == 0:\n return \"Empty\" # if no account yet created or wrong username\n elif len(rows) == 1 and rows[0][4] == pwd:\n return True\n return False # if wrong password inserted\n except Exception as e:\n print(\"userSearch(), error -\", e)\n self.con.close()\n return -1","title":""},{"docid":"cc295b480fcf5c6b853d0df7ea8f1d4f","score":"0.561518","text":"def _is_first_name(self, word):\n names = self._first_name_list\n word = word.lower()\n ind = bisect.bisect_left(names, word)\n if ind < len(names) and names[ind] == word:\n return True\n return False","title":""},{"docid":"0d71da97eb837b9720e897203c7687db","score":"0.56023335","text":"def test_get_user_by_username_r(self):\n pass","title":""},{"docid":"280fe52791e9ae728929c968bfb34157","score":"0.5601374","text":"def filter_user(tweets):\n \n return tweets.str.replace('<user>', '', case=False)","title":""},{"docid":"801fc57aa073f025190e7da688415a5e","score":"0.559805","text":"def _findUserNamePRIVMSG(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split('!')[0])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"","title":""},{"docid":"f6d7f959bb24b39ec9fb5bb435b9539a","score":"0.55943894","text":"def search_for_letters(phrase:str,letters:str='aeiou') ->set:\r\n return set(letters).intersection(set(phrase))","title":""},{"docid":"38c88ffee191a5c1841c81b8482b5ccf","score":"0.558165","text":"def get_service_usernames():\n usernames = [k[:-len('_passwd')] for k in leader_get()\n if k.endswith('_passwd') and k != 'admin_passwd']\n # now match against the service list.\n valid_service_names = valid_services.keys()\n known_service_usernames = []\n for username in usernames:\n # if a username has '_' in it, then it is a compound name.\n parts = username.split('_')\n if 'keystone' in parts:\n continue\n if not all(p in valid_service_names for p in parts):\n continue\n known_service_usernames.append(username)\n return known_service_usernames","title":""},{"docid":"39cb53ddf55ec2a3546f1b818d47090e","score":"0.5574706","text":"def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Nombre de usuario ya registrado.')\n return username","title":""},{"docid":"1d4427c955f075306234e38ed62c4af2","score":"0.5553871","text":"def generate_username(name):\n username = ''.join(name.split(' ')).lower()\n if not User.objects.filter(username=username).exists():\n return username\n else:\n random_username = username + str(randint(0, 1000))\n return generate_username(random_username)","title":""},{"docid":"aa644612e55a8ebbd61835fc8da001c0","score":"0.5548313","text":"def parse_name(guild, username):\r\n if '@' in username:\r\n try:\r\n return guild.get_member(int(username[3:-1]))\r\n except:\r\n raise NameError(username)\r\n else:\r\n return get_member_from_guild(guild.members, username)","title":""},{"docid":"8970f68548e77c567d874853c18c5454","score":"0.55467904","text":"def check():\r\n username = request.args.get(\"username\")\r\n if len(username) >= 1:\r\n usernames = [dct[\"username\"] for dct in db.execute(\"SELECT username FROM users\")]\r\n if username not in usernames:\r\n return jsonify(True)\r\n else:\r\n return jsonify(False)","title":""},{"docid":"d7f90c6d8429bb1d64141f7955edbd74","score":"0.55457246","text":"def username_exists_validator(value):\n try:\n User.objects.get(username=value)\n\n except User.DoesNotExist:\n raise ValidationError(_('Requested username does not exist.'))","title":""},{"docid":"e76690e229888ca6a3b5515f7b6a088f","score":"0.55418396","text":"def validate_username(cls, nick):\n if nick and nick != \"\":\n nick = nick.strip()\n if len(nick) > 0:\n if re.match(\"^[A-z0-9_\\-]+$\", nick):\n return nick, \"\"\n else:\n return \"\", \"Username can only contain letters, numbers, underscores, and dashes.\"\n return \"\", \"Username must not be empty.\"","title":""},{"docid":"566aa32677a8d6f412d11e734703c54a","score":"0.55270416","text":"def check_username_lower_limit(username):\n if len(username) >= 4:\n return True","title":""},{"docid":"8286201ec8571426d3ce568b4ff7d700","score":"0.55268127","text":"def get_by_natural_key(self, username):\r\n anycase_username_field = f'{self.model.USERNAME_FIELD}__iexact'\r\n return self.get(**{anycase_username_field: username})","title":""},{"docid":"40e99474b264ed23dbb9d1046298888f","score":"0.552202","text":"def check_username(cls, uid):\n if not uid:\n raise RBFatalError('Username must be given')\n if re.search(r'[^a-z0-9_.-]', uid):\n raise RBFatalError(\"Invalid characters in username\")\n if len(uid) > rbconfig.maxlen_uname:\n raise RBFatalError(\"Username can not be longer than %d characters\"\n % rbconfig.maxlen_uname)\n if re.search(r'^[^a-z0-9]', uid):\n raise RBFatalError(\"Username must begin with letter or number\")","title":""},{"docid":"bf3b9bd84177442eda8c6433534e636c","score":"0.5512801","text":"def validate_username(self, username):\n # onyl allow alphanumeric character, . _ -\n if username is not None:\n pattern = r\"^[a-zA-Z0-9_.-]+$\"\n if len(username) < 1:\n raise ValidationError('Invalid username, minimum is 1 character')\n if len(username) > 32:\n raise ValidationError('Invalid username, max is 32 character')\n if re.match(pattern, username) is None:\n raise ValidationError('Invalid username, only alphanumeric, . _ - allowed')","title":""},{"docid":"28fd7d5e2650faf027dced619ce0ed7e","score":"0.5505782","text":"def get_first_name(self):\n is_valid_first_name = False\n while not is_valid_first_name:\n print(\"First Name: \", end=\"\")\n fname = input().strip()\n fn_regex = re.compile(r\"^[A-z]+$\")\n is_valid_first_name = fn_regex.match(fname) is not None\n if not is_valid_first_name:\n print(\"{} is not a valid first name\".format(fname))\n return fname","title":""},{"docid":"d677b66b8bcbcc5abf29fbca1af46b39","score":"0.54982305","text":"def lookup_by_letter(letter):\n letterlist = []\n if len(letter) == 1 and letter.isalpha(): #tests if input is a letter and only one letter\n letter = letter.capitalize() #allows for case insensitivity, makes uppercase to match dictionary keys\n for i in name_dict.keys():\n if i[0] == letter:\n letterlist.append(i)\n letterlist.sort()\n return (letterlist)\n else:\n return (\"Invalid input\")","title":""},{"docid":"451862806c0c5103f25ac4f009c789e5","score":"0.54910004","text":"def checkUsername(self, username, cursor, UserAccount):\n \n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n\n while result:\n print(\"\\n\\n\\n\\n\\nSorry that username is taken\\n\")\n username = input(\"Please select another username: \")\n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n UserAccount.username = username","title":""},{"docid":"bcaf8476203abba535b097330b03e65a","score":"0.54815954","text":"def UIDStartsWith(uid: Text, prefix: Text) -> bool:\n return uid.find(prefix + '.') == 0","title":""},{"docid":"e9c7c4389e4800fba66a37280252c525","score":"0.5481196","text":"def get_user_by_username_or_user_id(cls, string):\n username = str(string).replace(\"@\", \"\").strip().lower()\n if username.isdigit(): # user_id\n return cls.objects.filter(user_id=int(username)).first()\n return cls.objects.filter(username__iexact=username).first()","title":""},{"docid":"ec61766f47b6fa904df432ea8eee3942","score":"0.54762703","text":"def find_person(self, name=None):\n try:\n if name != '' or name != ' ':\n fname = lname = ''\n if ' ' in name:\n fname = name.split(' ')[0]\n lname = name.split(' ')[1]\n else:\n fname = lname = name\n return (x.first_name for x in\n self.user.select().where(Person.last_name.contains(lname) & Person.first_name.contains(fname)))\n except Exception as ex:\n logging.error(\"Error in searching Person\" + str(ex))","title":""},{"docid":"880446522c0701f47fb1caa6d64f0ad7","score":"0.5466264","text":"def searchUser(self, nick=None):\n with open('/usr/local/apache2/cookies.csv', 'r') as csvFile:\n reader = csv.reader(csvFile)\n for row in reader:\n if nick == row[0]:\n return True\n return False","title":""},{"docid":"6183092b076a2e135b74342a8d89997e","score":"0.54581034","text":"def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tusername = username.strip()\n\t\tif not username:\n\t\t\traise ValidationError('Nickname mein harf likhna zaruri hain')\n\t\tvalidate_nickname_chars(username)\n\t\tif username[:1] == '.':\n\t\t\traise ValidationError('Nickname ke shuru mein . nah dalein')\n\t\tif username[-1:] == '.':\n\t\t\traise ValidationError('Nickname ke akhir mein . nah dalein')\n\t\texists = nick_already_exists(nickname=username)\n\t\taltered = {}\n\t\tif exists is None:\n\t\t\t#the redis DB is compromised, use PSQL DB. Check nick against DB, that's it\n\t\t\tif ' ' in username:\n\t\t\t\tusername_original = username\n\t\t\t\tusername = ''.join(username.split())\n\t\t\t\taltered = {'status':'joined'}\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username_original)\n\t\t\telse:\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\treturn [username], altered, username\n\t\t############################################\n\t\telse:\n\t\t\t# form variants and suggestions\n\t\t\t# check al against redis DB\n\t\t\tif ' ' in username:\n\t\t\t\talternatives = form_variants(username) #returns list of tuples containing variants and their statuses\n\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\tif not alt_choices:\n\t\t\t\t\t# no suggestions could be unearthed\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\telse:\n\t\t\t\t\t# some suggestions unearthed\n\t\t\t\t\taltered = {'status':'joined'}\n\t\t\t\t\treturn alt_choices, altered, username\n\t\t\telse:\n\t\t\t\tif exists:\n\t\t\t\t\t# nick is not available\n\t\t\t\t\talternatives = form_suggestions(username) #returns list of tuples containing suggestions and their statuses\n\t\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\t\tif not alt_choices:\n\t\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\t\telse:\n\t\t\t\t\t\taltered = {'status':'replaced'}\n\t\t\t\t\t\treturn alt_choices, altered, username\n\t\t\t\telse:\n\t\t\t\t\t#nick is available\n\t\t\t\t\t# altered = False\n\t\t\t\t\treturn [username], altered, username","title":""},{"docid":"00e4c3f38dce907d0d53bcbc58efa430","score":"0.54509556","text":"def _validate_username(self):\n username = self.cleaned_data.get(\"username\")\n\n if not username:\n return\n\n if settings.ST_CASE_INSENSITIVE_USERNAMES:\n username = username.lower()\n\n is_found = (\n User.objects\n .filter(username=username)\n .exists())\n if is_found:\n return\n\n if settings.ST_CASE_INSENSITIVE_EMAILS:\n username = username.lower()\n\n is_found_email = (\n User.objects\n .filter(email=username)\n .exists())\n if is_found_email:\n return\n\n raise forms.ValidationError(\n _(\"No account matches %(username)s.\") % {\n 'username': username})","title":""},{"docid":"9db4028ad184739fd82612b64ce1df4d","score":"0.5444739","text":"def fuzzy_nickname_search(request):\n logger('views', 'main: {}'.format(request.json_body))\n return fuzzy_string_matcher.get_nicknames(request.validated['user'], request.validated['value'])","title":""},{"docid":"ddc4a07d8aba14ec188a596bc8b9f270","score":"0.5443438","text":"def searchLetters(phrase: str, letters: str = 'aeiou') -> set:\n return set(letters).intersection(set(phrase))","title":""},{"docid":"373f0e4b464d81fd513c3c7bf09ae95f","score":"0.5439793","text":"def security_check_username(self, bot, update):\n\n full_name = ((update.message.from_user.first_name or \"\") + \" \"\n + (update.message.from_user.last_name or \"\"))\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(\n full_name.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n print(notify_user_id, \"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(\n update.message.from_user.username.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()","title":""},{"docid":"b46757792a081b3c67557f1b7341f309","score":"0.5436486","text":"def justice_league_by_real_name(real_name):\n print(\"i'm here though\")\n canonicalized = real_name.replace(\" \", \"\").lower()\n for character in justice_league_members:\n search_term = character[\"real_name\"].replace(\" \", \"\").lower()\n\n if search_term == canonicalized:\n return jsonify(character)\n\n return jsonify({\"error\": f\"Character with real_name {real_name} not found.\"}), 404","title":""},{"docid":"caded9656e227eede695bae6071645e2","score":"0.54354316","text":"def validate_first_name(self, value):\n if not all(c in string.ascii_letters for c in value.lower().replace(\" \", \"\")):\n raise serializers.ValidationError(\"First name is not valid, please enter again\")\n return value","title":""}],"string":"[\n {\n \"docid\": \"d8e566029b9c771acb958a39552c0017\",\n \"score\": \"0.85593385\",\n \"text\": \"def getUsersStartingWith(letters):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8664fa1d80739b81e26240163caa7420\",\n \"score\": \"0.6641168\",\n \"text\": \"def check_username(self, username):\\n match = re.findall(r\\\"^[a-zA-Z]+\\\\w+$\\\", username)\\n if match and username == match[0] and len(username) >= 4:\\n if not settings.first_run:\\n query = f\\\"SELECT user FROM {settings.users_table}\\\"\\n users = database.get_record(query)\\n if username in users[0]:\\n return None, \\\"Your username is taken\\\\n\\\"\\n return username, \\\"\\\"\\n else:\\n return None, \\\"Username is incorrect, check the tip in the input field\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d07a24adf7d116bc2430e9a8b3be28\",\n \"score\": \"0.6440513\",\n \"text\": \"def search_users_byusername(self, uid):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n self.cur.execute(\\n ('SELECT username, usertype, id, name, course, year, email '\\n 'FROM users WHERE username LIKE %s'), ('%%%s%%' % uid, ))\\n return self.cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53d6cdc39eb7021e73e9ba4c4996a088\",\n \"score\": \"0.64390105\",\n \"text\": \"def searchname(partialname, maxresults=32):\\n people = Person.select().where( \\\\\\n Person.name.contains(partialname) | \\\\\\n Person.username.contains(partialname)) \\\\\\n .order_by(Person.username).limit(maxresults)\\n return [p.username for p in people]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc18538883cccf5725b2dce2f8c60a0\",\n \"score\": \"0.6423979\",\n \"text\": \"def usernames_in(message):\\n # Don't parse usernames in the commands\\n if _command_re.match(message.split(\\\" \\\", 1)[0]):\\n message = message.split(\\\" \\\", 1)[1]\\n\\n # Strip email addresses from the message, in order to avoid matching the\\n # user's domain. Also strip URLs, in order to avoid usernames in them.\\n message = strip_urls(message)\\n\\n results = []\\n for result in _username_re.finditer(message):\\n if result.group(1):\\n results.append(result.group(1))\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4f8574b1f5698bd6006dda181c5d0b\",\n \"score\": \"0.6417107\",\n \"text\": \"def get_username(self):\\n is_valid_username = False\\n while not is_valid_username:\\n print(\\\"Username: \\\", end=\\\"\\\")\\n username = input().strip()\\n user_regex = re.compile(r\\\"^[A-z0-9]+$\\\")\\n is_valid_username = user_regex.match(username) is not None\\n if not is_valid_username:\\n print(\\\"{} is not a valid username\\\".format(username))\\n # check if username already exists in the system\\n if self.db.is_username_exists(username):\\n is_valid_username = False\\n print(\\\"The username {} already exists\\\".format(username))\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43cb84cbc70ae2360760c291046ee1c\",\n \"score\": \"0.63649565\",\n \"text\": \"def usernames(request):\\n term = request.GET.get('term', '')\\n query = request.GET.get('query', '')\\n pre = term or query\\n\\n if not pre:\\n return []\\n if not request.user.is_authenticated():\\n return []\\n with statsd.timer('users.api.usernames.search'):\\n profiles = (\\n Profile.objects.filter(Q(name__istartswith=pre))\\n .values_list('user_id', flat=True))\\n users = (\\n User.objects.filter(\\n Q(username__istartswith=pre) | Q(id__in=profiles))\\n .extra(select={'length': 'Length(username)'})\\n .order_by('length').select_related('profile'))\\n\\n if not waffle.switch_is_active('users-dont-limit-by-login'):\\n last_login = datetime.now() - timedelta(weeks=12)\\n users = users.filter(last_login__gte=last_login)\\n\\n return [{'username': u.username,\\n 'display_name': display_name_or_none(u)}\\n for u in users[:10]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a2c22b67b0f2a978e7780ae7c0336d\",\n \"score\": \"0.6349212\",\n \"text\": \"def username_condition(environ, match):\\n map_user = match.get('map_username')\\n if not map_user:\\n return False \\n if len(map_user) < 4:\\n return False\\n if map_user in CONFIG.BANNED_USERNAMES:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a13f74a9806a22a6449e0636d9524fe\",\n \"score\": \"0.6297197\",\n \"text\": \"def findUsers(conn, username):\\n curs = dbi.dict_cursor(conn)\\n curs.execute('''select * from user_accounts where username like %s ''', \\n ['%' + username + '%'])\\n return curs.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594beefe0a7b7fdf252067fc0a2c3576\",\n \"score\": \"0.6248869\",\n \"text\": \"def get_usernames(keyword):\\n con = get_binded_connection()\\n usernames = [result[1][\\\"uid\\\"][0] for result in\\\\\\n con.search_s(\\\"%s,%s\\\" % (settings.USERS_OU, settings.DC),\\n ldap.SCOPE_SUBTREE, \\\"(uid=*)\\\", ['uid'])]\\n con.unbind_s()\\n return filter(lambda username: re.search(keyword, username, re.IGNORECASE), usernames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8f38999dd1d2fd8616deec79bde372\",\n \"score\": \"0.6243985\",\n \"text\": \"def valid_name(username):\\n return username and re.match(r'[a-zA-Z][a-zA-Z0-9_]{2,}$', username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edbdd034511c2b18b1c0215116f2b0f0\",\n \"score\": \"0.62313473\",\n \"text\": \"def matching_user(self, name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccf8958baf7b0b08b5e164ed7102675\",\n \"score\": \"0.6210032\",\n \"text\": \"def check_username(cls, username):\\n return cls.db.keys('user:' + username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3730a4ec076f9fc8d1347cddd7de83\",\n \"score\": \"0.6201446\",\n \"text\": \"def check_username(username):\\n\\n usernames = db.execute(\\\"SELECT * FROM users WHERE username = :username\\\", username=username)\\n\\n return usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5acc83ec17386263af6a5d412e0fd45\",\n \"score\": \"0.616268\",\n \"text\": \"def search_users_byname(self, name):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n return self.search_users('name ILIKE', name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1ef7ea82d61048aa09dd3ed3e44c15\",\n \"score\": \"0.6128579\",\n \"text\": \"def search_usernames(self, query):\\n\\t\\tresults = self.t.users.search(q=query)\\n\\t\\tusernames = [(user['name'], user['screen_name']) for user in results]\\n\\t\\treturn usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df9359dba65ecb00cd096116cbc81acd\",\n \"score\": \"0.60037506\",\n \"text\": \"def test_user_is_a_johnny(self):\\n one_user = User.objects.first()\\n self.assertTrue(one_user.username.startswith('Johnny'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06d376bfd8ed8b7d44e8d44ecd462c9\",\n \"score\": \"0.5999096\",\n \"text\": \"def validate_username(self, username_field):\\n if re.match(r'[A-Z]', username_field.data):\\n raise ValidationError(\\\"you only can using lowercase for username\\\")\\n \\n if User.query.filter_by(username=username_field.data).first():\\n raise ValidationError(\\\"username already registered, try another one\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3ad88fbc1aaab9ad1becb48ae653c5\",\n \"score\": \"0.5974542\",\n \"text\": \"def search_users(self, where, var):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n self.cur.execute(\\n ('SELECT username, usertype, id, name, course, year, email '\\n 'FROM users WHERE ') + where + ' %s', ('%%%s%%' % var, ))\\n return self.cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b384e75efcc1d67949f0ef8c80505b6\",\n \"score\": \"0.5971448\",\n \"text\": \"def search_for_users(self, query: str) -> list:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a02b78a0e0147084bb2f8d657650deb0\",\n \"score\": \"0.59693974\",\n \"text\": \"def search(request):\\n global unknow_user_list\\n unknow_user_list = []\\n user_name = request.POST['res_user']\\n first = user_name.split(' ')[0]\\n second = user_name.split(' ')[1]\\n for i in SCUser.objects.filter(first_name=first,last_name=second)[:]:\\n unknow_user_list.append(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd486a999e8dc1924a47854a645327c\",\n \"score\": \"0.59549856\",\n \"text\": \"def valid_username(username):\\n return username and USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00be54a5087127aa2f0dc169f7128251\",\n \"score\": \"0.5947252\",\n \"text\": \"def userPrefix(self):\\n return self.userName[0].lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368ecd47b076d6bda76aab3a613cba06\",\n \"score\": \"0.5938804\",\n \"text\": \"def username(Username):\\n\\treturn Username.strip().lower().replace(\\\"_\\\", \\\"\\\").replace(\\\" \\\", \\\"-\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717f9b0b7330473e2adc7cf854da0e6e\",\n \"score\": \"0.59299797\",\n \"text\": \"def user_searcher(self, bot, name: str, max_users=5) -> List[any]:\\n username, discriminator = self.username_parser(name)\\n if discriminator:\\n users = [\\n x\\n for x in bot.get_all_members()\\n if x.name.lower() == username.lower()\\n and x.discriminator == discriminator\\n ]\\n else:\\n users = [\\n x for x in bot.get_all_members() if x.name.lower() == username.lower()\\n ]\\n if not users:\\n name = re.escape(name)\\n users = [\\n x\\n for x in bot.get_all_members()\\n if re.search(name.lower(), x.display_name.lower())\\n ]\\n if len(users) > max_users:\\n raise ValueError(\\\"Too many users returned.\\\")\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e9d832ff89507a1c9d784f031875b5\",\n \"score\": \"0.5920381\",\n \"text\": \"def find_account(cls,username):\\n for account in cls.users:\\n if account.firstName == username:\\n return account\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9af93bf0b1284fc4bd295ec2b5eef8\",\n \"score\": \"0.5917308\",\n \"text\": \"def _findUserNameCLEARCHAT(self):\\n\\t\\ttry:\\n\\t\\t\\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split(':')[2])\\n\\t\\t\\treturn userName\\n\\t\\texcept:\\n\\t\\t\\treturn \\\"error.finding.userName\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb6ee67c968d2081cd7cd8f23dba9b5f\",\n \"score\": \"0.59135354\",\n \"text\": \"def clean_username(self):\\n if not ALNUM_REGEXP.search(self.cleaned_data['username']):\\n raise forms.ValidationError(_(u'Uživatelské jméno může obsahovat pouze písmena, číslice a podtržítko'))\\n try:\\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\\n except User.DoesNotExist:\\n return self.cleaned_data['username']\\n raise forms.ValidationError(_(u'Toto uživatelské jméno už je zabráno'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45977a361e20948d23517a0937d6ba6b\",\n \"score\": \"0.5911364\",\n \"text\": \"def test_contains_lowercase(self):\\n assert re.search(\\\"[a-z]\\\", PasswordGenerator().create())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1747b598c0bd18602f0a68ac2c4399ca\",\n \"score\": \"0.58801764\",\n \"text\": \"def userfilter(text):\\n return stringutil.shortuser(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0d9d1c946f81589b520c6d0eb77721\",\n \"score\": \"0.5878371\",\n \"text\": \"def get_user_input():\\r\\n valid_inputs = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\\r\\n 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\\r\\n 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' ']\\r\\n username = input()\\r\\n if len(username) < 1:\\r\\n print(\\\"Name must be at least one letter. Please enter another name:\\\")\\r\\n username = get_user_input()\\r\\n for letter in username:\\r\\n if letter not in valid_inputs:\\r\\n print(\\\"Username can only contain letters. Please enter another name:\\\")\\r\\n username = get_user_input()\\r\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009a78a17b2eecd6200119ecaf2d1859\",\n \"score\": \"0.58749855\",\n \"text\": \"def validate_username(self, username):\\n regex = re.compile(r'[@_!#$%^&*()<>?/\\\\|}{~:]')\\n check_username = User.objects.filter(username=username)\\n if check_username.exists():\\n raise serializers.ValidationError(\\\"Provided username already exist, please provide a different one\\\")\\n elif regex.search(username):\\n raise serializers.ValidationError(\\\"should not contain special characters @_!#$%^&*()<>?/\\\\|}{~:\\\")\\n elif len(username.split()) > 1:\\n raise serializers.ValidationError(\\n \\\"Username should not contain spaces\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfebcbddfded50c6d9ec60be675c99bd\",\n \"score\": \"0.587404\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data[\\\"username\\\"]\\n\\n if Albergatore.objects.filter(username=username).exists():\\n raise forms.ValidationError('Username non disponibile!')\\n else:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b35357c4c9938c12a2a94fd81842d0\",\n \"score\": \"0.5864838\",\n \"text\": \"def find_user(self):\\n users = []\\n place = -1\\n not_found = True\\n # until a user is chosen\\n while not_found:\\n name = raw_input(\\\"Name: \\\")\\n users = self.searchForUsers(name)\\n # Printout 4 best matches\\n for i, user in enumerate(users[:4], 1):\\n print(f'#{i} {user.name}')\\n place = int(input(\\\"Enter number, in case the user is not listed, press enter and try their name again:\\\"))\\n if 0 <= place <= 3:\\n not_found = False\\n return users[place - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d070a3ee437c821903d36715620acb2\",\n \"score\": \"0.58620286\",\n \"text\": \"def username_validator(form, field):\\n username = field.data\\n if len(username) < 3:\\n raise ValidationError(_('Username must be at least 3 characters long'))\\n valid_chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._'\\n chars = list(username)\\n for char in chars:\\n if char not in valid_chars:\\n raise ValidationError(_(\\\"Username may only contain letters, numbers, '-', '.' and '_'.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6480d21680c8dd6952e8aea6c8929864\",\n \"score\": \"0.5858849\",\n \"text\": \"def clean_username(self):\\r\\n existing = User.objects.filter(username__iexact=self.cleaned_data['username'])\\r\\n if existing.exists():\\r\\n raise forms.ValidationError(_(\\\"O email já se encontra registado.\\\"))\\r\\n else:\\r\\n return self.cleaned_data['username']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb509753ebe1a402b4655be04aca08b9\",\n \"score\": \"0.585632\",\n \"text\": \"def check():\\n userNames = db.execute(\\\"SELECT username FROM users\\\")\\n userName = request.args.get(\\\"username\\\")\\n search = {'username' : userName}\\n if search in userNames:\\n print(\\\"user name found\\\")\\n return jsonify(\\\"false\\\")\\n return jsonify(\\\"TODO\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50cb820f73822e2b0f5cc90572b7b68\",\n \"score\": \"0.5852539\",\n \"text\": \"def valid_username(self, username):\\n return self.USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4695547fd1518cdc497f17e06805a09c\",\n \"score\": \"0.58498293\",\n \"text\": \"def valid_username(self, username):\\n return username and USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97828fd3b7f52be6efdb1d914cf15d1b\",\n \"score\": \"0.584555\",\n \"text\": \"def get_username(infile):\\n username = 0\\n myfile = open(infile, mode=\\\"r\\\", encoding=\\\"latin_1\\\")\\n what_to_look = 'Uploaded by '\\n for line_username in myfile:\\n if what_to_look in line_username:\\n username_length = line_username.find('</font>')\\n username = line_username[58:username_length]\\n print(\\\"Username: \\\" + username.strip())\\n return username.strip()\\n myfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b086f5cca7f18c70d9deeb692a32faf\",\n \"score\": \"0.58317703\",\n \"text\": \"def process_username(self, value) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cde03d4228e8685827660495aa1a6b1\",\n \"score\": \"0.58283216\",\n \"text\": \"def clean_username(self):\\n\\t\\tusername = self.cleaned_data['username']\\n\\t\\tif not re.search(r'^\\\\w+$', username):\\n\\t\\t\\traise forms.ValidationError ('Username can only contain '\\n\\t\\t\\t'alphanumeric characters and the underscore.')\\n\\t\\ttry:\\n\\t\\t\\tUser.objects.get(username=username)\\n\\t\\texcept User.DoesNotExist:\\n\\t\\t\\treturn username\\n\\t\\traise forms.ValidationError('Username already taken.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886622e20a72c5ffc26f7711c383fbfc\",\n \"score\": \"0.5823204\",\n \"text\": \"def clean_username(self):\\n existing = Users.objects.filter(username=self.cleaned_data['username'])\\n if existing.exists():\\n raise forms.ValidationError(_('A user with that username already exists.'))\\n else:\\n return self.cleaned_data['username']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27cb3f8ef14d1b9c8665d9cd4f0f134\",\n \"score\": \"0.5819259\",\n \"text\": \"def handle_search(request):\\n\\n query = request.GET.get('query')\\n if query:\\n users = User.objects.filter(username__icontains=query\\n ).values_list('username', flat=True\\n ).order_by('profile__score')\\n else:\\n users = User.objects.order_by('profile__score').values_list('username', flat=True)\\n\\n users = list(users[:20])\\n # Return list of users matching username\\n return ajax_success(users=users, msg=\\\"Username searched\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402f6542db3a31a58cd1343b3fc307c8\",\n \"score\": \"0.5805711\",\n \"text\": \"def validate_username(cls, username, skip_uniqueness_check=False):\\n un = username.lower()\\n\\n if (un in Config['blocked_usernames']\\n or any(fragment in un for fragment in Config['blocked_username_fragments'])\\n or any(fragment in un for fragment in Config['autoflag_words'])):\\n return \\\"Sorry, this username is not allowed.\\\"\\n\\n if not un:\\n return \\\"Please enter a username.\\\"\\n elif len(un) < 3:\\n return \\\"Username must be 3 or more characters.\\\"\\n elif len(un) > 16:\\n return \\\"Username must be 16 characters or less.\\\"\\n\\n alphabet = string.lowercase + string.uppercase + string.digits + '_'\\n if not all(char in alphabet for char in username):\\n return \\\"Usernames can only contain letters, digits and underscores.\\\"\\n\\n if not skip_uniqueness_check:\\n if cls.objects.filter(username__iexact=username):\\n return \\\"Sorry! This username is taken. Please pick a different username.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf82e5c207b54640641b69971396aec\",\n \"score\": \"0.5800986\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username'].lower()\\n if (User.objects.filter(username__iexact=username).exists() or\\n username in settings.ACCOUNT_USERNAME_BLACKLIST):\\n raise ValidationError(_('A user with that username already exists.'))\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b2719c2a52bb1843d98111dca318d8e\",\n \"score\": \"0.5790296\",\n \"text\": \"def get_users_like(self, name):\\n name_length = len(name)\\n if name_length > USER_ALL_NAME_LENGTH_MAX_WITH_DISCRIMINATOR:\\n return []\\n \\n users = self.users\\n \\n # name with discriminator\\n \\n name_with_discriminator = _parse_name_with_discriminator(name)\\n if (name_with_discriminator is not None):\\n for user in users.values():\\n if _is_user_matching_name_with_discriminator(user, name_with_discriminator):\\n return [user]\\n \\n if name_length > USER_ALL_NAME_LENGTH_MAX:\\n return []\\n \\n user_name_pattern = re_compile('.*?'.join(re_escape(char) for char in name), re_ignore_case)\\n matches = []\\n guild_id = self.id\\n \\n for user in users.values():\\n # name\\n \\n parsed = user_name_pattern.search(user.name)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_NAME, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n # display_name\\n \\n user_display_name = user.display_name\\n if (user_display_name is not None):\\n parsed = user_name_pattern.search(user_display_name)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_DISPLAY_NAME, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n # nick\\n \\n try:\\n guild_profile = user.guild_profiles[guild_id]\\n except KeyError:\\n pass\\n else:\\n user_nick = guild_profile.nick\\n if (user_nick is not None):\\n parsed = user_name_pattern.search(user_nick)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_NICK, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n \\n return [item[0] for item in sorted(matches, key = _user_match_sort_key)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9555fdcc9e54fd5f5f4380bc80d68d4\",\n \"score\": \"0.57825446\",\n \"text\": \"def validate_username_field(value):\\n msg = []\\n if value:\\n if not value.isalnum() and \\\"_\\\" not in value:\\n msg.append(\\n \\\"field mustn't contain any special character other than _\\\")\\n if not ((re.search(\\\"[a-z]\\\", value))or(re.search(\\\"[A-Z]\\\", value))):\\n msg.append(\\\"field must contain atleast one character\\\")\\n if len(msg) > 0:\\n raise serializers.ValidationError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35b0a5cfdaf998533757ab81ef80b7a\",\n \"score\": \"0.5759538\",\n \"text\": \"def validate_username(username: str) -> bool:\\n return True if re.match(\\\"^([a-zA-Z0-9]|\\\\.){6,32}$\\\", username) else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccfcb1a4faa69fafb35985b1726fcc8\",\n \"score\": \"0.5750044\",\n \"text\": \"def sanity_check_username(name):\\n VALID_CHARACTERS = string.ascii_letters+string.digits+\\\"_-.\\\"\\n rules = [ \\n len(name) > 3, # User name is longer than 3 characters\\n all([k in VALID_CHARACTERS for k in list(name)]) # Username is made of valid characters\\n ]\\n return all(rules)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73419325271abbbdc5c616ca0cd524bf\",\n \"score\": \"0.574605\",\n \"text\": \"def usersname_length(username):\\n try:\\n username = str(username)\\n # if username.length() > 3 and < 21:\\n if (len(username) >= 3) and (len(username) < 21):\\n return True\\n except ValueError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901627988b4967b6959f5399749d0ce6\",\n \"score\": \"0.57419056\",\n \"text\": \"def is_valid_username(u_name):\\n if not re.match(r\\\"^[A-Za-z\\\\.\\\\+_-]*$\\\", u_name) != None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921e0723b5599cc772717ee38636de85\",\n \"score\": \"0.57408136\",\n \"text\": \"def findUsersByName(conn, name):\\n query = \\\"SELECT * FROM users WHERE name LIKE %s\\\"\\n params = [\\\"%\\\" + name + \\\"%\\\"]\\n return getSqlQuery(conn, query, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11dd260e48c8b537315631ad539f4f7a\",\n \"score\": \"0.5739077\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data[\\\"username\\\"]\\n if not Albergatore.objects.filter(username=username).exists():\\n raise forms.ValidationError(\\\"Username non registrato\\\")\\n else:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10457fa52cefb6a629aa87718148e306\",\n \"score\": \"0.5726845\",\n \"text\": \"def check_user_name(self, username):\\n query = \\\"SELECT username FROM users WHERE username = '%s'\\\" % (username)\\n result = self.fetch_single_data_row(query)\\n if result is None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f0e453b2520b68c9cb1c756d008987\",\n \"score\": \"0.57148904\",\n \"text\": \"def check_valid_username(self, username : str):\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9c54c91fd6a80bbb5edc552561ef5b2\",\n \"score\": \"0.57078695\",\n \"text\": \"def security_check_username(self, bot, update):\\n\\n full_name = (update.message.from_user.first_name + \\\" \\\"\\n + update.message.from_user.last_name)\\n if self.name_ban_re and self.name_ban_re.search(full_name):\\n # Logging\\n log_message = \\\"Ban match full name: {}\\\".format(full_name.encode('utf-8'))\\n logger.info(log_message)\\n for notify_user_id in self.notify_user_ids:\\n logger.info(notify_user_id,\\\"gets notified\\\")\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n\\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\\n # Logging\\n log_message = \\\"Ban match username: {}\\\".format(update.message.from_user.username.encode('utf-8'))\\n logger.info(log_message)\\n for notify_user_id in self.notify_user_ids:\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60497a226d11be3cafe143f5f4baf5a3\",\n \"score\": \"0.56966424\",\n \"text\": \"def clean_username(self):\\r\\n username = self.cleaned_data[\\\"username\\\"]\\r\\n if '@' in username:\\r\\n # The user entered an email, so try to log them in by e-mail\\r\\n emails = ContactValue.objects.filter(value=username,\\r\\n field__field_type='email',\\r\\n contact__trash=False,\\r\\n contact__related_user__isnull=False)\\r\\n users = [email.contact.related_user.user for email in emails]\\r\\n else:\\r\\n users = User.objects.filter(user__username=username)\\r\\n if len(users) == 0:\\r\\n raise forms.ValidationError(\\r\\n _(\\\"Sorry, we don't know that user or e-mail.\\\"))\\r\\n else:\\r\\n username = users[0]\\r\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1353c764d75f75d8f8558f24aaa1ab2\",\n \"score\": \"0.5692759\",\n \"text\": \"def clean_username(self):\\n try:\\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\\n except User.DoesNotExist:\\n return self.cleaned_data['username']\\n raise forms.ValidationError(_(\\\"A user with that username already exists.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3de59944fb73806d9a0d76fdef129df\",\n \"score\": \"0.5677581\",\n \"text\": \"def auto_complete_model(request):\\n if request.is_ajax():\\n searchString=request.POST[\\\"search\\\"]\\n search_qs = User.objects.filter(username__startswith=searchString)\\n print search_qs\\n results = []\\n for r in search_qs:\\n results.append(r.username)\\n print results\\n return HttpResponse(json.dumps(results), content_type=\\\"application/json\\\", status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e0005f4d22a3f09122843f9e940b11\",\n \"score\": \"0.5675287\",\n \"text\": \"def is_letter_in_pattern(user_input, word):\\r\\n for letter in list(word):\\r\\n if user_input == letter:\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de1e6e4e7af2ec958fd08cc5adad7c4\",\n \"score\": \"0.56643456\",\n \"text\": \"def get_userName():\\n userName = input(\\\"Tapez Votre nom d'utilisateur: \\\")\\n\\n userName = userName.capitalize() # 1st letter in cap\\n\\n if not userName.isalnum() or len(userName)<4:\\n\\n print(\\\"Nom invalide\\\")\\n return get_userName()\\n\\n else:\\n return userName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11bb76360b33e6a116242866fab5edd2\",\n \"score\": \"0.5660173\",\n \"text\": \"def _check_username(self, username):\\n # Username cannot contain spaces\\n if ' ' in username:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc451def748fc8dda4ebad1e3f2dd02c\",\n \"score\": \"0.5657971\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data.get('username')\\n if len(username) < 3:\\n raise forms.ValidationError(\\\"001,Your username must be longer than 3 words.\\\")\\n elif len(username) > 20:\\n raise forms.ValidationError(\\\"002,Your username must be shorter than 20 words.\\\")\\n else:\\n\\n #To check whether the username already exists\\n \\n checkresult = User_Info.objects.filter(username=username)\\n if len(checkresult) > 0:\\n raise forms.ValidationError(\\\"003,Your username already exists.\\\")\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0411422ddb7c66213d92669fbfea4606\",\n \"score\": \"0.5654673\",\n \"text\": \"def find(word, letter, start):\\n\\n index = start\\n hits = []\\n\\n while index < len(word):\\n if word[index].lower() == letter.lower():\\n hits.append(index)\\n index += 1\\n\\n return(hits)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe06db84edc8d4419919a5d393e0d0da\",\n \"score\": \"0.5648181\",\n \"text\": \"def user_name():\\n while 1:\\n username = input(\\\"enter username, please: \\\")\\n if not len(username) == 0:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c82479f2897275c5c78a4cb5bfe4b88\",\n \"score\": \"0.56426215\",\n \"text\": \"def clean_username(self, username):\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c8af63dd87814589e29192c85d4b4c\",\n \"score\": \"0.5637166\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username']\\n if User.objects.filter(username=username):\\n raise forms.ValidationError('Este correo electronico ya existe')\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7833e36e5978bb94e34dd72ed08206\",\n \"score\": \"0.5629253\",\n \"text\": \"def _contains_lowercase_name(self, name):\\n return name in (x.lower() for x in self._distinct())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4746a4438fcd4d437bbb5a83cd66e336\",\n \"score\": \"0.56258893\",\n \"text\": \"def userSearch(self, username, pwd=\\\"abc\\\", usneed=False):\\n try:\\n self.cur.execute('''\\n SELECT * FROM \\\"Student\\\"\\n WHERE \\\"UserName\\\" = ?''', (username,))\\n\\n rows = self.cur.fetchall()\\n # print(rows)\\n if usneed:\\n return rows[0][1]\\n elif len(rows) == 0:\\n return \\\"Empty\\\" # if no account yet created or wrong username\\n elif len(rows) == 1 and rows[0][4] == pwd:\\n return True\\n return False # if wrong password inserted\\n except Exception as e:\\n print(\\\"userSearch(), error -\\\", e)\\n self.con.close()\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc295b480fcf5c6b853d0df7ea8f1d4f\",\n \"score\": \"0.561518\",\n \"text\": \"def _is_first_name(self, word):\\n names = self._first_name_list\\n word = word.lower()\\n ind = bisect.bisect_left(names, word)\\n if ind < len(names) and names[ind] == word:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d71da97eb837b9720e897203c7687db\",\n \"score\": \"0.56023335\",\n \"text\": \"def test_get_user_by_username_r(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280fe52791e9ae728929c968bfb34157\",\n \"score\": \"0.5601374\",\n \"text\": \"def filter_user(tweets):\\n \\n return tweets.str.replace('<user>', '', case=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801fc57aa073f025190e7da688415a5e\",\n \"score\": \"0.559805\",\n \"text\": \"def _findUserNamePRIVMSG(self):\\n\\t\\ttry:\\n\\t\\t\\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split('!')[0])\\n\\t\\t\\treturn userName\\n\\t\\texcept:\\n\\t\\t\\treturn \\\"error.finding.userName\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d7f959bb24b39ec9fb5bb435b9539a\",\n \"score\": \"0.55943894\",\n \"text\": \"def search_for_letters(phrase:str,letters:str='aeiou') ->set:\\r\\n return set(letters).intersection(set(phrase))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c88ffee191a5c1841c81b8482b5ccf\",\n \"score\": \"0.558165\",\n \"text\": \"def get_service_usernames():\\n usernames = [k[:-len('_passwd')] for k in leader_get()\\n if k.endswith('_passwd') and k != 'admin_passwd']\\n # now match against the service list.\\n valid_service_names = valid_services.keys()\\n known_service_usernames = []\\n for username in usernames:\\n # if a username has '_' in it, then it is a compound name.\\n parts = username.split('_')\\n if 'keystone' in parts:\\n continue\\n if not all(p in valid_service_names for p in parts):\\n continue\\n known_service_usernames.append(username)\\n return known_service_usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb53ddf55ec2a3546f1b818d47090e\",\n \"score\": \"0.5574706\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username']\\n if User.objects.filter(username=username):\\n raise forms.ValidationError('Nombre de usuario ya registrado.')\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4427c955f075306234e38ed62c4af2\",\n \"score\": \"0.5553871\",\n \"text\": \"def generate_username(name):\\n username = ''.join(name.split(' ')).lower()\\n if not User.objects.filter(username=username).exists():\\n return username\\n else:\\n random_username = username + str(randint(0, 1000))\\n return generate_username(random_username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa644612e55a8ebbd61835fc8da001c0\",\n \"score\": \"0.5548313\",\n \"text\": \"def parse_name(guild, username):\\r\\n if '@' in username:\\r\\n try:\\r\\n return guild.get_member(int(username[3:-1]))\\r\\n except:\\r\\n raise NameError(username)\\r\\n else:\\r\\n return get_member_from_guild(guild.members, username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8970f68548e77c567d874853c18c5454\",\n \"score\": \"0.55467904\",\n \"text\": \"def check():\\r\\n username = request.args.get(\\\"username\\\")\\r\\n if len(username) >= 1:\\r\\n usernames = [dct[\\\"username\\\"] for dct in db.execute(\\\"SELECT username FROM users\\\")]\\r\\n if username not in usernames:\\r\\n return jsonify(True)\\r\\n else:\\r\\n return jsonify(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f90c6d8429bb1d64141f7955edbd74\",\n \"score\": \"0.55457246\",\n \"text\": \"def username_exists_validator(value):\\n try:\\n User.objects.get(username=value)\\n\\n except User.DoesNotExist:\\n raise ValidationError(_('Requested username does not exist.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76690e229888ca6a3b5515f7b6a088f\",\n \"score\": \"0.55418396\",\n \"text\": \"def validate_username(cls, nick):\\n if nick and nick != \\\"\\\":\\n nick = nick.strip()\\n if len(nick) > 0:\\n if re.match(\\\"^[A-z0-9_\\\\-]+$\\\", nick):\\n return nick, \\\"\\\"\\n else:\\n return \\\"\\\", \\\"Username can only contain letters, numbers, underscores, and dashes.\\\"\\n return \\\"\\\", \\\"Username must not be empty.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"566aa32677a8d6f412d11e734703c54a\",\n \"score\": \"0.55270416\",\n \"text\": \"def check_username_lower_limit(username):\\n if len(username) >= 4:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8286201ec8571426d3ce568b4ff7d700\",\n \"score\": \"0.55268127\",\n \"text\": \"def get_by_natural_key(self, username):\\r\\n anycase_username_field = f'{self.model.USERNAME_FIELD}__iexact'\\r\\n return self.get(**{anycase_username_field: username})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e99474b264ed23dbb9d1046298888f\",\n \"score\": \"0.552202\",\n \"text\": \"def check_username(cls, uid):\\n if not uid:\\n raise RBFatalError('Username must be given')\\n if re.search(r'[^a-z0-9_.-]', uid):\\n raise RBFatalError(\\\"Invalid characters in username\\\")\\n if len(uid) > rbconfig.maxlen_uname:\\n raise RBFatalError(\\\"Username can not be longer than %d characters\\\"\\n % rbconfig.maxlen_uname)\\n if re.search(r'^[^a-z0-9]', uid):\\n raise RBFatalError(\\\"Username must begin with letter or number\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3b9bd84177442eda8c6433534e636c\",\n \"score\": \"0.5512801\",\n \"text\": \"def validate_username(self, username):\\n # onyl allow alphanumeric character, . _ -\\n if username is not None:\\n pattern = r\\\"^[a-zA-Z0-9_.-]+$\\\"\\n if len(username) < 1:\\n raise ValidationError('Invalid username, minimum is 1 character')\\n if len(username) > 32:\\n raise ValidationError('Invalid username, max is 32 character')\\n if re.match(pattern, username) is None:\\n raise ValidationError('Invalid username, only alphanumeric, . _ - allowed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28fd7d5e2650faf027dced619ce0ed7e\",\n \"score\": \"0.5505782\",\n \"text\": \"def get_first_name(self):\\n is_valid_first_name = False\\n while not is_valid_first_name:\\n print(\\\"First Name: \\\", end=\\\"\\\")\\n fname = input().strip()\\n fn_regex = re.compile(r\\\"^[A-z]+$\\\")\\n is_valid_first_name = fn_regex.match(fname) is not None\\n if not is_valid_first_name:\\n print(\\\"{} is not a valid first name\\\".format(fname))\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d677b66b8bcbcc5abf29fbca1af46b39\",\n \"score\": \"0.54982305\",\n \"text\": \"def lookup_by_letter(letter):\\n letterlist = []\\n if len(letter) == 1 and letter.isalpha(): #tests if input is a letter and only one letter\\n letter = letter.capitalize() #allows for case insensitivity, makes uppercase to match dictionary keys\\n for i in name_dict.keys():\\n if i[0] == letter:\\n letterlist.append(i)\\n letterlist.sort()\\n return (letterlist)\\n else:\\n return (\\\"Invalid input\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451862806c0c5103f25ac4f009c789e5\",\n \"score\": \"0.54910004\",\n \"text\": \"def checkUsername(self, username, cursor, UserAccount):\\n \\n query = \\\"SELECT username FROM accounts WHERE username = %s\\\"\\n cursor.execute(query, (username,))\\n result = cursor.fetchall()\\n\\n while result:\\n print(\\\"\\\\n\\\\n\\\\n\\\\n\\\\nSorry that username is taken\\\\n\\\")\\n username = input(\\\"Please select another username: \\\")\\n query = \\\"SELECT username FROM accounts WHERE username = %s\\\"\\n cursor.execute(query, (username,))\\n result = cursor.fetchall()\\n UserAccount.username = username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcaf8476203abba535b097330b03e65a\",\n \"score\": \"0.54815954\",\n \"text\": \"def UIDStartsWith(uid: Text, prefix: Text) -> bool:\\n return uid.find(prefix + '.') == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c7c4389e4800fba66a37280252c525\",\n \"score\": \"0.5481196\",\n \"text\": \"def get_user_by_username_or_user_id(cls, string):\\n username = str(string).replace(\\\"@\\\", \\\"\\\").strip().lower()\\n if username.isdigit(): # user_id\\n return cls.objects.filter(user_id=int(username)).first()\\n return cls.objects.filter(username__iexact=username).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec61766f47b6fa904df432ea8eee3942\",\n \"score\": \"0.54762703\",\n \"text\": \"def find_person(self, name=None):\\n try:\\n if name != '' or name != ' ':\\n fname = lname = ''\\n if ' ' in name:\\n fname = name.split(' ')[0]\\n lname = name.split(' ')[1]\\n else:\\n fname = lname = name\\n return (x.first_name for x in\\n self.user.select().where(Person.last_name.contains(lname) & Person.first_name.contains(fname)))\\n except Exception as ex:\\n logging.error(\\\"Error in searching Person\\\" + str(ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880446522c0701f47fb1caa6d64f0ad7\",\n \"score\": \"0.5466264\",\n \"text\": \"def searchUser(self, nick=None):\\n with open('/usr/local/apache2/cookies.csv', 'r') as csvFile:\\n reader = csv.reader(csvFile)\\n for row in reader:\\n if nick == row[0]:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6183092b076a2e135b74342a8d89997e\",\n \"score\": \"0.54581034\",\n \"text\": \"def clean_username(self):\\n\\t\\tusername = self.cleaned_data['username']\\n\\t\\tusername = username.strip()\\n\\t\\tif not username:\\n\\t\\t\\traise ValidationError('Nickname mein harf likhna zaruri hain')\\n\\t\\tvalidate_nickname_chars(username)\\n\\t\\tif username[:1] == '.':\\n\\t\\t\\traise ValidationError('Nickname ke shuru mein . nah dalein')\\n\\t\\tif username[-1:] == '.':\\n\\t\\t\\traise ValidationError('Nickname ke akhir mein . nah dalein')\\n\\t\\texists = nick_already_exists(nickname=username)\\n\\t\\taltered = {}\\n\\t\\tif exists is None:\\n\\t\\t\\t#the redis DB is compromised, use PSQL DB. Check nick against DB, that's it\\n\\t\\t\\tif ' ' in username:\\n\\t\\t\\t\\tusername_original = username\\n\\t\\t\\t\\tusername = ''.join(username.split())\\n\\t\\t\\t\\taltered = {'status':'joined'}\\n\\t\\t\\t\\tif User.objects.filter(username__iexact=username).exists():\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username_original)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif User.objects.filter(username__iexact=username).exists():\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\treturn [username], altered, username\\n\\t\\t############################################\\n\\t\\telse:\\n\\t\\t\\t# form variants and suggestions\\n\\t\\t\\t# check al against redis DB\\n\\t\\t\\tif ' ' in username:\\n\\t\\t\\t\\talternatives = form_variants(username) #returns list of tuples containing variants and their statuses\\n\\t\\t\\t\\talt_choices = process_choices(alternatives)\\n\\t\\t\\t\\tif not alt_choices:\\n\\t\\t\\t\\t\\t# no suggestions could be unearthed\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# some suggestions unearthed\\n\\t\\t\\t\\t\\taltered = {'status':'joined'}\\n\\t\\t\\t\\t\\treturn alt_choices, altered, username\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif exists:\\n\\t\\t\\t\\t\\t# nick is not available\\n\\t\\t\\t\\t\\talternatives = form_suggestions(username) #returns list of tuples containing suggestions and their statuses\\n\\t\\t\\t\\t\\talt_choices = process_choices(alternatives)\\n\\t\\t\\t\\t\\tif not alt_choices:\\n\\t\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\taltered = {'status':'replaced'}\\n\\t\\t\\t\\t\\t\\treturn alt_choices, altered, username\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t#nick is available\\n\\t\\t\\t\\t\\t# altered = False\\n\\t\\t\\t\\t\\treturn [username], altered, username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e4c3f38dce907d0d53bcbc58efa430\",\n \"score\": \"0.54509556\",\n \"text\": \"def _validate_username(self):\\n username = self.cleaned_data.get(\\\"username\\\")\\n\\n if not username:\\n return\\n\\n if settings.ST_CASE_INSENSITIVE_USERNAMES:\\n username = username.lower()\\n\\n is_found = (\\n User.objects\\n .filter(username=username)\\n .exists())\\n if is_found:\\n return\\n\\n if settings.ST_CASE_INSENSITIVE_EMAILS:\\n username = username.lower()\\n\\n is_found_email = (\\n User.objects\\n .filter(email=username)\\n .exists())\\n if is_found_email:\\n return\\n\\n raise forms.ValidationError(\\n _(\\\"No account matches %(username)s.\\\") % {\\n 'username': username})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db4028ad184739fd82612b64ce1df4d\",\n \"score\": \"0.5444739\",\n \"text\": \"def fuzzy_nickname_search(request):\\n logger('views', 'main: {}'.format(request.json_body))\\n return fuzzy_string_matcher.get_nicknames(request.validated['user'], request.validated['value'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc4a07d8aba14ec188a596bc8b9f270\",\n \"score\": \"0.5443438\",\n \"text\": \"def searchLetters(phrase: str, letters: str = 'aeiou') -> set:\\n return set(letters).intersection(set(phrase))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373f0e4b464d81fd513c3c7bf09ae95f\",\n \"score\": \"0.5439793\",\n \"text\": \"def security_check_username(self, bot, update):\\n\\n full_name = ((update.message.from_user.first_name or \\\"\\\") + \\\" \\\"\\n + (update.message.from_user.last_name or \\\"\\\"))\\n if self.name_ban_re and self.name_ban_re.search(full_name):\\n # Logging\\n log_message = \\\"Ban match full name: {}\\\".format(\\n full_name.encode('utf-8'))\\n if self.debug:\\n update.message.reply_text(log_message)\\n print(log_message)\\n for notify_user_id in self.notify_user_ids:\\n print(notify_user_id, \\\"gets notified\\\")\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n s = session()\\n userBan = UserBan(\\n user_id=update.message.from_user.id,\\n reason=log_message)\\n s.add(userBan)\\n s.commit()\\n s.close()\\n\\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\\n # Logging\\n log_message = \\\"Ban match username: {}\\\".format(\\n update.message.from_user.username.encode('utf-8'))\\n if self.debug:\\n update.message.reply_text(log_message)\\n print(log_message)\\n for notify_user_id in self.notify_user_ids:\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n s = session()\\n userBan = UserBan(\\n user_id=update.message.from_user.id,\\n reason=log_message)\\n s.add(userBan)\\n s.commit()\\n s.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46757792a081b3c67557f1b7341f309\",\n \"score\": \"0.5436486\",\n \"text\": \"def justice_league_by_real_name(real_name):\\n print(\\\"i'm here though\\\")\\n canonicalized = real_name.replace(\\\" \\\", \\\"\\\").lower()\\n for character in justice_league_members:\\n search_term = character[\\\"real_name\\\"].replace(\\\" \\\", \\\"\\\").lower()\\n\\n if search_term == canonicalized:\\n return jsonify(character)\\n\\n return jsonify({\\\"error\\\": f\\\"Character with real_name {real_name} not found.\\\"}), 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caded9656e227eede695bae6071645e2\",\n \"score\": \"0.54354316\",\n \"text\": \"def validate_first_name(self, value):\\n if not all(c in string.ascii_letters for c in value.lower().replace(\\\" \\\", \\\"\\\")):\\n raise serializers.ValidationError(\\\"First name is not valid, please enter again\\\")\\n return value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":790,"cells":{"query_id":{"kind":"string","value":"60a13f80bbbd52ab881d64ae67e7d878"},"query":{"kind":"string","value":"Test LimitTracker used in auvimport."},"positive_passages":{"kind":"list like","value":[{"docid":"376124658927b62692caa4f6e6bed547","score":"0.5882347","text":"def test_limit_tracker(self):\n\n # check direct values\n direct_track = LimitTracker()\n\n direct_track.check(1.0)\n\n self.assertEqual(direct_track.minimum, 1.0)\n self.assertEqual(direct_track.maximum, 1.0)\n\n direct_track.check(10.0)\n direct_track.check(-10.0)\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)\n\n # check values in a dictionary\n dict_value_track = LimitTracker('val')\n\n dict_value_track.check({'val': 1.0})\n\n self.assertEqual(dict_value_track.minimum, 1.0)\n\n dict_value_track.check({'val': 10.0})\n dict_value_track.check({'val': -10.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # the final (odd) cases\n # should throw error... or silently ignore?\n\n # using a dict, but give it a value\n self.assertRaises(TypeError, dict_value_track.check, (20.0))\n # or a dict with the wrong key\n dict_value_track.check({'other': -20.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # using a value but give it a dict\n self.assertRaises(TypeError, direct_track.check, ({'val': 20.0}))\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)","title":""}],"string":"[\n {\n \"docid\": \"376124658927b62692caa4f6e6bed547\",\n \"score\": \"0.5882347\",\n \"text\": \"def test_limit_tracker(self):\\n\\n # check direct values\\n direct_track = LimitTracker()\\n\\n direct_track.check(1.0)\\n\\n self.assertEqual(direct_track.minimum, 1.0)\\n self.assertEqual(direct_track.maximum, 1.0)\\n\\n direct_track.check(10.0)\\n direct_track.check(-10.0)\\n\\n self.assertEqual(direct_track.minimum, -10.0)\\n self.assertEqual(direct_track.maximum, 10.0)\\n\\n # check values in a dictionary\\n dict_value_track = LimitTracker('val')\\n\\n dict_value_track.check({'val': 1.0})\\n\\n self.assertEqual(dict_value_track.minimum, 1.0)\\n\\n dict_value_track.check({'val': 10.0})\\n dict_value_track.check({'val': -10.0})\\n\\n self.assertEqual(dict_value_track.maximum, 10.0)\\n self.assertEqual(dict_value_track.minimum, -10.0)\\n\\n # the final (odd) cases\\n # should throw error... or silently ignore?\\n\\n # using a dict, but give it a value\\n self.assertRaises(TypeError, dict_value_track.check, (20.0))\\n # or a dict with the wrong key\\n dict_value_track.check({'other': -20.0})\\n\\n self.assertEqual(dict_value_track.maximum, 10.0)\\n self.assertEqual(dict_value_track.minimum, -10.0)\\n\\n # using a value but give it a dict\\n self.assertRaises(TypeError, direct_track.check, ({'val': 20.0}))\\n\\n self.assertEqual(direct_track.minimum, -10.0)\\n self.assertEqual(direct_track.maximum, 10.0)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5e84c8ee36f03c4d423562b65adc6087","score":"0.6538664","text":"def test_rate_limit_interface():\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\n min_rate_def = RateDefinition(per_second=10)\n per_user_rate_def = PerUserRateDefinition(per_user_rate_def, min_rate_def)\n my_feature_rate_limiter = RateLimiter('my_feature', per_user_rate_def.get_rate_limits)\n if my_feature_rate_limiter.allow_usage('my_domain'):\n # ...do stuff...\n my_feature_rate_limiter.report_usage('my_domain')","title":""},{"docid":"3d93729a630c34495d64c4b65d915e78","score":"0.6360713","text":"def test_activity_api_limits(self):\n\n async def run_test():\n with self.assertRaises(ValueError):\n await self.test_camera.query_activity_history(limit=ACTIVITY_API_LIMIT + 1)\n\n self.loop.run_until_complete(run_test())","title":""},{"docid":"577aaff1b88b187d68a0fc9eb0d0a150","score":"0.60561013","text":"def on_limit(self, track):\n print 'Limitation notice has arrived...'\n return","title":""},{"docid":"f731d809af601dfc71952e039aedadcb","score":"0.5922246","text":"def test_no_participations(self):\n user = users.models.User.objects.create_user('test-user')\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c_prime)","title":""},{"docid":"cc47a7b5642e0be6e1843560d450fa4d","score":"0.58270365","text":"def test_limit_reached(self):\r\n chip_counter = Chips()\r\n limit_reached(chip_counter)\r\n self.assertEqual(chip_counter.total, 2000)","title":""},{"docid":"7f073275b6c9022ea447378594acfed2","score":"0.57859415","text":"def setUp(self):\n super(LimitsControllerTestV21, self).setUp()\n self.controller = wsgi.Resource(self.limits_controller())\n self.ctrler = self.limits_controller()","title":""},{"docid":"3e651565f2fa68e798f85ce96e80e1e5","score":"0.5757954","text":"def testCPULimit(self):\n result = self.RunFlow(\"CPULimitFlow\", cpu_limit=300)\n self.assertEqual(result[\"cpulimit\"], [300, 295, 255])","title":""},{"docid":"028e2c972cedb228cc34afa6e47ecdd1","score":"0.5748576","text":"def test_lagLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for lag in [100.0, 1100.0, 1200.0]:\n logger.observe(dict(\n type='operation', phase='start', user='user01',\n label='testing', lag=lag)\n )\n self.assertEqual(\n [\"Median TESTING scheduling lag greater than 1000.0ms\"],\n logger.failures())","title":""},{"docid":"f0ddee65968dc0db1bba54298341b73b","score":"0.5728882","text":"def test_rate_limiting_error(self):\n self.sources[0].RATE_LIMIT_HTTP_CODES = ('429', '456')\n\n error_body = json_dumps({\"meta\": {\n \"code\": 429, \"error_message\": \"The maximum number of requests...\",\n \"error_type\": \"OAuthRateLimitException\"}})\n self.expect_get_activities().AndRaise(\n urllib.error.HTTPError('url', 429, 'Rate limited', {},\n io.StringIO(error_body)))\n\n self.post_task(expect_poll=FakeSource.RATE_LIMITED_POLL,\n expect_last_polled=util.EPOCH)\n source = self.sources[0].key.get()\n self.assertEqual('error', source.poll_status)\n self.assertTrue(source.rate_limited)","title":""},{"docid":"429a1b753c13a4d10d6c92ab2a74cd5b","score":"0.57227284","text":"def l_rate_limit(self):","title":""},{"docid":"ce44371742d077c6779a50b7ed42e1ea","score":"0.5719269","text":"def test_failureLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for _ignore in range(98):\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=True)\n )\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=False)\n )\n self.assertEqual(\n [\"Greater than 1% TESTING failed\"],\n logger.failures())","title":""},{"docid":"e227dcc7289ee272f60fa0da1a07c9ce","score":"0.5711501","text":"def test_limit_complete(self):\n limit = Limit(item_limit=0)\n self.assertTrue(limit.complete)","title":""},{"docid":"0feeedd5f4c50591d83f4d5fcc7f880b","score":"0.57075286","text":"def patch_max_test_time(limit):\n from corehq.tests.noseplugins.timing import patch_max_test_time\n return patch_max_test_time(limit)","title":""},{"docid":"3cf5b7cc716afd8c4bbca17ff7c3acd0","score":"0.57040715","text":"def testNetworkLimit(self):\n result = self.RunFlow(\"NetworkLimitFlow\", network_bytes_limit=10000)\n self.assertEqual(result[\"networklimit\"], [10000, 9820, 8820, 8240])","title":""},{"docid":"ae6c67987520abd015cd9de754883cf2","score":"0.5701207","text":"def read_limits(self, for_who):","title":""},{"docid":"ff22e7cb9c2a3e700ca9155736fb84ab","score":"0.5698678","text":"def test_throttle(throttle_app: App, test_request):\n\n app = throttle_app\n\n # clear counter from previous test run\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)\n\n # We exceeded the limit of 1 request per hour\n app.get(\"/\", status=429)\n\n # Let's clear the counter\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)","title":""},{"docid":"5aaa63580662051245a41e49f13c4230","score":"0.56840473","text":"def setUp(self):\n self.max_integer = __import__('6-max_integer').max_integer","title":""},{"docid":"2543c7d0beb0ba1c91f9362f2f265831","score":"0.56803393","text":"def test_p_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='P')","title":""},{"docid":"e9ed7d357f3e9e2d9f4dc79158c4f26f","score":"0.5655471","text":"def test_custom_threshold_works(self, mock_req):\n uri = re.compile(\n r\"http://services\\.swpc\\.noaa\\.gov/text/aurora-nowcast-map\\.txt\"\n )\n mock_req.get(uri, text=load_fixture(\"aurora.txt\"))\n\n entities = []\n\n def mock_add_entities(new_entities, update_before_add=False):\n \"\"\"Mock add entities.\"\"\"\n if update_before_add:\n for entity in new_entities:\n entity.update()\n\n for entity in new_entities:\n entities.append(entity)\n\n config = {\"name\": \"Test\", \"forecast_threshold\": 1}\n self.hass.config.longitude = 18.987\n self.hass.config.latitude = 69.648\n\n aurora.setup_platform(self.hass, config, mock_add_entities)\n\n aurora_component = entities[0]\n assert aurora_component.aurora_data.visibility_level == \"16\"\n assert aurora_component.is_on","title":""},{"docid":"85bf876290c6c3c5d0355470cbad4ed4","score":"0.5648686","text":"def test_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = task.get_throughput() + self.error\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n task.get_throughput(), self.error)))\n\n self.get_stats(self.get_throughput, False)\n\n self.set_throughput_to_zero()","title":""},{"docid":"141469ad46b520837dd553482f37f6fe","score":"0.5639672","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_50():","title":""},{"docid":"0469a79b4e91fdf1eb75b14522575b4f","score":"0.5631249","text":"def test_invocation_unchanged_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"10000, 1000000\"\n rule = SleepingRateLimitRule()\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)","title":""},{"docid":"2599d758adb11097d473b0bfb0780c11","score":"0.56051433","text":"def _set_limits(self):\n raise NotImplementedError","title":""},{"docid":"a2319fb6aad76ef1c6ddf1cae92cd6c5","score":"0.5590528","text":"def __init__(self, reason, lim=0):\n self.successes = 0\n self.tests = 0\n self.reason = reason\n self.limit = lim","title":""},{"docid":"c4e9f03b8b2adbadb4b28bae3405579f","score":"0.5579144","text":"def test_invocation_changed_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"600, 30000\"\n\n # without limit enforcement (default)\n rule = SleepingRateLimitRule()\n rule(self.test_response)\n self.assertEqual(600, rule.short_limit)\n self.assertEqual(30000, rule.long_limit)\n\n # with limit enforcement\n rule = SleepingRateLimitRule(force_limits=True)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)","title":""},{"docid":"a03eb84647800d9093f40be90d5ba017","score":"0.5568088","text":"def test_validate_number_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})","title":""},{"docid":"eca65a20a04453254c85360acb20ea5e","score":"0.55434823","text":"def test_statistics_challenge(self):\n pass","title":""},{"docid":"df7e313bfb7a80e371ff9385d8417c86","score":"0.5538704","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_account_quota():","title":""},{"docid":"d018af22930d29f4c353b8dbfa60cafe","score":"0.5533047","text":"def test_no_quota(self, mock_google_credential):\n ae_api_client = ae.AppEngineClient(global_configs={})\n self.assertEqual(None, ae_api_client.repository._rate_limiter)","title":""},{"docid":"c788ea84b5ffad0c5ab893d8e475ac34","score":"0.5523266","text":"def test_below_to_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.sleep(5)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.set_throughput_to_zero()","title":""},{"docid":"21efddcaf5513005cc4e51e4780125a8","score":"0.552136","text":"def test_too_high_limit_value(self):\n self.mockRequest.args['limit'] = ['100']\n d = self.app.paginate_me(self.mockRequest)\n self.assertEqual(self.successResultOf(d), {'limit': 10})","title":""},{"docid":"fb84850547b9daa3559033fa86466ce2","score":"0.55199075","text":"def test_vehicle_error_quota_limit() -> None:\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\n f\"{fixtures.KAMEREON_FIXTURE_PATH}/error/quota_limit.json\",\n schemas.KamereonVehicleDataResponseSchema,\n )\n with pytest.raises(exceptions.QuotaLimitException) as excinfo:\n response.raise_for_error_code()\n assert excinfo.value.error_code == \"err.func.wired.overloaded\"\n assert excinfo.value.error_details == \"You have reached your quota limit\"","title":""},{"docid":"2db0e6d6b9538d21be903b3567d6778a","score":"0.551946","text":"def test_limits_boundary_values(self):\n def check_error_msg(status, output, storagelimit=False):\n import json\n if status == False:\n content = json.loads(output)[\"errors\"]\n if storagelimit:\n actual_error = content[\"dataStorageLimit\"]\n expected_error = '\"dataStorageLimit\" must be an integer between -1 and 100000'\n else:\n actual_error = content[\"dataThrottleLimit\"]\n expected_error = '\"dataThrottleLimit\" must be an integer between -1 and 2147483647'\n self.assertEqual(actual_error, expected_error)\n else:\n self.fail(\"expected to fail but passsed\")\n\n bucket = self.bucket_util.get_all_buckets(self.cluster)[0]\n server = random.choice(bucket.servers)\n bucket_helper = BucketHelper(server)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2)\n check_error_msg(status, content)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648)\n check_error_msg(status, content)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=-2)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=2147483648)\n check_error_msg(status, content, True)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2,\n storage_limit=-2)\n check_error_msg(status, content)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648,\n storage_limit=2147483648)\n check_error_msg(status, content)\n check_error_msg(status, content, True)","title":""},{"docid":"2c8cfd4e6547c5da4018fce338552fc1","score":"0.55178887","text":"def test_absLimits_get(self):\n # To check if all limits are present in the response (will be checked\n # by schema)\n self.client.show_limits()","title":""},{"docid":"f4559bad2a7fa45c28b461e0872b9faa","score":"0.55167836","text":"def testModel10(self):\n m = self.createModel ( 10 )\n ulComp = UpperLimitComputer( cl=.95 )\n ulProf = ulComp.getUpperLimitOnMu( m )\n self.assertAlmostEqual( ulProf / 365.6091713369213, 1.0, 2 )\n ## Nick's profiling code gets for n=10 ul=357.568","title":""},{"docid":"605f7410262f6675d7c4426a9b4c6acf","score":"0.5482882","text":"def test_above_to_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.sleep(5)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.set_throughput_to_zero()","title":""},{"docid":"d52e7cbd76427c029fb67e8abebba80c","score":"0.5475557","text":"def test_investment_limit(self):\n bus1 = solph.Bus(label='Bus1')\n solph.components.GenericStorage(\n label='storage_invest_limit',\n invest_relation_input_capacity=0.2,\n invest_relation_output_capacity=0.2,\n inputs={bus1: solph.Flow()},\n outputs={bus1: solph.Flow()},\n investment=solph.Investment(ep_costs=145))\n solph.Source(label='Source', outputs={bus1: solph.Flow(\n investment=solph.Investment(ep_costs=123))})\n om = self.get_om()\n solph.constraints.investment_limit(om, limit=900)\n\n self.compare_lp_files('investment_limit.lp', my_om=om)","title":""},{"docid":"b7463779b823b34dc5aa1bf07a24eca5","score":"0.5472236","text":"def __init__(self, \n loading_limit: float = 1.0,\n voltage_limit: dict = {\n 'overvoltage_threshold': 1.05,\n 'undervoltage_threshold': 0.95\n }\n ):\n\n self.loading_limit = data_model.ThermalLoadingLimit(threshold=loading_limit)\n self.voltage_limit = data_model.VoltageViolationLimit(**voltage_limit)\n self.sardi_aggregated = 0\n self.counter = 0","title":""},{"docid":"c4e2b672d4dc7204199b80307baf975e","score":"0.5468406","text":"def setUp(self):\n self.maxDiff = None\n pass","title":""},{"docid":"7bc25d8909e6aa209e9f2e2d0be7b68e","score":"0.54441905","text":"def test_warn_count_all_unnecessary(marker_trackerstore: TrackerStore):\n with pytest.warns(UserWarning):\n MarkerTrackerLoader(marker_trackerstore, STRATEGY_ALL, 3)","title":""},{"docid":"14d2bfa5e14c5ed85e25032ec06138e7","score":"0.54241455","text":"def _monkey_update_limits(self):\n self.monkey_update_limits_counter += 1","title":""},{"docid":"147689e8ba9730d21bd90b07a88450ca","score":"0.5423483","text":"async def test_warn_count_exceeds_store(marker_trackerstore: TrackerStore):\n loader = MarkerTrackerLoader(marker_trackerstore, STRATEGY_SAMPLE_N, 6)\n with pytest.warns(UserWarning):\n # Need to force the generator to evaluate to produce the warning\n [tracker async for tracker in loader.load()]","title":""},{"docid":"f563c2e499abfb074d540962c3d9af1f","score":"0.54190344","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_50():","title":""},{"docid":"861ab1172fa87007229aece7506960ca","score":"0.5406477","text":"def setUp(self):\n self.maxDiff = None","title":""},{"docid":"861ab1172fa87007229aece7506960ca","score":"0.5406477","text":"def setUp(self):\n self.maxDiff = None","title":""},{"docid":"effe8908484025bd651d30f0924e1980","score":"0.5385588","text":"def test_backend_job_limit(self):\n job_limit = self.backend.job_limit()\n self.assertIsNotNone(job_limit)\n self.assertIsNotNone(job_limit.active_jobs)\n if job_limit.maximum_jobs:\n self.assertGreater(job_limit.maximum_jobs, 0)","title":""},{"docid":"e6a5ca508718aa8278ec74bef4ab52dd","score":"0.5382612","text":"def test_api_loss_livestock_read(self):\n pass","title":""},{"docid":"aedb392579d434e71a5f9e1599a33c1a","score":"0.53748673","text":"def test_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = self.resource_limit * self.units\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n self.get_throughput, self.error)))\n\n # Once above threshold, ensure the expected error message is thrown\n if self.tasks and self.tasks[0].expected_error():\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check_error(self.tasks[0].error(), self.tasks[0].expected_error())))\n\n self.run_compaction()\n\n # set a higher limit to validate stats\n self.set_limits_for_all_users(self.resource_name, self.resource_limit*2)\n\n self.get_stats(self.get_throughput)\n self.set_throughput_to_zero()","title":""},{"docid":"6e9d17c01cda4ebe3809accad1368a5f","score":"0.5370684","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_account_quota():","title":""},{"docid":"22680aa0d2c40892f6f36114bc2bf5e0","score":"0.53467035","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled():","title":""},{"docid":"1483b065c225ad7c2faed1cc34cd61e5","score":"0.5340418","text":"def test_api_pings():","title":""},{"docid":"01008ac4145ca5c9d4ec1f51c92bfdf4","score":"0.5332597","text":"def test_datapoints_agent(self):\n pass","title":""},{"docid":"8e5e5b0d87f58a1a1bfbdbbd9d919cd0","score":"0.53280985","text":"def test_api_vulnerability_read(self):\n pass","title":""},{"docid":"8f91b4ce1ad365a009b2d61390489c8c","score":"0.53207844","text":"def test_get_stats_works(fixture):\n\tpass","title":""},{"docid":"8e9dc9174213a944552c00c74e42c711","score":"0.53145677","text":"def test_post_view_hits_rate_limit_will_return_429(self, throttled_function):\n url = reverse(self.VIEW_NAME)\n\n text = \"I am eating a hamburger\"\n data = {\"text\": text, \"email\": text, \"title\": text}\n\n client = self.registered_user_client\n\n for _ in range(3):\n response = client.post(url, data=data)\n\n # by the 3rd request, it should have hit the rate limit\n self.assertEqual(response.status_code, 429)","title":""},{"docid":"f7ce635d7ca31075dfd703bbb75fad7e","score":"0.5311275","text":"def test_stats_get(self):\n pass","title":""},{"docid":"9613fa7ebf25c4158b0f0913a2a1fffe","score":"0.53091764","text":"def test_api_rejects_invalid_limit_parameter(self):\n\n res = self.client().get(\"/bucketlists/v1.0/?limit=dkj\",\n headers={\"Authorization\": self.token}\n )\n self.assertEqual(res.status_code, 400)\n self.assertIn('\"limit\": \"Limit has to be a number between 1 and 100\"', str(res.data))","title":""},{"docid":"c38455204f979970e200d0fbc8af07fa","score":"0.5301058","text":"def test_setup(self):\n self.assertEqual(8, len(self.raw_metrics_list), \"incorrect number json files read\")","title":""},{"docid":"35f8ec51e964a32376250ad4abfdc991","score":"0.53001773","text":"def test_api_pollution_read(self):\n pass","title":""},{"docid":"1c00c0289ce5b30ecc63191737bf7a91","score":"0.5295825","text":"def test_no_quota(self, mock_google_credential):\n iam_api_client = iam.IAMClient(global_configs={})\n self.assertEqual(None, iam_api_client.repository._rate_limiter)","title":""},{"docid":"140403ea97ead15f21e0234c6e3bb831","score":"0.5290854","text":"def monkey_update_limits(self):\n self._monkey_update_limits_counter += 1","title":""},{"docid":"5210bac5e5864645dce2f6149ab7bd66","score":"0.52772236","text":"def throttle_failure(self):\n ...","title":""},{"docid":"cc2cb6f3650dbdde8c333a597208dd49","score":"0.5274103","text":"def test_4(self):\n pass","title":""},{"docid":"311741480132ebdb87164609f0028ed4","score":"0.526988","text":"def test_get_task_counts_for_project(self):\n pass","title":""},{"docid":"4bfcc5fa516f4fe2340f52fde18e6100","score":"0.5268455","text":"def test_limit_batches():\n command = [\n \"run.py\",\n \"trainer=default\",\n \"trainer.max_epochs=1\",\n \"trainer.limit_train_batches=0.25\",\n \"trainer.limit_val_batches=0.25\",\n \"trainer.limit_test_batches=0.25\",\n ]\n run_command(command)","title":""},{"docid":"791f0692a94751a7eb9dc52bcefd5e5c","score":"0.5268286","text":"def limit(self, count):\n ...","title":""},{"docid":"f988e2113eb41b9ef4a78efee021d3e0","score":"0.5256502","text":"def test_calc_requests_used_most_recent_quarterly_finished_mdm_import_dataset(self):\n pass","title":""},{"docid":"5d7cacf4f06d0c7199faf623f92e22eb","score":"0.52531564","text":"def test_get_loyalty_statistics(self):\n pass","title":""},{"docid":"90b4c969440d7d9ad5e7d45c2049f85c","score":"0.5247421","text":"def test_api_vulnerability_list(self):\n pass","title":""},{"docid":"eeadd5017709442f6f24c683f78a6ce2","score":"0.5246757","text":"def limits(self):\n pass","title":""},{"docid":"c8b4b1cdf058f1a8088794045a3af17d","score":"0.5246362","text":"def test_defaultmessage_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})","title":""},{"docid":"ba552db0941824167440874d2c526ad2","score":"0.5243879","text":"def test_api_loss_livestock_list(self):\n pass","title":""},{"docid":"ca5463fd9a59ec9898f825406dd45249","score":"0.52336663","text":"def test_assign_resources_on_sdp_in_low():","title":""},{"docid":"64401e16acebcdf491cc6144a1a120cc","score":"0.5232181","text":"def test_q_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='Q')","title":""},{"docid":"5648c8fcec5cc214d4d05a429bde0046","score":"0.5230283","text":"def test_mock_handle_rate_limiting_429(self, app, worker):\n mock_exception = SpotifyException(http_status=429, code=-1, msg=\"Mock exception\", headers={'Retry-After': 1})\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n\n assert job in app.task_queue.jobs\n worker.work(burst=True)\n assert job not in app.task_queue.jobs\n assert job.is_finished","title":""},{"docid":"af1ca4a11a40dbf7e53ee6a5ca9c4704","score":"0.52235043","text":"def monkey_update_limits(self, *args):\n self._monkey_update_limits_counter += 1","title":""},{"docid":"65a1187552ae4ec4473c938eb901fbb1","score":"0.52184767","text":"def test_update_limits(self, monkeypatch):\n # call function to test\n monkeypatch.setattr(DoublyFedInductionMotor, '__init__', self.monkey_init)\n\n test_object = DoublyFedInductionMotor()\n test_object._motor_parameter = self._motor_parameter\n test_object._limits = self._limit_values\n test_object._nominal_values = self._nominal_values\n # verify the expected results\n test_object._update_limits()\n\n assert test_object._limits['u_rbeta'] == 0.5 * self._limit_values['u']\n assert test_object._nominal_values['i_ralpha'] == (test_object._nominal_values.get('i', None) or test_object._nominal_values['u_ralpha'] / test_object._motor_parameter['r_r'])\n\n monkeypatch.setattr(InductionMotor, '_update_limits', self.monkey_super_update_limits)\n test_object._update_limits()\n assert self._monkey_super_update_limits_counter == 1, 'super().update_limits() is not called once'","title":""},{"docid":"6cfebc8db03c686495c135c813f57f06","score":"0.52165115","text":"def test_set_real_limit(self):\n ## Create a card\n self.app.post('/wallet/cards',\n data={**self.card_arguments,\n 'due_day': 27,\n 'max_limit': 1000.0},\n headers=dict(token=self.token), follow_redirects=True)\n\n # set real limit\n result = self.app.put('/wallet/real_limit/{}'.format(500.0), headers=dict(token=self.token),\n follow_redirects=True)\n self.assertEqual(result.status_code, 200)\n\n # Get wallet information and test free limit\n result = self.app.get('/wallet', headers=dict(token=self.token), follow_redirects=True)\n wallet = json.loads(result.get_data(as_text=True))\n\n self.assertEqual(wallet['real_limit'], 500.0)","title":""},{"docid":"5c001b9151f78ba6fa1c78d66717bde8","score":"0.52106315","text":"def test_1_0(self):\n pass","title":""},{"docid":"4eca299efee3d1176cf39ea28978c28a","score":"0.5209944","text":"def test_add_vote(self):\n pass","title":""},{"docid":"fc0a30a1ff8e7d599561ab6219a85a5a","score":"0.5197893","text":"def metrics_to_use(self):","title":""},{"docid":"e584f5b1e1944ae9128eb9a748fbacfe","score":"0.51940686","text":"def limit( bw=10, cpu=.1 ):\n intf = custom( TCIntf, bw=bw )\n myTopo = TreeTopo( depth=1, fanout=2 )\n for sched in 'rt', 'cfs':\n info( '*** Testing with', sched, 'bandwidth limiting\\n' )\n if sched == 'rt':\n release = quietRun( 'uname -r' ).strip('\\r\\n')\n output = quietRun( 'grep CONFIG_RT_GROUP_SCHED /boot/config-%s'\n % release )\n if output == '# CONFIG_RT_GROUP_SCHED is not set\\n':\n info( '*** RT Scheduler is not enabled in your kernel. '\n 'Skipping this test\\n' )\n continue\n host = custom( CPULimitedHost, sched=sched, cpu=cpu )\n net = Mininet( topo=myTopo, intf=intf, host=host )\n net.start()\n testLinkLimit( net, bw=bw )\n net.runCpuLimitTest( cpu=cpu )\n net.stop()","title":""},{"docid":"2dc1e789b8060a80568434a0a92ef969","score":"0.51926","text":"def test_get_target_low(self):\n self.assertEqual(90, int(self.decider.get_target_low()))","title":""},{"docid":"689d8b3e3e9cdd38fcfe0e2e1c61ad6f","score":"0.5189289","text":"def test_mock_handle_rate_limiting_other(self, app, worker):\n mock_exception = SpotifyException(http_status=500, code=-1, msg=\"Mock exception\")\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n assert job is None","title":""},{"docid":"46fcb98abd2ca1365a843b1d2872353e","score":"0.51855177","text":"def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckRequestPayload,\n **kwargs\n )\n\n payload = payloads.CheckRequestPayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )","title":""},{"docid":"552576c0a98079e2d0328df7aa439700","score":"0.51854384","text":"def test_add_loyalty_points(self):\n pass","title":""},{"docid":"90285dc105febdd849e5bb9c230d1dc5","score":"0.5181207","text":"def test_4_0(self):\n pass","title":""},{"docid":"99da783c6eb090328bfabeb8870bf314","score":"0.51730084","text":"def setUp(self):\n\n Test.maxDiff = None","title":""},{"docid":"5a92616e769a5d1cb3217b13b20a9983","score":"0.5170803","text":"def test_non_algorithmic(self):\n user = users.models.User.objects.create_user('test-user')\n user.school_participations.create(\n school=self.prev_school_1, parallel=self.s1_c_prime)\n user.school_participations.create(\n school=self.prev_school_2, parallel=self.s2_p)\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c)","title":""},{"docid":"e2de9a73ba436aa8b5014b39fd686afd","score":"0.5166248","text":"def test_change_default_throttling_settings_ws_with_overwrite_not_throttled():","title":""},{"docid":"25f824ca954e055d2f435e9035469b80","score":"0.516279","text":"def test_replica_rebuild_per_volume_limit():\n pass","title":""},{"docid":"ed82c281f0922ac9a7ecd142073448e4","score":"0.51620966","text":"def test_get_account_analytics(self):\n pass","title":""},{"docid":"9c2bcb75ddb3b932aa7bab229253b583","score":"0.51556665","text":"def test_case_4(self):","title":""},{"docid":"f464cac8f81208d141ebf17c1a20cacd","score":"0.51537263","text":"def test_functionality(self):\n pass","title":""},{"docid":"46fd953d06a0aa2f1c6ab23ae66d95a8","score":"0.515275","text":"def test_redundancy_increased_when_not_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=4)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers + 1)","title":""},{"docid":"927d00ec51cb64265439588d482b3f5f","score":"0.5147975","text":"def testCaseTBA(self):","title":""},{"docid":"96093b7512519cf8aa209c17e16fbbd9","score":"0.51442605","text":"def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckResponsePayload,\n **kwargs\n )\n\n payload = payloads.CheckResponsePayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )","title":""},{"docid":"0605695f28a7671760cd03cd4dd896e0","score":"0.5142084","text":"def test_redundancy_not_increased_when_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=3)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers)","title":""},{"docid":"c61b8cf2b199172f15801994e9c81fea","score":"0.513838","text":"def test_api_loss_list(self):\n pass","title":""},{"docid":"001da76050180758b4e0921641d6f7fe","score":"0.51352376","text":"def test_max_local_block_devices_0_force_bfv(self):\n self.flags(max_local_block_devices=0)\n server = self._build_server()\n ex = self.assertRaises(api_client.OpenStackApiException,\n self.admin_api.post_server,\n {'server': server})\n self.assertEqual(400, ex.response.status_code)\n self.assertIn('You specified more local devices than the limit allows',\n str(ex))","title":""}],"string":"[\n {\n \"docid\": \"5e84c8ee36f03c4d423562b65adc6087\",\n \"score\": \"0.6538664\",\n \"text\": \"def test_rate_limit_interface():\\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\\n min_rate_def = RateDefinition(per_second=10)\\n per_user_rate_def = PerUserRateDefinition(per_user_rate_def, min_rate_def)\\n my_feature_rate_limiter = RateLimiter('my_feature', per_user_rate_def.get_rate_limits)\\n if my_feature_rate_limiter.allow_usage('my_domain'):\\n # ...do stuff...\\n my_feature_rate_limiter.report_usage('my_domain')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d93729a630c34495d64c4b65d915e78\",\n \"score\": \"0.6360713\",\n \"text\": \"def test_activity_api_limits(self):\\n\\n async def run_test():\\n with self.assertRaises(ValueError):\\n await self.test_camera.query_activity_history(limit=ACTIVITY_API_LIMIT + 1)\\n\\n self.loop.run_until_complete(run_test())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577aaff1b88b187d68a0fc9eb0d0a150\",\n \"score\": \"0.60561013\",\n \"text\": \"def on_limit(self, track):\\n print 'Limitation notice has arrived...'\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f731d809af601dfc71952e039aedadcb\",\n \"score\": \"0.5922246\",\n \"text\": \"def test_no_participations(self):\\n user = users.models.User.objects.create_user('test-user')\\n limit = self.limiter.get_limit(user)\\n self.assertEqual(limit.min_level, self.c_prime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc47a7b5642e0be6e1843560d450fa4d\",\n \"score\": \"0.58270365\",\n \"text\": \"def test_limit_reached(self):\\r\\n chip_counter = Chips()\\r\\n limit_reached(chip_counter)\\r\\n self.assertEqual(chip_counter.total, 2000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f073275b6c9022ea447378594acfed2\",\n \"score\": \"0.57859415\",\n \"text\": \"def setUp(self):\\n super(LimitsControllerTestV21, self).setUp()\\n self.controller = wsgi.Resource(self.limits_controller())\\n self.ctrler = self.limits_controller()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e651565f2fa68e798f85ce96e80e1e5\",\n \"score\": \"0.5757954\",\n \"text\": \"def testCPULimit(self):\\n result = self.RunFlow(\\\"CPULimitFlow\\\", cpu_limit=300)\\n self.assertEqual(result[\\\"cpulimit\\\"], [300, 295, 255])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028e2c972cedb228cc34afa6e47ecdd1\",\n \"score\": \"0.5748576\",\n \"text\": \"def test_lagLimitExceeded(self):\\n logger = OperationLogger(outfile=StringIO())\\n for lag in [100.0, 1100.0, 1200.0]:\\n logger.observe(dict(\\n type='operation', phase='start', user='user01',\\n label='testing', lag=lag)\\n )\\n self.assertEqual(\\n [\\\"Median TESTING scheduling lag greater than 1000.0ms\\\"],\\n logger.failures())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ddee65968dc0db1bba54298341b73b\",\n \"score\": \"0.5728882\",\n \"text\": \"def test_rate_limiting_error(self):\\n self.sources[0].RATE_LIMIT_HTTP_CODES = ('429', '456')\\n\\n error_body = json_dumps({\\\"meta\\\": {\\n \\\"code\\\": 429, \\\"error_message\\\": \\\"The maximum number of requests...\\\",\\n \\\"error_type\\\": \\\"OAuthRateLimitException\\\"}})\\n self.expect_get_activities().AndRaise(\\n urllib.error.HTTPError('url', 429, 'Rate limited', {},\\n io.StringIO(error_body)))\\n\\n self.post_task(expect_poll=FakeSource.RATE_LIMITED_POLL,\\n expect_last_polled=util.EPOCH)\\n source = self.sources[0].key.get()\\n self.assertEqual('error', source.poll_status)\\n self.assertTrue(source.rate_limited)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429a1b753c13a4d10d6c92ab2a74cd5b\",\n \"score\": \"0.57227284\",\n \"text\": \"def l_rate_limit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce44371742d077c6779a50b7ed42e1ea\",\n \"score\": \"0.5719269\",\n \"text\": \"def test_failureLimitExceeded(self):\\n logger = OperationLogger(outfile=StringIO())\\n for _ignore in range(98):\\n logger.observe(dict(\\n type='operation', phase='end', user='user01',\\n duration=0.25, label='testing', success=True)\\n )\\n logger.observe(dict(\\n type='operation', phase='end', user='user01',\\n duration=0.25, label='testing', success=False)\\n )\\n self.assertEqual(\\n [\\\"Greater than 1% TESTING failed\\\"],\\n logger.failures())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e227dcc7289ee272f60fa0da1a07c9ce\",\n \"score\": \"0.5711501\",\n \"text\": \"def test_limit_complete(self):\\n limit = Limit(item_limit=0)\\n self.assertTrue(limit.complete)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0feeedd5f4c50591d83f4d5fcc7f880b\",\n \"score\": \"0.57075286\",\n \"text\": \"def patch_max_test_time(limit):\\n from corehq.tests.noseplugins.timing import patch_max_test_time\\n return patch_max_test_time(limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf5b7cc716afd8c4bbca17ff7c3acd0\",\n \"score\": \"0.57040715\",\n \"text\": \"def testNetworkLimit(self):\\n result = self.RunFlow(\\\"NetworkLimitFlow\\\", network_bytes_limit=10000)\\n self.assertEqual(result[\\\"networklimit\\\"], [10000, 9820, 8820, 8240])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6c67987520abd015cd9de754883cf2\",\n \"score\": \"0.5701207\",\n \"text\": \"def read_limits(self, for_who):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff22e7cb9c2a3e700ca9155736fb84ab\",\n \"score\": \"0.5698678\",\n \"text\": \"def test_throttle(throttle_app: App, test_request):\\n\\n app = throttle_app\\n\\n # clear counter from previous test run\\n clear_throttle(test_request, \\\"throttle_sample\\\")\\n\\n app.get(\\\"/\\\", status=200)\\n\\n # We exceeded the limit of 1 request per hour\\n app.get(\\\"/\\\", status=429)\\n\\n # Let's clear the counter\\n clear_throttle(test_request, \\\"throttle_sample\\\")\\n\\n app.get(\\\"/\\\", status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aaa63580662051245a41e49f13c4230\",\n \"score\": \"0.56840473\",\n \"text\": \"def setUp(self):\\n self.max_integer = __import__('6-max_integer').max_integer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2543c7d0beb0ba1c91f9362f2f265831\",\n \"score\": \"0.56803393\",\n \"text\": \"def test_p_rate_limit_switch(self):\\n return self._rate_limit_switch(apk_type='P')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ed7d357f3e9e2d9f4dc79158c4f26f\",\n \"score\": \"0.5655471\",\n \"text\": \"def test_custom_threshold_works(self, mock_req):\\n uri = re.compile(\\n r\\\"http://services\\\\.swpc\\\\.noaa\\\\.gov/text/aurora-nowcast-map\\\\.txt\\\"\\n )\\n mock_req.get(uri, text=load_fixture(\\\"aurora.txt\\\"))\\n\\n entities = []\\n\\n def mock_add_entities(new_entities, update_before_add=False):\\n \\\"\\\"\\\"Mock add entities.\\\"\\\"\\\"\\n if update_before_add:\\n for entity in new_entities:\\n entity.update()\\n\\n for entity in new_entities:\\n entities.append(entity)\\n\\n config = {\\\"name\\\": \\\"Test\\\", \\\"forecast_threshold\\\": 1}\\n self.hass.config.longitude = 18.987\\n self.hass.config.latitude = 69.648\\n\\n aurora.setup_platform(self.hass, config, mock_add_entities)\\n\\n aurora_component = entities[0]\\n assert aurora_component.aurora_data.visibility_level == \\\"16\\\"\\n assert aurora_component.is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bf876290c6c3c5d0355470cbad4ed4\",\n \"score\": \"0.5648686\",\n \"text\": \"def test_below_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.get_throughput = task.get_throughput() + self.error\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\\n task.get_throughput(), self.error)))\\n\\n self.get_stats(self.get_throughput, False)\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"141469ad46b520837dd553482f37f6fe\",\n \"score\": \"0.5639672\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_50():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0469a79b4e91fdf1eb75b14522575b4f\",\n \"score\": \"0.5631249\",\n \"text\": \"def test_invocation_unchanged_limits(self):\\n self.test_response[\\\"X-RateLimit-Usage\\\"] = \\\"0, 0\\\"\\n self.test_response[\\\"X-RateLimit-Limit\\\"] = \\\"10000, 1000000\\\"\\n rule = SleepingRateLimitRule()\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\\n rule(self.test_response)\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2599d758adb11097d473b0bfb0780c11\",\n \"score\": \"0.56051433\",\n \"text\": \"def _set_limits(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2319fb6aad76ef1c6ddf1cae92cd6c5\",\n \"score\": \"0.5590528\",\n \"text\": \"def __init__(self, reason, lim=0):\\n self.successes = 0\\n self.tests = 0\\n self.reason = reason\\n self.limit = lim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e9f03b8b2adbadb4b28bae3405579f\",\n \"score\": \"0.5579144\",\n \"text\": \"def test_invocation_changed_limits(self):\\n self.test_response[\\\"X-RateLimit-Usage\\\"] = \\\"0, 0\\\"\\n self.test_response[\\\"X-RateLimit-Limit\\\"] = \\\"600, 30000\\\"\\n\\n # without limit enforcement (default)\\n rule = SleepingRateLimitRule()\\n rule(self.test_response)\\n self.assertEqual(600, rule.short_limit)\\n self.assertEqual(30000, rule.long_limit)\\n\\n # with limit enforcement\\n rule = SleepingRateLimitRule(force_limits=True)\\n rule(self.test_response)\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03eb84647800d9093f40be90d5ba017\",\n \"score\": \"0.5568088\",\n \"text\": \"def test_validate_number_limits(self):\\n # Reset limits first.\\n self.reset_limits()\\n\\n # Request as often as allowed.\\n for i in range(0, self._app.limit_amount):\\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'ok'})\\n\\n # Request again and expect error.\\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'error', \\\"error\\\": \\\"limit_acceded\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca65a20a04453254c85360acb20ea5e\",\n \"score\": \"0.55434823\",\n \"text\": \"def test_statistics_challenge(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7e313bfb7a80e371ff9385d8417c86\",\n \"score\": \"0.5538704\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_account_quota():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d018af22930d29f4c353b8dbfa60cafe\",\n \"score\": \"0.5533047\",\n \"text\": \"def test_no_quota(self, mock_google_credential):\\n ae_api_client = ae.AppEngineClient(global_configs={})\\n self.assertEqual(None, ae_api_client.repository._rate_limiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c788ea84b5ffad0c5ab893d8e475ac34\",\n \"score\": \"0.5523266\",\n \"text\": \"def test_below_to_above_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\\n\\n self.sleep(5)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21efddcaf5513005cc4e51e4780125a8\",\n \"score\": \"0.552136\",\n \"text\": \"def test_too_high_limit_value(self):\\n self.mockRequest.args['limit'] = ['100']\\n d = self.app.paginate_me(self.mockRequest)\\n self.assertEqual(self.successResultOf(d), {'limit': 10})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb84850547b9daa3559033fa86466ce2\",\n \"score\": \"0.55199075\",\n \"text\": \"def test_vehicle_error_quota_limit() -> None:\\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\\n f\\\"{fixtures.KAMEREON_FIXTURE_PATH}/error/quota_limit.json\\\",\\n schemas.KamereonVehicleDataResponseSchema,\\n )\\n with pytest.raises(exceptions.QuotaLimitException) as excinfo:\\n response.raise_for_error_code()\\n assert excinfo.value.error_code == \\\"err.func.wired.overloaded\\\"\\n assert excinfo.value.error_details == \\\"You have reached your quota limit\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db0e6d6b9538d21be903b3567d6778a\",\n \"score\": \"0.551946\",\n \"text\": \"def test_limits_boundary_values(self):\\n def check_error_msg(status, output, storagelimit=False):\\n import json\\n if status == False:\\n content = json.loads(output)[\\\"errors\\\"]\\n if storagelimit:\\n actual_error = content[\\\"dataStorageLimit\\\"]\\n expected_error = '\\\"dataStorageLimit\\\" must be an integer between -1 and 100000'\\n else:\\n actual_error = content[\\\"dataThrottleLimit\\\"]\\n expected_error = '\\\"dataThrottleLimit\\\" must be an integer between -1 and 2147483647'\\n self.assertEqual(actual_error, expected_error)\\n else:\\n self.fail(\\\"expected to fail but passsed\\\")\\n\\n bucket = self.bucket_util.get_all_buckets(self.cluster)[0]\\n server = random.choice(bucket.servers)\\n bucket_helper = BucketHelper(server)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=-2)\\n check_error_msg(status, content)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=2147483648)\\n check_error_msg(status, content)\\n\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n storage_limit=-2)\\n check_error_msg(status, content, True)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n storage_limit=2147483648)\\n check_error_msg(status, content, True)\\n\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=-2,\\n storage_limit=-2)\\n check_error_msg(status, content)\\n check_error_msg(status, content, True)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=2147483648,\\n storage_limit=2147483648)\\n check_error_msg(status, content)\\n check_error_msg(status, content, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8cfd4e6547c5da4018fce338552fc1\",\n \"score\": \"0.55178887\",\n \"text\": \"def test_absLimits_get(self):\\n # To check if all limits are present in the response (will be checked\\n # by schema)\\n self.client.show_limits()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4559bad2a7fa45c28b461e0872b9faa\",\n \"score\": \"0.55167836\",\n \"text\": \"def testModel10(self):\\n m = self.createModel ( 10 )\\n ulComp = UpperLimitComputer( cl=.95 )\\n ulProf = ulComp.getUpperLimitOnMu( m )\\n self.assertAlmostEqual( ulProf / 365.6091713369213, 1.0, 2 )\\n ## Nick's profiling code gets for n=10 ul=357.568\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605f7410262f6675d7c4426a9b4c6acf\",\n \"score\": \"0.5482882\",\n \"text\": \"def test_above_to_below_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\\n\\n self.sleep(5)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52e7cbd76427c029fb67e8abebba80c\",\n \"score\": \"0.5475557\",\n \"text\": \"def test_investment_limit(self):\\n bus1 = solph.Bus(label='Bus1')\\n solph.components.GenericStorage(\\n label='storage_invest_limit',\\n invest_relation_input_capacity=0.2,\\n invest_relation_output_capacity=0.2,\\n inputs={bus1: solph.Flow()},\\n outputs={bus1: solph.Flow()},\\n investment=solph.Investment(ep_costs=145))\\n solph.Source(label='Source', outputs={bus1: solph.Flow(\\n investment=solph.Investment(ep_costs=123))})\\n om = self.get_om()\\n solph.constraints.investment_limit(om, limit=900)\\n\\n self.compare_lp_files('investment_limit.lp', my_om=om)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7463779b823b34dc5aa1bf07a24eca5\",\n \"score\": \"0.5472236\",\n \"text\": \"def __init__(self, \\n loading_limit: float = 1.0,\\n voltage_limit: dict = {\\n 'overvoltage_threshold': 1.05,\\n 'undervoltage_threshold': 0.95\\n }\\n ):\\n\\n self.loading_limit = data_model.ThermalLoadingLimit(threshold=loading_limit)\\n self.voltage_limit = data_model.VoltageViolationLimit(**voltage_limit)\\n self.sardi_aggregated = 0\\n self.counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e2b672d4dc7204199b80307baf975e\",\n \"score\": \"0.5468406\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bc25d8909e6aa209e9f2e2d0be7b68e\",\n \"score\": \"0.54441905\",\n \"text\": \"def test_warn_count_all_unnecessary(marker_trackerstore: TrackerStore):\\n with pytest.warns(UserWarning):\\n MarkerTrackerLoader(marker_trackerstore, STRATEGY_ALL, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d2bfa5e14c5ed85e25032ec06138e7\",\n \"score\": \"0.54241455\",\n \"text\": \"def _monkey_update_limits(self):\\n self.monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147689e8ba9730d21bd90b07a88450ca\",\n \"score\": \"0.5423483\",\n \"text\": \"async def test_warn_count_exceeds_store(marker_trackerstore: TrackerStore):\\n loader = MarkerTrackerLoader(marker_trackerstore, STRATEGY_SAMPLE_N, 6)\\n with pytest.warns(UserWarning):\\n # Need to force the generator to evaluate to produce the warning\\n [tracker async for tracker in loader.load()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f563c2e499abfb074d540962c3d9af1f\",\n \"score\": \"0.54190344\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_50():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab1172fa87007229aece7506960ca\",\n \"score\": \"0.5406477\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab1172fa87007229aece7506960ca\",\n \"score\": \"0.5406477\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effe8908484025bd651d30f0924e1980\",\n \"score\": \"0.5385588\",\n \"text\": \"def test_backend_job_limit(self):\\n job_limit = self.backend.job_limit()\\n self.assertIsNotNone(job_limit)\\n self.assertIsNotNone(job_limit.active_jobs)\\n if job_limit.maximum_jobs:\\n self.assertGreater(job_limit.maximum_jobs, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6a5ca508718aa8278ec74bef4ab52dd\",\n \"score\": \"0.5382612\",\n \"text\": \"def test_api_loss_livestock_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aedb392579d434e71a5f9e1599a33c1a\",\n \"score\": \"0.53748673\",\n \"text\": \"def test_above_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.get_throughput = self.resource_limit * self.units\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\\n self.get_throughput, self.error)))\\n\\n # Once above threshold, ensure the expected error message is thrown\\n if self.tasks and self.tasks[0].expected_error():\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check_error(self.tasks[0].error(), self.tasks[0].expected_error())))\\n\\n self.run_compaction()\\n\\n # set a higher limit to validate stats\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit*2)\\n\\n self.get_stats(self.get_throughput)\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9d17c01cda4ebe3809accad1368a5f\",\n \"score\": \"0.5370684\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_account_quota():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22680aa0d2c40892f6f36114bc2bf5e0\",\n \"score\": \"0.53467035\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1483b065c225ad7c2faed1cc34cd61e5\",\n \"score\": \"0.5340418\",\n \"text\": \"def test_api_pings():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01008ac4145ca5c9d4ec1f51c92bfdf4\",\n \"score\": \"0.5332597\",\n \"text\": \"def test_datapoints_agent(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5e5b0d87f58a1a1bfbdbbd9d919cd0\",\n \"score\": \"0.53280985\",\n \"text\": \"def test_api_vulnerability_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f91b4ce1ad365a009b2d61390489c8c\",\n \"score\": \"0.53207844\",\n \"text\": \"def test_get_stats_works(fixture):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9dc9174213a944552c00c74e42c711\",\n \"score\": \"0.53145677\",\n \"text\": \"def test_post_view_hits_rate_limit_will_return_429(self, throttled_function):\\n url = reverse(self.VIEW_NAME)\\n\\n text = \\\"I am eating a hamburger\\\"\\n data = {\\\"text\\\": text, \\\"email\\\": text, \\\"title\\\": text}\\n\\n client = self.registered_user_client\\n\\n for _ in range(3):\\n response = client.post(url, data=data)\\n\\n # by the 3rd request, it should have hit the rate limit\\n self.assertEqual(response.status_code, 429)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7ce635d7ca31075dfd703bbb75fad7e\",\n \"score\": \"0.5311275\",\n \"text\": \"def test_stats_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9613fa7ebf25c4158b0f0913a2a1fffe\",\n \"score\": \"0.53091764\",\n \"text\": \"def test_api_rejects_invalid_limit_parameter(self):\\n\\n res = self.client().get(\\\"/bucketlists/v1.0/?limit=dkj\\\",\\n headers={\\\"Authorization\\\": self.token}\\n )\\n self.assertEqual(res.status_code, 400)\\n self.assertIn('\\\"limit\\\": \\\"Limit has to be a number between 1 and 100\\\"', str(res.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38455204f979970e200d0fbc8af07fa\",\n \"score\": \"0.5301058\",\n \"text\": \"def test_setup(self):\\n self.assertEqual(8, len(self.raw_metrics_list), \\\"incorrect number json files read\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f8ec51e964a32376250ad4abfdc991\",\n \"score\": \"0.53001773\",\n \"text\": \"def test_api_pollution_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c00c0289ce5b30ecc63191737bf7a91\",\n \"score\": \"0.5295825\",\n \"text\": \"def test_no_quota(self, mock_google_credential):\\n iam_api_client = iam.IAMClient(global_configs={})\\n self.assertEqual(None, iam_api_client.repository._rate_limiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140403ea97ead15f21e0234c6e3bb831\",\n \"score\": \"0.5290854\",\n \"text\": \"def monkey_update_limits(self):\\n self._monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5210bac5e5864645dce2f6149ab7bd66\",\n \"score\": \"0.52772236\",\n \"text\": \"def throttle_failure(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2cb6f3650dbdde8c333a597208dd49\",\n \"score\": \"0.5274103\",\n \"text\": \"def test_4(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311741480132ebdb87164609f0028ed4\",\n \"score\": \"0.526988\",\n \"text\": \"def test_get_task_counts_for_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bfcc5fa516f4fe2340f52fde18e6100\",\n \"score\": \"0.5268455\",\n \"text\": \"def test_limit_batches():\\n command = [\\n \\\"run.py\\\",\\n \\\"trainer=default\\\",\\n \\\"trainer.max_epochs=1\\\",\\n \\\"trainer.limit_train_batches=0.25\\\",\\n \\\"trainer.limit_val_batches=0.25\\\",\\n \\\"trainer.limit_test_batches=0.25\\\",\\n ]\\n run_command(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791f0692a94751a7eb9dc52bcefd5e5c\",\n \"score\": \"0.5268286\",\n \"text\": \"def limit(self, count):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f988e2113eb41b9ef4a78efee021d3e0\",\n \"score\": \"0.5256502\",\n \"text\": \"def test_calc_requests_used_most_recent_quarterly_finished_mdm_import_dataset(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d7cacf4f06d0c7199faf623f92e22eb\",\n \"score\": \"0.52531564\",\n \"text\": \"def test_get_loyalty_statistics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b4c969440d7d9ad5e7d45c2049f85c\",\n \"score\": \"0.5247421\",\n \"text\": \"def test_api_vulnerability_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeadd5017709442f6f24c683f78a6ce2\",\n \"score\": \"0.5246757\",\n \"text\": \"def limits(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b4b1cdf058f1a8088794045a3af17d\",\n \"score\": \"0.5246362\",\n \"text\": \"def test_defaultmessage_limits(self):\\n # Reset limits first.\\n self.reset_limits()\\n\\n # Request as often as allowed.\\n for i in range(0, self._app.limit_amount):\\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'ok'})\\n\\n # Request again and expect error.\\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'error', \\\"error\\\": \\\"limit_acceded\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba552db0941824167440874d2c526ad2\",\n \"score\": \"0.5243879\",\n \"text\": \"def test_api_loss_livestock_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca5463fd9a59ec9898f825406dd45249\",\n \"score\": \"0.52336663\",\n \"text\": \"def test_assign_resources_on_sdp_in_low():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64401e16acebcdf491cc6144a1a120cc\",\n \"score\": \"0.5232181\",\n \"text\": \"def test_q_rate_limit_switch(self):\\n return self._rate_limit_switch(apk_type='Q')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5648c8fcec5cc214d4d05a429bde0046\",\n \"score\": \"0.5230283\",\n \"text\": \"def test_mock_handle_rate_limiting_429(self, app, worker):\\n mock_exception = SpotifyException(http_status=429, code=-1, msg=\\\"Mock exception\\\", headers={'Retry-After': 1})\\n job = findartist.spotify.handle_rate_limiting(mock_exception)\\n\\n assert job in app.task_queue.jobs\\n worker.work(burst=True)\\n assert job not in app.task_queue.jobs\\n assert job.is_finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1ca4a11a40dbf7e53ee6a5ca9c4704\",\n \"score\": \"0.52235043\",\n \"text\": \"def monkey_update_limits(self, *args):\\n self._monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a1187552ae4ec4473c938eb901fbb1\",\n \"score\": \"0.52184767\",\n \"text\": \"def test_update_limits(self, monkeypatch):\\n # call function to test\\n monkeypatch.setattr(DoublyFedInductionMotor, '__init__', self.monkey_init)\\n\\n test_object = DoublyFedInductionMotor()\\n test_object._motor_parameter = self._motor_parameter\\n test_object._limits = self._limit_values\\n test_object._nominal_values = self._nominal_values\\n # verify the expected results\\n test_object._update_limits()\\n\\n assert test_object._limits['u_rbeta'] == 0.5 * self._limit_values['u']\\n assert test_object._nominal_values['i_ralpha'] == (test_object._nominal_values.get('i', None) or test_object._nominal_values['u_ralpha'] / test_object._motor_parameter['r_r'])\\n\\n monkeypatch.setattr(InductionMotor, '_update_limits', self.monkey_super_update_limits)\\n test_object._update_limits()\\n assert self._monkey_super_update_limits_counter == 1, 'super().update_limits() is not called once'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfebc8db03c686495c135c813f57f06\",\n \"score\": \"0.52165115\",\n \"text\": \"def test_set_real_limit(self):\\n ## Create a card\\n self.app.post('/wallet/cards',\\n data={**self.card_arguments,\\n 'due_day': 27,\\n 'max_limit': 1000.0},\\n headers=dict(token=self.token), follow_redirects=True)\\n\\n # set real limit\\n result = self.app.put('/wallet/real_limit/{}'.format(500.0), headers=dict(token=self.token),\\n follow_redirects=True)\\n self.assertEqual(result.status_code, 200)\\n\\n # Get wallet information and test free limit\\n result = self.app.get('/wallet', headers=dict(token=self.token), follow_redirects=True)\\n wallet = json.loads(result.get_data(as_text=True))\\n\\n self.assertEqual(wallet['real_limit'], 500.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c001b9151f78ba6fa1c78d66717bde8\",\n \"score\": \"0.52106315\",\n \"text\": \"def test_1_0(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eca299efee3d1176cf39ea28978c28a\",\n \"score\": \"0.5209944\",\n \"text\": \"def test_add_vote(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0a30a1ff8e7d599561ab6219a85a5a\",\n \"score\": \"0.5197893\",\n \"text\": \"def metrics_to_use(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e584f5b1e1944ae9128eb9a748fbacfe\",\n \"score\": \"0.51940686\",\n \"text\": \"def limit( bw=10, cpu=.1 ):\\n intf = custom( TCIntf, bw=bw )\\n myTopo = TreeTopo( depth=1, fanout=2 )\\n for sched in 'rt', 'cfs':\\n info( '*** Testing with', sched, 'bandwidth limiting\\\\n' )\\n if sched == 'rt':\\n release = quietRun( 'uname -r' ).strip('\\\\r\\\\n')\\n output = quietRun( 'grep CONFIG_RT_GROUP_SCHED /boot/config-%s'\\n % release )\\n if output == '# CONFIG_RT_GROUP_SCHED is not set\\\\n':\\n info( '*** RT Scheduler is not enabled in your kernel. '\\n 'Skipping this test\\\\n' )\\n continue\\n host = custom( CPULimitedHost, sched=sched, cpu=cpu )\\n net = Mininet( topo=myTopo, intf=intf, host=host )\\n net.start()\\n testLinkLimit( net, bw=bw )\\n net.runCpuLimitTest( cpu=cpu )\\n net.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc1e789b8060a80568434a0a92ef969\",\n \"score\": \"0.51926\",\n \"text\": \"def test_get_target_low(self):\\n self.assertEqual(90, int(self.decider.get_target_low()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689d8b3e3e9cdd38fcfe0e2e1c61ad6f\",\n \"score\": \"0.5189289\",\n \"text\": \"def test_mock_handle_rate_limiting_other(self, app, worker):\\n mock_exception = SpotifyException(http_status=500, code=-1, msg=\\\"Mock exception\\\")\\n job = findartist.spotify.handle_rate_limiting(mock_exception)\\n assert job is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fcb98abd2ca1365a843b1d2872353e\",\n \"score\": \"0.51855177\",\n \"text\": \"def test_invalid_usage_limits_count(self):\\n kwargs = {'usage_limits_count': 'invalid'}\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n payloads.CheckRequestPayload,\\n **kwargs\\n )\\n\\n payload = payloads.CheckRequestPayload()\\n args = (payload, 'usage_limits_count', 'invalid')\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n setattr,\\n *args\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552576c0a98079e2d0328df7aa439700\",\n \"score\": \"0.51854384\",\n \"text\": \"def test_add_loyalty_points(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90285dc105febdd849e5bb9c230d1dc5\",\n \"score\": \"0.5181207\",\n \"text\": \"def test_4_0(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99da783c6eb090328bfabeb8870bf314\",\n \"score\": \"0.51730084\",\n \"text\": \"def setUp(self):\\n\\n Test.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a92616e769a5d1cb3217b13b20a9983\",\n \"score\": \"0.5170803\",\n \"text\": \"def test_non_algorithmic(self):\\n user = users.models.User.objects.create_user('test-user')\\n user.school_participations.create(\\n school=self.prev_school_1, parallel=self.s1_c_prime)\\n user.school_participations.create(\\n school=self.prev_school_2, parallel=self.s2_p)\\n limit = self.limiter.get_limit(user)\\n self.assertEqual(limit.min_level, self.c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2de9a73ba436aa8b5014b39fd686afd\",\n \"score\": \"0.5166248\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_not_throttled():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f824ca954e055d2f435e9035469b80\",\n \"score\": \"0.516279\",\n \"text\": \"def test_replica_rebuild_per_volume_limit():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed82c281f0922ac9a7ecd142073448e4\",\n \"score\": \"0.51620966\",\n \"text\": \"def test_get_account_analytics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2bcb75ddb3b932aa7bab229253b583\",\n \"score\": \"0.51556665\",\n \"text\": \"def test_case_4(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f464cac8f81208d141ebf17c1a20cacd\",\n \"score\": \"0.51537263\",\n \"text\": \"def test_functionality(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fd953d06a0aa2f1c6ab23ae66d95a8\",\n \"score\": \"0.515275\",\n \"text\": \"def test_redundancy_increased_when_not_max(self, mock_client):\\n n_answers = 3\\n target = 'example.com'\\n task = self.ctx.create_task(n_answers, target, max_answers=4)\\n for i in range(n_answers):\\n TaskRunFactory.create(task=task, info={\\n 'reference': i,\\n 'control_number': i,\\n 'comments': ''\\n })\\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\\n fake_search = MagicMock()\\n fake_search.return_value = []\\n mock_client.search_annotations = fake_search\\n self.z3950_analyst.analyse(result.id)\\n assert_equal(mock_client.create_annotation.called, False)\\n\\n updated_task = self.task_repo.get_task(task.id)\\n assert_equal(updated_task.n_answers, n_answers + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927d00ec51cb64265439588d482b3f5f\",\n \"score\": \"0.5147975\",\n \"text\": \"def testCaseTBA(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96093b7512519cf8aa209c17e16fbbd9\",\n \"score\": \"0.51442605\",\n \"text\": \"def test_invalid_usage_limits_count(self):\\n kwargs = {'usage_limits_count': 'invalid'}\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n payloads.CheckResponsePayload,\\n **kwargs\\n )\\n\\n payload = payloads.CheckResponsePayload()\\n args = (payload, 'usage_limits_count', 'invalid')\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n setattr,\\n *args\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0605695f28a7671760cd03cd4dd896e0\",\n \"score\": \"0.5142084\",\n \"text\": \"def test_redundancy_not_increased_when_max(self, mock_client):\\n n_answers = 3\\n target = 'example.com'\\n task = self.ctx.create_task(n_answers, target, max_answers=3)\\n for i in range(n_answers):\\n TaskRunFactory.create(task=task, info={\\n 'reference': i,\\n 'control_number': i,\\n 'comments': ''\\n })\\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\\n fake_search = MagicMock()\\n fake_search.return_value = []\\n mock_client.search_annotations = fake_search\\n self.z3950_analyst.analyse(result.id)\\n assert_equal(mock_client.create_annotation.called, False)\\n\\n updated_task = self.task_repo.get_task(task.id)\\n assert_equal(updated_task.n_answers, n_answers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61b8cf2b199172f15801994e9c81fea\",\n \"score\": \"0.513838\",\n \"text\": \"def test_api_loss_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001da76050180758b4e0921641d6f7fe\",\n \"score\": \"0.51352376\",\n \"text\": \"def test_max_local_block_devices_0_force_bfv(self):\\n self.flags(max_local_block_devices=0)\\n server = self._build_server()\\n ex = self.assertRaises(api_client.OpenStackApiException,\\n self.admin_api.post_server,\\n {'server': server})\\n self.assertEqual(400, ex.response.status_code)\\n self.assertIn('You specified more local devices than the limit allows',\\n str(ex))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":791,"cells":{"query_id":{"kind":"string","value":"ec66effa18f2468179bea5514cc33f27"},"query":{"kind":"string","value":"Changes each face when a turn is made"},"positive_passages":{"kind":"list like","value":[{"docid":"fbd8f96f5e48ea6fb17378d7c6c7e8a1","score":"0.0","text":"def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\n falseSide1, falseSide2,\n falseCopy1, falseCopy2,\n trueSide1, trueSide2, \n trueCopy1, trueCopy2,\n twoSide1, twoSide2, \n twoCopy1, twoCopy2,\n ):\n if numMoves == 1:\n if not isPrime:\n side[falseSide1][falseSide2] = copy1[falseCopy1][falseCopy2]\n elif isPrime:\n side[trueSide1][trueSide2] = copy2[trueCopy1][trueCopy2]\n elif numMoves == 2:\n side[twoSide1][twoSide2] = copy3[twoCopy1][twoCopy2]","title":""}],"string":"[\n {\n \"docid\": \"fbd8f96f5e48ea6fb17378d7c6c7e8a1\",\n \"score\": \"0.0\",\n \"text\": \"def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\\n falseSide1, falseSide2,\\n falseCopy1, falseCopy2,\\n trueSide1, trueSide2, \\n trueCopy1, trueCopy2,\\n twoSide1, twoSide2, \\n twoCopy1, twoCopy2,\\n ):\\n if numMoves == 1:\\n if not isPrime:\\n side[falseSide1][falseSide2] = copy1[falseCopy1][falseCopy2]\\n elif isPrime:\\n side[trueSide1][trueSide2] = copy2[trueCopy1][trueCopy2]\\n elif numMoves == 2:\\n side[twoSide1][twoSide2] = copy3[twoCopy1][twoCopy2]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ffc8669634c0df6b38b564f4c3edc344","score":"0.6458799","text":"def __init__(self):\n self.face = 0","title":""},{"docid":"0695201c5269bde707b3ec1907317f36","score":"0.6264643","text":"def changeTurn(self):\n self._players[self._current].deactivateAll()\n self._current += 1\n self._current %= 2\n self._players[self._current].activateAll()","title":""},{"docid":"5ca7ba0f0334c899a03277ab756a5f20","score":"0.6256967","text":"def faces(self, faces_list):\n self._faces = faces_list.copy()","title":""},{"docid":"d429617ce921bc8119fb2e901c727028","score":"0.62325376","text":"def faces(self) -> int:\n ...","title":""},{"docid":"1c1c70ce28713225d7cbc55049a7fbc4","score":"0.6092525","text":"def turn(self):\n pass","title":""},{"docid":"e08a8bc7b41d149c96a19da9d6335b8c","score":"0.60666806","text":"def face_north():\n while not_facing_north():\n turn_left()","title":""},{"docid":"e08a8bc7b41d149c96a19da9d6335b8c","score":"0.60666806","text":"def face_north():\n while not_facing_north():\n turn_left()","title":""},{"docid":"f0baaefbddfd7d10faccd30853b301b8","score":"0.60647076","text":"def setFaceValue(self, faceValue): # Private Method Please\n\t\tself.faceValue = faceValue","title":""},{"docid":"a177cbec4a85d538f345a12d654ba86f","score":"0.59847814","text":"def cube_perform_action(self, action):\n\n if action == 'left':\n self.turn_left()\n elif action == 'right':\n self.turn_right()\n elif action == 'front':\n self.turn_front()\n elif action == 'back':\n self.turn_back()\n elif action == 'up':\n self.turn_up()\n elif action == 'down':\n self.turn_down()\n self.__faces__ = [self.front(), self.back(), self.left(), self.right(), self.up(), self.down()]","title":""},{"docid":"a061d87f98199f1f9668b8d1a691a827","score":"0.5982775","text":"def __init__(self, model, size, faces_list):\n self.model = model\n self.screen = pygame.display.set_mode(size)\n self.faceImages = []\n for face in faces_list: #puts images of all available faces in a list\n self.faceImages.append(pygame.image.load('faces/'+face))\n for i in range(len(self.faceImages)): #scales the images to 80 x 80\n self.faceImages[i] = pygame.transform.scale(self.faceImages[i],(80,80))\n self.my_font = pygame.font.SysFont('Comic Sans MS', 32)\n self.text_playing = self.my_font.render('press spacebar to stack heads',\n False, (200,200,200))\n self.text_won = self.my_font.render('You Win! Press Enter to play again'\n , False, (127,255,63))","title":""},{"docid":"bff17381e1e3637051f3ef1d1ebe8330","score":"0.5980718","text":"def flip_card(self):\n if self.facedown:\n self.image = self.face\n self.facedown = False\n else:\n self.image = self.back\n self.facedown = True","title":""},{"docid":"700dc088225caa6f7c58cafc3c90f8cd","score":"0.5977117","text":"def _flip_all_card_face_up(self):\n card_update_dict = {}\n for card in self:\n card_update_dict[card.value]='U'\n card.face = 'U'\n new_event = gamestate.Event(\n type='Flip',\n cards_status=card_update_dict,\n )\n self._update_event_handle(new_event)","title":""},{"docid":"24eaf5602f6a342992aedd3c61992df8","score":"0.5971587","text":"def rotate_face(self, face):\n new_face = [[], [], []]\n for i in reversed(range(self.size)):\n for y in range(self.size):\n new_face[self.size - 1 - i].append(face[i][self.size - 1 - y])\n return new_face","title":""},{"docid":"f51b0f91c874812753159befb0d33d42","score":"0.5963223","text":"def update_ai_turn(self):\n self.is_ai_turn ^= True","title":""},{"docid":"e8aa1309b0c0dfe363ec073df8c793fa","score":"0.59623414","text":"def next(self):\n\n for i in range(len(self.face_names)):\n\n if self.face_names[i][0] == \"\":\n\n display_size_image = 600\n display_scale_image = display_size_image / 1000\n\n img_full = cv2.imread(self.face_rcg.get_known_face_files()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]) #, cv2.IMREAD_UNCHANGED)\n scale_percent = display_scale_image*1000/img_full.shape[1] # percent of original size\n width = int(img_full.shape[1] * scale_percent )\n height = int(img_full.shape[0] * scale_percent )\n dim = (width, height)\n # resize image\n img_bgr = cv2.resize(img_full, dim)\n\n (top, right, bottom, left) = self.face_rcg.get_known_face_locations()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]\n\n top = int(display_scale_image*top)\n right = int(display_scale_image*right)\n bottom = int(display_scale_image*bottom)\n left = int(display_scale_image*left)\n\n # Draw a box around the face\n cv2.rectangle(img_bgr, (left, top), (right, bottom), (255, 255, 255), 2)\n\n # Draw a label with a name below the face\n cv2.rectangle(img_bgr, (left, bottom), (right+120, bottom+30), (255, 255, 255), cv2.FILLED)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(img_bgr, self.face_ids[i][0], (left + 6, bottom + 25), font, 0.6, (0, 0, 0), 2)\n\n # change color channel\n b,g,r = cv2.split(img_bgr)\n img = cv2.merge((r,g,b))\n\n # Convert the Image object into a TkPhoto object\n im = Image.fromarray(img)\n imgtk = ImageTk.PhotoImage(image=im) \n self.canvas.configure(image=imgtk)\n self.canvas.image = imgtk\n\n\n self.status.set(\"Please type in the name of \" + self.face_ids[i][0] + \" and click 'Save' and 'Next'\")\n break\n\n else:\n\n if self.new_file_list:\n\n self.new_file_list = False\n\n self.face_rcg.encode([file[1] for file in self.file_list])\n\n self.face_clusters = self.face_rcg.cluster_faces_dbscan()\n #self.face_clusters = self.face_rcg.cluster_faces()\n self.face_ids,self.face_names,self.face_cluster_id = self.face_rcg.get_processed_clusters(self.face_clusters)\n\n self.status.set(\"Faces recognized. Click 'Next'\")\n\n \"\"\"else:\n\n self.face_rcg.write_images_with_names(\"/home/manuel/Pictures/test_face_recognition/known/\")\n self.status.set(\"Finished. Type path and click 'Get file list' to start new.\")\"\"\"","title":""},{"docid":"128d7614f3f4e709ea448f91f67b08b3","score":"0.59122604","text":"def alternateFace(self, alternateFace):\n pass","title":""},{"docid":"128d7614f3f4e709ea448f91f67b08b3","score":"0.591115","text":"def alternateFace(self, alternateFace):\n pass","title":""},{"docid":"2591ed688bb5b393fd66d89278a57ae7","score":"0.5884116","text":"def cycle_state_variable(self, step):\n if self.detection_state is DetectionState.FACELETS:\n self.curr_facelet_index = (self.curr_facelet_index + step) % len(self.facelets)\n if self.detection_state is DetectionState.COLORS:\n self.write_data()\n self.curr_face_index = (self.curr_face_index + step) % len(self.faces)","title":""},{"docid":"da933096f83382cb6f8e3a32d9ac169c","score":"0.58704185","text":"def before_turn(self):","title":""},{"docid":"86474847e2f3e487405a26113e2d7bb8","score":"0.58687705","text":"def make_turn(self, current_set):\n pass","title":""},{"docid":"603495cb7320afd8f075a41dd5a741b2","score":"0.57598776","text":"def flip_turn(self):\n self.current_player = self.other_player","title":""},{"docid":"1bba631564d46848b6f1088d1c18c4f5","score":"0.57119805","text":"def _turn(self):\n\n if self.dyingCounter:\n return\n for i in range(0, len(self.idle_anim)):\n self.idle_anim[i] = pygame.transform.flip(self.idle_anim[i], True, False)\n for i in range(0, len(self.sprint_anim)):\n self.sprint_anim[i] = pygame.transform.flip(\n self.sprint_anim[i], True, False\n )\n for i in range(0, len(self.death_anim)):\n self.death_anim[i] = pygame.transform.flip(self.death_anim[i], True, False)","title":""},{"docid":"6784a6fa82f3e8bc5c6a69e64aaa58aa","score":"0.5680338","text":"def face_west():\n while not_facing_west():\n turn_left()","title":""},{"docid":"6784a6fa82f3e8bc5c6a69e64aaa58aa","score":"0.5680338","text":"def face_west():\n while not_facing_west():\n turn_left()","title":""},{"docid":"549706dee04edf2bd2a164a4b62a7607","score":"0.5671197","text":"def change_turn(self):\n player = self.get_player_turn()\n if player == 1:\n self.set_player_turn(2) # change to player 2's turn\n elif player == 2:\n self.set_player_turn(1) # change to player 1's turn","title":""},{"docid":"aac0ed475a41f2c2daddd82cfcd1d9fe","score":"0.5666877","text":"def turn_to_face_v(self, direction=Vector3()):\n if direction.is_zero_vector():\n return\n direction = normalize(direction)\n forward = self.get_forward()\n axis = cross(forward, direction)\n angle = math.degrees(math.acos(dot(forward, direction)))\n self.rotate(angle=angle, axis=axis)","title":""},{"docid":"5ea4cf811fc403de72b690b5f67d56ef","score":"0.56437415","text":"def face_south():\n while not_facing_south():\n turn_left()","title":""},{"docid":"5ea4cf811fc403de72b690b5f67d56ef","score":"0.56437415","text":"def face_south():\n while not_facing_south():\n turn_left()","title":""},{"docid":"b5e928f657c1906b34842d1250d8fff6","score":"0.56321895","text":"def circularFace(self, circularFace):\n pass","title":""},{"docid":"189760f95d5ce7e234edde57548b548b","score":"0.5585415","text":"def face_east():\n while not_facing_east():\n turn_left()","title":""},{"docid":"189760f95d5ce7e234edde57548b548b","score":"0.5585415","text":"def face_east():\n while not_facing_east():\n turn_left()","title":""},{"docid":"ed4b503b10258e58a5c6803affaa2ed9","score":"0.55851406","text":"def update_turn(self):\n if self.get_turn() is True:\n self._turn = False\n elif self.get_turn() is False:\n self._turn = True","title":""},{"docid":"bb44be567a173befa4572a4772b065a6","score":"0.55826795","text":"def changePerspective(self):\n\n # We only need to loop through the 'top' of the board\n for index in range(32):\n # Set our current row and column with respect to index\n row = index//8\n column = index % 8\n # If we are currently on one of our peices\n if self.boardArray[row][column].isupper():\n flipPeice = self.boardArray[row][column].lower() # Set it as an enemy peice\n else:\n flipPeice = self.boardArray[row][column].upper() # Set enemy peice as friendly otherwise\n\n # We compute the same evaluations as above but for the peice at the opposite corner\n # If currently one of our peices\n if self.boardArray[7-row][7-column].isupper():\n self.boardArray[row][column] = self.boardArray[7-row][7-column].lower() # Set current peice to enemy peice\n else:\n self.boardArray[row][column] = self.boardArray[7-row][7-column].upper() # Set enemy peice as friendly otherwise\n\n self.boardArray[7-row][7-column] = flipPeice # Set peice to flipped peice\n\n kingFlipped = self.kingPosition_White # Set temporary kingFlipped variable for white position\n # Set new white and black king positions\n self.kingPosition_White = 63 - self.kingPosition_Black\n self.kingPosition_Black = 63 - kingFlipped","title":""},{"docid":"488f70727fcc85e584cfc0d19d827c55","score":"0.5572259","text":"def change_game(self,game):\r\n for i in range(0,len(game)):\r\n if(game[i] == UNEXPOSED):\r\n self.replace_character_at_index(i,UNEXPOSED)\r\n elif(game[i] == POKEMON):\r\n self.replace_character_at_index(i,POKEMON)\r\n elif(game[i] == FLAG):\r\n self.replace_character_at_index(i,FLAG)\r\n else:\r\n self.replace_character_at_index(i,game[i])","title":""},{"docid":"b8370e789cd50eff989a7a93fb7d4fe2","score":"0.5558109","text":"def SetFaceAttribute(*args):\r\n return _pynewton.TreeCollision_SetFaceAttribute(*args)","title":""},{"docid":"b69495d8208a4864bde80fc6e5d84f93","score":"0.5540498","text":"def on_face_known(self, faces):\n for person in faces:\n if self.is_greeting_appropriate(person.name):\n self.say(\"Hello, {}!\".format(person.name))","title":""},{"docid":"47e9fdbf3407bf11b9cddd6bc48483fa","score":"0.5509818","text":"def flip_faceverts(faces):\n flipped_faces = []\n for face in faces:\n flipped_face = []\n for vert in reversed(face):\n flipped_face.append(vert)\n flipped_faces.append(tuple(flipped_face))\n return flipped_faces","title":""},{"docid":"7863737e7a3789fdc123f7429e7e3abc","score":"0.5505532","text":"def turn(self, angle):\r\n self.angle += math.radians(angle)","title":""},{"docid":"170d87b5663fb2ec759bdc462737210a","score":"0.55026007","text":"def update(self):\n self.game_manager.play_turn()","title":""},{"docid":"6d48510aa9b5615f55195f6c7d5543b6","score":"0.54960114","text":"def GetFaces(self):\n ...","title":""},{"docid":"7cd6d059d0b5ecc8a587f69906f0edd0","score":"0.54950124","text":"def do_turn(self, player_index, game_info):","title":""},{"docid":"ff37c865ae0d503c86e439146c2d490a","score":"0.549461","text":"def poppy_turn(poppy, facee, frame):\r\n # Format of facee: [x, y, w, h]\r\n # print(facee)\r\n w = facee[2]\r\n x = facee[0] + 0.5 * w\r\n\r\n f_dist = find_distance(w)\r\n theta = find_angle(x - x_centre, f_dist)\r\n\r\n # print(\"angle: {0}\".format(theta)) \r\n # print(\"distance: {0}\".format(f_dist))\r\n # print()\r\n cv2.putText(frame, \"angle: \" + str(theta), \r\n (10, 100),\r\n cv2.FONT_HERSHEY_SIMPLEX,\r\n 1, (0, 255, 0), 2)\r\n\r\n if -TOLERANCE < theta < TOLERANCE:\r\n wave()\r\n else:\r\n poppy.head_z.goto_position(theta, wait=True)","title":""},{"docid":"ee602941d266468d60ff79ca0ad7f44d","score":"0.5484665","text":"def switch_turn(self):\n if self.whose_turn == self.player1:\n self.whose_turn = self.player2\n else:\n self.whose_turn = self.player1","title":""},{"docid":"9fc217b92dd7f13d55b1d8fe06e5bf9f","score":"0.54783547","text":"def switch_turn(self):\n self.black_turn = not self.black_turn","title":""},{"docid":"6c5dd10bcddb3d490fb9bd7562ec5609","score":"0.5469843","text":"def find_faces(self):\n if self.is_face:\n self.image = self.get_image()\n self.is_face = False\n self.rotate_right()\n else:\n if self.is_gray:\n self.set_image_colorful()\n self.is_gray = True\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\n faces = FACE_CASCADE.detectMultiScale(gray, 1.3, 5)\n for (x, y, w, h) in faces:\n cv2.rectangle(self.image, (x, y), (x+w, y+h), (255, 0, 0), 2)\n self.is_face = True","title":""},{"docid":"10358d736ae3f6c576c6bbcb9c6a8731","score":"0.5469517","text":"def rot(cube, face, direction):\n\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4] = np.roll(cube[0:4], -direction, axis=0)\n cube[4] = np.rot90(cube[4], k=-direction)\n cube[5] = np.rot90(cube[5], k=direction)\n elif face == Man.L.value:\n sl = [cube[Man.F.value], cube[Man.D.value], cube[Man.B.value, ::-1], cube[Man.U.value]]\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value] = sl[0]\n cube[Man.D.value] = sl[1]\n cube[Man.B.value, ::-1] = sl[2]\n cube[Man.U.value] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value], cube[Man.R.value], cube[Man.D.value], cube[Man.L.value]])\n sl = np.rot90(sl, k = -direction, axes=(1,2))\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value] = sl[0]\n cube[Man.R.value] = sl[1]\n cube[Man.D.value] = sl[2]\n cube[Man.L.value] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=direction)\n return cube","title":""},{"docid":"7300ea72bb79fa732ac3314284a70b79","score":"0.54683673","text":"def _update_turn(self) -> None:\n self._player = self._next_player(self._player)\n self._turns += 1","title":""},{"docid":"d90968dcc01c6270f40ffe213d050cb8","score":"0.54581696","text":"def do_viewfaces(self, arg):\n if len(self.inner) == 0:\n print(\"There are no faces to view. 1. loadchoices default 2. alterproperty 3. loadfaces personal 4. savechoices\")\n if len(self.inner) > con.MAX_PORTRAITS:\n print(\"Over 50 images have been selected. If you would like to view all fifty then adjust def do_viewfaces(self, arg)\")\n print(\"Otherwise, narrow your selection.\")\n return False\n filenames = []\n for face in self.inner:\n filename = face[0][0:-3]\n filenames.append(\"{}png\\n\".format(filename))\n with open(con.SAVE_IMAGE_NAMES, 'w') as f:\n for filename in filenames:\n # print(filename)\n f.write(filename)\n # for face in self.inner:\n # print(\"elem[0]: \", face[0])\n # # ----\n # filename = face[0][0:-3]\n # filename = \"{}png\".format(filename)\n # filepath = os.path.join(IMAGES_DIRECTORY, filename)\n # # print(filepath)\n # print(filepath)\n # img = mpimg.imread(filepath)\n # imgplot = plt.imshow(img)\n # plt.show()","title":""},{"docid":"81af7233c98af91768a4284a206a2ea5","score":"0.54511875","text":"def turn(self) -> None:\n #current position reset\n self.current = 0\n #increase sequence and play it\n self.addseq()\n self.playseq()","title":""},{"docid":"cdb50a9792a666e9020ee95cb88b7d81","score":"0.544412","text":"def turn_to_face_p(self, target=Point3()):\n my_pos = self.position()\n if my_pos == target:\n return\n direction = normalize(target - my_pos)\n self.turn_to_face_v(direction)","title":""},{"docid":"7cc4f5944b094acbfaeefcca5129c1f7","score":"0.5429818","text":"def draw(self):\n # rect_head = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n # pygame.draw.rect(screen, GREY + ALPHA, rect_head, 0)\n ''' I want to add two eyes! OR, use FACE and NECK! '''\n ball_face = (int((self.head[0] + 0.5) * UNIT_SIZE), int((self.head[1] + 0.5) * UNIT_SIZE))\n pygame.draw.circle(screen, GREY + ALPHA, ball_face, int(UNIT_SIZE / 2) - 1)\n if self.__direction == 'U':\n rect_neck = (self.head[0] * UNIT_SIZE, int((self.head[1] + 0.5) * UNIT_SIZE),\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n # ball_eye1 = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n # ball_eye2 = ((self.head[0] + 1) * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n elif self.__direction == 'D':\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n elif self.__direction == 'L':\n rect_neck = (int((self.head[0] + 0.5) * UNIT_SIZE), self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n else:\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n\n pygame.draw.rect(screen, GREY + ALPHA, rect_neck, 0)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye1, 5)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye2, 5)\n for body_node in self.body:\n rect_node = (body_node[0] * UNIT_SIZE, body_node[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n pygame.draw.rect(screen, GREY + ALPHA, rect_node, 0)","title":""},{"docid":"e005cb9924b5e7e58763ca9d7dd07829","score":"0.54284596","text":"def find_faces():\n\n labels = {}\n with open(\"./labels.pickle\", 'rb') as f:\n org_labels = pickle.load(f)\n labels = {v: k for k, v in org_labels.items()}\n\n face_cascade = CascadeClassifier(\n './haarcascade/haarcascade_frontalface_default.xml')\n recognizer = cv2.face.LBPHFaceRecognizer_create()\n recognizer.read(\"./trainer.yml\")\n\n lefteye_cascade = CascadeClassifier('./haarcascade/haarcascade_lefteye.xml')\n video_capture = cv2.VideoCapture(0)\n picture_counter = 0\n\n picture_flag = False\n while True:\n\n # Capture Frame by Frame\n ret, frame = video_capture.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(\n gray, scaleFactor=1.05, minNeighbors=8)\n face_dic = {\n \"known\": 0,\n \"total\": 0}\n \n # Draws a rectagle around the face\n for(x, y, w, h) in faces:\n face_dic[\"total\"] += 1\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = frame[y:y+h, x:x+w]\n id_, conf = recognizer.predict(roi_gray)\n eyes = lefteye_cascade.detectMultiScale(roi_gray)\n test = [i for i in eyes]\n if test:\n frame = cv2.rectangle(\n frame, (x, y,), (x+w, y+h), (0, 255, 0), 3)\n\n # conf is the confidence level that detected face has been trained\n if conf >= 85:\n print(id_)\n face_dic[\"known\"] += 1\n name = labels[id_]\n font = cv2. FONT_HERSHEY_SIMPLEX\n color = (255, 255, 255)\n stroke = 2\n cv2.putText(frame, name, (x, y), font, 1,\n color, stroke, cv2.LINE_AA)\n\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 0), 3)\n else:\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 255), 3)\n \n \n # Shows Number faces dectected/known in frame\n cv2.putText(frame, \"Number of faces detected: \" + str(\n face_dic[\"total\"]), (0, 100), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n cv2.putText(frame, \"Number of faces known: \" + str(\n face_dic[\"known\"]), (0, 120), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n\n # Dipsplay the resulting frame\n cv2.imshow('Video', frame)\n \n \n #Saves Picture \n if face_dic[\"known\"] == 0 and face_dic[\"total\"] >= 1:\n if not picture_flag:\n picture_counter += 1\n cv2.imwrite(\"./test_subjects/\" +\n str(picture_counter)+\".png\", frame)\n picture_flag = True\n picture_counter += 1\n if picture_counter == 120:\n picture_flag = False\n picture_counter = 0\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n video_capture.release()\n cv2.destroyAllWindows()","title":""},{"docid":"aca6c77e4382d682458f04ff0ba933f6","score":"0.5426279","text":"def roll(cube, face, direction):\n #print([\"L\", \"F\", \"R\", \"B\", \"U\", \"D\"][face] + str(direction))\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4, 0] = np.roll(cube[0:4, 0], -direction, axis=0)\n cube[Man.U.value] = np.rot90(cube[Man.U.value], k=-direction)\n elif face == Man.D.value:\n cube[0:4, -1] = np.roll(cube[0:4, -1], direction, axis=0)\n cube[Man.D.value] = np.rot90(cube[Man.D.value], k=-direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value, -1, :], cube[Man.R.value, :, 0],\n cube[Man.D.value, 0, ::-1], cube[Man.L.value, ::-1, -1]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value, -1, :] = sl[0]\n cube[Man.R.value, :, 0] = sl[1]\n cube[Man.D.value, 0, ::-1] = sl[2]\n cube[Man.L.value, ::-1, -1] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)#TODO: Use map function\n elif face == Man.B.value:\n sl = np.array([cube[Man.U.value, 0, :], cube[Man.R.value, :, -1], cube[Man.D.value, -1, ::-1], cube[Man.L.value, ::-1, 0]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.U.value, 0, :] = sl[0]\n cube[Man.R.value, :, -1] = sl[1]\n cube[Man.D.value, -1, ::-1] = sl[2]\n cube[Man.L.value, ::-1, 0] = sl[3]\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=-direction)#TODO: Use map function\n elif face == Man.L.value:\n sl = np.array([cube[Man.F.value, :, 0], cube[Man.D.value, :, 0], cube[Man.B.value, ::-1, -1], cube[Man.U.value, :, 0]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value, :, 0] = sl[0]\n cube[Man.D.value, :, 0] = sl[1]\n cube[Man.B.value, ::-1, -1] = sl[2]\n cube[Man.U.value, :, 0] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n elif face == Man.R.value:\n sl = np.array([cube[Man.F.value, :, -1], cube[Man.D.value, :, -1], cube[Man.B.value, ::-1, 0], cube[Man.U.value, :, -1]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.F.value, :, -1] = sl[0]\n cube[Man.D.value, :, -1] = sl[1]\n cube[Man.B.value, ::-1, 0] = sl[2]\n cube[Man.U.value, :, -1] = sl[3]\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=-direction)\n return cube\n #print(Man.__str__())","title":""},{"docid":"004a42186846ed8f1490595eec53dab7","score":"0.54207855","text":"def _run_initial_turn(self) -> None:","title":""},{"docid":"ba9e97f7f2908033657be66d1f35b5b9","score":"0.54142267","text":"def ChangeTurn(self):\r\n self.lsPlayer[self.turn] = self.ActivePlayer.copy()\r\n self.turn = (self.turn + 1) % self.numPlayers\r\n self.ActivePlayer = self.lsPlayer[self.turn].copy()\r\n if not self.CheckForValidMoves(self.ActivePlayer):\r\n self.ChangeTurn()","title":""},{"docid":"e3e708f7fd07391ba176ad2189cd3335","score":"0.5413524","text":"def faces(self):\n raise NotImplementedError(f\"faces not implemented for {type(self)}\")","title":""},{"docid":"36d5ea98cfd7541db5061101436cfeb6","score":"0.5406411","text":"def advance(self):\n self.x_shape_rot += self.x_rot_speed\n self.x_shape_rot %= 360\n self.y_shape_rot += self.y_rot_speed\n self.y_shape_rot %= 360\n self.z_shape_rot += self.z_rot_speed\n self.z_shape_rot %= 360\n self.updateGL()","title":""},{"docid":"fcb9346c79e67186b2b99f01f562d783","score":"0.53899795","text":"def turnLeft(self):","title":""},{"docid":"ce23e46ad1a8999548893082b51db47f","score":"0.5377675","text":"def setNext(self,game):\r\n self.nextFixture = game","title":""},{"docid":"85bf3aa342f06d6abe03dfabe04ff9e8","score":"0.53709286","text":"def faces(self):\r\n\t\tif self._faces is None:\r\n\t\t\tself._faces = [Face(self, i) for i in xrange(len(self.faceVertArray))]\r\n\t\treturn self._faces","title":""},{"docid":"4666aaedfe037607c73a5c0fa530b353","score":"0.53502315","text":"def targetFaces(self, targetFaces):\n pass","title":""},{"docid":"4666aaedfe037607c73a5c0fa530b353","score":"0.5349725","text":"def targetFaces(self, targetFaces):\n pass","title":""},{"docid":"9e7321b77232091343736848210ba289","score":"0.5335583","text":"def save_loop(camera, name: str) -> None:\n faces = []\n process_this = True\n face_to_be_saved = None\n while True:\n ret, frame = camera.read()\n if process_this:\n faces = prc.process_faces(frame)\n process_this = not process_this\n # changing name of each face to empty string\n message = ''\n for face in faces:\n face.assign_name('')\n if len(faces) == 1:\n face_to_be_saved = faces[0]\n elif len(faces) > 1:\n message = 'More than 1 face detected'\n else:\n message = 'No face detected'\n prc.mark_faces(frame, faces)\n cv2.putText(frame, message, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)\n cv2.imshow('Video', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n if cv2.getWindowProperty('Video',cv2.WND_PROP_VISIBLE) < 1:\n break;\n\n if face_to_be_saved is not None:\n save(face_to_be_saved, name)\n else:\n print(\"Couldn't save the face\")","title":""},{"docid":"f83a0ae88bdaf135a50b3fd54acc6388","score":"0.53346217","text":"def on_face_new(self, faces):\n\n if self.is_greeting_appropriate(\"new\"):\n self.say(\"I see a new person!, Hello stranger!\")","title":""},{"docid":"0a042e9a13a6051ebb7a09d57af4c404","score":"0.53295404","text":"def face_detect(self):\n self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n \n self.gray = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2GRAY)\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n \n # create an array of the positions of each face \n # and then iterates through it to draw a rectangle around all faces\n faces = self.face_cascade.detectMultiScale(self.gray, 1.3, 5)\n for (x, y, w, h) in faces:\n self.cv2img = cv2.rectangle(self.cv2img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n \n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n faceimg = Image.fromarray(self.cv2img)\n faceimg = ImageTk.PhotoImage(faceimg)\n self.panel.configure(image = faceimg)\n self.panel.image = faceimg\n \n #makes the button for crop faces visible\n self.btn.pack(side = 'right')\n\n self.states.append(self.cv2img)","title":""},{"docid":"05dddf2a206011046beb22ae40ed5ec9","score":"0.5327832","text":"def switch_turns(self):\n # TODO: This can be a one-liner, too lazy to do it now. Somehow it's\n # easier to write a comment about something that would take 10 seconds\n # to do than actually doing it\n if self.turn == self.tictactoe.player1:\n self.turn = self.tictactoe.player2\n else:\n self.turn = self.tictactoe.player1","title":""},{"docid":"8388d88bb7a30ab65434d4545028ee86","score":"0.53252715","text":"def flip(self):\r\n self.buffer,self.backbuffer = self.backbuffer,self.buffer\r\n self.Refresh()","title":""},{"docid":"264d4cbd2ab4610bba0e52cfc6791f00","score":"0.53248394","text":"def move_forward_or_back(self, face):\n\n while not ((self.get_face_distance(face) < 1.1) or (self.get_face_distance(face) > .9)):\n # checks if face is too small, then moves back\n if self.get_face_distance(face) < .9:\n self.move_bot(300)\n # checks if face is too big, then moves forward\n elif self.get_face_distance(face) > 1.1:\n self.move_bot(-300)\n self.motors = 6000\n self.tango.setTarget(self.MOTORS, self.motors)","title":""},{"docid":"8f1a405824a24625be6ae6172b8f61fd","score":"0.5323996","text":"def assBody(self):\n \n \n self.surfaceBody = self.spriteList[self.spriteIndex]\n self.surfaceBody.set_colorkey(constants.TRANS)","title":""},{"docid":"4717439d1124655b3c1bcf0d0ccb4854","score":"0.532059","text":"def update(self):\n self.surface.fill((0, 0, 0))\n # head\n self.surface.blit(pygame.transform.rotate(self.images[\"head\"],\n -90 * self.directions[0]),\n (CASE_SIZE[0] * self.coords[0][0],\n CASE_SIZE[1] * self.coords[0][1]))\n # body\n size_x, size_y = CASE_SIZE\n\n for i in range(len(self.coords[1:-1])):\n x, y = self.coords[i + 1]\n direction = self.directions[i + 1]\n direction_prec = self.directions[i]\n if direction == direction_prec: # straight\n self.surface.blit(self.rotate(\"body_straight\", -90*direction),\n (size_x * x, size_y * y))\n # turn right>top or botom>left\n if (direction == 0 and direction_prec == 1) or \\\n (direction == 3 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",0),\n (size_x * x, size_y * y))\n # turn left>top or botom>right\n if (direction == 0 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",-90),\n (size_x * x, size_y * y))\n # turn left>botom or top>right\n if (direction == 2 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 0):\n self.surface.blit(self.rotate(\"body_turn\",180),\n (size_x * x, size_y * y))\n # turn top>left or right>botom\n if (direction == 3 and direction_prec == 0) or \\\n (direction == 2 and direction_prec == 1):\n self.surface.blit(self.rotate(\"body_turn\",90),\n (size_x * x, size_y * y))\n # Tail\n self.surface.blit(pygame.transform.rotate(self.images[\"tail\"],\n -90 * self.directions[-2]),\n (size_x * self.coords[-1][0],\n size_y * self.coords[-1][1]))","title":""},{"docid":"9e9993859b70cb4a82352b878a93e3a4","score":"0.53183514","text":"def EditSkin(\n self,\n name: str = \"\",\n faces: tuple[Face] = (),\n edges: tuple[Edge] = (),\n elementFaces: tuple[MeshFace] = (),\n elementEdges: tuple[MeshEdge] = (),\n ):\n pass","title":""},{"docid":"4697beee66114fbfa28fcab3d916a7b4","score":"0.5313693","text":"def update(self, image):\n\n self._faces = []\n\n if (utils.is_gray(image)):\n image = cv2.equalizeHist(image)\n else:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n image = cv2.equalizeHist(image)\n\n min_size = utils.widthHeightDividedBy(image, 8)\n\n face_rects = self._face_classifier.detectMultiScale(\n image, self.scale_factor, self.min_neighbors, self.flags, min_size)\n\n if (face_rects is not None):\n for face_rect in face_rects:\n face = Face()\n face.face_rect = face_rect\n x, y, w, h = face_rect\n\n # look for an eye in the upper-left part of the face\n search_rect = (x+w//7, y, w*2//7, h//2)\n face.left_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an eye in the upper-right part of the face\n search_rect = (x + (w*4)//7, y, w*2//7, h//2)\n face.right_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an node in the middle part of the face\n search_rect = (x+w//4, y+h//4, w*2, h//2)\n face.nose_rect = self._detect_one_object(\n self._nose_classifier, image, search_rect, 32\n )\n\n # look for an mouth in the lower-middle part of the face\n search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)\n face.mouth_rect = self._detect_one_object(\n self._mouth_classifier, image, search_rect, 16\n )\n\n self._faces.append(face)","title":""},{"docid":"2f9a35c4e36f54cadb68c18c698cde3f","score":"0.5311607","text":"def faceSet(self):\r\n\t\tret = FaceSet(self, [])\r\n\t\tret.update(range(len(self.faceVertArray)))\r\n\t\treturn ret","title":""},{"docid":"7ee3af718f04065414eca6b36fe668b4","score":"0.53067994","text":"def face_amount(self, face_amount):\n\n self._face_amount = face_amount","title":""},{"docid":"b21fe36df6c0fae07d86a2b23bd7f9bb","score":"0.5304956","text":"def add_face_to_list(self, points):\n ids = [point.id for point in points]\n self.faces.append(ids)","title":""},{"docid":"a4bb2a8e7b4881fca6ca45efc0183e63","score":"0.53038234","text":"def online_face_recognition(profile_names, n_pictures=15):\n images = []\n labels = []\n label_names = []\n for i, name in enumerate(profile_names):\n p = load_profile(name)\n p = p[0:n_pictures, ]\n images += [p]\n labels += [np.ones(p.shape[0]) * (i + 1)]\n label_names += [name]\n faces = np.vstack(images)\n labels = np.hstack(labels).astype(np.int)\n # Generate model\n model = IncrementalKCenters(faces, labels)\n # Start camera\n cam = cv.VideoCapture(0)\n while True:\n ret_val, img = cam.read()\n grey_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n working_image = cv.bilateralFilter(grey_image, 9, 75, 75)\n working_image = cv.equalizeHist(working_image)\n working_image = cv.GaussianBlur(working_image, (5, 5), 0)\n box = face_haar_cascade.detectMultiScale(working_image)\n for b0 in box:\n x, y = b0[0], b0[1]\n x_range, y_range = b0[2], b0[3]\n # look for eye classifier\n local_image = img[y:(y + y_range), x:(x + x_range)]\n eye_box = eye_haar_cascade.detectMultiScale(local_image)\n if len(eye_box) == 0:\n cv.rectangle(img, tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4]),\n (0, 0, 255), 2)\n continue\n # select face\n local_image = grey_image[y:(y + y_range), x:(x + x_range)]\n x_t = preprocess_face(local_image)\n\n \"\"\"\n Centroids are updated here\n \"\"\"\n model.online_ssl_update_centroids(x_t)\n p1, p2 = tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4])\n\n \n \"\"\"\n HardHFS solution is computed here\n \"\"\"\n f = model.online_ssl_compute_solution()\n lab = np.argsort(f)\n \n \n \"\"\"\n Change False by something else to be able to disregard faces it cannot recognize (question 3.4)\n \"\"\" \n \n mean_dist_new_to_labeled = np.mean(distance.cdist(np.array([model.centroids[model.last_face]]), faces)[0])\n \n mean_dist_existing=0\n for person in set(labels):\n #Average distance between the faces of a same person\n mean_dist_existing += np.mean(distance.cdist(faces[labels==person], faces[labels==person])[0])\n mean_dist_existing=mean_dist_existing/len(list(set(labels))) \n \n if any(f==0.) and mean_dist_new_to_labeled>mean_dist_existing:\n color = (100, 100, 100)\n txt = \"unknown\"\n cv.putText(img, txt, (p1[0], p1[1] - 5), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, color)\n else:\n for i, l in enumerate(lab):\n color = [(0, 255, 0), (255, 0, 0), (0, 0, 255)][l]\n txt = label_names[l] + \" \" + ('%.4f' % np.abs(f[l]))\n cv.putText(img, txt, (p1[0], p1[1] - 5 - 10 * i), cv.FONT_HERSHEY_COMPLEX_SMALL,\n 0.5 + 0.5 * (i == f.shape[0] - 1), color)\n cv.rectangle(img, p1, p2, color, 2)\n cv.imshow(\"cam\", img)\n key = cv.waitKey(1)\n if key in [27, 101]:\n break\n if key == ord('s'):\n # Save face\n print('saved')\n cv.imwrite(\"frame.png\", img)\n ## cv.waitKey(1)\n cv.destroyAllWindows()","title":""},{"docid":"5a8f35e3b27d9bfb457cba006ae270ae","score":"0.5301556","text":"def turn():\n global o_both_steering\n\n o_both_steering.on_for_rotations(100, SpeedPercent(40), 1) # OPTIMAL VALUE 1","title":""},{"docid":"391dc969f17266ea0bca1dc38375864f","score":"0.5300656","text":"def generator(surface, angles):\n original_surf = surface.copy()\n for angle in angles:\n x, y = self.rect.center\n self.surface = pygame.transform.rotate(original_surf, angle)\n self.instant_move(x, y)\n yield True","title":""},{"docid":"f6eceb20ea200d1fa8569da8eae1e7bc","score":"0.5300277","text":"def updateCameraVectors(self):\n # Calculate the new Front vector\n x = np.cos(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n y = np.sin(np.radians(self.Pitch))\n z = np.sin(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n \n front = np.array([x, y, z], dtype=GLfloat)\n print(front)\n self.Front = front / np.sqrt(x**2 + y**2 + z**2)\n print(self.Front)\n \n temp = np.cross(self.Front, self.WorldUp)\n self.Right = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Right)\n \n temp = np.cross(self.Right, self.Front)\n self.Up = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Up)\n \n \n \n \n # front = glm.vec3(x, y, z)\n # print('*=========')\n # self.Front = np.array(glm.normalize(front), dtype=GLfloat)\n # print(self.Front)\n # print('#=========')\n #\n # # Also re-calculate the Right and Up vector\n # # Normalize the vectors, because their length gets closer to 0 the more you\n # # look up or down which results in slower movement.\n # print('**=========')\n # temp = np.cross(self.Front, self.WorldUp)\n # print(temp)\n # print('##=========')\n # self.Right = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n #\n # print('***=========')\n # temp = np.cross(self.Right, self.Front)\n # print(temp)\n # self.Up = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n # print('###=========')","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300243","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"ad3aa8cbac3e97b9e7256bd79a6f9a6c","score":"0.5299885","text":"def next_turn(self):\n temp = self.current_player\n self.current_player = self.opponent\n self.opponent = temp","title":""},{"docid":"57f4d58e0f16c22078ce9dd38381e616","score":"0.52914584","text":"def test_batch_redetect_with_one_face(self):\n for detector in self.detectors:\n with self.subTest(detectorType=detector.detectorType):\n detection = detector.detectOne(image=VLIMAGE_ONE_FACE)\n redetect = detector.redetect(\n images=[ImageForRedetection(image=VLIMAGE_ONE_FACE, bBoxes=[detection.boundingBox.rect])]\n )[0]\n self.assertFaceDetection(redetect, VLIMAGE_ONE_FACE)","title":""},{"docid":"37798494830af70cc4a3e549576796e7","score":"0.52862424","text":"def build_face():\n masterScale = 0.2\n create_hooks(masterScale)\n inputHooks = create_joint_drivers(masterScale)\n\n for poseInfo in faceposes:\n zone = poseInfo['zone']\n pose = poseInfo['pose']\n poseDriver = poseInfo['driver']\n poseRange = poseInfo['mapping']\n poseOverrides = poseInfo['overrides']\n poseOverrideMaps = poseInfo['overridemaps']\n\n create_pose_node(pose, zone, inputHooks, masterScale, poseDriver, poseRange, poseOverrides, poseOverrideMaps)\n print 'done'","title":""},{"docid":"7596177b3c91fc479cf4def251516cac","score":"0.5286083","text":"def UpdateRectColors(self, selectedRectId):\n\n for (rectId, rect) in enumerate(self.rects):\n\n (x, y, w, h) = rect\n color = (0,255,0) if rectId == selectedRectId-1 else (0,0,255) #selection is 1 based\n\n cv2.rectangle(self.img500, (x, y), ((x + w), (y + h)), color, 2)\n\n # show the face number\n cv2.putText(self.img500,\n \"Face #{}\".format(rectId + 1),\n (x - 10, y - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n\n cv2.imshow(self.faceSelectionWin, self.img500)","title":""},{"docid":"07ac0645a023800fc4dcf11143ec82b7","score":"0.5282528","text":"def setup_face_indices(self, reset=True):\n if reset or not self.fis:\n fa = self.domain.get_facets(force_faces=True)[1]\n\n if self.faces:\n faces = self.faces\n else:\n faces = self.edges\n\n self.fis = {}\n for ig in self.igs:\n rfaces = faces[ig]\n fi = fa.indices[rfaces]\n assert_(nm.all(fi[:,0] == ig))\n self.fis[ig] = fi[:,1:].copy()","title":""},{"docid":"8e58cb333ee43454817f059a30915849","score":"0.5273543","text":"def another_turn(self, pit: PitData) -> None:\n self.update_color(pit, 0)","title":""},{"docid":"4fc7f93fc6c3dd1ed8fcd5dd822f522a","score":"0.52639437","text":"def on_draw(self):\n arcade.start_render()","title":""},{"docid":"4b0b0f3d2a4cf4ba6735ee43f6bad2a4","score":"0.5263633","text":"def update_turn(self):\n self.p1.turn = not self.p1.turn\n self.p2.turn = not self.p2.turn\n self.current_player = self.p1 or self.p2","title":""},{"docid":"65698da0371ffe24258d721bc412f014","score":"0.52610844","text":"def updateCams(self, interface=\"match\"):\n activecams = getActiveCams(len(self.cameras[interface]))\n for activeind in activecams:\n camera = self.cameras[interface][activeind]\n if camera in self.gridded:\n camera.updateImgOnLabel()","title":""},{"docid":"09cf5d165983357f2dcc6a505dae41e3","score":"0.5259303","text":"def RubiksCube():\n solved = False\n while (input(\"Start game?: \") == \"yes\") and (not solved):\n c = Cube()\n c.shuffle()\n plotCube(c)\n plt.ion()\n plt.show()\n plt.pause(1)\n # pause needed to update matplotlib plots with keyboard inputs\n print(\"The possible faces to rotate are: U,D,L,R,F,B.\\n The possible directions to rotate are: clockwise, counterclockwise.\\n The possible numbers of rotations are: 1,2,3.\\n\")\n while not solved:\n interact(c)\n plt.close()\n plotCube(c)\n plt.show()\n plt.pause(1)\n if c._p == Cube()._p:\n solved = True\n if solved:\n print(\"You win!\")","title":""},{"docid":"0a4f80d41e6e547f5490b5de12845018","score":"0.52588135","text":"def get_any_face(self):\n return choice(list(self.faces()))","title":""},{"docid":"f2d84dce15832a9e4b877937b180ce8e","score":"0.5258472","text":"def Turn(self, amount):\n \n self.Angle -= amount\n self.Angle %= 360\n\n self.Image = pygame.transform.rotate(self.Orig, self.Angle)\n self.UpdateMask()\n \n newSize = self.Image.get_size()\n self.ModX = -(newSize[0]-self.Size[0])/2\n self.ModY = -(newSize[1]-self.Size[1])/2","title":""},{"docid":"ee707779d583c98c6499bd7652922ed3","score":"0.524997","text":"def toggle_turn(self):\n if self._turn == \"player1\":\n self._turn = \"player2\"\n else:\n self._turn = \"player1\"","title":""},{"docid":"c18c82af0b0743c846de867ee1593851","score":"0.52392316","text":"def update_surface(self):\n self.surface = pygame.surfarray.make_surface(self.arr)","title":""},{"docid":"2a23ba794220991e6386c2a7006d82da","score":"0.5238691","text":"def getSurface(self):\n self.frame += 1\n if self.frame == 3:\n self.isFirstPic = not self.isFirstPic\n self.frame = 0\n\n if self.direction == 0:\n self.surface = Pacman.images[self.isFirstPic]\n elif self.direction == 1:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 90)\n elif self.direction == 2:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 180)\n elif self.direction == 3:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 270)","title":""},{"docid":"9a00db76457ab7532cc3af1fdac12f64","score":"0.5236641","text":"def turn_inc(self) -> None:\n self.__current_turn += 1","title":""},{"docid":"b7e1b59538be1a46c3c180e932f6b9ca","score":"0.52359444","text":"def draw_changing(self):","title":""},{"docid":"20f025928f844ba5664d57864501e178","score":"0.5231487","text":"def turn(self):\n\n #print(self.coords[0] ,self.dx,self.dy, self.field.width,self.field.height)\n mis=list(self.field.mice.keys())\n if len(mis)>0:\n x=self.coords[0][0]\n y=self.coords[0][1]\n mx=1000\n my=1000\n for (tx,ty) in mis:\n if abs(x-tx)+abs(y-ty)<abs(x-mx)+abs(y-my):\n mx=tx\n my=ty\n\n #print(mx,my)\n if self.coords[0][0] < mx:\n if self.dx!=1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n \n\n elif self.coords[0][0] > mx:\n if self.dx!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n else:\n if self.coords[0][1] < my:\n if self.dy!=1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n elif self.coords[0][1] > my:\n if self.dy!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()","title":""}],"string":"[\n {\n \"docid\": \"ffc8669634c0df6b38b564f4c3edc344\",\n \"score\": \"0.6458799\",\n \"text\": \"def __init__(self):\\n self.face = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0695201c5269bde707b3ec1907317f36\",\n \"score\": \"0.6264643\",\n \"text\": \"def changeTurn(self):\\n self._players[self._current].deactivateAll()\\n self._current += 1\\n self._current %= 2\\n self._players[self._current].activateAll()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca7ba0f0334c899a03277ab756a5f20\",\n \"score\": \"0.6256967\",\n \"text\": \"def faces(self, faces_list):\\n self._faces = faces_list.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d429617ce921bc8119fb2e901c727028\",\n \"score\": \"0.62325376\",\n \"text\": \"def faces(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1c70ce28713225d7cbc55049a7fbc4\",\n \"score\": \"0.6092525\",\n \"text\": \"def turn(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08a8bc7b41d149c96a19da9d6335b8c\",\n \"score\": \"0.60666806\",\n \"text\": \"def face_north():\\n while not_facing_north():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08a8bc7b41d149c96a19da9d6335b8c\",\n \"score\": \"0.60666806\",\n \"text\": \"def face_north():\\n while not_facing_north():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0baaefbddfd7d10faccd30853b301b8\",\n \"score\": \"0.60647076\",\n \"text\": \"def setFaceValue(self, faceValue): # Private Method Please\\n\\t\\tself.faceValue = faceValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a177cbec4a85d538f345a12d654ba86f\",\n \"score\": \"0.59847814\",\n \"text\": \"def cube_perform_action(self, action):\\n\\n if action == 'left':\\n self.turn_left()\\n elif action == 'right':\\n self.turn_right()\\n elif action == 'front':\\n self.turn_front()\\n elif action == 'back':\\n self.turn_back()\\n elif action == 'up':\\n self.turn_up()\\n elif action == 'down':\\n self.turn_down()\\n self.__faces__ = [self.front(), self.back(), self.left(), self.right(), self.up(), self.down()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a061d87f98199f1f9668b8d1a691a827\",\n \"score\": \"0.5982775\",\n \"text\": \"def __init__(self, model, size, faces_list):\\n self.model = model\\n self.screen = pygame.display.set_mode(size)\\n self.faceImages = []\\n for face in faces_list: #puts images of all available faces in a list\\n self.faceImages.append(pygame.image.load('faces/'+face))\\n for i in range(len(self.faceImages)): #scales the images to 80 x 80\\n self.faceImages[i] = pygame.transform.scale(self.faceImages[i],(80,80))\\n self.my_font = pygame.font.SysFont('Comic Sans MS', 32)\\n self.text_playing = self.my_font.render('press spacebar to stack heads',\\n False, (200,200,200))\\n self.text_won = self.my_font.render('You Win! Press Enter to play again'\\n , False, (127,255,63))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff17381e1e3637051f3ef1d1ebe8330\",\n \"score\": \"0.5980718\",\n \"text\": \"def flip_card(self):\\n if self.facedown:\\n self.image = self.face\\n self.facedown = False\\n else:\\n self.image = self.back\\n self.facedown = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700dc088225caa6f7c58cafc3c90f8cd\",\n \"score\": \"0.5977117\",\n \"text\": \"def _flip_all_card_face_up(self):\\n card_update_dict = {}\\n for card in self:\\n card_update_dict[card.value]='U'\\n card.face = 'U'\\n new_event = gamestate.Event(\\n type='Flip',\\n cards_status=card_update_dict,\\n )\\n self._update_event_handle(new_event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24eaf5602f6a342992aedd3c61992df8\",\n \"score\": \"0.5971587\",\n \"text\": \"def rotate_face(self, face):\\n new_face = [[], [], []]\\n for i in reversed(range(self.size)):\\n for y in range(self.size):\\n new_face[self.size - 1 - i].append(face[i][self.size - 1 - y])\\n return new_face\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51b0f91c874812753159befb0d33d42\",\n \"score\": \"0.5963223\",\n \"text\": \"def update_ai_turn(self):\\n self.is_ai_turn ^= True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8aa1309b0c0dfe363ec073df8c793fa\",\n \"score\": \"0.59623414\",\n \"text\": \"def next(self):\\n\\n for i in range(len(self.face_names)):\\n\\n if self.face_names[i][0] == \\\"\\\":\\n\\n display_size_image = 600\\n display_scale_image = display_size_image / 1000\\n\\n img_full = cv2.imread(self.face_rcg.get_known_face_files()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]) #, cv2.IMREAD_UNCHANGED)\\n scale_percent = display_scale_image*1000/img_full.shape[1] # percent of original size\\n width = int(img_full.shape[1] * scale_percent )\\n height = int(img_full.shape[0] * scale_percent )\\n dim = (width, height)\\n # resize image\\n img_bgr = cv2.resize(img_full, dim)\\n\\n (top, right, bottom, left) = self.face_rcg.get_known_face_locations()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]\\n\\n top = int(display_scale_image*top)\\n right = int(display_scale_image*right)\\n bottom = int(display_scale_image*bottom)\\n left = int(display_scale_image*left)\\n\\n # Draw a box around the face\\n cv2.rectangle(img_bgr, (left, top), (right, bottom), (255, 255, 255), 2)\\n\\n # Draw a label with a name below the face\\n cv2.rectangle(img_bgr, (left, bottom), (right+120, bottom+30), (255, 255, 255), cv2.FILLED)\\n font = cv2.FONT_HERSHEY_DUPLEX\\n cv2.putText(img_bgr, self.face_ids[i][0], (left + 6, bottom + 25), font, 0.6, (0, 0, 0), 2)\\n\\n # change color channel\\n b,g,r = cv2.split(img_bgr)\\n img = cv2.merge((r,g,b))\\n\\n # Convert the Image object into a TkPhoto object\\n im = Image.fromarray(img)\\n imgtk = ImageTk.PhotoImage(image=im) \\n self.canvas.configure(image=imgtk)\\n self.canvas.image = imgtk\\n\\n\\n self.status.set(\\\"Please type in the name of \\\" + self.face_ids[i][0] + \\\" and click 'Save' and 'Next'\\\")\\n break\\n\\n else:\\n\\n if self.new_file_list:\\n\\n self.new_file_list = False\\n\\n self.face_rcg.encode([file[1] for file in self.file_list])\\n\\n self.face_clusters = self.face_rcg.cluster_faces_dbscan()\\n #self.face_clusters = self.face_rcg.cluster_faces()\\n self.face_ids,self.face_names,self.face_cluster_id = self.face_rcg.get_processed_clusters(self.face_clusters)\\n\\n self.status.set(\\\"Faces recognized. Click 'Next'\\\")\\n\\n \\\"\\\"\\\"else:\\n\\n self.face_rcg.write_images_with_names(\\\"/home/manuel/Pictures/test_face_recognition/known/\\\")\\n self.status.set(\\\"Finished. Type path and click 'Get file list' to start new.\\\")\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128d7614f3f4e709ea448f91f67b08b3\",\n \"score\": \"0.59122604\",\n \"text\": \"def alternateFace(self, alternateFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128d7614f3f4e709ea448f91f67b08b3\",\n \"score\": \"0.591115\",\n \"text\": \"def alternateFace(self, alternateFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2591ed688bb5b393fd66d89278a57ae7\",\n \"score\": \"0.5884116\",\n \"text\": \"def cycle_state_variable(self, step):\\n if self.detection_state is DetectionState.FACELETS:\\n self.curr_facelet_index = (self.curr_facelet_index + step) % len(self.facelets)\\n if self.detection_state is DetectionState.COLORS:\\n self.write_data()\\n self.curr_face_index = (self.curr_face_index + step) % len(self.faces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da933096f83382cb6f8e3a32d9ac169c\",\n \"score\": \"0.58704185\",\n \"text\": \"def before_turn(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86474847e2f3e487405a26113e2d7bb8\",\n \"score\": \"0.58687705\",\n \"text\": \"def make_turn(self, current_set):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603495cb7320afd8f075a41dd5a741b2\",\n \"score\": \"0.57598776\",\n \"text\": \"def flip_turn(self):\\n self.current_player = self.other_player\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bba631564d46848b6f1088d1c18c4f5\",\n \"score\": \"0.57119805\",\n \"text\": \"def _turn(self):\\n\\n if self.dyingCounter:\\n return\\n for i in range(0, len(self.idle_anim)):\\n self.idle_anim[i] = pygame.transform.flip(self.idle_anim[i], True, False)\\n for i in range(0, len(self.sprint_anim)):\\n self.sprint_anim[i] = pygame.transform.flip(\\n self.sprint_anim[i], True, False\\n )\\n for i in range(0, len(self.death_anim)):\\n self.death_anim[i] = pygame.transform.flip(self.death_anim[i], True, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6784a6fa82f3e8bc5c6a69e64aaa58aa\",\n \"score\": \"0.5680338\",\n \"text\": \"def face_west():\\n while not_facing_west():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6784a6fa82f3e8bc5c6a69e64aaa58aa\",\n \"score\": \"0.5680338\",\n \"text\": \"def face_west():\\n while not_facing_west():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549706dee04edf2bd2a164a4b62a7607\",\n \"score\": \"0.5671197\",\n \"text\": \"def change_turn(self):\\n player = self.get_player_turn()\\n if player == 1:\\n self.set_player_turn(2) # change to player 2's turn\\n elif player == 2:\\n self.set_player_turn(1) # change to player 1's turn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac0ed475a41f2c2daddd82cfcd1d9fe\",\n \"score\": \"0.5666877\",\n \"text\": \"def turn_to_face_v(self, direction=Vector3()):\\n if direction.is_zero_vector():\\n return\\n direction = normalize(direction)\\n forward = self.get_forward()\\n axis = cross(forward, direction)\\n angle = math.degrees(math.acos(dot(forward, direction)))\\n self.rotate(angle=angle, axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea4cf811fc403de72b690b5f67d56ef\",\n \"score\": \"0.56437415\",\n \"text\": \"def face_south():\\n while not_facing_south():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea4cf811fc403de72b690b5f67d56ef\",\n \"score\": \"0.56437415\",\n \"text\": \"def face_south():\\n while not_facing_south():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e928f657c1906b34842d1250d8fff6\",\n \"score\": \"0.56321895\",\n \"text\": \"def circularFace(self, circularFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189760f95d5ce7e234edde57548b548b\",\n \"score\": \"0.5585415\",\n \"text\": \"def face_east():\\n while not_facing_east():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189760f95d5ce7e234edde57548b548b\",\n \"score\": \"0.5585415\",\n \"text\": \"def face_east():\\n while not_facing_east():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4b503b10258e58a5c6803affaa2ed9\",\n \"score\": \"0.55851406\",\n \"text\": \"def update_turn(self):\\n if self.get_turn() is True:\\n self._turn = False\\n elif self.get_turn() is False:\\n self._turn = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb44be567a173befa4572a4772b065a6\",\n \"score\": \"0.55826795\",\n \"text\": \"def changePerspective(self):\\n\\n # We only need to loop through the 'top' of the board\\n for index in range(32):\\n # Set our current row and column with respect to index\\n row = index//8\\n column = index % 8\\n # If we are currently on one of our peices\\n if self.boardArray[row][column].isupper():\\n flipPeice = self.boardArray[row][column].lower() # Set it as an enemy peice\\n else:\\n flipPeice = self.boardArray[row][column].upper() # Set enemy peice as friendly otherwise\\n\\n # We compute the same evaluations as above but for the peice at the opposite corner\\n # If currently one of our peices\\n if self.boardArray[7-row][7-column].isupper():\\n self.boardArray[row][column] = self.boardArray[7-row][7-column].lower() # Set current peice to enemy peice\\n else:\\n self.boardArray[row][column] = self.boardArray[7-row][7-column].upper() # Set enemy peice as friendly otherwise\\n\\n self.boardArray[7-row][7-column] = flipPeice # Set peice to flipped peice\\n\\n kingFlipped = self.kingPosition_White # Set temporary kingFlipped variable for white position\\n # Set new white and black king positions\\n self.kingPosition_White = 63 - self.kingPosition_Black\\n self.kingPosition_Black = 63 - kingFlipped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488f70727fcc85e584cfc0d19d827c55\",\n \"score\": \"0.5572259\",\n \"text\": \"def change_game(self,game):\\r\\n for i in range(0,len(game)):\\r\\n if(game[i] == UNEXPOSED):\\r\\n self.replace_character_at_index(i,UNEXPOSED)\\r\\n elif(game[i] == POKEMON):\\r\\n self.replace_character_at_index(i,POKEMON)\\r\\n elif(game[i] == FLAG):\\r\\n self.replace_character_at_index(i,FLAG)\\r\\n else:\\r\\n self.replace_character_at_index(i,game[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8370e789cd50eff989a7a93fb7d4fe2\",\n \"score\": \"0.5558109\",\n \"text\": \"def SetFaceAttribute(*args):\\r\\n return _pynewton.TreeCollision_SetFaceAttribute(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69495d8208a4864bde80fc6e5d84f93\",\n \"score\": \"0.5540498\",\n \"text\": \"def on_face_known(self, faces):\\n for person in faces:\\n if self.is_greeting_appropriate(person.name):\\n self.say(\\\"Hello, {}!\\\".format(person.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e9fdbf3407bf11b9cddd6bc48483fa\",\n \"score\": \"0.5509818\",\n \"text\": \"def flip_faceverts(faces):\\n flipped_faces = []\\n for face in faces:\\n flipped_face = []\\n for vert in reversed(face):\\n flipped_face.append(vert)\\n flipped_faces.append(tuple(flipped_face))\\n return flipped_faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7863737e7a3789fdc123f7429e7e3abc\",\n \"score\": \"0.5505532\",\n \"text\": \"def turn(self, angle):\\r\\n self.angle += math.radians(angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170d87b5663fb2ec759bdc462737210a\",\n \"score\": \"0.55026007\",\n \"text\": \"def update(self):\\n self.game_manager.play_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d48510aa9b5615f55195f6c7d5543b6\",\n \"score\": \"0.54960114\",\n \"text\": \"def GetFaces(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd6d059d0b5ecc8a587f69906f0edd0\",\n \"score\": \"0.54950124\",\n \"text\": \"def do_turn(self, player_index, game_info):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff37c865ae0d503c86e439146c2d490a\",\n \"score\": \"0.549461\",\n \"text\": \"def poppy_turn(poppy, facee, frame):\\r\\n # Format of facee: [x, y, w, h]\\r\\n # print(facee)\\r\\n w = facee[2]\\r\\n x = facee[0] + 0.5 * w\\r\\n\\r\\n f_dist = find_distance(w)\\r\\n theta = find_angle(x - x_centre, f_dist)\\r\\n\\r\\n # print(\\\"angle: {0}\\\".format(theta)) \\r\\n # print(\\\"distance: {0}\\\".format(f_dist))\\r\\n # print()\\r\\n cv2.putText(frame, \\\"angle: \\\" + str(theta), \\r\\n (10, 100),\\r\\n cv2.FONT_HERSHEY_SIMPLEX,\\r\\n 1, (0, 255, 0), 2)\\r\\n\\r\\n if -TOLERANCE < theta < TOLERANCE:\\r\\n wave()\\r\\n else:\\r\\n poppy.head_z.goto_position(theta, wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee602941d266468d60ff79ca0ad7f44d\",\n \"score\": \"0.5484665\",\n \"text\": \"def switch_turn(self):\\n if self.whose_turn == self.player1:\\n self.whose_turn = self.player2\\n else:\\n self.whose_turn = self.player1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc217b92dd7f13d55b1d8fe06e5bf9f\",\n \"score\": \"0.54783547\",\n \"text\": \"def switch_turn(self):\\n self.black_turn = not self.black_turn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5dd10bcddb3d490fb9bd7562ec5609\",\n \"score\": \"0.5469843\",\n \"text\": \"def find_faces(self):\\n if self.is_face:\\n self.image = self.get_image()\\n self.is_face = False\\n self.rotate_right()\\n else:\\n if self.is_gray:\\n self.set_image_colorful()\\n self.is_gray = True\\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\\n faces = FACE_CASCADE.detectMultiScale(gray, 1.3, 5)\\n for (x, y, w, h) in faces:\\n cv2.rectangle(self.image, (x, y), (x+w, y+h), (255, 0, 0), 2)\\n self.is_face = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10358d736ae3f6c576c6bbcb9c6a8731\",\n \"score\": \"0.5469517\",\n \"text\": \"def rot(cube, face, direction):\\n\\n cube = np.copy(cube)\\n if face == Man.U.value:\\n cube[0:4] = np.roll(cube[0:4], -direction, axis=0)\\n cube[4] = np.rot90(cube[4], k=-direction)\\n cube[5] = np.rot90(cube[5], k=direction)\\n elif face == Man.L.value:\\n sl = [cube[Man.F.value], cube[Man.D.value], cube[Man.B.value, ::-1], cube[Man.U.value]]\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.F.value] = sl[0]\\n cube[Man.D.value] = sl[1]\\n cube[Man.B.value, ::-1] = sl[2]\\n cube[Man.U.value] = sl[3]\\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=direction)\\n elif face == Man.F.value:\\n sl = np.array([cube[Man.U.value], cube[Man.R.value], cube[Man.D.value], cube[Man.L.value]])\\n sl = np.rot90(sl, k = -direction, axes=(1,2))\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.U.value] = sl[0]\\n cube[Man.R.value] = sl[1]\\n cube[Man.D.value] = sl[2]\\n cube[Man.L.value] = sl[3]\\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)\\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=direction)\\n return cube\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7300ea72bb79fa732ac3314284a70b79\",\n \"score\": \"0.54683673\",\n \"text\": \"def _update_turn(self) -> None:\\n self._player = self._next_player(self._player)\\n self._turns += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90968dcc01c6270f40ffe213d050cb8\",\n \"score\": \"0.54581696\",\n \"text\": \"def do_viewfaces(self, arg):\\n if len(self.inner) == 0:\\n print(\\\"There are no faces to view. 1. loadchoices default 2. alterproperty 3. loadfaces personal 4. savechoices\\\")\\n if len(self.inner) > con.MAX_PORTRAITS:\\n print(\\\"Over 50 images have been selected. If you would like to view all fifty then adjust def do_viewfaces(self, arg)\\\")\\n print(\\\"Otherwise, narrow your selection.\\\")\\n return False\\n filenames = []\\n for face in self.inner:\\n filename = face[0][0:-3]\\n filenames.append(\\\"{}png\\\\n\\\".format(filename))\\n with open(con.SAVE_IMAGE_NAMES, 'w') as f:\\n for filename in filenames:\\n # print(filename)\\n f.write(filename)\\n # for face in self.inner:\\n # print(\\\"elem[0]: \\\", face[0])\\n # # ----\\n # filename = face[0][0:-3]\\n # filename = \\\"{}png\\\".format(filename)\\n # filepath = os.path.join(IMAGES_DIRECTORY, filename)\\n # # print(filepath)\\n # print(filepath)\\n # img = mpimg.imread(filepath)\\n # imgplot = plt.imshow(img)\\n # plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81af7233c98af91768a4284a206a2ea5\",\n \"score\": \"0.54511875\",\n \"text\": \"def turn(self) -> None:\\n #current position reset\\n self.current = 0\\n #increase sequence and play it\\n self.addseq()\\n self.playseq()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb50a9792a666e9020ee95cb88b7d81\",\n \"score\": \"0.544412\",\n \"text\": \"def turn_to_face_p(self, target=Point3()):\\n my_pos = self.position()\\n if my_pos == target:\\n return\\n direction = normalize(target - my_pos)\\n self.turn_to_face_v(direction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc4f5944b094acbfaeefcca5129c1f7\",\n \"score\": \"0.5429818\",\n \"text\": \"def draw(self):\\n # rect_head = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\\n # pygame.draw.rect(screen, GREY + ALPHA, rect_head, 0)\\n ''' I want to add two eyes! OR, use FACE and NECK! '''\\n ball_face = (int((self.head[0] + 0.5) * UNIT_SIZE), int((self.head[1] + 0.5) * UNIT_SIZE))\\n pygame.draw.circle(screen, GREY + ALPHA, ball_face, int(UNIT_SIZE / 2) - 1)\\n if self.__direction == 'U':\\n rect_neck = (self.head[0] * UNIT_SIZE, int((self.head[1] + 0.5) * UNIT_SIZE),\\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\\n # ball_eye1 = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE)\\n # ball_eye2 = ((self.head[0] + 1) * UNIT_SIZE, self.head[1] * UNIT_SIZE)\\n elif self.__direction == 'D':\\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\\n elif self.__direction == 'L':\\n rect_neck = (int((self.head[0] + 0.5) * UNIT_SIZE), self.head[1] * UNIT_SIZE,\\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\\n else:\\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\\n\\n pygame.draw.rect(screen, GREY + ALPHA, rect_neck, 0)\\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye1, 5)\\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye2, 5)\\n for body_node in self.body:\\n rect_node = (body_node[0] * UNIT_SIZE, body_node[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\\n pygame.draw.rect(screen, GREY + ALPHA, rect_node, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e005cb9924b5e7e58763ca9d7dd07829\",\n \"score\": \"0.54284596\",\n \"text\": \"def find_faces():\\n\\n labels = {}\\n with open(\\\"./labels.pickle\\\", 'rb') as f:\\n org_labels = pickle.load(f)\\n labels = {v: k for k, v in org_labels.items()}\\n\\n face_cascade = CascadeClassifier(\\n './haarcascade/haarcascade_frontalface_default.xml')\\n recognizer = cv2.face.LBPHFaceRecognizer_create()\\n recognizer.read(\\\"./trainer.yml\\\")\\n\\n lefteye_cascade = CascadeClassifier('./haarcascade/haarcascade_lefteye.xml')\\n video_capture = cv2.VideoCapture(0)\\n picture_counter = 0\\n\\n picture_flag = False\\n while True:\\n\\n # Capture Frame by Frame\\n ret, frame = video_capture.read()\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n faces = face_cascade.detectMultiScale(\\n gray, scaleFactor=1.05, minNeighbors=8)\\n face_dic = {\\n \\\"known\\\": 0,\\n \\\"total\\\": 0}\\n \\n # Draws a rectagle around the face\\n for(x, y, w, h) in faces:\\n face_dic[\\\"total\\\"] += 1\\n roi_gray = gray[y:y+h, x:x+w]\\n roi_color = frame[y:y+h, x:x+w]\\n id_, conf = recognizer.predict(roi_gray)\\n eyes = lefteye_cascade.detectMultiScale(roi_gray)\\n test = [i for i in eyes]\\n if test:\\n frame = cv2.rectangle(\\n frame, (x, y,), (x+w, y+h), (0, 255, 0), 3)\\n\\n # conf is the confidence level that detected face has been trained\\n if conf >= 85:\\n print(id_)\\n face_dic[\\\"known\\\"] += 1\\n name = labels[id_]\\n font = cv2. FONT_HERSHEY_SIMPLEX\\n color = (255, 255, 255)\\n stroke = 2\\n cv2.putText(frame, name, (x, y), font, 1,\\n color, stroke, cv2.LINE_AA)\\n\\n for (ex, ey, ew, eh) in eyes:\\n cv2.rectangle(roi_color, (ex, ey),\\n (ex+ew, ey+eh), (255, 0, 0), 3)\\n else:\\n for (ex, ey, ew, eh) in eyes:\\n cv2.rectangle(roi_color, (ex, ey),\\n (ex+ew, ey+eh), (255, 0, 255), 3)\\n \\n \\n # Shows Number faces dectected/known in frame\\n cv2.putText(frame, \\\"Number of faces detected: \\\" + str(\\n face_dic[\\\"total\\\"]), (0, 100), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\\n cv2.putText(frame, \\\"Number of faces known: \\\" + str(\\n face_dic[\\\"known\\\"]), (0, 120), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\\n\\n # Dipsplay the resulting frame\\n cv2.imshow('Video', frame)\\n \\n \\n #Saves Picture \\n if face_dic[\\\"known\\\"] == 0 and face_dic[\\\"total\\\"] >= 1:\\n if not picture_flag:\\n picture_counter += 1\\n cv2.imwrite(\\\"./test_subjects/\\\" +\\n str(picture_counter)+\\\".png\\\", frame)\\n picture_flag = True\\n picture_counter += 1\\n if picture_counter == 120:\\n picture_flag = False\\n picture_counter = 0\\n\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n\\n video_capture.release()\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca6c77e4382d682458f04ff0ba933f6\",\n \"score\": \"0.5426279\",\n \"text\": \"def roll(cube, face, direction):\\n #print([\\\"L\\\", \\\"F\\\", \\\"R\\\", \\\"B\\\", \\\"U\\\", \\\"D\\\"][face] + str(direction))\\n cube = np.copy(cube)\\n if face == Man.U.value:\\n cube[0:4, 0] = np.roll(cube[0:4, 0], -direction, axis=0)\\n cube[Man.U.value] = np.rot90(cube[Man.U.value], k=-direction)\\n elif face == Man.D.value:\\n cube[0:4, -1] = np.roll(cube[0:4, -1], direction, axis=0)\\n cube[Man.D.value] = np.rot90(cube[Man.D.value], k=-direction)\\n elif face == Man.F.value:\\n sl = np.array([cube[Man.U.value, -1, :], cube[Man.R.value, :, 0],\\n cube[Man.D.value, 0, ::-1], cube[Man.L.value, ::-1, -1]])\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.U.value, -1, :] = sl[0]\\n cube[Man.R.value, :, 0] = sl[1]\\n cube[Man.D.value, 0, ::-1] = sl[2]\\n cube[Man.L.value, ::-1, -1] = sl[3]\\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)#TODO: Use map function\\n elif face == Man.B.value:\\n sl = np.array([cube[Man.U.value, 0, :], cube[Man.R.value, :, -1], cube[Man.D.value, -1, ::-1], cube[Man.L.value, ::-1, 0]])\\n sl = np.roll(sl, -direction, axis=0)\\n cube[Man.U.value, 0, :] = sl[0]\\n cube[Man.R.value, :, -1] = sl[1]\\n cube[Man.D.value, -1, ::-1] = sl[2]\\n cube[Man.L.value, ::-1, 0] = sl[3]\\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=-direction)#TODO: Use map function\\n elif face == Man.L.value:\\n sl = np.array([cube[Man.F.value, :, 0], cube[Man.D.value, :, 0], cube[Man.B.value, ::-1, -1], cube[Man.U.value, :, 0]])\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.F.value, :, 0] = sl[0]\\n cube[Man.D.value, :, 0] = sl[1]\\n cube[Man.B.value, ::-1, -1] = sl[2]\\n cube[Man.U.value, :, 0] = sl[3]\\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\\n elif face == Man.R.value:\\n sl = np.array([cube[Man.F.value, :, -1], cube[Man.D.value, :, -1], cube[Man.B.value, ::-1, 0], cube[Man.U.value, :, -1]])\\n sl = np.roll(sl, -direction, axis=0)\\n cube[Man.F.value, :, -1] = sl[0]\\n cube[Man.D.value, :, -1] = sl[1]\\n cube[Man.B.value, ::-1, 0] = sl[2]\\n cube[Man.U.value, :, -1] = sl[3]\\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=-direction)\\n return cube\\n #print(Man.__str__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004a42186846ed8f1490595eec53dab7\",\n \"score\": \"0.54207855\",\n \"text\": \"def _run_initial_turn(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba9e97f7f2908033657be66d1f35b5b9\",\n \"score\": \"0.54142267\",\n \"text\": \"def ChangeTurn(self):\\r\\n self.lsPlayer[self.turn] = self.ActivePlayer.copy()\\r\\n self.turn = (self.turn + 1) % self.numPlayers\\r\\n self.ActivePlayer = self.lsPlayer[self.turn].copy()\\r\\n if not self.CheckForValidMoves(self.ActivePlayer):\\r\\n self.ChangeTurn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e708f7fd07391ba176ad2189cd3335\",\n \"score\": \"0.5413524\",\n \"text\": \"def faces(self):\\n raise NotImplementedError(f\\\"faces not implemented for {type(self)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5ea98cfd7541db5061101436cfeb6\",\n \"score\": \"0.5406411\",\n \"text\": \"def advance(self):\\n self.x_shape_rot += self.x_rot_speed\\n self.x_shape_rot %= 360\\n self.y_shape_rot += self.y_rot_speed\\n self.y_shape_rot %= 360\\n self.z_shape_rot += self.z_rot_speed\\n self.z_shape_rot %= 360\\n self.updateGL()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb9346c79e67186b2b99f01f562d783\",\n \"score\": \"0.53899795\",\n \"text\": \"def turnLeft(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce23e46ad1a8999548893082b51db47f\",\n \"score\": \"0.5377675\",\n \"text\": \"def setNext(self,game):\\r\\n self.nextFixture = game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bf3aa342f06d6abe03dfabe04ff9e8\",\n \"score\": \"0.53709286\",\n \"text\": \"def faces(self):\\r\\n\\t\\tif self._faces is None:\\r\\n\\t\\t\\tself._faces = [Face(self, i) for i in xrange(len(self.faceVertArray))]\\r\\n\\t\\treturn self._faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4666aaedfe037607c73a5c0fa530b353\",\n \"score\": \"0.53502315\",\n \"text\": \"def targetFaces(self, targetFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4666aaedfe037607c73a5c0fa530b353\",\n \"score\": \"0.5349725\",\n \"text\": \"def targetFaces(self, targetFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7321b77232091343736848210ba289\",\n \"score\": \"0.5335583\",\n \"text\": \"def save_loop(camera, name: str) -> None:\\n faces = []\\n process_this = True\\n face_to_be_saved = None\\n while True:\\n ret, frame = camera.read()\\n if process_this:\\n faces = prc.process_faces(frame)\\n process_this = not process_this\\n # changing name of each face to empty string\\n message = ''\\n for face in faces:\\n face.assign_name('')\\n if len(faces) == 1:\\n face_to_be_saved = faces[0]\\n elif len(faces) > 1:\\n message = 'More than 1 face detected'\\n else:\\n message = 'No face detected'\\n prc.mark_faces(frame, faces)\\n cv2.putText(frame, message, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)\\n cv2.imshow('Video', frame)\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n if cv2.getWindowProperty('Video',cv2.WND_PROP_VISIBLE) < 1:\\n break;\\n\\n if face_to_be_saved is not None:\\n save(face_to_be_saved, name)\\n else:\\n print(\\\"Couldn't save the face\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83a0ae88bdaf135a50b3fd54acc6388\",\n \"score\": \"0.53346217\",\n \"text\": \"def on_face_new(self, faces):\\n\\n if self.is_greeting_appropriate(\\\"new\\\"):\\n self.say(\\\"I see a new person!, Hello stranger!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a042e9a13a6051ebb7a09d57af4c404\",\n \"score\": \"0.53295404\",\n \"text\": \"def face_detect(self):\\n self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\\n \\n self.gray = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2GRAY)\\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\\n \\n # create an array of the positions of each face \\n # and then iterates through it to draw a rectangle around all faces\\n faces = self.face_cascade.detectMultiScale(self.gray, 1.3, 5)\\n for (x, y, w, h) in faces:\\n self.cv2img = cv2.rectangle(self.cv2img, (x, y), (x+w, y+h), (255, 0, 0), 2)\\n \\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\\n faceimg = Image.fromarray(self.cv2img)\\n faceimg = ImageTk.PhotoImage(faceimg)\\n self.panel.configure(image = faceimg)\\n self.panel.image = faceimg\\n \\n #makes the button for crop faces visible\\n self.btn.pack(side = 'right')\\n\\n self.states.append(self.cv2img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dddf2a206011046beb22ae40ed5ec9\",\n \"score\": \"0.5327832\",\n \"text\": \"def switch_turns(self):\\n # TODO: This can be a one-liner, too lazy to do it now. Somehow it's\\n # easier to write a comment about something that would take 10 seconds\\n # to do than actually doing it\\n if self.turn == self.tictactoe.player1:\\n self.turn = self.tictactoe.player2\\n else:\\n self.turn = self.tictactoe.player1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8388d88bb7a30ab65434d4545028ee86\",\n \"score\": \"0.53252715\",\n \"text\": \"def flip(self):\\r\\n self.buffer,self.backbuffer = self.backbuffer,self.buffer\\r\\n self.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264d4cbd2ab4610bba0e52cfc6791f00\",\n \"score\": \"0.53248394\",\n \"text\": \"def move_forward_or_back(self, face):\\n\\n while not ((self.get_face_distance(face) < 1.1) or (self.get_face_distance(face) > .9)):\\n # checks if face is too small, then moves back\\n if self.get_face_distance(face) < .9:\\n self.move_bot(300)\\n # checks if face is too big, then moves forward\\n elif self.get_face_distance(face) > 1.1:\\n self.move_bot(-300)\\n self.motors = 6000\\n self.tango.setTarget(self.MOTORS, self.motors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1a405824a24625be6ae6172b8f61fd\",\n \"score\": \"0.5323996\",\n \"text\": \"def assBody(self):\\n \\n \\n self.surfaceBody = self.spriteList[self.spriteIndex]\\n self.surfaceBody.set_colorkey(constants.TRANS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4717439d1124655b3c1bcf0d0ccb4854\",\n \"score\": \"0.532059\",\n \"text\": \"def update(self):\\n self.surface.fill((0, 0, 0))\\n # head\\n self.surface.blit(pygame.transform.rotate(self.images[\\\"head\\\"],\\n -90 * self.directions[0]),\\n (CASE_SIZE[0] * self.coords[0][0],\\n CASE_SIZE[1] * self.coords[0][1]))\\n # body\\n size_x, size_y = CASE_SIZE\\n\\n for i in range(len(self.coords[1:-1])):\\n x, y = self.coords[i + 1]\\n direction = self.directions[i + 1]\\n direction_prec = self.directions[i]\\n if direction == direction_prec: # straight\\n self.surface.blit(self.rotate(\\\"body_straight\\\", -90*direction),\\n (size_x * x, size_y * y))\\n # turn right>top or botom>left\\n if (direction == 0 and direction_prec == 1) or \\\\\\n (direction == 3 and direction_prec == 2):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",0),\\n (size_x * x, size_y * y))\\n # turn left>top or botom>right\\n if (direction == 0 and direction_prec == 3) or \\\\\\n (direction == 1 and direction_prec == 2):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",-90),\\n (size_x * x, size_y * y))\\n # turn left>botom or top>right\\n if (direction == 2 and direction_prec == 3) or \\\\\\n (direction == 1 and direction_prec == 0):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",180),\\n (size_x * x, size_y * y))\\n # turn top>left or right>botom\\n if (direction == 3 and direction_prec == 0) or \\\\\\n (direction == 2 and direction_prec == 1):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",90),\\n (size_x * x, size_y * y))\\n # Tail\\n self.surface.blit(pygame.transform.rotate(self.images[\\\"tail\\\"],\\n -90 * self.directions[-2]),\\n (size_x * self.coords[-1][0],\\n size_y * self.coords[-1][1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9993859b70cb4a82352b878a93e3a4\",\n \"score\": \"0.53183514\",\n \"text\": \"def EditSkin(\\n self,\\n name: str = \\\"\\\",\\n faces: tuple[Face] = (),\\n edges: tuple[Edge] = (),\\n elementFaces: tuple[MeshFace] = (),\\n elementEdges: tuple[MeshEdge] = (),\\n ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4697beee66114fbfa28fcab3d916a7b4\",\n \"score\": \"0.5313693\",\n \"text\": \"def update(self, image):\\n\\n self._faces = []\\n\\n if (utils.is_gray(image)):\\n image = cv2.equalizeHist(image)\\n else:\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n image = cv2.equalizeHist(image)\\n\\n min_size = utils.widthHeightDividedBy(image, 8)\\n\\n face_rects = self._face_classifier.detectMultiScale(\\n image, self.scale_factor, self.min_neighbors, self.flags, min_size)\\n\\n if (face_rects is not None):\\n for face_rect in face_rects:\\n face = Face()\\n face.face_rect = face_rect\\n x, y, w, h = face_rect\\n\\n # look for an eye in the upper-left part of the face\\n search_rect = (x+w//7, y, w*2//7, h//2)\\n face.left_eye_rect = self._detect_one_object(\\n self._eye_classifier, image, search_rect, 64\\n )\\n\\n # look for an eye in the upper-right part of the face\\n search_rect = (x + (w*4)//7, y, w*2//7, h//2)\\n face.right_eye_rect = self._detect_one_object(\\n self._eye_classifier, image, search_rect, 64\\n )\\n\\n # look for an node in the middle part of the face\\n search_rect = (x+w//4, y+h//4, w*2, h//2)\\n face.nose_rect = self._detect_one_object(\\n self._nose_classifier, image, search_rect, 32\\n )\\n\\n # look for an mouth in the lower-middle part of the face\\n search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)\\n face.mouth_rect = self._detect_one_object(\\n self._mouth_classifier, image, search_rect, 16\\n )\\n\\n self._faces.append(face)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9a35c4e36f54cadb68c18c698cde3f\",\n \"score\": \"0.5311607\",\n \"text\": \"def faceSet(self):\\r\\n\\t\\tret = FaceSet(self, [])\\r\\n\\t\\tret.update(range(len(self.faceVertArray)))\\r\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee3af718f04065414eca6b36fe668b4\",\n \"score\": \"0.53067994\",\n \"text\": \"def face_amount(self, face_amount):\\n\\n self._face_amount = face_amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21fe36df6c0fae07d86a2b23bd7f9bb\",\n \"score\": \"0.5304956\",\n \"text\": \"def add_face_to_list(self, points):\\n ids = [point.id for point in points]\\n self.faces.append(ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bb2a8e7b4881fca6ca45efc0183e63\",\n \"score\": \"0.53038234\",\n \"text\": \"def online_face_recognition(profile_names, n_pictures=15):\\n images = []\\n labels = []\\n label_names = []\\n for i, name in enumerate(profile_names):\\n p = load_profile(name)\\n p = p[0:n_pictures, ]\\n images += [p]\\n labels += [np.ones(p.shape[0]) * (i + 1)]\\n label_names += [name]\\n faces = np.vstack(images)\\n labels = np.hstack(labels).astype(np.int)\\n # Generate model\\n model = IncrementalKCenters(faces, labels)\\n # Start camera\\n cam = cv.VideoCapture(0)\\n while True:\\n ret_val, img = cam.read()\\n grey_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n working_image = cv.bilateralFilter(grey_image, 9, 75, 75)\\n working_image = cv.equalizeHist(working_image)\\n working_image = cv.GaussianBlur(working_image, (5, 5), 0)\\n box = face_haar_cascade.detectMultiScale(working_image)\\n for b0 in box:\\n x, y = b0[0], b0[1]\\n x_range, y_range = b0[2], b0[3]\\n # look for eye classifier\\n local_image = img[y:(y + y_range), x:(x + x_range)]\\n eye_box = eye_haar_cascade.detectMultiScale(local_image)\\n if len(eye_box) == 0:\\n cv.rectangle(img, tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4]),\\n (0, 0, 255), 2)\\n continue\\n # select face\\n local_image = grey_image[y:(y + y_range), x:(x + x_range)]\\n x_t = preprocess_face(local_image)\\n\\n \\\"\\\"\\\"\\n Centroids are updated here\\n \\\"\\\"\\\"\\n model.online_ssl_update_centroids(x_t)\\n p1, p2 = tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4])\\n\\n \\n \\\"\\\"\\\"\\n HardHFS solution is computed here\\n \\\"\\\"\\\"\\n f = model.online_ssl_compute_solution()\\n lab = np.argsort(f)\\n \\n \\n \\\"\\\"\\\"\\n Change False by something else to be able to disregard faces it cannot recognize (question 3.4)\\n \\\"\\\"\\\" \\n \\n mean_dist_new_to_labeled = np.mean(distance.cdist(np.array([model.centroids[model.last_face]]), faces)[0])\\n \\n mean_dist_existing=0\\n for person in set(labels):\\n #Average distance between the faces of a same person\\n mean_dist_existing += np.mean(distance.cdist(faces[labels==person], faces[labels==person])[0])\\n mean_dist_existing=mean_dist_existing/len(list(set(labels))) \\n \\n if any(f==0.) and mean_dist_new_to_labeled>mean_dist_existing:\\n color = (100, 100, 100)\\n txt = \\\"unknown\\\"\\n cv.putText(img, txt, (p1[0], p1[1] - 5), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, color)\\n else:\\n for i, l in enumerate(lab):\\n color = [(0, 255, 0), (255, 0, 0), (0, 0, 255)][l]\\n txt = label_names[l] + \\\" \\\" + ('%.4f' % np.abs(f[l]))\\n cv.putText(img, txt, (p1[0], p1[1] - 5 - 10 * i), cv.FONT_HERSHEY_COMPLEX_SMALL,\\n 0.5 + 0.5 * (i == f.shape[0] - 1), color)\\n cv.rectangle(img, p1, p2, color, 2)\\n cv.imshow(\\\"cam\\\", img)\\n key = cv.waitKey(1)\\n if key in [27, 101]:\\n break\\n if key == ord('s'):\\n # Save face\\n print('saved')\\n cv.imwrite(\\\"frame.png\\\", img)\\n ## cv.waitKey(1)\\n cv.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8f35e3b27d9bfb457cba006ae270ae\",\n \"score\": \"0.5301556\",\n \"text\": \"def turn():\\n global o_both_steering\\n\\n o_both_steering.on_for_rotations(100, SpeedPercent(40), 1) # OPTIMAL VALUE 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"391dc969f17266ea0bca1dc38375864f\",\n \"score\": \"0.5300656\",\n \"text\": \"def generator(surface, angles):\\n original_surf = surface.copy()\\n for angle in angles:\\n x, y = self.rect.center\\n self.surface = pygame.transform.rotate(original_surf, angle)\\n self.instant_move(x, y)\\n yield True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6eceb20ea200d1fa8569da8eae1e7bc\",\n \"score\": \"0.5300277\",\n \"text\": \"def updateCameraVectors(self):\\n # Calculate the new Front vector\\n x = np.cos(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\\n y = np.sin(np.radians(self.Pitch))\\n z = np.sin(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\\n \\n front = np.array([x, y, z], dtype=GLfloat)\\n print(front)\\n self.Front = front / np.sqrt(x**2 + y**2 + z**2)\\n print(self.Front)\\n \\n temp = np.cross(self.Front, self.WorldUp)\\n self.Right = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\\n print(self.Right)\\n \\n temp = np.cross(self.Right, self.Front)\\n self.Up = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\\n print(self.Up)\\n \\n \\n \\n \\n # front = glm.vec3(x, y, z)\\n # print('*=========')\\n # self.Front = np.array(glm.normalize(front), dtype=GLfloat)\\n # print(self.Front)\\n # print('#=========')\\n #\\n # # Also re-calculate the Right and Up vector\\n # # Normalize the vectors, because their length gets closer to 0 the more you\\n # # look up or down which results in slower movement.\\n # print('**=========')\\n # temp = np.cross(self.Front, self.WorldUp)\\n # print(temp)\\n # print('##=========')\\n # self.Right = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\\n #\\n # print('***=========')\\n # temp = np.cross(self.Right, self.Front)\\n # print(temp)\\n # self.Up = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\\n # print('###=========')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300243\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad3aa8cbac3e97b9e7256bd79a6f9a6c\",\n \"score\": \"0.5299885\",\n \"text\": \"def next_turn(self):\\n temp = self.current_player\\n self.current_player = self.opponent\\n self.opponent = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f4d58e0f16c22078ce9dd38381e616\",\n \"score\": \"0.52914584\",\n \"text\": \"def test_batch_redetect_with_one_face(self):\\n for detector in self.detectors:\\n with self.subTest(detectorType=detector.detectorType):\\n detection = detector.detectOne(image=VLIMAGE_ONE_FACE)\\n redetect = detector.redetect(\\n images=[ImageForRedetection(image=VLIMAGE_ONE_FACE, bBoxes=[detection.boundingBox.rect])]\\n )[0]\\n self.assertFaceDetection(redetect, VLIMAGE_ONE_FACE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37798494830af70cc4a3e549576796e7\",\n \"score\": \"0.52862424\",\n \"text\": \"def build_face():\\n masterScale = 0.2\\n create_hooks(masterScale)\\n inputHooks = create_joint_drivers(masterScale)\\n\\n for poseInfo in faceposes:\\n zone = poseInfo['zone']\\n pose = poseInfo['pose']\\n poseDriver = poseInfo['driver']\\n poseRange = poseInfo['mapping']\\n poseOverrides = poseInfo['overrides']\\n poseOverrideMaps = poseInfo['overridemaps']\\n\\n create_pose_node(pose, zone, inputHooks, masterScale, poseDriver, poseRange, poseOverrides, poseOverrideMaps)\\n print 'done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7596177b3c91fc479cf4def251516cac\",\n \"score\": \"0.5286083\",\n \"text\": \"def UpdateRectColors(self, selectedRectId):\\n\\n for (rectId, rect) in enumerate(self.rects):\\n\\n (x, y, w, h) = rect\\n color = (0,255,0) if rectId == selectedRectId-1 else (0,0,255) #selection is 1 based\\n\\n cv2.rectangle(self.img500, (x, y), ((x + w), (y + h)), color, 2)\\n\\n # show the face number\\n cv2.putText(self.img500,\\n \\\"Face #{}\\\".format(rectId + 1),\\n (x - 10, y - 10),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\\n\\n cv2.imshow(self.faceSelectionWin, self.img500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ac0645a023800fc4dcf11143ec82b7\",\n \"score\": \"0.5282528\",\n \"text\": \"def setup_face_indices(self, reset=True):\\n if reset or not self.fis:\\n fa = self.domain.get_facets(force_faces=True)[1]\\n\\n if self.faces:\\n faces = self.faces\\n else:\\n faces = self.edges\\n\\n self.fis = {}\\n for ig in self.igs:\\n rfaces = faces[ig]\\n fi = fa.indices[rfaces]\\n assert_(nm.all(fi[:,0] == ig))\\n self.fis[ig] = fi[:,1:].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e58cb333ee43454817f059a30915849\",\n \"score\": \"0.5273543\",\n \"text\": \"def another_turn(self, pit: PitData) -> None:\\n self.update_color(pit, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc7f93fc6c3dd1ed8fcd5dd822f522a\",\n \"score\": \"0.52639437\",\n \"text\": \"def on_draw(self):\\n arcade.start_render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0b0f3d2a4cf4ba6735ee43f6bad2a4\",\n \"score\": \"0.5263633\",\n \"text\": \"def update_turn(self):\\n self.p1.turn = not self.p1.turn\\n self.p2.turn = not self.p2.turn\\n self.current_player = self.p1 or self.p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65698da0371ffe24258d721bc412f014\",\n \"score\": \"0.52610844\",\n \"text\": \"def updateCams(self, interface=\\\"match\\\"):\\n activecams = getActiveCams(len(self.cameras[interface]))\\n for activeind in activecams:\\n camera = self.cameras[interface][activeind]\\n if camera in self.gridded:\\n camera.updateImgOnLabel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09cf5d165983357f2dcc6a505dae41e3\",\n \"score\": \"0.5259303\",\n \"text\": \"def RubiksCube():\\n solved = False\\n while (input(\\\"Start game?: \\\") == \\\"yes\\\") and (not solved):\\n c = Cube()\\n c.shuffle()\\n plotCube(c)\\n plt.ion()\\n plt.show()\\n plt.pause(1)\\n # pause needed to update matplotlib plots with keyboard inputs\\n print(\\\"The possible faces to rotate are: U,D,L,R,F,B.\\\\n The possible directions to rotate are: clockwise, counterclockwise.\\\\n The possible numbers of rotations are: 1,2,3.\\\\n\\\")\\n while not solved:\\n interact(c)\\n plt.close()\\n plotCube(c)\\n plt.show()\\n plt.pause(1)\\n if c._p == Cube()._p:\\n solved = True\\n if solved:\\n print(\\\"You win!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f80d41e6e547f5490b5de12845018\",\n \"score\": \"0.52588135\",\n \"text\": \"def get_any_face(self):\\n return choice(list(self.faces()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d84dce15832a9e4b877937b180ce8e\",\n \"score\": \"0.5258472\",\n \"text\": \"def Turn(self, amount):\\n \\n self.Angle -= amount\\n self.Angle %= 360\\n\\n self.Image = pygame.transform.rotate(self.Orig, self.Angle)\\n self.UpdateMask()\\n \\n newSize = self.Image.get_size()\\n self.ModX = -(newSize[0]-self.Size[0])/2\\n self.ModY = -(newSize[1]-self.Size[1])/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee707779d583c98c6499bd7652922ed3\",\n \"score\": \"0.524997\",\n \"text\": \"def toggle_turn(self):\\n if self._turn == \\\"player1\\\":\\n self._turn = \\\"player2\\\"\\n else:\\n self._turn = \\\"player1\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c18c82af0b0743c846de867ee1593851\",\n \"score\": \"0.52392316\",\n \"text\": \"def update_surface(self):\\n self.surface = pygame.surfarray.make_surface(self.arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a23ba794220991e6386c2a7006d82da\",\n \"score\": \"0.5238691\",\n \"text\": \"def getSurface(self):\\n self.frame += 1\\n if self.frame == 3:\\n self.isFirstPic = not self.isFirstPic\\n self.frame = 0\\n\\n if self.direction == 0:\\n self.surface = Pacman.images[self.isFirstPic]\\n elif self.direction == 1:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 90)\\n elif self.direction == 2:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 180)\\n elif self.direction == 3:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 270)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a00db76457ab7532cc3af1fdac12f64\",\n \"score\": \"0.5236641\",\n \"text\": \"def turn_inc(self) -> None:\\n self.__current_turn += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e1b59538be1a46c3c180e932f6b9ca\",\n \"score\": \"0.52359444\",\n \"text\": \"def draw_changing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f025928f844ba5664d57864501e178\",\n \"score\": \"0.5231487\",\n \"text\": \"def turn(self):\\n\\n #print(self.coords[0] ,self.dx,self.dy, self.field.width,self.field.height)\\n mis=list(self.field.mice.keys())\\n if len(mis)>0:\\n x=self.coords[0][0]\\n y=self.coords[0][1]\\n mx=1000\\n my=1000\\n for (tx,ty) in mis:\\n if abs(x-tx)+abs(y-ty)<abs(x-mx)+abs(y-my):\\n mx=tx\\n my=ty\\n\\n #print(mx,my)\\n if self.coords[0][0] < mx:\\n if self.dx!=1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\\n \\n\\n elif self.coords[0][0] > mx:\\n if self.dx!=-1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\\n else:\\n if self.coords[0][1] < my:\\n if self.dy!=1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\\n elif self.coords[0][1] > my:\\n if self.dy!=-1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":792,"cells":{"query_id":{"kind":"string","value":"9003693612e2496d1b8ec20c13e967fc"},"query":{"kind":"string","value":"Takes a project UUID and either creates a new document in the desprojects index or updates the document if one already exists for that project."},"positive_passages":{"kind":"list like","value":[{"docid":"809657dc207ab465236a7ea3711a9617","score":"0.74024814","text":"def index_or_update_project(self, uuid):\n from designsafe.apps.api.projects.models import Project\n\n client = get_service_account_client()\n project_model = Project(client)\n project = project_model.search({'uuid': uuid}, client)[0]\n project_meta = project.to_dict()\n\n to_index = {key: value for key, value in project_meta.iteritems() if key != '_links'}\n to_index['value'] = {key: value for key, value in project_meta['value'].iteritems() if key != 'teamMember'}\n\n project_search = IndexedProject.search().filter(\n Q({'term': \n {'uuid._exact': uuid}\n })\n )\n res = project_search.execute()\n\n if res.hits.total == 0:\n # Create an ES record for the new metadata.\n # project_info_args = {key:value for key,value in project_info.iteritems() if key != '_links'}\n project_ES = IndexedProject(**to_index)\n project_ES.save()\n elif res.hits.total == 1:\n # Update the record.\n doc = res[0]\n doc.update(**to_index)\n else:\n # If we're here we've somehow indexed the same project multiple times. \n # Delete all records and replace with the metadata passed to the task.\n for doc in res:\n doc.delete()\n project_ES = IndexedProject(**to_index) \n project_ES.save()","title":""}],"string":"[\n {\n \"docid\": \"809657dc207ab465236a7ea3711a9617\",\n \"score\": \"0.74024814\",\n \"text\": \"def index_or_update_project(self, uuid):\\n from designsafe.apps.api.projects.models import Project\\n\\n client = get_service_account_client()\\n project_model = Project(client)\\n project = project_model.search({'uuid': uuid}, client)[0]\\n project_meta = project.to_dict()\\n\\n to_index = {key: value for key, value in project_meta.iteritems() if key != '_links'}\\n to_index['value'] = {key: value for key, value in project_meta['value'].iteritems() if key != 'teamMember'}\\n\\n project_search = IndexedProject.search().filter(\\n Q({'term': \\n {'uuid._exact': uuid}\\n })\\n )\\n res = project_search.execute()\\n\\n if res.hits.total == 0:\\n # Create an ES record for the new metadata.\\n # project_info_args = {key:value for key,value in project_info.iteritems() if key != '_links'}\\n project_ES = IndexedProject(**to_index)\\n project_ES.save()\\n elif res.hits.total == 1:\\n # Update the record.\\n doc = res[0]\\n doc.update(**to_index)\\n else:\\n # If we're here we've somehow indexed the same project multiple times. \\n # Delete all records and replace with the metadata passed to the task.\\n for doc in res:\\n doc.delete()\\n project_ES = IndexedProject(**to_index) \\n project_ES.save()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3eb70541f86495a1a812503637a60949","score":"0.58575755","text":"def reindex_projects(self):\n client = get_service_account_client()\n query = {'name': 'designsafe.project'}\n\n in_loop = True\n offset = 0\n while in_loop:\n listing = client.meta.listMetadata(q=json.dumps(query), offset=offset, limit=100)\n offset += 100\n\n if len(listing) == 0:\n in_loop = False\n else:\n for project in listing:\n index_or_update_project.apply_async(args=[project.uuid], queue='api')","title":""},{"docid":"25f60d64bc8a92d3f3948d857b5134ec","score":"0.57789236","text":"def create(self, document):\n self.index(document)","title":""},{"docid":"1e467fdbb386338ac8ce8d4cd52f433a","score":"0.57180494","text":"def create_or_update_doc(self, index, doc):\n body = self.bulk_body[index]\n body.write(json.dumps({'index': {'_id': doc['id']}}))\n body.write('\\n')\n body.write(json.dumps(doc))\n body.write('\\n')","title":""},{"docid":"56f028452184cb6eea1ae6844841d69e","score":"0.5655644","text":"def update_document(self, **fields):\r\n \r\n # Check which of the supplied fields are unique\r\n unique_fields = [name for name, field\r\n in self.index.schema.fields()\r\n if name in fields and field.unique]\r\n if not unique_fields:\r\n raise IndexingError(\"None of the fields in %r are unique\" % fields.keys())\r\n \r\n # Delete documents in which the supplied unique fields match\r\n s = self.searcher()\r\n for name in unique_fields:\r\n self.delete_by_term(name, fields[name])\r\n \r\n # Add the given fields\r\n self.add_document(**fields)","title":""},{"docid":"8b403dda7150c949fc6ce049474e9990","score":"0.56132174","text":"def create_document(doc_id, data):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[data[\"object_type\"]]):\n conn.create(index=alias, doc_type=GLOBAL_DOC_TYPE, body=data, id=doc_id)","title":""},{"docid":"a5241a27b6867e7f2755bbd925f12a74","score":"0.5546349","text":"def update_doc(c, i, d, u=False):\n try:\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\n return True\n except:\n return False","title":""},{"docid":"4b4b83ea1b879b88eda2c72b53e3c52c","score":"0.5532153","text":"def post_project_document(project_id,\n document_type,\n document_name,\n document_content_type,\n document_content,\n document_preamble,\n document_legal_entity_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_content_type(document_content_type)\n document.set_document_content(document_content)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n project.save()\n return project.to_dict()","title":""},{"docid":"f019a8aa87ac1017e067247c87599fa5","score":"0.5523262","text":"def process_project(project_data: dict):\n project_id = project_data.get('projectid')\n\n if project_id is not None:\n Project.objects.update_or_create(\n ona_pk=project_id,\n defaults={\n 'name': project_data.get('name'),\n 'deleted_at': project_data.get('deleted_at'),\n 'last_updated': project_data.get('date_modified'),\n 'json': project_data,\n })","title":""},{"docid":"a74cc25a8d7944eb373349e99b2e6179","score":"0.5489646","text":"def update_project(uuid, **kwargs):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n if \"name\" in kwargs:\n name = kwargs[\"name\"]\n if name != project.name:\n check_project_name = db_session.query(Project).filter_by(name=name).first()\n if check_project_name:\n raise BadRequest(\"a project with that name already exists\")\n\n data = {\"updated_at\": datetime.utcnow()}\n data.update(kwargs)\n\n try:\n db_session.query(Project).filter_by(uuid=uuid).update(data)\n db_session.commit()\n except (InvalidRequestError, ProgrammingError) as e:\n raise BadRequest(str(e))\n\n return project.as_dict()","title":""},{"docid":"968af9fd450edbe0146f7b5d25cc0cd7","score":"0.5369272","text":"def post_project_document_template(project_id,\n document_type,\n document_name,\n document_preamble,\n document_legal_entity_name,\n template_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n # Need to take the template, inject the preamble and legal entity name, and add the tabs.\n tmplt = getattr(cla.resources.contract_templates, template_name)\n template = tmplt(document_type=document_type.capitalize(),\n major_version=document.get_document_major_version(),\n minor_version=document.get_document_minor_version())\n content = template.get_html_contract(document_legal_entity_name, document_preamble)\n pdf_generator = get_pdf_service()\n pdf_content = pdf_generator.generate(content)\n document.set_document_content_type('storage+pdf')\n document.set_document_content(pdf_content, b64_encoded=False)\n document.set_raw_document_tabs(template.get_tabs())\n project.save()\n return project.to_dict()","title":""},{"docid":"18db39f126f585a872029737f5e29364","score":"0.52629536","text":"def create_document(self, entity, wait=False):\n docs = self.build_document(entity, 'single')\n for doc in docs:\n logger.info(f\"Making Doc={doc}\")\n res = self.es.index(index=self.index_name(entity.project.pk),\n id=doc['_id'],\n refresh=wait,\n routing=1,\n body={**doc['_source']})","title":""},{"docid":"397a433b2bb4643fa322625c06217544","score":"0.5236747","text":"def setup():\n index_template = Doc._index.as_template(\"base\")\n index_template.save()","title":""},{"docid":"cd1f125fa2856f065487eb0b6eafe3e6","score":"0.5214013","text":"def update_project(self, project_name, from_path):\n session = self.session\n\n # check if project exists.\n query_result = session.query(Project).filter_by(\n name=project_name).first()\n if not query_result:\n logging.info(f\"{project_name} not found, skipping.\")\n return\n\n if query_result.fully_indexed and query_result.external:\n logging.debug(\n f\"{project_name} already fully indexed, skipping.\")\n return\n\n if query_result.external and from_path:\n module_name = Path(from_path).name\n if query_result.is_module_in_project(module_name):\n logging.info(f\"{project_name} is already enough indexed, skipping.\")\n return\n\n logging.info(f\"Indexing {project_name} from {from_path}.\")\n query_result.index(from_path)\n logging.info(f\"Building {project_name}.\")\n query_result.build()\n\n session.commit()\n # session.close()","title":""},{"docid":"f69d0545c80cf079a73906a79de1b5e3","score":"0.52051336","text":"def create_index(index=None):\n index = index or INDEX\n document_types = ['project', 'component', 'registration', 'user', 'file', 'institution', 'preprint', 'collectionSubmission']\n guid_metadata_types = ['project', 'component', 'registration', 'preprint', 'file']\n\n client().indices.create(index, ignore=[400]) # HTTP 400 if index already exists\n for type_ in document_types:\n if type_ == 'collectionSubmission':\n mapping = {\n 'properties': {\n 'collectedType': NOT_ANALYZED_PROPERTY,\n 'subjects': NOT_ANALYZED_PROPERTY,\n 'status': NOT_ANALYZED_PROPERTY,\n 'issue': NOT_ANALYZED_PROPERTY,\n 'volume': NOT_ANALYZED_PROPERTY,\n 'programArea': NOT_ANALYZED_PROPERTY,\n 'provider': NOT_ANALYZED_PROPERTY,\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'abstract': ENGLISH_ANALYZER_PROPERTY,\n 'schoolType': NOT_ANALYZED_PROPERTY,\n 'studyDesign': NOT_ANALYZED_PROPERTY,\n }\n }\n else:\n mapping = {\n 'properties': {\n 'tags': NOT_ANALYZED_PROPERTY,\n 'license': {\n 'properties': {\n 'id': NOT_ANALYZED_PROPERTY,\n 'name': NOT_ANALYZED_PROPERTY,\n # Elasticsearch automatically infers mappings from content-type. `year` needs to\n # be explicitly mapped as a string to allow date ranges, which break on the inferred type\n 'year': {'type': 'string'},\n }\n },\n\n }\n }\n if type_ in guid_metadata_types:\n mapping['properties'].update({\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'description': ENGLISH_ANALYZER_PROPERTY,\n 'language': NOT_ANALYZED_PROPERTY,\n 'resource_type_general': NOT_ANALYZED_PROPERTY,\n 'funder_name': {'type': 'string'},\n 'funder_identifier': NOT_ANALYZED_PROPERTY,\n 'award_number': NOT_ANALYZED_PROPERTY,\n 'award_uri': NOT_ANALYZED_PROPERTY,\n 'award_title': {'type': 'string'},\n })\n\n if type_ == 'user':\n fields = {\n 'job': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_jobs': {\n 'type': 'string',\n 'boost': '0.01',\n },\n 'school': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_schools': {\n 'type': 'string',\n 'boost': '0.01'\n },\n }\n mapping['properties'].update(fields)\n client().indices.put_mapping(index=index, doc_type=type_, body=mapping, ignore=[400, 404])","title":""},{"docid":"8f0e31de144dba037550aa04b481e8d6","score":"0.5165092","text":"def update_index():\n # TODO: check that it's been more than a day\n deb.update_index()","title":""},{"docid":"105792b5a3b9e9dbd05f13689425dbf9","score":"0.5146721","text":"def update_projects():","title":""},{"docid":"105792b5a3b9e9dbd05f13689425dbf9","score":"0.5146721","text":"def update_projects():","title":""},{"docid":"68dbdd7332da5f3c6e2202cf083bbe01","score":"0.51065445","text":"def update(self, document):\n pass","title":""},{"docid":"dfb05f5d631251af0f718137c6fa6bec","score":"0.5101003","text":"async def replace_project(project_id: ProjectID, _replace: ProjectReplace):","title":""},{"docid":"05a62e64c02e498d0d8bdea8b6527342","score":"0.5084844","text":"def reindex_document(document):\n\n try:\n record = document._record or DocumentRecord.objects.get(pk=document.id)\n except DocumentRecord.DoesNotExist:\n return\n\n qs = TokenFieldIndex.objects.filter(\n record_id=document.id,\n revision=document.revision\n )\n defer_iteration_with_finalize(\n qs, _destroy_record, _finalize, _shards=1, _queue=_SEARCH_QUEUE\n )\n\n # Generate a brand new revision ID for this document\n record.revision = uuid.uuid4()\n record.save()\n\n index_document(document.index_name, document)","title":""},{"docid":"2bf09988a232c0f4a78f1201328c3b11","score":"0.50620395","text":"def test_get_project_by_id(app):\n\n # insert a projects into the database","title":""},{"docid":"33d3852abae5ff5e392637b5bcc96cc4","score":"0.5043024","text":"def addDocumentToIndex(self, name, vrr, nodeIdNum, title, content, tags):\n raise NotImplementedError","title":""},{"docid":"e705ba684fa2a02064f83326f33c8ec5","score":"0.5042402","text":"def projects_delete(self, perun_id):\n self.projects_update(perun_id, scratched=True)","title":""},{"docid":"3ba7858e4f524c1eb763fba5c6cd64be","score":"0.50327057","text":"def add(self, document):\n return self.db.update({document['id']: document})","title":""},{"docid":"4bcd77babd05f969641766c089631885","score":"0.5025321","text":"def delete_project_document(project_id, document_type, major_version, minor_version, username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = cla.utils.get_project_document(project, document_type, major_version, minor_version)\n if document is None:\n return {'errors': {'document': 'Document version not found'}}\n if document_type == 'individual':\n project.remove_project_individual_document(document)\n else:\n project.remove_project_corporate_document(document)\n project.save()\n return {'success': True}","title":""},{"docid":"437d1058dc5f0f770d721c6f2b83c40c","score":"0.50245684","text":"def update_project(self, database):\n opended_after_140630 = comp_dates('2014-06-30', self.ordered_opened)\n try:\n LOG.info('Handeling {proj}'.format(proj = self.name))\n project = database.ProjectDB(lims, self.id, self.samp_db)\n key = find_proj_from_view(self.proj_db, self.name)\n project.obj['_id'] = find_or_make_key(key)\n if not opended_after_140630:\n project.obj = load_status_from_google_docs.get(self.name, project.obj)\n if self.upload_data:\n info = save_couchdb_obj(self.proj_db, project.obj)\n else:\n info = self.print_couchdb_obj_to_file(project.obj)\n return \"project {name} is handled and {info}: _id = {id}\".format(\n name=self.name, info=info, id=project.obj['_id'])\n except:\n return ('Issues geting info for {name}. The \"Application\" udf might'\n ' be missing'.format(name = self.name))","title":""},{"docid":"311ac2a59dda5e4adc79b73d50426ccc","score":"0.5006261","text":"def create_day_doc(doc_datetime):\n timestampStr = doc_datetime.strftime(\"%Y-%m-%d\")\n\n if not COL_TELEMETRY.document(timestampStr).get().exists:\n try:\n COL_TELEMETRY.document(timestampStr).set({\"Date\": timestampStr})\n return \"Documents Created\", 201\n except Exception as e:\n reporting_client.report_exception()\n return f\"An Error Occured: {e}\", 400\n return \"Document already exists\", 200","title":""},{"docid":"5b85dff9afb87d73bc6a1318ae161f8e","score":"0.5004508","text":"def create_legacy_projects():\n for slug, title, pubdate in dbs:\n r = requests.get('https://physionet.org/physiobank/database/{}/HEADER.shtml'.format(slug))\n if r.status_code != 200:\n r = requests.get('https://physionet.org/physiobank/database/{}/index.shtml'.format(slug))\n if r.status_code != 200:\n print('{} does not exist'.format(slug))\n continue\n content = load_legacy_html(slug=slug)\n p = LegacyProject.objects.create(title=title, slug=slug,\n full_description=content, publish_date=datetime.datetime.strptime(pubdate.strip(), '%d %B %Y'))","title":""},{"docid":"f2401688b53bc9cf60745ef665dd2c14","score":"0.5003635","text":"def update_project(self):\n pass","title":""},{"docid":"192c820a883dfe716f2d4126f0080317","score":"0.49894994","text":"def test_create_project(self):\n payload = {\n \"name\": {\"en\": \"Test project\", \"fr\": \"Projet test\"},\n \"description\": {\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n }\n res = self.admin_client.post(\n PROJECT_URL, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n exists = Project.objects.all().filter(\n name=payload['name']).exists()\n self.assertTrue(exists)\n Project.objects.all().filter(name=payload['name']).delete()","title":""},{"docid":"98e81e7036e3f2d0f04ad8b471ec4984","score":"0.49818078","text":"def update_document(id, body):\n\n es.update(\n index=INDEX_NAME,\n doc_type=DOC_TYPE,\n id=id,\n body=body\n )","title":""},{"docid":"0130808ec40d1e1a19bb584b322e719c","score":"0.4969886","text":"def test_update(dvc):\n index = Index.from_repo(dvc)\n new_stage = Stage(dvc, path=\"path1\")\n new_index = index.update({new_stage})\n\n assert not index.stages\n assert new_index.stages == [new_stage]\n\n dup_stage1 = Stage(dvc, path=\"path1\")\n dup_stage2 = Stage(dvc, path=\"path2\")\n dup_index = index.update([dup_stage1, dup_stage2])\n assert not index.stages\n assert len(new_index.stages) == 1\n assert new_index.stages[0] is new_stage\n assert set(map(id, dup_index.stages)) == {id(dup_stage1), id(dup_stage2)}","title":""},{"docid":"61353c81ed9deebb3ce5644b5255533a","score":"0.4959378","text":"def projects_create(self, perun_id, name=None, description=None, members=None, enabled=True):\n\n perun_id = str(perun_id)\n if name is None:\n name = perun_id\n\n if not self.ro:\n os_project = self.keystone.projects.create(name=str(name),\n perun_id=perun_id,\n domain=self.target_domain_id,\n description=description,\n enabled=bool(enabled),\n scratched=False,\n flag=self.flag,\n parent=self.parent_project_id if self.nested else None)\n denbi_project = {'id': str(os_project.id),\n 'name': str(os_project.name),\n 'perun_id': str(os_project.perun_id),\n 'description': os_project.description,\n 'enabled': bool(os_project.enabled),\n 'scratched': bool(os_project.scratched),\n 'members': []}\n else:\n denbi_project = {'id': 'read-only-fake',\n 'name': name,\n 'perun_id': perun_id,\n 'description': description,\n 'enabled': enabled,\n 'scratched': False,\n 'members': []}\n # Log keystone update\n self.log2.debug(f\"project [{denbi_project['perun_id']},{denbi_project['id']}]: created.\")\n\n self.denbi_project_map[denbi_project['perun_id']] = denbi_project\n self.__project_id2perun_id__[denbi_project['id']] = denbi_project['perun_id']\n\n # if a list of members is given append them to current project\n if members:\n for member in members:\n self.projects_append_user(perun_id, member)\n\n return denbi_project","title":""},{"docid":"f37cbd0c4d6a7707fd3977bd2ed8fbb1","score":"0.49592552","text":"def projects_create(context, values):\n session = get_session()\n project = models.Project()\n if not values.get('id'):\n values['id'] = uuid.uuid4()\n with session.begin():\n project.update(values)\n project.save(session)\n return project","title":""},{"docid":"a68db4d9ea28e8d68eef7684f1c15da2","score":"0.4956459","text":"def put_design(self, designname, doc, rebuild=True):\n response = self.server._GET(self.name, \"_design\", designname,\n errors={404: None})\n if response.status_code == 200:\n current_doc = response.json(object_pairs_hook=odict)\n doc[\"_id\"] = current_doc[\"_id\"]\n doc[\"_rev\"] = current_doc[\"_rev\"]\n if doc == current_doc:\n return False\n response = self.server._PUT(self.name, \"_design\", designname, json=doc)\n if rebuild:\n for view in doc.get(\"views\", {}):\n self.view(designname, view, limit=1)\n return True","title":""},{"docid":"8b15b5501a521c21a539203e0e8fe5b9","score":"0.49440858","text":"def check_and_update_run(self, run_number):\n run_doc = self.get_run(run_number)\n if not run_doc:\n run_doc = self.run_doc_class.build_document(run_number)\n if run_doc:\n print \"Run %i is not in database, inserting...\" % int(run_number)\n self.insert_rundoc(run_doc)","title":""},{"docid":"1e868dbc23aaa9f84999b81f877cb4a9","score":"0.49372107","text":"def _add_document(doc_id, data, database=MAIN_DB):\n return requests.put(\n os.path.join(COUCH_URL, database, doc_id),\n json=data,\n headers={\"Content-Type\": \"application/json\"}\n )","title":""},{"docid":"4667675eb9f0fee441a3449ca58fd4e8","score":"0.4923755","text":"def test_project_index(project_client, session, project, project2):\n assert project.releases\n assert project.tags\n assert project.comments\n assert project.user\n assert project.user.projects\n assert project.user.projectcomments\n assert project.tag_counts == [('arcade', 2, 16), ('game', 1, 14)]\n\n resp = project_client.get('/project/1/')\n assert resp.status_code == 200\n assert b'<h1>Some project title 1' in resp.data\n assert b'<h1>Some project title 2' not in resp.data\n assert b'game' in resp.data\n assert b'arcade' in resp.data\n\n resp = project_client.get('/project-blabla+bla-1-.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'<h1>Some project title 1' in resp.data\n\n resp = project_client.get('/project/1/1')\n assert resp.status_code == 200\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project-blabla+blasbla+-1-1.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project/66/')\n assert resp.status_code == 404, 'when the project is not there'\n resp = project_client.get('/project/1/66')\n assert resp.status_code == 404, 'when the release is not there either'","title":""},{"docid":"3899bcc0fbdc4a74c83b775d8510dce1","score":"0.49015632","text":"def set_document(request, doc_id, body, errors):\n if doc_id in DB:\n errors['doc_id'].add('Document already exists')\n\n if errors:\n raise errors\n\n body['doc_id'] = doc_id\n DB[doc_id] = body\n\n # dict response for jsonify middleware\n return body","title":""},{"docid":"68855beaa907077bdd1096e7fe3a3d2d","score":"0.48790357","text":"def get_or_create(self, index_name): \r\n raise NotImplementedError","title":""},{"docid":"68855beaa907077bdd1096e7fe3a3d2d","score":"0.48790357","text":"def get_or_create(self, index_name): \r\n raise NotImplementedError","title":""},{"docid":"6423f40815445f520211d18edecdff79","score":"0.487891","text":"def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(ProjectTemplateDocRef).create_doc_instances(**kwargs)","title":""},{"docid":"8ef3b2bce4ef4353b9113c49562ff49c","score":"0.4876358","text":"def add_project():\n\n # Get an access class\n da=DomainAccess()\n ea=EmployeeAccess()\n pa=ProjectAccess()\n\n # Acquire form data\n title = request.form[\"Title\"]\n maxStudents = request.form[\"Maxstudents\"]\n descriptionTextNl = request.form[\"nlDescription\"]\n descriptionTextEng = request.form[\"engDescription\"]\n\n researchGroupNrs = request.form.getlist(\"Researchgroup\")\n typeNrs = request.form.getlist(\"Type\")\n disciplineNrs = request.form.getlist(\"Discipline\")\n tags = request.form.getlist(\"Tags\")\n related = request.form.getlist(\"Related\")\n promotorNames = request.form.getlist(\"Promotors\")\n supervisorNames = request.form.getlist(\"Supervisors\")\n externNames = request.form.getlist(\"Extern\")\n\n # Acquire request files\n files_nl = request.files.getlist(\"nlUploads\")\n files_en = request.files.getlist(\"engUploads\")\n\n # Create basic project\n project = Project(None, title, maxStudents, True)\n\n # Assign research group ID's\n for researchGroupNr in researchGroupNrs:\n if int(researchGroupNr) == 0:\n continue\n project.researchGroup.append(int(researchGroupNr))\n\n # Create dutch document\n docNL = Document(None, \"dutch\", descriptionTextNl)\n # Link attachments to document\n this_dir = os.path.dirname(__file__)\n upload_folder = os.path.join(this_dir, app.config['UPLOAD_FOLDER'])\n for file in files_nl:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(upload_folder, nameFile))\n docNL.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docNL)\n\n # Create english document\n docEn = Document(None, \"english\", descriptionTextEng)\n # Link attachments to document\n for file in files_en:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(os.path.join(upload_folder, nameFile)))\n docEn.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docEn)\n\n # Append types to the project\n typeOptions = da.get_projectType()\n for typeNr in typeNrs:\n if int(typeNr) == 0:\n continue\n project.type.append(typeOptions[int(typeNr) - 1])\n\n # Append disciplines to the project\n disciplineOptions = da.get_disciplines()\n for disciplineNr in disciplineNrs:\n if int(disciplineNr) == 0:\n continue\n project.discipline.append(disciplineOptions[int(disciplineNr) - 1])\n\n # Fetch all employees from the database\n employeeOptions = ea.get_employees()\n\n # Add promotors to the project\n # Create dictionary of promotor name and his id, used to easily append ID to the project\n promotor_id_dict = {promotorOption.name: promotorOption.id for promotorOption in employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for promotorName in promotorNames:\n if promotorName in promotor_id_dict:\n project.promotors.append(promotor_id_dict[promotorName])\n\n # Add supervisors to the project\n # Create dictionary of supervisor name and his id, used to easily append ID to the project\n supervisor_id_dict = {staffOption.name: staffOption.id for staffOption in\n employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for staffName in supervisorNames:\n if staffName in supervisor_id_dict:\n project.supervisors.append(supervisor_id_dict[staffName])\n\n # Add extern employees to the project\n for name in externNames:\n project.extern_employees.append(name)\n\n # Add tags to the project\n project.tag = list(tags)\n\n # Add related projects to the project\n # Fetch all projects from the database\n relatedProjectOptions = pa.get_projects()\n # Create dictionary of project title and its id, used to easily find the required ID\n related_project_id_dict = {relatedProjectOption.title: relatedProjectOption.ID for relatedProjectOption in\n relatedProjectOptions}\n # Loop over related projects and append the associated ID to the project relations\n for relatedProjectTitle in related:\n if relatedProjectTitle in related_project_id_dict:\n project.relatedProject.append(related_project_id_dict[relatedProjectTitle])\n\n # Assign active year\n now = datetime.now()\n project.activeYear.append(now.year)\n\n # Finalize project and add it to the database\n pa.add_project(project)\n findTag(project)\n\n # Return result to javascript\n return jsonify(result=True)","title":""},{"docid":"dcbe91f25d0a54837a18c3ca816e5125","score":"0.4869392","text":"def add_doc():\n c_id = request.args[\"c_id\"]\n doc_number = request.args[\"doc_number\"]\n\n if s.query(Documents). \\\n filter(and_(Documents.c_id == c_id,\n Documents.qpulse_no == doc_number)). \\\n count() == 0:\n doc = Documents(c_id=c_id, qpulse_no=doc_number)\n s.add(doc)\n s.commit()\n return jsonify({\"success\": True})","title":""},{"docid":"198535543a3034d8ac97e5d93610f18c","score":"0.48673","text":"def test_existing_index(self, orion_db):\n assert (\n \"new_field_1\" not in get_db(orion_db)[\"new_collection\"].index_information()\n )\n\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()\n\n # reattempt\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()","title":""},{"docid":"c466a7bd7ef34e6dfc1db2eba47deba4","score":"0.4845863","text":"def update_inv_index(self, processed_tokens, document_id):\n for token_index in range(0, len(processed_tokens)):\n if processed_tokens[token_index] not in self.terms:\n self.terms.append(processed_tokens[token_index])\n new_postings_list = list()\n if self.purpose == \"vsm\":\n new_doc = Document(document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n new_postings_list.append(new_doc)\n self.posting_lists.append(new_postings_list)\n else:\n existing_posting_list = self.get_postings_list(\n processed_tokens[token_index])\n doc_exists = False\n for i in range(len(existing_posting_list)):\n if existing_posting_list[i].id == document_id:\n if self.purpose == \"vsm\":\n existing_posting_list[i].increment_frequency()\n else:\n existing_posting_list[i].add_position(\n token_index + 1)\n doc_exists = True\n if doc_exists is False:\n if self.purpose == \"vsm\":\n new_doc = Document(\n document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n existing_posting_list.append(new_doc)","title":""},{"docid":"c0486cd11ffc6867d49d30635859999a","score":"0.48408973","text":"def create_index():\n try:\n print(\"[*] creating an index in elasticsearch\")\n r = requests.put(INDEX_URL)\n if \"index_already_exists_exception\" in r.text:\n print(\"[*] index already exists\")\n return\n mapping = {\n \"capture\": {\n \"properties\": { \n \"timestamp\": {\n \"type\": \"date\",\n },\n \"capture\": {\n \"type\": \"string\",\n },\n }\n }\n }\n r = requests.put(TYPE_URL + \"/_mapping\", data=json.dumps(mapping))\n print(r.text)\n except:\n raise Exception(\"Elasticsearch is not running\")","title":""},{"docid":"79550ceba64c00d7cd1ed2e8ab6d2e3b","score":"0.48389933","text":"def insert(self, doc):\n params = self._params_for_insert\n params.update({'body': doc, 'refresh': True})\n if not self._index_exists():\n self._create_index()\n doc = ELASTICSEARCH.index(**params)\n return doc['_id']","title":""},{"docid":"91c9aafa4e4dd3f4de10a590538ed54f","score":"0.48380858","text":"def create(self, id: str, data: Optional[JsonDict] = None) -> Document:\n if any(id == d.id for d in self._docs):\n raise ValueError(\n f\"There is already another Document instance for {id} part of the BulkOperation\"\n )\n\n doc = Document(self._database, id, data=data)\n self._docs.append(doc)\n\n return doc","title":""},{"docid":"c338b5a256e496f6123de3c7a5c4fc5b","score":"0.48324662","text":"def _import_project_data(self):\n for data in self._get_fyle_data(\n self.fyle_api.Projects, {'active_only': False}):\n defaults = {\n 'name': data['name'],\n 'description': data['description'],\n 'active': data['active'],\n }\n Project.objects.update_or_create(\n id=data['id'], user=self.batch.user, defaults=defaults)","title":""},{"docid":"8266958ea8686836d449b0440c676007","score":"0.48165312","text":"def index(self, doctype, docid, document):\n dbname = self.dbname\n self.broker.DB.index(index=dbname, doc_type=doctype, id=docid, body=document)","title":""},{"docid":"b95af5d41d231f0ee4aff7660a7f0d47","score":"0.48141915","text":"def add_new_doc(self, document):\n document_dictionary = document.term_doc_dictionary\n document_capitals = document.capital_letter_indexer\n document_date = document.tweet_date\n for key_term in document_capitals:\n if key_term not in self.global_capitals:\n self.global_capitals[key_term] = document_capitals[key_term]\n else:\n if not document_capitals[key_term]:\n self.global_capitals[key_term] = False\n\n document_entities = document.named_entities\n\n for entity in document_entities:\n self.entities_dict[entity] += 1\n document_vec = np.zeros(shape=25)\n is_covid = False\n for term in document_dictionary:\n if term == 'covid':\n is_covid = True\n if term in self.glove_dict:\n document_vec += self.glove_dict[term]\n document_vec /= len(document_dictionary)\n # document_vec, # numpy array of size 25 which\n # represents the document in 25 dimensional space(GloVe)\n if is_covid:\n self.document_posting_covid[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting_covid\" + str(self.doc_posting_covid_counter)\n else:\n self.doc_posting_dict[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting\"+str(self.doc_posting_counter)\n\n if len(self.doc_posting_dict) == 100000:\n self.save_doc_posting()\n if len(self.document_posting_covid) == 100000:\n self.save_doc_covid()\n\n\n # Go over each term in the doc\n for term in document_dictionary.keys():\n try:\n if term in self.inverted_idx:\n if term not in self.posting_dict:\n self.posting_dict[term] = None\n self.inverted_idx[term][0] += 1\n\n else:\n self.inverted_idx[term] = [1, str(self.counter_of_postings)]\n self.posting_dict[term] = None\n\n insert_tuple = (document.tweet_id, # tweet id\n document.doc_length, # total number of words in tweet\n document.max_tf, # number of occurrences of most common term in tweet\n document.unique_terms, # number of unique words in tweet\n document_dictionary[term], # number of times term is in tweet - tf!\n )\n # if there're no documents for the current term, insert the first document\n if self.posting_dict[term] is None:\n self.posting_list.append((term, [insert_tuple]))\n self.posting_dict[term] = len(self.posting_list) - 1\n else:\n tuple_idx = self.posting_dict[term]\n self.posting_list[tuple_idx][1].append(insert_tuple)\n\n # check if posting_dict is full\n if len(self.posting_list) == self.postingDict_size:\n self.save_postings()\n\n except:\n print('problem with the following key {}'.format(term))","title":""},{"docid":"3ad5d272bb1d56172468a84a457c5e10","score":"0.4809561","text":"def test_indexed_doc(es):\n investor_company = CompanyFactory()\n\n large_investor_profile = LargeCapitalInvestorProfileFactory(\n investor_company=investor_company,\n )\n\n doc = ESLargeInvestorProfile.es_document(large_investor_profile)\n elasticsearch.bulk(actions=(doc, ), chunk_size=1)\n\n es.indices.refresh()\n\n indexed_large_investor_profile = es.get(\n index=ESLargeInvestorProfile.get_write_index(),\n id=large_investor_profile.pk,\n )\n\n assert indexed_large_investor_profile['_id'] == str(large_investor_profile.pk)\n assert indexed_large_investor_profile['_source'] == {\n '_document_type': LargeInvestorProfileSearchApp.name,\n 'id': str(large_investor_profile.pk),\n 'asset_classes_of_interest': [],\n 'country_of_origin': {\n 'id': str(large_investor_profile.country_of_origin.pk),\n 'name': str(large_investor_profile.country_of_origin.name),\n },\n 'investor_company': {\n 'id': str(investor_company.pk),\n 'name': str(investor_company.name),\n 'trading_names': investor_company.trading_names,\n },\n 'created_by': None,\n 'investor_type': None,\n 'required_checks_conducted': None,\n 'deal_ticket_sizes': [],\n 'investment_types': [],\n 'minimum_return_rate': None,\n 'time_horizons': [],\n 'restrictions': [],\n 'construction_risks': [],\n 'minimum_equity_percentage': None,\n 'desired_deal_roles': [],\n 'uk_region_locations': [],\n 'other_countries_being_considered': [],\n 'investable_capital': None,\n 'investor_description': '',\n 'created_on': '2019-01-01T00:00:00+00:00',\n 'notes_on_locations': '',\n 'global_assets_under_management': None,\n 'modified_on': '2019-01-01T00:00:00+00:00',\n }","title":""},{"docid":"51f2637b70c16067a94a6bd4e0258048","score":"0.48014212","text":"def read_document(self, d):\n if not self.in_collection(d):\n self.documents[d] = self.index.add_document(d)\n\n if len(self.index) >= READ_LIMIT_TO_WRITE_TO_MONGO:\n self.flush_to_mongo()","title":""},{"docid":"ce5a9df77a8dea388f8abce2e0a3ba20","score":"0.47926098","text":"def project(name, description, skip_if_exists):\n if skip_if_exists:\n result = Client().graphql(\n query={\n \"query\": {\n with_args(\"project\", {\"where\": {\"name\": {\"_eq\": name}}}): {\n \"id\": True\n }\n }\n }\n )\n if result.data.project:\n click.secho(\"{} already exists\".format(name), fg=\"green\")\n return\n\n try:\n Client().create_project(project_name=name, project_description=description)\n except ClientError as exc:\n click.echo(f\"{type(exc).__name__}: {exc}\")\n click.secho(\"Error creating project\", fg=\"red\")\n return\n\n click.secho(\"{} created\".format(name), fg=\"green\")","title":""},{"docid":"d308c3fb5e0f7925f8a90beabd7769b1","score":"0.47813076","text":"def test_create(self):\n doc = Document(DocumentID=\"123\")","title":""},{"docid":"3653cfc90294d606c691f6002160e051","score":"0.47683114","text":"async def update_project(project_id: ProjectID, update: ProjectUpdate):","title":""},{"docid":"3cb2ea4fbdea7b30cfb8fc035ec33b63","score":"0.4765857","text":"def delete(project, version=None):","title":""},{"docid":"304ed14f24d509d80cc30d9e20a000bb","score":"0.47605696","text":"def update_document(self, document_id, mappings):\n\n if len(mappings) == 0:\n return None\n\n requests = []\n for (src, dest) in mappings.items():\n requests.append(\n {\n \"replaceAllText\": {\n \"containsText\": {\n \"text\": src,\n \"matchCase\": \"true\",\n },\n \"replaceText\": dest,\n }\n }\n )\n\n body = {\"requests\": requests}\n results = self.docs.batchUpdate(documentId=document_id, body=body).execute()\n\n return results","title":""},{"docid":"0b0ff57f645529136b9fca1f29ca8a3d","score":"0.47586724","text":"def assign_weather_to_project(delphin_id: str, weather_ids: list) -> str:\n\n # Save climate class to delphin document\n delphin_document = delphin_db.Delphin.objects(id=delphin_id).first()\n\n for id_ in weather_ids:\n weather_document = weather_db.Weather.objects(id=id_).first()\n delphin_document.update(push__weather=weather_document)\n\n return delphin_document.id","title":""},{"docid":"da1b60c6b85b7a75cb23675cb817f78d","score":"0.4754898","text":"def insert_document(self, index_name, type, document_id, document):\n\n try:\n if self.es_client is None or not self.es_client.ping():\n self.connect()\n\n if document_id is not None and document_id != \"\":\n return self.es_client.index(index=index_name, doc_type=type, id=document_id, body=document, refresh='wait_for', request_timeout=30)\n else:\n return self.es_client.index(index=index_name, doc_type=type, body=document, refresh='wait_for', request_timeout=30)\n except:\n logger.exception(\"could not insert document is %s in index %s \"%(document_id, index_name))","title":""},{"docid":"1f767eaef4e45cf976afc1f230f7464f","score":"0.47510174","text":"def upsert(self):\n\n # Raise error if index is not writable\n if not self.config.get(\"writable\"):\n raise ReadOnlyError(\"Attempting to upsert a read-only index (writable != True)\")\n\n if self.embeddings and self.documents:\n with self.lock:\n # Run upsert\n self.embeddings.upsert(self.documents)\n\n # Save index if path available, otherwise this is an memory-only index\n if self.config.get(\"path\"):\n self.embeddings.save(self.config[\"path\"], self.config.get(\"cloud\"))\n\n # Reset document stream\n self.documents.close()\n self.documents = None","title":""},{"docid":"da970d3a07959bb70343da54b9e0f1dc","score":"0.4724849","text":"def updateDocs():\n docs = Dataset.search()\n for doc in docs.scan():\n created = getattr(getattr(doc, \"_ts\", None), \"date_created\", None)\n updated = getattr(getattr(doc, \"_ts\", None), \"last_updated\", None)\n if not created and not updated:\n existing_dc = getattr(getattr(doc, \"_meta\", None), \"date_created\", None)\n existing_lu = getattr(getattr(doc, \"_meta\", None), \"last_updated\", None)\n\n if existing_dc and existing_lu:\n doc.update(**{\"_ts\": {\"date_created\": existing_dc, \"last_updated\": existing_lu}})\n logging.info(\"Updating fields for doc with existing\")\n else:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n logging.info(\"Adding fields for doc with defaults\")\n else:\n created_bug = getattr(getattr(doc, \"_ts\", None), \"guide\", None)\n if created_bug:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n else:\n logging.info(\"Doc already has TS last updated field\")","title":""},{"docid":"4c0614bb9a344bbcb7774f40d315a663","score":"0.4718103","text":"def _write_projects(projects):\n path = _get_projects_index_file()\n contents = util.read_yaml(path)\n contents.update({'projects': projects})\n util.write_yaml(path, contents)","title":""},{"docid":"f3b73dd77df050a21496f43cdef09149","score":"0.47118863","text":"def delete(self, project, query):\n self.es.delete_by_query(\n index=self.index_name(project),\n body=query,\n conflicts='proceed',\n )","title":""},{"docid":"3024c1ab542c8ad983221027239d933a","score":"0.47102568","text":"def add_doc(self, doc_id, contents):\n self.__es.index(index=self.__index_name, doc_type=self.DOC_TYPE, id=doc_id, body=contents)","title":""},{"docid":"af9147664a4952bc73e1c324e25ce1b2","score":"0.47069666","text":"def test_designs_id_put(self):\n pass","title":""},{"docid":"6d6dd2c023bd9b98b8654db1f500b71a","score":"0.4705125","text":"def insert(dictObject, db):\n\n \"\"\" res = db[\"menus\"].update_one(\n {\"id\": dictObject[\"id\"],\n \"date\": dictObject[\"date\"],\n \"mensaName\": dictObject[\"mensaName\"],\n \"lang\": dictObject[\"lang\"]\n }, {\"$set\": dictObject}, upsert=True)\n\n print(\"modifed: id: \" + str(dictObject[\"id\"].encode('utf-8')) + \" Date: \" + dictObject[\"date\"] + \" lang: \" +\n dictObject[\"lang\"])\n if res.upserted_id is None:\n print(\"res: modified: \" + str(res.modified_count) + \" matched: \" + str(res.matched_count))\n else:\n print(\"res: inserted\") \"\"\"","title":""},{"docid":"876fbf36475b5fdab80415b990d77feb","score":"0.46986815","text":"def scrape_new_data(index=\"verdicts\"):\n if es.indices.exists(index=index):\n tic = time.time()\n scraper.update_database()\n toc = time.time()\n print(\"Scraping done! Time needed: {}\".format(str(toc - tic)))\n else:\n print(\"Index to be updated does not exists!\")","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"620ced752c4b779874c9474695caa26d","score":"0.4695401","text":"def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(TaskTemplateDocRef).create_doc_instances(**kwargs)","title":""},{"docid":"de775a146f47d74e09cd6d9324f8dbab","score":"0.4694352","text":"def create_update_project(request, slug=None):\n request_user = request.user\n if slug is not None:\n project = Project.objects.get(slug=slug)\n if project.owner != request.user:\n raise PermissionDenied\n else:\n project_form = CreateProjectForm(instance=project)\n position_formset = PositionFormset(\n queryset=Position.objects.filter(\n project=project\n ))\n else:\n project = None\n project_form = CreateProjectForm()\n position_formset = PositionFormset(\n queryset=Position.objects.none()\n )\n\n if request.method == 'POST':\n project_form = CreateProjectForm(\n request.POST, \n instance=project\n )\n position_formset = PositionFormset(\n request.POST,\n queryset=Position.objects.filter(project=project)\n )\n if project_form.is_valid() and position_formset.is_valid():\n project = project_form.save(commit=False)\n project.owner = request_user\n new_project = False\n if project.status is None:\n new_project = True\n project.status = 'A'\n project.save()\n positions = position_formset.save(commit=False)\n for position in positions:\n position.project = project\n if position.status not in ('E', 'F'):\n position.status = 'E'\n position.save()\n position_formset.save_m2m()\n position_formset.save()\n if new_project:\n messages.success(\n request, 'Project {} created!'.format(project.title))\n else:\n messages.success(\n request, 'Project {} updated!'.format(project.title))\n return HttpResponseRedirect(reverse('projects:project', kwargs={'slug': project.slug}))\n\n context = {\n 'project_form': project_form,\n 'position_formset': position_formset,\n }\n return render(request, 'projects/create_update_project.html', context)","title":""},{"docid":"1df57eb7f6c6f2712debcc484e3347d8","score":"0.46942607","text":"def test_update_project(self):\n pass","title":""},{"docid":"c17dc383ee2e366b1a5fc4e1604f9b99","score":"0.4692427","text":"def refresh(self, project):\n self.es.indices.refresh(index=self.index_name(project))","title":""},{"docid":"6d957caf110468efdc93c17755a223f5","score":"0.46918374","text":"def test_rud_document(self):\n\n # upload a documnet\n self.upload_document()\n\n # retrieve document PK to test\n test_document = Document.objects.all().first()\n\n # test retrive document with invvalid pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': 99}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 404)\n\n # test update document with invvalid pk\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n self.assertEqual(response.status_code, 404)\n\n # test delete document with invvalid pk\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 404)\n\n # test retrieve document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 200)\n\n # test update document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n test_document.refresh_from_db()\n self.assertEqual(response.status_code, 200)\n self.assertEqual(test_document.language.short_name, 'af')\n self.assertEqual(test_document.document_type.short_name, 'pdf')\n self.assertEqual(Document.objects.all().count(), 1)\n\n # test delete document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 204)\n self.assertEqual(Document.objects.all().count(), 0)","title":""},{"docid":"a42d660681fa0dea1386fc66c5c6f7ae","score":"0.46850002","text":"def indexDocElement(es_Url, awsauth, docData):\n try:\n headers = {\"Content-Type\": \"application/json\"}\n resp = requests.put(es_Url, auth=awsauth,\n headers=headers, json=docData)\n print(resp.content)\n if resp.status_code == 201:\n logger.info('INFO: Successfully created element into ES')\n elif resp.status_code == 200:\n logger.info('INFO: Successfully updated element into ES')\n else:\n logger.error(f'FAILURE: Unable to index element {resp.content}')\n raise\n except Exception as e:\n logger.error(f'ERROR: {str(e)}')\n logger.error(f\"ERROR: Unable to index line:{docData['content']}\")\n raise","title":""},{"docid":"11e4d54117e769ca06d0c90afeab7608","score":"0.46833104","text":"def unindex_object(documentId):","title":""},{"docid":"7a6374eccd3ecd3ac8f9c0c4077d249a","score":"0.46808988","text":"async def update_data(doc_id: int, response: IntakeData,\n db_file: str = tinydb_file):\n db0 = await get_db(db_file)\n db0.update(response.dict(), doc_ids=[doc_id])\n return db0","title":""},{"docid":"f6ca95cb848f9130b79bfd3e7d693228","score":"0.4669554","text":"def uploadDesignDocuments(self,directory):\n\n import glob\n pattern = os.path.join(directory,'*.py')\n viewFiles = glob.glob(pattern)\n for viewFile in viewFiles:\n execfile(viewFile)\n for view in views:\n viewID = os.path.join('_design',view)\n print (\"uploading \", view, \" of \", viewFile, \" to \", self.databaseName)\n document = self.db.get(viewID)\n if document:\n self.db.delete(document)\n self.db[viewID] = views[view]","title":""},{"docid":"4c6b0bfb6c8ba313b8b13141fe7abd40","score":"0.46691608","text":"def update(self, index, document, **options):\n\n index = base._resolve_index(index)\n\n index_name = index.get_name()\n index_doc_type = options.pop('doc_type', index.get_doc_type())\n\n id, adapted_document = index.adapt_document(document)\n\n if id:\n options.setdefault(\"id\", id)\n\n self._es.index(index_name, index_doc_type, adapted_document, **options)","title":""},{"docid":"e43a3a310e72a471695196fe25c4e8b9","score":"0.46609166","text":"def test_delete_project(self):\n name = {\"en\": \"Test project\", \"fr\": \"Projet test\"}\n project = Project.objects.create(\n name=name,\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.delete(detail_url)\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\n exists = Project.objects.all().filter(name=name).exists()\n self.assertFalse(exists)","title":""},{"docid":"0e611165d35dc8ad0d765ab9c84890b1","score":"0.4655304","text":"def update_project(project_id, update_dict):\n # update_dict = convert_project_object_to_dict(update_dict)\n # try:\n Project.objects.get_or_404(id=project_id).update(**update_dict)\n return (True, \"success\")\n # except Exception, e:\n # return (False, repr(e))","title":""},{"docid":"4ec582f4f431c4caf8adbe8f976d8607","score":"0.46348995","text":"def project(id):\n\t#Call load() in the data layer to load the database and store it's contents in the variable db\n\tdb = data.load('data.json')\n\t#Call get_project() in the data layer and store the project in the variable project\n\tproject = data.get_project(db, id)\n\t#If None was returned by get_project():\n\t\t#No project with the specified ID exists, ergo a nonexistent page has been visited, ergo abort with a 404\n\tif project is None:\n\t\tabort(404)\n\telse:\n\t\t#adding a counter to the project-page.\n\t\tcounter = open(\"doc/counter.log\", \"r+\")\t\t\t\t\t#opens counter.log\n\t\tcount = 1\n\t\tpid = str(\"project \")+str(project['project_id'])+\"\\n\"\n\t\tfor line in counter:\n\t\t\tif pid in line:\n\t\t\t\tcount += 1\n\t\tproject['counter'] = count\n\t\tcounter.write(pid)\n\t\tcounter.close()\n\t\t#Render project.html with:\n\t\t\t#project --> used to display information about the project\n\t\treturn render_template('project.html', project=project)","title":""},{"docid":"cb39d6df0b7b3498a570ea1bba71feb3","score":"0.46262908","text":"def _update_document_by_id(doc_id, body, object_type, *, retry_on_conflict=0, **kwargs):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[object_type]):\n try:\n conn.update(\n index=alias,\n doc_type=GLOBAL_DOC_TYPE,\n body=body,\n id=doc_id,\n params={\"retry_on_conflict\": retry_on_conflict, **kwargs},\n )\n # Our policy for document update-related version conflicts right now is to log them\n # and allow the app to continue as normal.\n except ConflictError:\n log.error(\n \"Update API request resulted in a version conflict (alias: %s, doc id: %s)\",\n alias,\n doc_id,\n )","title":""},{"docid":"331d110c41a2530b147f583ddccbeb0a","score":"0.46188155","text":"def create_opensearch_index(index_name: str):\n with open(Path(__file__).parent / \"pipeline_logs_mapping.json5\") as fd:\n index_mappings = pyjson5.load(fd)\n res = requests.put(\n f\"{OPENSEARCH_ENDPOINT}/{index_name}\",\n data=json.dumps(index_mappings),\n headers={\"Content-Type\": \"application/json\"},\n auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),\n )\n if res.status_code >= 400:\n logging.error(\n f'Failed to create opensearch index \"{index_name}\", '\n f\"server responded with status {res.status_code}\"\n )\n try:\n logging.error(res.json())\n except json.JSONDecodeError:\n logging.error(res.text)","title":""},{"docid":"74d8d50b59c45411d8f14763398d3d38","score":"0.46130043","text":"def createDoc(self, docInfos):\n if docInfos['id'] != '':\n newDocId = self.plominoDatabase.invokeFactory(docInfos['type'], \n id=docInfos['id'])\n newDoc = self.plominoDatabase.getDocument(docInfos['id'])\n else:\n newDoc = self.plominoDatabase.createDocument()\n \n if newDoc is not None:\n if self.plominoDatabase.getForm(docInfos['form']) is not None:\n newDoc.setItem('Form', docInfos['form'])\n\n # TODO: create a default form if form does not exist ? in a later version\n # REM: doc is not saved if not associated with a existing form\n \n # Set the items of this document\n for itemInfos in docInfos['items']:\n newDoc.setItem(itemInfos['name'], itemInfos['value'])\n # TODO: check the value before set ?\n #self.createItem(itemInfos, newDoc)\n\n # Add the files inserted in the document\n if docInfos['files'] != []:\n if not hasattr(newDoc.getForm(), 'imported_files'):\n fieldId = newDoc.getForm().invokeFactory('PlominoField', \n id='imported_files',\n title='Imported Files',\n FieldType='ATTACHMENT')\n newDoc.getForm().setFormLayout(newDoc.getForm().getFormLayout() + \\\n '<p>Imported files: <span class=\"plominoFieldClass\">imported_files</span></p>')\n for fileInfos in docInfos['files']:\n newDoc.setfile(a2b_base64(str(fileInfos['content'])), fileInfos['name'])\n\n newDoc.save()\n\n else:\n raise Exception\n\n self.plominoDatabase.getIndex().refresh()","title":""},{"docid":"a5eea1dedb85eb1c7282df2ad603ce7f","score":"0.46129036","text":"def upsert_document(doc_id, doc, object_type, *, retry_on_conflict=0, **kwargs):\n _update_document_by_id(\n doc_id,\n {\"doc\": doc, \"doc_as_upsert\": True},\n object_type,\n retry_on_conflict=retry_on_conflict,\n **kwargs,\n )","title":""},{"docid":"10ab54ba9546b01682521f015050756d","score":"0.46108896","text":"def delete_project(uuid):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n experiments = Experiment.query.filter(Experiment.project_id == uuid).all()\n for experiment in experiments:\n # remove dependencies\n operators = db_session.query(Operator).filter(Operator.experiment_id == experiment.uuid).all()\n for operator in operators:\n Dependency.query.filter(Dependency.operator_id == operator.uuid).delete()\n\n # remove operators\n Operator.query.filter(Operator.experiment_id == experiment.uuid).delete()\n\n Experiment.query.filter(Experiment.project_id == uuid).delete()\n\n db_session.delete(project)\n db_session.commit()\n\n prefix = join(\"experiments\", uuid)\n remove_objects(prefix=prefix)\n\n return {\"message\": \"Project deleted\"}","title":""},{"docid":"1acfc33f6ce86672083340d2f05e83f7","score":"0.46102795","text":"def index_document(body):\n es.index(\n index='documents',\n doc_type='document',\n body=body\n )","title":""},{"docid":"ce2ba6ec3062bc8b00d66042e7c58f95","score":"0.46075854","text":"def post(self, request, *args, **kwargs):\n self.project_slug = self.kwargs.get('project_slug', None)\n self.project = Project.objects.get(slug=self.project_slug)\n return super(VersionDeleteView, self).post(request, *args, **kwargs)","title":""},{"docid":"7cca9d7d23d55aea633a1d309353db55","score":"0.46073022","text":"def add(self, document_or_documents):\n\n added_document_ids = []\n\n if isinstance(document_or_documents, Document):\n was_list = False\n documents = [document_or_documents]\n else:\n was_list = True\n documents = document_or_documents[:]\n\n # First-pass validation\n self._validate_documents(documents)\n\n with transaction.atomic(independent=True):\n for document in documents:\n record = document._record\n\n # We go through the document fields, pull out the values that have been set\n # then we index them.\n field_data = {\n f: getattr(document, document.get_field(f).attname)\n for f in document.get_fields() if f != \"id\"\n }\n\n # Generate a database representation of this Document use\n # the passed ID if there is one\n record, created = DocumentRecord.objects.update_or_create(\n pk=document.id,\n defaults={\n \"index_stats\": self.index,\n \"data\": field_data\n }\n )\n document.id = record.id\n document._record = record\n\n if created:\n index_document(self.name, document)\n added_document_ids.append(record.id)\n else:\n # This wipes out any existing document, bumps the revision\n # and then indexes this one\n reindex_document(document)\n\n return added_document_ids if was_list else (added_document_ids[0] if added_document_ids else 0)","title":""},{"docid":"7df4233a622d8556ec5399e88e8dcaf1","score":"0.4595747","text":"def create_document(record):\n # TODO: Write an iterator over the INDEX_MAP to fetch fields from the record and\n # return a dictionary representing the document.\n return None","title":""},{"docid":"b994723d25f0a197f745292df18fa72c","score":"0.45953628","text":"def test_update_project(self):\n project = Project.objects.create(\n name={\"en\": \"Test project\", \"fr\": \"Projet test\"},\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n payload = {\"name\": {\"en\": \"New name\", \"fr\": \"Nouveau nom\"}}\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.patch(detail_url, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n project.refresh_from_db()\n self.assertEqual(project.name, payload['name'])","title":""},{"docid":"4b3a5c81140b78c9a4413be72272e6f4","score":"0.45898458","text":"def CreateProjects(projRef):\r\n\r\n print(\"Creating projects\")\r\n # make root directory with permit date\r\n if (len(projRef) == 0):\r\n print(\"No project available\")\r\n return\r\n\r\n dt = str(projRef.values()[0].PERMIT_DATE).split(\" \")[0]\r\n project_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.join(NFOutputPath.OUTPUT, dt))\r\n if not os.path.exists(project_root):\r\n os.makedirs(project_root)\r\n\r\n summary = []\r\n\r\n try:\r\n edit = arcpy.da.Editor(NFDataSource.COMMONGIS_CONN)\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n # create one folder for each project ref\r\n for key in projRef:\r\n arcpy.AddMessage(key)\r\n\r\n project_folder = os.path.join(project_root, key)\r\n\r\n # if the project folder already contains plans - skip; this project has been imported before; will not be included in new MaxDia report too\r\n if not os.path.exists(project_folder):\r\n os.makedirs(project_folder)\r\n file_list = []\r\n else:\r\n file_list = os.listdir(project_folder)\r\n\r\n pdf_exists = False\r\n\r\n for f in file_list:\r\n\r\n if \".pdf\" in f:\r\n arcpy.AddMessage(\"PDF in project folder: \" + project_folder)\r\n pdf_exists = True\r\n break\r\n if pdf_exists:\r\n continue\r\n\r\n cwdPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.CWD)))\r\n\r\n UpdateCWD(projRef[key])\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.CWDTEMPLATE), projRef[key], cwdPdf, [], \"\")\r\n\r\n wsnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WSN)))\r\n wsnasset = UpdateWSN(projRef[key])\r\n\r\n if (len(wsnasset) > 0):\r\n # GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WSNTEMPLATE), projRef[key], wsnPdf, wsnasset, \"WSN\")\r\n print \"wsnasset\"\r\n\r\n wrnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WRN)))\r\n wrnasset = UpdateWRN(projRef[key])\r\n\r\n if (len(wrnasset) > 0):\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WRNTEMPLATE), projRef[key], wrnPdf, wrnasset, \"WRN\")\r\n summary.extend(GenerateReport(projRef[key], wrnasset))\r\n\r\n editCounter = editCounter + 1\r\n\r\n if (editCounter == 10):\r\n print (\"intermediate save @ 10\")\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n del edit\r\n\r\n max_dia_report_name = GenerateMaxDiaReport(summary, dt, projRef)\r\n arcpy.AddMessage(\"END Creating projects\")\r\n arcpy.AddMessage(max_dia_report_name)\r\n print(\"Generated maxdia report\")\r\n return max_dia_report_name\r\n\r\n except Exception as err:\r\n print err\r\n Error_Handler(\"Create Projects Failed\")\r\n Error_Handler(repr(traceback.format_exc()))","title":""},{"docid":"86d93df5e9bcd0bbd1dbc95aa839899d","score":"0.45813572","text":"def insert_into_index(es_object, index_name, record): \n try:\n outcome = es_object.index(index=index_name, body=record)\n print(f\"Inserted object into Index '{index_name}'.\")\n print(str(record))\n except Exception as ex:\n print('Error in indexing data')\n print(str(ex))","title":""},{"docid":"f4a20f89cdf668abaec4a099362659d0","score":"0.4577445","text":"def test_get_or_create_project__call():\n project_name = str(uuid.uuid1())\n project = synapseclient.Project(name=project_name)\n returned = synapseclient.Project(name=project_name,\n id=str(uuid.uuid1()))\n with patch.object(CREATE_CLS,\n \"_find_by_obj_or_create\",\n return_value=returned) as patch_find_or_create:\n new_project = CREATE_CLS.get_or_create_project(name=project_name)\n assert new_project == returned\n patch_find_or_create.assert_called_once_with(project)","title":""},{"docid":"01878f3733f7e6fa22c36b45e3f46ae8","score":"0.45740703","text":"def test_create_project_adds_a_project(db: Session) -> None:\n\n # there is no project to start with\n assert db.query(models.Project).count() == 0\n\n # create a project\n project = schemas.ProjectCreate(\n name=\"My Project\", directory=\"/wherever\", command=\"whatever\"\n )\n create_project(db, project)\n\n # there is a project now...\n assert db.query(models.Project).count() == 1\n\n # ... and it has the correct content\n db_project = db.query(models.Project).first()\n assert db_project.id is not None\n assert db_project.command == project.command\n assert db_project.directory == project.directory\n assert db_project.name == project.name","title":""},{"docid":"8dd10092dad3a1e670dc70a758f1a80d","score":"0.45728135","text":"def add_guid_index():\n catalog = api.portal.get_tool('portal_catalog')\n if GUID_INDEX_NAME not in catalog.indexes():\n log.info(\"Adding GUID index %r\" % GUID_INDEX_NAME)\n catalog.addIndex(GUID_INDEX_NAME, 'FieldIndex')\n else:\n log.info(\"GUID index %r already exists\" % GUID_INDEX_NAME)","title":""}],"string":"[\n {\n \"docid\": \"3eb70541f86495a1a812503637a60949\",\n \"score\": \"0.58575755\",\n \"text\": \"def reindex_projects(self):\\n client = get_service_account_client()\\n query = {'name': 'designsafe.project'}\\n\\n in_loop = True\\n offset = 0\\n while in_loop:\\n listing = client.meta.listMetadata(q=json.dumps(query), offset=offset, limit=100)\\n offset += 100\\n\\n if len(listing) == 0:\\n in_loop = False\\n else:\\n for project in listing:\\n index_or_update_project.apply_async(args=[project.uuid], queue='api')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f60d64bc8a92d3f3948d857b5134ec\",\n \"score\": \"0.57789236\",\n \"text\": \"def create(self, document):\\n self.index(document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e467fdbb386338ac8ce8d4cd52f433a\",\n \"score\": \"0.57180494\",\n \"text\": \"def create_or_update_doc(self, index, doc):\\n body = self.bulk_body[index]\\n body.write(json.dumps({'index': {'_id': doc['id']}}))\\n body.write('\\\\n')\\n body.write(json.dumps(doc))\\n body.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f028452184cb6eea1ae6844841d69e\",\n \"score\": \"0.5655644\",\n \"text\": \"def update_document(self, **fields):\\r\\n \\r\\n # Check which of the supplied fields are unique\\r\\n unique_fields = [name for name, field\\r\\n in self.index.schema.fields()\\r\\n if name in fields and field.unique]\\r\\n if not unique_fields:\\r\\n raise IndexingError(\\\"None of the fields in %r are unique\\\" % fields.keys())\\r\\n \\r\\n # Delete documents in which the supplied unique fields match\\r\\n s = self.searcher()\\r\\n for name in unique_fields:\\r\\n self.delete_by_term(name, fields[name])\\r\\n \\r\\n # Add the given fields\\r\\n self.add_document(**fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b403dda7150c949fc6ce049474e9990\",\n \"score\": \"0.56132174\",\n \"text\": \"def create_document(doc_id, data):\\n conn = get_conn()\\n for alias in get_active_aliases(conn, object_types=[data[\\\"object_type\\\"]]):\\n conn.create(index=alias, doc_type=GLOBAL_DOC_TYPE, body=data, id=doc_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5241a27b6867e7f2755bbd925f12a74\",\n \"score\": \"0.5546349\",\n \"text\": \"def update_doc(c, i, d, u=False):\\n try:\\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b4b83ea1b879b88eda2c72b53e3c52c\",\n \"score\": \"0.5532153\",\n \"text\": \"def post_project_document(project_id,\\n document_type,\\n document_name,\\n document_content_type,\\n document_content,\\n document_preamble,\\n document_legal_entity_name,\\n new_major_version=None,\\n username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = get_document_instance()\\n document.set_document_name(document_name)\\n document.set_document_content_type(document_content_type)\\n document.set_document_content(document_content)\\n document.set_document_preamble(document_preamble)\\n document.set_document_legal_entity_name(document_legal_entity_name)\\n if document_type == 'individual':\\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n if major == 0:\\n major = 1\\n document.set_document_major_version(major)\\n document.set_document_minor_version(minor + 1)\\n project.add_project_individual_document(document)\\n else:\\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n if major == 0:\\n major = 1\\n document.set_document_major_version(major)\\n document.set_document_minor_version(minor + 1)\\n project.add_project_corporate_document(document)\\n project.save()\\n return project.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f019a8aa87ac1017e067247c87599fa5\",\n \"score\": \"0.5523262\",\n \"text\": \"def process_project(project_data: dict):\\n project_id = project_data.get('projectid')\\n\\n if project_id is not None:\\n Project.objects.update_or_create(\\n ona_pk=project_id,\\n defaults={\\n 'name': project_data.get('name'),\\n 'deleted_at': project_data.get('deleted_at'),\\n 'last_updated': project_data.get('date_modified'),\\n 'json': project_data,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74cc25a8d7944eb373349e99b2e6179\",\n \"score\": \"0.5489646\",\n \"text\": \"def update_project(uuid, **kwargs):\\n project = Project.query.get(uuid)\\n\\n if project is None:\\n raise NotFound(\\\"The specified project does not exist\\\")\\n\\n if \\\"name\\\" in kwargs:\\n name = kwargs[\\\"name\\\"]\\n if name != project.name:\\n check_project_name = db_session.query(Project).filter_by(name=name).first()\\n if check_project_name:\\n raise BadRequest(\\\"a project with that name already exists\\\")\\n\\n data = {\\\"updated_at\\\": datetime.utcnow()}\\n data.update(kwargs)\\n\\n try:\\n db_session.query(Project).filter_by(uuid=uuid).update(data)\\n db_session.commit()\\n except (InvalidRequestError, ProgrammingError) as e:\\n raise BadRequest(str(e))\\n\\n return project.as_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968af9fd450edbe0146f7b5d25cc0cd7\",\n \"score\": \"0.5369272\",\n \"text\": \"def post_project_document_template(project_id,\\n document_type,\\n document_name,\\n document_preamble,\\n document_legal_entity_name,\\n template_name,\\n new_major_version=None,\\n username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = get_document_instance()\\n document.set_document_name(document_name)\\n document.set_document_preamble(document_preamble)\\n document.set_document_legal_entity_name(document_legal_entity_name)\\n if document_type == 'individual':\\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n document.set_document_minor_version(minor + 1)\\n project.add_project_individual_document(document)\\n else:\\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n document.set_document_minor_version(minor + 1)\\n project.add_project_corporate_document(document)\\n # Need to take the template, inject the preamble and legal entity name, and add the tabs.\\n tmplt = getattr(cla.resources.contract_templates, template_name)\\n template = tmplt(document_type=document_type.capitalize(),\\n major_version=document.get_document_major_version(),\\n minor_version=document.get_document_minor_version())\\n content = template.get_html_contract(document_legal_entity_name, document_preamble)\\n pdf_generator = get_pdf_service()\\n pdf_content = pdf_generator.generate(content)\\n document.set_document_content_type('storage+pdf')\\n document.set_document_content(pdf_content, b64_encoded=False)\\n document.set_raw_document_tabs(template.get_tabs())\\n project.save()\\n return project.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18db39f126f585a872029737f5e29364\",\n \"score\": \"0.52629536\",\n \"text\": \"def create_document(self, entity, wait=False):\\n docs = self.build_document(entity, 'single')\\n for doc in docs:\\n logger.info(f\\\"Making Doc={doc}\\\")\\n res = self.es.index(index=self.index_name(entity.project.pk),\\n id=doc['_id'],\\n refresh=wait,\\n routing=1,\\n body={**doc['_source']})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397a433b2bb4643fa322625c06217544\",\n \"score\": \"0.5236747\",\n \"text\": \"def setup():\\n index_template = Doc._index.as_template(\\\"base\\\")\\n index_template.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1f125fa2856f065487eb0b6eafe3e6\",\n \"score\": \"0.5214013\",\n \"text\": \"def update_project(self, project_name, from_path):\\n session = self.session\\n\\n # check if project exists.\\n query_result = session.query(Project).filter_by(\\n name=project_name).first()\\n if not query_result:\\n logging.info(f\\\"{project_name} not found, skipping.\\\")\\n return\\n\\n if query_result.fully_indexed and query_result.external:\\n logging.debug(\\n f\\\"{project_name} already fully indexed, skipping.\\\")\\n return\\n\\n if query_result.external and from_path:\\n module_name = Path(from_path).name\\n if query_result.is_module_in_project(module_name):\\n logging.info(f\\\"{project_name} is already enough indexed, skipping.\\\")\\n return\\n\\n logging.info(f\\\"Indexing {project_name} from {from_path}.\\\")\\n query_result.index(from_path)\\n logging.info(f\\\"Building {project_name}.\\\")\\n query_result.build()\\n\\n session.commit()\\n # session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69d0545c80cf079a73906a79de1b5e3\",\n \"score\": \"0.52051336\",\n \"text\": \"def create_index(index=None):\\n index = index or INDEX\\n document_types = ['project', 'component', 'registration', 'user', 'file', 'institution', 'preprint', 'collectionSubmission']\\n guid_metadata_types = ['project', 'component', 'registration', 'preprint', 'file']\\n\\n client().indices.create(index, ignore=[400]) # HTTP 400 if index already exists\\n for type_ in document_types:\\n if type_ == 'collectionSubmission':\\n mapping = {\\n 'properties': {\\n 'collectedType': NOT_ANALYZED_PROPERTY,\\n 'subjects': NOT_ANALYZED_PROPERTY,\\n 'status': NOT_ANALYZED_PROPERTY,\\n 'issue': NOT_ANALYZED_PROPERTY,\\n 'volume': NOT_ANALYZED_PROPERTY,\\n 'programArea': NOT_ANALYZED_PROPERTY,\\n 'provider': NOT_ANALYZED_PROPERTY,\\n 'title': ENGLISH_ANALYZER_PROPERTY,\\n 'abstract': ENGLISH_ANALYZER_PROPERTY,\\n 'schoolType': NOT_ANALYZED_PROPERTY,\\n 'studyDesign': NOT_ANALYZED_PROPERTY,\\n }\\n }\\n else:\\n mapping = {\\n 'properties': {\\n 'tags': NOT_ANALYZED_PROPERTY,\\n 'license': {\\n 'properties': {\\n 'id': NOT_ANALYZED_PROPERTY,\\n 'name': NOT_ANALYZED_PROPERTY,\\n # Elasticsearch automatically infers mappings from content-type. `year` needs to\\n # be explicitly mapped as a string to allow date ranges, which break on the inferred type\\n 'year': {'type': 'string'},\\n }\\n },\\n\\n }\\n }\\n if type_ in guid_metadata_types:\\n mapping['properties'].update({\\n 'title': ENGLISH_ANALYZER_PROPERTY,\\n 'description': ENGLISH_ANALYZER_PROPERTY,\\n 'language': NOT_ANALYZED_PROPERTY,\\n 'resource_type_general': NOT_ANALYZED_PROPERTY,\\n 'funder_name': {'type': 'string'},\\n 'funder_identifier': NOT_ANALYZED_PROPERTY,\\n 'award_number': NOT_ANALYZED_PROPERTY,\\n 'award_uri': NOT_ANALYZED_PROPERTY,\\n 'award_title': {'type': 'string'},\\n })\\n\\n if type_ == 'user':\\n fields = {\\n 'job': {\\n 'type': 'string',\\n 'boost': '1',\\n },\\n 'all_jobs': {\\n 'type': 'string',\\n 'boost': '0.01',\\n },\\n 'school': {\\n 'type': 'string',\\n 'boost': '1',\\n },\\n 'all_schools': {\\n 'type': 'string',\\n 'boost': '0.01'\\n },\\n }\\n mapping['properties'].update(fields)\\n client().indices.put_mapping(index=index, doc_type=type_, body=mapping, ignore=[400, 404])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0e31de144dba037550aa04b481e8d6\",\n \"score\": \"0.5165092\",\n \"text\": \"def update_index():\\n # TODO: check that it's been more than a day\\n deb.update_index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105792b5a3b9e9dbd05f13689425dbf9\",\n \"score\": \"0.5146721\",\n \"text\": \"def update_projects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105792b5a3b9e9dbd05f13689425dbf9\",\n \"score\": \"0.5146721\",\n \"text\": \"def update_projects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68dbdd7332da5f3c6e2202cf083bbe01\",\n \"score\": \"0.51065445\",\n \"text\": \"def update(self, document):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb05f5d631251af0f718137c6fa6bec\",\n \"score\": \"0.5101003\",\n \"text\": \"async def replace_project(project_id: ProjectID, _replace: ProjectReplace):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a62e64c02e498d0d8bdea8b6527342\",\n \"score\": \"0.5084844\",\n \"text\": \"def reindex_document(document):\\n\\n try:\\n record = document._record or DocumentRecord.objects.get(pk=document.id)\\n except DocumentRecord.DoesNotExist:\\n return\\n\\n qs = TokenFieldIndex.objects.filter(\\n record_id=document.id,\\n revision=document.revision\\n )\\n defer_iteration_with_finalize(\\n qs, _destroy_record, _finalize, _shards=1, _queue=_SEARCH_QUEUE\\n )\\n\\n # Generate a brand new revision ID for this document\\n record.revision = uuid.uuid4()\\n record.save()\\n\\n index_document(document.index_name, document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf09988a232c0f4a78f1201328c3b11\",\n \"score\": \"0.50620395\",\n \"text\": \"def test_get_project_by_id(app):\\n\\n # insert a projects into the database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d3852abae5ff5e392637b5bcc96cc4\",\n \"score\": \"0.5043024\",\n \"text\": \"def addDocumentToIndex(self, name, vrr, nodeIdNum, title, content, tags):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e705ba684fa2a02064f83326f33c8ec5\",\n \"score\": \"0.5042402\",\n \"text\": \"def projects_delete(self, perun_id):\\n self.projects_update(perun_id, scratched=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba7858e4f524c1eb763fba5c6cd64be\",\n \"score\": \"0.50327057\",\n \"text\": \"def add(self, document):\\n return self.db.update({document['id']: document})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcd77babd05f969641766c089631885\",\n \"score\": \"0.5025321\",\n \"text\": \"def delete_project_document(project_id, document_type, major_version, minor_version, username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = cla.utils.get_project_document(project, document_type, major_version, minor_version)\\n if document is None:\\n return {'errors': {'document': 'Document version not found'}}\\n if document_type == 'individual':\\n project.remove_project_individual_document(document)\\n else:\\n project.remove_project_corporate_document(document)\\n project.save()\\n return {'success': True}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d1058dc5f0f770d721c6f2b83c40c\",\n \"score\": \"0.50245684\",\n \"text\": \"def update_project(self, database):\\n opended_after_140630 = comp_dates('2014-06-30', self.ordered_opened)\\n try:\\n LOG.info('Handeling {proj}'.format(proj = self.name))\\n project = database.ProjectDB(lims, self.id, self.samp_db)\\n key = find_proj_from_view(self.proj_db, self.name)\\n project.obj['_id'] = find_or_make_key(key)\\n if not opended_after_140630:\\n project.obj = load_status_from_google_docs.get(self.name, project.obj)\\n if self.upload_data:\\n info = save_couchdb_obj(self.proj_db, project.obj)\\n else:\\n info = self.print_couchdb_obj_to_file(project.obj)\\n return \\\"project {name} is handled and {info}: _id = {id}\\\".format(\\n name=self.name, info=info, id=project.obj['_id'])\\n except:\\n return ('Issues geting info for {name}. The \\\"Application\\\" udf might'\\n ' be missing'.format(name = self.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311ac2a59dda5e4adc79b73d50426ccc\",\n \"score\": \"0.5006261\",\n \"text\": \"def create_day_doc(doc_datetime):\\n timestampStr = doc_datetime.strftime(\\\"%Y-%m-%d\\\")\\n\\n if not COL_TELEMETRY.document(timestampStr).get().exists:\\n try:\\n COL_TELEMETRY.document(timestampStr).set({\\\"Date\\\": timestampStr})\\n return \\\"Documents Created\\\", 201\\n except Exception as e:\\n reporting_client.report_exception()\\n return f\\\"An Error Occured: {e}\\\", 400\\n return \\\"Document already exists\\\", 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b85dff9afb87d73bc6a1318ae161f8e\",\n \"score\": \"0.5004508\",\n \"text\": \"def create_legacy_projects():\\n for slug, title, pubdate in dbs:\\n r = requests.get('https://physionet.org/physiobank/database/{}/HEADER.shtml'.format(slug))\\n if r.status_code != 200:\\n r = requests.get('https://physionet.org/physiobank/database/{}/index.shtml'.format(slug))\\n if r.status_code != 200:\\n print('{} does not exist'.format(slug))\\n continue\\n content = load_legacy_html(slug=slug)\\n p = LegacyProject.objects.create(title=title, slug=slug,\\n full_description=content, publish_date=datetime.datetime.strptime(pubdate.strip(), '%d %B %Y'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2401688b53bc9cf60745ef665dd2c14\",\n \"score\": \"0.5003635\",\n \"text\": \"def update_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192c820a883dfe716f2d4126f0080317\",\n \"score\": \"0.49894994\",\n \"text\": \"def test_create_project(self):\\n payload = {\\n \\\"name\\\": {\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"},\\n \\\"description\\\": {\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n }\\n res = self.admin_client.post(\\n PROJECT_URL, payload, format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n exists = Project.objects.all().filter(\\n name=payload['name']).exists()\\n self.assertTrue(exists)\\n Project.objects.all().filter(name=payload['name']).delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e81e7036e3f2d0f04ad8b471ec4984\",\n \"score\": \"0.49818078\",\n \"text\": \"def update_document(id, body):\\n\\n es.update(\\n index=INDEX_NAME,\\n doc_type=DOC_TYPE,\\n id=id,\\n body=body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0130808ec40d1e1a19bb584b322e719c\",\n \"score\": \"0.4969886\",\n \"text\": \"def test_update(dvc):\\n index = Index.from_repo(dvc)\\n new_stage = Stage(dvc, path=\\\"path1\\\")\\n new_index = index.update({new_stage})\\n\\n assert not index.stages\\n assert new_index.stages == [new_stage]\\n\\n dup_stage1 = Stage(dvc, path=\\\"path1\\\")\\n dup_stage2 = Stage(dvc, path=\\\"path2\\\")\\n dup_index = index.update([dup_stage1, dup_stage2])\\n assert not index.stages\\n assert len(new_index.stages) == 1\\n assert new_index.stages[0] is new_stage\\n assert set(map(id, dup_index.stages)) == {id(dup_stage1), id(dup_stage2)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61353c81ed9deebb3ce5644b5255533a\",\n \"score\": \"0.4959378\",\n \"text\": \"def projects_create(self, perun_id, name=None, description=None, members=None, enabled=True):\\n\\n perun_id = str(perun_id)\\n if name is None:\\n name = perun_id\\n\\n if not self.ro:\\n os_project = self.keystone.projects.create(name=str(name),\\n perun_id=perun_id,\\n domain=self.target_domain_id,\\n description=description,\\n enabled=bool(enabled),\\n scratched=False,\\n flag=self.flag,\\n parent=self.parent_project_id if self.nested else None)\\n denbi_project = {'id': str(os_project.id),\\n 'name': str(os_project.name),\\n 'perun_id': str(os_project.perun_id),\\n 'description': os_project.description,\\n 'enabled': bool(os_project.enabled),\\n 'scratched': bool(os_project.scratched),\\n 'members': []}\\n else:\\n denbi_project = {'id': 'read-only-fake',\\n 'name': name,\\n 'perun_id': perun_id,\\n 'description': description,\\n 'enabled': enabled,\\n 'scratched': False,\\n 'members': []}\\n # Log keystone update\\n self.log2.debug(f\\\"project [{denbi_project['perun_id']},{denbi_project['id']}]: created.\\\")\\n\\n self.denbi_project_map[denbi_project['perun_id']] = denbi_project\\n self.__project_id2perun_id__[denbi_project['id']] = denbi_project['perun_id']\\n\\n # if a list of members is given append them to current project\\n if members:\\n for member in members:\\n self.projects_append_user(perun_id, member)\\n\\n return denbi_project\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37cbd0c4d6a7707fd3977bd2ed8fbb1\",\n \"score\": \"0.49592552\",\n \"text\": \"def projects_create(context, values):\\n session = get_session()\\n project = models.Project()\\n if not values.get('id'):\\n values['id'] = uuid.uuid4()\\n with session.begin():\\n project.update(values)\\n project.save(session)\\n return project\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a68db4d9ea28e8d68eef7684f1c15da2\",\n \"score\": \"0.4956459\",\n \"text\": \"def put_design(self, designname, doc, rebuild=True):\\n response = self.server._GET(self.name, \\\"_design\\\", designname,\\n errors={404: None})\\n if response.status_code == 200:\\n current_doc = response.json(object_pairs_hook=odict)\\n doc[\\\"_id\\\"] = current_doc[\\\"_id\\\"]\\n doc[\\\"_rev\\\"] = current_doc[\\\"_rev\\\"]\\n if doc == current_doc:\\n return False\\n response = self.server._PUT(self.name, \\\"_design\\\", designname, json=doc)\\n if rebuild:\\n for view in doc.get(\\\"views\\\", {}):\\n self.view(designname, view, limit=1)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b15b5501a521c21a539203e0e8fe5b9\",\n \"score\": \"0.49440858\",\n \"text\": \"def check_and_update_run(self, run_number):\\n run_doc = self.get_run(run_number)\\n if not run_doc:\\n run_doc = self.run_doc_class.build_document(run_number)\\n if run_doc:\\n print \\\"Run %i is not in database, inserting...\\\" % int(run_number)\\n self.insert_rundoc(run_doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e868dbc23aaa9f84999b81f877cb4a9\",\n \"score\": \"0.49372107\",\n \"text\": \"def _add_document(doc_id, data, database=MAIN_DB):\\n return requests.put(\\n os.path.join(COUCH_URL, database, doc_id),\\n json=data,\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4667675eb9f0fee441a3449ca58fd4e8\",\n \"score\": \"0.4923755\",\n \"text\": \"def test_project_index(project_client, session, project, project2):\\n assert project.releases\\n assert project.tags\\n assert project.comments\\n assert project.user\\n assert project.user.projects\\n assert project.user.projectcomments\\n assert project.tag_counts == [('arcade', 2, 16), ('game', 1, 14)]\\n\\n resp = project_client.get('/project/1/')\\n assert resp.status_code == 200\\n assert b'<h1>Some project title 1' in resp.data\\n assert b'<h1>Some project title 2' not in resp.data\\n assert b'game' in resp.data\\n assert b'arcade' in resp.data\\n\\n resp = project_client.get('/project-blabla+bla-1-.html')\\n assert resp.status_code == 200, 'because this url works too.'\\n assert b'<h1>Some project title 1' in resp.data\\n\\n resp = project_client.get('/project/1/1')\\n assert resp.status_code == 200\\n assert b'A release title.' in resp.data\\n\\n resp = project_client.get('/project-blabla+blasbla+-1-1.html')\\n assert resp.status_code == 200, 'because this url works too.'\\n assert b'A release title.' in resp.data\\n\\n resp = project_client.get('/project/66/')\\n assert resp.status_code == 404, 'when the project is not there'\\n resp = project_client.get('/project/1/66')\\n assert resp.status_code == 404, 'when the release is not there either'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3899bcc0fbdc4a74c83b775d8510dce1\",\n \"score\": \"0.49015632\",\n \"text\": \"def set_document(request, doc_id, body, errors):\\n if doc_id in DB:\\n errors['doc_id'].add('Document already exists')\\n\\n if errors:\\n raise errors\\n\\n body['doc_id'] = doc_id\\n DB[doc_id] = body\\n\\n # dict response for jsonify middleware\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68855beaa907077bdd1096e7fe3a3d2d\",\n \"score\": \"0.48790357\",\n \"text\": \"def get_or_create(self, index_name): \\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68855beaa907077bdd1096e7fe3a3d2d\",\n \"score\": \"0.48790357\",\n \"text\": \"def get_or_create(self, index_name): \\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6423f40815445f520211d18edecdff79\",\n \"score\": \"0.487891\",\n \"text\": \"def create_doc_instances(self, **kwargs):\\n kwargs[u\\\"cdb_project_id\\\"] = self.cdb_project_id\\n return self.Super(ProjectTemplateDocRef).create_doc_instances(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef3b2bce4ef4353b9113c49562ff49c\",\n \"score\": \"0.4876358\",\n \"text\": \"def add_project():\\n\\n # Get an access class\\n da=DomainAccess()\\n ea=EmployeeAccess()\\n pa=ProjectAccess()\\n\\n # Acquire form data\\n title = request.form[\\\"Title\\\"]\\n maxStudents = request.form[\\\"Maxstudents\\\"]\\n descriptionTextNl = request.form[\\\"nlDescription\\\"]\\n descriptionTextEng = request.form[\\\"engDescription\\\"]\\n\\n researchGroupNrs = request.form.getlist(\\\"Researchgroup\\\")\\n typeNrs = request.form.getlist(\\\"Type\\\")\\n disciplineNrs = request.form.getlist(\\\"Discipline\\\")\\n tags = request.form.getlist(\\\"Tags\\\")\\n related = request.form.getlist(\\\"Related\\\")\\n promotorNames = request.form.getlist(\\\"Promotors\\\")\\n supervisorNames = request.form.getlist(\\\"Supervisors\\\")\\n externNames = request.form.getlist(\\\"Extern\\\")\\n\\n # Acquire request files\\n files_nl = request.files.getlist(\\\"nlUploads\\\")\\n files_en = request.files.getlist(\\\"engUploads\\\")\\n\\n # Create basic project\\n project = Project(None, title, maxStudents, True)\\n\\n # Assign research group ID's\\n for researchGroupNr in researchGroupNrs:\\n if int(researchGroupNr) == 0:\\n continue\\n project.researchGroup.append(int(researchGroupNr))\\n\\n # Create dutch document\\n docNL = Document(None, \\\"dutch\\\", descriptionTextNl)\\n # Link attachments to document\\n this_dir = os.path.dirname(__file__)\\n upload_folder = os.path.join(this_dir, app.config['UPLOAD_FOLDER'])\\n for file in files_nl:\\n nameFile = secure_filename(title + '_' + file.filename)\\n file.save(os.path.join(upload_folder, nameFile))\\n docNL.attachment.append(nameFile)\\n # Assign document as description\\n project.desc.append(docNL)\\n\\n # Create english document\\n docEn = Document(None, \\\"english\\\", descriptionTextEng)\\n # Link attachments to document\\n for file in files_en:\\n nameFile = secure_filename(title + '_' + file.filename)\\n file.save(os.path.join(os.path.join(upload_folder, nameFile)))\\n docEn.attachment.append(nameFile)\\n # Assign document as description\\n project.desc.append(docEn)\\n\\n # Append types to the project\\n typeOptions = da.get_projectType()\\n for typeNr in typeNrs:\\n if int(typeNr) == 0:\\n continue\\n project.type.append(typeOptions[int(typeNr) - 1])\\n\\n # Append disciplines to the project\\n disciplineOptions = da.get_disciplines()\\n for disciplineNr in disciplineNrs:\\n if int(disciplineNr) == 0:\\n continue\\n project.discipline.append(disciplineOptions[int(disciplineNr) - 1])\\n\\n # Fetch all employees from the database\\n employeeOptions = ea.get_employees()\\n\\n # Add promotors to the project\\n # Create dictionary of promotor name and his id, used to easily append ID to the project\\n promotor_id_dict = {promotorOption.name: promotorOption.id for promotorOption in employeeOptions}\\n # Loop over names and connect the associated ID's to the project\\n for promotorName in promotorNames:\\n if promotorName in promotor_id_dict:\\n project.promotors.append(promotor_id_dict[promotorName])\\n\\n # Add supervisors to the project\\n # Create dictionary of supervisor name and his id, used to easily append ID to the project\\n supervisor_id_dict = {staffOption.name: staffOption.id for staffOption in\\n employeeOptions}\\n # Loop over names and connect the associated ID's to the project\\n for staffName in supervisorNames:\\n if staffName in supervisor_id_dict:\\n project.supervisors.append(supervisor_id_dict[staffName])\\n\\n # Add extern employees to the project\\n for name in externNames:\\n project.extern_employees.append(name)\\n\\n # Add tags to the project\\n project.tag = list(tags)\\n\\n # Add related projects to the project\\n # Fetch all projects from the database\\n relatedProjectOptions = pa.get_projects()\\n # Create dictionary of project title and its id, used to easily find the required ID\\n related_project_id_dict = {relatedProjectOption.title: relatedProjectOption.ID for relatedProjectOption in\\n relatedProjectOptions}\\n # Loop over related projects and append the associated ID to the project relations\\n for relatedProjectTitle in related:\\n if relatedProjectTitle in related_project_id_dict:\\n project.relatedProject.append(related_project_id_dict[relatedProjectTitle])\\n\\n # Assign active year\\n now = datetime.now()\\n project.activeYear.append(now.year)\\n\\n # Finalize project and add it to the database\\n pa.add_project(project)\\n findTag(project)\\n\\n # Return result to javascript\\n return jsonify(result=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbe91f25d0a54837a18c3ca816e5125\",\n \"score\": \"0.4869392\",\n \"text\": \"def add_doc():\\n c_id = request.args[\\\"c_id\\\"]\\n doc_number = request.args[\\\"doc_number\\\"]\\n\\n if s.query(Documents). \\\\\\n filter(and_(Documents.c_id == c_id,\\n Documents.qpulse_no == doc_number)). \\\\\\n count() == 0:\\n doc = Documents(c_id=c_id, qpulse_no=doc_number)\\n s.add(doc)\\n s.commit()\\n return jsonify({\\\"success\\\": True})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198535543a3034d8ac97e5d93610f18c\",\n \"score\": \"0.48673\",\n \"text\": \"def test_existing_index(self, orion_db):\\n assert (\\n \\\"new_field_1\\\" not in get_db(orion_db)[\\\"new_collection\\\"].index_information()\\n )\\n\\n orion_db.ensure_index(\\\"new_collection\\\", \\\"new_field\\\", unique=True)\\n assert \\\"new_field_1\\\" in get_db(orion_db)[\\\"new_collection\\\"].index_information()\\n\\n # reattempt\\n orion_db.ensure_index(\\\"new_collection\\\", \\\"new_field\\\", unique=True)\\n assert \\\"new_field_1\\\" in get_db(orion_db)[\\\"new_collection\\\"].index_information()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c466a7bd7ef34e6dfc1db2eba47deba4\",\n \"score\": \"0.4845863\",\n \"text\": \"def update_inv_index(self, processed_tokens, document_id):\\n for token_index in range(0, len(processed_tokens)):\\n if processed_tokens[token_index] not in self.terms:\\n self.terms.append(processed_tokens[token_index])\\n new_postings_list = list()\\n if self.purpose == \\\"vsm\\\":\\n new_doc = Document(document_id, store_term_weights=True)\\n else:\\n new_doc = Document(document_id, token_index + 1)\\n new_postings_list.append(new_doc)\\n self.posting_lists.append(new_postings_list)\\n else:\\n existing_posting_list = self.get_postings_list(\\n processed_tokens[token_index])\\n doc_exists = False\\n for i in range(len(existing_posting_list)):\\n if existing_posting_list[i].id == document_id:\\n if self.purpose == \\\"vsm\\\":\\n existing_posting_list[i].increment_frequency()\\n else:\\n existing_posting_list[i].add_position(\\n token_index + 1)\\n doc_exists = True\\n if doc_exists is False:\\n if self.purpose == \\\"vsm\\\":\\n new_doc = Document(\\n document_id, store_term_weights=True)\\n else:\\n new_doc = Document(document_id, token_index + 1)\\n existing_posting_list.append(new_doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0486cd11ffc6867d49d30635859999a\",\n \"score\": \"0.48408973\",\n \"text\": \"def create_index():\\n try:\\n print(\\\"[*] creating an index in elasticsearch\\\")\\n r = requests.put(INDEX_URL)\\n if \\\"index_already_exists_exception\\\" in r.text:\\n print(\\\"[*] index already exists\\\")\\n return\\n mapping = {\\n \\\"capture\\\": {\\n \\\"properties\\\": { \\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\",\\n },\\n \\\"capture\\\": {\\n \\\"type\\\": \\\"string\\\",\\n },\\n }\\n }\\n }\\n r = requests.put(TYPE_URL + \\\"/_mapping\\\", data=json.dumps(mapping))\\n print(r.text)\\n except:\\n raise Exception(\\\"Elasticsearch is not running\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79550ceba64c00d7cd1ed2e8ab6d2e3b\",\n \"score\": \"0.48389933\",\n \"text\": \"def insert(self, doc):\\n params = self._params_for_insert\\n params.update({'body': doc, 'refresh': True})\\n if not self._index_exists():\\n self._create_index()\\n doc = ELASTICSEARCH.index(**params)\\n return doc['_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c9aafa4e4dd3f4de10a590538ed54f\",\n \"score\": \"0.48380858\",\n \"text\": \"def create(self, id: str, data: Optional[JsonDict] = None) -> Document:\\n if any(id == d.id for d in self._docs):\\n raise ValueError(\\n f\\\"There is already another Document instance for {id} part of the BulkOperation\\\"\\n )\\n\\n doc = Document(self._database, id, data=data)\\n self._docs.append(doc)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c338b5a256e496f6123de3c7a5c4fc5b\",\n \"score\": \"0.48324662\",\n \"text\": \"def _import_project_data(self):\\n for data in self._get_fyle_data(\\n self.fyle_api.Projects, {'active_only': False}):\\n defaults = {\\n 'name': data['name'],\\n 'description': data['description'],\\n 'active': data['active'],\\n }\\n Project.objects.update_or_create(\\n id=data['id'], user=self.batch.user, defaults=defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8266958ea8686836d449b0440c676007\",\n \"score\": \"0.48165312\",\n \"text\": \"def index(self, doctype, docid, document):\\n dbname = self.dbname\\n self.broker.DB.index(index=dbname, doc_type=doctype, id=docid, body=document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95af5d41d231f0ee4aff7660a7f0d47\",\n \"score\": \"0.48141915\",\n \"text\": \"def add_new_doc(self, document):\\n document_dictionary = document.term_doc_dictionary\\n document_capitals = document.capital_letter_indexer\\n document_date = document.tweet_date\\n for key_term in document_capitals:\\n if key_term not in self.global_capitals:\\n self.global_capitals[key_term] = document_capitals[key_term]\\n else:\\n if not document_capitals[key_term]:\\n self.global_capitals[key_term] = False\\n\\n document_entities = document.named_entities\\n\\n for entity in document_entities:\\n self.entities_dict[entity] += 1\\n document_vec = np.zeros(shape=25)\\n is_covid = False\\n for term in document_dictionary:\\n if term == 'covid':\\n is_covid = True\\n if term in self.glove_dict:\\n document_vec += self.glove_dict[term]\\n document_vec /= len(document_dictionary)\\n # document_vec, # numpy array of size 25 which\\n # represents the document in 25 dimensional space(GloVe)\\n if is_covid:\\n self.document_posting_covid[document.tweet_id] = (document_vec, document_date)\\n self.document_dict[document.tweet_id] = \\\"doc_posting_covid\\\" + str(self.doc_posting_covid_counter)\\n else:\\n self.doc_posting_dict[document.tweet_id] = (document_vec, document_date)\\n self.document_dict[document.tweet_id] = \\\"doc_posting\\\"+str(self.doc_posting_counter)\\n\\n if len(self.doc_posting_dict) == 100000:\\n self.save_doc_posting()\\n if len(self.document_posting_covid) == 100000:\\n self.save_doc_covid()\\n\\n\\n # Go over each term in the doc\\n for term in document_dictionary.keys():\\n try:\\n if term in self.inverted_idx:\\n if term not in self.posting_dict:\\n self.posting_dict[term] = None\\n self.inverted_idx[term][0] += 1\\n\\n else:\\n self.inverted_idx[term] = [1, str(self.counter_of_postings)]\\n self.posting_dict[term] = None\\n\\n insert_tuple = (document.tweet_id, # tweet id\\n document.doc_length, # total number of words in tweet\\n document.max_tf, # number of occurrences of most common term in tweet\\n document.unique_terms, # number of unique words in tweet\\n document_dictionary[term], # number of times term is in tweet - tf!\\n )\\n # if there're no documents for the current term, insert the first document\\n if self.posting_dict[term] is None:\\n self.posting_list.append((term, [insert_tuple]))\\n self.posting_dict[term] = len(self.posting_list) - 1\\n else:\\n tuple_idx = self.posting_dict[term]\\n self.posting_list[tuple_idx][1].append(insert_tuple)\\n\\n # check if posting_dict is full\\n if len(self.posting_list) == self.postingDict_size:\\n self.save_postings()\\n\\n except:\\n print('problem with the following key {}'.format(term))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad5d272bb1d56172468a84a457c5e10\",\n \"score\": \"0.4809561\",\n \"text\": \"def test_indexed_doc(es):\\n investor_company = CompanyFactory()\\n\\n large_investor_profile = LargeCapitalInvestorProfileFactory(\\n investor_company=investor_company,\\n )\\n\\n doc = ESLargeInvestorProfile.es_document(large_investor_profile)\\n elasticsearch.bulk(actions=(doc, ), chunk_size=1)\\n\\n es.indices.refresh()\\n\\n indexed_large_investor_profile = es.get(\\n index=ESLargeInvestorProfile.get_write_index(),\\n id=large_investor_profile.pk,\\n )\\n\\n assert indexed_large_investor_profile['_id'] == str(large_investor_profile.pk)\\n assert indexed_large_investor_profile['_source'] == {\\n '_document_type': LargeInvestorProfileSearchApp.name,\\n 'id': str(large_investor_profile.pk),\\n 'asset_classes_of_interest': [],\\n 'country_of_origin': {\\n 'id': str(large_investor_profile.country_of_origin.pk),\\n 'name': str(large_investor_profile.country_of_origin.name),\\n },\\n 'investor_company': {\\n 'id': str(investor_company.pk),\\n 'name': str(investor_company.name),\\n 'trading_names': investor_company.trading_names,\\n },\\n 'created_by': None,\\n 'investor_type': None,\\n 'required_checks_conducted': None,\\n 'deal_ticket_sizes': [],\\n 'investment_types': [],\\n 'minimum_return_rate': None,\\n 'time_horizons': [],\\n 'restrictions': [],\\n 'construction_risks': [],\\n 'minimum_equity_percentage': None,\\n 'desired_deal_roles': [],\\n 'uk_region_locations': [],\\n 'other_countries_being_considered': [],\\n 'investable_capital': None,\\n 'investor_description': '',\\n 'created_on': '2019-01-01T00:00:00+00:00',\\n 'notes_on_locations': '',\\n 'global_assets_under_management': None,\\n 'modified_on': '2019-01-01T00:00:00+00:00',\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f2637b70c16067a94a6bd4e0258048\",\n \"score\": \"0.48014212\",\n \"text\": \"def read_document(self, d):\\n if not self.in_collection(d):\\n self.documents[d] = self.index.add_document(d)\\n\\n if len(self.index) >= READ_LIMIT_TO_WRITE_TO_MONGO:\\n self.flush_to_mongo()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5a9df77a8dea388f8abce2e0a3ba20\",\n \"score\": \"0.47926098\",\n \"text\": \"def project(name, description, skip_if_exists):\\n if skip_if_exists:\\n result = Client().graphql(\\n query={\\n \\\"query\\\": {\\n with_args(\\\"project\\\", {\\\"where\\\": {\\\"name\\\": {\\\"_eq\\\": name}}}): {\\n \\\"id\\\": True\\n }\\n }\\n }\\n )\\n if result.data.project:\\n click.secho(\\\"{} already exists\\\".format(name), fg=\\\"green\\\")\\n return\\n\\n try:\\n Client().create_project(project_name=name, project_description=description)\\n except ClientError as exc:\\n click.echo(f\\\"{type(exc).__name__}: {exc}\\\")\\n click.secho(\\\"Error creating project\\\", fg=\\\"red\\\")\\n return\\n\\n click.secho(\\\"{} created\\\".format(name), fg=\\\"green\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d308c3fb5e0f7925f8a90beabd7769b1\",\n \"score\": \"0.47813076\",\n \"text\": \"def test_create(self):\\n doc = Document(DocumentID=\\\"123\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3653cfc90294d606c691f6002160e051\",\n \"score\": \"0.47683114\",\n \"text\": \"async def update_project(project_id: ProjectID, update: ProjectUpdate):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb2ea4fbdea7b30cfb8fc035ec33b63\",\n \"score\": \"0.4765857\",\n \"text\": \"def delete(project, version=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304ed14f24d509d80cc30d9e20a000bb\",\n \"score\": \"0.47605696\",\n \"text\": \"def update_document(self, document_id, mappings):\\n\\n if len(mappings) == 0:\\n return None\\n\\n requests = []\\n for (src, dest) in mappings.items():\\n requests.append(\\n {\\n \\\"replaceAllText\\\": {\\n \\\"containsText\\\": {\\n \\\"text\\\": src,\\n \\\"matchCase\\\": \\\"true\\\",\\n },\\n \\\"replaceText\\\": dest,\\n }\\n }\\n )\\n\\n body = {\\\"requests\\\": requests}\\n results = self.docs.batchUpdate(documentId=document_id, body=body).execute()\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b0ff57f645529136b9fca1f29ca8a3d\",\n \"score\": \"0.47586724\",\n \"text\": \"def assign_weather_to_project(delphin_id: str, weather_ids: list) -> str:\\n\\n # Save climate class to delphin document\\n delphin_document = delphin_db.Delphin.objects(id=delphin_id).first()\\n\\n for id_ in weather_ids:\\n weather_document = weather_db.Weather.objects(id=id_).first()\\n delphin_document.update(push__weather=weather_document)\\n\\n return delphin_document.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1b60c6b85b7a75cb23675cb817f78d\",\n \"score\": \"0.4754898\",\n \"text\": \"def insert_document(self, index_name, type, document_id, document):\\n\\n try:\\n if self.es_client is None or not self.es_client.ping():\\n self.connect()\\n\\n if document_id is not None and document_id != \\\"\\\":\\n return self.es_client.index(index=index_name, doc_type=type, id=document_id, body=document, refresh='wait_for', request_timeout=30)\\n else:\\n return self.es_client.index(index=index_name, doc_type=type, body=document, refresh='wait_for', request_timeout=30)\\n except:\\n logger.exception(\\\"could not insert document is %s in index %s \\\"%(document_id, index_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f767eaef4e45cf976afc1f230f7464f\",\n \"score\": \"0.47510174\",\n \"text\": \"def upsert(self):\\n\\n # Raise error if index is not writable\\n if not self.config.get(\\\"writable\\\"):\\n raise ReadOnlyError(\\\"Attempting to upsert a read-only index (writable != True)\\\")\\n\\n if self.embeddings and self.documents:\\n with self.lock:\\n # Run upsert\\n self.embeddings.upsert(self.documents)\\n\\n # Save index if path available, otherwise this is an memory-only index\\n if self.config.get(\\\"path\\\"):\\n self.embeddings.save(self.config[\\\"path\\\"], self.config.get(\\\"cloud\\\"))\\n\\n # Reset document stream\\n self.documents.close()\\n self.documents = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da970d3a07959bb70343da54b9e0f1dc\",\n \"score\": \"0.4724849\",\n \"text\": \"def updateDocs():\\n docs = Dataset.search()\\n for doc in docs.scan():\\n created = getattr(getattr(doc, \\\"_ts\\\", None), \\\"date_created\\\", None)\\n updated = getattr(getattr(doc, \\\"_ts\\\", None), \\\"last_updated\\\", None)\\n if not created and not updated:\\n existing_dc = getattr(getattr(doc, \\\"_meta\\\", None), \\\"date_created\\\", None)\\n existing_lu = getattr(getattr(doc, \\\"_meta\\\", None), \\\"last_updated\\\", None)\\n\\n if existing_dc and existing_lu:\\n doc.update(**{\\\"_ts\\\": {\\\"date_created\\\": existing_dc, \\\"last_updated\\\": existing_lu}})\\n logging.info(\\\"Updating fields for doc with existing\\\")\\n else:\\n doc.update(\\n **{\\n \\\"_ts\\\": {\\n \\\"date_created\\\": default_create,\\n \\\"last_updated\\\": default_last_updated,\\n }\\n }\\n )\\n logging.info(\\\"Adding fields for doc with defaults\\\")\\n else:\\n created_bug = getattr(getattr(doc, \\\"_ts\\\", None), \\\"guide\\\", None)\\n if created_bug:\\n doc.update(\\n **{\\n \\\"_ts\\\": {\\n \\\"date_created\\\": default_create,\\n \\\"last_updated\\\": default_last_updated,\\n }\\n }\\n )\\n else:\\n logging.info(\\\"Doc already has TS last updated field\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0614bb9a344bbcb7774f40d315a663\",\n \"score\": \"0.4718103\",\n \"text\": \"def _write_projects(projects):\\n path = _get_projects_index_file()\\n contents = util.read_yaml(path)\\n contents.update({'projects': projects})\\n util.write_yaml(path, contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b73dd77df050a21496f43cdef09149\",\n \"score\": \"0.47118863\",\n \"text\": \"def delete(self, project, query):\\n self.es.delete_by_query(\\n index=self.index_name(project),\\n body=query,\\n conflicts='proceed',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3024c1ab542c8ad983221027239d933a\",\n \"score\": \"0.47102568\",\n \"text\": \"def add_doc(self, doc_id, contents):\\n self.__es.index(index=self.__index_name, doc_type=self.DOC_TYPE, id=doc_id, body=contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9147664a4952bc73e1c324e25ce1b2\",\n \"score\": \"0.47069666\",\n \"text\": \"def test_designs_id_put(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6dd2c023bd9b98b8654db1f500b71a\",\n \"score\": \"0.4705125\",\n \"text\": \"def insert(dictObject, db):\\n\\n \\\"\\\"\\\" res = db[\\\"menus\\\"].update_one(\\n {\\\"id\\\": dictObject[\\\"id\\\"],\\n \\\"date\\\": dictObject[\\\"date\\\"],\\n \\\"mensaName\\\": dictObject[\\\"mensaName\\\"],\\n \\\"lang\\\": dictObject[\\\"lang\\\"]\\n }, {\\\"$set\\\": dictObject}, upsert=True)\\n\\n print(\\\"modifed: id: \\\" + str(dictObject[\\\"id\\\"].encode('utf-8')) + \\\" Date: \\\" + dictObject[\\\"date\\\"] + \\\" lang: \\\" +\\n dictObject[\\\"lang\\\"])\\n if res.upserted_id is None:\\n print(\\\"res: modified: \\\" + str(res.modified_count) + \\\" matched: \\\" + str(res.matched_count))\\n else:\\n print(\\\"res: inserted\\\") \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"876fbf36475b5fdab80415b990d77feb\",\n \"score\": \"0.46986815\",\n \"text\": \"def scrape_new_data(index=\\\"verdicts\\\"):\\n if es.indices.exists(index=index):\\n tic = time.time()\\n scraper.update_database()\\n toc = time.time()\\n print(\\\"Scraping done! Time needed: {}\\\".format(str(toc - tic)))\\n else:\\n print(\\\"Index to be updated does not exists!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620ced752c4b779874c9474695caa26d\",\n \"score\": \"0.4695401\",\n \"text\": \"def create_doc_instances(self, **kwargs):\\n kwargs[u\\\"cdb_project_id\\\"] = self.cdb_project_id\\n return self.Super(TaskTemplateDocRef).create_doc_instances(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de775a146f47d74e09cd6d9324f8dbab\",\n \"score\": \"0.4694352\",\n \"text\": \"def create_update_project(request, slug=None):\\n request_user = request.user\\n if slug is not None:\\n project = Project.objects.get(slug=slug)\\n if project.owner != request.user:\\n raise PermissionDenied\\n else:\\n project_form = CreateProjectForm(instance=project)\\n position_formset = PositionFormset(\\n queryset=Position.objects.filter(\\n project=project\\n ))\\n else:\\n project = None\\n project_form = CreateProjectForm()\\n position_formset = PositionFormset(\\n queryset=Position.objects.none()\\n )\\n\\n if request.method == 'POST':\\n project_form = CreateProjectForm(\\n request.POST, \\n instance=project\\n )\\n position_formset = PositionFormset(\\n request.POST,\\n queryset=Position.objects.filter(project=project)\\n )\\n if project_form.is_valid() and position_formset.is_valid():\\n project = project_form.save(commit=False)\\n project.owner = request_user\\n new_project = False\\n if project.status is None:\\n new_project = True\\n project.status = 'A'\\n project.save()\\n positions = position_formset.save(commit=False)\\n for position in positions:\\n position.project = project\\n if position.status not in ('E', 'F'):\\n position.status = 'E'\\n position.save()\\n position_formset.save_m2m()\\n position_formset.save()\\n if new_project:\\n messages.success(\\n request, 'Project {} created!'.format(project.title))\\n else:\\n messages.success(\\n request, 'Project {} updated!'.format(project.title))\\n return HttpResponseRedirect(reverse('projects:project', kwargs={'slug': project.slug}))\\n\\n context = {\\n 'project_form': project_form,\\n 'position_formset': position_formset,\\n }\\n return render(request, 'projects/create_update_project.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df57eb7f6c6f2712debcc484e3347d8\",\n \"score\": \"0.46942607\",\n \"text\": \"def test_update_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17dc383ee2e366b1a5fc4e1604f9b99\",\n \"score\": \"0.4692427\",\n \"text\": \"def refresh(self, project):\\n self.es.indices.refresh(index=self.index_name(project))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d957caf110468efdc93c17755a223f5\",\n \"score\": \"0.46918374\",\n \"text\": \"def test_rud_document(self):\\n\\n # upload a documnet\\n self.upload_document()\\n\\n # retrieve document PK to test\\n test_document = Document.objects.all().first()\\n\\n # test retrive document with invvalid pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': 99}\\n )\\n response = self.client.get(\\n rud_document_url,\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test update document with invvalid pk\\n response = self.client.patch(\\n rud_document_url,\\n {\\n 'language': 'af',\\n 'document_type': 'pdf'\\n }\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test delete document with invvalid pk\\n response = self.client.delete(\\n rud_document_url\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test retrieve document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.get(\\n rud_document_url,\\n )\\n self.assertEqual(response.status_code, 200)\\n\\n # test update document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.patch(\\n rud_document_url,\\n {\\n 'language': 'af',\\n 'document_type': 'pdf'\\n }\\n )\\n test_document.refresh_from_db()\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(test_document.language.short_name, 'af')\\n self.assertEqual(test_document.document_type.short_name, 'pdf')\\n self.assertEqual(Document.objects.all().count(), 1)\\n\\n # test delete document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.delete(\\n rud_document_url\\n )\\n self.assertEqual(response.status_code, 204)\\n self.assertEqual(Document.objects.all().count(), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a42d660681fa0dea1386fc66c5c6f7ae\",\n \"score\": \"0.46850002\",\n \"text\": \"def indexDocElement(es_Url, awsauth, docData):\\n try:\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n resp = requests.put(es_Url, auth=awsauth,\\n headers=headers, json=docData)\\n print(resp.content)\\n if resp.status_code == 201:\\n logger.info('INFO: Successfully created element into ES')\\n elif resp.status_code == 200:\\n logger.info('INFO: Successfully updated element into ES')\\n else:\\n logger.error(f'FAILURE: Unable to index element {resp.content}')\\n raise\\n except Exception as e:\\n logger.error(f'ERROR: {str(e)}')\\n logger.error(f\\\"ERROR: Unable to index line:{docData['content']}\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11e4d54117e769ca06d0c90afeab7608\",\n \"score\": \"0.46833104\",\n \"text\": \"def unindex_object(documentId):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6374eccd3ecd3ac8f9c0c4077d249a\",\n \"score\": \"0.46808988\",\n \"text\": \"async def update_data(doc_id: int, response: IntakeData,\\n db_file: str = tinydb_file):\\n db0 = await get_db(db_file)\\n db0.update(response.dict(), doc_ids=[doc_id])\\n return db0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ca95cb848f9130b79bfd3e7d693228\",\n \"score\": \"0.4669554\",\n \"text\": \"def uploadDesignDocuments(self,directory):\\n\\n import glob\\n pattern = os.path.join(directory,'*.py')\\n viewFiles = glob.glob(pattern)\\n for viewFile in viewFiles:\\n execfile(viewFile)\\n for view in views:\\n viewID = os.path.join('_design',view)\\n print (\\\"uploading \\\", view, \\\" of \\\", viewFile, \\\" to \\\", self.databaseName)\\n document = self.db.get(viewID)\\n if document:\\n self.db.delete(document)\\n self.db[viewID] = views[view]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6b0bfb6c8ba313b8b13141fe7abd40\",\n \"score\": \"0.46691608\",\n \"text\": \"def update(self, index, document, **options):\\n\\n index = base._resolve_index(index)\\n\\n index_name = index.get_name()\\n index_doc_type = options.pop('doc_type', index.get_doc_type())\\n\\n id, adapted_document = index.adapt_document(document)\\n\\n if id:\\n options.setdefault(\\\"id\\\", id)\\n\\n self._es.index(index_name, index_doc_type, adapted_document, **options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43a3a310e72a471695196fe25c4e8b9\",\n \"score\": \"0.46609166\",\n \"text\": \"def test_delete_project(self):\\n name = {\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"}\\n project = Project.objects.create(\\n name=name,\\n description={\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n )\\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\\n res = self.admin_client.delete(detail_url)\\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\\n exists = Project.objects.all().filter(name=name).exists()\\n self.assertFalse(exists)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e611165d35dc8ad0d765ab9c84890b1\",\n \"score\": \"0.4655304\",\n \"text\": \"def update_project(project_id, update_dict):\\n # update_dict = convert_project_object_to_dict(update_dict)\\n # try:\\n Project.objects.get_or_404(id=project_id).update(**update_dict)\\n return (True, \\\"success\\\")\\n # except Exception, e:\\n # return (False, repr(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec582f4f431c4caf8adbe8f976d8607\",\n \"score\": \"0.46348995\",\n \"text\": \"def project(id):\\n\\t#Call load() in the data layer to load the database and store it's contents in the variable db\\n\\tdb = data.load('data.json')\\n\\t#Call get_project() in the data layer and store the project in the variable project\\n\\tproject = data.get_project(db, id)\\n\\t#If None was returned by get_project():\\n\\t\\t#No project with the specified ID exists, ergo a nonexistent page has been visited, ergo abort with a 404\\n\\tif project is None:\\n\\t\\tabort(404)\\n\\telse:\\n\\t\\t#adding a counter to the project-page.\\n\\t\\tcounter = open(\\\"doc/counter.log\\\", \\\"r+\\\")\\t\\t\\t\\t\\t#opens counter.log\\n\\t\\tcount = 1\\n\\t\\tpid = str(\\\"project \\\")+str(project['project_id'])+\\\"\\\\n\\\"\\n\\t\\tfor line in counter:\\n\\t\\t\\tif pid in line:\\n\\t\\t\\t\\tcount += 1\\n\\t\\tproject['counter'] = count\\n\\t\\tcounter.write(pid)\\n\\t\\tcounter.close()\\n\\t\\t#Render project.html with:\\n\\t\\t\\t#project --> used to display information about the project\\n\\t\\treturn render_template('project.html', project=project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb39d6df0b7b3498a570ea1bba71feb3\",\n \"score\": \"0.46262908\",\n \"text\": \"def _update_document_by_id(doc_id, body, object_type, *, retry_on_conflict=0, **kwargs):\\n conn = get_conn()\\n for alias in get_active_aliases(conn, object_types=[object_type]):\\n try:\\n conn.update(\\n index=alias,\\n doc_type=GLOBAL_DOC_TYPE,\\n body=body,\\n id=doc_id,\\n params={\\\"retry_on_conflict\\\": retry_on_conflict, **kwargs},\\n )\\n # Our policy for document update-related version conflicts right now is to log them\\n # and allow the app to continue as normal.\\n except ConflictError:\\n log.error(\\n \\\"Update API request resulted in a version conflict (alias: %s, doc id: %s)\\\",\\n alias,\\n doc_id,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331d110c41a2530b147f583ddccbeb0a\",\n \"score\": \"0.46188155\",\n \"text\": \"def create_opensearch_index(index_name: str):\\n with open(Path(__file__).parent / \\\"pipeline_logs_mapping.json5\\\") as fd:\\n index_mappings = pyjson5.load(fd)\\n res = requests.put(\\n f\\\"{OPENSEARCH_ENDPOINT}/{index_name}\\\",\\n data=json.dumps(index_mappings),\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),\\n )\\n if res.status_code >= 400:\\n logging.error(\\n f'Failed to create opensearch index \\\"{index_name}\\\", '\\n f\\\"server responded with status {res.status_code}\\\"\\n )\\n try:\\n logging.error(res.json())\\n except json.JSONDecodeError:\\n logging.error(res.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d8d50b59c45411d8f14763398d3d38\",\n \"score\": \"0.46130043\",\n \"text\": \"def createDoc(self, docInfos):\\n if docInfos['id'] != '':\\n newDocId = self.plominoDatabase.invokeFactory(docInfos['type'], \\n id=docInfos['id'])\\n newDoc = self.plominoDatabase.getDocument(docInfos['id'])\\n else:\\n newDoc = self.plominoDatabase.createDocument()\\n \\n if newDoc is not None:\\n if self.plominoDatabase.getForm(docInfos['form']) is not None:\\n newDoc.setItem('Form', docInfos['form'])\\n\\n # TODO: create a default form if form does not exist ? in a later version\\n # REM: doc is not saved if not associated with a existing form\\n \\n # Set the items of this document\\n for itemInfos in docInfos['items']:\\n newDoc.setItem(itemInfos['name'], itemInfos['value'])\\n # TODO: check the value before set ?\\n #self.createItem(itemInfos, newDoc)\\n\\n # Add the files inserted in the document\\n if docInfos['files'] != []:\\n if not hasattr(newDoc.getForm(), 'imported_files'):\\n fieldId = newDoc.getForm().invokeFactory('PlominoField', \\n id='imported_files',\\n title='Imported Files',\\n FieldType='ATTACHMENT')\\n newDoc.getForm().setFormLayout(newDoc.getForm().getFormLayout() + \\\\\\n '<p>Imported files: <span class=\\\"plominoFieldClass\\\">imported_files</span></p>')\\n for fileInfos in docInfos['files']:\\n newDoc.setfile(a2b_base64(str(fileInfos['content'])), fileInfos['name'])\\n\\n newDoc.save()\\n\\n else:\\n raise Exception\\n\\n self.plominoDatabase.getIndex().refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5eea1dedb85eb1c7282df2ad603ce7f\",\n \"score\": \"0.46129036\",\n \"text\": \"def upsert_document(doc_id, doc, object_type, *, retry_on_conflict=0, **kwargs):\\n _update_document_by_id(\\n doc_id,\\n {\\\"doc\\\": doc, \\\"doc_as_upsert\\\": True},\\n object_type,\\n retry_on_conflict=retry_on_conflict,\\n **kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ab54ba9546b01682521f015050756d\",\n \"score\": \"0.46108896\",\n \"text\": \"def delete_project(uuid):\\n project = Project.query.get(uuid)\\n\\n if project is None:\\n raise NotFound(\\\"The specified project does not exist\\\")\\n\\n experiments = Experiment.query.filter(Experiment.project_id == uuid).all()\\n for experiment in experiments:\\n # remove dependencies\\n operators = db_session.query(Operator).filter(Operator.experiment_id == experiment.uuid).all()\\n for operator in operators:\\n Dependency.query.filter(Dependency.operator_id == operator.uuid).delete()\\n\\n # remove operators\\n Operator.query.filter(Operator.experiment_id == experiment.uuid).delete()\\n\\n Experiment.query.filter(Experiment.project_id == uuid).delete()\\n\\n db_session.delete(project)\\n db_session.commit()\\n\\n prefix = join(\\\"experiments\\\", uuid)\\n remove_objects(prefix=prefix)\\n\\n return {\\\"message\\\": \\\"Project deleted\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1acfc33f6ce86672083340d2f05e83f7\",\n \"score\": \"0.46102795\",\n \"text\": \"def index_document(body):\\n es.index(\\n index='documents',\\n doc_type='document',\\n body=body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce2ba6ec3062bc8b00d66042e7c58f95\",\n \"score\": \"0.46075854\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n self.project_slug = self.kwargs.get('project_slug', None)\\n self.project = Project.objects.get(slug=self.project_slug)\\n return super(VersionDeleteView, self).post(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cca9d7d23d55aea633a1d309353db55\",\n \"score\": \"0.46073022\",\n \"text\": \"def add(self, document_or_documents):\\n\\n added_document_ids = []\\n\\n if isinstance(document_or_documents, Document):\\n was_list = False\\n documents = [document_or_documents]\\n else:\\n was_list = True\\n documents = document_or_documents[:]\\n\\n # First-pass validation\\n self._validate_documents(documents)\\n\\n with transaction.atomic(independent=True):\\n for document in documents:\\n record = document._record\\n\\n # We go through the document fields, pull out the values that have been set\\n # then we index them.\\n field_data = {\\n f: getattr(document, document.get_field(f).attname)\\n for f in document.get_fields() if f != \\\"id\\\"\\n }\\n\\n # Generate a database representation of this Document use\\n # the passed ID if there is one\\n record, created = DocumentRecord.objects.update_or_create(\\n pk=document.id,\\n defaults={\\n \\\"index_stats\\\": self.index,\\n \\\"data\\\": field_data\\n }\\n )\\n document.id = record.id\\n document._record = record\\n\\n if created:\\n index_document(self.name, document)\\n added_document_ids.append(record.id)\\n else:\\n # This wipes out any existing document, bumps the revision\\n # and then indexes this one\\n reindex_document(document)\\n\\n return added_document_ids if was_list else (added_document_ids[0] if added_document_ids else 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df4233a622d8556ec5399e88e8dcaf1\",\n \"score\": \"0.4595747\",\n \"text\": \"def create_document(record):\\n # TODO: Write an iterator over the INDEX_MAP to fetch fields from the record and\\n # return a dictionary representing the document.\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b994723d25f0a197f745292df18fa72c\",\n \"score\": \"0.45953628\",\n \"text\": \"def test_update_project(self):\\n project = Project.objects.create(\\n name={\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"},\\n description={\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n )\\n payload = {\\\"name\\\": {\\\"en\\\": \\\"New name\\\", \\\"fr\\\": \\\"Nouveau nom\\\"}}\\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\\n res = self.admin_client.patch(detail_url, payload, format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n project.refresh_from_db()\\n self.assertEqual(project.name, payload['name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3a5c81140b78c9a4413be72272e6f4\",\n \"score\": \"0.45898458\",\n \"text\": \"def CreateProjects(projRef):\\r\\n\\r\\n print(\\\"Creating projects\\\")\\r\\n # make root directory with permit date\\r\\n if (len(projRef) == 0):\\r\\n print(\\\"No project available\\\")\\r\\n return\\r\\n\\r\\n dt = str(projRef.values()[0].PERMIT_DATE).split(\\\" \\\")[0]\\r\\n project_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.join(NFOutputPath.OUTPUT, dt))\\r\\n if not os.path.exists(project_root):\\r\\n os.makedirs(project_root)\\r\\n\\r\\n summary = []\\r\\n\\r\\n try:\\r\\n edit = arcpy.da.Editor(NFDataSource.COMMONGIS_CONN)\\r\\n edit.startEditing(False, False)\\r\\n edit.startOperation()\\r\\n editCounter = 0\\r\\n\\r\\n # create one folder for each project ref\\r\\n for key in projRef:\\r\\n arcpy.AddMessage(key)\\r\\n\\r\\n project_folder = os.path.join(project_root, key)\\r\\n\\r\\n # if the project folder already contains plans - skip; this project has been imported before; will not be included in new MaxDia report too\\r\\n if not os.path.exists(project_folder):\\r\\n os.makedirs(project_folder)\\r\\n file_list = []\\r\\n else:\\r\\n file_list = os.listdir(project_folder)\\r\\n\\r\\n pdf_exists = False\\r\\n\\r\\n for f in file_list:\\r\\n\\r\\n if \\\".pdf\\\" in f:\\r\\n arcpy.AddMessage(\\\"PDF in project folder: \\\" + project_folder)\\r\\n pdf_exists = True\\r\\n break\\r\\n if pdf_exists:\\r\\n continue\\r\\n\\r\\n cwdPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.CWD)))\\r\\n\\r\\n UpdateCWD(projRef[key])\\r\\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.CWDTEMPLATE), projRef[key], cwdPdf, [], \\\"\\\")\\r\\n\\r\\n wsnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.WSN)))\\r\\n wsnasset = UpdateWSN(projRef[key])\\r\\n\\r\\n if (len(wsnasset) > 0):\\r\\n # GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WSNTEMPLATE), projRef[key], wsnPdf, wsnasset, \\\"WSN\\\")\\r\\n print \\\"wsnasset\\\"\\r\\n\\r\\n wrnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.WRN)))\\r\\n wrnasset = UpdateWRN(projRef[key])\\r\\n\\r\\n if (len(wrnasset) > 0):\\r\\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WRNTEMPLATE), projRef[key], wrnPdf, wrnasset, \\\"WRN\\\")\\r\\n summary.extend(GenerateReport(projRef[key], wrnasset))\\r\\n\\r\\n editCounter = editCounter + 1\\r\\n\\r\\n if (editCounter == 10):\\r\\n print (\\\"intermediate save @ 10\\\")\\r\\n edit.stopOperation()\\r\\n edit.stopEditing(True)\\r\\n\\r\\n edit.startEditing(False, False)\\r\\n edit.startOperation()\\r\\n editCounter = 0\\r\\n\\r\\n edit.stopOperation()\\r\\n edit.stopEditing(True)\\r\\n del edit\\r\\n\\r\\n max_dia_report_name = GenerateMaxDiaReport(summary, dt, projRef)\\r\\n arcpy.AddMessage(\\\"END Creating projects\\\")\\r\\n arcpy.AddMessage(max_dia_report_name)\\r\\n print(\\\"Generated maxdia report\\\")\\r\\n return max_dia_report_name\\r\\n\\r\\n except Exception as err:\\r\\n print err\\r\\n Error_Handler(\\\"Create Projects Failed\\\")\\r\\n Error_Handler(repr(traceback.format_exc()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d93df5e9bcd0bbd1dbc95aa839899d\",\n \"score\": \"0.45813572\",\n \"text\": \"def insert_into_index(es_object, index_name, record): \\n try:\\n outcome = es_object.index(index=index_name, body=record)\\n print(f\\\"Inserted object into Index '{index_name}'.\\\")\\n print(str(record))\\n except Exception as ex:\\n print('Error in indexing data')\\n print(str(ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a20f89cdf668abaec4a099362659d0\",\n \"score\": \"0.4577445\",\n \"text\": \"def test_get_or_create_project__call():\\n project_name = str(uuid.uuid1())\\n project = synapseclient.Project(name=project_name)\\n returned = synapseclient.Project(name=project_name,\\n id=str(uuid.uuid1()))\\n with patch.object(CREATE_CLS,\\n \\\"_find_by_obj_or_create\\\",\\n return_value=returned) as patch_find_or_create:\\n new_project = CREATE_CLS.get_or_create_project(name=project_name)\\n assert new_project == returned\\n patch_find_or_create.assert_called_once_with(project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01878f3733f7e6fa22c36b45e3f46ae8\",\n \"score\": \"0.45740703\",\n \"text\": \"def test_create_project_adds_a_project(db: Session) -> None:\\n\\n # there is no project to start with\\n assert db.query(models.Project).count() == 0\\n\\n # create a project\\n project = schemas.ProjectCreate(\\n name=\\\"My Project\\\", directory=\\\"/wherever\\\", command=\\\"whatever\\\"\\n )\\n create_project(db, project)\\n\\n # there is a project now...\\n assert db.query(models.Project).count() == 1\\n\\n # ... and it has the correct content\\n db_project = db.query(models.Project).first()\\n assert db_project.id is not None\\n assert db_project.command == project.command\\n assert db_project.directory == project.directory\\n assert db_project.name == project.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd10092dad3a1e670dc70a758f1a80d\",\n \"score\": \"0.45728135\",\n \"text\": \"def add_guid_index():\\n catalog = api.portal.get_tool('portal_catalog')\\n if GUID_INDEX_NAME not in catalog.indexes():\\n log.info(\\\"Adding GUID index %r\\\" % GUID_INDEX_NAME)\\n catalog.addIndex(GUID_INDEX_NAME, 'FieldIndex')\\n else:\\n log.info(\\\"GUID index %r already exists\\\" % GUID_INDEX_NAME)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":793,"cells":{"query_id":{"kind":"string","value":"68e37ec088ae4267c593779e2ace4a03"},"query":{"kind":"string","value":"r\"\"\"The number of graphs in the dataset."},"positive_passages":{"kind":"list like","value":[{"docid":"179a0ee94a6a7e7d1a87da777afe7be5","score":"0.0","text":"def __len__(self): # -> Literal[1]:\n ...","title":""}],"string":"[\n {\n \"docid\": \"179a0ee94a6a7e7d1a87da777afe7be5\",\n \"score\": \"0.0\",\n \"text\": \"def __len__(self): # -> Literal[1]:\\n ...\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0355f1833468a8bd7a3a10e653f39a43","score":"0.8215049","text":"def number_of_graphs(self):\n return self.get_table_row_count(self.GRAPH_TABLE._name_)","title":""},{"docid":"d5cf06f4008d0a5795c841c5b8657580","score":"0.75362337","text":"def n_datasets(self) -> int:\n n_others = len(self._dcs().keys())\n\n return n_others + 1","title":""},{"docid":"bfee6f97e0581fbcdbcde40548c486ef","score":"0.73872626","text":"def number_of_edges(self):\n ...","title":""},{"docid":"fb0738fdb10f050aab0ef67e1a4adca5","score":"0.73707473","text":"def n_plots(self):\n return 0","title":""},{"docid":"08bad50437dea7ff65c93298e103c51c","score":"0.7311434","text":"def number_of_nodes(self):\n ...","title":""},{"docid":"6d31cacdd1126a7080e038de5ebd6812","score":"0.71918505","text":"def size(self):\n\t\treturn len(self._datasets)","title":""},{"docid":"1d4a94f52e9f13a3c54d0cf45633160c","score":"0.71886915","text":"def number_of_nodes(self):\r\n return len(self.nodes)","title":""},{"docid":"d2591fef6647a6fc549f24fb36a1d180","score":"0.71552324","text":"def number_of_nodes(self):\n return len(self.nodes)","title":""},{"docid":"3558f2eb5a7ab0338cede409267fcf02","score":"0.71441734","text":"def number_of_edges(self):\r\n return len(self.edges)","title":""},{"docid":"0d8b2036bbf102da85429eefe047c8bb","score":"0.7133862","text":"def number_of_nodes(self):\n return self.number_of_vertices()","title":""},{"docid":"b1aa83c24a0ee2ef2a53638baa576fba","score":"0.70328444","text":"def num_of_node(graph):\r\n return len(list(graph.nodes()))","title":""},{"docid":"5dfad0d59845d099bb0f3b0393f7ef0a","score":"0.7019014","text":"def get_number_of_nodes(self):\r\n return len(self.node_Ids)","title":""},{"docid":"76154412818646208517a4ae81add28b","score":"0.6967071","text":"def n_nodes(self):\n return self._G.number_of_nodes()","title":""},{"docid":"df2614d63ee491efd9a8e9666e3a4e4b","score":"0.6942869","text":"def get_number_of_nodes(self):\n return len(self.__adjacency_list)","title":""},{"docid":"6afa6f8cf49e64e4b7e5c061e51ef1ae","score":"0.6938715","text":"def count(self) -> int:\n return self.vis[\"count\"]","title":""},{"docid":"e93592d0020887f7c40a21c6d1cb6144","score":"0.6929193","text":"def getGraphSize(self):\n return self.graphSize","title":""},{"docid":"2edb31256fae3bd25a640fed242c1827","score":"0.692044","text":"def number_of_nodes(self):\r\n return len(self.node)","title":""},{"docid":"04898e55628d19987e50d0a139129b10","score":"0.68733644","text":"def n_data_points(self)->int:\n return self._n_data_points","title":""},{"docid":"04898e55628d19987e50d0a139129b10","score":"0.68733644","text":"def n_data_points(self)->int:\n return self._n_data_points","title":""},{"docid":"4a5be17166b29fc298d961638225e1c0","score":"0.68576306","text":"def number_of_edges(self):\n return sum(1 for _ in self.edges())","title":""},{"docid":"1f728335c8c6b9ff1422511a87bbe86c","score":"0.6832707","text":"def num_edges(self):\n return","title":""},{"docid":"bcc6f50b575f6617d5901ee7b86f07fe","score":"0.6823159","text":"def number_of_nodes(self):\n return sum(1 for _ in self.nodes_iter())","title":""},{"docid":"4e249348a7eb7edd50e8b905bb41dfed","score":"0.68057483","text":"def dataset_image_count(self):\n return self._dataset_image_count","title":""},{"docid":"474979992ba794a05d9cb3e18ca10e96","score":"0.6793165","text":"def n_edges(self) -> int:\n return len(self.edges())","title":""},{"docid":"62ae97345eb9260c4e91c922f20583df","score":"0.67880934","text":"def num_nodes(self):\n return len(self.nodes())","title":""},{"docid":"89ac151061059e2521eaf9256758b63b","score":"0.67822707","text":"def num_nodes(self):\n return len(self.adj_list)","title":""},{"docid":"5c116b37f3519214798b58931e4d1faa","score":"0.6774768","text":"def node_count(self):\n return len(self.nodes)","title":""},{"docid":"08727f4e597c0ab82643a0042d218190","score":"0.6736805","text":"def numNodes(self):\n return len(self.dicN)","title":""},{"docid":"ab03c6856efb9d62a0fac78658e743a5","score":"0.67211145","text":"def number_of_hidden_nodes(self):\r\n return len(self.hidden_nodes)","title":""},{"docid":"c920b53677faaa969bbfeff023116cf0","score":"0.6689106","text":"def number_of_hidden_edges(self):\r\n return len(self.hidden_edges)","title":""},{"docid":"87c4b1315c12da4ea656b9cbb315a03c","score":"0.6685002","text":"def connection_count(self):\n\n connections = 0\n if \"num-connections\" in self.profile:\n connections = self.profile[\"num-connections\"]\n\n return connections","title":""},{"docid":"690e135c7c131dc14231b535172a0282","score":"0.66796535","text":"def nconsiderednodes(self):\n assert self._node_count is not None\n return self._node_count","title":""},{"docid":"3e1aa29ec90d6b77fd0e7c4c31b8563f","score":"0.66766924","text":"def size(self):\n\n return self.n_edges","title":""},{"docid":"e6faad0a0d83c063cfbc933606dce2f7","score":"0.6675746","text":"def number_of_edges(self):\n num_edges = graph_wrapper.number_of_edges(self.graph_ptr)\n\n return num_edges","title":""},{"docid":"cf57ddcd35b62ee350b622ab89f97184","score":"0.66686505","text":"def n_nodes(self) -> int:\n raise NotImplementedError","title":""},{"docid":"cf57ddcd35b62ee350b622ab89f97184","score":"0.66686505","text":"def n_nodes(self) -> int:\n raise NotImplementedError","title":""},{"docid":"936e2734149056a985cf042958e7a1e8","score":"0.66666746","text":"def numNodes(self) -> int:\n return self._call_java(\"numNodes\")","title":""},{"docid":"a4f7605f501cc4a7659341266aecc6d0","score":"0.6652915","text":"def number_of_docs(self) -> int:\n return self._G.number_of_nodes()","title":""},{"docid":"d1efe6a5c532bd8a32dcb9d7931a89fe","score":"0.66516787","text":"def num_edges(self):\n return lib.graph_num_edges(self._obj)","title":""},{"docid":"540eef509583d426ab07af850429427c","score":"0.6622153","text":"def num_nodes(self) -> int:\n return self._c_csc_graph.num_nodes()","title":""},{"docid":"8f56c5b5b9c9b332a52dd9b25e86da94","score":"0.6622133","text":"def _get_number_of_instances(self):\n return self.__number_of_instances","title":""},{"docid":"8f56c5b5b9c9b332a52dd9b25e86da94","score":"0.6622133","text":"def _get_number_of_instances(self):\n return self.__number_of_instances","title":""},{"docid":"d3bbbace7d5e517b7c1f0c9cd2c64b76","score":"0.66186965","text":"def example_count(self):\n return len(self.torch_loader.dataset)","title":""},{"docid":"78f630810ff63c673af6a4e80f02b9fd","score":"0.6618202","text":"def number_of_vertices(self):\n num_vertices = graph_wrapper.number_of_vertices(self.graph_ptr)\n\n return num_vertices","title":""},{"docid":"a3a4abff35b148272092124f43be0f8b","score":"0.6610146","text":"def dataset_count():\n count = 0\n result = toolkit.get_action('package_search')({}, {'rows': 1})\n if result.get('count'):\n count = result.get('count')\n return count","title":""},{"docid":"6574458beda02c6c4c70bbfeed0306cd","score":"0.66042715","text":"def get_point_count(self):\n return len(self.edges)","title":""},{"docid":"49918347258b797a343003e19f1f7cfb","score":"0.6601892","text":"def GetNodeCount(self):\r\n pass","title":""},{"docid":"ed8ffb037c28d99430f82940f7ff1ebe","score":"0.65973693","text":"def num_instances(self):\n return _NvRules.IMetric_num_instances(self)","title":""},{"docid":"d071495a425e30a7937c01a0e1cd00d3","score":"0.6597358","text":"def nnodes(self):\n return len(self.matrix.keys())","title":""},{"docid":"5bb32be2f53661002c27c87273f37b8d","score":"0.65965056","text":"def num_datapoints(self):\n\t\treturn self.predictions.shape[0]","title":""},{"docid":"3464e5965295ae9a8d80778d86c6411c","score":"0.6596318","text":"def count(self):\n return self._label_fdist.N()","title":""},{"docid":"d0b4f79eedd7716a980fec7fd1fcc86c","score":"0.65911674","text":"def size(self):\n return self.nodes.size()","title":""},{"docid":"a0788dee9a35d6dfc6c7c25db7fe1e68","score":"0.65862036","text":"def size(self):\n return self.digr.num_vertices()","title":""},{"docid":"1e40b28ac771634a9df3b7c41ccd5c0c","score":"0.658214","text":"def number_of_nodes(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"number_of_nodes\")","title":""},{"docid":"6b5270f9cba24d292600124909b27116","score":"0.6581056","text":"def plot_rank(self):\n return len(self.plot_shape)","title":""},{"docid":"4748f20ccc2d3768007027e69e5cabb6","score":"0.65771747","text":"def node_count(graph):\n return len(graph.keys())","title":""},{"docid":"958ed92b89a2de823e72cf2d18df1875","score":"0.6570017","text":"def num_nodes(self):\n return len(self._nodes)","title":""},{"docid":"460f42002a43c9de8ba845e635ed3be5","score":"0.6568672","text":"def dataset_size(self):\n return self.dataset.size","title":""},{"docid":"a5956dcf005f5a551ed08f05bb482489","score":"0.6568589","text":"def number_of_paths(self):\n return self.num_paths","title":""},{"docid":"562189beb582fc5db5475ef106ed37f2","score":"0.65611106","text":"def __len__(self) -> int:\n return len(list(self.graph.items(self.uri)))","title":""},{"docid":"d90f41a954dbd4af2a4f891ba47a0b2f","score":"0.65585494","text":"def totalConnections(analyzer):\n return gr.numEdges(analyzer['connections'])","title":""},{"docid":"5b99a28bdd0c7483f89649d3657a11fa","score":"0.6558188","text":"def num_edges(self) -> int:\n return self._c_csc_graph.num_edges()","title":""},{"docid":"0af950419a6e9ba755be627bf4a55852","score":"0.6527257","text":"def __len__(self):\n return self.num_edges","title":""},{"docid":"9edc1dad30b39aefcfd0285b42b8a328","score":"0.65193063","text":"def number_of_monitors(): ###\n return len(monitors())","title":""},{"docid":"d15ec172779f9d6b32207a8a088d435d","score":"0.65185875","text":"def numNodes(self):\n return len(self.G)","title":""},{"docid":"2fc7ddd99e64240ecf316bf6d1594e93","score":"0.65136","text":"def get_frame_count(self):\n\n return len(self.plot_list)","title":""},{"docid":"34cb4e9110db43ea35180523b3ca3d70","score":"0.6512665","text":"def dimensions(self):\n return self._N","title":""},{"docid":"4aee5267e5f73468783933e887a0efdd","score":"0.65110767","text":"def n_records(self) -> int:\n\n n_records = sum([len(data.initial_molecules) for data in self.dataset.values()])\n return n_records","title":""},{"docid":"719d02468234d1aa3c2dec1d8689c58b","score":"0.65079","text":"def getNdim(self):\n try:\n ndim = self.segments.ndim\n except AttributeError:\n ndim = self._ndim\n return ndim","title":""},{"docid":"dddcc96fb145ad82d5cb2cacd1bd7931","score":"0.6505912","text":"def get_num_nodes(self):\n return len([sec for sec in self.config.sections if self.is_node(sec)])","title":""},{"docid":"a6b1e31b262dd6bc1276e39245392cb2","score":"0.6504585","text":"def num_datapoints(self):\n\t\treturn self.pred_probs.shape[0]","title":""},{"docid":"9cd1df339740f4fb4043640e049d656c","score":"0.6499093","text":"def n_scales(self):\n return len(self.scales)","title":""},{"docid":"9cd1df339740f4fb4043640e049d656c","score":"0.6499093","text":"def n_scales(self):\n return len(self.scales)","title":""},{"docid":"df460ebad4eecf19324ad19e3c7f59de","score":"0.64894354","text":"def num_subgraphs(self, subgraph):\n return self.num_subgraphs_by_parameters(\n subgraph.num_verts(),\n subgraph.num_edges(),\n subgraph.automorphism_group().order()\n )","title":""},{"docid":"df52ea3bf5d0498742b099a84939ac22","score":"0.64827776","text":"def __len__(self):\n\t\treturn self._nnodes","title":""},{"docid":"94ce00d1dbe4096809876f0f4d3ae624","score":"0.6477803","text":"def n_nodes(self):\n return len(self.xs)","title":""},{"docid":"df97dcfd539cfe6c227b63c8b4f920c5","score":"0.64721024","text":"def num_per_epoch(self):\n\n return len(self.images)","title":""},{"docid":"52821838253f58673b5a1a49ed8e44ff","score":"0.64649993","text":"def getDimension(self):\n return len(self.components)","title":""},{"docid":"b4a960f137d0cfa646f638b56d43f468","score":"0.64609027","text":"def __len__(self):\n return self._n_nodes","title":""},{"docid":"4a26f0ff4891642c6b0af7c69c3bfb35","score":"0.6458422","text":"def _get_dataset_size(self):","title":""},{"docid":"717dba2db4cbce87a82ddae8a8cd9c7f","score":"0.6457629","text":"def dimension(self):\r\n return self.n","title":""},{"docid":"98d322b0f1269415c560ad757aef43fe","score":"0.64518964","text":"def get_count(self):\n return len(self._bars)","title":""},{"docid":"d413aebbb9a88ec559169b4fa899caac","score":"0.6450254","text":"def dimension(self):\n return self.n+1","title":""},{"docid":"c49bcb084caca4964216fc2fefd6e5af","score":"0.6448196","text":"def nums(self):\n return len(self.data)","title":""},{"docid":"0f345f1295ad50bfc21811d69dea3f0d","score":"0.6447515","text":"def get_count(self) -> int:\n return len(self._bars)","title":""},{"docid":"8bc33751eeb84b817b313f95c0b2585c","score":"0.64436454","text":"def number_of_layers(self):\n return self.len","title":""},{"docid":"4ec5df087a97bd61e2a2f090a708bec3","score":"0.6435416","text":"def get_dataset_size(self, dataset):\n \n i = 0\n for batch, (image, audio, label) in enumerate(dataset):\n assert tf.shape(image)[0] == tf.shape(audio)[0] == tf.shape(label)[0]\n i += tf.shape(image)[0]\n \n return i","title":""},{"docid":"6e5b492619ae4b1681fc7d6804c9e76e","score":"0.642131","text":"def __len__(self):\r\n return len(Dataset(self._id).shape)","title":""},{"docid":"383ab9ad77c69e54df5b8d330e1e757c","score":"0.6417246","text":"def qg8_graph_get_number_chunks(graph: qg8_graph):\n if not isinstance(graph, qg8_graph):\n raise TypeError(\"Argument is not a qg8_graph\")\n return len(graph.chunks)","title":""},{"docid":"640890e72855245f1a2545c881ce98fb","score":"0.640243","text":"def num_node_groups(self) -> int:\n return pulumi.get(self, \"num_node_groups\")","title":""},{"docid":"725a9f9c5e60e3ed272d60d7960d7fb6","score":"0.6393146","text":"def dim(self):\n return len(self)","title":""},{"docid":"c5fbe652b1d9985ecc168146e0cfc634","score":"0.63930035","text":"def num_nodes(self):\n return len(self.myDict.keys())","title":""},{"docid":"4a45541a366dd2442b53dbbeb223cb07","score":"0.6390137","text":"def getNodes(self):\n return self.getParameter(\"NODE_COUNT\")","title":""},{"docid":"ba0f614b73a3c53ccb3754672c4c9142","score":"0.63867104","text":"def N(self):\n return self.inputdata().GetNumberOfPoints()","title":""},{"docid":"54c448bb78dddb0d6d8e65382178e863","score":"0.6385197","text":"def number_of_selfloops(self):\r\n return len(self.selfloop_edges())","title":""},{"docid":"ba62341117af99dc48f054cda06be3a1","score":"0.6381643","text":"def totalNumNodes(self) -> int:\n return self._call_java(\"totalNumNodes\")","title":""},{"docid":"79be64a597a7eff7ef9ee35a4799f694","score":"0.63810533","text":"def num_of_edge(graph):\r\n return len(list(graph.edges()))","title":""},{"docid":"9b618bf04b85a724d0516cc64b89edc5","score":"0.6379052","text":"def dim(self) -> int:\n return self._dim","title":""},{"docid":"9b618bf04b85a724d0516cc64b89edc5","score":"0.6379052","text":"def dim(self) -> int:\n return self._dim","title":""},{"docid":"e1c525285756cd9712e8ee8baecfed8d","score":"0.63684386","text":"def number_of_values(self):","title":""},{"docid":"d31cf30662de8229efe8544a66397df2","score":"0.63648313","text":"def num_frames( self ):\r\n return self.data.num_frames","title":""}],"string":"[\n {\n \"docid\": \"0355f1833468a8bd7a3a10e653f39a43\",\n \"score\": \"0.8215049\",\n \"text\": \"def number_of_graphs(self):\\n return self.get_table_row_count(self.GRAPH_TABLE._name_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cf06f4008d0a5795c841c5b8657580\",\n \"score\": \"0.75362337\",\n \"text\": \"def n_datasets(self) -> int:\\n n_others = len(self._dcs().keys())\\n\\n return n_others + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfee6f97e0581fbcdbcde40548c486ef\",\n \"score\": \"0.73872626\",\n \"text\": \"def number_of_edges(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0738fdb10f050aab0ef67e1a4adca5\",\n \"score\": \"0.73707473\",\n \"text\": \"def n_plots(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bad50437dea7ff65c93298e103c51c\",\n \"score\": \"0.7311434\",\n \"text\": \"def number_of_nodes(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d31cacdd1126a7080e038de5ebd6812\",\n \"score\": \"0.71918505\",\n \"text\": \"def size(self):\\n\\t\\treturn len(self._datasets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4a94f52e9f13a3c54d0cf45633160c\",\n \"score\": \"0.71886915\",\n \"text\": \"def number_of_nodes(self):\\r\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2591fef6647a6fc549f24fb36a1d180\",\n \"score\": \"0.71552324\",\n \"text\": \"def number_of_nodes(self):\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3558f2eb5a7ab0338cede409267fcf02\",\n \"score\": \"0.71441734\",\n \"text\": \"def number_of_edges(self):\\r\\n return len(self.edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8b2036bbf102da85429eefe047c8bb\",\n \"score\": \"0.7133862\",\n \"text\": \"def number_of_nodes(self):\\n return self.number_of_vertices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1aa83c24a0ee2ef2a53638baa576fba\",\n \"score\": \"0.70328444\",\n \"text\": \"def num_of_node(graph):\\r\\n return len(list(graph.nodes()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dfad0d59845d099bb0f3b0393f7ef0a\",\n \"score\": \"0.7019014\",\n \"text\": \"def get_number_of_nodes(self):\\r\\n return len(self.node_Ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76154412818646208517a4ae81add28b\",\n \"score\": \"0.6967071\",\n \"text\": \"def n_nodes(self):\\n return self._G.number_of_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2614d63ee491efd9a8e9666e3a4e4b\",\n \"score\": \"0.6942869\",\n \"text\": \"def get_number_of_nodes(self):\\n return len(self.__adjacency_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afa6f8cf49e64e4b7e5c061e51ef1ae\",\n \"score\": \"0.6938715\",\n \"text\": \"def count(self) -> int:\\n return self.vis[\\\"count\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93592d0020887f7c40a21c6d1cb6144\",\n \"score\": \"0.6929193\",\n \"text\": \"def getGraphSize(self):\\n return self.graphSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edb31256fae3bd25a640fed242c1827\",\n \"score\": \"0.692044\",\n \"text\": \"def number_of_nodes(self):\\r\\n return len(self.node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04898e55628d19987e50d0a139129b10\",\n \"score\": \"0.68733644\",\n \"text\": \"def n_data_points(self)->int:\\n return self._n_data_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04898e55628d19987e50d0a139129b10\",\n \"score\": \"0.68733644\",\n \"text\": \"def n_data_points(self)->int:\\n return self._n_data_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5be17166b29fc298d961638225e1c0\",\n \"score\": \"0.68576306\",\n \"text\": \"def number_of_edges(self):\\n return sum(1 for _ in self.edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f728335c8c6b9ff1422511a87bbe86c\",\n \"score\": \"0.6832707\",\n \"text\": \"def num_edges(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc6f50b575f6617d5901ee7b86f07fe\",\n \"score\": \"0.6823159\",\n \"text\": \"def number_of_nodes(self):\\n return sum(1 for _ in self.nodes_iter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e249348a7eb7edd50e8b905bb41dfed\",\n \"score\": \"0.68057483\",\n \"text\": \"def dataset_image_count(self):\\n return self._dataset_image_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474979992ba794a05d9cb3e18ca10e96\",\n \"score\": \"0.6793165\",\n \"text\": \"def n_edges(self) -> int:\\n return len(self.edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ae97345eb9260c4e91c922f20583df\",\n \"score\": \"0.67880934\",\n \"text\": \"def num_nodes(self):\\n return len(self.nodes())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ac151061059e2521eaf9256758b63b\",\n \"score\": \"0.67822707\",\n \"text\": \"def num_nodes(self):\\n return len(self.adj_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c116b37f3519214798b58931e4d1faa\",\n \"score\": \"0.6774768\",\n \"text\": \"def node_count(self):\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08727f4e597c0ab82643a0042d218190\",\n \"score\": \"0.6736805\",\n \"text\": \"def numNodes(self):\\n return len(self.dicN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab03c6856efb9d62a0fac78658e743a5\",\n \"score\": \"0.67211145\",\n \"text\": \"def number_of_hidden_nodes(self):\\r\\n return len(self.hidden_nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c920b53677faaa969bbfeff023116cf0\",\n \"score\": \"0.6689106\",\n \"text\": \"def number_of_hidden_edges(self):\\r\\n return len(self.hidden_edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c4b1315c12da4ea656b9cbb315a03c\",\n \"score\": \"0.6685002\",\n \"text\": \"def connection_count(self):\\n\\n connections = 0\\n if \\\"num-connections\\\" in self.profile:\\n connections = self.profile[\\\"num-connections\\\"]\\n\\n return connections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690e135c7c131dc14231b535172a0282\",\n \"score\": \"0.66796535\",\n \"text\": \"def nconsiderednodes(self):\\n assert self._node_count is not None\\n return self._node_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1aa29ec90d6b77fd0e7c4c31b8563f\",\n \"score\": \"0.66766924\",\n \"text\": \"def size(self):\\n\\n return self.n_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6faad0a0d83c063cfbc933606dce2f7\",\n \"score\": \"0.6675746\",\n \"text\": \"def number_of_edges(self):\\n num_edges = graph_wrapper.number_of_edges(self.graph_ptr)\\n\\n return num_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf57ddcd35b62ee350b622ab89f97184\",\n \"score\": \"0.66686505\",\n \"text\": \"def n_nodes(self) -> int:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf57ddcd35b62ee350b622ab89f97184\",\n \"score\": \"0.66686505\",\n \"text\": \"def n_nodes(self) -> int:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936e2734149056a985cf042958e7a1e8\",\n \"score\": \"0.66666746\",\n \"text\": \"def numNodes(self) -> int:\\n return self._call_java(\\\"numNodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f7605f501cc4a7659341266aecc6d0\",\n \"score\": \"0.6652915\",\n \"text\": \"def number_of_docs(self) -> int:\\n return self._G.number_of_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1efe6a5c532bd8a32dcb9d7931a89fe\",\n \"score\": \"0.66516787\",\n \"text\": \"def num_edges(self):\\n return lib.graph_num_edges(self._obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540eef509583d426ab07af850429427c\",\n \"score\": \"0.6622153\",\n \"text\": \"def num_nodes(self) -> int:\\n return self._c_csc_graph.num_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f56c5b5b9c9b332a52dd9b25e86da94\",\n \"score\": \"0.6622133\",\n \"text\": \"def _get_number_of_instances(self):\\n return self.__number_of_instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f56c5b5b9c9b332a52dd9b25e86da94\",\n \"score\": \"0.6622133\",\n \"text\": \"def _get_number_of_instances(self):\\n return self.__number_of_instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bbbace7d5e517b7c1f0c9cd2c64b76\",\n \"score\": \"0.66186965\",\n \"text\": \"def example_count(self):\\n return len(self.torch_loader.dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78f630810ff63c673af6a4e80f02b9fd\",\n \"score\": \"0.6618202\",\n \"text\": \"def number_of_vertices(self):\\n num_vertices = graph_wrapper.number_of_vertices(self.graph_ptr)\\n\\n return num_vertices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a4abff35b148272092124f43be0f8b\",\n \"score\": \"0.6610146\",\n \"text\": \"def dataset_count():\\n count = 0\\n result = toolkit.get_action('package_search')({}, {'rows': 1})\\n if result.get('count'):\\n count = result.get('count')\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6574458beda02c6c4c70bbfeed0306cd\",\n \"score\": \"0.66042715\",\n \"text\": \"def get_point_count(self):\\n return len(self.edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49918347258b797a343003e19f1f7cfb\",\n \"score\": \"0.6601892\",\n \"text\": \"def GetNodeCount(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8ffb037c28d99430f82940f7ff1ebe\",\n \"score\": \"0.65973693\",\n \"text\": \"def num_instances(self):\\n return _NvRules.IMetric_num_instances(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d071495a425e30a7937c01a0e1cd00d3\",\n \"score\": \"0.6597358\",\n \"text\": \"def nnodes(self):\\n return len(self.matrix.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb32be2f53661002c27c87273f37b8d\",\n \"score\": \"0.65965056\",\n \"text\": \"def num_datapoints(self):\\n\\t\\treturn self.predictions.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3464e5965295ae9a8d80778d86c6411c\",\n \"score\": \"0.6596318\",\n \"text\": \"def count(self):\\n return self._label_fdist.N()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b4f79eedd7716a980fec7fd1fcc86c\",\n \"score\": \"0.65911674\",\n \"text\": \"def size(self):\\n return self.nodes.size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0788dee9a35d6dfc6c7c25db7fe1e68\",\n \"score\": \"0.65862036\",\n \"text\": \"def size(self):\\n return self.digr.num_vertices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e40b28ac771634a9df3b7c41ccd5c0c\",\n \"score\": \"0.658214\",\n \"text\": \"def number_of_nodes(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"number_of_nodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5270f9cba24d292600124909b27116\",\n \"score\": \"0.6581056\",\n \"text\": \"def plot_rank(self):\\n return len(self.plot_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4748f20ccc2d3768007027e69e5cabb6\",\n \"score\": \"0.65771747\",\n \"text\": \"def node_count(graph):\\n return len(graph.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958ed92b89a2de823e72cf2d18df1875\",\n \"score\": \"0.6570017\",\n \"text\": \"def num_nodes(self):\\n return len(self._nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460f42002a43c9de8ba845e635ed3be5\",\n \"score\": \"0.6568672\",\n \"text\": \"def dataset_size(self):\\n return self.dataset.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5956dcf005f5a551ed08f05bb482489\",\n \"score\": \"0.6568589\",\n \"text\": \"def number_of_paths(self):\\n return self.num_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562189beb582fc5db5475ef106ed37f2\",\n \"score\": \"0.65611106\",\n \"text\": \"def __len__(self) -> int:\\n return len(list(self.graph.items(self.uri)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90f41a954dbd4af2a4f891ba47a0b2f\",\n \"score\": \"0.65585494\",\n \"text\": \"def totalConnections(analyzer):\\n return gr.numEdges(analyzer['connections'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b99a28bdd0c7483f89649d3657a11fa\",\n \"score\": \"0.6558188\",\n \"text\": \"def num_edges(self) -> int:\\n return self._c_csc_graph.num_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af950419a6e9ba755be627bf4a55852\",\n \"score\": \"0.6527257\",\n \"text\": \"def __len__(self):\\n return self.num_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9edc1dad30b39aefcfd0285b42b8a328\",\n \"score\": \"0.65193063\",\n \"text\": \"def number_of_monitors(): ###\\n return len(monitors())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15ec172779f9d6b32207a8a088d435d\",\n \"score\": \"0.65185875\",\n \"text\": \"def numNodes(self):\\n return len(self.G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc7ddd99e64240ecf316bf6d1594e93\",\n \"score\": \"0.65136\",\n \"text\": \"def get_frame_count(self):\\n\\n return len(self.plot_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cb4e9110db43ea35180523b3ca3d70\",\n \"score\": \"0.6512665\",\n \"text\": \"def dimensions(self):\\n return self._N\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aee5267e5f73468783933e887a0efdd\",\n \"score\": \"0.65110767\",\n \"text\": \"def n_records(self) -> int:\\n\\n n_records = sum([len(data.initial_molecules) for data in self.dataset.values()])\\n return n_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719d02468234d1aa3c2dec1d8689c58b\",\n \"score\": \"0.65079\",\n \"text\": \"def getNdim(self):\\n try:\\n ndim = self.segments.ndim\\n except AttributeError:\\n ndim = self._ndim\\n return ndim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dddcc96fb145ad82d5cb2cacd1bd7931\",\n \"score\": \"0.6505912\",\n \"text\": \"def get_num_nodes(self):\\n return len([sec for sec in self.config.sections if self.is_node(sec)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b1e31b262dd6bc1276e39245392cb2\",\n \"score\": \"0.6504585\",\n \"text\": \"def num_datapoints(self):\\n\\t\\treturn self.pred_probs.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd1df339740f4fb4043640e049d656c\",\n \"score\": \"0.6499093\",\n \"text\": \"def n_scales(self):\\n return len(self.scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd1df339740f4fb4043640e049d656c\",\n \"score\": \"0.6499093\",\n \"text\": \"def n_scales(self):\\n return len(self.scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df460ebad4eecf19324ad19e3c7f59de\",\n \"score\": \"0.64894354\",\n \"text\": \"def num_subgraphs(self, subgraph):\\n return self.num_subgraphs_by_parameters(\\n subgraph.num_verts(),\\n subgraph.num_edges(),\\n subgraph.automorphism_group().order()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df52ea3bf5d0498742b099a84939ac22\",\n \"score\": \"0.64827776\",\n \"text\": \"def __len__(self):\\n\\t\\treturn self._nnodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94ce00d1dbe4096809876f0f4d3ae624\",\n \"score\": \"0.6477803\",\n \"text\": \"def n_nodes(self):\\n return len(self.xs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df97dcfd539cfe6c227b63c8b4f920c5\",\n \"score\": \"0.64721024\",\n \"text\": \"def num_per_epoch(self):\\n\\n return len(self.images)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52821838253f58673b5a1a49ed8e44ff\",\n \"score\": \"0.64649993\",\n \"text\": \"def getDimension(self):\\n return len(self.components)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a960f137d0cfa646f638b56d43f468\",\n \"score\": \"0.64609027\",\n \"text\": \"def __len__(self):\\n return self._n_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a26f0ff4891642c6b0af7c69c3bfb35\",\n \"score\": \"0.6458422\",\n \"text\": \"def _get_dataset_size(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717dba2db4cbce87a82ddae8a8cd9c7f\",\n \"score\": \"0.6457629\",\n \"text\": \"def dimension(self):\\r\\n return self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d322b0f1269415c560ad757aef43fe\",\n \"score\": \"0.64518964\",\n \"text\": \"def get_count(self):\\n return len(self._bars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d413aebbb9a88ec559169b4fa899caac\",\n \"score\": \"0.6450254\",\n \"text\": \"def dimension(self):\\n return self.n+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49bcb084caca4964216fc2fefd6e5af\",\n \"score\": \"0.6448196\",\n \"text\": \"def nums(self):\\n return len(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f345f1295ad50bfc21811d69dea3f0d\",\n \"score\": \"0.6447515\",\n \"text\": \"def get_count(self) -> int:\\n return len(self._bars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc33751eeb84b817b313f95c0b2585c\",\n \"score\": \"0.64436454\",\n \"text\": \"def number_of_layers(self):\\n return self.len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec5df087a97bd61e2a2f090a708bec3\",\n \"score\": \"0.6435416\",\n \"text\": \"def get_dataset_size(self, dataset):\\n \\n i = 0\\n for batch, (image, audio, label) in enumerate(dataset):\\n assert tf.shape(image)[0] == tf.shape(audio)[0] == tf.shape(label)[0]\\n i += tf.shape(image)[0]\\n \\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5b492619ae4b1681fc7d6804c9e76e\",\n \"score\": \"0.642131\",\n \"text\": \"def __len__(self):\\r\\n return len(Dataset(self._id).shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383ab9ad77c69e54df5b8d330e1e757c\",\n \"score\": \"0.6417246\",\n \"text\": \"def qg8_graph_get_number_chunks(graph: qg8_graph):\\n if not isinstance(graph, qg8_graph):\\n raise TypeError(\\\"Argument is not a qg8_graph\\\")\\n return len(graph.chunks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640890e72855245f1a2545c881ce98fb\",\n \"score\": \"0.640243\",\n \"text\": \"def num_node_groups(self) -> int:\\n return pulumi.get(self, \\\"num_node_groups\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725a9f9c5e60e3ed272d60d7960d7fb6\",\n \"score\": \"0.6393146\",\n \"text\": \"def dim(self):\\n return len(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fbe652b1d9985ecc168146e0cfc634\",\n \"score\": \"0.63930035\",\n \"text\": \"def num_nodes(self):\\n return len(self.myDict.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a45541a366dd2442b53dbbeb223cb07\",\n \"score\": \"0.6390137\",\n \"text\": \"def getNodes(self):\\n return self.getParameter(\\\"NODE_COUNT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba0f614b73a3c53ccb3754672c4c9142\",\n \"score\": \"0.63867104\",\n \"text\": \"def N(self):\\n return self.inputdata().GetNumberOfPoints()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c448bb78dddb0d6d8e65382178e863\",\n \"score\": \"0.6385197\",\n \"text\": \"def number_of_selfloops(self):\\r\\n return len(self.selfloop_edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba62341117af99dc48f054cda06be3a1\",\n \"score\": \"0.6381643\",\n \"text\": \"def totalNumNodes(self) -> int:\\n return self._call_java(\\\"totalNumNodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79be64a597a7eff7ef9ee35a4799f694\",\n \"score\": \"0.63810533\",\n \"text\": \"def num_of_edge(graph):\\r\\n return len(list(graph.edges()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b618bf04b85a724d0516cc64b89edc5\",\n \"score\": \"0.6379052\",\n \"text\": \"def dim(self) -> int:\\n return self._dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b618bf04b85a724d0516cc64b89edc5\",\n \"score\": \"0.6379052\",\n \"text\": \"def dim(self) -> int:\\n return self._dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c525285756cd9712e8ee8baecfed8d\",\n \"score\": \"0.63684386\",\n \"text\": \"def number_of_values(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31cf30662de8229efe8544a66397df2\",\n \"score\": \"0.63648313\",\n \"text\": \"def num_frames( self ):\\r\\n return self.data.num_frames\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":794,"cells":{"query_id":{"kind":"string","value":"e436a651273cc71240d278e3bae026c5"},"query":{"kind":"string","value":"Show the given variable on the plot. Undoes `ignore_variable`. If a dataframe name is specified, all of its columns will be shown."},"positive_passages":{"kind":"list like","value":[{"docid":"13c4e6f3f61e09eaa750fd207f32f767","score":"0.4953621","text":"def show_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.show_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement showing variables\", ToastType.warning)","title":""}],"string":"[\n {\n \"docid\": \"13c4e6f3f61e09eaa750fd207f32f767\",\n \"score\": \"0.4953621\",\n \"text\": \"def show_variable(self, toast: Toast, var_name: str) -> None:\\n try:\\n return self.active_view.show_variable(toast, var_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement showing variables\\\", ToastType.warning)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f3416a3c42880a57556602bd26db1797","score":"0.57033026","text":"def ignore_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.ignore_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement ignoring variables\", ToastType.warning)","title":""},{"docid":"a30be2073bb4ee8b98885fcb1f16c1f5","score":"0.5620072","text":"def plot_proper(df: pd.DataFrame, xax: str, yax: str, var: str, title: str) -> None:\n pd.options.plotting.backend = \"plotly\"\n fig = df.plot(title=title, template=\"simple_white\",\n labels=dict(index=xax, value=yax, variable=var))\n fig.update_layout(\n xaxis_title=xax,\n yaxis_title=yax,\n )\n fig.show()","title":""},{"docid":"0372b371887346e02e57dac3f55b655a","score":"0.545876","text":"def ignore_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError","title":""},{"docid":"b21493415e595924fcbbdc5510e9902e","score":"0.51881444","text":"def show(*args, block=True, **kwargs):\n # Remove reserved rewords\n try:\n kwargs.pop('block')\n except:\n pass\n # Get the variable names in the scope show() was called from\n callers_local_vars = inspect.currentframe().f_back.f_locals.items()\n\n # Make a dictionary of the DataFrames from the position args and get their variable names using inspect\n dataframes = {}\n for i, df_object in enumerate(args):\n df_name = 'untitled' + str(i + 1)\n\n for var_name, var_val in callers_local_vars:\n if var_val is df_object:\n df_name = var_name\n\n dataframes[df_name] = df_object\n\n # Add the dictionary of positional args to the kwargs\n if (any([key in kwargs.keys() for key in dataframes.keys()])):\n print(\"Warning! Duplicate DataFrame names were given, duplicates were ignored.\")\n kwargs = {**kwargs, **dataframes}\n\n pandas_gui = PandasGUI(**kwargs)\n\n if block:\n pandas_gui.app.exec_()","title":""},{"docid":"a51ef5db5e8ccb230c8b182b1588e161","score":"0.51808095","text":"def no_detour_boxplot(df,country,save=False,fig=None, ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='no detour', grid=False, ax=ax,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% No detour\")\n ax.set_title(\"% routes between NUTS-3 regions in {} without detour\".format(country))\n\n #Todo (possible): give fig ax as args; enable saving possiblity\n\n if save: # TODO REPLACE ALL INSTANCES OF THIS PART OF CODE WITH A SPECIAL FUNCTION\n save_figs = load_config(config_file)['paths']['output_images'] / 'no_detour_boxplot'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"noDT_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax","title":""},{"docid":"53a5704bd479ea2ddbc6c7a3c6679479","score":"0.51140994","text":"def display_data_frame(data_frame):\n\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n print(data_frame)","title":""},{"docid":"cf1e92a529a23ea32701ac6ec9e46e36","score":"0.5068718","text":"def plot_log(df, var) -> None:\n\n df = df.copy()\n df[var] = np.log(df[var] + 1)\n sns.displot(df, x=var)","title":""},{"docid":"0af0d61f97e9c1df8737454662caad4a","score":"0.5064501","text":"def plot_continuous(df, var) -> None:\n\n df = df.copy()\n df['Income'] = df['Income'].astype('category')\n var_name = var\n if var in config.SKEWED_NUMERIC_VARS:\n df[var] = df[var]\n var_name = 'Log of ' + var_name\n ax = sns.boxplot(x='Income', y=var, data=df)\n ax.set(ylabel=var_name, title=f\"Effect of {var} on Income\")\n plt.show()","title":""},{"docid":"005efd6f5fe0c2f341addce0816a3c8e","score":"0.50505435","text":"def visualize_continuous_variables(df, label, method={'type': 'histogram', 'bins': 20}, outlier='on'):\n # create vector of the variable of interest\n v = df[label]\n # define mean and standard deviation\n m = v.mean()\n s = v.std()\n # prep the figure\n fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n ax[0].set_title('Distribution of ' + label)\n ax[1].set_title('Tip % by ' + label)\n if outlier == 'off': # remove outliers accordingly and update titles\n v = v[(v - m) <= 3 * s]\n ax[0].set_title('Distribution of ' + label + '(no outliers)')\n ax[1].set_title('Tip % by ' + label + '(no outliers)')\n if method['type'] == 'histogram': # plot the histogram\n v.hist(bins=method['bins'], ax=ax[0])\n if method['type'] == 'boxplot': # plot the box plot\n df.loc[v.index].boxplot(label, ax=ax[0])\n ax[1].plot(v, df.loc[v.index].tip_percentage, '.', alpha=0.4)\n ax[0].set_xlabel(label)\n ax[1].set_xlabel(label)\n ax[0].set_ylabel('Count')\n ax[1].set_ylabel('Tip (%)')","title":""},{"docid":"a3c79f8c6106edcd31610020abb8ff8b","score":"0.5001463","text":"def plot_continuous_column(df, target_variable, column_name, fig_size=(9, 12)):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.distplot(tmp_df[column_name], ax=ax1)\n sns.boxplot(x=target_variable, y=column_name, data=tmp_df, ax=ax2)","title":""},{"docid":"db0fbaeaed5291893aca4beb006c0d75","score":"0.49557826","text":"def set_df_violin_plot(data, header):\n df = records_to_df(data)\n try:\n df[['user', 'condition', 'block', 'task']] = df[['user', 'condition', 'block', 'task']].astype('category')\n except KeyError:\n col_names = [c['id'] for c in header]\n df = pd.DataFrame(columns=col_names)\n df.rename(columns={'df1 mean': 'df1', 'df2 mean': 'df2'}, inplace=True)\n fig = plotting.generate_violin_figure(df, columns=['df1', 'df2'], ytitle='Mean', legend_title=\"DOF\")\n return fig","title":""},{"docid":"e2c885c87d0ff416a36c8e524bf40d4a","score":"0.49128348","text":"def plot_var(var='positiveIncrease',\n state='NY'):\n assert type(var) == str, \"Expected string as the variable name\"\n assert type(state) == str, \"Expected string as the state name\"\n\n y = df[df['state'] == state][var]\n x = df[df['state'] == state]['date']\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" for {}\".format(var, state), fontsize=18)\n plt.bar(x=x, height=y, edgecolor='k', color='orange')\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()","title":""},{"docid":"5c80fdeda37d80a6aae7f35d9019dc81","score":"0.48900345","text":"def show_labels(self, var, datasets, text_key):\n\t\tif datasets == ['all']: datasets = self.ds_names\n\t\tds = [self[datasets]] if len(datasets)==1 else self[datasets]\n\t\talias = self._get_alias(datasets)\n\t\ttk = text_key.split('~')\n\t\tetk = tk[1].split()[0] if len(tk) > 1 else None\n\t\ttk = tk[0]\n\t\tall_df = []\n\t\tfor v in var:\n\t\t\tif not all(v in d for d in ds): continue\n\t\t\ttexts = [d.text(v, False, tk, etk) if v in d else None\n\t\t\t\t\t for d in ds]\n\t\t\tindex = pd.MultiIndex.from_tuples([(v, a) for a in alias])\n\t\t\tdf = pd.DataFrame({text_key: texts}, index=index)\n\t\t\tall_df.append(df)\n\t\tif not all_df:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(all_df, axis=0)","title":""},{"docid":"7f303d01580491b054b8ee02bf003a5d","score":"0.48365968","text":"def plot(ds: DatasetLike.TYPE,\n var: VarName.TYPE,\n indexers: DictLike.TYPE = None,\n title: str = None,\n properties: DictLike.TYPE = None,\n file: str = None) -> Figure:\n ds = DatasetLike.convert(ds)\n\n var_name = VarName.convert(var)\n if not var_name:\n raise ValidationError(\"Missing name for 'var'\")\n var = ds[var_name]\n\n indexers = DictLike.convert(indexers)\n properties = DictLike.convert(properties) or {}\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n\n var_data = get_var_data(var, indexers)\n var_data.plot(ax=ax, **properties)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None","title":""},{"docid":"147a7cca10cae4658a76af694603d9ef","score":"0.47928968","text":"def removeVariable(self, *args):\n return _libsedml.SedDataGenerator_removeVariable(self, *args)","title":""},{"docid":"739695fe3ea743b4e0a8fb833a68fa2c","score":"0.4776736","text":"def plot_discrete(df, var) -> None:\n\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n plt.title(f\"Effect of {var} on Income\")\n if var == 'weeks worked in year':\n plt.title(f\"Effect of Months worked in year on Income\")\n axis.set(xlabel=\"Months worked in year\")\n plt.show()","title":""},{"docid":"4ce3d4781b9c8774a39083c0bd03c66a","score":"0.4768098","text":"def plot_missing(self, axis = 1, show=True, **figure_kwargs):\n if axis == 1:\n if self.record_missing_cols is None:\n raise NotImplementedError(\"Columns missing values have not been calculated. Run `find_missing with axis = 1`\")\n elif axis == 0:\n if self.record_missing_rows is None:\n raise NotImplementedError(\"Rows missing values have not been calculated. Run `find_missing with axis = 0`\")\n self.reset_plot()\n # Histogram of missing values\n plt.style.use('seaborn-white')\n plt.figure(figsize = (7, 5))\n if axis == 1:\n plt.hist(self.missing_stats_cols['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n elif axis == 0:\n plt.hist(self.missing_stats_rows['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n plt.xticks(np.linspace(0, 1, 11))\n plt.xlabel('Missing Fraction', size = 14)\n plt.ylabel('Count of Features', size = 14)\n plt.title(\"Fraction of Missing Values Histogram\", size = 16)\n missing_plot = self.prepro_dir + '/missing.png'\n plt.savefig(missing_plot, dpi=300)\n if self.isDisplay == False:\n plt.close()","title":""},{"docid":"f81b4edbff3dfdaaacd61d1269c7a70d","score":"0.47633633","text":"def displayDataFrame(dataFrame):\n pd.set_option('display.max_rows', dataFrame.shape[0] + 1)\n pd.set_option('display.max_columns', dataFrame.shape[1] + 1)\n print(dataFrame)\n pd.reset_option('display.max_rows')\n pd.reset_option('display.max_columns')","title":""},{"docid":"d6b28d65ea32c34efacc762f1e62c65c","score":"0.4750205","text":"def removeVariable(self, *args):\n return _libsedml.SedComputeChange_removeVariable(self, *args)","title":""},{"docid":"c57f8fcc11be295aa3a4e5d2127121d3","score":"0.47035733","text":"def plot_graph(dfs,\n variables_to_plot,\n titles,\n ylim=None,\n xlim=None,\n save=None,\n eventname=None,\n show=True):\n answer = {} \n custom_lim = np.ndarray((2,1))\n def printspanselector(xmin, xmax):\n if isinstance(xmin, np.datetime64):\n xmin = date2num(xmin)\n xmax = date2num(xmax)\n\n ind = np.logical_and(date2num(df.index) >= xmin,\n date2num(df.index) <= xmax)\n axs[-1].plot(df.index[ind],\n df[v][ind],\n 'r+',\n gid='selected')\n if 'delete' not in answer:\n answer['delete'] = []\n answer['delete'].append([xmin, xmax])\n\n def onclick(event, ax):\n # Only clicks inside this axis are valid.\n try: # use try/except in case we are not using Qt backend\n zooming_panning = ( fig.canvas.cursor().shape() != 0 ) # 0 is the arrow, which means we are not zooming or panning.\n except:\n zooming_panning = False\n if zooming_panning: \n return\n\n if eventname == 'choose':\n plt.close('all')\n answer['axes'] = event.inaxes.get_title()\n \n \n plt.close('all')\n fig = plt.figure(figsize = (11.69*(2/3), 8.27/2))\n gs1 = gridspec.GridSpec(len(dfs), 1)\n axs=[]\n for n, df in enumerate(dfs):\n if n == 0:\n axs.append(fig.add_subplot(gs1[n],) )\n else:\n axs.append(fig.add_subplot(gs1[n], sharex=axs[0]) )\n\n plt.title(titles[n])\n\n for i,v in enumerate(variables_to_plot):\n if v in df:\n if not check_timeseries(df[v].values):\n typ = '-'\n else:\n typ = '+'\n\n axs[-1].plot(df.index,\n df[v],\n typ,\n label=v)\n\n if xlim:\n plt.xlim(xlim[0], xlim[1])\n\n if ylim:\n plt.ylim(ylim[0], ylim[1])\n\n plt.legend(bbox_to_anchor=(0.0, -.5, 1, 0),\n loc=\"lower left\",\n mode=\"expand\", ncol=2)\n \n fig.autofmt_xdate()\n\n if save:\n plt.savefig(save,\n format='png',\n bbox_inches=\"tight\")\n\n if eventname=='choose':\n cid = fig.canvas.mpl_connect('button_press_event', lambda event: onclick(event, axs[-1]))\n plt.show()\n elif eventname=='clean':\n selector = SpanSelector(axs[-1],\n onselect=printspanselector,\n direction='horizontal',\n useblit=True,\n span_stays=False,\n button=1,\n rectprops={'facecolor': 'grey', 'alpha': 0.3})\n fig.canvas.mpl_connect('key_press_event',\n selector)\n plt.show()\n else:\n if show:\n plt.show()\n\n return answer","title":""},{"docid":"84b3cc6dad55e823688a60a9dd9f1d6a","score":"0.47027907","text":"def drop_superfluous_trace_vars(data):\n return data.drop([\"P0\", \"co2vmr\", \"gw\", \"hyai\", \"date\", \"date_written\",\n \"datesec\", \"hyai\", \"hyam\", \"hybi\", \"hybm\", \"mdt\",\n \"nbdate\", \"nbsec\", \"ndbase\", \"ndcur\", \"nlon\", \"nsbase\",\n \"nscur\", \"nsteph\", \"ntrk\", \"ntrm\", \"ntrn\",\n \"time_written\", \"wnummax\"])","title":""},{"docid":"62e424f4c821b66a9f0683dc1362dd3c","score":"0.468913","text":"def plot_variable(lon, lat, variable, varname):\n\n # Create a figure\n plt.figure(figsize=(12,12))\n ax = plt.axes(projection=ccrs.PlateCarree())\n ax.coastlines()\n \n # Plot data\n plt.pcolor(lon, lat, variable, transform=ccrs.PlateCarree(), cmap='RdYlBu_r')\n plt.title(varname)\n plt.colorbar(orientation=\"horizontal\", pad=0.06)#fraction=0.05)\n \n # Extent from metadata: ULC: 060.00 -120.00 LRC: 000.00 -030.00 RES:1.00 1.0\n ax.set_xticks([-120,-60,-30])\n ax.set_yticks([60,30,0])\n \n plt.savefig('snapshot_' + varname + '.png', dpi=300, bbox_inches='tight')\n plt.show()\n plt.close()","title":""},{"docid":"7e890b9f93a94f0a34c0d76af1446424","score":"0.4683768","text":"def show_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError","title":""},{"docid":"b971ee4f2989e077c9d161fa3847216b","score":"0.46831873","text":"def plot_3d(df, target_variable):\n unique_labels = target_variable.unique()\n ordinal_encoding = [np.where(unique_labels == label)[0][0]\n for label in target_variable]\n color_dict = {0: 'red', 1: 'green', 2: 'blue'}\n colors = [color_dict[each] for each in ordinal_encoding]\n threedee = plt.figure().gca(projection='3d')\n threedee.scatter(df[[0]], df[[1]],\n df[[2]], color=colors)\n threedee.set_xlabel(df.columns.values[0])\n threedee.set_ylabel(df.columns.values[1])\n threedee.set_zlabel(df.columns.values[2])\n plt.show()\n return None","title":""},{"docid":"fbd0f849e14a511d0e2aa2a2fe32b21e","score":"0.46819878","text":"def visualize(self, df:DataFrame):# -> dict:\n pass","title":""},{"docid":"bc766e7a0cd6d7c24b37fe6c46efb114","score":"0.46767002","text":"def get_var(data, variable):\n\n var = data.variables[variable]\n var_plot = ma.masked_equal(var, var.missing_value)\n\n lons = data.variables['lon'][...]\n lats = data.variables['lat'][...]\n\n return var_plot, lons, lats","title":""},{"docid":"0ad4c798ac7dc8256ba71957ce08c2f2","score":"0.46681195","text":"def plot_solution(self, variable):\n all_points = [*(self.nodes), *self.internal_points]\n x = sorted(set([point.coords[0] for point in all_points]))\n y = sorted(set([point.coords[1] for point in all_points]))\n z = [[None] * len(x)] * len(y)\n variable = variable.lower()\n for point in all_points:\n point_x = x.index(point.coords[0])\n point_y = y.index(point.coords[1])\n if variable == \"ux\":\n z[point_y][point_x] = point.u[0]\n elif variable == \"uy\":\n z[point_y][point_x] = point.u[1]\n elif variable == \"sx\":\n z[point_y][point_x] = point.p[0]\n elif variable == \"sy\":\n z[point_y][point_x] = point.p[1]\n elif variable == \"sxy\":\n z[point_y][point_x] = point.p[2]\n else:\n print(f\"Invalid option: {variable}\")\n self.plot_model(auto_open=False)\n self.fig.add_contour(\n name=variable,\n z=z,\n x=x,\n y=y,\n connectgaps=True,\n contours=dict(coloring=\"heatmap\", showlabels=True),\n colorbar=dict(title=variable, titleside=\"right\", x=1.2),\n )\n self.fig[\"layout\"].update(title=f\"<b>{self.name} ({variable})</b>\")\n return plotly.offline.plot(self.fig, filename=f\"{self.name}-{variable}.html\")","title":""},{"docid":"aa5d60f3a2f6d30cef38d85daa28dec1","score":"0.46618274","text":"def set_proj_var_plot(data):\n df = records_to_df(data)\n long_df = analysis.wide_to_long(df, ['parallel', 'orthogonal'], suffixes='variance', j='projection')\n fig = plotting.generate_lines_plot(long_df, \"variance\", by='user', color_col='projection')\n return fig","title":""},{"docid":"ee269e097d579cb2a24ab667fa58037b","score":"0.46423653","text":"def plot_against_target(self, feature):\n assert feature != self._target_variable\n\n if feature in self.numeric_features:\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\n y=self._target_variable,\n alpha=0.1,\n title='{0} vs. target (`{1}`)'.\n format(feature,\n self._target_variable))\n else:\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\n plt.ylabel(self.target_variable)\n plt.title(title)\n plt.suptitle(\"\")\n plt.tight_layout()","title":""},{"docid":"9e9637d4e9abc6369930a68c1502b95e","score":"0.46198583","text":"def set_variance_graph(table_data):\n df = records_to_df(table_data)\n df.dropna(inplace=True)\n return plotting.generate_means_figure(df)","title":""},{"docid":"82efd8c8b683ed6091a8576b3df1086d","score":"0.46060088","text":"def plotData(dataFrame, titleString, saveToFile=False, nsamples = 100, vars=None):\n\n\t#plot nsamples random elements from the sample\n\trandom_elements = random.sample(range(dataFrame.shape[0]), nsamples)\n\tdataFrame_plot = dataFrame.iloc[random_elements]\n\n\tif vars != None:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', vars=vars, dropna=True)\n\telse:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', dropna=True)\n\n\tplt.suptitle(titleString, y = 1.025)\n\n\tif saveToFile == True:\n\t\tplt.savefig('testfig.png', format='png', pad_inches=0.5, bbox_inches='tight')\n\t\tplt.close()\n\telse:\n\t\tpass\n\t\t#plt.show()","title":""},{"docid":"174b0d1fc32bac49ceac43e70e10a012","score":"0.46051374","text":"def plot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.plot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"d347fcfa2ffeceefe3ec788c6b9ec835","score":"0.45513475","text":"def removeVariable(self, *args):\n return _libsedml.SedSetValue_removeVariable(self, *args)","title":""},{"docid":"52d761854d1c29b36faeda1e5af7e583","score":"0.4534381","text":"def show_cats(self, var, text_key):\n\t\ttext_key = text_key.split('~')\n\t\tetk = text_key[1].split()[0] if len(text_key) > 1 else None\n\t\ttext_key = text_key[0]\n\t\tdf_all_v = []\n\t\tfor v in var:\n\t\t\tall_df = []\n\t\t\tfor name in list(self.ds_alias.values()):\n\t\t\t\tif v in self[name]:\n\t\t\t\t\tval = self[name].value_texts(v, text_key, etk)\n\t\t\t\t\tcodes = self[name].codes(v)\n\t\t\t\t\tindex = pd.MultiIndex.from_tuples([(v, c) for c in codes])\n\t\t\t\t\tdf = pd.DataFrame(val, index=index, columns=[name])\n\t\t\t\t\tall_df.append(df)\n\t\t\tall_df = pd.concat(all_df, axis=1)\n\t\t\tdf_all_v.append(all_df)\n\t\tif not df_all_v:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(df_all_v, axis=0)","title":""},{"docid":"c460d6f20fc4d69bed840f326ee71a3a","score":"0.44996905","text":"def plot_categorical_column(df, target_variable, column_name, fig_size=(9, 12), y_lim=None):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.countplot(x=column_name, data=tmp_df, ax=ax1)\n sns.pointplot(x=column_name, y=target_variable, data=tmp_df, ax=ax2)\n if y_lim is not None:\n ax2.set_ylim(0, y_lim)","title":""},{"docid":"31dcef4c8847e4789643aaf5cb79239e","score":"0.44970563","text":"def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,\n **kwargs):\n fdf = df.filter(**kwargs)\n if len(fdf.data) > 0:\n vdf = fdf.require_variable(variable=variable, unit=unit, year=year,\n exclude_on_fail=exclude_on_fail)\n df.meta['exclude'] |= fdf.meta['exclude'] # update if any excluded\n return vdf","title":""},{"docid":"5a9b710f33eaca9bece16fa9e4362da0","score":"0.44876218","text":"def unsuppress(self):\n self._pipe.send((None, 'show', None))","title":""},{"docid":"9a073a4e1e8b5823d0c2be47feae53f7","score":"0.4475288","text":"def plot(self, df, plotdir):\n df.plot()","title":""},{"docid":"4aed811f4a0431f31970dec2f4ca576a","score":"0.44718388","text":"def plot_single_variable(self, results, xlab=None, ylab=None, save=None):\n\n n, m = self.x.shape\n if m != 2:\n raise ValueError('Error: can only plot single explanatory variable. Current X shape: ({},{})'.format(n, m))\n X = self.x.drop('const', axis=1)\n X = np.squeeze(X.values)\n Y = np.squeeze(self.y.values)\n plt.plot(X, Y, 'o', c='steelblue')\n Xmin = np.argmin(X)\n Xmax = np.argmax(X)\n plt.plot([X[Xmin], X[Xmax]], [self.preds[Xmin], self.preds[Xmax]], c='darkorange')\n if xlab:\n plt.xlabel(xlab)\n if ylab:\n plt.ylabel(ylab)\n if save:\n plt.savefig('{}.png'.format(save), dpi=200)\n plt.show()","title":""},{"docid":"acb0f674d7c2303a1019fee9596426f2","score":"0.44626507","text":"def display(df):","title":""},{"docid":"7f92ec7c3a117c72a81c791bdbfc54a3","score":"0.4445461","text":"def print_dataframe(data):\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print(data)","title":""},{"docid":"215c3df97025653743c1890ed0d2c221","score":"0.44444445","text":"def remove_variable(self, varname):\n self.graph.remove_node(self.variables[varname])\n \n #removes the class of varname (should there be no other variables that share that class) \n #from classes_imported set \n for key,value in self.classes_available.items():\n if isinstance(self.variables[varname], value):\n self.classes_imported.remove(key) \n\n #remove the varname from the variables dictionary \n del self.variables[varname]\n self.convert_to_script()\n self.graph_changed_event = True","title":""},{"docid":"edc07226f64f3c33738730d8089e6428","score":"0.44427666","text":"def _plot_var(self, ax, **kwargs):\n self.__xmin, self.__xmax = ax.get_xlim()\n self.__ymin, self.__ymax = ax.get_ylim()\n plot_digit = kwargs.get('plot_digit', None)\n if plot_digit is not None:\n self.__prec = '%.' + '%d' % plot_digit + 'f'","title":""},{"docid":"05543b631cc28eab1499d4a0303f5622","score":"0.44299516","text":"def set_dropextradata(self, dropextradata):\n self.options['dropextradata'] = dropextradata","title":""},{"docid":"498c7472384572bf9b63e89977b3b9a2","score":"0.4422164","text":"def box_plot() -> str:\n\n session_manager.cache_analysis_option()\n return StatsModel().get_box_plot()","title":""},{"docid":"2b2024b879b7e0c085a117ee9ab652b2","score":"0.4414972","text":"def logplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.logplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"bc7637e0f986c8d599c82f0c7bfa9bbd","score":"0.44135877","text":"def plot(filename, column):\n df=pd.read_csv(filename)\n if column is None:\n df.hist();\n else:\n df[column].hist()\n plt.show() # if slow, this blocks all subsequent code from running # it waits for the window to close before continuing","title":""},{"docid":"c17307ab1248182037bdaadea9cc6336","score":"0.44075608","text":"def x_vs_y_plots(X,y,save_toggle=False,file_prefix=''):\n if len(file_prefix)>0:\n file_prefix += '_'\n \n df = pd.concat([X, pd.DataFrame(y, index=X.index)], axis=1)\n for x in X.columns:\n df[[x,y.name]].groupby(x).mean().plot()\n if save_toggle:\n plt.savefig(file_prefix+x+'_vs_'+y.name+'.png')\n plt.show()\n return","title":""},{"docid":"0889a47d34fccef58e97603b04c39123","score":"0.43964615","text":"def show_figure(self, *args, **kwargs):\n if 'divider' in dir(self):\n del self.divider\n plt.show(*args, **kwargs)","title":""},{"docid":"e42c056ce7bb2a7c997fe17f18e2da84","score":"0.43961328","text":"def implot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.implot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"94c1e8bd58d88d907afa606c72266977","score":"0.4391739","text":"def plot_xy(varx='totalTestResultsIncrease',\n vary='positiveIncrease',\n state='NY'):\n assert type(varx) == str, \"Expected string as the variable x name\"\n assert type(vary) == str, \"Expected string as the variable y name\"\n\n y = df[df['state'] == state][vary]\n x = df[df['state'] == state][varx]\n if (x.nunique() != 1) and (y.nunique() != 1):\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" vs. \\\"{}\\\" for {}\".format(varx, vary, state), fontsize=18)\n plt.scatter(x=x, y=y, edgecolor='k', color='lightgreen', s=100)\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()\n else:\n print(\"Some of the data unavailable for a scatter plot. Sorry!\")","title":""},{"docid":"bb8afd28e29829a317cacd43faa15d70","score":"0.4388029","text":"def plot(\n self,\n components=None,\n *,\n ax=None,\n logy=False,\n show=False,\n dates=False,\n legend=True,\n grid=True,\n ):\n ax = ax or plt.gca()\n kwargs = {\"logy\": logy, \"ax\": ax, \"grid\": grid, \"legend\": legend}\n\n def get_column(col):\n if dates:\n col += \":dates\"\n data = self[col]\n return data\n\n components = self.meta.variables if components is None else components\n for col in components:\n data = get_column(col)\n data.plot(**kwargs)\n if show:\n plt.show()","title":""},{"docid":"3cf0aa71c26b31f4c997c8c2afaf8893","score":"0.4385104","text":"def print_df(df: pd.DataFrame) -> None:\n\twith pd.option_context('display.max_rows', None, 'display.max_columns', None):\n\t\tprint(df)","title":""},{"docid":"72e57320b475c20dc2f96fd48da6fca7","score":"0.43605706","text":"def display_na(df):\r\n \r\n display(HTML(\"<h4>Percentage of missing variables for each feature:\"))\r\n print(df.isnull().sum(axis=0) * 100 / len(df))","title":""},{"docid":"7d004de844130a45592187867f7c68fd","score":"0.4355372","text":"def show(self, *args, **kwargs):\n self.get_plot(*args, **kwargs).show()","title":""},{"docid":"c08b4f4854337048144d53f798e073eb","score":"0.43472216","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show() # must be called to show plots in some environments","title":""},{"docid":"2e2c6fd0d9dca232109a4340a5c5a1a9","score":"0.43427277","text":"def extra_plot_variance_explained(var_explained, component,var_explained_2, component_2, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n ax = plt.subplot(111)\n plt.plot(component, np.array(var_explained), \"-v\", color=\"red\", mfc=\"red\", label=\"train\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.plot(component_2, np.array(var_explained_2), \"-v\", color=\"blue\", mfc=\"blue\", label=\"test\")\n plt.plot(\n component_2[opt_n_comp], np.array(var_explained_2)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n ax.legend()\n plt.show()","title":""},{"docid":"0a4f7b6cbfef0a5defdf6be992e65669","score":"0.4340944","text":"def plot_runner(ds, variable, s3upload=False) -> str:\n\n model_type = get_model_type(ds)\n if model_type == 'roms':\n imagename = plot_roms(ds_ofs, var, s3upload=True)\n elif model_type == 'fvcom':\n imagename = plot_fvcom(ds_ofs, var, s3upload=True)\n else:\n print(f\"ERROR: Unsupported model type - {modeltype}\")","title":""},{"docid":"473bf765662306bc925b6e3848dabee6","score":"0.43398735","text":"def hide_column(self, column_name):\n if not isinstance(column_name, str):\n raise TypeError(\"column_name must be of type str\")\n\n column_names = self.get_column_names()\n if column_name not in column_names:\n raise ValueError(f\"{column_name} not in available columns ({column_names})\")\n new_column_names = [cn for cn in self.get_column_names(True)\n if cn not in column_name]\n return self.set_visible_columns(new_column_names)","title":""},{"docid":"715c6a713767779ca425cadeff548c21","score":"0.43344194","text":"def display(self, *fields):\n try:\n if self.df is None:\n self.warning(\"Dataframe is empty: nothing to display\")\n return\n df2 = self.df[list(fields)]\n return df2.head()\n except Exception as e:\n self.err(e, self.display, \"Can not display dataframe\")","title":""},{"docid":"ab189fb4de3b5e34800eba35dea524ba","score":"0.43340218","text":"def plot_hovmoeller(ds: xr.Dataset,\n var: VarName.TYPE = None,\n x_axis: DimName.TYPE = None,\n y_axis: DimName.TYPE = None,\n method: str = 'mean',\n contour: bool = True,\n title: str = None,\n file: str = None,\n monitor: Monitor = Monitor.NONE,\n **kwargs) -> Figure:\n var_name = None\n if not var:\n for key in ds.data_vars.keys():\n var_name = key\n break\n else:\n var_name = VarName.convert(var)\n var = ds[var_name]\n\n if not x_axis:\n x_axis = var.dims[0]\n else:\n x_axis = DimName.convert(x_axis)\n\n if not y_axis:\n try:\n y_axis = var.dims[1]\n except IndexError:\n raise ValidationError('Given dataset variable should have at least two dimensions.')\n else:\n y_axis = DimName.convert(y_axis)\n\n if x_axis == y_axis:\n raise ValidationError('Dimensions should differ between plot axis.')\n\n dims = list(var.dims)\n try:\n dims.remove(x_axis)\n dims.remove(y_axis)\n except ValueError:\n raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\\\n {}, {}.'.format(var_name, x_axis, y_axis))\n\n ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean,\n 'median': np.nanmedian, 'sum': np.nansum}\n\n with monitor.starting(\"Plot Hovmoeller\", total_work=100):\n monitor.progress(5)\n with monitor.child(90).observing(\"Aggregate\"):\n var = var.reduce(ufuncs[method], dim=dims)\n monitor.progress(5)\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n if x_axis == 'time':\n figure.autofmt_xdate()\n\n if contour:\n var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs)\n else:\n var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None","title":""},{"docid":"e45e26120e690f3dad49ac9e218a7480","score":"0.43270665","text":"def remove_variable(self, var):\n self.vars.remove(var)","title":""},{"docid":"6f45be729aa0e30132fc58c88ddb34ca","score":"0.4326928","text":"def subset_variables(ds, variableList): # {{{\n\n allvars = ds.data_vars.keys()\n\n # get set of variables to drop (all ds variables not in vlist)\n dropvars = set(allvars) - set(variableList)\n\n # drop spurious variables\n ds = ds.drop(dropvars)\n\n # must also drop all coordinates that are not associated with the variables\n coords = set()\n for avar in ds.data_vars.keys():\n coords |= set(ds[avar].coords.keys())\n dropcoords = set(ds.coords.keys()) - coords\n\n # drop spurious coordinates\n ds = ds.drop(dropcoords)\n\n if len(ds.data_vars.keys()) == 0:\n raise ValueError(\n 'Empty dataset is returned.\\n'\n 'Variables {}\\n'\n 'are not found within the dataset '\n 'variables: {}.'.format(variableList, allvars))\n\n return ds # }}}","title":""},{"docid":"64cfecc9efd06e5098ce91f2f6a118a5","score":"0.43260407","text":"def distribution_plot(dataframe: pd.DataFrame, var: str, target: str = None, **kwargs):\n\n row = kwargs.get('row', None)\n col = kwargs.get('col', None)\n\n size = kwargs.get('size', None)\n aspect = kwargs.get('aspect', None)\n\n facet = sns.FacetGrid(\n dataframe,\n hue=target,\n size=size if size else 3,\n aspect=aspect if aspect else 4,\n row=row,\n col=col\n )\n\n facet.map(sns.kdeplot, var, shade=True)\n facet.set(title=\"Distribution of '{}' over '{}'\".format(var, target))\n\n # Defines limits\n if kwargs.get('clean', False):\n xmin = dataframe[var].quantile(0.02)\n xmax = dataframe[var].quantile(0.98)\n facet.set(xlim=(xmin, xmax))\n\n facet.add_legend()\n plt.show()","title":""},{"docid":"54230d6985c38be8af9232a0bf01ee18","score":"0.43237677","text":"def rename_variable(self, toast: Toast, var_name: str, display_name: str) -> None:\n try:\n return self.active_view.rename_variable(toast, var_name, display_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement variable renaming\", ToastType.warning)","title":""},{"docid":"5290b0dbee071b9ff806e7ed58e3ceb1","score":"0.43195853","text":"def setShowInX(self, x):\r\n\t\tself.__showInX=x","title":""},{"docid":"1ca95d946d23156598f83a8c528ad2ac","score":"0.43146688","text":"def plot_dis(df_master, df_dis, units):\n modul = df_master.modul\n stor = '{}_stor'.format(modul)\n loss = '{}_loss'.format(modul)\n relax = '{}_relax'.format(modul)\n\n if df_master.domain == 'freq':\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,0.75*4))\n df_master.plot(x='f', y=[stor], label=[\"{}'(filter)\".format(modul)],\n ax=ax1, logx=True, logy=True, color=['C0'], alpha=0.5)\n df_master.plot(x='f', y=[loss], label=[\"{}''(filter)\".format(modul)],\n ax=ax2, logx=True, logy=True, color=['C1'], alpha=0.5)\n df_dis.plot(x='f', y=[stor], label=['tau_i'], ax=ax1, \n logx=True, logy=True, ls='', marker='o', color=['C0'])\n df_dis.plot(x='f', y=[loss], label=['tau_i'], ax=ax2, \n logx=True, logy=True, ls='', marker='o', color=['C1'])\n ax1.set_xlabel('Frequency (Hz)')\n ax1.set_ylabel('Storage modulus ({})'.format(units[stor]))\n ax2.set_xlabel('Frequency (Hz)')\n ax2.set_ylabel('Loss modulus ({})'.format(units[stor])) \n ax1.legend()\n ax2.legend()\n fig.show()\n return fig\n elif df_master.domain == 'time':\n fig, ax1 = plt.subplots(figsize=(4,0.75*4))\n df_master.plot(x='t', y=[relax], \n ax=ax1, logx=True, logy=True, color=['k'])\n df_dis.plot(x='t', y=[relax], label = ['tau_i'], \n ax=ax1, logx=True, logy=True, ls='', marker='o', color=['red'])\n ax1.set_xlabel('Time ({})'.format(units['t']))\n ax1.set_ylabel('Relaxation modulus ({})'.format(units[relax]))\n ax1.legend()\n fig.show()\n return fig","title":""},{"docid":"35832c1e396d3f8c41dcf63d2a46273b","score":"0.43139654","text":"def hide_xticks(ax):\n for tick in ax.axes.get_xticklabels():\n tick.set_visible(False)","title":""},{"docid":"5e63b785758284832390cce3c328b022","score":"0.43101996","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()","title":""},{"docid":"5e63b785758284832390cce3c328b022","score":"0.43101996","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()","title":""},{"docid":"eb45a6f55ba0db29f2a75fa25b824f96","score":"0.43083325","text":"def show(self, *args, **kwds):\n self.plot(*args, **kwds).show()\n return","title":""},{"docid":"0b798880aba7d93b422ba79a8dafd758","score":"0.43063605","text":"def plot_discharge(self, data, **kw):\n x = 0\n print 'data', data\n for key in data:\n print 'key', key\n if key == '11462125 peak':\n print 'skipping peak discharge df'\n elif key == 'CLV - Cloverdale hourly':\n for col in self._col_list[:2]:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n\n elif key == 'COY - Coyote hourly':\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n else:\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n print 'nan values: {}'.format(count_nonzero(isnan(ser)))\n print 'len ser: {}'.format(len(ser))\n if len(ser) != count_nonzero(isnan(ser)):\n self._plot(col, key, ser, **kw)","title":""},{"docid":"8ae8dc9da6845384c103464f8a1f1451","score":"0.43056202","text":"def set_display():\r\n # Plots display settings\r\n plt.style.use('fivethirtyeight')\r\n plt.rcParams['figure.figsize'] = 12, 8\r\n plt.rcParams.update({'font.size': 14})\r\n # DataFrame display settings\r\n pd.set_option('display.max_columns', None)\r\n pd.set_option('display.max_rows', None)\r\n pd.options.display.float_format = '{:.4f}'.format","title":""},{"docid":"30b66c30a50d93c31d6e8fbe9796c40a","score":"0.42991424","text":"def boxplot_one_country(df,country,AoIs= 'All',save=False,fig=None,ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n #if AoIs != 'All': df2 = df2.loc[df2['AoI combinations'].isin(AoIs)]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='disrupted', ax=ax,grid=False,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% preferred routes disrupted\")\n ax.set_title(\"% routes between NUTS-3 regions in {} disrupted\".format(country))\n\n if save:\n save_figs = load_config(config_file)['paths']['output_images'] / 'disrupted'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"disrupted_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax","title":""},{"docid":"7cf843c3246768fbe1cd025b86929885","score":"0.42943284","text":"def plot_categoricial(df, var) -> None:\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n axis.set_xticklabels(config.vis_dict[var], rotation=75)\n plt.title(f\"Effect of {var} on Income\")\n plt.show()","title":""},{"docid":"e6b30d96205aae6597f9e8a95779b74e","score":"0.42883715","text":"def plot_line_chart(df: pd.DataFrame, title: str, y: str = 'y', x: str = 'x',):\n px.line(df, x=x, y=y, title=title).show()","title":""},{"docid":"99f63f08277116886413b5dfbbc45efa","score":"0.42838115","text":"def datatoplot(self, track_choice, track_no=None, track_index=None):\r\n if track_choice == 'evo':\r\n tracks = self.data\r\n elif track_choice == 'ranged':\r\n if self.ranged_tracks is None:\r\n raise NameError('No ranged tracks!')\r\n else: tracks = self.ranged_tracks\r\n else: raise NameError('Wrong track name!')\r\n \r\n max_tracks = tracks['track_no'].nunique()\r\n uniques = tracks['track_no'].unique()\r\n if track_no != None:\r\n if max_tracks > track_no:\r\n if type(track_index)==type(None):\r\n track_index=np.random.choice(uniques, track_no)\r\n else: track_index = uniques\r\n else: track_index = uniques\r\n if len(track_index)>200:\r\n raise ValueError('Too many tracks, are you sure you want to plot '+str(len(track_index))+' tracks??')\r\n \r\n selected = tracks.loc[tracks['track_no'].isin(track_index)]\r\n plot_tracks = [selected['Teff'], selected['L']]\r\n plot_m = selected['mass']\r\n return plot_tracks, plot_m","title":""},{"docid":"5513626174155422f31e167cfb56c003","score":"0.42826742","text":"def set_df_mean_plot(data):\n df = records_to_df(data)\n try:\n # For error bars we need standard deviation.\n df[['df1 std', 'df2 std']] = df[['df1 variance', 'df2 variance']].transform(np.sqrt)\n except KeyError:\n pass\n # Convert to long format for easier plotting.\n long_df = analysis.wide_to_long(df, stubs=['df1', 'df2'], suffixes=['mean', 'std'], j='dof')\n fig = plotting.generate_lines_plot(long_df, \"mean\", by='user', color_col='dof', errors='std', jitter=True)\n return fig","title":""},{"docid":"a2d4e630a8059c1b12652584c43c00fd","score":"0.4279645","text":"def easy_print_data_frame(data_frame):\n\n print(data_frame.to_string())","title":""},{"docid":"eef7324d0a5850a8c8ac3a856b4b9dfb","score":"0.42760804","text":"def _plot_variance_explained(var_explained, component, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n plt.plot(component, np.array(var_explained), \"-v\", color=\"blue\", mfc=\"blue\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n\n plt.show()","title":""},{"docid":"d83e8777568cac18f63c1b5039fa7e43","score":"0.4275368","text":"def show():\n plt.show(block=False)","title":""},{"docid":"4441f564cdf782290c8b56b76d3b9153","score":"0.4270931","text":"def show_plot(self, table):\n assert (self._current_panel is not None)\n graph = wxtbx.plots.iotbx_data_plot_base(\n parent=self._current_panel,\n tables=[table],\n size=(TEXT_WIDTH - 40, min(500, TEXT_WIDTH * 0.75)))\n graph.set_plot(table.only_plot())\n self._graphs.append((graph, table.title))\n self._current_sizer.Add(graph, 0, wx.ALL|wx.EXPAND, 5)","title":""},{"docid":"a774101cb8988ac9fa189c9a6f651b21","score":"0.42703143","text":"def plot_two_vars(self,\n xy_data, # ['my_datadict_out', e.g., 'merge_summstat_out']\n x_name, # [name of x variable to use for x-axis]\n y_name, # [name of y variable to use for y-axis]\n ent_axis = 'col', # [<'row', 'col'>] => how variables are situated]\n err_data = None, # [<None, 'my_datadict_out'> => e.g., 'merge_summstat_out']\n x_err = None, # [<None, size, name of x variable to use for error statistic> => create bubbles]\n y_err = None, # [<None, size, name of y variable to use for error statistic> => create bubbles]\n color_by = None, # [<None, 'g', '0.75', (0.5,0.2,0.7), 'rand', ['gender', {'male':'b', 'female':'r'}], ['age', 'g']> => color-code bubbles]\n cosine_correct = 'coord|corr', # [<None, 'coord', 'corr', 'coord|corr'> => correct for cosine between variables]\n max_cos = 0.95, # <unsigned corr> => trigger exception when cos(x, y) > max_cos]\n plot = None, # [<None, {plotting parameters}> => see docs to customize]\n savefig = 'xy_plot.png' # [<None, 'show', 'filename.png'> => how to output chart]\n ):\n\n if self.verbose is True:\n print 'plot_two_vars() is working...\\n'\n\n # Run the damon utility\n plot_two_vars_out = dmn.utils._plot_two_vars(locals())\n self.plot_two_vars_out = plot_two_vars_out\n\n if self.verbose is True:\n print 'plot_two_vars() is done -- see my_obj.plot_two_vars_out'\n\n return None","title":""},{"docid":"0d4531cd130c5272a5d9ffa4d886b3ee","score":"0.42631972","text":"def _plot_skipped_well_file(config, raw_data, well_name):\n if config.plot_skipped:\n plot_params = dict()\n plot_params['savepath'] = config.work_dir\n plot_params['well_name'] = well_name\n plot_params['fg_conc_edges'] = config.fg_conc_edges\n plot_params['fg_fret_edges'] = config.fg_fret_edges\n plot_params['data_df'] = raw_data\n plot_params['fine_grid_xlim'] = None\n plot_params['fine_grid_ylim'] = None\n plot_params['plot_type'] = config.plot_type\n plot_fine_grid_profiles(plot_params)","title":""},{"docid":"11adb8baf5e82cdd7884c2a1f79cd1e1","score":"0.42607546","text":"def do_variable(self, arg):\n self.display_cmake_help('variable', arg)","title":""},{"docid":"1862e5d6a2dd0e4bdab216750261911f","score":"0.42592025","text":"def strip_data(df: pd.DataFrame, data_to_keep: dict):\n gene_name = 'symbol'\n if df.index.name == data_to_keep.get(gene_name):\n data_to_keep.pop(gene_name)\n new_df = df[data_to_keep.values()]\n return new_df","title":""},{"docid":"a745c78232fa241c5ee33a4d7e4af63e","score":"0.4254119","text":"def bokeh_show(bokeh_object, filename=None, **save_kwargs):\n if filename is None:\n filename = \"bokehplot_00000000.html\"\n i = 1\n while i < 99999999 and os.path.isfile(filename):\n filename = \"bokehplot_{0:08d}.html\".format(i)\n i += 1\n\n if i == 99999999:\n raise RuntimeError(\n \"Cannot generate generic file name, as all filenames are used.\"\n )\n\n if \"resources\" in save_kwargs:\n resources = save_kwargs.pop(\"resources\")\n else:\n resources = bokeh.resources.CDN\n\n if \"title\" in save_kwargs:\n title = save_kwargs.pop(\"title\")\n else:\n title = \"Bokeh Plot\"\n\n bokeh.io.save(\n bokeh_object, filename=filename, resources=resources, title=title, **save_kwargs\n )\n\n return HTML(filename)","title":""},{"docid":"338a9905885d36026a7596b50df165de","score":"0.42535764","text":"def oplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.oplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"8d7c065190303a0310e9f02a8760beaa","score":"0.4251952","text":"def print_1d_visual(self, df, tuning_param):\n axis = {\n \"accuracy\": \"Accuracy (%)\",\n \"false_positive\": \"False Positive Rate (%)\",\n \"false_negative\": \"False Negative Rate (%)\"\n }\n plt.clf()\n sns.set(style='darkgrid')\n sns.lineplot(x=tuning_param, y=axis[self.metric], data=df)\n fig = plt.gcf()\n fig.savefig('%s/OptimizationLineGraph.png' % self.path, bbox_inches='tight')\n plt.close()\n df.to_csv(\"%s/optimize_results.csv\" % self.path)","title":""},{"docid":"11a762413a0129120eb397564ca2dc97","score":"0.42496905","text":"def plot(self, variables_values, axes=None):\n assert len(self.variables) == len(variables_values), \\\n f\"You need to pass as many variable values as this visualiser has variables. Required:\" \\\n f\"{len(self.variables)}, Given: {len(variables_values)}\"\n\n fig = None\n if axes is None:\n fig, axes = self.create_default_axes()\n # TODO: Probably should not do that, but it prevents an empty plot from popping up in IPython\n plt.close(fig)\n\n self.plot_init(variables_values, axes)\n self.plot_update(variables_values, axes)\n if fig is not None and in_ipython_context:\n ip_display(fig)","title":""},{"docid":"81280fdc16829c5aafa79a33822634eb","score":"0.4248225","text":"def plot_my_data(x,y,verbose=True):\n\n # Note the indentation for the if block\n if (verbose):\n print \"\"\n print \"Now plotting the data.\"\n\n # In the plot command below, the 'bo' tells the program to plot blue ('b')\n # circles ('o')\n print \"\"\n #print \"Close the plot window to continue...\"\n plt.plot(x,y,'bo')","title":""},{"docid":"45ad991ab71a43a553c913e63ea1f2af","score":"0.42471483","text":"def variable_importance_plot(model, log, result_path=\"result\"):\n algo = model.params['model_id']['actual']['name']\n\n if model.varimp() is not None:\n plt.tight_layout()\n\n plt.figure(figsize=(12, 8))\n var_imp = model.varimp_plot()\n plt.title(\"Variable Importance : \" + algo)\n plt.xlabel(\"Relative Importance\")\n\n plt.savefig(result_path + \"/\" + \"variable_importance.png\", dpi=600)\n plt.close()\n else:\n log.warning(\"[\" + algo + \"]\" + \"This model doesn't have variable importances\")","title":""},{"docid":"2e224ac6f166a66c30181dc30cb08c5b","score":"0.42448515","text":"def turn_visualization_off(self):\n self.visualize = False","title":""},{"docid":"aaa4f7f0ce31f350d847feb2209d7261","score":"0.4238392","text":"def drop(self, var_index):\n return self.masked([i != var_index for i in range(self.num_vars)])","title":""},{"docid":"da9570ff3d6ed2dd5db6f03efbc01781","score":"0.4237229","text":"def display_only(self, frame):\n cv2.imshow(self.display_name, frame)","title":""},{"docid":"f997c2c75ad0ef1126c60105cf176510","score":"0.42370698","text":"def remove_NoVal_col(df: pd.core.frame.DataFrame, label: str) -> pd.core.series.Series:\n mask = df[label] != 99.0\n col = df[label][mask]\n return col","title":""},{"docid":"fa0ae72e1dcc3d68e8cfcd5580d6d362","score":"0.42370147","text":"def display_data(df):\n response= ['yes', 'no']\n rawdata = ''\n #counter variable is initialized as a tag to ensure only details from a particular point is displayed\n counter = 0\n while rawdata not in response:\n print(\"\\nDo you wish to view the raw data?\")\n print(\"\\nAccepted responses:\\nYes or no\")\n rawdata = input().lower()\n #the raw data from the df is displayed if user opts for it\n if rawdata == \"yes\":\n print(df.head())\n elif rawdata not in response:\n print(\"\\nPlease check your input.\")\n print(\"Input does not seem to match any of the accepted responses.\")\n print(\"\\nRestarting...\\n\")\n\n #Extra while loop here to ask user if they want to continue viewing data\n while rawdata == 'yes':\n print(\"Do you wish to view raw data?\")\n counter += 5\n rawdata = input().lower()\n #If user opts for it, this displays next 5 rows of data\n if rawdata == \"yes\":\n print(df[counter:counter+5])\n elif rawdata != \"yes\":\n break\n\n print('-'*80)","title":""},{"docid":"d09bbfaaacfe251e374b812438d2d174","score":"0.42354107","text":"def viewVariables(dataset):\r\n for entry in dataset['DATA'][0]:\r\n pprint.pprint(str(entry) + \" : \" + str(dataset['VARIABLES'][str(entry)]))","title":""},{"docid":"227b29295221cfa88a8937ea82c250ad","score":"0.42240646","text":"def remove_plot(self):\n \n if self.has_plant: \n #Updates the variables to show that the plot is empty\n self.plant_type = None\n self.has_plant = False\n self.age = 0\n self.time_planted = 0\n self.can_harvest = False\n \n else:\n #Once the plot itself is removed, the switch turns off and the plot is disabled\n self.plotted = False","title":""},{"docid":"68478c1344086aa8df9dc257fd0cc6cd","score":"0.42235586","text":"def suppress(self):\n self._pipe.send((None, 'hide', None))","title":""}],"string":"[\n {\n \"docid\": \"f3416a3c42880a57556602bd26db1797\",\n \"score\": \"0.57033026\",\n \"text\": \"def ignore_variable(self, toast: Toast, var_name: str) -> None:\\n try:\\n return self.active_view.ignore_variable(toast, var_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement ignoring variables\\\", ToastType.warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30be2073bb4ee8b98885fcb1f16c1f5\",\n \"score\": \"0.5620072\",\n \"text\": \"def plot_proper(df: pd.DataFrame, xax: str, yax: str, var: str, title: str) -> None:\\n pd.options.plotting.backend = \\\"plotly\\\"\\n fig = df.plot(title=title, template=\\\"simple_white\\\",\\n labels=dict(index=xax, value=yax, variable=var))\\n fig.update_layout(\\n xaxis_title=xax,\\n yaxis_title=yax,\\n )\\n fig.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0372b371887346e02e57dac3f55b655a\",\n \"score\": \"0.545876\",\n \"text\": \"def ignore_variable(self, toast: Toast, var_name: str) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21493415e595924fcbbdc5510e9902e\",\n \"score\": \"0.51881444\",\n \"text\": \"def show(*args, block=True, **kwargs):\\n # Remove reserved rewords\\n try:\\n kwargs.pop('block')\\n except:\\n pass\\n # Get the variable names in the scope show() was called from\\n callers_local_vars = inspect.currentframe().f_back.f_locals.items()\\n\\n # Make a dictionary of the DataFrames from the position args and get their variable names using inspect\\n dataframes = {}\\n for i, df_object in enumerate(args):\\n df_name = 'untitled' + str(i + 1)\\n\\n for var_name, var_val in callers_local_vars:\\n if var_val is df_object:\\n df_name = var_name\\n\\n dataframes[df_name] = df_object\\n\\n # Add the dictionary of positional args to the kwargs\\n if (any([key in kwargs.keys() for key in dataframes.keys()])):\\n print(\\\"Warning! Duplicate DataFrame names were given, duplicates were ignored.\\\")\\n kwargs = {**kwargs, **dataframes}\\n\\n pandas_gui = PandasGUI(**kwargs)\\n\\n if block:\\n pandas_gui.app.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51ef5db5e8ccb230c8b182b1588e161\",\n \"score\": \"0.51808095\",\n \"text\": \"def no_detour_boxplot(df,country,save=False,fig=None, ax=None,**kwargs):\\n df = df.loc[df['country'] == country]\\n\\n if (fig == None and ax == None): # if No axes and no figure is provided\\n fig, ax = plt.subplots(figsize=(12, 6))\\n\\n df.boxplot(by='AoI combinations', column='no detour', grid=False, ax=ax,**kwargs)\\n ax.set_xlabel(\\\"Number of combinations of flood events (AoI)\\\")\\n ax.set_ylabel(\\\"% No detour\\\")\\n ax.set_title(\\\"% routes between NUTS-3 regions in {} without detour\\\".format(country))\\n\\n #Todo (possible): give fig ax as args; enable saving possiblity\\n\\n if save: # TODO REPLACE ALL INSTANCES OF THIS PART OF CODE WITH A SPECIAL FUNCTION\\n save_figs = load_config(config_file)['paths']['output_images'] / 'no_detour_boxplot'\\n if not save_figs.exists(): save_figs.mkdir()\\n filename = \\\"noDT_boxplot_{}.png\\\".format(country)\\n fig.savefig(save_figs / filename)\\n\\n return fig,ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a5704bd479ea2ddbc6c7a3c6679479\",\n \"score\": \"0.51140994\",\n \"text\": \"def display_data_frame(data_frame):\\n\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\\n print(data_frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1e92a529a23ea32701ac6ec9e46e36\",\n \"score\": \"0.5068718\",\n \"text\": \"def plot_log(df, var) -> None:\\n\\n df = df.copy()\\n df[var] = np.log(df[var] + 1)\\n sns.displot(df, x=var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af0d61f97e9c1df8737454662caad4a\",\n \"score\": \"0.5064501\",\n \"text\": \"def plot_continuous(df, var) -> None:\\n\\n df = df.copy()\\n df['Income'] = df['Income'].astype('category')\\n var_name = var\\n if var in config.SKEWED_NUMERIC_VARS:\\n df[var] = df[var]\\n var_name = 'Log of ' + var_name\\n ax = sns.boxplot(x='Income', y=var, data=df)\\n ax.set(ylabel=var_name, title=f\\\"Effect of {var} on Income\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005efd6f5fe0c2f341addce0816a3c8e\",\n \"score\": \"0.50505435\",\n \"text\": \"def visualize_continuous_variables(df, label, method={'type': 'histogram', 'bins': 20}, outlier='on'):\\n # create vector of the variable of interest\\n v = df[label]\\n # define mean and standard deviation\\n m = v.mean()\\n s = v.std()\\n # prep the figure\\n fig, ax = plt.subplots(1, 2, figsize=(14, 4))\\n ax[0].set_title('Distribution of ' + label)\\n ax[1].set_title('Tip % by ' + label)\\n if outlier == 'off': # remove outliers accordingly and update titles\\n v = v[(v - m) <= 3 * s]\\n ax[0].set_title('Distribution of ' + label + '(no outliers)')\\n ax[1].set_title('Tip % by ' + label + '(no outliers)')\\n if method['type'] == 'histogram': # plot the histogram\\n v.hist(bins=method['bins'], ax=ax[0])\\n if method['type'] == 'boxplot': # plot the box plot\\n df.loc[v.index].boxplot(label, ax=ax[0])\\n ax[1].plot(v, df.loc[v.index].tip_percentage, '.', alpha=0.4)\\n ax[0].set_xlabel(label)\\n ax[1].set_xlabel(label)\\n ax[0].set_ylabel('Count')\\n ax[1].set_ylabel('Tip (%)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c79f8c6106edcd31610020abb8ff8b\",\n \"score\": \"0.5001463\",\n \"text\": \"def plot_continuous_column(df, target_variable, column_name, fig_size=(9, 12)):\\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\\n tmp_df = df.dropna()\\n sns.distplot(tmp_df[column_name], ax=ax1)\\n sns.boxplot(x=target_variable, y=column_name, data=tmp_df, ax=ax2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0fbaeaed5291893aca4beb006c0d75\",\n \"score\": \"0.49557826\",\n \"text\": \"def set_df_violin_plot(data, header):\\n df = records_to_df(data)\\n try:\\n df[['user', 'condition', 'block', 'task']] = df[['user', 'condition', 'block', 'task']].astype('category')\\n except KeyError:\\n col_names = [c['id'] for c in header]\\n df = pd.DataFrame(columns=col_names)\\n df.rename(columns={'df1 mean': 'df1', 'df2 mean': 'df2'}, inplace=True)\\n fig = plotting.generate_violin_figure(df, columns=['df1', 'df2'], ytitle='Mean', legend_title=\\\"DOF\\\")\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c885c87d0ff416a36c8e524bf40d4a\",\n \"score\": \"0.49128348\",\n \"text\": \"def plot_var(var='positiveIncrease',\\n state='NY'):\\n assert type(var) == str, \\\"Expected string as the variable name\\\"\\n assert type(state) == str, \\\"Expected string as the state name\\\"\\n\\n y = df[df['state'] == state][var]\\n x = df[df['state'] == state]['date']\\n plt.figure(figsize=(12, 4))\\n plt.title(\\\"Plot of \\\\\\\"{}\\\\\\\" for {}\\\".format(var, state), fontsize=18)\\n plt.bar(x=x, height=y, edgecolor='k', color='orange')\\n plt.grid(True)\\n plt.xticks(fontsize=14, rotation=45)\\n plt.yticks(fontsize=14)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c80fdeda37d80a6aae7f35d9019dc81\",\n \"score\": \"0.48900345\",\n \"text\": \"def show_labels(self, var, datasets, text_key):\\n\\t\\tif datasets == ['all']: datasets = self.ds_names\\n\\t\\tds = [self[datasets]] if len(datasets)==1 else self[datasets]\\n\\t\\talias = self._get_alias(datasets)\\n\\t\\ttk = text_key.split('~')\\n\\t\\tetk = tk[1].split()[0] if len(tk) > 1 else None\\n\\t\\ttk = tk[0]\\n\\t\\tall_df = []\\n\\t\\tfor v in var:\\n\\t\\t\\tif not all(v in d for d in ds): continue\\n\\t\\t\\ttexts = [d.text(v, False, tk, etk) if v in d else None\\n\\t\\t\\t\\t\\t for d in ds]\\n\\t\\t\\tindex = pd.MultiIndex.from_tuples([(v, a) for a in alias])\\n\\t\\t\\tdf = pd.DataFrame({text_key: texts}, index=index)\\n\\t\\t\\tall_df.append(df)\\n\\t\\tif not all_df:\\n\\t\\t\\tprint('No variables to show.')\\n\\t\\telse:\\n\\t\\t\\treturn pd.concat(all_df, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f303d01580491b054b8ee02bf003a5d\",\n \"score\": \"0.48365968\",\n \"text\": \"def plot(ds: DatasetLike.TYPE,\\n var: VarName.TYPE,\\n indexers: DictLike.TYPE = None,\\n title: str = None,\\n properties: DictLike.TYPE = None,\\n file: str = None) -> Figure:\\n ds = DatasetLike.convert(ds)\\n\\n var_name = VarName.convert(var)\\n if not var_name:\\n raise ValidationError(\\\"Missing name for 'var'\\\")\\n var = ds[var_name]\\n\\n indexers = DictLike.convert(indexers)\\n properties = DictLike.convert(properties) or {}\\n\\n figure = plt.figure()\\n ax = figure.add_subplot(111)\\n\\n var_data = get_var_data(var, indexers)\\n var_data.plot(ax=ax, **properties)\\n\\n if title:\\n ax.set_title(title)\\n\\n figure.tight_layout()\\n\\n if file:\\n figure.savefig(file)\\n\\n return figure if not in_notebook() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147a7cca10cae4658a76af694603d9ef\",\n \"score\": \"0.47928968\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedDataGenerator_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739695fe3ea743b4e0a8fb833a68fa2c\",\n \"score\": \"0.4776736\",\n \"text\": \"def plot_discrete(df, var) -> None:\\n\\n temp = df[['Income', var]].groupby(var).mean().reset_index()\\n axis = sns.barplot(x=var, y='Income', data=temp)\\n axis.set(ylabel=\\\"Probability of earning over 50K\\\")\\n plt.title(f\\\"Effect of {var} on Income\\\")\\n if var == 'weeks worked in year':\\n plt.title(f\\\"Effect of Months worked in year on Income\\\")\\n axis.set(xlabel=\\\"Months worked in year\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce3d4781b9c8774a39083c0bd03c66a\",\n \"score\": \"0.4768098\",\n \"text\": \"def plot_missing(self, axis = 1, show=True, **figure_kwargs):\\n if axis == 1:\\n if self.record_missing_cols is None:\\n raise NotImplementedError(\\\"Columns missing values have not been calculated. Run `find_missing with axis = 1`\\\")\\n elif axis == 0:\\n if self.record_missing_rows is None:\\n raise NotImplementedError(\\\"Rows missing values have not been calculated. Run `find_missing with axis = 0`\\\")\\n self.reset_plot()\\n # Histogram of missing values\\n plt.style.use('seaborn-white')\\n plt.figure(figsize = (7, 5))\\n if axis == 1:\\n plt.hist(self.missing_stats_cols['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\\n elif axis == 0:\\n plt.hist(self.missing_stats_rows['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\\n plt.xticks(np.linspace(0, 1, 11))\\n plt.xlabel('Missing Fraction', size = 14)\\n plt.ylabel('Count of Features', size = 14)\\n plt.title(\\\"Fraction of Missing Values Histogram\\\", size = 16)\\n missing_plot = self.prepro_dir + '/missing.png'\\n plt.savefig(missing_plot, dpi=300)\\n if self.isDisplay == False:\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81b4edbff3dfdaaacd61d1269c7a70d\",\n \"score\": \"0.47633633\",\n \"text\": \"def displayDataFrame(dataFrame):\\n pd.set_option('display.max_rows', dataFrame.shape[0] + 1)\\n pd.set_option('display.max_columns', dataFrame.shape[1] + 1)\\n print(dataFrame)\\n pd.reset_option('display.max_rows')\\n pd.reset_option('display.max_columns')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b28d65ea32c34efacc762f1e62c65c\",\n \"score\": \"0.4750205\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedComputeChange_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57f8fcc11be295aa3a4e5d2127121d3\",\n \"score\": \"0.47035733\",\n \"text\": \"def plot_graph(dfs,\\n variables_to_plot,\\n titles,\\n ylim=None,\\n xlim=None,\\n save=None,\\n eventname=None,\\n show=True):\\n answer = {} \\n custom_lim = np.ndarray((2,1))\\n def printspanselector(xmin, xmax):\\n if isinstance(xmin, np.datetime64):\\n xmin = date2num(xmin)\\n xmax = date2num(xmax)\\n\\n ind = np.logical_and(date2num(df.index) >= xmin,\\n date2num(df.index) <= xmax)\\n axs[-1].plot(df.index[ind],\\n df[v][ind],\\n 'r+',\\n gid='selected')\\n if 'delete' not in answer:\\n answer['delete'] = []\\n answer['delete'].append([xmin, xmax])\\n\\n def onclick(event, ax):\\n # Only clicks inside this axis are valid.\\n try: # use try/except in case we are not using Qt backend\\n zooming_panning = ( fig.canvas.cursor().shape() != 0 ) # 0 is the arrow, which means we are not zooming or panning.\\n except:\\n zooming_panning = False\\n if zooming_panning: \\n return\\n\\n if eventname == 'choose':\\n plt.close('all')\\n answer['axes'] = event.inaxes.get_title()\\n \\n \\n plt.close('all')\\n fig = plt.figure(figsize = (11.69*(2/3), 8.27/2))\\n gs1 = gridspec.GridSpec(len(dfs), 1)\\n axs=[]\\n for n, df in enumerate(dfs):\\n if n == 0:\\n axs.append(fig.add_subplot(gs1[n],) )\\n else:\\n axs.append(fig.add_subplot(gs1[n], sharex=axs[0]) )\\n\\n plt.title(titles[n])\\n\\n for i,v in enumerate(variables_to_plot):\\n if v in df:\\n if not check_timeseries(df[v].values):\\n typ = '-'\\n else:\\n typ = '+'\\n\\n axs[-1].plot(df.index,\\n df[v],\\n typ,\\n label=v)\\n\\n if xlim:\\n plt.xlim(xlim[0], xlim[1])\\n\\n if ylim:\\n plt.ylim(ylim[0], ylim[1])\\n\\n plt.legend(bbox_to_anchor=(0.0, -.5, 1, 0),\\n loc=\\\"lower left\\\",\\n mode=\\\"expand\\\", ncol=2)\\n \\n fig.autofmt_xdate()\\n\\n if save:\\n plt.savefig(save,\\n format='png',\\n bbox_inches=\\\"tight\\\")\\n\\n if eventname=='choose':\\n cid = fig.canvas.mpl_connect('button_press_event', lambda event: onclick(event, axs[-1]))\\n plt.show()\\n elif eventname=='clean':\\n selector = SpanSelector(axs[-1],\\n onselect=printspanselector,\\n direction='horizontal',\\n useblit=True,\\n span_stays=False,\\n button=1,\\n rectprops={'facecolor': 'grey', 'alpha': 0.3})\\n fig.canvas.mpl_connect('key_press_event',\\n selector)\\n plt.show()\\n else:\\n if show:\\n plt.show()\\n\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b3cc6dad55e823688a60a9dd9f1d6a\",\n \"score\": \"0.47027907\",\n \"text\": \"def drop_superfluous_trace_vars(data):\\n return data.drop([\\\"P0\\\", \\\"co2vmr\\\", \\\"gw\\\", \\\"hyai\\\", \\\"date\\\", \\\"date_written\\\",\\n \\\"datesec\\\", \\\"hyai\\\", \\\"hyam\\\", \\\"hybi\\\", \\\"hybm\\\", \\\"mdt\\\",\\n \\\"nbdate\\\", \\\"nbsec\\\", \\\"ndbase\\\", \\\"ndcur\\\", \\\"nlon\\\", \\\"nsbase\\\",\\n \\\"nscur\\\", \\\"nsteph\\\", \\\"ntrk\\\", \\\"ntrm\\\", \\\"ntrn\\\",\\n \\\"time_written\\\", \\\"wnummax\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e424f4c821b66a9f0683dc1362dd3c\",\n \"score\": \"0.468913\",\n \"text\": \"def plot_variable(lon, lat, variable, varname):\\n\\n # Create a figure\\n plt.figure(figsize=(12,12))\\n ax = plt.axes(projection=ccrs.PlateCarree())\\n ax.coastlines()\\n \\n # Plot data\\n plt.pcolor(lon, lat, variable, transform=ccrs.PlateCarree(), cmap='RdYlBu_r')\\n plt.title(varname)\\n plt.colorbar(orientation=\\\"horizontal\\\", pad=0.06)#fraction=0.05)\\n \\n # Extent from metadata: ULC: 060.00 -120.00 LRC: 000.00 -030.00 RES:1.00 1.0\\n ax.set_xticks([-120,-60,-30])\\n ax.set_yticks([60,30,0])\\n \\n plt.savefig('snapshot_' + varname + '.png', dpi=300, bbox_inches='tight')\\n plt.show()\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e890b9f93a94f0a34c0d76af1446424\",\n \"score\": \"0.4683768\",\n \"text\": \"def show_variable(self, toast: Toast, var_name: str) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b971ee4f2989e077c9d161fa3847216b\",\n \"score\": \"0.46831873\",\n \"text\": \"def plot_3d(df, target_variable):\\n unique_labels = target_variable.unique()\\n ordinal_encoding = [np.where(unique_labels == label)[0][0]\\n for label in target_variable]\\n color_dict = {0: 'red', 1: 'green', 2: 'blue'}\\n colors = [color_dict[each] for each in ordinal_encoding]\\n threedee = plt.figure().gca(projection='3d')\\n threedee.scatter(df[[0]], df[[1]],\\n df[[2]], color=colors)\\n threedee.set_xlabel(df.columns.values[0])\\n threedee.set_ylabel(df.columns.values[1])\\n threedee.set_zlabel(df.columns.values[2])\\n plt.show()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd0f849e14a511d0e2aa2a2fe32b21e\",\n \"score\": \"0.46819878\",\n \"text\": \"def visualize(self, df:DataFrame):# -> dict:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc766e7a0cd6d7c24b37fe6c46efb114\",\n \"score\": \"0.46767002\",\n \"text\": \"def get_var(data, variable):\\n\\n var = data.variables[variable]\\n var_plot = ma.masked_equal(var, var.missing_value)\\n\\n lons = data.variables['lon'][...]\\n lats = data.variables['lat'][...]\\n\\n return var_plot, lons, lats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad4c798ac7dc8256ba71957ce08c2f2\",\n \"score\": \"0.46681195\",\n \"text\": \"def plot_solution(self, variable):\\n all_points = [*(self.nodes), *self.internal_points]\\n x = sorted(set([point.coords[0] for point in all_points]))\\n y = sorted(set([point.coords[1] for point in all_points]))\\n z = [[None] * len(x)] * len(y)\\n variable = variable.lower()\\n for point in all_points:\\n point_x = x.index(point.coords[0])\\n point_y = y.index(point.coords[1])\\n if variable == \\\"ux\\\":\\n z[point_y][point_x] = point.u[0]\\n elif variable == \\\"uy\\\":\\n z[point_y][point_x] = point.u[1]\\n elif variable == \\\"sx\\\":\\n z[point_y][point_x] = point.p[0]\\n elif variable == \\\"sy\\\":\\n z[point_y][point_x] = point.p[1]\\n elif variable == \\\"sxy\\\":\\n z[point_y][point_x] = point.p[2]\\n else:\\n print(f\\\"Invalid option: {variable}\\\")\\n self.plot_model(auto_open=False)\\n self.fig.add_contour(\\n name=variable,\\n z=z,\\n x=x,\\n y=y,\\n connectgaps=True,\\n contours=dict(coloring=\\\"heatmap\\\", showlabels=True),\\n colorbar=dict(title=variable, titleside=\\\"right\\\", x=1.2),\\n )\\n self.fig[\\\"layout\\\"].update(title=f\\\"<b>{self.name} ({variable})</b>\\\")\\n return plotly.offline.plot(self.fig, filename=f\\\"{self.name}-{variable}.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5d60f3a2f6d30cef38d85daa28dec1\",\n \"score\": \"0.46618274\",\n \"text\": \"def set_proj_var_plot(data):\\n df = records_to_df(data)\\n long_df = analysis.wide_to_long(df, ['parallel', 'orthogonal'], suffixes='variance', j='projection')\\n fig = plotting.generate_lines_plot(long_df, \\\"variance\\\", by='user', color_col='projection')\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee269e097d579cb2a24ab667fa58037b\",\n \"score\": \"0.46423653\",\n \"text\": \"def plot_against_target(self, feature):\\n assert feature != self._target_variable\\n\\n if feature in self.numeric_features:\\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\\n y=self._target_variable,\\n alpha=0.1,\\n title='{0} vs. target (`{1}`)'.\\n format(feature,\\n self._target_variable))\\n else:\\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\\n plt.ylabel(self.target_variable)\\n plt.title(title)\\n plt.suptitle(\\\"\\\")\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9637d4e9abc6369930a68c1502b95e\",\n \"score\": \"0.46198583\",\n \"text\": \"def set_variance_graph(table_data):\\n df = records_to_df(table_data)\\n df.dropna(inplace=True)\\n return plotting.generate_means_figure(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82efd8c8b683ed6091a8576b3df1086d\",\n \"score\": \"0.46060088\",\n \"text\": \"def plotData(dataFrame, titleString, saveToFile=False, nsamples = 100, vars=None):\\n\\n\\t#plot nsamples random elements from the sample\\n\\trandom_elements = random.sample(range(dataFrame.shape[0]), nsamples)\\n\\tdataFrame_plot = dataFrame.iloc[random_elements]\\n\\n\\tif vars != None:\\n\\t\\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', vars=vars, dropna=True)\\n\\telse:\\n\\t\\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', dropna=True)\\n\\n\\tplt.suptitle(titleString, y = 1.025)\\n\\n\\tif saveToFile == True:\\n\\t\\tplt.savefig('testfig.png', format='png', pad_inches=0.5, bbox_inches='tight')\\n\\t\\tplt.close()\\n\\telse:\\n\\t\\tpass\\n\\t\\t#plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"174b0d1fc32bac49ceac43e70e10a012\",\n \"score\": \"0.46051374\",\n \"text\": \"def plot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.plot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d347fcfa2ffeceefe3ec788c6b9ec835\",\n \"score\": \"0.45513475\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedSetValue_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d761854d1c29b36faeda1e5af7e583\",\n \"score\": \"0.4534381\",\n \"text\": \"def show_cats(self, var, text_key):\\n\\t\\ttext_key = text_key.split('~')\\n\\t\\tetk = text_key[1].split()[0] if len(text_key) > 1 else None\\n\\t\\ttext_key = text_key[0]\\n\\t\\tdf_all_v = []\\n\\t\\tfor v in var:\\n\\t\\t\\tall_df = []\\n\\t\\t\\tfor name in list(self.ds_alias.values()):\\n\\t\\t\\t\\tif v in self[name]:\\n\\t\\t\\t\\t\\tval = self[name].value_texts(v, text_key, etk)\\n\\t\\t\\t\\t\\tcodes = self[name].codes(v)\\n\\t\\t\\t\\t\\tindex = pd.MultiIndex.from_tuples([(v, c) for c in codes])\\n\\t\\t\\t\\t\\tdf = pd.DataFrame(val, index=index, columns=[name])\\n\\t\\t\\t\\t\\tall_df.append(df)\\n\\t\\t\\tall_df = pd.concat(all_df, axis=1)\\n\\t\\t\\tdf_all_v.append(all_df)\\n\\t\\tif not df_all_v:\\n\\t\\t\\tprint('No variables to show.')\\n\\t\\telse:\\n\\t\\t\\treturn pd.concat(df_all_v, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c460d6f20fc4d69bed840f326ee71a3a\",\n \"score\": \"0.44996905\",\n \"text\": \"def plot_categorical_column(df, target_variable, column_name, fig_size=(9, 12), y_lim=None):\\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\\n tmp_df = df.dropna()\\n sns.countplot(x=column_name, data=tmp_df, ax=ax1)\\n sns.pointplot(x=column_name, y=target_variable, data=tmp_df, ax=ax2)\\n if y_lim is not None:\\n ax2.set_ylim(0, y_lim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31dcef4c8847e4789643aaf5cb79239e\",\n \"score\": \"0.44970563\",\n \"text\": \"def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,\\n **kwargs):\\n fdf = df.filter(**kwargs)\\n if len(fdf.data) > 0:\\n vdf = fdf.require_variable(variable=variable, unit=unit, year=year,\\n exclude_on_fail=exclude_on_fail)\\n df.meta['exclude'] |= fdf.meta['exclude'] # update if any excluded\\n return vdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9b710f33eaca9bece16fa9e4362da0\",\n \"score\": \"0.44876218\",\n \"text\": \"def unsuppress(self):\\n self._pipe.send((None, 'show', None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a073a4e1e8b5823d0c2be47feae53f7\",\n \"score\": \"0.4475288\",\n \"text\": \"def plot(self, df, plotdir):\\n df.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aed811f4a0431f31970dec2f4ca576a\",\n \"score\": \"0.44718388\",\n \"text\": \"def plot_single_variable(self, results, xlab=None, ylab=None, save=None):\\n\\n n, m = self.x.shape\\n if m != 2:\\n raise ValueError('Error: can only plot single explanatory variable. Current X shape: ({},{})'.format(n, m))\\n X = self.x.drop('const', axis=1)\\n X = np.squeeze(X.values)\\n Y = np.squeeze(self.y.values)\\n plt.plot(X, Y, 'o', c='steelblue')\\n Xmin = np.argmin(X)\\n Xmax = np.argmax(X)\\n plt.plot([X[Xmin], X[Xmax]], [self.preds[Xmin], self.preds[Xmax]], c='darkorange')\\n if xlab:\\n plt.xlabel(xlab)\\n if ylab:\\n plt.ylabel(ylab)\\n if save:\\n plt.savefig('{}.png'.format(save), dpi=200)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb0f674d7c2303a1019fee9596426f2\",\n \"score\": \"0.44626507\",\n \"text\": \"def display(df):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f92ec7c3a117c72a81c791bdbfc54a3\",\n \"score\": \"0.4445461\",\n \"text\": \"def print_dataframe(data):\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n print(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"215c3df97025653743c1890ed0d2c221\",\n \"score\": \"0.44444445\",\n \"text\": \"def remove_variable(self, varname):\\n self.graph.remove_node(self.variables[varname])\\n \\n #removes the class of varname (should there be no other variables that share that class) \\n #from classes_imported set \\n for key,value in self.classes_available.items():\\n if isinstance(self.variables[varname], value):\\n self.classes_imported.remove(key) \\n\\n #remove the varname from the variables dictionary \\n del self.variables[varname]\\n self.convert_to_script()\\n self.graph_changed_event = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc07226f64f3c33738730d8089e6428\",\n \"score\": \"0.44427666\",\n \"text\": \"def _plot_var(self, ax, **kwargs):\\n self.__xmin, self.__xmax = ax.get_xlim()\\n self.__ymin, self.__ymax = ax.get_ylim()\\n plot_digit = kwargs.get('plot_digit', None)\\n if plot_digit is not None:\\n self.__prec = '%.' + '%d' % plot_digit + 'f'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05543b631cc28eab1499d4a0303f5622\",\n \"score\": \"0.44299516\",\n \"text\": \"def set_dropextradata(self, dropextradata):\\n self.options['dropextradata'] = dropextradata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498c7472384572bf9b63e89977b3b9a2\",\n \"score\": \"0.4422164\",\n \"text\": \"def box_plot() -> str:\\n\\n session_manager.cache_analysis_option()\\n return StatsModel().get_box_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b2024b879b7e0c085a117ee9ab652b2\",\n \"score\": \"0.4414972\",\n \"text\": \"def logplot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.logplot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7637e0f986c8d599c82f0c7bfa9bbd\",\n \"score\": \"0.44135877\",\n \"text\": \"def plot(filename, column):\\n df=pd.read_csv(filename)\\n if column is None:\\n df.hist();\\n else:\\n df[column].hist()\\n plt.show() # if slow, this blocks all subsequent code from running # it waits for the window to close before continuing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17307ab1248182037bdaadea9cc6336\",\n \"score\": \"0.44075608\",\n \"text\": \"def x_vs_y_plots(X,y,save_toggle=False,file_prefix=''):\\n if len(file_prefix)>0:\\n file_prefix += '_'\\n \\n df = pd.concat([X, pd.DataFrame(y, index=X.index)], axis=1)\\n for x in X.columns:\\n df[[x,y.name]].groupby(x).mean().plot()\\n if save_toggle:\\n plt.savefig(file_prefix+x+'_vs_'+y.name+'.png')\\n plt.show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0889a47d34fccef58e97603b04c39123\",\n \"score\": \"0.43964615\",\n \"text\": \"def show_figure(self, *args, **kwargs):\\n if 'divider' in dir(self):\\n del self.divider\\n plt.show(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42c056ce7bb2a7c997fe17f18e2da84\",\n \"score\": \"0.43961328\",\n \"text\": \"def implot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.implot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c1e8bd58d88d907afa606c72266977\",\n \"score\": \"0.4391739\",\n \"text\": \"def plot_xy(varx='totalTestResultsIncrease',\\n vary='positiveIncrease',\\n state='NY'):\\n assert type(varx) == str, \\\"Expected string as the variable x name\\\"\\n assert type(vary) == str, \\\"Expected string as the variable y name\\\"\\n\\n y = df[df['state'] == state][vary]\\n x = df[df['state'] == state][varx]\\n if (x.nunique() != 1) and (y.nunique() != 1):\\n plt.figure(figsize=(12, 4))\\n plt.title(\\\"Plot of \\\\\\\"{}\\\\\\\" vs. \\\\\\\"{}\\\\\\\" for {}\\\".format(varx, vary, state), fontsize=18)\\n plt.scatter(x=x, y=y, edgecolor='k', color='lightgreen', s=100)\\n plt.grid(True)\\n plt.xticks(fontsize=14, rotation=45)\\n plt.yticks(fontsize=14)\\n plt.show()\\n else:\\n print(\\\"Some of the data unavailable for a scatter plot. Sorry!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb8afd28e29829a317cacd43faa15d70\",\n \"score\": \"0.4388029\",\n \"text\": \"def plot(\\n self,\\n components=None,\\n *,\\n ax=None,\\n logy=False,\\n show=False,\\n dates=False,\\n legend=True,\\n grid=True,\\n ):\\n ax = ax or plt.gca()\\n kwargs = {\\\"logy\\\": logy, \\\"ax\\\": ax, \\\"grid\\\": grid, \\\"legend\\\": legend}\\n\\n def get_column(col):\\n if dates:\\n col += \\\":dates\\\"\\n data = self[col]\\n return data\\n\\n components = self.meta.variables if components is None else components\\n for col in components:\\n data = get_column(col)\\n data.plot(**kwargs)\\n if show:\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf0aa71c26b31f4c997c8c2afaf8893\",\n \"score\": \"0.4385104\",\n \"text\": \"def print_df(df: pd.DataFrame) -> None:\\n\\twith pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n\\t\\tprint(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e57320b475c20dc2f96fd48da6fca7\",\n \"score\": \"0.43605706\",\n \"text\": \"def display_na(df):\\r\\n \\r\\n display(HTML(\\\"<h4>Percentage of missing variables for each feature:\\\"))\\r\\n print(df.isnull().sum(axis=0) * 100 / len(df))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d004de844130a45592187867f7c68fd\",\n \"score\": \"0.4355372\",\n \"text\": \"def show(self, *args, **kwargs):\\n self.get_plot(*args, **kwargs).show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c08b4f4854337048144d53f798e073eb\",\n \"score\": \"0.43472216\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show() # must be called to show plots in some environments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2c6fd0d9dca232109a4340a5c5a1a9\",\n \"score\": \"0.43427277\",\n \"text\": \"def extra_plot_variance_explained(var_explained, component,var_explained_2, component_2, *args):\\n opt_n_comp = np.argmin(var_explained) - 1\\n stdout.write(\\\"\\\\n\\\")\\n # plot var_explained for each component\\n with plt.style.context((\\\"ggplot\\\")):\\n ax = plt.subplot(111)\\n plt.plot(component, np.array(var_explained), \\\"-v\\\", color=\\\"red\\\", mfc=\\\"red\\\", label=\\\"train\\\")\\n plt.plot(\\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.plot(component_2, np.array(var_explained_2), \\\"-v\\\", color=\\\"blue\\\", mfc=\\\"blue\\\", label=\\\"test\\\")\\n plt.plot(\\n component_2[opt_n_comp], np.array(var_explained_2)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.xlabel(\\\"Number of PLS components\\\")\\n plt.ylabel(\\\"Variance explained %\\\")\\n plt.title(\\\"PLS\\\")\\n plt.xlim(0-1)\\n ax.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f7b6cbfef0a5defdf6be992e65669\",\n \"score\": \"0.4340944\",\n \"text\": \"def plot_runner(ds, variable, s3upload=False) -> str:\\n\\n model_type = get_model_type(ds)\\n if model_type == 'roms':\\n imagename = plot_roms(ds_ofs, var, s3upload=True)\\n elif model_type == 'fvcom':\\n imagename = plot_fvcom(ds_ofs, var, s3upload=True)\\n else:\\n print(f\\\"ERROR: Unsupported model type - {modeltype}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473bf765662306bc925b6e3848dabee6\",\n \"score\": \"0.43398735\",\n \"text\": \"def hide_column(self, column_name):\\n if not isinstance(column_name, str):\\n raise TypeError(\\\"column_name must be of type str\\\")\\n\\n column_names = self.get_column_names()\\n if column_name not in column_names:\\n raise ValueError(f\\\"{column_name} not in available columns ({column_names})\\\")\\n new_column_names = [cn for cn in self.get_column_names(True)\\n if cn not in column_name]\\n return self.set_visible_columns(new_column_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715c6a713767779ca425cadeff548c21\",\n \"score\": \"0.43344194\",\n \"text\": \"def display(self, *fields):\\n try:\\n if self.df is None:\\n self.warning(\\\"Dataframe is empty: nothing to display\\\")\\n return\\n df2 = self.df[list(fields)]\\n return df2.head()\\n except Exception as e:\\n self.err(e, self.display, \\\"Can not display dataframe\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab189fb4de3b5e34800eba35dea524ba\",\n \"score\": \"0.43340218\",\n \"text\": \"def plot_hovmoeller(ds: xr.Dataset,\\n var: VarName.TYPE = None,\\n x_axis: DimName.TYPE = None,\\n y_axis: DimName.TYPE = None,\\n method: str = 'mean',\\n contour: bool = True,\\n title: str = None,\\n file: str = None,\\n monitor: Monitor = Monitor.NONE,\\n **kwargs) -> Figure:\\n var_name = None\\n if not var:\\n for key in ds.data_vars.keys():\\n var_name = key\\n break\\n else:\\n var_name = VarName.convert(var)\\n var = ds[var_name]\\n\\n if not x_axis:\\n x_axis = var.dims[0]\\n else:\\n x_axis = DimName.convert(x_axis)\\n\\n if not y_axis:\\n try:\\n y_axis = var.dims[1]\\n except IndexError:\\n raise ValidationError('Given dataset variable should have at least two dimensions.')\\n else:\\n y_axis = DimName.convert(y_axis)\\n\\n if x_axis == y_axis:\\n raise ValidationError('Dimensions should differ between plot axis.')\\n\\n dims = list(var.dims)\\n try:\\n dims.remove(x_axis)\\n dims.remove(y_axis)\\n except ValueError:\\n raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\\\\\\n {}, {}.'.format(var_name, x_axis, y_axis))\\n\\n ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean,\\n 'median': np.nanmedian, 'sum': np.nansum}\\n\\n with monitor.starting(\\\"Plot Hovmoeller\\\", total_work=100):\\n monitor.progress(5)\\n with monitor.child(90).observing(\\\"Aggregate\\\"):\\n var = var.reduce(ufuncs[method], dim=dims)\\n monitor.progress(5)\\n\\n figure = plt.figure()\\n ax = figure.add_subplot(111)\\n if x_axis == 'time':\\n figure.autofmt_xdate()\\n\\n if contour:\\n var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs)\\n else:\\n var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs)\\n\\n if title:\\n ax.set_title(title)\\n\\n figure.tight_layout()\\n\\n if file:\\n figure.savefig(file)\\n\\n return figure if not in_notebook() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45e26120e690f3dad49ac9e218a7480\",\n \"score\": \"0.43270665\",\n \"text\": \"def remove_variable(self, var):\\n self.vars.remove(var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f45be729aa0e30132fc58c88ddb34ca\",\n \"score\": \"0.4326928\",\n \"text\": \"def subset_variables(ds, variableList): # {{{\\n\\n allvars = ds.data_vars.keys()\\n\\n # get set of variables to drop (all ds variables not in vlist)\\n dropvars = set(allvars) - set(variableList)\\n\\n # drop spurious variables\\n ds = ds.drop(dropvars)\\n\\n # must also drop all coordinates that are not associated with the variables\\n coords = set()\\n for avar in ds.data_vars.keys():\\n coords |= set(ds[avar].coords.keys())\\n dropcoords = set(ds.coords.keys()) - coords\\n\\n # drop spurious coordinates\\n ds = ds.drop(dropcoords)\\n\\n if len(ds.data_vars.keys()) == 0:\\n raise ValueError(\\n 'Empty dataset is returned.\\\\n'\\n 'Variables {}\\\\n'\\n 'are not found within the dataset '\\n 'variables: {}.'.format(variableList, allvars))\\n\\n return ds # }}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cfecc9efd06e5098ce91f2f6a118a5\",\n \"score\": \"0.43260407\",\n \"text\": \"def distribution_plot(dataframe: pd.DataFrame, var: str, target: str = None, **kwargs):\\n\\n row = kwargs.get('row', None)\\n col = kwargs.get('col', None)\\n\\n size = kwargs.get('size', None)\\n aspect = kwargs.get('aspect', None)\\n\\n facet = sns.FacetGrid(\\n dataframe,\\n hue=target,\\n size=size if size else 3,\\n aspect=aspect if aspect else 4,\\n row=row,\\n col=col\\n )\\n\\n facet.map(sns.kdeplot, var, shade=True)\\n facet.set(title=\\\"Distribution of '{}' over '{}'\\\".format(var, target))\\n\\n # Defines limits\\n if kwargs.get('clean', False):\\n xmin = dataframe[var].quantile(0.02)\\n xmax = dataframe[var].quantile(0.98)\\n facet.set(xlim=(xmin, xmax))\\n\\n facet.add_legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54230d6985c38be8af9232a0bf01ee18\",\n \"score\": \"0.43237677\",\n \"text\": \"def rename_variable(self, toast: Toast, var_name: str, display_name: str) -> None:\\n try:\\n return self.active_view.rename_variable(toast, var_name, display_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement variable renaming\\\", ToastType.warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5290b0dbee071b9ff806e7ed58e3ceb1\",\n \"score\": \"0.43195853\",\n \"text\": \"def setShowInX(self, x):\\r\\n\\t\\tself.__showInX=x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca95d946d23156598f83a8c528ad2ac\",\n \"score\": \"0.43146688\",\n \"text\": \"def plot_dis(df_master, df_dis, units):\\n modul = df_master.modul\\n stor = '{}_stor'.format(modul)\\n loss = '{}_loss'.format(modul)\\n relax = '{}_relax'.format(modul)\\n\\n if df_master.domain == 'freq':\\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,0.75*4))\\n df_master.plot(x='f', y=[stor], label=[\\\"{}'(filter)\\\".format(modul)],\\n ax=ax1, logx=True, logy=True, color=['C0'], alpha=0.5)\\n df_master.plot(x='f', y=[loss], label=[\\\"{}''(filter)\\\".format(modul)],\\n ax=ax2, logx=True, logy=True, color=['C1'], alpha=0.5)\\n df_dis.plot(x='f', y=[stor], label=['tau_i'], ax=ax1, \\n logx=True, logy=True, ls='', marker='o', color=['C0'])\\n df_dis.plot(x='f', y=[loss], label=['tau_i'], ax=ax2, \\n logx=True, logy=True, ls='', marker='o', color=['C1'])\\n ax1.set_xlabel('Frequency (Hz)')\\n ax1.set_ylabel('Storage modulus ({})'.format(units[stor]))\\n ax2.set_xlabel('Frequency (Hz)')\\n ax2.set_ylabel('Loss modulus ({})'.format(units[stor])) \\n ax1.legend()\\n ax2.legend()\\n fig.show()\\n return fig\\n elif df_master.domain == 'time':\\n fig, ax1 = plt.subplots(figsize=(4,0.75*4))\\n df_master.plot(x='t', y=[relax], \\n ax=ax1, logx=True, logy=True, color=['k'])\\n df_dis.plot(x='t', y=[relax], label = ['tau_i'], \\n ax=ax1, logx=True, logy=True, ls='', marker='o', color=['red'])\\n ax1.set_xlabel('Time ({})'.format(units['t']))\\n ax1.set_ylabel('Relaxation modulus ({})'.format(units[relax]))\\n ax1.legend()\\n fig.show()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35832c1e396d3f8c41dcf63d2a46273b\",\n \"score\": \"0.43139654\",\n \"text\": \"def hide_xticks(ax):\\n for tick in ax.axes.get_xticklabels():\\n tick.set_visible(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e63b785758284832390cce3c328b022\",\n \"score\": \"0.43101996\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e63b785758284832390cce3c328b022\",\n \"score\": \"0.43101996\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb45a6f55ba0db29f2a75fa25b824f96\",\n \"score\": \"0.43083325\",\n \"text\": \"def show(self, *args, **kwds):\\n self.plot(*args, **kwds).show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b798880aba7d93b422ba79a8dafd758\",\n \"score\": \"0.43063605\",\n \"text\": \"def plot_discharge(self, data, **kw):\\n x = 0\\n print 'data', data\\n for key in data:\\n print 'key', key\\n if key == '11462125 peak':\\n print 'skipping peak discharge df'\\n elif key == 'CLV - Cloverdale hourly':\\n for col in self._col_list[:2]:\\n ser = data[key]['Dataframe'][col]\\n if not ser.empty:\\n self._plot(col, key, ser, **kw)\\n\\n elif key == 'COY - Coyote hourly':\\n for col in self._col_list:\\n ser = data[key]['Dataframe'][col]\\n if not ser.empty:\\n self._plot(col, key, ser, **kw)\\n else:\\n for col in self._col_list:\\n ser = data[key]['Dataframe'][col]\\n print 'nan values: {}'.format(count_nonzero(isnan(ser)))\\n print 'len ser: {}'.format(len(ser))\\n if len(ser) != count_nonzero(isnan(ser)):\\n self._plot(col, key, ser, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8dc9da6845384c103464f8a1f1451\",\n \"score\": \"0.43056202\",\n \"text\": \"def set_display():\\r\\n # Plots display settings\\r\\n plt.style.use('fivethirtyeight')\\r\\n plt.rcParams['figure.figsize'] = 12, 8\\r\\n plt.rcParams.update({'font.size': 14})\\r\\n # DataFrame display settings\\r\\n pd.set_option('display.max_columns', None)\\r\\n pd.set_option('display.max_rows', None)\\r\\n pd.options.display.float_format = '{:.4f}'.format\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b66c30a50d93c31d6e8fbe9796c40a\",\n \"score\": \"0.42991424\",\n \"text\": \"def boxplot_one_country(df,country,AoIs= 'All',save=False,fig=None,ax=None,**kwargs):\\n df = df.loc[df['country'] == country]\\n #if AoIs != 'All': df2 = df2.loc[df2['AoI combinations'].isin(AoIs)]\\n\\n if (fig == None and ax == None): # if No axes and no figure is provided\\n fig, ax = plt.subplots(figsize=(12, 6))\\n\\n df.boxplot(by='AoI combinations', column='disrupted', ax=ax,grid=False,**kwargs)\\n ax.set_xlabel(\\\"Number of combinations of flood events (AoI)\\\")\\n ax.set_ylabel(\\\"% preferred routes disrupted\\\")\\n ax.set_title(\\\"% routes between NUTS-3 regions in {} disrupted\\\".format(country))\\n\\n if save:\\n save_figs = load_config(config_file)['paths']['output_images'] / 'disrupted'\\n if not save_figs.exists(): save_figs.mkdir()\\n filename = \\\"disrupted_boxplot_{}.png\\\".format(country)\\n fig.savefig(save_figs / filename)\\n\\n return fig,ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf843c3246768fbe1cd025b86929885\",\n \"score\": \"0.42943284\",\n \"text\": \"def plot_categoricial(df, var) -> None:\\n temp = df[['Income', var]].groupby(var).mean().reset_index()\\n axis = sns.barplot(x=var, y='Income', data=temp)\\n axis.set(ylabel=\\\"Probability of earning over 50K\\\")\\n axis.set_xticklabels(config.vis_dict[var], rotation=75)\\n plt.title(f\\\"Effect of {var} on Income\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b30d96205aae6597f9e8a95779b74e\",\n \"score\": \"0.42883715\",\n \"text\": \"def plot_line_chart(df: pd.DataFrame, title: str, y: str = 'y', x: str = 'x',):\\n px.line(df, x=x, y=y, title=title).show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f63f08277116886413b5dfbbc45efa\",\n \"score\": \"0.42838115\",\n \"text\": \"def datatoplot(self, track_choice, track_no=None, track_index=None):\\r\\n if track_choice == 'evo':\\r\\n tracks = self.data\\r\\n elif track_choice == 'ranged':\\r\\n if self.ranged_tracks is None:\\r\\n raise NameError('No ranged tracks!')\\r\\n else: tracks = self.ranged_tracks\\r\\n else: raise NameError('Wrong track name!')\\r\\n \\r\\n max_tracks = tracks['track_no'].nunique()\\r\\n uniques = tracks['track_no'].unique()\\r\\n if track_no != None:\\r\\n if max_tracks > track_no:\\r\\n if type(track_index)==type(None):\\r\\n track_index=np.random.choice(uniques, track_no)\\r\\n else: track_index = uniques\\r\\n else: track_index = uniques\\r\\n if len(track_index)>200:\\r\\n raise ValueError('Too many tracks, are you sure you want to plot '+str(len(track_index))+' tracks??')\\r\\n \\r\\n selected = tracks.loc[tracks['track_no'].isin(track_index)]\\r\\n plot_tracks = [selected['Teff'], selected['L']]\\r\\n plot_m = selected['mass']\\r\\n return plot_tracks, plot_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5513626174155422f31e167cfb56c003\",\n \"score\": \"0.42826742\",\n \"text\": \"def set_df_mean_plot(data):\\n df = records_to_df(data)\\n try:\\n # For error bars we need standard deviation.\\n df[['df1 std', 'df2 std']] = df[['df1 variance', 'df2 variance']].transform(np.sqrt)\\n except KeyError:\\n pass\\n # Convert to long format for easier plotting.\\n long_df = analysis.wide_to_long(df, stubs=['df1', 'df2'], suffixes=['mean', 'std'], j='dof')\\n fig = plotting.generate_lines_plot(long_df, \\\"mean\\\", by='user', color_col='dof', errors='std', jitter=True)\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d4e630a8059c1b12652584c43c00fd\",\n \"score\": \"0.4279645\",\n \"text\": \"def easy_print_data_frame(data_frame):\\n\\n print(data_frame.to_string())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef7324d0a5850a8c8ac3a856b4b9dfb\",\n \"score\": \"0.42760804\",\n \"text\": \"def _plot_variance_explained(var_explained, component, *args):\\n opt_n_comp = np.argmin(var_explained) - 1\\n stdout.write(\\\"\\\\n\\\")\\n # plot var_explained for each component\\n with plt.style.context((\\\"ggplot\\\")):\\n plt.plot(component, np.array(var_explained), \\\"-v\\\", color=\\\"blue\\\", mfc=\\\"blue\\\")\\n plt.plot(\\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.xlabel(\\\"Number of PLS components\\\")\\n plt.ylabel(\\\"Variance explained %\\\")\\n plt.title(\\\"PLS\\\")\\n plt.xlim(0-1)\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83e8777568cac18f63c1b5039fa7e43\",\n \"score\": \"0.4275368\",\n \"text\": \"def show():\\n plt.show(block=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4441f564cdf782290c8b56b76d3b9153\",\n \"score\": \"0.4270931\",\n \"text\": \"def show_plot(self, table):\\n assert (self._current_panel is not None)\\n graph = wxtbx.plots.iotbx_data_plot_base(\\n parent=self._current_panel,\\n tables=[table],\\n size=(TEXT_WIDTH - 40, min(500, TEXT_WIDTH * 0.75)))\\n graph.set_plot(table.only_plot())\\n self._graphs.append((graph, table.title))\\n self._current_sizer.Add(graph, 0, wx.ALL|wx.EXPAND, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a774101cb8988ac9fa189c9a6f651b21\",\n \"score\": \"0.42703143\",\n \"text\": \"def plot_two_vars(self,\\n xy_data, # ['my_datadict_out', e.g., 'merge_summstat_out']\\n x_name, # [name of x variable to use for x-axis]\\n y_name, # [name of y variable to use for y-axis]\\n ent_axis = 'col', # [<'row', 'col'>] => how variables are situated]\\n err_data = None, # [<None, 'my_datadict_out'> => e.g., 'merge_summstat_out']\\n x_err = None, # [<None, size, name of x variable to use for error statistic> => create bubbles]\\n y_err = None, # [<None, size, name of y variable to use for error statistic> => create bubbles]\\n color_by = None, # [<None, 'g', '0.75', (0.5,0.2,0.7), 'rand', ['gender', {'male':'b', 'female':'r'}], ['age', 'g']> => color-code bubbles]\\n cosine_correct = 'coord|corr', # [<None, 'coord', 'corr', 'coord|corr'> => correct for cosine between variables]\\n max_cos = 0.95, # <unsigned corr> => trigger exception when cos(x, y) > max_cos]\\n plot = None, # [<None, {plotting parameters}> => see docs to customize]\\n savefig = 'xy_plot.png' # [<None, 'show', 'filename.png'> => how to output chart]\\n ):\\n\\n if self.verbose is True:\\n print 'plot_two_vars() is working...\\\\n'\\n\\n # Run the damon utility\\n plot_two_vars_out = dmn.utils._plot_two_vars(locals())\\n self.plot_two_vars_out = plot_two_vars_out\\n\\n if self.verbose is True:\\n print 'plot_two_vars() is done -- see my_obj.plot_two_vars_out'\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4531cd130c5272a5d9ffa4d886b3ee\",\n \"score\": \"0.42631972\",\n \"text\": \"def _plot_skipped_well_file(config, raw_data, well_name):\\n if config.plot_skipped:\\n plot_params = dict()\\n plot_params['savepath'] = config.work_dir\\n plot_params['well_name'] = well_name\\n plot_params['fg_conc_edges'] = config.fg_conc_edges\\n plot_params['fg_fret_edges'] = config.fg_fret_edges\\n plot_params['data_df'] = raw_data\\n plot_params['fine_grid_xlim'] = None\\n plot_params['fine_grid_ylim'] = None\\n plot_params['plot_type'] = config.plot_type\\n plot_fine_grid_profiles(plot_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11adb8baf5e82cdd7884c2a1f79cd1e1\",\n \"score\": \"0.42607546\",\n \"text\": \"def do_variable(self, arg):\\n self.display_cmake_help('variable', arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1862e5d6a2dd0e4bdab216750261911f\",\n \"score\": \"0.42592025\",\n \"text\": \"def strip_data(df: pd.DataFrame, data_to_keep: dict):\\n gene_name = 'symbol'\\n if df.index.name == data_to_keep.get(gene_name):\\n data_to_keep.pop(gene_name)\\n new_df = df[data_to_keep.values()]\\n return new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a745c78232fa241c5ee33a4d7e4af63e\",\n \"score\": \"0.4254119\",\n \"text\": \"def bokeh_show(bokeh_object, filename=None, **save_kwargs):\\n if filename is None:\\n filename = \\\"bokehplot_00000000.html\\\"\\n i = 1\\n while i < 99999999 and os.path.isfile(filename):\\n filename = \\\"bokehplot_{0:08d}.html\\\".format(i)\\n i += 1\\n\\n if i == 99999999:\\n raise RuntimeError(\\n \\\"Cannot generate generic file name, as all filenames are used.\\\"\\n )\\n\\n if \\\"resources\\\" in save_kwargs:\\n resources = save_kwargs.pop(\\\"resources\\\")\\n else:\\n resources = bokeh.resources.CDN\\n\\n if \\\"title\\\" in save_kwargs:\\n title = save_kwargs.pop(\\\"title\\\")\\n else:\\n title = \\\"Bokeh Plot\\\"\\n\\n bokeh.io.save(\\n bokeh_object, filename=filename, resources=resources, title=title, **save_kwargs\\n )\\n\\n return HTML(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338a9905885d36026a7596b50df165de\",\n \"score\": \"0.42535764\",\n \"text\": \"def oplot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.oplot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7c065190303a0310e9f02a8760beaa\",\n \"score\": \"0.4251952\",\n \"text\": \"def print_1d_visual(self, df, tuning_param):\\n axis = {\\n \\\"accuracy\\\": \\\"Accuracy (%)\\\",\\n \\\"false_positive\\\": \\\"False Positive Rate (%)\\\",\\n \\\"false_negative\\\": \\\"False Negative Rate (%)\\\"\\n }\\n plt.clf()\\n sns.set(style='darkgrid')\\n sns.lineplot(x=tuning_param, y=axis[self.metric], data=df)\\n fig = plt.gcf()\\n fig.savefig('%s/OptimizationLineGraph.png' % self.path, bbox_inches='tight')\\n plt.close()\\n df.to_csv(\\\"%s/optimize_results.csv\\\" % self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a762413a0129120eb397564ca2dc97\",\n \"score\": \"0.42496905\",\n \"text\": \"def plot(self, variables_values, axes=None):\\n assert len(self.variables) == len(variables_values), \\\\\\n f\\\"You need to pass as many variable values as this visualiser has variables. Required:\\\" \\\\\\n f\\\"{len(self.variables)}, Given: {len(variables_values)}\\\"\\n\\n fig = None\\n if axes is None:\\n fig, axes = self.create_default_axes()\\n # TODO: Probably should not do that, but it prevents an empty plot from popping up in IPython\\n plt.close(fig)\\n\\n self.plot_init(variables_values, axes)\\n self.plot_update(variables_values, axes)\\n if fig is not None and in_ipython_context:\\n ip_display(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81280fdc16829c5aafa79a33822634eb\",\n \"score\": \"0.4248225\",\n \"text\": \"def plot_my_data(x,y,verbose=True):\\n\\n # Note the indentation for the if block\\n if (verbose):\\n print \\\"\\\"\\n print \\\"Now plotting the data.\\\"\\n\\n # In the plot command below, the 'bo' tells the program to plot blue ('b')\\n # circles ('o')\\n print \\\"\\\"\\n #print \\\"Close the plot window to continue...\\\"\\n plt.plot(x,y,'bo')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ad991ab71a43a553c913e63ea1f2af\",\n \"score\": \"0.42471483\",\n \"text\": \"def variable_importance_plot(model, log, result_path=\\\"result\\\"):\\n algo = model.params['model_id']['actual']['name']\\n\\n if model.varimp() is not None:\\n plt.tight_layout()\\n\\n plt.figure(figsize=(12, 8))\\n var_imp = model.varimp_plot()\\n plt.title(\\\"Variable Importance : \\\" + algo)\\n plt.xlabel(\\\"Relative Importance\\\")\\n\\n plt.savefig(result_path + \\\"/\\\" + \\\"variable_importance.png\\\", dpi=600)\\n plt.close()\\n else:\\n log.warning(\\\"[\\\" + algo + \\\"]\\\" + \\\"This model doesn't have variable importances\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e224ac6f166a66c30181dc30cb08c5b\",\n \"score\": \"0.42448515\",\n \"text\": \"def turn_visualization_off(self):\\n self.visualize = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa4f7f0ce31f350d847feb2209d7261\",\n \"score\": \"0.4238392\",\n \"text\": \"def drop(self, var_index):\\n return self.masked([i != var_index for i in range(self.num_vars)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da9570ff3d6ed2dd5db6f03efbc01781\",\n \"score\": \"0.4237229\",\n \"text\": \"def display_only(self, frame):\\n cv2.imshow(self.display_name, frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f997c2c75ad0ef1126c60105cf176510\",\n \"score\": \"0.42370698\",\n \"text\": \"def remove_NoVal_col(df: pd.core.frame.DataFrame, label: str) -> pd.core.series.Series:\\n mask = df[label] != 99.0\\n col = df[label][mask]\\n return col\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0ae72e1dcc3d68e8cfcd5580d6d362\",\n \"score\": \"0.42370147\",\n \"text\": \"def display_data(df):\\n response= ['yes', 'no']\\n rawdata = ''\\n #counter variable is initialized as a tag to ensure only details from a particular point is displayed\\n counter = 0\\n while rawdata not in response:\\n print(\\\"\\\\nDo you wish to view the raw data?\\\")\\n print(\\\"\\\\nAccepted responses:\\\\nYes or no\\\")\\n rawdata = input().lower()\\n #the raw data from the df is displayed if user opts for it\\n if rawdata == \\\"yes\\\":\\n print(df.head())\\n elif rawdata not in response:\\n print(\\\"\\\\nPlease check your input.\\\")\\n print(\\\"Input does not seem to match any of the accepted responses.\\\")\\n print(\\\"\\\\nRestarting...\\\\n\\\")\\n\\n #Extra while loop here to ask user if they want to continue viewing data\\n while rawdata == 'yes':\\n print(\\\"Do you wish to view raw data?\\\")\\n counter += 5\\n rawdata = input().lower()\\n #If user opts for it, this displays next 5 rows of data\\n if rawdata == \\\"yes\\\":\\n print(df[counter:counter+5])\\n elif rawdata != \\\"yes\\\":\\n break\\n\\n print('-'*80)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09bbfaaacfe251e374b812438d2d174\",\n \"score\": \"0.42354107\",\n \"text\": \"def viewVariables(dataset):\\r\\n for entry in dataset['DATA'][0]:\\r\\n pprint.pprint(str(entry) + \\\" : \\\" + str(dataset['VARIABLES'][str(entry)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227b29295221cfa88a8937ea82c250ad\",\n \"score\": \"0.42240646\",\n \"text\": \"def remove_plot(self):\\n \\n if self.has_plant: \\n #Updates the variables to show that the plot is empty\\n self.plant_type = None\\n self.has_plant = False\\n self.age = 0\\n self.time_planted = 0\\n self.can_harvest = False\\n \\n else:\\n #Once the plot itself is removed, the switch turns off and the plot is disabled\\n self.plotted = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68478c1344086aa8df9dc257fd0cc6cd\",\n \"score\": \"0.42235586\",\n \"text\": \"def suppress(self):\\n self._pipe.send((None, 'hide', None))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":795,"cells":{"query_id":{"kind":"string","value":"2be6d369e19ba2cf155c71d55a982262"},"query":{"kind":"string","value":"Start a player off with the default starting deck, an empty hand, and an empty discard pile. Set the game stage to 'early_game'. Initialize a turn counter. Draw a random starting hand from the deck."},"positive_passages":{"kind":"list like","value":[{"docid":"9ab052ff693422df14ac76b389410470","score":"0.7242505","text":"def __init__(self):\n\n self.hand = [] # empty hand\n self.discard = [] # empty discard pile\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\n self.game_stage = GameStage.early_game # start the game in early stage\n self.turn_count = 0\n\n random.shuffle(\n self.deck\n ) # shuffle the deck before drawing (for the first time)\n self.draw_hand() # start by drawing a hand","title":""}],"string":"[\n {\n \"docid\": \"9ab052ff693422df14ac76b389410470\",\n \"score\": \"0.7242505\",\n \"text\": \"def __init__(self):\\n\\n self.hand = [] # empty hand\\n self.discard = [] # empty discard pile\\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\\n self.game_stage = GameStage.early_game # start the game in early stage\\n self.turn_count = 0\\n\\n random.shuffle(\\n self.deck\\n ) # shuffle the deck before drawing (for the first time)\\n self.draw_hand() # start by drawing a hand\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5100836fd6ace5c376bcbe31fb141463","score":"0.7105495","text":"def start_new_game(self, player_hand):\n self.deck_of_cards += self.hand\n self.deck_of_cards += player_hand\n self.deck_of_cards.shuffle()","title":""},{"docid":"73b8d8128052bfd7bd5a587d480672ce","score":"0.6954077","text":"def start_game(self):\n initial_draw = int((len(self.stock.cards) * 0.5) / len(self.participants))\n\n if initial_draw == 0:\n initial_draw = 1\n\n for player in self.participants:\n cards = [self.stock.deal() for x in range(initial_draw)]\n player.hand = cards\n\n self.act_player = self.participants[0]\n self.turn = self.next_turn()","title":""},{"docid":"6662d1a0a763307463d759a7f7232e3a","score":"0.66947067","text":"def init_game(self):\n if self.is_basic: #create quick simple game\n p1 = 6 #priest\n p2 = 7 #rogue\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game\n\n else:\n p1 = random.randint(1, 9)\n p2 = random.randint(1, 9)\n #initialize players and randomly draft decks\n #pdb.set_trace()\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n #begin the game\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan for now\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game","title":""},{"docid":"db336ace779d9c035eacad3d1e43a2c3","score":"0.6674635","text":"def play_hand(self, player):\n if not player.has_blackjack():\n while player.get_hand_value() <= 21:\n self.print_current_state(player, sd=False)\n action = player.get_action()\n if action == 'Hit':\n self.deal_top(player)\n if action == 'Stand':\n break\n if action == 'Split':\n pass\n if action == 'Double':\n player.stack -= player.amount_bet\n player.amount_bet *= 2\n self.deal_top(player)\n break\n # If player did not bust dealer plays\n if (player.get_hand_value() <= 21):\n self.dealer_plays()\n self.print_current_state(player, sd=True)\n self.declare_winner(player)\n self.reset_table()\n print(self.players[0])","title":""},{"docid":"cefe9cdd368f1bc5c556b592bd28b0ba","score":"0.6657983","text":"def computer_turn():\n global winner, winning_hand, time\n computer.draw_card(deck.pile[0])\n winning_hand = computer.declare_game()\n if winning_hand[0]:\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n else:\n score = [0] * 14\n min_pile = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_pile[1]:\n min_pile = (i,score[i])\n elif score[i] == min_pile[1]:\n if computer.hand[i].rank >= computer.hand[min_pile[0]].rank:\n min_pile = (i, score[i])\n\n computer.discard_card(deck.pile[0])\n\n joker_card = Card('3','spades',True)\n computer.draw_card(joker_card)\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n computer.discard_card(joker_card)\n\n\n if min_pile[1] <= min_deck[1]:\n #draw from pile\n if time == 0 :\n computer.draw_card(deck.pile.pop(0))\n deal_card_sound.play()\n time += 1\n # print(\"pile initialisation\")\n elif time >= computer_delay:\n #DELAY\n deck.update_pile(computer.hand[min_pile[0]])\n computer.discard_card(computer.hand[min_pile[0]])\n deal_card_sound.play()\n # print(\"pile time condition\")\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n time = 0\n else:\n # print(\"pile increment\")\n time += 1\n else:\n #draw from deck\n #DELAY\n if time == 0:\n computer.draw_card(deck.draw_card())\n deal_card_sound.play()\n # print(\"deck initialisation\")\n time += 1\n elif time >= computer_delay:\n # print(\"Deck time condition\")\n winning_hand = computer.declare_hand()\n if winning_hand[0] :\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n time = 0\n else:\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n deck.update_pile(computer.hand[min_deck[0]])\n computer.discard_card(computer.hand[min_deck[0]])\n time = 0\n deal_card_sound.play()\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n else:\n # print(\"deck increment condition\")\n time += 1","title":""},{"docid":"ae5ce86f91fa809eba12df1bb5fd7837","score":"0.66456765","text":"def start_hand(player, dealer, bet_amount):\n\n player.place_bet(bet_amount)\n player.get_hand(dealer.give_card())\n player.get_hand(dealer.give_card())\n\n print(player.show_hand())\n\n dealer.get_hand(dealer.give_card())\n dealer.get_hand(dealer.give_card())\n\n print(dealer.show_first_hand())\n\n if player.hand_total_value() == 21:\n player.has_blackjack = True\n\n if dealer.hand_total_value() == 21:\n dealer.has_blackjack = True\n\n if player.has_blackjack:\n print(\"Blackjack!!!\")","title":""},{"docid":"f23e94ed0fa96fae14e8750676a9722f","score":"0.65526325","text":"def play(self):\n\n #############\n # Game loop #\n #############\n number_bets = 0\n print \"###############\"\n print \"# Game starts #\"\n print \"###############\"\n while self.player.chips > 0:\n self.play_hands()\n number_bets += 1\n #wants_exit = raw_input(\"> Press e for exit \").lower()\n #if wants_exit == 'e':\n # break\n print \"###############\"\n print \"#########################\"\n print \"# Game end with %s bets #\" % (number_bets)\n print \"#########################\"","title":""},{"docid":"cb175cb8d195e3b844ef7dd03c55afb8","score":"0.6526096","text":"def initialSetUp():\n deck = cardGame.getDeck()\n cardGame.shuffleDeck(deck)\n numberOfPlayers = getNumberOfPlayers()\n hands = cardGame.dealCards(deck, 0, numberOfPlayers)\n sortHands(hands)\n startingPlayer = getStartingPlayer(hands)\n return deck, hands, numberOfPlayers, startingPlayer","title":""},{"docid":"b1ec6eee21dc39ea3e1cc703759281ee","score":"0.6525598","text":"def start(self):\n\n self.view.display_game_start()\n self.cardgame.add_players()\n\n game_over = False\n\n while not game_over:\n for player in self.cardgame.get_players():\n move = self.parse_move(player.name)\n self.cardgame.step(player, move)\n self.view.display_hand(player.name, player.get_hand())\n game_over = self.cardgame.is_game_over()\n\n winner, score = self.cardgame.pick_winner()\n\n if not winner:\n self.view.display_draw_game()\n else:\n self.view.display_winner(winner.name, score, winner.get_hand())","title":""},{"docid":"a3e313dae13a0e559e08ea35e111a9f2","score":"0.65084654","text":"def new_game():\n global cards, exposed, hand, state\n global card_1, card_2, turns\n\n hand = cards[:]\n random.shuffle(hand) # hand for the game\n\n exposed = 16 * [False] # initialize uncovered cards\n card_1 = None\n card_2 = None\n turns = 0\n\n state = 0 # number of cards initially uncovered","title":""},{"docid":"fee53164e852b2bd36967c77375c4a68","score":"0.64621794","text":"def setup_game(self):\n if self.is_ready():\n for player in self.players:\n player.draw(5)\n self.wild.draw(5)\n self.starting_positions = [p.player_id for p in self.players]\n self.current_player = self.next_player()\n else:\n print 'Not enough players to setup game'","title":""},{"docid":"caf9e7f2d74b953dbf52004e56ba4100","score":"0.6429044","text":"def start_hand(self):\n [self.draw() for _ in range(5)] #initially draw 5","title":""},{"docid":"d389423337a21da474a9da118a77413d","score":"0.64132434","text":"def start_game(self):\n self.display_info()\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n \"Computer\", player=False)\n self.computer_board = temporary_board\n player_name = input(\"Please enter your name: \\n\")\n print(\"-\" * 60)\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n player_name, player=True)\n self.player_board = temporary_board\n\n self.play_game()","title":""},{"docid":"eb5a90f61dcbcc1ddbbe1831103505af","score":"0.6401434","text":"def reset_new_game(self):\n\t\tself._piles['deck'] = Pile('copper',7)\n\t\tself._piles['deck'].add_new_card_to_pile('estate',3)\n\t\tself._piles['deck'].shuffle()\n\t\tself._piles['hand'] = Pile()\n\t\tself._piles['discard'] = Pile()\n\t\tself._piles['in_play'] = Pile()\n\t\tself.draw_n(5)","title":""},{"docid":"6d74b8502b3dc12a43ce7f6e15d3c209","score":"0.6401383","text":"def play_hands(self):\n\n #############\n # First hand\n #############\n # Deal two cards to each opponent\n self.player.cards = self.dealer.deal(2)\n self.dealer.cards = self.dealer.deal(2)\n\n # Show hand\n self.player.show_hand()\n self.dealer.show_hand(1) # hide second card\n\n # End if player has blackjack\n if self.player.has_blackjack():\n self.end_bet()\n return\n\n #################################\n # Hand loop for player and dealer\n #################################\n opponents = [self.player, self.dealer]\n dealer_turn = 0 # 0: is player turn, 1: is dealer turn\n continue_bet = True\n round = 1\n # Bet\n self.player.chips -= self.player.chips_per_bet\n # Repeat until bet end condition\n while continue_bet:\n # Set who plays: player or dealer\n opponent = opponents[dealer_turn]\n continue_hand = True\n while (continue_hand):\n # Ask for action: hit or stand?\n action = opponent.choose_action()\n # If hit, deal cards and show hand\n if action == settings.ACTION_HIT:\n opponent.cards.extend(self.dealer.deal())\n opponent.show_hand()\n # check end conditions\n if opponent.has_blackjack() or opponent.has_busted():\n continue_hand = False\n continue_bet = False\n # If stand, it's dealer's turn\n elif action == settings.ACTION_STAND:\n dealer_turn = 1 - dealer_turn # pass turn to other opponent\n round+=1\n continue_hand = False\n # Player stands and dealer stands so end bet\n if round == 4:\n continue_bet = False\n else:\n raise Exception(\"Unknown action: '%s'\" % action)\n self.end_bet()","title":""},{"docid":"bd044cf068a3a9a276486c7f13ec9422","score":"0.6370955","text":"def start_game(self):\n print(\"\"\"\n Welcome to the Hi/Lo game!\n \n Guess if the next card will be higher\n or lower than the last drawn card.\n If correct, you get 100pts! Guess wrong \n and you lose 75pts!\n \n You lose if you hit, or go below, 0pts.\n You win if you score 1,000 or more pts.\n \n Good luck!\n \"\"\")\n while self.keep_playing == True:\n print(f\"The card is : {self.old_card}\")\n self.player.guess()\n self.draw_card()\n self.display_card()\n self.get_points()\n print(f\"Score: {self.points}\")\n self.keep_playing = self.to_play()\n self.card_update()","title":""},{"docid":"a74aa24f1c77ebf72c7629e86f7d25ca","score":"0.6350413","text":"def main():\n newdeck = deck.Deck()\n newdeck.shuffle()\n firstplayer_cards = []\n secondplayer_cards = []\n for x in range(5):\n firstplayer_cards.append(newdeck.dealacard())\n secondplayer_cards.append(newdeck.dealacard())\n playing_game(firstplayer_cards, secondplayer_cards)","title":""},{"docid":"c6a583f2c39d810c3d4c6a06f580b22f","score":"0.6337075","text":"def start(self):\n while self._pyramid[0][0].rank is not None: # if top card is not None\n self._menu()\n command = input('\\nCommand: ').strip()\n if self._enter_command(command) is None:\n system('cls')\n continue\n else:\n system('cls')\n input('\\nGame Over')\n print(game.instruction())\n input('Press any key to continue...') # wait user\n system('cls')\n self.start()","title":""},{"docid":"293c5613cb568b29524346857329a8d3","score":"0.6327848","text":"def reset(self) -> None:\n\n self.deck = make_cards()\n self.player = Player([self.deck.pop(), self.deck.pop()], 0, self.player_name)\n self.dealer = Dealer([self.deck.pop(), self.deck.pop()])\n self.player_split = Player([], 0, self.player_name + \" hand_two\")\n self.split = False","title":""},{"docid":"b7122669f6b8f76c9b6655a98923e290","score":"0.63025635","text":"def start_round(self):\r\n self.round_number += 1\r\n self.dealer = (self.dealer + 1) % 4\r\n self.round = Round(self.round_number, self.dealer)\r\n trump_suit = None\r\n self.deal()\r\n self.round.starting_hands = [player.cards[:] for player in self.players] + [\r\n self.kitty.cards[:]\r\n ]\r\n\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\r\n print(\"Round %s\" % (self.round_number))\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")","title":""},{"docid":"e90754c4fcc810d1e8439fabac48bf0d","score":"0.6301797","text":"def start_game(self):\n\t\tpass\n\t\t# Welcome the player\n\t\t# Get the player name\n\t\t# Tell the player how to navigate the game\n\t\t\t# How to access the rules\n\t\t\t# How to quit at any input \n\t\t# Reveal the blank game board \n\t\t# \"Ready to start? (Y)es or (N)o\"","title":""},{"docid":"5f40568fed3de59176404cedb08d0e3a","score":"0.62817025","text":"def user_start_play(tower, discard):\n\n print('\\nNOW IT\\'S YOUR TURN!')\n print('Your Tower', end = ': ')\n print(tower)\n\n top_discard_brick = see_top_brick_discard(discard)\n print('The top brick on the discard pile is', top_discard_brick)","title":""},{"docid":"713dc46b4c49cbc94a8c0af15906bb56","score":"0.6278261","text":"def game_reset():\n global deck, user, computer, draw, swap, insert, sort, declare, discard, showCP, user_name, user_name_rect, computer_name, computer_name_rect\n global name_text, name_rect, score_text, score_rect, discard_mode, swap_mode, insert_mode, index\n deck = Deck(2)\n deck.shuffle_cards()\n deck.set_joker()\n user.deal_cards(deck)\n computer.deal_cards(deck)\n cardss = deck.draw_card()\n deck.update_pile(cardss)\n user.turn = True\n discard_mode = False\n swap_mode = False\n insert_mode = False\n index = []","title":""},{"docid":"4235cb633939d598736e432d17bdfc7f","score":"0.62765485","text":"def play_turn(game):\n print('=================================')\n if game.is_current_player_a_computer():\n print(game.get_current_player_name() + \"'s turn to play\")\n time.sleep(0.5)\n cards_drawn = game.play_computer_turn()\n for card in cards_drawn:\n if card is not None:\n print(\"Hit! Drew card %s\" % card)\n time.sleep(0.5)\n else:\n print(\"Deck is empty! Finished turn.\")\n if (game.current_player.is_over_21()):\n print(game.get_current_player_name() + \" lost! \" + game.get_current_player_name() + \" is out.\")\n time.sleep(0.5)\n else:\n if game.get_current_player_name != 'Dealer':\n print(game.get_current_player_name() + \" finished with count of \" + str(game.get_current_player_hand_count()))\n time.sleep(0.5)\n else:\n print(\"Your turn to play! Your count is: %s\" % game.get_current_player_hand_count())\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n while game.human_can_draw() and input(\"Do you want to hit? [y/N] \") is 'y':\n card = game.human_player_draw()\n if card is not None:\n print(\"Hit! Delt card %s. Your count is now: %s\" % (card, game.get_current_player_hand_count()))\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n else:\n print(\"Deck is empty! Finished turn.\")\n break\n if game.is_current_player_over_21():\n print(\"You lost! You score is over %s.\" % game.max_count())","title":""},{"docid":"2222c78926dcf1f22dafb55d4be49df5","score":"0.626647","text":"def computer_turn(self):\n print(\"You have \" + str(len(self.player1.deck)) + \" cards\")\n print(self.player2.name + \" has \" + str(len(self.player2.deck)) + \" cards\")\n print(\"Here is your top card\\n\")\n print_card(self.player1.deck[0])\n print(\"\\nYour opponent is choosing a trump stat\")\n rand_num = randint(1, 6)\n chosen_stat = None\n\n if rand_num == 1:\n chosen_stat = \"hp\"\n elif rand_num == 2:\n chosen_stat = \"attack\"\n elif rand_num == 3:\n chosen_stat = \"defense\"\n elif rand_num == 4:\n chosen_stat = \"speed\"\n elif rand_num == 5:\n chosen_stat = \"special attack\"\n else:\n chosen_stat = \"special defense\"\n time.sleep(2)\n print(\"\\n Your opponent picked \" + chosen_stat + \"!\\n\")\n\n self.check_stats(chosen_stat)","title":""},{"docid":"334b7600b2adc90874380ae9d38902c7","score":"0.625842","text":"def new_game():\n global exposed, deck_cards, state, turns\n turns = 0\n # Game state\n state = 0\n exposed = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 1: Model the deck of cards \n deck_cards = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]\n correct_cards = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 3: Shuffle the deck using random.shuffle\n random.shuffle(deck_cards)","title":""},{"docid":"98b10c5fbb5553c5b37d0874b9667ae6","score":"0.624704","text":"def __init__(self):\n self.keep_playing = True\n self.old_card = r.randint(1,13)\n self.new_card = 0\n self.player = Player()\n self.guess = \"\"\n self.points = 300","title":""},{"docid":"2efe4a4c4937f3e8e134bfdda416f06d","score":"0.62439346","text":"def reset_game(self):\n # The player always goes first.\n self.current_turn = 'player'\n\n # Set the current cards to an empty array.\n self.cards = {\n 'dealer': [],\n 'player': []\n }\n\n # Set the totals for each player.\n self.totals = {\n 'dealer': 0,\n 'player': 0\n }\n\n # Create a new new deck of cards.\n self.deck = self.deck_generator.get_new_deck()","title":""},{"docid":"4315b4a7c52331d868ebcd95500313ae","score":"0.6240726","text":"def take_initial_turn(self, player, dealer, deck_list):\n\n dist_counter = 0\n\n # Distribute first 2 cards to player and dealer\n while dist_counter < 2:\n \n # Take 1 card for player\n player, deck_list = self.take_1_card(player, deck_list)\n print(f'{player.name} hand: {[pair[0] for pair in player.hand]}\\nCurrent deck len: {len(deck_list)}')\n \n # Take 1 card for dealer, showing only the first drawn card\n dealer, deck_list = self.take_1_card(dealer, deck_list)\n if dist_counter == 0:\n print(f'{dealer.name} hand: {[pair[0] for pair in dealer.hand]}\\nCurrent deck len: {len(deck_list)}')\n else:\n print(f'Current deck len: {len(deck_list)}')\n\n dist_counter += 1\n \n print('\\nEnd of initial distribution.')\n\n return player, dealer, deck_list","title":""},{"docid":"aeccbe58a2576153b7edf0796e66999c","score":"0.62265307","text":"def black_jack_hand(player1, dealer): \n #this should be the black_jack hand, while the begging should be setting up a new game\n dealer.get_all_bets(player1)\n ##deal two cards each, one to the dealer should be set to face down\n for x in xrange(1,3):\n dealer.give_card_to_player(player1)\n dealer.give_card_to_player(dealer)\n\n ###Now the game is ready to start showing the hands of the dealer and the player should be done\n print \"Dealer's Hand\"\n dealer.get_hand().showHand()\n print \"Player's Hand\"\n player1.get_hand().showHand()\n while(player1.want_card()):\n dealer.give_card_to_player(player1)\n print \"Players Hand\"\n player1.get_hand().showHand()\n\n \n dealer.flip_card_over()\n while(dealer.want_card()):\n dealer.give_card_to_player(dealer)\n \n print \"Dealers Hand\"\n dealer.get_hand().showHand()\n\n dealer.set_winner(player1)","title":""},{"docid":"06a062fdec911de20686003a63b70bb4","score":"0.6199962","text":"def play_game(self):\n\n # Print the beginning game message.\n print(\"\\nBeginning \" + str(self))\n\n time.sleep(1.5)\n\n os.system('cls' if os.name == 'nt' else 'clear')\n\n # It is the initial turn.\n turn = 0\n\n # This is the game loop. When the game ends we will exit this loop.\n has_ended = False\n while not has_ended:\n\n # Increment the turn number.\n turn += 1\n\n # Iterate through each player that is playing.\n for player_num, player in enumerate(self.config.players):\n \n # Set the current player.\n self.config.set_current_player(player_num)\n \n # Before each player's turn, check to see if the game ended.\n if self.config.supply.check_end_game():\n has_ended = True\n break\n \n # The player's turn starts here.\n try:\n print(f\"\\n********** Turn #{turn} **********\")\n player.turn(self.config)\n time.sleep(1.5)\n os.system('cls' if os.name == 'nt' else 'clear')\n \n # Here we catch unexpected errors while playing the game.\n except DominionError as e:\n print(\"Encountered an error while playing. \" + e)\n return\n\n # If anyone quits, exit the game here.\n except DominionQuitError:\n print(f\"Game quit by {player.name}\")\n return\n\n # Once the game has ended, run the end_game method.\n self.end_game(turn)","title":""},{"docid":"e85dcd6a656f2d43ae2291615c21760e","score":"0.6190354","text":"def start_game(self):\n\n # Change starting screen to Chess board\n self.board = Board(square_width=self.view.square_width, \n square_height=self.view.square_height,\n is_flipped=self.is_flipped)\n\n # Create players\n self.board.player_list.append('Human')\n \n if self.settings['vs_computer']: self.board.player_list.append('AI')\n else: self.board.player_list.append('Human')","title":""},{"docid":"f4717ddff3d01a3cc83e1621cdcdf452","score":"0.6188858","text":"def playAgain(self):\n self.game_setup()","title":""},{"docid":"08f12d4c17062b04529efa4006ecc7bb","score":"0.6175251","text":"def _start_new_game(self):\n D = DeckGenerator()\n D.shuffle()\n T = TableCard(D)\n T.generate_pyramid()\n gL = GameLogic(T)\n gL.level = self._level\n for item in T.pyramid_deck[-1]:\n item.status = True\n return Game(D, T, gL)","title":""},{"docid":"6dcbee6750907da85989d529de59453d","score":"0.6170448","text":"def initTurn(self,players,current_player,draw):\n if self.__oneSide:\n for player in players:\n i=0\n for card in player.getHand():\n if callable(getattr(player, \"getSpecial\", None)):\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n i=0\n for card in player.getDiscard():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n else:\n for player in players:\n i=0\n for card in player.getHand():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,-716)\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,716)\n i+=1\n i=0\n for card in player.getDiscard():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,-358)\n else :\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,358)\n i+=1\n if len(draw) == 0:\n self.__canvas.itemconfigure(self.__canvas_items[\"draw\"],image=\"\")\n self.__updateDisplay()","title":""},{"docid":"020fa0581c40ec60e6532db6e2adb650","score":"0.6161986","text":"def main():\n game = poker_game()\n game.add_player(player_1) #Temp solution to add players to game\n game.add_player(player_2)\n game.form_player_hands() #Deal phase one, creating card objects and card_pack \n game.update_all_hands() #Used after each round, to sort hand + calculate each persons strongest hand\n game.deal_flop()\n game.update_all_hands()\n game.deal_turn()\n game.update_all_hands()\n game.deal_river()\n game.update_all_hands()\n print(game.player_list[1].hand_value) #Prints hand_value of player at index 1\n #hand_compare.hand_compare(player_hands, board) --Incomplete\n return","title":""},{"docid":"8e986e6106c6e9afad81891509fe2a59","score":"0.6153859","text":"def start_game(self):\n if self.gameover:\n self.init_game()\n self.gameover = False","title":""},{"docid":"ff428e6a7a8d16851ada513c3fdd2f4b","score":"0.61526495","text":"def run_play_loop(self): \n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n self.gui.create_play_screen()\n self.pause(500)\n \n while True:\n deck = Deck()\n\n # Prompt the user to place a wager\n wager = self.get_wager(player, dealer)\n\n # Handle the card selection process in triple pocket holdem\n # Present the user with his/her options for cards and have him/her select the cards\n self.select_cards(deck, player, dealer)\n community_cards = deck.draw_five_community_cards()\n \n # Now, reveal the cards held by the user and the dealer, as well as the \n # five community cards\n self.gui.explain_card_reveal()\n self.pause(2000)\n self.gui.reveal_player_cards(player, dealer)\n self.pause(2000)\n self.gui.reveal_common_cards(community_cards)\n self.pause(5000)\n\n # Determine the best hand held by each player and who won the round\n # Add or subtract chips from the player and dealer's totals depending on the outcome\n # Output to the user which poker hand the dealer had and what the consequent result was\n (player_hand, dealer_hand, player_wager_multiple, dealer_wager_multiple) = determine_outcome(\n player.hands[0], \n dealer.hands[0], \n dealer.hands[1], \n community_cards\n )\n player.alter_chip_balance(player_wager_multiple * wager)\n dealer.alter_chip_balance(dealer_wager_multiple * wager)\n self.gui.explain_outcome(\n player_hand, \n dealer_hand, \n player_wager_multiple, \n player.num_chips, \n dealer.num_chips\n )\n self.pause(4000)\n \n # Get the next action from the user\n # If the game has ended (i.e. the user or dealer has run out of chips),\n # the user can elect to play again. Otherwise, the user can elect to continue with\n # another round in the current game or to exit\n next_action = self.get_action_at_round_end(player, dealer)\n if next_action == MENU_NOW:\n return next_action\n elif next_action == PLAY_AGAIN:\n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n else:\n player.clear_hands()\n dealer.clear_hands()","title":""},{"docid":"94b5e7b4a39d3ad3dd48208c7ef542b8","score":"0.614256","text":"def play(self):\n # Clear/Reset attributes/values\n self.winners = []\n self.count = 0\n for player in self.players:\n # Reset hands\n del(player.cards[:])\n # Increment number of unique games played\n player.gamesPlayed += 1.0\n # Increment total number of games played\n self.totalGamesPlayed += 1.0\n # Prepare the deck, deal the cards\n self.deck = Deck()\n self.deck.shuffle()\n self.deck.deal(3, self)\n # Top card is placed onto discard pile\n self.discard.add(self.deck.pop())\n # While no one has satisfied winning conditions, take turns\n while not self.check_won():\n self.turn_seq()","title":""},{"docid":"ad995044d769ec0161ae9924becdf18b","score":"0.61161804","text":"def initiate_deck(self):\n\n deck = Deck()\n deck.build_deck()\n deck.shuffle()\n self.deck1, self.deck2 = deck.split_deck()","title":""},{"docid":"0706d71c979b7b26af67ecd63b9ff690","score":"0.61084616","text":"def reset_hands(self):\n self.my_player.reset_hand()\n self.my_computer.reset_hand()","title":""},{"docid":"b7dc1ffc7670027b24ea1494978535af","score":"0.61073416","text":"def reset(self, test=None):\n self.health = self.start_health\n self.armor = 0\n self.this_turn_mana = 0\n self._init_heropower()\n self._init_deck(self.fix_deck)\n self.inhands = [] # cards in self hands\n self.intable = [] # cards on self table\n self.used_cards = defaultdict(int) # used card counts. key: cidx, value: used counts\n if self.first_player:\n self.draw_as_first_player()\n else:\n self.draw_as_second_player()\n if test is not None:\n self.test = test","title":""},{"docid":"f1a212e0c2daeb3463048aa45a876849","score":"0.60954237","text":"def setup_game():\n\t\n\tglobal bullet_chamber\n\tglobal current_chamber\n\tglobal player_turn\n\t\n\tbullet_chamber = math.ceil(random.random()*CHAMBER_COUNT)\n\tcurrent_chamber = 1\n\t\n\tprint \"Gun prepared...\"\n\t# determine who's first\n\tif random.random() >= 0.5:\n\t\tplayer_turn = True\n\t\tprint \"Player goes first\"\n\telse:\n\t\tplayer_turn = False\n\t\tprint \"Computer goes first\"","title":""},{"docid":"dd4b311a54ba395986edf37e3444a0a1","score":"0.60823447","text":"def start_new_game(players):\n game = SchnapsenGame()\n game.create_cards()\n game.shuffle_cards()\n for player in players:\n for dummy in range(5):\n card = game.draw_from_stock()\n player.add_card(card)\n return game","title":""},{"docid":"b8c0d6666ca0db5b99f471105ab90a1f","score":"0.6074004","text":"def show_next_hand(*args):\n global twentyfive_cards\n # disable show_next and enable show_best\n # show_best25_hand_button[\"state\"] = \"normal\"\n # show_next_hand_button[\"state\"] = \"disabled\"\n\n window.title(\"Play Pyramid Poker\")\n w.delete(\"all\") # clear out last hand\n show_twentyfive_cards()","title":""},{"docid":"0d3d9925f75608d29e6f3f04e3b60fe8","score":"0.60738045","text":"def start_hand(deck, onCoin):\n size = 4 if onCoin else 3\n return [deck.pop() for i in range(size)]","title":""},{"docid":"abc03548cf453e649529ab559af2bfe6","score":"0.607288","text":"def initialise_deck(self):\n deck = Deck()\n shuffle(deck.card)\n self.deck = deck\n return","title":""},{"docid":"40e5d29991114394fd48b8ffdeb3fb70","score":"0.60725707","text":"def main():\n numHands = eval(input('Enter number of player: '))\n while (numHands < 2 or numHands > 10):\n numHands = eval(input('Enter number of hands to play[2~6]: '))\n game = Poker(numHands)\n print(\"## Game start\")\n game.play()\n print(\"## Caculating hands\")\n print(game.evaluate(game.hands))","title":""},{"docid":"d4317741c257ecaff2039978f0c82dd3","score":"0.60713416","text":"def choose_hand(self):\n\n w = random.randint(0, 4)\n if len(Player.history) > 0:\n if Player.history[len(Player.history)-1] == \"rock\":\n w = 4\n self.weapon = Weapons.weapon[w]\n self.turtle.shape(Weapons.computer_images[w])","title":""},{"docid":"de83074b480ba3c367ce9ddc04565f9a","score":"0.60338426","text":"def start(self):\n self.start_game()\n self.init_playing_order()\n while self.winning_camel is None:\n self.run_leg()\n self.report()\n print(\"------------------- End leg ---------------------\")\n self.game_stage = \"result\"\n self.losing_camel = self.orders[-1]\n self.game_scoring_round()\n self.determine_game_result()","title":""},{"docid":"5f2270c443ff6f39200566891a90e09b","score":"0.60226154","text":"def playUpgrade(self, card, target = None):\n logging.info(card)\n active = self.activePlayer.board[\"Creature\"]\n inactive = self.inactivePlayer.board[\"Creature\"]\n hand = self.activePlayer.hand\n broken = False\n \n if target:\n self.activePlayer.board[\"Upgrade\"].append(card)\n if card in hand:\n hand.remove(card)\n side, choice = target\n if side == \"fr\":\n active[choice].upgrade.append(card)\n else:\n inactive[choice].upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, side, choice)\")\n self.cardChanged(True) # this would actually make the play abilities I put on blood of titans, a bit redundant, but redundancy is ok\n return\n \n drawMe = []\n # surfs\n selectedSurf = Surface((self.target_cardw, self.target_cardh))\n selectedSurf.convert_alpha()\n selectedSurf.set_alpha(80)\n selectedSurf.fill(COLORS[\"LIGHT_GREEN\"])\n\n selectedSurfTapped = Surface((self.target_cardh, self.target_cardw))\n selectedSurfTapped.convert_alpha()\n selectedSurfTapped.set_alpha(80)\n selectedSurfTapped.fill(COLORS[\"LIGHT_GREEN\"])\n # draw all targets\n for temp_card in active + inactive:\n if temp_card.ready:\n drawMe.append((selectedSurf, temp_card.rect))\n else:\n drawMe.append((selectedSurfTapped, temp_card.tapped_rect))\n \n if self.canDiscard(card, reset = False):\n ## discard stuff here - if you can play it, you can discard it\n discSurf = Surface((self.target_cardw, self.target_cardh))\n discSurf.convert_alpha()\n discSurf.set_alpha(80)\n discSurf.fill(COLORS[\"LIGHT_GREEN\"])\n discRect = discSurf.get_rect()\n discRect.topleft = self.discard1_rect.topleft\n drawMe.append((discSurf, discRect))\n \n while True:\n self.extraDraws = drawMe.copy()\n \n for e in pygame.event.get():\n if e.type == MOUSEMOTION:\n #update mouse position\n self.mousex, self.mousey = e.pos\n \n if e.type == QUIT:\n pygame.quit()\n\n if e.type == MOUSEBUTTONUP and e.button == 1:\n activeHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in active]\n inactiveHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in inactive]\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n hand.append(self.dragging.pop())\n self.extraDraws = []\n return\n elif True in activeHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = activeHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif True in inactiveHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = inactiveHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif self.canDiscard(card, reset=False) and Rect.collidepoint(discRect, (self.mousex, self.mousey)):\n hand.append(self.dragging.pop())\n self.discardCard(-1)\n self.extraDraws = []\n self.cardChanged()\n return\n else:\n hand.append(self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n\n if e.type == MOUSEBUTTONDOWN and e.button == 1:\n logging.error(\"You shouldn't be able to trigger MOUSEBUTTONDOWN in dragCard.\")\n\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.invisicard)\n self.cardChanged()\n break\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.invisicard)\n self.cardChanged()\n break\n\n self.CLOCK.tick(self.FPS)\n self.hovercard = []\n self.check_hover()\n self.draw(False)\n try:\n hand.remove(self.invisicard)\n except:\n pass\n pygame.display.flip()\n self.extraDraws = []\n if broken:\n self.cardChanged()\n break\n \n if not target and card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{card.title} gave you {str(card.amber)} amber. You now have {str(self.activePlayer.amber)} amber.\\n\\nChange to a log when you fix the amber display issue.\"\"\")\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True)\n return","title":""},{"docid":"c09bb9eea918d6798de1ca216dd3a63a","score":"0.60097355","text":"def player_start(self):\n for card in self._target_hand:\n if card.pip is False:\n self._dealer.flip_deal(card)\n return self._target_hand_stats","title":""},{"docid":"fe63242c7be7c36cf950c836bb9a76e8","score":"0.60043347","text":"def initialize_game():\n\n MIN_SCORE = 0\n\n p1_card = create_score_card()\n p2_card = create_score_card()\n p1_bonus = MIN_SCORE\n p2_bonus = MIN_SCORE\n\n round_ongoing = True\n\n while round_ongoing:\n if -1 in p1_card.values():\n print(\"-- IT IS PLAYER ONE'S TURN --\")\n take_turn(p1_card, p1_bonus)\n\n print(\"\\n\")\n\n if -1 in p2_card.values():\n print(\"-- IT IS PLAYER TWO'S TURN --\")\n take_turn(p2_card, p2_bonus)\n\n if -1 not in p1_card.values() and -1 not in p2_card.values():\n round_ongoing = False\n\n game_verdict(calculate_score(p1_card), p1_bonus, calculate_score(p2_card), p2_bonus)","title":""},{"docid":"45c738d403cfef32cc315c30b913debe","score":"0.6004059","text":"def __init__(self):\n self.shoe = deck.Deck(False)\n self.shuffle_shoe()\n self.hand = []","title":""},{"docid":"d0f4de22f0f5ef385e52aa0594aba3bc","score":"0.6000726","text":"def begin_game(self):\n pass","title":""},{"docid":"307a4bb97dc9ff3d6387bcf1682a04b0","score":"0.59878075","text":"def play(self):\n # randomly remove a Cards in cards\n card_picked = choice(self.cards)\n self.cards.remove(card_picked)\n\n # add the Card to the Player's history\n self.history.append(card_picked)\n\n # increment the Player's turn count by 1\n self.turn_count += 1\n\n # decrement the Player's number of cards by 1\n self.number_of_cards -= 1\n \n # if self.number_of_cards == 0:\n # print(f\"{self.name} {self.turn_count} has no card\")\n\n # display the play\n print(f\"{self.name} {self.turn_count} played: {card_picked}\")\n\n return card_picked","title":""},{"docid":"06c3a907ddc1c52dcecd2b011db5a5c4","score":"0.5981489","text":"def black_jack():\n dealer = Dealer()\n player1 = Player(input(\"How much money does the player have?\"))\n want_to_play = 1\n while(want_to_play == 1):\n dealer.shuffle_deck()\n dealer.ready_for_next_hand()\n player1.ready_for_next_hand()\n\n black_jack_hand(player1, dealer)\n want_to_play = input(\"Would you like to play another hand? Select 1 for Yes and 0 for no\")","title":""},{"docid":"3cdc313404535e52d43ab7e2d590aff0","score":"0.59767616","text":"def startTurn():\n\tglobal current_piece\n\tcurrent_piece = 0\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.PickD)\n\ti = 0\n\twhile current_piece == 0:\n\t\tcurrent_piece = IDBlock(left_image)\n\t\trospy.sleep(5)\n\t\ti+=1\t\t\n\t\tif i >= 6:#After 30 seconds of inaction by the human player, Lucas assumes victory\n\t\t\tprint \"Huzzah! Another glorious victory for your robotic overlord!\"\n\t\t\tsys.exit()\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tscanBoard()\n\tlimbL.move_to_joint_positions(coords.ResetPos)","title":""},{"docid":"9c181062fcadfcfb7154f204b991d32b","score":"0.5975167","text":"def play_one_turn(self):\n #XXX: My laziness knows no bounds. Only allows two players.\n #XXX: I pass the whole game object through to players. Ugh.\n \n if self.current_player == 1:\n self.p1.new_hand()\n self.p1.play_one_turn(self)\n self.current_player = 2\n else:\n self.p2.new_hand()\n self.p2.play_one_turn(self)\n self.current_player = 1","title":""},{"docid":"cd5f840201383788c385fab45b96b7da","score":"0.59595335","text":"def reset(self, bank=50):\n self.gm = Game(\"P1\", \"Qtable\", \"P2\", \"Qtable\", bank)\n\n # Random starting money distribution p=0.4\n if self.rc is not None:\n if random() < self.rc:\n left = round(random() * ((bank * 2) - 4))\n self.gm.sb_player().bank = left + 2\n self.gm.bb_player().bank = ((bank * 2) - 4) - left + 2\n\n self.gm.place_blinds()\n if self.dc:\n self.gm.players_draw_cards()\n # Return observation\n # [ Small Blind Player, Big Blind Player ]\n self.ob = [self.gm.sb_player(), self.gm.bb_player()]\n return self.ob","title":""},{"docid":"21166b5cdbe4125442fd133b0444ff07","score":"0.5957577","text":"def test_playcard_cost0(self):\n self.plr.play_card(self.card)\n self.plr.gain_card(\"Copper\")\n self.plr.test_input = [\"end phase\", \"end phase\"]\n self.plr.turn()\n self.assertNotIn(\"Horse\", self.plr.piles[Piles.DISCARD])","title":""},{"docid":"6436110836fe4d65b16594b43045efd8","score":"0.59550923","text":"def play_round(self):\n playing_players = self.enter_bets()\n self.give_cards()\n for player in self.players:\n if player.name in playing_players:\n self.play_hand(player)","title":""},{"docid":"60e9ce3200b27a029258f903fb33497d","score":"0.5942199","text":"def play_1(player):\r\n playing = True\r\n deck = Deck()\r\n deck.shuffle()\r\n score = 0\r\n print(input(\"Welcome to the casino, \" + player + \". Press Enter to play the game.\"))\r\n while playing:\r\n if deck.out():\r\n print(\"Deck is out of cards. Game over. Your score was \", str(score))\r\n playing = False\r\n else:\r\n hand_a = PokerHand()\r\n hand_b = PokerHand()\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_a.add_card(card)\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_b.add_card(card)\r\n print(\"Hand #1: \", hand_a)\r\n print(\"Hand #2: \", hand_b, \"\\n\")\r\n user_answer = int(input(\"Which hand is worth more? \"\r\n \"Type 1 if Hand #1, -1 if Hand #2, and 0 if they are worth the same\\n \"))\r\n correct_answer = hand_a.compare_to(hand_b)\r\n if user_answer == correct_answer:\r\n print(\"Correct! Let's try again\\n\")\r\n score += 1\r\n else:\r\n print(\"Game over. Your score was \", str(score))\r\n playing = False","title":""},{"docid":"e2e8b14207df9b875bc1f700b9f25012","score":"0.5939224","text":"def start_self_play(self, player, display=False):\n player.reset_player()\n self.init_game_state()\n\n states, mcts_probs, current_players = [], [], []\n while True:\n move, move_probs = player.get_action(self.game_state)\n\n if move == PASS_MOVE:\n move = PASS_MOVE\n else:\n move = (move // self.game_state.size, move % self.game_state.size)\n\n # store the data\n states.append(self.game_state.copy())\n mcts_probs.append(move_probs)\n current_players.append(self.game_state.current_player)\n\n # perform a move\n is_game_end = self.game_state.do_move(move)\n if display:\n self.print_board(self.game_state, str(player), str(player), move_probs)\n if is_game_end:\n winner_id = self.game_state.get_winner()\n # winner from the perspective of the current player of each state\n winners_z = np.zeros(len(current_players))\n if winner_id is not None:\n winners_z[np.array(current_players) == winner_id] = 1.0\n winners_z[np.array(current_players) != winner_id] = -1.0\n if display:\n if winner_id is not None:\n print(\n \"Game end. Winner is player:\",\n \"X\" if winner_id == go.BLACK else \"O\",\n )\n else:\n print(\"Game end. Tie\")\n return winner_id, zip(states, mcts_probs, winners_z)","title":""},{"docid":"a2d452612ccda9a3382eabefd3e5e2c2","score":"0.5927983","text":"def UCTPlaySampleGame():\n # state = OthelloState(4) # uncomment to play Othello on a square board of the given size\n # state = OXOState() # uncomment to play OXO\n # state = NimState(15) # uncomment to play Nim with the given number of starting chips\n # state = SimpState((5,5)) # uncomment to play Simp with the given number of starting chips\n state = CapGoState(sz) # uncomment to play Capture Go with the given board size\n UCTPlayGame(state)","title":""},{"docid":"3c125026b6cbd995192ce2d8c656d968","score":"0.59240186","text":"def resetGame():\n del CompHand[:] \n del UserHand[:]\n del CompWinningList[:]\n del UserWinningList[:]\n del deck[:]\n deck.append(\"Ah\")\n deck.append(\"Ad\")\n deck.append(\"Ac\")\n deck.append(\"As\")\n deck.append(\"2h\")\n deck.append(\"2d\")\n deck.append(\"2c\")\n deck.append(\"2s\")\n deck.append(\"3h\")\n deck.append(\"3d\")\n deck.append(\"3k\")\n deck.append(\"3s\")\n deck.append(\"4h\")\n deck.append(\"4d\")\n deck.append(\"4c\")\n deck.append(\"4s\")\n deck.append(\"5h\")\n deck.append(\"5d\")\n deck.append(\"5c\")\n deck.append(\"5s\")\n deck.append(\"6h\")\n deck.append(\"6d\")\n deck.append(\"6c\")\n deck.append(\"6s\")\n deck.append(\"7h\")\n deck.append(\"7d\")\n deck.append(\"7c\")\n deck.append(\"7s\")\n deck.append(\"8h\")\n deck.append(\"8d\")\n deck.append(\"8c\")\n deck.append(\"8s\")\n deck.append(\"9h\")\n deck.append(\"9c\")\n deck.append(\"9d\")\n deck.append(\"9s\")\n deck.append(\"10h\")\n deck.append(\"10d\")\n deck.append(\"10c\")\n deck.append(\"10s\")\n deck.append(\"Jh\")\n deck.append(\"Jd\")\n deck.append(\"Jc\")\n deck.append(\"Js\")\n deck.append(\"Qh\")\n deck.append(\"Qd\")\n deck.append(\"Qc\")\n deck.append(\"Qs\")\n deck.append(\"Kh\")\n deck.append(\"Kd\")\n deck.append(\"Kc\")\n deck.append(\"Ks\") \n return deck","title":""},{"docid":"55054711f5324eaa84db3999ab4d245c","score":"0.59172213","text":"def test_play_no_pickup(self):\n self.plr.piles[Piles.HAND].set(\"Copper\", \"Copper\", \"Estate\", \"Duchy\")\n self.plr.piles[Piles.DECK].set(\"Gold\", \"Silver\", \"Copper\", \"Copper\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.test_input = [\n \"Discard\",\n \"Discard Estate\",\n \"Discard Duchy\",\n \"Finish\",\n \"nothing\",\n ]\n self.plr.play_card(self.card)\n self.assertEqual(self.plr.coins.get(), 2)\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 4 + 1 - 2)\n self.assertNotIn(\"Duchy\", self.plr.piles[Piles.HAND])\n self.assertNotIn(\"Silver\", self.plr.piles[Piles.HAND])","title":""},{"docid":"f18317543fe0e81636342952f64a9f20","score":"0.5912951","text":"def start_game():\n\tprint intro_msg\n\tplayer_name = raw_input(\"What' do they call you son ? \")\n\tend = False\t\t\n\twhile not end :\n\t\tchoice,door = show_options()\n\t\tif choice == 'yes':\n\t\t\t#Enter the door chosen\n\t\t\tproceed = proceed_in_door(door)\n\t\t\tif proceed:\n\t\t\t\tprint \"Proceeding....\"\n\t\t\t\ttime.sleep(3)\n\t\t\t\tos.system('clear')\n\t\t\t\tcontinue\t\n\t\t\telse :\n\t\t\t\tend = True\n\n\t\telif choice == 'no' :\n\t\t\t#Show the random waiting message\n\t\t\tprint random.randint(0,len(waiting_messages))\n\t\t\ttime.sleep(5) #wait\n\n\t\telif choice == 'info':\n\t\t\t#shows the player info\n\t\t\tshow_player_info()\n\t\telse :\n\t\t\tprint \"Whaaaaaaaat the heck is that ?, Just YES or no\"","title":""},{"docid":"754749c503bb5ff4f5d8ff50d7501951","score":"0.5910271","text":"def start_game(self, name):\n\t\tself.guesses = ''\n\t\tself.incorrect = 0\n\t\tself.correct = 0\n\t\tself.gameover = False\n\t\tself.won = False\n\t\tself.username = name\n\n\t\tword_list = ['3dhubs', 'marvin', 'print', 'filament', 'order', 'layer']\n\t\tself.target = word_list[random.randint(0, 5)]","title":""},{"docid":"f5c861602d872a650894343f6703dbbb","score":"0.5908629","text":"def start(self):\n # <<-- Creer-Merge: start -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\n # replace with your start logic\n self.game_data = GameData()\n\n self.random = random.random()\n # <<-- /Creer-Merge: start -->>\n\n self.game_data.humans = [human for human in self.player.units if human.job.title == 'fresh human']\n\n self.first_run = True\n self.no_income = True\n self.harvester_returning = False","title":""},{"docid":"c9805fd2876acedb227ef8bbf31c53e4","score":"0.59040797","text":"def reset_game():\n global tmp_score, current_player\n for data in players:\n data[1] = 0\n tmp_score = 0\n current_player = 0 # 'the user always starts'\n cls()\n start_game()","title":""},{"docid":"2a582deca9da8cb98d30740d848ec22b","score":"0.5902361","text":"def startGame(self):\n while True:\n whiteType = input('Welcome to King-Queen Endgame Simulatior \\nWould you like a (1) Computer or (2) Human to play as White (the attacker with a queen and a king)?\\n')\n if whiteType == (1 or 'computer' or 'Computer'):\n whiteType = 'comp'\n break\n elif whiteType == (2 or 'human' or 'Human'):\n whiteType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n while True:\n blackType = input('Would you like a (1) Computer or (2) Human to play as Black (the defender with a king)?\\n')\n if blackType == 1 or blackType=='computer' or blackType=='Computer':\n blackType = 'comp'\n break\n elif blackType == 2 or blackType=='human' or blackType=='Human':\n blackType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n self.setBoard('Q')\n print b\n self.playGame(whiteType, blackType)","title":""},{"docid":"c413ae6f91a0d0648f1e7f342387a287","score":"0.58975714","text":"def _start_game(self):\n\t\tself.settings.initialize_dynamic_settings()\n\n\t\t# Reset the game statistics\n\t\tself.stats.reset_stats()\n\t\tself.stats.game_active = True\n\t\tself.sb.prep_score()\n\t\tself.sb.prep_level()\n\t\tself.sb.prep_strikers()\n\n\t\t# Get rid of any remaining kids and candy.\n# \t\tself.kids.empty()\n# \t\tself.candies.empty()\n\n\t\t# Hide the mouse cursor.\n\t\tpygame.mouse.set_visible(False)\n\n\t\tpygame.mixer.music.set_endevent(self.SONG_END)\n\t\tpygame.mixer.music.load(self.song_fn)\n\t\tpygame.mixer.music.play()","title":""},{"docid":"26f3a2bfdeb59a4fb079e12aaccf6ffa","score":"0.5896248","text":"def create_random(cls, players, deck='52-card'):\n\n current_deck = Card.get_shuffled_deck(size=int(deck[:2]))\n\n bottom_card = current_deck[0]\n\n player_hands = {}\n\n for uid, name in players:\n player_hands[uid] = set(current_deck.pop() for _ in xrange(6))\n\n if deck[-4:] == 'fast':\n current_deck = current_deck[:len(players)]\n\n ordered_players = list(players)\n urandom_shuffle_inplace(ordered_players)\n\n cls._choose_first_player(bottom_card.suit, ordered_players, player_hands)\n\n return GameState(ordered_players, player_hands, [], current_deck, [], bottom_card)","title":""},{"docid":"ca6db0fb483345ac87b4cf31144f780c","score":"0.58839583","text":"def initialize_trump(self) -> None:\r\n input(\"Time to decide trump. Pass the computer to \" + self.first_player.name)\r\n euchre_utils.clear_screen()\r\n starting = self.players.index(self.first_player)\r\n i=starting\r\n\r\n self.add_card(self.deck.get_card())\r\n \r\n while i <= starting+7:\r\n player = self.players[i%4]\r\n self.print_status_update(player)\r\n face_up_card: Card = self.cards_on_table[0]\r\n\r\n if i <= starting+3: #first time around\r\n answer = euchre_utils.get_yes_no(\"Do you want the dealer\"+\r\n \" to pick up the card on the table and that card to\" +\r\n \" become trump?\".format(player.name))\r\n if answer:\r\n #alone_answer = euchre_utils.get_yes_no(\"Do you want to go alone?\")\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, Announce that {} is trump. Pass the computer to the dealer({})\".format(\r\n face_up_card.suit, self.dealer.name))\r\n euchre_utils.clear_screen()\r\n self.dealer.new_card(face_up_card)\r\n self.clear_cards_on_table()\r\n self.trump = face_up_card.suit\r\n self.player_that_called_suit = player\r\n \r\n #dealer chooses which cards to remove\r\n self.dealer.sort_hand(self.trump)\r\n self.print_status_update(self.dealer)\r\n print(\"Dealer, {} told you to pick it up. \".format(player.name)+\r\n \"Which card in your hand do you want to remove?\")\r\n self.dealer.remove_card(self.dealer.select_card(self.dealer.hand))\r\n euchre_utils.clear_screen()\r\n input(\"Pass the computer to {} to start the round\".format(self.first_player.name))\r\n euchre_utils.clear_screen()\r\n break\r\n else: \r\n euchre_utils.clear_screen()\r\n input(\"\\nPass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n else: #second time around, choose which suit to be trump\r\n response = euchre_utils.get_answer_no(\"Do you want to select the trump suit?\"+\r\n \" If so, say which. You're playing 'stick the dealer'\\n\", \"diamonds|hearts|spades|clubs\")\r\n if response and response != face_up_card.suit: #if they say an availible suit\r\n print(\"OK, {} is Trump. Pass the computer to {} to start the round\".format(\r\n response, self.first_player.name))\r\n self.trump = response\r\n self.player_that_called_suit = player\r\n self.clear_cards_on_table()\r\n break\r\n elif response == face_up_card.suit:\r\n input(\"Sorry, you can't choose the suit that was initially face up. Try again\")\r\n i-=1 # loop around to the same player again to give another chance\r\n elif i == starting+7: #STICK THE DEALER\r\n #this if clause needs to come after the first two to take lower precedence\r\n input(\"You're playing 'stick the dealer', so the dealer has to choose a suit.\")\r\n i-=1\r\n else: #empty string, they said no\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, pass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n euchre_utils.clear_screen()\r\n i+=1\r\n euchre_utils.clear_screen()\r\n #sort everybodys hands according to trump\r\n [player.sort_hand(self.trump) for player in self.players]","title":""},{"docid":"25edc669fdded7b1f82cd0c502985d64","score":"0.5880057","text":"def make_step(self, action=\"reset\"):\n reward = 0\n if not self.active and action != \"reset\":\n print(\"You specified an action, but no active game is being played. Please reset the game using action='reset'.\")\n elif self.active and action == \"reset\":\n print(\"WARNING! You specified action='reset' although the last game has not ended, yet.\")\n elif action == \"reset\":\n self.reset_game()\n if self.player_sum == 21:\n self.active = False\n if self.verbose:\n print(\"Player has Blackjack!\")\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n if np.sum(self.dealer_cards == 1) == 1 and np.sum(self.dealer_cards == 10) == 1:\n if self.verbose:\n print(\"Dealer has Blackjack, too!\")\n print(\"DRAW!\")\n else:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n elif action == \"hit\":\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Player draws card:\", new_card)\n self.player_sum += new_card\n if self.player_sum > 21:\n if self.player_has_usable_ace:\n if self.verbose:\n print(\"Player converts a usable ace (11) into 1.\")\n self.player_cards[self.player_cards == 11] = 1\n self.player_has_usable_ace = False\n self.player_sum -= 10\n if self.player_sum > 21:\n # Player goes bust\n if self.verbose:\n print(\"Player goes BUST!\")\n self.active = False\n reward = -1\n else:\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"New sum of player's cards:\", self.player_sum)\n reward = 0\n elif action == \"stick\":\n self.active = False\n if self.verbose:\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n print(\"The dealer has\", np.sum(self.dealer_cards), \"points.\")\n # Let dealer play\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[self.dealer_cards == 1] = 11\n while self.dealer_sum < 17:\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Dealer draws card:\", new_card)\n self.dealer_cards = np.append(self.dealer_cards, new_card)\n self.dealer_sum += new_card\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[np.where(self.dealer_cards == 1)[0]] = 11\n if self.verbose:\n print(\"New dealer sum\", self.dealer_sum)\n\n # Check whether dealer goes bust.\n dealer_difference = 21 - self.dealer_sum\n if dealer_difference < 0:\n if self.verbose:\n print(\"Dealer goes BUST!\")\n reward = 1\n else:\n # Compare player to dealer\n player_difference = 21 - self.player_sum\n if player_difference == dealer_difference:\n if self.verbose:\n print(\"DRAW!\")\n reward = 0\n elif player_difference < dealer_difference:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n else:\n if self.verbose:\n print(\"DEALER WINS!\")\n reward = -1\n else:\n raise ValueError(\"'action' has to be either 'stick' or 'hit' when gameover is False.\")\n\n if not self.active:\n self.state = np.array([-1, -1, -1])\n\n return copy.deepcopy(self.state), reward","title":""},{"docid":"2b5ed06f45caf7e937b1b9ad3a3a65f4","score":"0.5879681","text":"def test_others_playing(self):\n while True:\n card = self.g[\"Clashes\"].remove()\n if card.name == \"Warlord\":\n break\n self.plr.add_card(card, Piles.HAND)\n self.plr.play_card(card)\n mil = self.g[\"Militia\"].remove()\n self.oth.add_card(mil, Piles.HAND)\n self.oth.piles[Piles.PLAYED].set(\"Militia\", \"Militia\", \"Copper\")\n self.oth.play_card(mil)\n self.g.print_state()","title":""},{"docid":"2063217bde657141e0f5d1728c6fe9fb","score":"0.5867167","text":"def playCard(self, chosen: int, played_from: str = \"Hand\", cheat: bool = False, flank = \"Right\", ask = True):\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n if played_from == \"Deck\":\n source = self.activePlayer.deck\n elif played_from == \"Discard\":\n source = self.activePlayer.discard\n elif played_from == \"Mimicry\":\n source = self.inactivePlayer.discard\n else:\n source = self.activePlayer.hand\n card = source[chosen]\n if not self.canPlay(card, cheat = cheat):\n return\n # canPlay() makes sure that you can't try to play a card you're not allowed to play\n # Increases amber, adds the card to the action section of the board, then calls the card's play function\n if card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{source[chosen].title} gave {self.activePlayer.name} {card.amber} amber. {self.activePlayer.name} now has {self.activePlayer.amber} amber.\")\n if ask:\n if card.type == \"Creature\" and len(self.activePlayer.board[\"Creature\"]) > 0:\n flank = self.chooseFlank(card)\n # left flank\n if card.type != \"Upgrade\" and flank == \"Left\":\n self.activePlayer.board[card.type].insert(0, source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # default case: right flank\n elif card.type != \"Upgrade\" and played_from != \"Mimicry\":\n self.activePlayer.board[card.type].append(source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n elif played_from == \"Mimicry\":\n self.activePlayer.board[card.type].append(source[chosen])\n logging.info(f\"Mimicry branch.\")\n else:\n targeted = self.chooseCards(\"Creature\", \"Choose a creature to attach the upgrade to:\")\n if targeted:\n targeted = targeted[0]\n else:\n return\n self.playUpgrade(card, targeted)\n self.cardChanged()\n return\n #once the card has been added, then we trigger any play effects (eg smaaash will target himself if played on an empty board), use stored new position\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True) # definitely need to recalc power here, in case we play something next to a staunch knight so now it is dead\n self.draw()\n pygame.display.update()\n card.play(self, card)\n logging.info(f\"{card.title} play ability resolved.\")\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # if the card is an action, now add it to the discard pile - remote access or poltergeist or nexus on masterplan can potentially play cards that belong to the other player\n if card.type == \"Action\" and played_from != \"Mimicry\":\n if card.title == \"library_access\":\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n elif played_from == \"Mimicry\":\n if card.title == \"library_access\":\n purge_mimic = True\n self.activePlayer.board[\"Action\"].pop()\n logging.info(f\"Removing Mimicried card.\")\n if purge_mimic:\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n self.cardChanged(True)","title":""},{"docid":"430302c7890fa57ebc3b3eed949f5b6c","score":"0.5861373","text":"def new_game():\n \n # Initialize global variable that will hold the \"deck\n # of cards\"; we model the \"deck of cards\" as a list \n # consisting of 16 numbers with each number lying in \n # the range [0,8) and appearing twice. The list is\n # created by concatenating two lists with range [0,8) \n # together. Although Player can play the game with\n # \"textual numbers\" or \"images\", the above mentioned \n # technique is being used modeling the game in both\n # game \"modes\". \n global deck_of_cards\n deck_of_cards = range(CARDS_NUMBER // 2) + range(CARDS_NUMBER // 2)\n # Shuffle the \"deck\".\n random.shuffle(deck_of_cards)\n # Remove comment if in DEBUG mode.\n #print deck_of_cards\n \n # Initialize global variable that will hold the a list,\n # with size equal to the size of the \"deck of cards\"\n # consisting of boolean values. The boolean value\n # at a certain list index indicates whether the \"card\"\n # is \"exposed\" (True) or not (False). Particularly,\n # the ith entry should be \"True\" if the ith card is \n # face up and its value is visible or \"False\" if the \n # ith card is face down and it's value is hidden. \n global deck_of_cards_exposed\n deck_of_cards_exposed = [False] * CARDS_NUMBER\n \n # Initialize global variable that will hold the game\n # state (0,1 and 2), i.e. beginning of the game, single \n # \"exposed\" unpaired \"card\" and end of a \"turn\" \n # respectively (have a look at the comments of \n # \"mouseclick()\" for a detailed description \n # concerning this variable).\n global state\n state = 0 \n \n # Initialize global variable that will hold the number\n # of \"turns\" playing the game.\n global turn \n turn = 0\n label.set_text(\"Turns = \" + str(turn))\n \n # Initialize global variable that will hold a \"helper\"\n # list, keeping the index of the cards \"exposed\" in \n # a single \"turn\". \n global index_of_cards_exposed_in_a_turn \n index_of_cards_exposed_in_a_turn = [-1, -1]\n \n return None","title":""},{"docid":"92bac313e3373abca83de4f803c9c470","score":"0.5860306","text":"def play(deck, verbose):\r\n\r\n\t#generate first 2 'visible' cards from deck\r\n\tvisible = [deck.pop(0), deck.pop(0)]\r\n\t\r\n\twhile len(visible) <= 9: #game over if there are more than 9 'piles'\r\n\t\t\t\r\n\t\twhile add_to_11(visible) or jqk(visible):\t\t\r\n\t\t\r\n\t\t\tif add_to_11(visible):\r\n\t\t\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 2:\r\n\t\t\t\t\t\r\n\t\t\t\t\tfor a in add_to_11(visible):\r\n\t\t\t\t\t\tvisible[a] = deck.pop(0) #'cover' cards\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\t\t\telse: \r\n\t\t\t\t\treturn 0 \r\n\t\t\t\t\t#if len(deck) < 2 and 2 cards need to be covered then player wins\r\n\t\r\n\t\t\tif jqk(visible):\r\n\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 3 : \r\n\t\t\t\t\t\r\n\t\t\t\t\tfor j in jqk(visible):\r\n\t\t\t\t\t\tvisible[j] = deck.pop(0)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\r\n\t\t\t\telse:\r\n\t\t\t\t\treturn 0\r\n\t\r\n\t\tif len(visible) <= 9 and len(deck) > 0: \r\n\t\t# no cards can be covered but game not over:\r\n\t\r\n\t\t\tvisible.append(deck.pop(0)) #add new pile\r\n\t\t\t\r\n\t\t\tif verbose:\r\n\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\tif len(visible) > 9 or len(deck) == 0: \r\n\t\t\tbreak #once there are 10 piles/deck empty game is over\r\n\t\r\n\treturn(len(deck))","title":""},{"docid":"d95982ee89a1979bb0988195bad9f0f4","score":"0.58343667","text":"def reset_game(self):\n if self.verbose:\n print(\"The game is reset.\")\n self.active = True\n self.player_cards = []\n self.player_sum = 0\n self.player_has_usable_ace = False\n while self.player_sum < 12:\n new_card = self.draw_cards(size=1)\n if new_card == 1 and self.player_sum < 11:\n new_card = 11\n self.player_has_usable_ace = True\n self.player_cards = np.append(self.player_cards, new_card)\n self.player_sum += new_card\n\n self.dealer_cards = self.draw_cards(size=2)\n self.dealer_sum = np.sum(self.dealer_cards)\n self.dealers_showing_card = self.dealer_cards[1]\n\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"Player's cards:\", [int(x) for x in self.player_cards]) # , \"; player sum:\", self.player_sum[0]\n print(\"Dealer's showing card:\", [self.dealer_cards[1]])","title":""},{"docid":"6159fbc53dc2432a972ab889872f9fc7","score":"0.5832458","text":"def start_turn(self, character):\n character.db.combat_actionsleft = ACTIONS_PER_TURN # Replenish actions\n # Prompt the character for their turn and give some information.\n character.msg(\"|wIt's your turn! You have %i HP remaining.|n\" % character.db.hp)","title":""},{"docid":"53efab2740a1c1c957c7cc649f014514","score":"0.5830657","text":"def initial(self, state):\n\n self.player_number = min(state.turn_order, 0) % self.size\n self.current_player = self.players[self.player_number]","title":""},{"docid":"d948450c72319e3b6261eb24a0525aa5","score":"0.581744","text":"def play(self):\n print('Welcome to Blackjack v1.0')\n # give dealer 2 cards\n # give player 2 cards\n # player goes first in the gameplay, dealer hits until the player stays\n # dealer then goes\n # if player wins, they double their bet\n\n # if both the player and dealer are the same value, it's called a PUSH and\n # no money changes hands.\n\n # it's a 1:1 payout in blackjack, if you put down 10, you win another 10.\n\n # if you hit BLACKJACK! - 21, then you will get a 3:1 payout, so for putting down\n # 10, you get 15.\n\n while True:\n\n break","title":""},{"docid":"f9ef2eb3926070baf2d966906da516c8","score":"0.5817214","text":"def shuffle_deck(self):\n self.__deck = Deck()","title":""},{"docid":"dece00dfe6866bb147e30f9a31ea4d1c","score":"0.58131605","text":"def select_cards(self, deck, player, dealer):\n self.gui.show_hand_select_instructions()\n self.pause(2000)\n \n first_hand = deck.draw_two_card_hand()\n second_hand = deck.draw_two_card_hand()\n third_hand = deck.draw_two_card_hand()\n \n self.gui.show_first_hand(first_hand[0].img_path, first_hand[1].img_path)\n if self.picked_up_cards():\n player.add_hand(first_hand)\n dealer.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(first_hand)\n self.gui.show_second_hand(second_hand[0].img_path, second_hand[1].img_path)\n\n if self.picked_up_cards():\n player.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(second_hand)\n player.add_hand(third_hand)\n self.gui.alert_to_third_hand()\n self.pause(1500)","title":""},{"docid":"2589a760be7715f758b3933ef4b784ee","score":"0.58054376","text":"def game(self) -> None:\n print(RULES) \n print(f\"You have been credited with ${self.start_amount}\")\n\n # main game loop, loop infinitely until\n # player decides to quit or player looses\n # all his/her money\n while True: \n if self.start_amount < 1:\n print(\"You've run out of money\")\n print(\"Good thing you weren't using real money.\")\n print(\"Try to seek professional help for your gambling problems.\")\n sys.exit() #player is broke, exit game \n\n print(f\"Money available: ${self.start_amount}\") \n\n # Get the amount player wants to stake.\n amt_to_bet = self.entity_bet(self.player, self.start_amount)\n print(f\"Player bet ${amt_to_bet}\")\n\n # Display player scores\n self.display_scores()\n\n # play for both player and player's second\n # hand if he does split\n self.entity_play(self.player)\n self.entity_play(self.player_split)\n\n # after player busts or (St)ands\n # check if dealer needs to play\n self.check_if_dealer_plays()\n\n # check which hand - player main or second hand\n # if existent- won.\n self.check_who_wins(self.player)\n self.check_who_wins(self.player_split)\n\n # start all over again\n input(\"Press Enter to continue...\")\n print('\\n')\n\n # reset stats.\n self.reset()\n # clear console\n os.system('cls') if sys.platform == \"win32\" else os.system('clear')","title":""},{"docid":"35cf530c7c6f7b8e383a4fe612d5570d","score":"0.58013165","text":"def __init__(self, deck=None, p1_class=Player, p2_class=Player):\n #XXX: Currently defaults to playing with VP and Money only.\n self.card_set = 'trivial'\n self.current_player = 1\n self.banked_cards = copy.deepcopy(cards.CARD_SETS[self.card_set])\n\n # Variables for game stats\n self.turn_num = 1\n self.winning_player = []\n self.player_vp = []\n\n initial_deck = deck or cards.STARTING_DECK\n self.p1 = p1_class(initial_deck)\n self.p2 = p2_class(initial_deck)","title":""},{"docid":"fa0e423f621be5257e0f20a164254566","score":"0.5795721","text":"def main():\n\n print(\"Welcome to BlackJack!!!\")\n\n # Ask for number of players\n n_players = get_num_players()\n\n # Initialize the game\n human_name = 'You'\n game = Game(n_players, human_name=human_name)\n\n while (True):\n # Deal\n print_deal(game.deal())\n\n # Play one turn per player\n while game.can_play():\n play_turn(game)\n game.next_player()\n\n # Output results\n print_score(game, *game.get_winners())\n if (input(\"Play again? [Y/n] \") is 'n'):\n break\n\n # End the game\n print(\"Thanks for playing! Have a great day!\")","title":""},{"docid":"74b353cb03f1abea549580a71817cbcb","score":"0.57908016","text":"def play(self):\n self._shuffle()\n self._deal()\n self._display_all()\n results = {}\n for player in self.player_list:\n score = player.play_hand()\n results[f'{player.type} {player.name}'] = score\n print('Dealer flips over second card...')\n print(self._dealer.hand)\n dealer_score = self._dealer.play_hand()\n print('\\n---------------------------------\\n')\n for name, score in results.items():\n if score == 0:\n print(f'{name} lost')\n elif score > dealer_score:\n print(f'{name} wins')\n elif score == dealer_score:\n print(f'{name} pushes')\n else:\n print(f'{name} lost')","title":""},{"docid":"64492e3d35024b21d7f7db18a48eadff","score":"0.57886285","text":"def draw_initial_cards(self):\n\n # Draw cards for player and dealer.\n for current in ('dealer', 'player'):\n for i in range(0, 2):\n card = self.draw_card()\n self.cards[current].append(card)","title":""},{"docid":"a3cba110962d7ea7079e68e96fc17b49","score":"0.5776046","text":"def main():\n game = WarGame()\n game.deal()\n while not game.winner():\n game.step()\n print(game)\n print(game.winner())","title":""},{"docid":"2ad22698074c27bd70352a6b42bde555","score":"0.57631284","text":"def test_Engine__insure_zero(engine, hand):\n player = players.BetterPlayer(name='Eric', chips=100)\n player.bet = 20\n player.hands = (cards.Hand(),)\n engine.dealer.hands = (hand,)\n engine._insure(player)\n assert player.insured == 0\n assert player.chips == 100\n assert engine.ui.mock_calls == []","title":""},{"docid":"b156f8f72b972997f3e5d63cee14036e","score":"0.5762807","text":"def pollPlay(self, state):\n hand = self.player.getHand()\n if not len(hand) == 0:\n cardOfChoice = random.choice(hand)\n # returns the choosen card here\n return cardOfChoice\n print(\"Oops, you are trying to play a card from an empty hand\")\n return None","title":""},{"docid":"7976e1729bf6638650ad8b2cd77acfb2","score":"0.5760314","text":"def start(self):\n Screen.clear()\n print('STARTING NEW GAME')\n print('ENTERING MAIN GAME STATE IN A MOMENT...')\n self.pause(pause_time=1)","title":""},{"docid":"39032af7fa0996e1f761e776ab5a59b6","score":"0.5759432","text":"def drawPlayerCard(self):\n player = self.level.players[0]\n card = self.level.playerDeck.draw()\n if card is not None:\n controller = PlayerDrawController(player, card)\n controller.run()","title":""},{"docid":"c4bb3a70ea09d37ce93a834cc0900ec8","score":"0.57525396","text":"def resetHand(self):\n self.hand = Hand()","title":""},{"docid":"b29b99ebb0ebc7579986cd9ef088010b","score":"0.5751541","text":"def prep(self, hand):\r\n self.hand = hand\r\n active_count = 0\r\n\r\n for player in self.players:\r\n if player.stack >= self.BLINDS[1]:\r\n player.activate()\r\n active_count += 1\r\n else:\r\n player.deactivate()\r\n \r\n if active_count < 2:\r\n c = \"Table needs at least 2 active players for a game.\"\r\n raise StructureError(c)\r\n\r\n if self.button == None:\r\n self.button = 0\r\n else:\r\n self.button += 1","title":""},{"docid":"2114aa08ab856de7ecb735cbc2255963","score":"0.574097","text":"def start_game(self):\n self.round = 0\n self.drawing_player = None\n self.score = {}\n\n for client_id in self.clients:\n self.score[client_id] = 0\n\n self.notify_all('game_starting')\n self.next_turn()","title":""},{"docid":"1fdb99a447976b8d96b16eef1d320019","score":"0.5738883","text":"def PlayRound(self):\n with self._lock:\n if self._active_round:\n logging.error('HypeJack game already active.')\n return\n bets = self._core.bets.LookupBets(\n self.name, resolver=self._core.name.lower())\n if not bets:\n logging.error('Attempted to start HypeJack with no players.')\n return\n\n self._pending_start = False\n\n # Shuffle the deck when it gets low. We assume a reasonable number of\n # cards needed per player, but with lots of splits / low cards we may\n # still run out of cards to play the hand.\n if len(self._shoe) < (len(self._peeps) + 1) * 7:\n self._ShuffleCards()\n\n # Deal cards to plebs.\n for user_id, user_bets in bets.items():\n hand = Hand(user_bets[0], self._shoe.pop(), self._shoe.pop())\n self._peeps[user_id] = [hand]\n self._DisplayUser(user_bets[0].user)\n\n # Deal cards to hypebot.\n self._dealer_hand = Hand(None, self._shoe.pop(), self._shoe.pop())\n # self._dealer_hand = Hand(playing_cards_lib.Card('Hearts', 8),\n # playing_cards_lib.Card('Spades', 8))\n self._msg_fn(None, 'Dealer: [%s, %s]' % (self._dealer_hand.cards[0], '🂠'))\n\n self._active_round = True\n\n # Short-circuit game play if the dealer has a hypejack or if all peeps\n # have hypejacks.\n if not self._dealer_hand.IsHypeJack() and any(\n [self._IsActive(user_id) for user_id in self._peeps.keys()]):\n # Force the round to end after some time if some peep ran away. Waiting\n # on a condition releases the lock while waiting, then reacquires it\n # automatically. Will shortcircuit if notified when all peeps have\n # finished their hands.\n self._game_ender.wait(timeout=self.MAX_ROUND_LENGTH)\n\n # Complete dealer hand.\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n while self._dealer_hand.Score() < 17:\n self._dealer_hand.cards.append(self._shoe.pop())\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n\n self._core.bets.SettleBets(self, self._core.name.lower(), self._msg_fn)\n\n # Reset game state.\n self._peeps = {}\n self._active_round = False","title":""}],"string":"[\n {\n \"docid\": \"5100836fd6ace5c376bcbe31fb141463\",\n \"score\": \"0.7105495\",\n \"text\": \"def start_new_game(self, player_hand):\\n self.deck_of_cards += self.hand\\n self.deck_of_cards += player_hand\\n self.deck_of_cards.shuffle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b8d8128052bfd7bd5a587d480672ce\",\n \"score\": \"0.6954077\",\n \"text\": \"def start_game(self):\\n initial_draw = int((len(self.stock.cards) * 0.5) / len(self.participants))\\n\\n if initial_draw == 0:\\n initial_draw = 1\\n\\n for player in self.participants:\\n cards = [self.stock.deal() for x in range(initial_draw)]\\n player.hand = cards\\n\\n self.act_player = self.participants[0]\\n self.turn = self.next_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6662d1a0a763307463d759a7f7232e3a\",\n \"score\": \"0.66947067\",\n \"text\": \"def init_game(self):\\n if self.is_basic: #create quick simple game\\n p1 = 6 #priest\\n p2 = 7 #rogue\\n deck1 = random_draft(CardClass(p1))\\n deck2 = random_draft(CardClass(p2))\\n self.players[0] = Player(\\\"Player1\\\", deck1, CardClass(p1).default_hero)\\n self.players[1] = Player(\\\"Player2\\\", deck2, CardClass(p2).default_hero)\\n self.game = Game(players=self.players)\\n self.game.start()\\n\\n #Skip mulligan\\n for player in self.game.players:\\n cards_to_mulligan = random.sample(player.choice.cards, 0)\\n player.choice.choose(*cards_to_mulligan)\\n\\n return self.game\\n\\n else:\\n p1 = random.randint(1, 9)\\n p2 = random.randint(1, 9)\\n #initialize players and randomly draft decks\\n #pdb.set_trace()\\n deck1 = random_draft(CardClass(p1))\\n deck2 = random_draft(CardClass(p2))\\n self.players[0] = Player(\\\"Player1\\\", deck1, CardClass(p1).default_hero)\\n self.players[1] = Player(\\\"Player2\\\", deck2, CardClass(p2).default_hero)\\n #begin the game\\n self.game = Game(players=self.players)\\n self.game.start()\\n\\n #Skip mulligan for now\\n for player in self.game.players:\\n cards_to_mulligan = random.sample(player.choice.cards, 0)\\n player.choice.choose(*cards_to_mulligan)\\n\\n return self.game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db336ace779d9c035eacad3d1e43a2c3\",\n \"score\": \"0.6674635\",\n \"text\": \"def play_hand(self, player):\\n if not player.has_blackjack():\\n while player.get_hand_value() <= 21:\\n self.print_current_state(player, sd=False)\\n action = player.get_action()\\n if action == 'Hit':\\n self.deal_top(player)\\n if action == 'Stand':\\n break\\n if action == 'Split':\\n pass\\n if action == 'Double':\\n player.stack -= player.amount_bet\\n player.amount_bet *= 2\\n self.deal_top(player)\\n break\\n # If player did not bust dealer plays\\n if (player.get_hand_value() <= 21):\\n self.dealer_plays()\\n self.print_current_state(player, sd=True)\\n self.declare_winner(player)\\n self.reset_table()\\n print(self.players[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefe9cdd368f1bc5c556b592bd28b0ba\",\n \"score\": \"0.6657983\",\n \"text\": \"def computer_turn():\\n global winner, winning_hand, time\\n computer.draw_card(deck.pile[0])\\n winning_hand = computer.declare_game()\\n if winning_hand[0]:\\n #winning condition\\n winner = computer\\n round_over()\\n stage[0] += 1\\n else:\\n score = [0] * 14\\n min_pile = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_pile[1]:\\n min_pile = (i,score[i])\\n elif score[i] == min_pile[1]:\\n if computer.hand[i].rank >= computer.hand[min_pile[0]].rank:\\n min_pile = (i, score[i])\\n\\n computer.discard_card(deck.pile[0])\\n\\n joker_card = Card('3','spades',True)\\n computer.draw_card(joker_card)\\n score = [0] * 14\\n min_deck = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_deck[1]:\\n min_deck = (i,score[i])\\n elif score[i] == min_deck[1]:\\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\\n min_deck = (i, score[i])\\n computer.discard_card(joker_card)\\n\\n\\n if min_pile[1] <= min_deck[1]:\\n #draw from pile\\n if time == 0 :\\n computer.draw_card(deck.pile.pop(0))\\n deal_card_sound.play()\\n time += 1\\n # print(\\\"pile initialisation\\\")\\n elif time >= computer_delay:\\n #DELAY\\n deck.update_pile(computer.hand[min_pile[0]])\\n computer.discard_card(computer.hand[min_pile[0]])\\n deal_card_sound.play()\\n # print(\\\"pile time condition\\\")\\n computer.hand = sort_hand(computer.hand)\\n computer.turn = False\\n user.turn = True\\n time = 0\\n else:\\n # print(\\\"pile increment\\\")\\n time += 1\\n else:\\n #draw from deck\\n #DELAY\\n if time == 0:\\n computer.draw_card(deck.draw_card())\\n deal_card_sound.play()\\n # print(\\\"deck initialisation\\\")\\n time += 1\\n elif time >= computer_delay:\\n # print(\\\"Deck time condition\\\")\\n winning_hand = computer.declare_hand()\\n if winning_hand[0] :\\n #winning condition\\n winner = computer\\n round_over()\\n stage[0] += 1\\n time = 0\\n else:\\n score = [0] * 14\\n min_deck = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_deck[1]:\\n min_deck = (i,score[i])\\n elif score[i] == min_deck[1]:\\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\\n min_deck = (i, score[i])\\n deck.update_pile(computer.hand[min_deck[0]])\\n computer.discard_card(computer.hand[min_deck[0]])\\n time = 0\\n deal_card_sound.play()\\n computer.hand = sort_hand(computer.hand)\\n computer.turn = False\\n user.turn = True\\n else:\\n # print(\\\"deck increment condition\\\")\\n time += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5ce86f91fa809eba12df1bb5fd7837\",\n \"score\": \"0.66456765\",\n \"text\": \"def start_hand(player, dealer, bet_amount):\\n\\n player.place_bet(bet_amount)\\n player.get_hand(dealer.give_card())\\n player.get_hand(dealer.give_card())\\n\\n print(player.show_hand())\\n\\n dealer.get_hand(dealer.give_card())\\n dealer.get_hand(dealer.give_card())\\n\\n print(dealer.show_first_hand())\\n\\n if player.hand_total_value() == 21:\\n player.has_blackjack = True\\n\\n if dealer.hand_total_value() == 21:\\n dealer.has_blackjack = True\\n\\n if player.has_blackjack:\\n print(\\\"Blackjack!!!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23e94ed0fa96fae14e8750676a9722f\",\n \"score\": \"0.65526325\",\n \"text\": \"def play(self):\\n\\n #############\\n # Game loop #\\n #############\\n number_bets = 0\\n print \\\"###############\\\"\\n print \\\"# Game starts #\\\"\\n print \\\"###############\\\"\\n while self.player.chips > 0:\\n self.play_hands()\\n number_bets += 1\\n #wants_exit = raw_input(\\\"> Press e for exit \\\").lower()\\n #if wants_exit == 'e':\\n # break\\n print \\\"###############\\\"\\n print \\\"#########################\\\"\\n print \\\"# Game end with %s bets #\\\" % (number_bets)\\n print \\\"#########################\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb175cb8d195e3b844ef7dd03c55afb8\",\n \"score\": \"0.6526096\",\n \"text\": \"def initialSetUp():\\n deck = cardGame.getDeck()\\n cardGame.shuffleDeck(deck)\\n numberOfPlayers = getNumberOfPlayers()\\n hands = cardGame.dealCards(deck, 0, numberOfPlayers)\\n sortHands(hands)\\n startingPlayer = getStartingPlayer(hands)\\n return deck, hands, numberOfPlayers, startingPlayer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ec6eee21dc39ea3e1cc703759281ee\",\n \"score\": \"0.6525598\",\n \"text\": \"def start(self):\\n\\n self.view.display_game_start()\\n self.cardgame.add_players()\\n\\n game_over = False\\n\\n while not game_over:\\n for player in self.cardgame.get_players():\\n move = self.parse_move(player.name)\\n self.cardgame.step(player, move)\\n self.view.display_hand(player.name, player.get_hand())\\n game_over = self.cardgame.is_game_over()\\n\\n winner, score = self.cardgame.pick_winner()\\n\\n if not winner:\\n self.view.display_draw_game()\\n else:\\n self.view.display_winner(winner.name, score, winner.get_hand())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e313dae13a0e559e08ea35e111a9f2\",\n \"score\": \"0.65084654\",\n \"text\": \"def new_game():\\n global cards, exposed, hand, state\\n global card_1, card_2, turns\\n\\n hand = cards[:]\\n random.shuffle(hand) # hand for the game\\n\\n exposed = 16 * [False] # initialize uncovered cards\\n card_1 = None\\n card_2 = None\\n turns = 0\\n\\n state = 0 # number of cards initially uncovered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee53164e852b2bd36967c77375c4a68\",\n \"score\": \"0.64621794\",\n \"text\": \"def setup_game(self):\\n if self.is_ready():\\n for player in self.players:\\n player.draw(5)\\n self.wild.draw(5)\\n self.starting_positions = [p.player_id for p in self.players]\\n self.current_player = self.next_player()\\n else:\\n print 'Not enough players to setup game'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf9e7f2d74b953dbf52004e56ba4100\",\n \"score\": \"0.6429044\",\n \"text\": \"def start_hand(self):\\n [self.draw() for _ in range(5)] #initially draw 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d389423337a21da474a9da118a77413d\",\n \"score\": \"0.64132434\",\n \"text\": \"def start_game(self):\\n self.display_info()\\n temporary_board = Board_game(self.board_size, self.number_of_ships,\\n \\\"Computer\\\", player=False)\\n self.computer_board = temporary_board\\n player_name = input(\\\"Please enter your name: \\\\n\\\")\\n print(\\\"-\\\" * 60)\\n temporary_board = Board_game(self.board_size, self.number_of_ships,\\n player_name, player=True)\\n self.player_board = temporary_board\\n\\n self.play_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5a90f61dcbcc1ddbbe1831103505af\",\n \"score\": \"0.6401434\",\n \"text\": \"def reset_new_game(self):\\n\\t\\tself._piles['deck'] = Pile('copper',7)\\n\\t\\tself._piles['deck'].add_new_card_to_pile('estate',3)\\n\\t\\tself._piles['deck'].shuffle()\\n\\t\\tself._piles['hand'] = Pile()\\n\\t\\tself._piles['discard'] = Pile()\\n\\t\\tself._piles['in_play'] = Pile()\\n\\t\\tself.draw_n(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d74b8502b3dc12a43ce7f6e15d3c209\",\n \"score\": \"0.6401383\",\n \"text\": \"def play_hands(self):\\n\\n #############\\n # First hand\\n #############\\n # Deal two cards to each opponent\\n self.player.cards = self.dealer.deal(2)\\n self.dealer.cards = self.dealer.deal(2)\\n\\n # Show hand\\n self.player.show_hand()\\n self.dealer.show_hand(1) # hide second card\\n\\n # End if player has blackjack\\n if self.player.has_blackjack():\\n self.end_bet()\\n return\\n\\n #################################\\n # Hand loop for player and dealer\\n #################################\\n opponents = [self.player, self.dealer]\\n dealer_turn = 0 # 0: is player turn, 1: is dealer turn\\n continue_bet = True\\n round = 1\\n # Bet\\n self.player.chips -= self.player.chips_per_bet\\n # Repeat until bet end condition\\n while continue_bet:\\n # Set who plays: player or dealer\\n opponent = opponents[dealer_turn]\\n continue_hand = True\\n while (continue_hand):\\n # Ask for action: hit or stand?\\n action = opponent.choose_action()\\n # If hit, deal cards and show hand\\n if action == settings.ACTION_HIT:\\n opponent.cards.extend(self.dealer.deal())\\n opponent.show_hand()\\n # check end conditions\\n if opponent.has_blackjack() or opponent.has_busted():\\n continue_hand = False\\n continue_bet = False\\n # If stand, it's dealer's turn\\n elif action == settings.ACTION_STAND:\\n dealer_turn = 1 - dealer_turn # pass turn to other opponent\\n round+=1\\n continue_hand = False\\n # Player stands and dealer stands so end bet\\n if round == 4:\\n continue_bet = False\\n else:\\n raise Exception(\\\"Unknown action: '%s'\\\" % action)\\n self.end_bet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd044cf068a3a9a276486c7f13ec9422\",\n \"score\": \"0.6370955\",\n \"text\": \"def start_game(self):\\n print(\\\"\\\"\\\"\\n Welcome to the Hi/Lo game!\\n \\n Guess if the next card will be higher\\n or lower than the last drawn card.\\n If correct, you get 100pts! Guess wrong \\n and you lose 75pts!\\n \\n You lose if you hit, or go below, 0pts.\\n You win if you score 1,000 or more pts.\\n \\n Good luck!\\n \\\"\\\"\\\")\\n while self.keep_playing == True:\\n print(f\\\"The card is : {self.old_card}\\\")\\n self.player.guess()\\n self.draw_card()\\n self.display_card()\\n self.get_points()\\n print(f\\\"Score: {self.points}\\\")\\n self.keep_playing = self.to_play()\\n self.card_update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74aa24f1c77ebf72c7629e86f7d25ca\",\n \"score\": \"0.6350413\",\n \"text\": \"def main():\\n newdeck = deck.Deck()\\n newdeck.shuffle()\\n firstplayer_cards = []\\n secondplayer_cards = []\\n for x in range(5):\\n firstplayer_cards.append(newdeck.dealacard())\\n secondplayer_cards.append(newdeck.dealacard())\\n playing_game(firstplayer_cards, secondplayer_cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a583f2c39d810c3d4c6a06f580b22f\",\n \"score\": \"0.6337075\",\n \"text\": \"def start(self):\\n while self._pyramid[0][0].rank is not None: # if top card is not None\\n self._menu()\\n command = input('\\\\nCommand: ').strip()\\n if self._enter_command(command) is None:\\n system('cls')\\n continue\\n else:\\n system('cls')\\n input('\\\\nGame Over')\\n print(game.instruction())\\n input('Press any key to continue...') # wait user\\n system('cls')\\n self.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293c5613cb568b29524346857329a8d3\",\n \"score\": \"0.6327848\",\n \"text\": \"def reset(self) -> None:\\n\\n self.deck = make_cards()\\n self.player = Player([self.deck.pop(), self.deck.pop()], 0, self.player_name)\\n self.dealer = Dealer([self.deck.pop(), self.deck.pop()])\\n self.player_split = Player([], 0, self.player_name + \\\" hand_two\\\")\\n self.split = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7122669f6b8f76c9b6655a98923e290\",\n \"score\": \"0.63025635\",\n \"text\": \"def start_round(self):\\r\\n self.round_number += 1\\r\\n self.dealer = (self.dealer + 1) % 4\\r\\n self.round = Round(self.round_number, self.dealer)\\r\\n trump_suit = None\\r\\n self.deal()\\r\\n self.round.starting_hands = [player.cards[:] for player in self.players] + [\\r\\n self.kitty.cards[:]\\r\\n ]\\r\\n\\r\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\\r\\n print(\\\"Round %s\\\" % (self.round_number))\\r\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90754c4fcc810d1e8439fabac48bf0d\",\n \"score\": \"0.6301797\",\n \"text\": \"def start_game(self):\\n\\t\\tpass\\n\\t\\t# Welcome the player\\n\\t\\t# Get the player name\\n\\t\\t# Tell the player how to navigate the game\\n\\t\\t\\t# How to access the rules\\n\\t\\t\\t# How to quit at any input \\n\\t\\t# Reveal the blank game board \\n\\t\\t# \\\"Ready to start? (Y)es or (N)o\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f40568fed3de59176404cedb08d0e3a\",\n \"score\": \"0.62817025\",\n \"text\": \"def user_start_play(tower, discard):\\n\\n print('\\\\nNOW IT\\\\'S YOUR TURN!')\\n print('Your Tower', end = ': ')\\n print(tower)\\n\\n top_discard_brick = see_top_brick_discard(discard)\\n print('The top brick on the discard pile is', top_discard_brick)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713dc46b4c49cbc94a8c0af15906bb56\",\n \"score\": \"0.6278261\",\n \"text\": \"def game_reset():\\n global deck, user, computer, draw, swap, insert, sort, declare, discard, showCP, user_name, user_name_rect, computer_name, computer_name_rect\\n global name_text, name_rect, score_text, score_rect, discard_mode, swap_mode, insert_mode, index\\n deck = Deck(2)\\n deck.shuffle_cards()\\n deck.set_joker()\\n user.deal_cards(deck)\\n computer.deal_cards(deck)\\n cardss = deck.draw_card()\\n deck.update_pile(cardss)\\n user.turn = True\\n discard_mode = False\\n swap_mode = False\\n insert_mode = False\\n index = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4235cb633939d598736e432d17bdfc7f\",\n \"score\": \"0.62765485\",\n \"text\": \"def play_turn(game):\\n print('=================================')\\n if game.is_current_player_a_computer():\\n print(game.get_current_player_name() + \\\"'s turn to play\\\")\\n time.sleep(0.5)\\n cards_drawn = game.play_computer_turn()\\n for card in cards_drawn:\\n if card is not None:\\n print(\\\"Hit! Drew card %s\\\" % card)\\n time.sleep(0.5)\\n else:\\n print(\\\"Deck is empty! Finished turn.\\\")\\n if (game.current_player.is_over_21()):\\n print(game.get_current_player_name() + \\\" lost! \\\" + game.get_current_player_name() + \\\" is out.\\\")\\n time.sleep(0.5)\\n else:\\n if game.get_current_player_name != 'Dealer':\\n print(game.get_current_player_name() + \\\" finished with count of \\\" + str(game.get_current_player_hand_count()))\\n time.sleep(0.5)\\n else:\\n print(\\\"Your turn to play! Your count is: %s\\\" % game.get_current_player_hand_count())\\n print(\\\"Your hand is: %s\\\" % game.get_current_player_hand())\\n while game.human_can_draw() and input(\\\"Do you want to hit? [y/N] \\\") is 'y':\\n card = game.human_player_draw()\\n if card is not None:\\n print(\\\"Hit! Delt card %s. Your count is now: %s\\\" % (card, game.get_current_player_hand_count()))\\n print(\\\"Your hand is: %s\\\" % game.get_current_player_hand())\\n else:\\n print(\\\"Deck is empty! Finished turn.\\\")\\n break\\n if game.is_current_player_over_21():\\n print(\\\"You lost! You score is over %s.\\\" % game.max_count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2222c78926dcf1f22dafb55d4be49df5\",\n \"score\": \"0.626647\",\n \"text\": \"def computer_turn(self):\\n print(\\\"You have \\\" + str(len(self.player1.deck)) + \\\" cards\\\")\\n print(self.player2.name + \\\" has \\\" + str(len(self.player2.deck)) + \\\" cards\\\")\\n print(\\\"Here is your top card\\\\n\\\")\\n print_card(self.player1.deck[0])\\n print(\\\"\\\\nYour opponent is choosing a trump stat\\\")\\n rand_num = randint(1, 6)\\n chosen_stat = None\\n\\n if rand_num == 1:\\n chosen_stat = \\\"hp\\\"\\n elif rand_num == 2:\\n chosen_stat = \\\"attack\\\"\\n elif rand_num == 3:\\n chosen_stat = \\\"defense\\\"\\n elif rand_num == 4:\\n chosen_stat = \\\"speed\\\"\\n elif rand_num == 5:\\n chosen_stat = \\\"special attack\\\"\\n else:\\n chosen_stat = \\\"special defense\\\"\\n time.sleep(2)\\n print(\\\"\\\\n Your opponent picked \\\" + chosen_stat + \\\"!\\\\n\\\")\\n\\n self.check_stats(chosen_stat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334b7600b2adc90874380ae9d38902c7\",\n \"score\": \"0.625842\",\n \"text\": \"def new_game():\\n global exposed, deck_cards, state, turns\\n turns = 0\\n # Game state\\n state = 0\\n exposed = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \\n # STEP 1: Model the deck of cards \\n deck_cards = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]\\n correct_cards = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \\n # STEP 3: Shuffle the deck using random.shuffle\\n random.shuffle(deck_cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b10c5fbb5553c5b37d0874b9667ae6\",\n \"score\": \"0.624704\",\n \"text\": \"def __init__(self):\\n self.keep_playing = True\\n self.old_card = r.randint(1,13)\\n self.new_card = 0\\n self.player = Player()\\n self.guess = \\\"\\\"\\n self.points = 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efe4a4c4937f3e8e134bfdda416f06d\",\n \"score\": \"0.62439346\",\n \"text\": \"def reset_game(self):\\n # The player always goes first.\\n self.current_turn = 'player'\\n\\n # Set the current cards to an empty array.\\n self.cards = {\\n 'dealer': [],\\n 'player': []\\n }\\n\\n # Set the totals for each player.\\n self.totals = {\\n 'dealer': 0,\\n 'player': 0\\n }\\n\\n # Create a new new deck of cards.\\n self.deck = self.deck_generator.get_new_deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4315b4a7c52331d868ebcd95500313ae\",\n \"score\": \"0.6240726\",\n \"text\": \"def take_initial_turn(self, player, dealer, deck_list):\\n\\n dist_counter = 0\\n\\n # Distribute first 2 cards to player and dealer\\n while dist_counter < 2:\\n \\n # Take 1 card for player\\n player, deck_list = self.take_1_card(player, deck_list)\\n print(f'{player.name} hand: {[pair[0] for pair in player.hand]}\\\\nCurrent deck len: {len(deck_list)}')\\n \\n # Take 1 card for dealer, showing only the first drawn card\\n dealer, deck_list = self.take_1_card(dealer, deck_list)\\n if dist_counter == 0:\\n print(f'{dealer.name} hand: {[pair[0] for pair in dealer.hand]}\\\\nCurrent deck len: {len(deck_list)}')\\n else:\\n print(f'Current deck len: {len(deck_list)}')\\n\\n dist_counter += 1\\n \\n print('\\\\nEnd of initial distribution.')\\n\\n return player, dealer, deck_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeccbe58a2576153b7edf0796e66999c\",\n \"score\": \"0.62265307\",\n \"text\": \"def black_jack_hand(player1, dealer): \\n #this should be the black_jack hand, while the begging should be setting up a new game\\n dealer.get_all_bets(player1)\\n ##deal two cards each, one to the dealer should be set to face down\\n for x in xrange(1,3):\\n dealer.give_card_to_player(player1)\\n dealer.give_card_to_player(dealer)\\n\\n ###Now the game is ready to start showing the hands of the dealer and the player should be done\\n print \\\"Dealer's Hand\\\"\\n dealer.get_hand().showHand()\\n print \\\"Player's Hand\\\"\\n player1.get_hand().showHand()\\n while(player1.want_card()):\\n dealer.give_card_to_player(player1)\\n print \\\"Players Hand\\\"\\n player1.get_hand().showHand()\\n\\n \\n dealer.flip_card_over()\\n while(dealer.want_card()):\\n dealer.give_card_to_player(dealer)\\n \\n print \\\"Dealers Hand\\\"\\n dealer.get_hand().showHand()\\n\\n dealer.set_winner(player1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a062fdec911de20686003a63b70bb4\",\n \"score\": \"0.6199962\",\n \"text\": \"def play_game(self):\\n\\n # Print the beginning game message.\\n print(\\\"\\\\nBeginning \\\" + str(self))\\n\\n time.sleep(1.5)\\n\\n os.system('cls' if os.name == 'nt' else 'clear')\\n\\n # It is the initial turn.\\n turn = 0\\n\\n # This is the game loop. When the game ends we will exit this loop.\\n has_ended = False\\n while not has_ended:\\n\\n # Increment the turn number.\\n turn += 1\\n\\n # Iterate through each player that is playing.\\n for player_num, player in enumerate(self.config.players):\\n \\n # Set the current player.\\n self.config.set_current_player(player_num)\\n \\n # Before each player's turn, check to see if the game ended.\\n if self.config.supply.check_end_game():\\n has_ended = True\\n break\\n \\n # The player's turn starts here.\\n try:\\n print(f\\\"\\\\n********** Turn #{turn} **********\\\")\\n player.turn(self.config)\\n time.sleep(1.5)\\n os.system('cls' if os.name == 'nt' else 'clear')\\n \\n # Here we catch unexpected errors while playing the game.\\n except DominionError as e:\\n print(\\\"Encountered an error while playing. \\\" + e)\\n return\\n\\n # If anyone quits, exit the game here.\\n except DominionQuitError:\\n print(f\\\"Game quit by {player.name}\\\")\\n return\\n\\n # Once the game has ended, run the end_game method.\\n self.end_game(turn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85dcd6a656f2d43ae2291615c21760e\",\n \"score\": \"0.6190354\",\n \"text\": \"def start_game(self):\\n\\n # Change starting screen to Chess board\\n self.board = Board(square_width=self.view.square_width, \\n square_height=self.view.square_height,\\n is_flipped=self.is_flipped)\\n\\n # Create players\\n self.board.player_list.append('Human')\\n \\n if self.settings['vs_computer']: self.board.player_list.append('AI')\\n else: self.board.player_list.append('Human')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4717ddff3d01a3cc83e1621cdcdf452\",\n \"score\": \"0.6188858\",\n \"text\": \"def playAgain(self):\\n self.game_setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f12d4c17062b04529efa4006ecc7bb\",\n \"score\": \"0.6175251\",\n \"text\": \"def _start_new_game(self):\\n D = DeckGenerator()\\n D.shuffle()\\n T = TableCard(D)\\n T.generate_pyramid()\\n gL = GameLogic(T)\\n gL.level = self._level\\n for item in T.pyramid_deck[-1]:\\n item.status = True\\n return Game(D, T, gL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dcbee6750907da85989d529de59453d\",\n \"score\": \"0.6170448\",\n \"text\": \"def initTurn(self,players,current_player,draw):\\n if self.__oneSide:\\n for player in players:\\n i=0\\n for card in player.getHand():\\n if callable(getattr(player, \\\"getSpecial\\\", None)):\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[\\\"back\\\"])\\n else:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n i+=1\\n i=0\\n for card in player.getDiscard():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n i+=1\\n else:\\n for player in players:\\n i=0\\n for card in player.getHand():\\n if current_player.getId() == player.getId():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[\\\"back\\\"])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],0,-716)\\n else:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],0,716)\\n i+=1\\n i=0\\n for card in player.getDiscard():\\n if current_player.getId() == player.getId():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],0,-358)\\n else :\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],0,358)\\n i+=1\\n if len(draw) == 0:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"draw\\\"],image=\\\"\\\")\\n self.__updateDisplay()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"020fa0581c40ec60e6532db6e2adb650\",\n \"score\": \"0.6161986\",\n \"text\": \"def main():\\n game = poker_game()\\n game.add_player(player_1) #Temp solution to add players to game\\n game.add_player(player_2)\\n game.form_player_hands() #Deal phase one, creating card objects and card_pack \\n game.update_all_hands() #Used after each round, to sort hand + calculate each persons strongest hand\\n game.deal_flop()\\n game.update_all_hands()\\n game.deal_turn()\\n game.update_all_hands()\\n game.deal_river()\\n game.update_all_hands()\\n print(game.player_list[1].hand_value) #Prints hand_value of player at index 1\\n #hand_compare.hand_compare(player_hands, board) --Incomplete\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e986e6106c6e9afad81891509fe2a59\",\n \"score\": \"0.6153859\",\n \"text\": \"def start_game(self):\\n if self.gameover:\\n self.init_game()\\n self.gameover = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff428e6a7a8d16851ada513c3fdd2f4b\",\n \"score\": \"0.61526495\",\n \"text\": \"def run_play_loop(self): \\n player = Player('Player', STARTING_CHIP_NUMBER)\\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\\n self.gui.create_play_screen()\\n self.pause(500)\\n \\n while True:\\n deck = Deck()\\n\\n # Prompt the user to place a wager\\n wager = self.get_wager(player, dealer)\\n\\n # Handle the card selection process in triple pocket holdem\\n # Present the user with his/her options for cards and have him/her select the cards\\n self.select_cards(deck, player, dealer)\\n community_cards = deck.draw_five_community_cards()\\n \\n # Now, reveal the cards held by the user and the dealer, as well as the \\n # five community cards\\n self.gui.explain_card_reveal()\\n self.pause(2000)\\n self.gui.reveal_player_cards(player, dealer)\\n self.pause(2000)\\n self.gui.reveal_common_cards(community_cards)\\n self.pause(5000)\\n\\n # Determine the best hand held by each player and who won the round\\n # Add or subtract chips from the player and dealer's totals depending on the outcome\\n # Output to the user which poker hand the dealer had and what the consequent result was\\n (player_hand, dealer_hand, player_wager_multiple, dealer_wager_multiple) = determine_outcome(\\n player.hands[0], \\n dealer.hands[0], \\n dealer.hands[1], \\n community_cards\\n )\\n player.alter_chip_balance(player_wager_multiple * wager)\\n dealer.alter_chip_balance(dealer_wager_multiple * wager)\\n self.gui.explain_outcome(\\n player_hand, \\n dealer_hand, \\n player_wager_multiple, \\n player.num_chips, \\n dealer.num_chips\\n )\\n self.pause(4000)\\n \\n # Get the next action from the user\\n # If the game has ended (i.e. the user or dealer has run out of chips),\\n # the user can elect to play again. Otherwise, the user can elect to continue with\\n # another round in the current game or to exit\\n next_action = self.get_action_at_round_end(player, dealer)\\n if next_action == MENU_NOW:\\n return next_action\\n elif next_action == PLAY_AGAIN:\\n player = Player('Player', STARTING_CHIP_NUMBER)\\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\\n else:\\n player.clear_hands()\\n dealer.clear_hands()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b5e7b4a39d3ad3dd48208c7ef542b8\",\n \"score\": \"0.614256\",\n \"text\": \"def play(self):\\n # Clear/Reset attributes/values\\n self.winners = []\\n self.count = 0\\n for player in self.players:\\n # Reset hands\\n del(player.cards[:])\\n # Increment number of unique games played\\n player.gamesPlayed += 1.0\\n # Increment total number of games played\\n self.totalGamesPlayed += 1.0\\n # Prepare the deck, deal the cards\\n self.deck = Deck()\\n self.deck.shuffle()\\n self.deck.deal(3, self)\\n # Top card is placed onto discard pile\\n self.discard.add(self.deck.pop())\\n # While no one has satisfied winning conditions, take turns\\n while not self.check_won():\\n self.turn_seq()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad995044d769ec0161ae9924becdf18b\",\n \"score\": \"0.61161804\",\n \"text\": \"def initiate_deck(self):\\n\\n deck = Deck()\\n deck.build_deck()\\n deck.shuffle()\\n self.deck1, self.deck2 = deck.split_deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0706d71c979b7b26af67ecd63b9ff690\",\n \"score\": \"0.61084616\",\n \"text\": \"def reset_hands(self):\\n self.my_player.reset_hand()\\n self.my_computer.reset_hand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7dc1ffc7670027b24ea1494978535af\",\n \"score\": \"0.61073416\",\n \"text\": \"def reset(self, test=None):\\n self.health = self.start_health\\n self.armor = 0\\n self.this_turn_mana = 0\\n self._init_heropower()\\n self._init_deck(self.fix_deck)\\n self.inhands = [] # cards in self hands\\n self.intable = [] # cards on self table\\n self.used_cards = defaultdict(int) # used card counts. key: cidx, value: used counts\\n if self.first_player:\\n self.draw_as_first_player()\\n else:\\n self.draw_as_second_player()\\n if test is not None:\\n self.test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a212e0c2daeb3463048aa45a876849\",\n \"score\": \"0.60954237\",\n \"text\": \"def setup_game():\\n\\t\\n\\tglobal bullet_chamber\\n\\tglobal current_chamber\\n\\tglobal player_turn\\n\\t\\n\\tbullet_chamber = math.ceil(random.random()*CHAMBER_COUNT)\\n\\tcurrent_chamber = 1\\n\\t\\n\\tprint \\\"Gun prepared...\\\"\\n\\t# determine who's first\\n\\tif random.random() >= 0.5:\\n\\t\\tplayer_turn = True\\n\\t\\tprint \\\"Player goes first\\\"\\n\\telse:\\n\\t\\tplayer_turn = False\\n\\t\\tprint \\\"Computer goes first\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4b311a54ba395986edf37e3444a0a1\",\n \"score\": \"0.60823447\",\n \"text\": \"def start_new_game(players):\\n game = SchnapsenGame()\\n game.create_cards()\\n game.shuffle_cards()\\n for player in players:\\n for dummy in range(5):\\n card = game.draw_from_stock()\\n player.add_card(card)\\n return game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c0d6666ca0db5b99f471105ab90a1f\",\n \"score\": \"0.6074004\",\n \"text\": \"def show_next_hand(*args):\\n global twentyfive_cards\\n # disable show_next and enable show_best\\n # show_best25_hand_button[\\\"state\\\"] = \\\"normal\\\"\\n # show_next_hand_button[\\\"state\\\"] = \\\"disabled\\\"\\n\\n window.title(\\\"Play Pyramid Poker\\\")\\n w.delete(\\\"all\\\") # clear out last hand\\n show_twentyfive_cards()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3d9925f75608d29e6f3f04e3b60fe8\",\n \"score\": \"0.60738045\",\n \"text\": \"def start_hand(deck, onCoin):\\n size = 4 if onCoin else 3\\n return [deck.pop() for i in range(size)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc03548cf453e649529ab559af2bfe6\",\n \"score\": \"0.607288\",\n \"text\": \"def initialise_deck(self):\\n deck = Deck()\\n shuffle(deck.card)\\n self.deck = deck\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e5d29991114394fd48b8ffdeb3fb70\",\n \"score\": \"0.60725707\",\n \"text\": \"def main():\\n numHands = eval(input('Enter number of player: '))\\n while (numHands < 2 or numHands > 10):\\n numHands = eval(input('Enter number of hands to play[2~6]: '))\\n game = Poker(numHands)\\n print(\\\"## Game start\\\")\\n game.play()\\n print(\\\"## Caculating hands\\\")\\n print(game.evaluate(game.hands))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4317741c257ecaff2039978f0c82dd3\",\n \"score\": \"0.60713416\",\n \"text\": \"def choose_hand(self):\\n\\n w = random.randint(0, 4)\\n if len(Player.history) > 0:\\n if Player.history[len(Player.history)-1] == \\\"rock\\\":\\n w = 4\\n self.weapon = Weapons.weapon[w]\\n self.turtle.shape(Weapons.computer_images[w])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de83074b480ba3c367ce9ddc04565f9a\",\n \"score\": \"0.60338426\",\n \"text\": \"def start(self):\\n self.start_game()\\n self.init_playing_order()\\n while self.winning_camel is None:\\n self.run_leg()\\n self.report()\\n print(\\\"------------------- End leg ---------------------\\\")\\n self.game_stage = \\\"result\\\"\\n self.losing_camel = self.orders[-1]\\n self.game_scoring_round()\\n self.determine_game_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2270c443ff6f39200566891a90e09b\",\n \"score\": \"0.60226154\",\n \"text\": \"def playUpgrade(self, card, target = None):\\n logging.info(card)\\n active = self.activePlayer.board[\\\"Creature\\\"]\\n inactive = self.inactivePlayer.board[\\\"Creature\\\"]\\n hand = self.activePlayer.hand\\n broken = False\\n \\n if target:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(card)\\n if card in hand:\\n hand.remove(card)\\n side, choice = target\\n if side == \\\"fr\\\":\\n active[choice].upgrade.append(card)\\n else:\\n inactive[choice].upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, side, choice)\\\")\\n self.cardChanged(True) # this would actually make the play abilities I put on blood of titans, a bit redundant, but redundancy is ok\\n return\\n \\n drawMe = []\\n # surfs\\n selectedSurf = Surface((self.target_cardw, self.target_cardh))\\n selectedSurf.convert_alpha()\\n selectedSurf.set_alpha(80)\\n selectedSurf.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n\\n selectedSurfTapped = Surface((self.target_cardh, self.target_cardw))\\n selectedSurfTapped.convert_alpha()\\n selectedSurfTapped.set_alpha(80)\\n selectedSurfTapped.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n # draw all targets\\n for temp_card in active + inactive:\\n if temp_card.ready:\\n drawMe.append((selectedSurf, temp_card.rect))\\n else:\\n drawMe.append((selectedSurfTapped, temp_card.tapped_rect))\\n \\n if self.canDiscard(card, reset = False):\\n ## discard stuff here - if you can play it, you can discard it\\n discSurf = Surface((self.target_cardw, self.target_cardh))\\n discSurf.convert_alpha()\\n discSurf.set_alpha(80)\\n discSurf.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n discRect = discSurf.get_rect()\\n discRect.topleft = self.discard1_rect.topleft\\n drawMe.append((discSurf, discRect))\\n \\n while True:\\n self.extraDraws = drawMe.copy()\\n \\n for e in pygame.event.get():\\n if e.type == MOUSEMOTION:\\n #update mouse position\\n self.mousex, self.mousey = e.pos\\n \\n if e.type == QUIT:\\n pygame.quit()\\n\\n if e.type == MOUSEBUTTONUP and e.button == 1:\\n activeHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in active]\\n inactiveHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in inactive]\\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\\n l = len(hand)\\n for x in range(len(hand)):\\n temp_card = hand[x]\\n if x == 0 and self.mousex < temp_card.rect.centerx:\\n hand.insert(0, self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\\n hand.insert(x + 1, self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n hand.append(self.dragging.pop())\\n self.extraDraws = []\\n return\\n elif True in activeHit:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(self.dragging.pop())\\n choice = activeHit.index(True)\\n target = active[choice]\\n target.upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, target)\\\")\\n broken = True\\n break\\n elif True in inactiveHit:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(self.dragging.pop())\\n choice = inactiveHit.index(True)\\n target = active[choice]\\n target.upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, target)\\\")\\n broken = True\\n break\\n elif self.canDiscard(card, reset=False) and Rect.collidepoint(discRect, (self.mousex, self.mousey)):\\n hand.append(self.dragging.pop())\\n self.discardCard(-1)\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n else:\\n hand.append(self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n\\n if e.type == MOUSEBUTTONDOWN and e.button == 1:\\n logging.error(\\\"You shouldn't be able to trigger MOUSEBUTTONDOWN in dragCard.\\\")\\n\\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\\n l = len(hand)\\n for x in range(len(hand)):\\n temp_card = hand[x]\\n if x == 0 and self.mousex < temp_card.rect.centerx:\\n hand.insert(0, self.invisicard)\\n self.cardChanged()\\n break\\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\\n hand.insert(x + 1, self.invisicard)\\n self.cardChanged()\\n break\\n\\n self.CLOCK.tick(self.FPS)\\n self.hovercard = []\\n self.check_hover()\\n self.draw(False)\\n try:\\n hand.remove(self.invisicard)\\n except:\\n pass\\n pygame.display.flip()\\n self.extraDraws = []\\n if broken:\\n self.cardChanged()\\n break\\n \\n if not target and card.amber > 0:\\n self.activePlayer.gainAmber(card.amber, self)\\n logging.info(f\\\"{card.title} gave you {str(card.amber)} amber. You now have {str(self.activePlayer.amber)} amber.\\\\n\\\\nChange to a log when you fix the amber display issue.\\\"\\\"\\\")\\n if card not in self.playedThisTurn:\\n self.playedThisTurn[card] = 1\\n else:\\n self.playedThisTurn[card] += 1\\n self.cardChanged(True)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09bb9eea918d6798de1ca216dd3a63a\",\n \"score\": \"0.60097355\",\n \"text\": \"def player_start(self):\\n for card in self._target_hand:\\n if card.pip is False:\\n self._dealer.flip_deal(card)\\n return self._target_hand_stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe63242c7be7c36cf950c836bb9a76e8\",\n \"score\": \"0.60043347\",\n \"text\": \"def initialize_game():\\n\\n MIN_SCORE = 0\\n\\n p1_card = create_score_card()\\n p2_card = create_score_card()\\n p1_bonus = MIN_SCORE\\n p2_bonus = MIN_SCORE\\n\\n round_ongoing = True\\n\\n while round_ongoing:\\n if -1 in p1_card.values():\\n print(\\\"-- IT IS PLAYER ONE'S TURN --\\\")\\n take_turn(p1_card, p1_bonus)\\n\\n print(\\\"\\\\n\\\")\\n\\n if -1 in p2_card.values():\\n print(\\\"-- IT IS PLAYER TWO'S TURN --\\\")\\n take_turn(p2_card, p2_bonus)\\n\\n if -1 not in p1_card.values() and -1 not in p2_card.values():\\n round_ongoing = False\\n\\n game_verdict(calculate_score(p1_card), p1_bonus, calculate_score(p2_card), p2_bonus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45c738d403cfef32cc315c30b913debe\",\n \"score\": \"0.6004059\",\n \"text\": \"def __init__(self):\\n self.shoe = deck.Deck(False)\\n self.shuffle_shoe()\\n self.hand = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f4de22f0f5ef385e52aa0594aba3bc\",\n \"score\": \"0.6000726\",\n \"text\": \"def begin_game(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307a4bb97dc9ff3d6387bcf1682a04b0\",\n \"score\": \"0.59878075\",\n \"text\": \"def play(self):\\n # randomly remove a Cards in cards\\n card_picked = choice(self.cards)\\n self.cards.remove(card_picked)\\n\\n # add the Card to the Player's history\\n self.history.append(card_picked)\\n\\n # increment the Player's turn count by 1\\n self.turn_count += 1\\n\\n # decrement the Player's number of cards by 1\\n self.number_of_cards -= 1\\n \\n # if self.number_of_cards == 0:\\n # print(f\\\"{self.name} {self.turn_count} has no card\\\")\\n\\n # display the play\\n print(f\\\"{self.name} {self.turn_count} played: {card_picked}\\\")\\n\\n return card_picked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c3a907ddc1c52dcecd2b011db5a5c4\",\n \"score\": \"0.5981489\",\n \"text\": \"def black_jack():\\n dealer = Dealer()\\n player1 = Player(input(\\\"How much money does the player have?\\\"))\\n want_to_play = 1\\n while(want_to_play == 1):\\n dealer.shuffle_deck()\\n dealer.ready_for_next_hand()\\n player1.ready_for_next_hand()\\n\\n black_jack_hand(player1, dealer)\\n want_to_play = input(\\\"Would you like to play another hand? Select 1 for Yes and 0 for no\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cdc313404535e52d43ab7e2d590aff0\",\n \"score\": \"0.59767616\",\n \"text\": \"def startTurn():\\n\\tglobal current_piece\\n\\tcurrent_piece = 0\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\\n\\tlimbL.move_to_joint_positions(coords.PickU)\\n\\tlimbL.move_to_joint_positions(coords.PickD)\\n\\ti = 0\\n\\twhile current_piece == 0:\\n\\t\\tcurrent_piece = IDBlock(left_image)\\n\\t\\trospy.sleep(5)\\n\\t\\ti+=1\\t\\t\\n\\t\\tif i >= 6:#After 30 seconds of inaction by the human player, Lucas assumes victory\\n\\t\\t\\tprint \\\"Huzzah! Another glorious victory for your robotic overlord!\\\"\\n\\t\\t\\tsys.exit()\\n\\tlimbL.move_to_joint_positions(coords.PickU)\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\\n\\tscanBoard()\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c181062fcadfcfb7154f204b991d32b\",\n \"score\": \"0.5975167\",\n \"text\": \"def play_one_turn(self):\\n #XXX: My laziness knows no bounds. Only allows two players.\\n #XXX: I pass the whole game object through to players. Ugh.\\n \\n if self.current_player == 1:\\n self.p1.new_hand()\\n self.p1.play_one_turn(self)\\n self.current_player = 2\\n else:\\n self.p2.new_hand()\\n self.p2.play_one_turn(self)\\n self.current_player = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5f840201383788c385fab45b96b7da\",\n \"score\": \"0.59595335\",\n \"text\": \"def reset(self, bank=50):\\n self.gm = Game(\\\"P1\\\", \\\"Qtable\\\", \\\"P2\\\", \\\"Qtable\\\", bank)\\n\\n # Random starting money distribution p=0.4\\n if self.rc is not None:\\n if random() < self.rc:\\n left = round(random() * ((bank * 2) - 4))\\n self.gm.sb_player().bank = left + 2\\n self.gm.bb_player().bank = ((bank * 2) - 4) - left + 2\\n\\n self.gm.place_blinds()\\n if self.dc:\\n self.gm.players_draw_cards()\\n # Return observation\\n # [ Small Blind Player, Big Blind Player ]\\n self.ob = [self.gm.sb_player(), self.gm.bb_player()]\\n return self.ob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21166b5cdbe4125442fd133b0444ff07\",\n \"score\": \"0.5957577\",\n \"text\": \"def test_playcard_cost0(self):\\n self.plr.play_card(self.card)\\n self.plr.gain_card(\\\"Copper\\\")\\n self.plr.test_input = [\\\"end phase\\\", \\\"end phase\\\"]\\n self.plr.turn()\\n self.assertNotIn(\\\"Horse\\\", self.plr.piles[Piles.DISCARD])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6436110836fe4d65b16594b43045efd8\",\n \"score\": \"0.59550923\",\n \"text\": \"def play_round(self):\\n playing_players = self.enter_bets()\\n self.give_cards()\\n for player in self.players:\\n if player.name in playing_players:\\n self.play_hand(player)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e9ce3200b27a029258f903fb33497d\",\n \"score\": \"0.5942199\",\n \"text\": \"def play_1(player):\\r\\n playing = True\\r\\n deck = Deck()\\r\\n deck.shuffle()\\r\\n score = 0\\r\\n print(input(\\\"Welcome to the casino, \\\" + player + \\\". Press Enter to play the game.\\\"))\\r\\n while playing:\\r\\n if deck.out():\\r\\n print(\\\"Deck is out of cards. Game over. Your score was \\\", str(score))\\r\\n playing = False\\r\\n else:\\r\\n hand_a = PokerHand()\\r\\n hand_b = PokerHand()\\r\\n for i in range(0, MAX_HAND_SIZE):\\r\\n card = deck.deal()\\r\\n hand_a.add_card(card)\\r\\n for i in range(0, MAX_HAND_SIZE):\\r\\n card = deck.deal()\\r\\n hand_b.add_card(card)\\r\\n print(\\\"Hand #1: \\\", hand_a)\\r\\n print(\\\"Hand #2: \\\", hand_b, \\\"\\\\n\\\")\\r\\n user_answer = int(input(\\\"Which hand is worth more? \\\"\\r\\n \\\"Type 1 if Hand #1, -1 if Hand #2, and 0 if they are worth the same\\\\n \\\"))\\r\\n correct_answer = hand_a.compare_to(hand_b)\\r\\n if user_answer == correct_answer:\\r\\n print(\\\"Correct! Let's try again\\\\n\\\")\\r\\n score += 1\\r\\n else:\\r\\n print(\\\"Game over. Your score was \\\", str(score))\\r\\n playing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2e8b14207df9b875bc1f700b9f25012\",\n \"score\": \"0.5939224\",\n \"text\": \"def start_self_play(self, player, display=False):\\n player.reset_player()\\n self.init_game_state()\\n\\n states, mcts_probs, current_players = [], [], []\\n while True:\\n move, move_probs = player.get_action(self.game_state)\\n\\n if move == PASS_MOVE:\\n move = PASS_MOVE\\n else:\\n move = (move // self.game_state.size, move % self.game_state.size)\\n\\n # store the data\\n states.append(self.game_state.copy())\\n mcts_probs.append(move_probs)\\n current_players.append(self.game_state.current_player)\\n\\n # perform a move\\n is_game_end = self.game_state.do_move(move)\\n if display:\\n self.print_board(self.game_state, str(player), str(player), move_probs)\\n if is_game_end:\\n winner_id = self.game_state.get_winner()\\n # winner from the perspective of the current player of each state\\n winners_z = np.zeros(len(current_players))\\n if winner_id is not None:\\n winners_z[np.array(current_players) == winner_id] = 1.0\\n winners_z[np.array(current_players) != winner_id] = -1.0\\n if display:\\n if winner_id is not None:\\n print(\\n \\\"Game end. Winner is player:\\\",\\n \\\"X\\\" if winner_id == go.BLACK else \\\"O\\\",\\n )\\n else:\\n print(\\\"Game end. Tie\\\")\\n return winner_id, zip(states, mcts_probs, winners_z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d452612ccda9a3382eabefd3e5e2c2\",\n \"score\": \"0.5927983\",\n \"text\": \"def UCTPlaySampleGame():\\n # state = OthelloState(4) # uncomment to play Othello on a square board of the given size\\n # state = OXOState() # uncomment to play OXO\\n # state = NimState(15) # uncomment to play Nim with the given number of starting chips\\n # state = SimpState((5,5)) # uncomment to play Simp with the given number of starting chips\\n state = CapGoState(sz) # uncomment to play Capture Go with the given board size\\n UCTPlayGame(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c125026b6cbd995192ce2d8c656d968\",\n \"score\": \"0.59240186\",\n \"text\": \"def resetGame():\\n del CompHand[:] \\n del UserHand[:]\\n del CompWinningList[:]\\n del UserWinningList[:]\\n del deck[:]\\n deck.append(\\\"Ah\\\")\\n deck.append(\\\"Ad\\\")\\n deck.append(\\\"Ac\\\")\\n deck.append(\\\"As\\\")\\n deck.append(\\\"2h\\\")\\n deck.append(\\\"2d\\\")\\n deck.append(\\\"2c\\\")\\n deck.append(\\\"2s\\\")\\n deck.append(\\\"3h\\\")\\n deck.append(\\\"3d\\\")\\n deck.append(\\\"3k\\\")\\n deck.append(\\\"3s\\\")\\n deck.append(\\\"4h\\\")\\n deck.append(\\\"4d\\\")\\n deck.append(\\\"4c\\\")\\n deck.append(\\\"4s\\\")\\n deck.append(\\\"5h\\\")\\n deck.append(\\\"5d\\\")\\n deck.append(\\\"5c\\\")\\n deck.append(\\\"5s\\\")\\n deck.append(\\\"6h\\\")\\n deck.append(\\\"6d\\\")\\n deck.append(\\\"6c\\\")\\n deck.append(\\\"6s\\\")\\n deck.append(\\\"7h\\\")\\n deck.append(\\\"7d\\\")\\n deck.append(\\\"7c\\\")\\n deck.append(\\\"7s\\\")\\n deck.append(\\\"8h\\\")\\n deck.append(\\\"8d\\\")\\n deck.append(\\\"8c\\\")\\n deck.append(\\\"8s\\\")\\n deck.append(\\\"9h\\\")\\n deck.append(\\\"9c\\\")\\n deck.append(\\\"9d\\\")\\n deck.append(\\\"9s\\\")\\n deck.append(\\\"10h\\\")\\n deck.append(\\\"10d\\\")\\n deck.append(\\\"10c\\\")\\n deck.append(\\\"10s\\\")\\n deck.append(\\\"Jh\\\")\\n deck.append(\\\"Jd\\\")\\n deck.append(\\\"Jc\\\")\\n deck.append(\\\"Js\\\")\\n deck.append(\\\"Qh\\\")\\n deck.append(\\\"Qd\\\")\\n deck.append(\\\"Qc\\\")\\n deck.append(\\\"Qs\\\")\\n deck.append(\\\"Kh\\\")\\n deck.append(\\\"Kd\\\")\\n deck.append(\\\"Kc\\\")\\n deck.append(\\\"Ks\\\") \\n return deck\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55054711f5324eaa84db3999ab4d245c\",\n \"score\": \"0.59172213\",\n \"text\": \"def test_play_no_pickup(self):\\n self.plr.piles[Piles.HAND].set(\\\"Copper\\\", \\\"Copper\\\", \\\"Estate\\\", \\\"Duchy\\\")\\n self.plr.piles[Piles.DECK].set(\\\"Gold\\\", \\\"Silver\\\", \\\"Copper\\\", \\\"Copper\\\")\\n self.plr.add_card(self.card, Piles.HAND)\\n self.plr.test_input = [\\n \\\"Discard\\\",\\n \\\"Discard Estate\\\",\\n \\\"Discard Duchy\\\",\\n \\\"Finish\\\",\\n \\\"nothing\\\",\\n ]\\n self.plr.play_card(self.card)\\n self.assertEqual(self.plr.coins.get(), 2)\\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 4 + 1 - 2)\\n self.assertNotIn(\\\"Duchy\\\", self.plr.piles[Piles.HAND])\\n self.assertNotIn(\\\"Silver\\\", self.plr.piles[Piles.HAND])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18317543fe0e81636342952f64a9f20\",\n \"score\": \"0.5912951\",\n \"text\": \"def start_game():\\n\\tprint intro_msg\\n\\tplayer_name = raw_input(\\\"What' do they call you son ? \\\")\\n\\tend = False\\t\\t\\n\\twhile not end :\\n\\t\\tchoice,door = show_options()\\n\\t\\tif choice == 'yes':\\n\\t\\t\\t#Enter the door chosen\\n\\t\\t\\tproceed = proceed_in_door(door)\\n\\t\\t\\tif proceed:\\n\\t\\t\\t\\tprint \\\"Proceeding....\\\"\\n\\t\\t\\t\\ttime.sleep(3)\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tcontinue\\t\\n\\t\\t\\telse :\\n\\t\\t\\t\\tend = True\\n\\n\\t\\telif choice == 'no' :\\n\\t\\t\\t#Show the random waiting message\\n\\t\\t\\tprint random.randint(0,len(waiting_messages))\\n\\t\\t\\ttime.sleep(5) #wait\\n\\n\\t\\telif choice == 'info':\\n\\t\\t\\t#shows the player info\\n\\t\\t\\tshow_player_info()\\n\\t\\telse :\\n\\t\\t\\tprint \\\"Whaaaaaaaat the heck is that ?, Just YES or no\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754749c503bb5ff4f5d8ff50d7501951\",\n \"score\": \"0.5910271\",\n \"text\": \"def start_game(self, name):\\n\\t\\tself.guesses = ''\\n\\t\\tself.incorrect = 0\\n\\t\\tself.correct = 0\\n\\t\\tself.gameover = False\\n\\t\\tself.won = False\\n\\t\\tself.username = name\\n\\n\\t\\tword_list = ['3dhubs', 'marvin', 'print', 'filament', 'order', 'layer']\\n\\t\\tself.target = word_list[random.randint(0, 5)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c861602d872a650894343f6703dbbb\",\n \"score\": \"0.5908629\",\n \"text\": \"def start(self):\\n # <<-- Creer-Merge: start -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\\n # replace with your start logic\\n self.game_data = GameData()\\n\\n self.random = random.random()\\n # <<-- /Creer-Merge: start -->>\\n\\n self.game_data.humans = [human for human in self.player.units if human.job.title == 'fresh human']\\n\\n self.first_run = True\\n self.no_income = True\\n self.harvester_returning = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9805fd2876acedb227ef8bbf31c53e4\",\n \"score\": \"0.59040797\",\n \"text\": \"def reset_game():\\n global tmp_score, current_player\\n for data in players:\\n data[1] = 0\\n tmp_score = 0\\n current_player = 0 # 'the user always starts'\\n cls()\\n start_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a582deca9da8cb98d30740d848ec22b\",\n \"score\": \"0.5902361\",\n \"text\": \"def startGame(self):\\n while True:\\n whiteType = input('Welcome to King-Queen Endgame Simulatior \\\\nWould you like a (1) Computer or (2) Human to play as White (the attacker with a queen and a king)?\\\\n')\\n if whiteType == (1 or 'computer' or 'Computer'):\\n whiteType = 'comp'\\n break\\n elif whiteType == (2 or 'human' or 'Human'):\\n whiteType = 'human'\\n break\\n else:\\n print 'Please input 1 or 2.'\\n while True:\\n blackType = input('Would you like a (1) Computer or (2) Human to play as Black (the defender with a king)?\\\\n')\\n if blackType == 1 or blackType=='computer' or blackType=='Computer':\\n blackType = 'comp'\\n break\\n elif blackType == 2 or blackType=='human' or blackType=='Human':\\n blackType = 'human'\\n break\\n else:\\n print 'Please input 1 or 2.'\\n self.setBoard('Q')\\n print b\\n self.playGame(whiteType, blackType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c413ae6f91a0d0648f1e7f342387a287\",\n \"score\": \"0.58975714\",\n \"text\": \"def _start_game(self):\\n\\t\\tself.settings.initialize_dynamic_settings()\\n\\n\\t\\t# Reset the game statistics\\n\\t\\tself.stats.reset_stats()\\n\\t\\tself.stats.game_active = True\\n\\t\\tself.sb.prep_score()\\n\\t\\tself.sb.prep_level()\\n\\t\\tself.sb.prep_strikers()\\n\\n\\t\\t# Get rid of any remaining kids and candy.\\n# \\t\\tself.kids.empty()\\n# \\t\\tself.candies.empty()\\n\\n\\t\\t# Hide the mouse cursor.\\n\\t\\tpygame.mouse.set_visible(False)\\n\\n\\t\\tpygame.mixer.music.set_endevent(self.SONG_END)\\n\\t\\tpygame.mixer.music.load(self.song_fn)\\n\\t\\tpygame.mixer.music.play()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f3a2bfdeb59a4fb079e12aaccf6ffa\",\n \"score\": \"0.5896248\",\n \"text\": \"def create_random(cls, players, deck='52-card'):\\n\\n current_deck = Card.get_shuffled_deck(size=int(deck[:2]))\\n\\n bottom_card = current_deck[0]\\n\\n player_hands = {}\\n\\n for uid, name in players:\\n player_hands[uid] = set(current_deck.pop() for _ in xrange(6))\\n\\n if deck[-4:] == 'fast':\\n current_deck = current_deck[:len(players)]\\n\\n ordered_players = list(players)\\n urandom_shuffle_inplace(ordered_players)\\n\\n cls._choose_first_player(bottom_card.suit, ordered_players, player_hands)\\n\\n return GameState(ordered_players, player_hands, [], current_deck, [], bottom_card)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6db0fb483345ac87b4cf31144f780c\",\n \"score\": \"0.58839583\",\n \"text\": \"def initialize_trump(self) -> None:\\r\\n input(\\\"Time to decide trump. Pass the computer to \\\" + self.first_player.name)\\r\\n euchre_utils.clear_screen()\\r\\n starting = self.players.index(self.first_player)\\r\\n i=starting\\r\\n\\r\\n self.add_card(self.deck.get_card())\\r\\n \\r\\n while i <= starting+7:\\r\\n player = self.players[i%4]\\r\\n self.print_status_update(player)\\r\\n face_up_card: Card = self.cards_on_table[0]\\r\\n\\r\\n if i <= starting+3: #first time around\\r\\n answer = euchre_utils.get_yes_no(\\\"Do you want the dealer\\\"+\\r\\n \\\" to pick up the card on the table and that card to\\\" +\\r\\n \\\" become trump?\\\".format(player.name))\\r\\n if answer:\\r\\n #alone_answer = euchre_utils.get_yes_no(\\\"Do you want to go alone?\\\")\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nOK, Announce that {} is trump. Pass the computer to the dealer({})\\\".format(\\r\\n face_up_card.suit, self.dealer.name))\\r\\n euchre_utils.clear_screen()\\r\\n self.dealer.new_card(face_up_card)\\r\\n self.clear_cards_on_table()\\r\\n self.trump = face_up_card.suit\\r\\n self.player_that_called_suit = player\\r\\n \\r\\n #dealer chooses which cards to remove\\r\\n self.dealer.sort_hand(self.trump)\\r\\n self.print_status_update(self.dealer)\\r\\n print(\\\"Dealer, {} told you to pick it up. \\\".format(player.name)+\\r\\n \\\"Which card in your hand do you want to remove?\\\")\\r\\n self.dealer.remove_card(self.dealer.select_card(self.dealer.hand))\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"Pass the computer to {} to start the round\\\".format(self.first_player.name))\\r\\n euchre_utils.clear_screen()\\r\\n break\\r\\n else: \\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nPass the computer to {}\\\".format(self.players[(i+1)%4].name))\\r\\n euchre_utils.clear_screen()\\r\\n \\r\\n else: #second time around, choose which suit to be trump\\r\\n response = euchre_utils.get_answer_no(\\\"Do you want to select the trump suit?\\\"+\\r\\n \\\" If so, say which. You're playing 'stick the dealer'\\\\n\\\", \\\"diamonds|hearts|spades|clubs\\\")\\r\\n if response and response != face_up_card.suit: #if they say an availible suit\\r\\n print(\\\"OK, {} is Trump. Pass the computer to {} to start the round\\\".format(\\r\\n response, self.first_player.name))\\r\\n self.trump = response\\r\\n self.player_that_called_suit = player\\r\\n self.clear_cards_on_table()\\r\\n break\\r\\n elif response == face_up_card.suit:\\r\\n input(\\\"Sorry, you can't choose the suit that was initially face up. Try again\\\")\\r\\n i-=1 # loop around to the same player again to give another chance\\r\\n elif i == starting+7: #STICK THE DEALER\\r\\n #this if clause needs to come after the first two to take lower precedence\\r\\n input(\\\"You're playing 'stick the dealer', so the dealer has to choose a suit.\\\")\\r\\n i-=1\\r\\n else: #empty string, they said no\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nOK, pass the computer to {}\\\".format(self.players[(i+1)%4].name))\\r\\n euchre_utils.clear_screen()\\r\\n \\r\\n euchre_utils.clear_screen()\\r\\n i+=1\\r\\n euchre_utils.clear_screen()\\r\\n #sort everybodys hands according to trump\\r\\n [player.sort_hand(self.trump) for player in self.players]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25edc669fdded7b1f82cd0c502985d64\",\n \"score\": \"0.5880057\",\n \"text\": \"def make_step(self, action=\\\"reset\\\"):\\n reward = 0\\n if not self.active and action != \\\"reset\\\":\\n print(\\\"You specified an action, but no active game is being played. Please reset the game using action='reset'.\\\")\\n elif self.active and action == \\\"reset\\\":\\n print(\\\"WARNING! You specified action='reset' although the last game has not ended, yet.\\\")\\n elif action == \\\"reset\\\":\\n self.reset_game()\\n if self.player_sum == 21:\\n self.active = False\\n if self.verbose:\\n print(\\\"Player has Blackjack!\\\")\\n print(\\\"The dealer's cards are:\\\", [int(x) for x in self.dealer_cards])\\n if np.sum(self.dealer_cards == 1) == 1 and np.sum(self.dealer_cards == 10) == 1:\\n if self.verbose:\\n print(\\\"Dealer has Blackjack, too!\\\")\\n print(\\\"DRAW!\\\")\\n else:\\n if self.verbose:\\n print(\\\"PLAYER WINS!\\\")\\n reward = 1\\n elif action == \\\"hit\\\":\\n new_card = self.draw_cards(size=1)\\n if self.verbose:\\n print(\\\"Player draws card:\\\", new_card)\\n self.player_sum += new_card\\n if self.player_sum > 21:\\n if self.player_has_usable_ace:\\n if self.verbose:\\n print(\\\"Player converts a usable ace (11) into 1.\\\")\\n self.player_cards[self.player_cards == 11] = 1\\n self.player_has_usable_ace = False\\n self.player_sum -= 10\\n if self.player_sum > 21:\\n # Player goes bust\\n if self.verbose:\\n print(\\\"Player goes BUST!\\\")\\n self.active = False\\n reward = -1\\n else:\\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\\n if self.verbose:\\n print(\\\"New sum of player's cards:\\\", self.player_sum)\\n reward = 0\\n elif action == \\\"stick\\\":\\n self.active = False\\n if self.verbose:\\n print(\\\"The dealer's cards are:\\\", [int(x) for x in self.dealer_cards])\\n print(\\\"The dealer has\\\", np.sum(self.dealer_cards), \\\"points.\\\")\\n # Let dealer play\\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\\n if self.verbose:\\n print(\\\"Dealer converts 1 into 11\\\")\\n self.dealer_sum += 10\\n self.dealer_cards[self.dealer_cards == 1] = 11\\n while self.dealer_sum < 17:\\n new_card = self.draw_cards(size=1)\\n if self.verbose:\\n print(\\\"Dealer draws card:\\\", new_card)\\n self.dealer_cards = np.append(self.dealer_cards, new_card)\\n self.dealer_sum += new_card\\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\\n if self.verbose:\\n print(\\\"Dealer converts 1 into 11\\\")\\n self.dealer_sum += 10\\n self.dealer_cards[np.where(self.dealer_cards == 1)[0]] = 11\\n if self.verbose:\\n print(\\\"New dealer sum\\\", self.dealer_sum)\\n\\n # Check whether dealer goes bust.\\n dealer_difference = 21 - self.dealer_sum\\n if dealer_difference < 0:\\n if self.verbose:\\n print(\\\"Dealer goes BUST!\\\")\\n reward = 1\\n else:\\n # Compare player to dealer\\n player_difference = 21 - self.player_sum\\n if player_difference == dealer_difference:\\n if self.verbose:\\n print(\\\"DRAW!\\\")\\n reward = 0\\n elif player_difference < dealer_difference:\\n if self.verbose:\\n print(\\\"PLAYER WINS!\\\")\\n reward = 1\\n else:\\n if self.verbose:\\n print(\\\"DEALER WINS!\\\")\\n reward = -1\\n else:\\n raise ValueError(\\\"'action' has to be either 'stick' or 'hit' when gameover is False.\\\")\\n\\n if not self.active:\\n self.state = np.array([-1, -1, -1])\\n\\n return copy.deepcopy(self.state), reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5ed06f45caf7e937b1b9ad3a3a65f4\",\n \"score\": \"0.5879681\",\n \"text\": \"def test_others_playing(self):\\n while True:\\n card = self.g[\\\"Clashes\\\"].remove()\\n if card.name == \\\"Warlord\\\":\\n break\\n self.plr.add_card(card, Piles.HAND)\\n self.plr.play_card(card)\\n mil = self.g[\\\"Militia\\\"].remove()\\n self.oth.add_card(mil, Piles.HAND)\\n self.oth.piles[Piles.PLAYED].set(\\\"Militia\\\", \\\"Militia\\\", \\\"Copper\\\")\\n self.oth.play_card(mil)\\n self.g.print_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2063217bde657141e0f5d1728c6fe9fb\",\n \"score\": \"0.5867167\",\n \"text\": \"def playCard(self, chosen: int, played_from: str = \\\"Hand\\\", cheat: bool = False, flank = \\\"Right\\\", ask = True):\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n if played_from == \\\"Deck\\\":\\n source = self.activePlayer.deck\\n elif played_from == \\\"Discard\\\":\\n source = self.activePlayer.discard\\n elif played_from == \\\"Mimicry\\\":\\n source = self.inactivePlayer.discard\\n else:\\n source = self.activePlayer.hand\\n card = source[chosen]\\n if not self.canPlay(card, cheat = cheat):\\n return\\n # canPlay() makes sure that you can't try to play a card you're not allowed to play\\n # Increases amber, adds the card to the action section of the board, then calls the card's play function\\n if card.amber > 0:\\n self.activePlayer.gainAmber(card.amber, self)\\n logging.info(f\\\"{source[chosen].title} gave {self.activePlayer.name} {card.amber} amber. {self.activePlayer.name} now has {self.activePlayer.amber} amber.\\\")\\n if ask:\\n if card.type == \\\"Creature\\\" and len(self.activePlayer.board[\\\"Creature\\\"]) > 0:\\n flank = self.chooseFlank(card)\\n # left flank\\n if card.type != \\\"Upgrade\\\" and flank == \\\"Left\\\":\\n self.activePlayer.board[card.type].insert(0, source.pop(chosen))\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n # default case: right flank\\n elif card.type != \\\"Upgrade\\\" and played_from != \\\"Mimicry\\\":\\n self.activePlayer.board[card.type].append(source.pop(chosen))\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n elif played_from == \\\"Mimicry\\\":\\n self.activePlayer.board[card.type].append(source[chosen])\\n logging.info(f\\\"Mimicry branch.\\\")\\n else:\\n targeted = self.chooseCards(\\\"Creature\\\", \\\"Choose a creature to attach the upgrade to:\\\")\\n if targeted:\\n targeted = targeted[0]\\n else:\\n return\\n self.playUpgrade(card, targeted)\\n self.cardChanged()\\n return\\n #once the card has been added, then we trigger any play effects (eg smaaash will target himself if played on an empty board), use stored new position\\n if card not in self.playedThisTurn:\\n self.playedThisTurn[card] = 1\\n else:\\n self.playedThisTurn[card] += 1\\n self.cardChanged(True) # definitely need to recalc power here, in case we play something next to a staunch knight so now it is dead\\n self.draw()\\n pygame.display.update()\\n card.play(self, card)\\n logging.info(f\\\"{card.title} play ability resolved.\\\")\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n # if the card is an action, now add it to the discard pile - remote access or poltergeist or nexus on masterplan can potentially play cards that belong to the other player\\n if card.type == \\\"Action\\\" and played_from != \\\"Mimicry\\\":\\n if card.title == \\\"library_access\\\":\\n if card not in self.activePlayer.board[\\\"Action\\\"]:\\n return\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n if card not in self.activePlayer.board[\\\"Action\\\"]:\\n return\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.discard.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.discard.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n elif played_from == \\\"Mimicry\\\":\\n if card.title == \\\"library_access\\\":\\n purge_mimic = True\\n self.activePlayer.board[\\\"Action\\\"].pop()\\n logging.info(f\\\"Removing Mimicried card.\\\")\\n if purge_mimic:\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n self.cardChanged(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430302c7890fa57ebc3b3eed949f5b6c\",\n \"score\": \"0.5861373\",\n \"text\": \"def new_game():\\n \\n # Initialize global variable that will hold the \\\"deck\\n # of cards\\\"; we model the \\\"deck of cards\\\" as a list \\n # consisting of 16 numbers with each number lying in \\n # the range [0,8) and appearing twice. The list is\\n # created by concatenating two lists with range [0,8) \\n # together. Although Player can play the game with\\n # \\\"textual numbers\\\" or \\\"images\\\", the above mentioned \\n # technique is being used modeling the game in both\\n # game \\\"modes\\\". \\n global deck_of_cards\\n deck_of_cards = range(CARDS_NUMBER // 2) + range(CARDS_NUMBER // 2)\\n # Shuffle the \\\"deck\\\".\\n random.shuffle(deck_of_cards)\\n # Remove comment if in DEBUG mode.\\n #print deck_of_cards\\n \\n # Initialize global variable that will hold the a list,\\n # with size equal to the size of the \\\"deck of cards\\\"\\n # consisting of boolean values. The boolean value\\n # at a certain list index indicates whether the \\\"card\\\"\\n # is \\\"exposed\\\" (True) or not (False). Particularly,\\n # the ith entry should be \\\"True\\\" if the ith card is \\n # face up and its value is visible or \\\"False\\\" if the \\n # ith card is face down and it's value is hidden. \\n global deck_of_cards_exposed\\n deck_of_cards_exposed = [False] * CARDS_NUMBER\\n \\n # Initialize global variable that will hold the game\\n # state (0,1 and 2), i.e. beginning of the game, single \\n # \\\"exposed\\\" unpaired \\\"card\\\" and end of a \\\"turn\\\" \\n # respectively (have a look at the comments of \\n # \\\"mouseclick()\\\" for a detailed description \\n # concerning this variable).\\n global state\\n state = 0 \\n \\n # Initialize global variable that will hold the number\\n # of \\\"turns\\\" playing the game.\\n global turn \\n turn = 0\\n label.set_text(\\\"Turns = \\\" + str(turn))\\n \\n # Initialize global variable that will hold a \\\"helper\\\"\\n # list, keeping the index of the cards \\\"exposed\\\" in \\n # a single \\\"turn\\\". \\n global index_of_cards_exposed_in_a_turn \\n index_of_cards_exposed_in_a_turn = [-1, -1]\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bac313e3373abca83de4f803c9c470\",\n \"score\": \"0.5860306\",\n \"text\": \"def play(deck, verbose):\\r\\n\\r\\n\\t#generate first 2 'visible' cards from deck\\r\\n\\tvisible = [deck.pop(0), deck.pop(0)]\\r\\n\\t\\r\\n\\twhile len(visible) <= 9: #game over if there are more than 9 'piles'\\r\\n\\t\\t\\t\\r\\n\\t\\twhile add_to_11(visible) or jqk(visible):\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t\\tif add_to_11(visible):\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\t#avoid popping from empty list:\\r\\n\\t\\t\\t\\tif len(deck) >= 2:\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tfor a in add_to_11(visible):\\r\\n\\t\\t\\t\\t\\t\\tvisible[a] = deck.pop(0) #'cover' cards\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\telse: \\r\\n\\t\\t\\t\\t\\treturn 0 \\r\\n\\t\\t\\t\\t\\t#if len(deck) < 2 and 2 cards need to be covered then player wins\\r\\n\\t\\r\\n\\t\\t\\tif jqk(visible):\\r\\n\\t\\r\\n\\t\\t\\t\\t#avoid popping from empty list:\\r\\n\\t\\t\\t\\tif len(deck) >= 3 : \\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tfor j in jqk(visible):\\r\\n\\t\\t\\t\\t\\t\\tvisible[j] = deck.pop(0)\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\treturn 0\\r\\n\\t\\r\\n\\t\\tif len(visible) <= 9 and len(deck) > 0: \\r\\n\\t\\t# no cards can be covered but game not over:\\r\\n\\t\\r\\n\\t\\t\\tvisible.append(deck.pop(0)) #add new pile\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\t\\r\\n\\t\\tif len(visible) > 9 or len(deck) == 0: \\r\\n\\t\\t\\tbreak #once there are 10 piles/deck empty game is over\\r\\n\\t\\r\\n\\treturn(len(deck))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95982ee89a1979bb0988195bad9f0f4\",\n \"score\": \"0.58343667\",\n \"text\": \"def reset_game(self):\\n if self.verbose:\\n print(\\\"The game is reset.\\\")\\n self.active = True\\n self.player_cards = []\\n self.player_sum = 0\\n self.player_has_usable_ace = False\\n while self.player_sum < 12:\\n new_card = self.draw_cards(size=1)\\n if new_card == 1 and self.player_sum < 11:\\n new_card = 11\\n self.player_has_usable_ace = True\\n self.player_cards = np.append(self.player_cards, new_card)\\n self.player_sum += new_card\\n\\n self.dealer_cards = self.draw_cards(size=2)\\n self.dealer_sum = np.sum(self.dealer_cards)\\n self.dealers_showing_card = self.dealer_cards[1]\\n\\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\\n if self.verbose:\\n print(\\\"Player's cards:\\\", [int(x) for x in self.player_cards]) # , \\\"; player sum:\\\", self.player_sum[0]\\n print(\\\"Dealer's showing card:\\\", [self.dealer_cards[1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6159fbc53dc2432a972ab889872f9fc7\",\n \"score\": \"0.5832458\",\n \"text\": \"def start_turn(self, character):\\n character.db.combat_actionsleft = ACTIONS_PER_TURN # Replenish actions\\n # Prompt the character for their turn and give some information.\\n character.msg(\\\"|wIt's your turn! You have %i HP remaining.|n\\\" % character.db.hp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53efab2740a1c1c957c7cc649f014514\",\n \"score\": \"0.5830657\",\n \"text\": \"def initial(self, state):\\n\\n self.player_number = min(state.turn_order, 0) % self.size\\n self.current_player = self.players[self.player_number]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d948450c72319e3b6261eb24a0525aa5\",\n \"score\": \"0.581744\",\n \"text\": \"def play(self):\\n print('Welcome to Blackjack v1.0')\\n # give dealer 2 cards\\n # give player 2 cards\\n # player goes first in the gameplay, dealer hits until the player stays\\n # dealer then goes\\n # if player wins, they double their bet\\n\\n # if both the player and dealer are the same value, it's called a PUSH and\\n # no money changes hands.\\n\\n # it's a 1:1 payout in blackjack, if you put down 10, you win another 10.\\n\\n # if you hit BLACKJACK! - 21, then you will get a 3:1 payout, so for putting down\\n # 10, you get 15.\\n\\n while True:\\n\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ef2eb3926070baf2d966906da516c8\",\n \"score\": \"0.5817214\",\n \"text\": \"def shuffle_deck(self):\\n self.__deck = Deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dece00dfe6866bb147e30f9a31ea4d1c\",\n \"score\": \"0.58131605\",\n \"text\": \"def select_cards(self, deck, player, dealer):\\n self.gui.show_hand_select_instructions()\\n self.pause(2000)\\n \\n first_hand = deck.draw_two_card_hand()\\n second_hand = deck.draw_two_card_hand()\\n third_hand = deck.draw_two_card_hand()\\n \\n self.gui.show_first_hand(first_hand[0].img_path, first_hand[1].img_path)\\n if self.picked_up_cards():\\n player.add_hand(first_hand)\\n dealer.add_hand(second_hand)\\n dealer.add_hand(third_hand)\\n else:\\n dealer.add_hand(first_hand)\\n self.gui.show_second_hand(second_hand[0].img_path, second_hand[1].img_path)\\n\\n if self.picked_up_cards():\\n player.add_hand(second_hand)\\n dealer.add_hand(third_hand)\\n else:\\n dealer.add_hand(second_hand)\\n player.add_hand(third_hand)\\n self.gui.alert_to_third_hand()\\n self.pause(1500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2589a760be7715f758b3933ef4b784ee\",\n \"score\": \"0.58054376\",\n \"text\": \"def game(self) -> None:\\n print(RULES) \\n print(f\\\"You have been credited with ${self.start_amount}\\\")\\n\\n # main game loop, loop infinitely until\\n # player decides to quit or player looses\\n # all his/her money\\n while True: \\n if self.start_amount < 1:\\n print(\\\"You've run out of money\\\")\\n print(\\\"Good thing you weren't using real money.\\\")\\n print(\\\"Try to seek professional help for your gambling problems.\\\")\\n sys.exit() #player is broke, exit game \\n\\n print(f\\\"Money available: ${self.start_amount}\\\") \\n\\n # Get the amount player wants to stake.\\n amt_to_bet = self.entity_bet(self.player, self.start_amount)\\n print(f\\\"Player bet ${amt_to_bet}\\\")\\n\\n # Display player scores\\n self.display_scores()\\n\\n # play for both player and player's second\\n # hand if he does split\\n self.entity_play(self.player)\\n self.entity_play(self.player_split)\\n\\n # after player busts or (St)ands\\n # check if dealer needs to play\\n self.check_if_dealer_plays()\\n\\n # check which hand - player main or second hand\\n # if existent- won.\\n self.check_who_wins(self.player)\\n self.check_who_wins(self.player_split)\\n\\n # start all over again\\n input(\\\"Press Enter to continue...\\\")\\n print('\\\\n')\\n\\n # reset stats.\\n self.reset()\\n # clear console\\n os.system('cls') if sys.platform == \\\"win32\\\" else os.system('clear')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35cf530c7c6f7b8e383a4fe612d5570d\",\n \"score\": \"0.58013165\",\n \"text\": \"def __init__(self, deck=None, p1_class=Player, p2_class=Player):\\n #XXX: Currently defaults to playing with VP and Money only.\\n self.card_set = 'trivial'\\n self.current_player = 1\\n self.banked_cards = copy.deepcopy(cards.CARD_SETS[self.card_set])\\n\\n # Variables for game stats\\n self.turn_num = 1\\n self.winning_player = []\\n self.player_vp = []\\n\\n initial_deck = deck or cards.STARTING_DECK\\n self.p1 = p1_class(initial_deck)\\n self.p2 = p2_class(initial_deck)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0e423f621be5257e0f20a164254566\",\n \"score\": \"0.5795721\",\n \"text\": \"def main():\\n\\n print(\\\"Welcome to BlackJack!!!\\\")\\n\\n # Ask for number of players\\n n_players = get_num_players()\\n\\n # Initialize the game\\n human_name = 'You'\\n game = Game(n_players, human_name=human_name)\\n\\n while (True):\\n # Deal\\n print_deal(game.deal())\\n\\n # Play one turn per player\\n while game.can_play():\\n play_turn(game)\\n game.next_player()\\n\\n # Output results\\n print_score(game, *game.get_winners())\\n if (input(\\\"Play again? [Y/n] \\\") is 'n'):\\n break\\n\\n # End the game\\n print(\\\"Thanks for playing! Have a great day!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b353cb03f1abea549580a71817cbcb\",\n \"score\": \"0.57908016\",\n \"text\": \"def play(self):\\n self._shuffle()\\n self._deal()\\n self._display_all()\\n results = {}\\n for player in self.player_list:\\n score = player.play_hand()\\n results[f'{player.type} {player.name}'] = score\\n print('Dealer flips over second card...')\\n print(self._dealer.hand)\\n dealer_score = self._dealer.play_hand()\\n print('\\\\n---------------------------------\\\\n')\\n for name, score in results.items():\\n if score == 0:\\n print(f'{name} lost')\\n elif score > dealer_score:\\n print(f'{name} wins')\\n elif score == dealer_score:\\n print(f'{name} pushes')\\n else:\\n print(f'{name} lost')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64492e3d35024b21d7f7db18a48eadff\",\n \"score\": \"0.57886285\",\n \"text\": \"def draw_initial_cards(self):\\n\\n # Draw cards for player and dealer.\\n for current in ('dealer', 'player'):\\n for i in range(0, 2):\\n card = self.draw_card()\\n self.cards[current].append(card)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3cba110962d7ea7079e68e96fc17b49\",\n \"score\": \"0.5776046\",\n \"text\": \"def main():\\n game = WarGame()\\n game.deal()\\n while not game.winner():\\n game.step()\\n print(game)\\n print(game.winner())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad22698074c27bd70352a6b42bde555\",\n \"score\": \"0.57631284\",\n \"text\": \"def test_Engine__insure_zero(engine, hand):\\n player = players.BetterPlayer(name='Eric', chips=100)\\n player.bet = 20\\n player.hands = (cards.Hand(),)\\n engine.dealer.hands = (hand,)\\n engine._insure(player)\\n assert player.insured == 0\\n assert player.chips == 100\\n assert engine.ui.mock_calls == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b156f8f72b972997f3e5d63cee14036e\",\n \"score\": \"0.5762807\",\n \"text\": \"def pollPlay(self, state):\\n hand = self.player.getHand()\\n if not len(hand) == 0:\\n cardOfChoice = random.choice(hand)\\n # returns the choosen card here\\n return cardOfChoice\\n print(\\\"Oops, you are trying to play a card from an empty hand\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7976e1729bf6638650ad8b2cd77acfb2\",\n \"score\": \"0.5760314\",\n \"text\": \"def start(self):\\n Screen.clear()\\n print('STARTING NEW GAME')\\n print('ENTERING MAIN GAME STATE IN A MOMENT...')\\n self.pause(pause_time=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39032af7fa0996e1f761e776ab5a59b6\",\n \"score\": \"0.5759432\",\n \"text\": \"def drawPlayerCard(self):\\n player = self.level.players[0]\\n card = self.level.playerDeck.draw()\\n if card is not None:\\n controller = PlayerDrawController(player, card)\\n controller.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4bb3a70ea09d37ce93a834cc0900ec8\",\n \"score\": \"0.57525396\",\n \"text\": \"def resetHand(self):\\n self.hand = Hand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b29b99ebb0ebc7579986cd9ef088010b\",\n \"score\": \"0.5751541\",\n \"text\": \"def prep(self, hand):\\r\\n self.hand = hand\\r\\n active_count = 0\\r\\n\\r\\n for player in self.players:\\r\\n if player.stack >= self.BLINDS[1]:\\r\\n player.activate()\\r\\n active_count += 1\\r\\n else:\\r\\n player.deactivate()\\r\\n \\r\\n if active_count < 2:\\r\\n c = \\\"Table needs at least 2 active players for a game.\\\"\\r\\n raise StructureError(c)\\r\\n\\r\\n if self.button == None:\\r\\n self.button = 0\\r\\n else:\\r\\n self.button += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2114aa08ab856de7ecb735cbc2255963\",\n \"score\": \"0.574097\",\n \"text\": \"def start_game(self):\\n self.round = 0\\n self.drawing_player = None\\n self.score = {}\\n\\n for client_id in self.clients:\\n self.score[client_id] = 0\\n\\n self.notify_all('game_starting')\\n self.next_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fdb99a447976b8d96b16eef1d320019\",\n \"score\": \"0.5738883\",\n \"text\": \"def PlayRound(self):\\n with self._lock:\\n if self._active_round:\\n logging.error('HypeJack game already active.')\\n return\\n bets = self._core.bets.LookupBets(\\n self.name, resolver=self._core.name.lower())\\n if not bets:\\n logging.error('Attempted to start HypeJack with no players.')\\n return\\n\\n self._pending_start = False\\n\\n # Shuffle the deck when it gets low. We assume a reasonable number of\\n # cards needed per player, but with lots of splits / low cards we may\\n # still run out of cards to play the hand.\\n if len(self._shoe) < (len(self._peeps) + 1) * 7:\\n self._ShuffleCards()\\n\\n # Deal cards to plebs.\\n for user_id, user_bets in bets.items():\\n hand = Hand(user_bets[0], self._shoe.pop(), self._shoe.pop())\\n self._peeps[user_id] = [hand]\\n self._DisplayUser(user_bets[0].user)\\n\\n # Deal cards to hypebot.\\n self._dealer_hand = Hand(None, self._shoe.pop(), self._shoe.pop())\\n # self._dealer_hand = Hand(playing_cards_lib.Card('Hearts', 8),\\n # playing_cards_lib.Card('Spades', 8))\\n self._msg_fn(None, 'Dealer: [%s, %s]' % (self._dealer_hand.cards[0], '🂠'))\\n\\n self._active_round = True\\n\\n # Short-circuit game play if the dealer has a hypejack or if all peeps\\n # have hypejacks.\\n if not self._dealer_hand.IsHypeJack() and any(\\n [self._IsActive(user_id) for user_id in self._peeps.keys()]):\\n # Force the round to end after some time if some peep ran away. Waiting\\n # on a condition releases the lock while waiting, then reacquires it\\n # automatically. Will shortcircuit if notified when all peeps have\\n # finished their hands.\\n self._game_ender.wait(timeout=self.MAX_ROUND_LENGTH)\\n\\n # Complete dealer hand.\\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\\n while self._dealer_hand.Score() < 17:\\n self._dealer_hand.cards.append(self._shoe.pop())\\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\\n\\n self._core.bets.SettleBets(self, self._core.name.lower(), self._msg_fn)\\n\\n # Reset game state.\\n self._peeps = {}\\n self._active_round = False\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":796,"cells":{"query_id":{"kind":"string","value":"878430aedb1cf2faa0d7a646f80b93dd"},"query":{"kind":"string","value":"Returns True if `n` is a prime number. Uses a sieve based approach."},"positive_passages":{"kind":"list like","value":[{"docid":"f6562e93978739d735e33a2f03e547ef","score":"0.82913023","text":"def is_prime(n):\n if n < 2:\n return False\n ps = primes()\n p = next(ps)\n while p * p <= n:\n if n % p == 0:\n return False\n p = next(ps)\n return True","title":""}],"string":"[\n {\n \"docid\": \"f6562e93978739d735e33a2f03e547ef\",\n \"score\": \"0.82913023\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n ps = primes()\\n p = next(ps)\\n while p * p <= n:\\n if n % p == 0:\\n return False\\n p = next(ps)\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"78a25bc4f1590805de217f19646d793b","score":"0.8519759","text":"def isPrime(n):\n\tif n > 1:\n\t\tsqrtN = int(math.sqrt(n))\n\t\tfor i in xrange(2, sqrtN + 1):\n\t\t\tif n % i == 0:\n\t\t\t\treturn False\n\t\treturn True\n\telse:\n\t\treturn False","title":""},{"docid":"dc7a4b3b514e80626aae9ddf8b01d248","score":"0.85149896","text":"def is_prime(n):\n return divisors_count(n) == 2","title":""},{"docid":"8552f164b83d92b64ee1a20078ac78f5","score":"0.85020345","text":"def is_prime(n):\n n=int(n)\n if n <= 1:\n return False\n else:\n val = round(m.sqrt(n))\n for i in range(2, val + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"270a8e44a4956d1698003b4d5c40e13c","score":"0.8487371","text":"def isPrime(n):\n primes=getPrimesLT(n)\n return n in primes","title":""},{"docid":"ba5d05a02c5d60a51d738a646aff60ba","score":"0.8479271","text":"def is_prime(self, n: int) -> bool:\n assert isinstance(n, int) and n >= 2, \"n is an integer greater than 1\"\n\n s = math.sqrt(n)\n for p in self.first_1e6_prime:\n if p <= s and n % p == 0:\n return False\n\n while p <= s:\n p += 2\n if n % p == 0:\n return False\n\n return True","title":""},{"docid":"b19d63e9d42c2b218f79ec6c0d98fd6f","score":"0.84770465","text":"def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n < 2:\n return False\n for x in xrange(2, int(n ** 0.5) + 1):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"477eda70153ff8b8c7ccaf2fc536d8b1","score":"0.8416152","text":"def IsPrime(n):\n if n%2 == 0:\n return False\n else:\n prime = True\n for j in range(2,int(np.sqrt(n))+1):\n if n%j == 0:\n prime = False\n break\n return prime","title":""},{"docid":"901a0f3a40b40d64ffc53c9ea7bf1c30","score":"0.84116584","text":"def isprime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n for i in range(2, int(math.ceil(math.sqrt(n)))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"dfeec13bc07ab476d32355abbc4c85d4","score":"0.8396946","text":"def prime(n):\n\tif n <= 1:\n\t\treturn False\n\tfor i in range(2, int(sqrt(n) + 1)):\n\t\tif n % i == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"36a9b3e3ec56f716139c2690f52c09f0","score":"0.8389916","text":"def isPrime(n):\n result = False;\n if isinstance(n, int) == False:\n raise TypeError(\"Input is not an Integer.\");\n elif n <= 0:\n raise ValueError(\"Input is zero or less.\");\n elif n < 2:\n result = False;\n elif n == 2:\n result = True;\n else:\n for i in range(2, n):\n if i == n:\n return;\n elif n % i == 0:\n result = False;\n break;\n else:\n result = True;\n\n return result;","title":""},{"docid":"50906bb1d2d6ef7da5820bf7438979a9","score":"0.8389429","text":"def isprime(n):\r\n prime_ind = False\r\n init_primes = [\r\n 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,\r\n 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137,\r\n 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199\r\n ]\r\n\r\n if n in init_primes:\r\n prime_ind = True\r\n\r\n elif (n<=1 or n%2==0 or n%3==0 or n%5==0):\r\n prime_ind = False\r\n\r\n else:\r\n # Determine upper limit of test range.\r\n ulimit = (int(math.ceil(math.sqrt(n)))+1)\r\n prime_ind = not any(n%k==0 for k in range(3, ulimit, 2))\r\n return(prime_ind)","title":""},{"docid":"60a114091880ba14ed9eca16e709d998","score":"0.83860743","text":"def is_prime(n):\n\tfor num in range(2, floor(sqrt(n)) + 1):\n\t\tif n % num == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"8d62ce4c046658b6480d22f00130fb9f","score":"0.8377889","text":"def is_prime(n):\n\n\t# Find the square root of n.\n\n\tp = int(math.sqrt(n))\n\n\twhile p > 1:\n\t\tif n % p == 0:\n\t\t\treturn 0\n\n\t\tp = p - 1\n\n\treturn 1","title":""},{"docid":"56dadae9af134db9fa497ca8a6ff4e8b","score":"0.8371619","text":"def is_prime(n):\n if n in (0, 1):\n return False\n for i in range(2, n):\n\tif n % i == 0:\n\t return False\n return True","title":""},{"docid":"7635e87c98a226c663071d70fe5328cb","score":"0.83457136","text":"def is_prime(n):\n for i in range(2,n):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"3c2d0c0ed09c36e073b03e7a086dc47b","score":"0.83377486","text":"def is_prime(n):\n\tfor i in range(2,int(abs(n)**0.5)+1):\n\t\tif n%i == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"856b5803e82eb6c5d40330c721906474","score":"0.8323594","text":"def prime_test(n):\n if n < 2:\n return False\n for number in islice(count(2), int(sqrt(n)-1)):\n if not n % number:\n return False\n return True","title":""},{"docid":"589b2f8b21d0d8b67d7c6e53a4057c7e","score":"0.83179694","text":"def is_prime(n):\n \n if n == 2: return True\n if n % 2 == 0: \n return False\n root_n = int(math.sqrt(n))+1\n up_to_root_n = root_n\n quotients = map(lambda divisor: n % divisor, \n range(3, up_to_root_n))\n n_is_evenly_divisible = 0 in quotients\n return False if n_is_evenly_divisible else True","title":""},{"docid":"272d0471d12b21e6b69f97f48baac25a","score":"0.83177346","text":"def is_prime(n: int) -> bool:\n result: bool = True\n\n if n <= 1 or (n % 2 == 0 and n > 2) or (n % 3 == 0 and n > 3):\n result = False\n\n if n == 2:\n result = True\n\n for x in range(3, int(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n result = False\n\n return result","title":""},{"docid":"dd2265a5869ef4466755a84a4f2bdbe7","score":"0.8312044","text":"def is_prime(n):\n if n == 1:\n return False\n\n if n == 2:\n return True\n\n if n % 2 == 0:\n return False\n\n for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):\n if n % i == 0:\n return False\n\n return True","title":""},{"docid":"74aa430d379576b0fc6af3d55d5f6748","score":"0.8284265","text":"def is_prime(n):","title":""},{"docid":"0fed4b5a70448034b4da8242726ede89","score":"0.82800364","text":"def is_prime(n):\n from math import sqrt\n\n # Primes starts on 2\n if n <= 1: return False\n # Caso especial da porra\n if n == 2: return True\n # Numeros pares > 2 nao sao primos\n if n % 2 == 0: return False\n\n square_root = int(sqrt(n))\n for m in xrange(3, square_root + 1, 2): # lista de nums impares ate sqrt\n if n % m == 0:\n return False\n return True","title":""},{"docid":"5f6716ef495b945079b2a2119c7894cf","score":"0.82623875","text":"def is_prime(n):\n if n < 2:\n return False\n else:\n for x in range(2,n):\n if(n % x == 0):\n return False\n return True","title":""},{"docid":"511006c76add766235762394700bec07","score":"0.8257107","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 0 or n == 1 or n % 2 == 0:\n return False\n for i in range(3, int(math.sqrt(n))+1, 2):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"79fb7fa454db5d1e238880467ff4ddee","score":"0.82552886","text":"def is_prime(n):\n\n if n <= 1:\n return False # 0 and 1 are prime numbers\n if n == 2:\n return True # 2 is a prime number\n if n % 2 == 0:\n return False # even numbers greater than 2 are not prime\n # For every integer 1 < i < n, if n is divisible by i,\n # then n is not prime. We only need to check from 3 to\n # sqrt(n), because sqrt(n) is the second largest factor of n,\n # and we can increase i by 2 at every iteration, since we know\n # that n is not even\n limit = math.floor( math.sqrt(n) ) + 1\n for i in range(3, limit, 2):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"2afa11d6ef7e50b2a833a1cf51f971e6","score":"0.82412285","text":"def is_prime(n):\n if n <= 0:\n return False\n if n == 1:\n return False\n if n == 2:\n return True\n for m in range (2, n): # We only have to check until n/2 \n if n % m == 0:\n return False\n return True","title":""},{"docid":"422b20b7c441b1037d7b6a7cc0e0ae1b","score":"0.8240911","text":"def is_prime(n):\n if n<2:\n return False\n\n i=int(n**0.5)\n\n if has_divisor_smaller_than(n, i) == False:\n return True\n return False","title":""},{"docid":"f6e0478c7ea72acda213a781f826bddd","score":"0.82399696","text":"def is_prime(n: int) -> bool: # not solved\n if n == 2 or n == 3:\n return True\n if n % 2 == 0 or n % 3 == 0 or n == 1:\n return False\n else:\n for i in range(2, int(n**.5) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"725ba95a76ce6d82f495fe8d1132f873","score":"0.82367325","text":"def isprime(n):\n # make sure n is a positive integer\n n = abs(int(n))\n # 0 and 1 are not primes\n if n < 2:\n return False\n # 2 is the only even prime number\n if n == 2:\n return True\n # all other even numbers are not primes\n if not n & 1:\n return False\n # range starts with 3 and only needs to go up the square root of n\n # for all odd numbers\n for x in range(3, int(n**0.5)+1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"fc31081f55ec0723a789771bb6d30774","score":"0.82352555","text":"def is_prime(n):\n if n==1:\n return False\n elif n==2:\n return True\n else:\n for i in range(2,n):\n if(n % i==0):\n return False\n return True","title":""},{"docid":"07348f41c7be602f12b436214ff72d1a","score":"0.82299316","text":"def isprime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"08cee8ee7adddb537a8b7d371a94a6ce","score":"0.8216398","text":"def prime(n):\n if n == 2:\n return True\n elif n < 2 or n % 2 == 0:\n return False\n else:\n return not any(n % x == 0\n for x in range(3, math.ceil(math.sqrt(n)) + 1, 2))","title":""},{"docid":"a2ce1f52c9105aa1973c3712ae2bb925","score":"0.82160985","text":"def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"a2ce1f52c9105aa1973c3712ae2bb925","score":"0.82160985","text":"def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"f0820534545c1052093724d0be6a8cb0","score":"0.82139057","text":"def isprime(n):\n if n == 2: return True\n if n == 3: return True\n if n % 2 == 0: return False\n if n % 3 == 0: return False\n\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"09f7f61a6383ab12105f728487bdd534","score":"0.82113105","text":"def isprime(n):\n if n % 2 == 0 and n > 2:\n return False\n for i in range(3, n):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"d0cccf3620b1352e5c60bf0346de7b01","score":"0.82056844","text":"def is_prime(n):\n if n <= 1:\n return False\n m = 2\n while m <= sqrt(n):\n if n % m == 0:\n return False\n m += 1\n return True","title":""},{"docid":"635525dbae85557bdc340c8b9b10edc4","score":"0.8201573","text":"def is_prime(n):\n\n for i in range(2, int(n**(.5))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"41ac96a2995b84c5582172f7c60743c7","score":"0.81952757","text":"def is_prime(n):\n if n < 2: return False\n if n % 2 == 0: return n == 2\n d = int(math.sqrt(n))\n for k in range(3, d+1, 2):\n if n % k == 0: return False\n return True","title":""},{"docid":"d7bafae931b71a704e193fea70413010","score":"0.8193707","text":"def is_prime(n):\n if n < 2:\n return False\n for i in range(2, int(n**0.5) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"af516e3c8b4214cefaaeb5e242c9e5de","score":"0.8186336","text":"def is_prime(n):\r\n end = int(sqrt(n))\r\n for i in range(2, end + 1):\r\n if n % i == 0:\r\n return False\r\n return True","title":""},{"docid":"c278d5413913dd21598d0be7b8b5beeb","score":"0.81617874","text":"def is_prime(n):\n if n < 2:\n return False\n\n if n == 2:\n return True\n \n if n % 2 == 0:\n return False\n \n for i in range(3, n):\n if n % i == 0:\n return False\n \n return True","title":""},{"docid":"bc24c2eda47040b95d6de2a18c0f451a","score":"0.81556326","text":"def is_prime(n):\n for i in range(2,int(abs(n)**0.5)+1):\n if(n%i == 0):\n return False\n return True","title":""},{"docid":"1b4342cdcfc8e1b7e350391ff6fdd457","score":"0.8154981","text":"def is_prime(n):\n if n < 2:\n return False\n i = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i = i + 1\n return True","title":""},{"docid":"0e4c66c8caa96007d8a7ed30223725ce","score":"0.81473047","text":"def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"0e4c66c8caa96007d8a7ed30223725ce","score":"0.81473047","text":"def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"78c57cc85d45fb203d479ef719ae28e9","score":"0.81402576","text":"def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n % 2 == 0:\n return False\n for x in range(3, int(n**0.5)+1,2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"0718b32ecb5751a279a2c58ec2ec1cf7","score":"0.8138055","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"54ae235b788cc942b94ba04ca81c3c9d","score":"0.8134483","text":"def is_prime(n):\n for i in range(2, int(n ** 0.5) + 1):\n if not n % i:\n return False\n return True","title":""},{"docid":"6809b1cd33dd1d0ebe49e0e30145952f","score":"0.8124355","text":"def is_prime(n):\n prime = True # assume prime until proven otherwise\n\n for i in range(2, int(sqrt(n) + 1)):\n if n % i == 0: # factor found -> not prime\n prime = False\n break\n\n return prime","title":""},{"docid":"a1e711a66dbcaeb4c4f5cd3fef7cac5c","score":"0.81242764","text":"def is_prime_v2(n):\n if n == 1:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(2, 1 + max_divisor):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"5d81ef705192336ab08758c7cb15039d","score":"0.81232667","text":"def isPrime(n):\n\n if n == 0 or n == 1:\n return False\n\n for div in range(2, int(n**0.5)+1):\n if not n % div:\n return False\n return True","title":""},{"docid":"148222020cd78bf2d22c5e4326000805","score":"0.81167865","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"4c5a33abd4d265953025486942673076","score":"0.81161934","text":"def isPrime(n):\n\tif n <= 1: return False\n\telif n < 4: return True #2, 3\n\telif n % 2 == 0: return False\n\telif n < 9: return True #5, 7\n\telif n % 3 == 0: return False\n\telse:\n\t\t#make use of the fact that all primes > 5 have have the form 6k +- 1\n\t\tr = math.floor(math.sqrt(n))\n\t\tf = 5\n\t\twhile f <= r:\n\t\t\tif n % f == 0: return False\n\t\t\tif n % (f + 2) == 0: return False\n\t\t\tf += 6\n\t\treturn True","title":""},{"docid":"18968f383dac5e1e0b0a60d0bb0988ba","score":"0.80963194","text":"def is_prime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n d = 2\n while d <= int(math.sqrt(n)) + 1:\n if n % d == 0:\n return False\n d += 1\n return True","title":""},{"docid":"40d0e26c0162295a4d5cec18cca267db","score":"0.8092706","text":"def isprime(n):\n return len(factors(n)) == 2","title":""},{"docid":"bda83ee0a8ba13b9b662c98c048c8736","score":"0.80838317","text":"def isprime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"6f088fa3e414e6a8725d83eb028394b2","score":"0.8081018","text":"def is_prime_v1(n):\n if n == 1:\n return False\n\n for d in range(2, n):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"ae0faea9835cf4d2c6ce4970efa17b8f","score":"0.8067031","text":"def is_prime(n):\n def divisible_by_known_prime(n):\n for x in known_primes:\n if divides(x, n):\n return x\n return False\n\n # Quick filters\n if n < 2:\n return False\n elif n in known_primes:\n return True\n elif divisible_by_known_prime(n):\n return False\n else:\n # Start searching again at the max known prime\n start = known_primes[-1] + 2\n for y in xrange(start, int(sqrt(n)), 2):\n if divisible_by_known_prime(y):\n continue\n else:\n if n % y == 0:\n return False\n known_primes.append(y)\n return True","title":""},{"docid":"464021a33de3106c49185f714bf98982","score":"0.8051832","text":"def test_prime(n):\n if n==2:\n return True\n #we only need to check divisors up to square root of a number\n for i in range(2,int(n**(.5))+1):\n if n % i ==0:\n return False\n else:\n return True","title":""},{"docid":"4c6829a46657473e9890c8e4a7c737a3","score":"0.8046635","text":"def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True","title":""},{"docid":"4c6829a46657473e9890c8e4a7c737a3","score":"0.8046635","text":"def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True","title":""},{"docid":"e993486b8ff4626cd7a77a6f4541959e","score":"0.80411047","text":"def is_prime_number(n:int) -> bool:\r\n\r\n i = 0\r\n is_prime = True\r\n\r\n if (n == 2 or n == 3):\r\n return True\r\n while(i < len(_prime_list)):\r\n if (not n % _prime_list[i] and is_prime):\r\n is_prime = False\r\n return is_prime\r\n i += 1\r\n if (is_prime):\r\n _prime_list.append(n)\r\n return is_prime","title":""},{"docid":"bc04899d0ad229677ff3b7c4f040ac5d","score":"0.8024999","text":"def isPrime(n):\n Dmax = scipy.sqrt(n)\n if n == 2:\n return True\n if isEven(n):\n return False\n d = 3\n while n%d != 0 and d <= Dmax:\n d += 2\n return d > Dmax","title":""},{"docid":"b5a485022c94922bb7f2ae2c0922016c","score":"0.7937437","text":"def is_prime(n):\n test_vals = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n if n in test_vals:\n return True\n d = n - 1\n s = 0\n while not d & 1:\n d = d >> 1\n s += 1\n for a in test_vals:\n for r in range(0, s):\n if (a ** (d * (1 << r))) % n != (n - 1) \\\n and (a ** d) % n != 1:\n return False\n return True","title":""},{"docid":"7a6588f6a60f59af54b5b30212aef90f","score":"0.78840125","text":"def is_prime(n):\n if n < 2 :\n return False\n\n # OFF BY ONE Error, solution is to add +1 \n #for i in range (2, int(math.sqrt(n)) + 1 ):\n for i in range (2, int(math.sqrt(n)) ):\n if n % i == 0 :\n return False\n return True","title":""},{"docid":"8c240bfae6683bf9bd96ef3272a3f9b7","score":"0.78501946","text":"def prime_number(n):\n\n if n > 1:\n\n for divisor in range(2,n):\n\n if n%divisor == 0:\n #if n and divisor cannot complete full division, n is not a \n #prime number\n\n return False\n\n return True\n\n else:\n #negative number is not prime number\n\n return False","title":""},{"docid":"9168442a99017fb7004b5deea6142afb","score":"0.7763757","text":"def is_prime(n):\n \n if n == 2:\n return True\n if n < 2 or n % 2 == 0:\n return False\n \n trail_factor = 3\n \n root = sqrt(n)\n \n while trail_factor <= root:\n if n % trail_factor == 0:\n return False\n trail_factor += 2 \n \n return True","title":""},{"docid":"e931ff2a59cbd8806ee6f819d4efc108","score":"0.77405703","text":"def is_prime(n):\n def prime_helper(index):\n if index == n:\n return True\n elif n % index == 0 or n == 1:\n return False\n else:\n return prime_helper(index + 1)\n return prime_helper(2)","title":""},{"docid":"1ad782a50c1962258b23b6d5a2dfd97c","score":"0.7739098","text":"def primality(n):\n if n == 1:\n return NOT_PRIME\n return PRIME if all(n % i for i in range(2, int(sqrt(n)) + 1)) else NOT_PRIME","title":""},{"docid":"2440dc2a1445e687d40f229c33daa7cd","score":"0.77390164","text":"def isprime(n):\r\n if n % 3 == 0:\r\n return (False, 3)\r\n\r\n i = 5\r\n w = 2\r\n\r\n while i * i <= n:\r\n if n % i == 0:\r\n return (False, i)\r\n\r\n i += w\r\n w = 6 - w\r\n\r\n return (True, None)","title":""},{"docid":"1373ba4fdce84a62e9becedccf9c9fad","score":"0.7735379","text":"def is_prime(n):\n prime = True\n d = 2\n while prime and d <= round(n**(1/2)):\n if n%d != 0:\n d += 1\n else:\n prime = False\n return prime","title":""},{"docid":"5fb53f51042964c6e86ff53aeb84cd0f","score":"0.76981086","text":"def is_prime_sieve(x):\n\n if (x <= 1):\n return False\n elif (x <= 3):\n return True\n x_mod_6 = x % 6\n if ((x_mod_6 != 1) and (x_mod_6 != 5)):\n return False\n\n sqrt_x = int_sqrt(x)\n for i in generate_primes(sqrt_x):\n if ((x % i) == 0):\n return False\n return True","title":""},{"docid":"ebc0590b9895849fc3e3f49891941a7f","score":"0.7671711","text":"def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in (\n (100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in xrange(t):\n a = smallprimes[i]\n y = pow(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = pow(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True","title":""},{"docid":"07e51bd8d669673e6777f4dbb96d36b8","score":"0.76641953","text":"def is_prime(n):\n\n # Checks if given a list...\n if type(n) == list:\n if_prime_list = [False, ] * len(n)\n c = 0\n for i in n:\n if is_prime(i):\n if_prime_list[c] = True\n c += 1\n # and returns one with weather or not it's prime in place\n return if_prime_list\n\n # When a single number ..\n\n if n < 2:\n return False\n if n == 2 or n == 3:\n return True\n\n for i in range(2, int(sqrt(n))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"b167f7b47ee5341083441f6a31a32272","score":"0.7655478","text":"def isPrime(n):\n\n if n % 2 == 0:\n return n == 2\n d = 3\n while d * d <= n and n % d != 0:\n d += 2\n return d * d > n","title":""},{"docid":"0be316dbe44f91fe2b2e34bac2ceba31","score":"0.7647462","text":"def is_prime_cacheless(n):\n if n < 2:\n return False\n for i in xrange(2, int(math.sqrt(n)) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"5809882f1b0d4c24bf26aadb26e9c4cb","score":"0.7642497","text":"def is_superprime(n):\n while n > 0:\n if not is_prime(n):\n return False\n n = n // 10\n return True","title":""},{"docid":"6f9804bea1d4da046f3722898d4b15ae","score":"0.7615486","text":"def is_prime_exact(n):\n if n <= 3:\n return n >= 2\n if n % 2 == 0 or n % 3 == 0:\n return False\n for i in range(5, int(n ** 0.5) + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"5259c60cb0e66026bd62a7cf1ba6f28e","score":"0.76105756","text":"def is_prime(number: int) -> bool:\n prop_div = proper_divisors(number)\n return len(prop_div) == 1","title":""},{"docid":"2e333a6a531610d72ac921f35578efc7","score":"0.76068914","text":"def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"2e333a6a531610d72ac921f35578efc7","score":"0.76068914","text":"def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"bd441534aae1f3e8f4a3213bf868fdb0","score":"0.75916487","text":"def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in ((100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in range(t):\n a = smallprimes[i]\n y = modular_exp(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = modular_exp(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True","title":""},{"docid":"31c7eb72160691d2a58b025131a986ac","score":"0.758809","text":"def is_prime(n):\n r = 0\n d = n - 1\n while d % 2 == 0:\n r += 1\n d //= 2\n\n for _ in range(40):\n a = random.randint(2, n - 2)\n x = pow(a, d, n)\n if x == 1 or x == n - 1:\n continue\n \n for _ in range(r - 1):\n x = pow(x, 2, n)\n if x == n - 1:\n break\n\n else:\n return False\n\n return True","title":""},{"docid":"0b537c6a55e7ef96df0d686053d637c4","score":"0.75501156","text":"def is_prime(i, primes):\n pass","title":""},{"docid":"1fbbb15cd7dd7e9b4d3490d51da7e317","score":"0.74986815","text":"def is_prime(number):\n for i in range(2, number):\n if number % i == 0:\n return False\n\n return True","title":""},{"docid":"d3e14a0482f5920322f17e25db1e4d4f","score":"0.7495828","text":"def is_prime(n, prime_numbers = []):\n if n < 2:\n return False\n\n if len(prime_numbers) == 0 or prime_numbers[-1] < n:\n prime_numbers = _generate_primes(n + 1, prime_numbers)\n\n for p in prime_numbers:\n if n < p:\n return False\n if n == p:\n return True\n\n return False","title":""},{"docid":"83d11c11d34ac275d0984b8b705ea571","score":"0.7481891","text":"def is_prime(number):\r\n if number <= 0:\r\n return False\r\n elif number > 2:\r\n for i in range(2, number):\r\n if number % i == 0:\r\n return False\r\n return True","title":""},{"docid":"2bbe1ee06cee20708acd22de4008554f","score":"0.7473216","text":"def prime(n):\n # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\n sieve = np.ones(int(n/3 + (n%6==2)), dtype=np.bool)\n sieve[0] = False\n for i in range(int(n**0.5/3+1)):\n if sieve[i]:\n k=3*i+1|1\n sieve[ int((k*k)/3) ::2*k] = False\n sieve[int((k*k+4*k-2*k*(i&1))/3)::2*k] = False\n return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]","title":""},{"docid":"589dda1ed65ef55d34a2407a75760f86","score":"0.74541706","text":"def is_prime(num):\r\n for n in range(2, num):\r\n if num % n == 0:\r\n return False\r\n return True","title":""},{"docid":"557caac191fadc8d79ddadae1a0b768c","score":"0.74398","text":"def _is_prime(number):\n if number <= 1:\n return False\n\n divisor = 2\n while divisor < number:\n if ((number % divisor) == 0) or (divisor > (number / 2)):\n return False\n divisor += 1\n return True","title":""},{"docid":"07f0a11ff3c7fc1e8870986532afedf0","score":"0.7438905","text":"def is_prime(number: int) -> bool:\n min_divisor = 2\n max_divisor = math.ceil(math.sqrt(number))\n for divisor in range(min_divisor, max_divisor + 1):\n if number % divisor == 0:\n return False\n return True","title":""},{"docid":"0b7752fe5a1cf97814e82aa201face01","score":"0.7425838","text":"def probable_prime(n):\n return pow(2, n-1, n) == 1","title":""},{"docid":"89e87a69f7b9cd088605a16cdbb01c27","score":"0.74213374","text":"def is_prime_v1(n):\n if n == 1:\n return False # 1 is not prime (1 is not composite, either)\n # d stands for divisor. Since any number greater than 1 (prime or composite) can be exactly divided by 1 and itself,\n # we skip 1 and n and only iterate over the items in the sequence from 2 through n - 1 (Remember in the range()\n # function the stop value is not included.)\n for d in range(2, n):\n # % is the modulus operator. The right-hand side is the remainder of the operation of division of n by d.\n # If n is divisible by d, the remainder is equal to 0; if not, the remainder is not equal to 0.\n # So the test_expression of n % d == 0 means n is divisible by d. If the test_expression is True, control of the\n # program flows to the body of if and execute the return statement. The function exits and meanwhile the boolean\n # value of False is returned.\n if n % d == 0:\n return False\n # If each items in the sequence generated from the range doesn't make the test_expression True, meaning n is not\n # divisible by any smaller number, then we can consider n is prime and return True after the items in the sequence\n # exhaust without the for loop being stopped halfway.\n return True","title":""},{"docid":"460b8ab60da7496aa79fef6e6731ca9f","score":"0.74172294","text":"def isPrime(p):\n\n return True","title":""},{"docid":"6248ff0357b4b512961650451529bc9c","score":"0.7411656","text":"def is_prime(number: int) -> bool:\n\n if 1 < number < 4:\n # 2 and 3 are primes\n return True\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\n return False\n\n # All primes number are in format of 6k +/- 1\n for i in range(5, int(math.sqrt(number) + 1), 6):\n if number % i == 0 or number % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"6d50325cb8c5ccefa49472a84ce23913","score":"0.7410716","text":"def is_prime(number):\n if number <= 1:\n return False\n if number == 2:\n return True\n else:\n maximum = int(math.sqrt(number)) + 1\n for i in range(2, maximum):\n if number % i == 0:\n return False\n return True","title":""},{"docid":"b2b3903b7ed71035196f37c9778c5e81","score":"0.74050426","text":"def check_prime(number):\n if number in primes:\n return True\n return False","title":""},{"docid":"1a3617d3ebdd114f5eeefb2a8c559fad","score":"0.73954546","text":"def primes(n):\n sieve = [True] * n\n for i in range(3,int(n**0.5)+1,2): #in py3, range and xrange are same\n if sieve[i]:\n sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)\n return [2] + [i for i in range(3,n,2) if sieve[i]]","title":""},{"docid":"9d7593a29f9a46b123950c8a508ec279","score":"0.7380684","text":"def is_prime(number: int) -> bool:\r\n\r\n if 1 < number < 4:\r\n # 2 and 3 are primes\r\n return True\r\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\r\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\r\n return False\r\n\r\n # All primes number are in format of 6k +/- 1\r\n for i in range(5, int(math.sqrt(number) + 1), 6):\r\n if number % i == 0 or number % (i + 2) == 0:\r\n return False\r\n return True","title":""}],"string":"[\n {\n \"docid\": \"78a25bc4f1590805de217f19646d793b\",\n \"score\": \"0.8519759\",\n \"text\": \"def isPrime(n):\\n\\tif n > 1:\\n\\t\\tsqrtN = int(math.sqrt(n))\\n\\t\\tfor i in xrange(2, sqrtN + 1):\\n\\t\\t\\tif n % i == 0:\\n\\t\\t\\t\\treturn False\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc7a4b3b514e80626aae9ddf8b01d248\",\n \"score\": \"0.85149896\",\n \"text\": \"def is_prime(n):\\n return divisors_count(n) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8552f164b83d92b64ee1a20078ac78f5\",\n \"score\": \"0.85020345\",\n \"text\": \"def is_prime(n):\\n n=int(n)\\n if n <= 1:\\n return False\\n else:\\n val = round(m.sqrt(n))\\n for i in range(2, val + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270a8e44a4956d1698003b4d5c40e13c\",\n \"score\": \"0.8487371\",\n \"text\": \"def isPrime(n):\\n primes=getPrimesLT(n)\\n return n in primes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5d05a02c5d60a51d738a646aff60ba\",\n \"score\": \"0.8479271\",\n \"text\": \"def is_prime(self, n: int) -> bool:\\n assert isinstance(n, int) and n >= 2, \\\"n is an integer greater than 1\\\"\\n\\n s = math.sqrt(n)\\n for p in self.first_1e6_prime:\\n if p <= s and n % p == 0:\\n return False\\n\\n while p <= s:\\n p += 2\\n if n % p == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19d63e9d42c2b218f79ec6c0d98fd6f\",\n \"score\": \"0.84770465\",\n \"text\": \"def isprime(n):\\n # range starts with 2 and only needs to go up the squareroot of n\\n if n < 2:\\n return False\\n for x in xrange(2, int(n ** 0.5) + 1):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477eda70153ff8b8c7ccaf2fc536d8b1\",\n \"score\": \"0.8416152\",\n \"text\": \"def IsPrime(n):\\n if n%2 == 0:\\n return False\\n else:\\n prime = True\\n for j in range(2,int(np.sqrt(n))+1):\\n if n%j == 0:\\n prime = False\\n break\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901a0f3a40b40d64ffc53c9ea7bf1c30\",\n \"score\": \"0.84116584\",\n \"text\": \"def isprime(n):\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n for i in range(2, int(math.ceil(math.sqrt(n)))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfeec13bc07ab476d32355abbc4c85d4\",\n \"score\": \"0.8396946\",\n \"text\": \"def prime(n):\\n\\tif n <= 1:\\n\\t\\treturn False\\n\\tfor i in range(2, int(sqrt(n) + 1)):\\n\\t\\tif n % i == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a9b3e3ec56f716139c2690f52c09f0\",\n \"score\": \"0.8389916\",\n \"text\": \"def isPrime(n):\\n result = False;\\n if isinstance(n, int) == False:\\n raise TypeError(\\\"Input is not an Integer.\\\");\\n elif n <= 0:\\n raise ValueError(\\\"Input is zero or less.\\\");\\n elif n < 2:\\n result = False;\\n elif n == 2:\\n result = True;\\n else:\\n for i in range(2, n):\\n if i == n:\\n return;\\n elif n % i == 0:\\n result = False;\\n break;\\n else:\\n result = True;\\n\\n return result;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50906bb1d2d6ef7da5820bf7438979a9\",\n \"score\": \"0.8389429\",\n \"text\": \"def isprime(n):\\r\\n prime_ind = False\\r\\n init_primes = [\\r\\n 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,\\r\\n 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137,\\r\\n 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199\\r\\n ]\\r\\n\\r\\n if n in init_primes:\\r\\n prime_ind = True\\r\\n\\r\\n elif (n<=1 or n%2==0 or n%3==0 or n%5==0):\\r\\n prime_ind = False\\r\\n\\r\\n else:\\r\\n # Determine upper limit of test range.\\r\\n ulimit = (int(math.ceil(math.sqrt(n)))+1)\\r\\n prime_ind = not any(n%k==0 for k in range(3, ulimit, 2))\\r\\n return(prime_ind)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a114091880ba14ed9eca16e709d998\",\n \"score\": \"0.83860743\",\n \"text\": \"def is_prime(n):\\n\\tfor num in range(2, floor(sqrt(n)) + 1):\\n\\t\\tif n % num == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d62ce4c046658b6480d22f00130fb9f\",\n \"score\": \"0.8377889\",\n \"text\": \"def is_prime(n):\\n\\n\\t# Find the square root of n.\\n\\n\\tp = int(math.sqrt(n))\\n\\n\\twhile p > 1:\\n\\t\\tif n % p == 0:\\n\\t\\t\\treturn 0\\n\\n\\t\\tp = p - 1\\n\\n\\treturn 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56dadae9af134db9fa497ca8a6ff4e8b\",\n \"score\": \"0.8371619\",\n \"text\": \"def is_prime(n):\\n if n in (0, 1):\\n return False\\n for i in range(2, n):\\n\\tif n % i == 0:\\n\\t return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7635e87c98a226c663071d70fe5328cb\",\n \"score\": \"0.83457136\",\n \"text\": \"def is_prime(n):\\n for i in range(2,n):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2d0c0ed09c36e073b03e7a086dc47b\",\n \"score\": \"0.83377486\",\n \"text\": \"def is_prime(n):\\n\\tfor i in range(2,int(abs(n)**0.5)+1):\\n\\t\\tif n%i == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856b5803e82eb6c5d40330c721906474\",\n \"score\": \"0.8323594\",\n \"text\": \"def prime_test(n):\\n if n < 2:\\n return False\\n for number in islice(count(2), int(sqrt(n)-1)):\\n if not n % number:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589b2f8b21d0d8b67d7c6e53a4057c7e\",\n \"score\": \"0.83179694\",\n \"text\": \"def is_prime(n):\\n \\n if n == 2: return True\\n if n % 2 == 0: \\n return False\\n root_n = int(math.sqrt(n))+1\\n up_to_root_n = root_n\\n quotients = map(lambda divisor: n % divisor, \\n range(3, up_to_root_n))\\n n_is_evenly_divisible = 0 in quotients\\n return False if n_is_evenly_divisible else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272d0471d12b21e6b69f97f48baac25a\",\n \"score\": \"0.83177346\",\n \"text\": \"def is_prime(n: int) -> bool:\\n result: bool = True\\n\\n if n <= 1 or (n % 2 == 0 and n > 2) or (n % 3 == 0 and n > 3):\\n result = False\\n\\n if n == 2:\\n result = True\\n\\n for x in range(3, int(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n result = False\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2265a5869ef4466755a84a4f2bdbe7\",\n \"score\": \"0.8312044\",\n \"text\": \"def is_prime(n):\\n if n == 1:\\n return False\\n\\n if n == 2:\\n return True\\n\\n if n % 2 == 0:\\n return False\\n\\n for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):\\n if n % i == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74aa430d379576b0fc6af3d55d5f6748\",\n \"score\": \"0.8284265\",\n \"text\": \"def is_prime(n):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fed4b5a70448034b4da8242726ede89\",\n \"score\": \"0.82800364\",\n \"text\": \"def is_prime(n):\\n from math import sqrt\\n\\n # Primes starts on 2\\n if n <= 1: return False\\n # Caso especial da porra\\n if n == 2: return True\\n # Numeros pares > 2 nao sao primos\\n if n % 2 == 0: return False\\n\\n square_root = int(sqrt(n))\\n for m in xrange(3, square_root + 1, 2): # lista de nums impares ate sqrt\\n if n % m == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6716ef495b945079b2a2119c7894cf\",\n \"score\": \"0.82623875\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n else:\\n for x in range(2,n):\\n if(n % x == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"511006c76add766235762394700bec07\",\n \"score\": \"0.8257107\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 0 or n == 1 or n % 2 == 0:\\n return False\\n for i in range(3, int(math.sqrt(n))+1, 2):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fb7fa454db5d1e238880467ff4ddee\",\n \"score\": \"0.82552886\",\n \"text\": \"def is_prime(n):\\n\\n if n <= 1:\\n return False # 0 and 1 are prime numbers\\n if n == 2:\\n return True # 2 is a prime number\\n if n % 2 == 0:\\n return False # even numbers greater than 2 are not prime\\n # For every integer 1 < i < n, if n is divisible by i,\\n # then n is not prime. We only need to check from 3 to\\n # sqrt(n), because sqrt(n) is the second largest factor of n,\\n # and we can increase i by 2 at every iteration, since we know\\n # that n is not even\\n limit = math.floor( math.sqrt(n) ) + 1\\n for i in range(3, limit, 2):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2afa11d6ef7e50b2a833a1cf51f971e6\",\n \"score\": \"0.82412285\",\n \"text\": \"def is_prime(n):\\n if n <= 0:\\n return False\\n if n == 1:\\n return False\\n if n == 2:\\n return True\\n for m in range (2, n): # We only have to check until n/2 \\n if n % m == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422b20b7c441b1037d7b6a7cc0e0ae1b\",\n \"score\": \"0.8240911\",\n \"text\": \"def is_prime(n):\\n if n<2:\\n return False\\n\\n i=int(n**0.5)\\n\\n if has_divisor_smaller_than(n, i) == False:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e0478c7ea72acda213a781f826bddd\",\n \"score\": \"0.82399696\",\n \"text\": \"def is_prime(n: int) -> bool: # not solved\\n if n == 2 or n == 3:\\n return True\\n if n % 2 == 0 or n % 3 == 0 or n == 1:\\n return False\\n else:\\n for i in range(2, int(n**.5) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725ba95a76ce6d82f495fe8d1132f873\",\n \"score\": \"0.82367325\",\n \"text\": \"def isprime(n):\\n # make sure n is a positive integer\\n n = abs(int(n))\\n # 0 and 1 are not primes\\n if n < 2:\\n return False\\n # 2 is the only even prime number\\n if n == 2:\\n return True\\n # all other even numbers are not primes\\n if not n & 1:\\n return False\\n # range starts with 3 and only needs to go up the square root of n\\n # for all odd numbers\\n for x in range(3, int(n**0.5)+1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc31081f55ec0723a789771bb6d30774\",\n \"score\": \"0.82352555\",\n \"text\": \"def is_prime(n):\\n if n==1:\\n return False\\n elif n==2:\\n return True\\n else:\\n for i in range(2,n):\\n if(n % i==0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07348f41c7be602f12b436214ff72d1a\",\n \"score\": \"0.82299316\",\n \"text\": \"def isprime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08cee8ee7adddb537a8b7d371a94a6ce\",\n \"score\": \"0.8216398\",\n \"text\": \"def prime(n):\\n if n == 2:\\n return True\\n elif n < 2 or n % 2 == 0:\\n return False\\n else:\\n return not any(n % x == 0\\n for x in range(3, math.ceil(math.sqrt(n)) + 1, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ce1f52c9105aa1973c3712ae2bb925\",\n \"score\": \"0.82160985\",\n \"text\": \"def is_prime(n: int) -> bool:\\n # simple test for small n: 2 and 3 are prime, but 1 is not\\n if n <= 3:\\n return n > 1\\n\\n # check if multiple of 2 or 3\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n\\n # search for subsequent prime factors around multiples of 6\\n max_factor = int(math.sqrt(n))\\n for i in range(5, max_factor + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ce1f52c9105aa1973c3712ae2bb925\",\n \"score\": \"0.82160985\",\n \"text\": \"def is_prime(n: int) -> bool:\\n # simple test for small n: 2 and 3 are prime, but 1 is not\\n if n <= 3:\\n return n > 1\\n\\n # check if multiple of 2 or 3\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n\\n # search for subsequent prime factors around multiples of 6\\n max_factor = int(math.sqrt(n))\\n for i in range(5, max_factor + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0820534545c1052093724d0be6a8cb0\",\n \"score\": \"0.82139057\",\n \"text\": \"def isprime(n):\\n if n == 2: return True\\n if n == 3: return True\\n if n % 2 == 0: return False\\n if n % 3 == 0: return False\\n\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f7f61a6383ab12105f728487bdd534\",\n \"score\": \"0.82113105\",\n \"text\": \"def isprime(n):\\n if n % 2 == 0 and n > 2:\\n return False\\n for i in range(3, n):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cccf3620b1352e5c60bf0346de7b01\",\n \"score\": \"0.82056844\",\n \"text\": \"def is_prime(n):\\n if n <= 1:\\n return False\\n m = 2\\n while m <= sqrt(n):\\n if n % m == 0:\\n return False\\n m += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"635525dbae85557bdc340c8b9b10edc4\",\n \"score\": \"0.8201573\",\n \"text\": \"def is_prime(n):\\n\\n for i in range(2, int(n**(.5))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41ac96a2995b84c5582172f7c60743c7\",\n \"score\": \"0.81952757\",\n \"text\": \"def is_prime(n):\\n if n < 2: return False\\n if n % 2 == 0: return n == 2\\n d = int(math.sqrt(n))\\n for k in range(3, d+1, 2):\\n if n % k == 0: return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bafae931b71a704e193fea70413010\",\n \"score\": \"0.8193707\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n for i in range(2, int(n**0.5) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af516e3c8b4214cefaaeb5e242c9e5de\",\n \"score\": \"0.8186336\",\n \"text\": \"def is_prime(n):\\r\\n end = int(sqrt(n))\\r\\n for i in range(2, end + 1):\\r\\n if n % i == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c278d5413913dd21598d0be7b8b5beeb\",\n \"score\": \"0.81617874\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n\\n if n == 2:\\n return True\\n \\n if n % 2 == 0:\\n return False\\n \\n for i in range(3, n):\\n if n % i == 0:\\n return False\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc24c2eda47040b95d6de2a18c0f451a\",\n \"score\": \"0.81556326\",\n \"text\": \"def is_prime(n):\\n for i in range(2,int(abs(n)**0.5)+1):\\n if(n%i == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4342cdcfc8e1b7e350391ff6fdd457\",\n \"score\": \"0.8154981\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n i = 2\\n while i*i <= n:\\n if n % i == 0:\\n return False\\n i = i + 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4c66c8caa96007d8a7ed30223725ce\",\n \"score\": \"0.81473047\",\n \"text\": \"def isPrime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4c66c8caa96007d8a7ed30223725ce\",\n \"score\": \"0.81473047\",\n \"text\": \"def isPrime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c57cc85d45fb203d479ef719ae28e9\",\n \"score\": \"0.81402576\",\n \"text\": \"def isprime(n):\\n # range starts with 2 and only needs to go up the squareroot of n\\n if n % 2 == 0:\\n return False\\n for x in range(3, int(n**0.5)+1,2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0718b32ecb5751a279a2c58ec2ec1cf7\",\n \"score\": \"0.8138055\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n while i*i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ae235b788cc942b94ba04ca81c3c9d\",\n \"score\": \"0.8134483\",\n \"text\": \"def is_prime(n):\\n for i in range(2, int(n ** 0.5) + 1):\\n if not n % i:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6809b1cd33dd1d0ebe49e0e30145952f\",\n \"score\": \"0.8124355\",\n \"text\": \"def is_prime(n):\\n prime = True # assume prime until proven otherwise\\n\\n for i in range(2, int(sqrt(n) + 1)):\\n if n % i == 0: # factor found -> not prime\\n prime = False\\n break\\n\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e711a66dbcaeb4c4f5cd3fef7cac5c\",\n \"score\": \"0.81242764\",\n \"text\": \"def is_prime_v2(n):\\n if n == 1:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(2, 1 + max_divisor):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d81ef705192336ab08758c7cb15039d\",\n \"score\": \"0.81232667\",\n \"text\": \"def isPrime(n):\\n\\n if n == 0 or n == 1:\\n return False\\n\\n for div in range(2, int(n**0.5)+1):\\n if not n % div:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148222020cd78bf2d22c5e4326000805\",\n \"score\": \"0.81167865\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5a33abd4d265953025486942673076\",\n \"score\": \"0.81161934\",\n \"text\": \"def isPrime(n):\\n\\tif n <= 1: return False\\n\\telif n < 4: return True #2, 3\\n\\telif n % 2 == 0: return False\\n\\telif n < 9: return True #5, 7\\n\\telif n % 3 == 0: return False\\n\\telse:\\n\\t\\t#make use of the fact that all primes > 5 have have the form 6k +- 1\\n\\t\\tr = math.floor(math.sqrt(n))\\n\\t\\tf = 5\\n\\t\\twhile f <= r:\\n\\t\\t\\tif n % f == 0: return False\\n\\t\\t\\tif n % (f + 2) == 0: return False\\n\\t\\t\\tf += 6\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18968f383dac5e1e0b0a60d0bb0988ba\",\n \"score\": \"0.80963194\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n d = 2\\n while d <= int(math.sqrt(n)) + 1:\\n if n % d == 0:\\n return False\\n d += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40d0e26c0162295a4d5cec18cca267db\",\n \"score\": \"0.8092706\",\n \"text\": \"def isprime(n):\\n return len(factors(n)) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda83ee0a8ba13b9b662c98c048c8736\",\n \"score\": \"0.80838317\",\n \"text\": \"def isprime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f088fa3e414e6a8725d83eb028394b2\",\n \"score\": \"0.8081018\",\n \"text\": \"def is_prime_v1(n):\\n if n == 1:\\n return False\\n\\n for d in range(2, n):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0faea9835cf4d2c6ce4970efa17b8f\",\n \"score\": \"0.8067031\",\n \"text\": \"def is_prime(n):\\n def divisible_by_known_prime(n):\\n for x in known_primes:\\n if divides(x, n):\\n return x\\n return False\\n\\n # Quick filters\\n if n < 2:\\n return False\\n elif n in known_primes:\\n return True\\n elif divisible_by_known_prime(n):\\n return False\\n else:\\n # Start searching again at the max known prime\\n start = known_primes[-1] + 2\\n for y in xrange(start, int(sqrt(n)), 2):\\n if divisible_by_known_prime(y):\\n continue\\n else:\\n if n % y == 0:\\n return False\\n known_primes.append(y)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464021a33de3106c49185f714bf98982\",\n \"score\": \"0.8051832\",\n \"text\": \"def test_prime(n):\\n if n==2:\\n return True\\n #we only need to check divisors up to square root of a number\\n for i in range(2,int(n**(.5))+1):\\n if n % i ==0:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6829a46657473e9890c8e4a7c737a3\",\n \"score\": \"0.8046635\",\n \"text\": \"def isprime(n):\\n\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6829a46657473e9890c8e4a7c737a3\",\n \"score\": \"0.8046635\",\n \"text\": \"def isprime(n):\\n\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e993486b8ff4626cd7a77a6f4541959e\",\n \"score\": \"0.80411047\",\n \"text\": \"def is_prime_number(n:int) -> bool:\\r\\n\\r\\n i = 0\\r\\n is_prime = True\\r\\n\\r\\n if (n == 2 or n == 3):\\r\\n return True\\r\\n while(i < len(_prime_list)):\\r\\n if (not n % _prime_list[i] and is_prime):\\r\\n is_prime = False\\r\\n return is_prime\\r\\n i += 1\\r\\n if (is_prime):\\r\\n _prime_list.append(n)\\r\\n return is_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc04899d0ad229677ff3b7c4f040ac5d\",\n \"score\": \"0.8024999\",\n \"text\": \"def isPrime(n):\\n Dmax = scipy.sqrt(n)\\n if n == 2:\\n return True\\n if isEven(n):\\n return False\\n d = 3\\n while n%d != 0 and d <= Dmax:\\n d += 2\\n return d > Dmax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a485022c94922bb7f2ae2c0922016c\",\n \"score\": \"0.7937437\",\n \"text\": \"def is_prime(n):\\n test_vals = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\\n if n in test_vals:\\n return True\\n d = n - 1\\n s = 0\\n while not d & 1:\\n d = d >> 1\\n s += 1\\n for a in test_vals:\\n for r in range(0, s):\\n if (a ** (d * (1 << r))) % n != (n - 1) \\\\\\n and (a ** d) % n != 1:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6588f6a60f59af54b5b30212aef90f\",\n \"score\": \"0.78840125\",\n \"text\": \"def is_prime(n):\\n if n < 2 :\\n return False\\n\\n # OFF BY ONE Error, solution is to add +1 \\n #for i in range (2, int(math.sqrt(n)) + 1 ):\\n for i in range (2, int(math.sqrt(n)) ):\\n if n % i == 0 :\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c240bfae6683bf9bd96ef3272a3f9b7\",\n \"score\": \"0.78501946\",\n \"text\": \"def prime_number(n):\\n\\n if n > 1:\\n\\n for divisor in range(2,n):\\n\\n if n%divisor == 0:\\n #if n and divisor cannot complete full division, n is not a \\n #prime number\\n\\n return False\\n\\n return True\\n\\n else:\\n #negative number is not prime number\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9168442a99017fb7004b5deea6142afb\",\n \"score\": \"0.7763757\",\n \"text\": \"def is_prime(n):\\n \\n if n == 2:\\n return True\\n if n < 2 or n % 2 == 0:\\n return False\\n \\n trail_factor = 3\\n \\n root = sqrt(n)\\n \\n while trail_factor <= root:\\n if n % trail_factor == 0:\\n return False\\n trail_factor += 2 \\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e931ff2a59cbd8806ee6f819d4efc108\",\n \"score\": \"0.77405703\",\n \"text\": \"def is_prime(n):\\n def prime_helper(index):\\n if index == n:\\n return True\\n elif n % index == 0 or n == 1:\\n return False\\n else:\\n return prime_helper(index + 1)\\n return prime_helper(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad782a50c1962258b23b6d5a2dfd97c\",\n \"score\": \"0.7739098\",\n \"text\": \"def primality(n):\\n if n == 1:\\n return NOT_PRIME\\n return PRIME if all(n % i for i in range(2, int(sqrt(n)) + 1)) else NOT_PRIME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2440dc2a1445e687d40f229c33daa7cd\",\n \"score\": \"0.77390164\",\n \"text\": \"def isprime(n):\\r\\n if n % 3 == 0:\\r\\n return (False, 3)\\r\\n\\r\\n i = 5\\r\\n w = 2\\r\\n\\r\\n while i * i <= n:\\r\\n if n % i == 0:\\r\\n return (False, i)\\r\\n\\r\\n i += w\\r\\n w = 6 - w\\r\\n\\r\\n return (True, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1373ba4fdce84a62e9becedccf9c9fad\",\n \"score\": \"0.7735379\",\n \"text\": \"def is_prime(n):\\n prime = True\\n d = 2\\n while prime and d <= round(n**(1/2)):\\n if n%d != 0:\\n d += 1\\n else:\\n prime = False\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb53f51042964c6e86ff53aeb84cd0f\",\n \"score\": \"0.76981086\",\n \"text\": \"def is_prime_sieve(x):\\n\\n if (x <= 1):\\n return False\\n elif (x <= 3):\\n return True\\n x_mod_6 = x % 6\\n if ((x_mod_6 != 1) and (x_mod_6 != 5)):\\n return False\\n\\n sqrt_x = int_sqrt(x)\\n for i in generate_primes(sqrt_x):\\n if ((x % i) == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc0590b9895849fc3e3f49891941a7f\",\n \"score\": \"0.7671711\",\n \"text\": \"def is_prime(n):\\n\\n # (This is used to study the risk of false positives:)\\n global miller_rabin_test_count\\n\\n miller_rabin_test_count = 0\\n\\n if n <= smallprimes[-1]:\\n if n in smallprimes:\\n return True\\n else:\\n return False\\n\\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\\n return False\\n\\n # Choose a number of iterations sufficient to reduce the\\n # probability of accepting a composite below 2**-80\\n # (from Menezes et al. Table 4.4):\\n\\n t = 40\\n n_bits = 1 + int(math.log(n, 2))\\n for k, tt in (\\n (100, 27),\\n (150, 18),\\n (200, 15),\\n (250, 12),\\n (300, 9),\\n (350, 8),\\n (400, 7),\\n (450, 6),\\n (550, 5),\\n (650, 4),\\n (850, 3),\\n (1300, 2),\\n ):\\n if n_bits < k:\\n break\\n t = tt\\n\\n # Run the test t times:\\n\\n s = 0\\n r = n - 1\\n while (r % 2) == 0:\\n s = s + 1\\n r = r // 2\\n for i in xrange(t):\\n a = smallprimes[i]\\n y = pow(a, r, n)\\n if y != 1 and y != n - 1:\\n j = 1\\n while j <= s - 1 and y != n - 1:\\n y = pow(y, 2, n)\\n if y == 1:\\n miller_rabin_test_count = i + 1\\n return False\\n j = j + 1\\n if y != n - 1:\\n miller_rabin_test_count = i + 1\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e51bd8d669673e6777f4dbb96d36b8\",\n \"score\": \"0.76641953\",\n \"text\": \"def is_prime(n):\\n\\n # Checks if given a list...\\n if type(n) == list:\\n if_prime_list = [False, ] * len(n)\\n c = 0\\n for i in n:\\n if is_prime(i):\\n if_prime_list[c] = True\\n c += 1\\n # and returns one with weather or not it's prime in place\\n return if_prime_list\\n\\n # When a single number ..\\n\\n if n < 2:\\n return False\\n if n == 2 or n == 3:\\n return True\\n\\n for i in range(2, int(sqrt(n))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b167f7b47ee5341083441f6a31a32272\",\n \"score\": \"0.7655478\",\n \"text\": \"def isPrime(n):\\n\\n if n % 2 == 0:\\n return n == 2\\n d = 3\\n while d * d <= n and n % d != 0:\\n d += 2\\n return d * d > n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be316dbe44f91fe2b2e34bac2ceba31\",\n \"score\": \"0.7647462\",\n \"text\": \"def is_prime_cacheless(n):\\n if n < 2:\\n return False\\n for i in xrange(2, int(math.sqrt(n)) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5809882f1b0d4c24bf26aadb26e9c4cb\",\n \"score\": \"0.7642497\",\n \"text\": \"def is_superprime(n):\\n while n > 0:\\n if not is_prime(n):\\n return False\\n n = n // 10\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9804bea1d4da046f3722898d4b15ae\",\n \"score\": \"0.7615486\",\n \"text\": \"def is_prime_exact(n):\\n if n <= 3:\\n return n >= 2\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n for i in range(5, int(n ** 0.5) + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5259c60cb0e66026bd62a7cf1ba6f28e\",\n \"score\": \"0.76105756\",\n \"text\": \"def is_prime(number: int) -> bool:\\n prop_div = proper_divisors(number)\\n return len(prop_div) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e333a6a531610d72ac921f35578efc7\",\n \"score\": \"0.76068914\",\n \"text\": \"def is_prime_v3(n):\\n if n == 1:\\n return False\\n\\n # If it's even and not 2, then it's not prime\\n if n == 2:\\n return True\\n if n > 2 and n % 2 == 0:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(3, 1 + max_divisor, 2):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e333a6a531610d72ac921f35578efc7\",\n \"score\": \"0.76068914\",\n \"text\": \"def is_prime_v3(n):\\n if n == 1:\\n return False\\n\\n # If it's even and not 2, then it's not prime\\n if n == 2:\\n return True\\n if n > 2 and n % 2 == 0:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(3, 1 + max_divisor, 2):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd441534aae1f3e8f4a3213bf868fdb0\",\n \"score\": \"0.75916487\",\n \"text\": \"def is_prime(n):\\n\\n # (This is used to study the risk of false positives:)\\n global miller_rabin_test_count\\n\\n miller_rabin_test_count = 0\\n\\n if n <= smallprimes[-1]:\\n if n in smallprimes:\\n return True\\n else:\\n return False\\n\\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\\n return False\\n\\n # Choose a number of iterations sufficient to reduce the\\n # probability of accepting a composite below 2**-80\\n # (from Menezes et al. Table 4.4):\\n\\n t = 40\\n n_bits = 1 + int(math.log(n, 2))\\n for k, tt in ((100, 27),\\n (150, 18),\\n (200, 15),\\n (250, 12),\\n (300, 9),\\n (350, 8),\\n (400, 7),\\n (450, 6),\\n (550, 5),\\n (650, 4),\\n (850, 3),\\n (1300, 2),\\n ):\\n if n_bits < k:\\n break\\n t = tt\\n\\n # Run the test t times:\\n\\n s = 0\\n r = n - 1\\n while (r % 2) == 0:\\n s = s + 1\\n r = r // 2\\n for i in range(t):\\n a = smallprimes[i]\\n y = modular_exp(a, r, n)\\n if y != 1 and y != n - 1:\\n j = 1\\n while j <= s - 1 and y != n - 1:\\n y = modular_exp(y, 2, n)\\n if y == 1:\\n miller_rabin_test_count = i + 1\\n return False\\n j = j + 1\\n if y != n - 1:\\n miller_rabin_test_count = i + 1\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c7eb72160691d2a58b025131a986ac\",\n \"score\": \"0.758809\",\n \"text\": \"def is_prime(n):\\n r = 0\\n d = n - 1\\n while d % 2 == 0:\\n r += 1\\n d //= 2\\n\\n for _ in range(40):\\n a = random.randint(2, n - 2)\\n x = pow(a, d, n)\\n if x == 1 or x == n - 1:\\n continue\\n \\n for _ in range(r - 1):\\n x = pow(x, 2, n)\\n if x == n - 1:\\n break\\n\\n else:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b537c6a55e7ef96df0d686053d637c4\",\n \"score\": \"0.75501156\",\n \"text\": \"def is_prime(i, primes):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fbbb15cd7dd7e9b4d3490d51da7e317\",\n \"score\": \"0.74986815\",\n \"text\": \"def is_prime(number):\\n for i in range(2, number):\\n if number % i == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e14a0482f5920322f17e25db1e4d4f\",\n \"score\": \"0.7495828\",\n \"text\": \"def is_prime(n, prime_numbers = []):\\n if n < 2:\\n return False\\n\\n if len(prime_numbers) == 0 or prime_numbers[-1] < n:\\n prime_numbers = _generate_primes(n + 1, prime_numbers)\\n\\n for p in prime_numbers:\\n if n < p:\\n return False\\n if n == p:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d11c11d34ac275d0984b8b705ea571\",\n \"score\": \"0.7481891\",\n \"text\": \"def is_prime(number):\\r\\n if number <= 0:\\r\\n return False\\r\\n elif number > 2:\\r\\n for i in range(2, number):\\r\\n if number % i == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bbe1ee06cee20708acd22de4008554f\",\n \"score\": \"0.7473216\",\n \"text\": \"def prime(n):\\n # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\\n sieve = np.ones(int(n/3 + (n%6==2)), dtype=np.bool)\\n sieve[0] = False\\n for i in range(int(n**0.5/3+1)):\\n if sieve[i]:\\n k=3*i+1|1\\n sieve[ int((k*k)/3) ::2*k] = False\\n sieve[int((k*k+4*k-2*k*(i&1))/3)::2*k] = False\\n return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589dda1ed65ef55d34a2407a75760f86\",\n \"score\": \"0.74541706\",\n \"text\": \"def is_prime(num):\\r\\n for n in range(2, num):\\r\\n if num % n == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557caac191fadc8d79ddadae1a0b768c\",\n \"score\": \"0.74398\",\n \"text\": \"def _is_prime(number):\\n if number <= 1:\\n return False\\n\\n divisor = 2\\n while divisor < number:\\n if ((number % divisor) == 0) or (divisor > (number / 2)):\\n return False\\n divisor += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f0a11ff3c7fc1e8870986532afedf0\",\n \"score\": \"0.7438905\",\n \"text\": \"def is_prime(number: int) -> bool:\\n min_divisor = 2\\n max_divisor = math.ceil(math.sqrt(number))\\n for divisor in range(min_divisor, max_divisor + 1):\\n if number % divisor == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7752fe5a1cf97814e82aa201face01\",\n \"score\": \"0.7425838\",\n \"text\": \"def probable_prime(n):\\n return pow(2, n-1, n) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e87a69f7b9cd088605a16cdbb01c27\",\n \"score\": \"0.74213374\",\n \"text\": \"def is_prime_v1(n):\\n if n == 1:\\n return False # 1 is not prime (1 is not composite, either)\\n # d stands for divisor. Since any number greater than 1 (prime or composite) can be exactly divided by 1 and itself,\\n # we skip 1 and n and only iterate over the items in the sequence from 2 through n - 1 (Remember in the range()\\n # function the stop value is not included.)\\n for d in range(2, n):\\n # % is the modulus operator. The right-hand side is the remainder of the operation of division of n by d.\\n # If n is divisible by d, the remainder is equal to 0; if not, the remainder is not equal to 0.\\n # So the test_expression of n % d == 0 means n is divisible by d. If the test_expression is True, control of the\\n # program flows to the body of if and execute the return statement. The function exits and meanwhile the boolean\\n # value of False is returned.\\n if n % d == 0:\\n return False\\n # If each items in the sequence generated from the range doesn't make the test_expression True, meaning n is not\\n # divisible by any smaller number, then we can consider n is prime and return True after the items in the sequence\\n # exhaust without the for loop being stopped halfway.\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460b8ab60da7496aa79fef6e6731ca9f\",\n \"score\": \"0.74172294\",\n \"text\": \"def isPrime(p):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6248ff0357b4b512961650451529bc9c\",\n \"score\": \"0.7411656\",\n \"text\": \"def is_prime(number: int) -> bool:\\n\\n if 1 < number < 4:\\n # 2 and 3 are primes\\n return True\\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\\n return False\\n\\n # All primes number are in format of 6k +/- 1\\n for i in range(5, int(math.sqrt(number) + 1), 6):\\n if number % i == 0 or number % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d50325cb8c5ccefa49472a84ce23913\",\n \"score\": \"0.7410716\",\n \"text\": \"def is_prime(number):\\n if number <= 1:\\n return False\\n if number == 2:\\n return True\\n else:\\n maximum = int(math.sqrt(number)) + 1\\n for i in range(2, maximum):\\n if number % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b3903b7ed71035196f37c9778c5e81\",\n \"score\": \"0.74050426\",\n \"text\": \"def check_prime(number):\\n if number in primes:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3617d3ebdd114f5eeefb2a8c559fad\",\n \"score\": \"0.73954546\",\n \"text\": \"def primes(n):\\n sieve = [True] * n\\n for i in range(3,int(n**0.5)+1,2): #in py3, range and xrange are same\\n if sieve[i]:\\n sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)\\n return [2] + [i for i in range(3,n,2) if sieve[i]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7593a29f9a46b123950c8a508ec279\",\n \"score\": \"0.7380684\",\n \"text\": \"def is_prime(number: int) -> bool:\\r\\n\\r\\n if 1 < number < 4:\\r\\n # 2 and 3 are primes\\r\\n return True\\r\\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\\r\\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\\r\\n return False\\r\\n\\r\\n # All primes number are in format of 6k +/- 1\\r\\n for i in range(5, int(math.sqrt(number) + 1), 6):\\r\\n if number % i == 0 or number % (i + 2) == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":797,"cells":{"query_id":{"kind":"string","value":"c4d430e66bb3d21f6d5a3c0b14362f75"},"query":{"kind":"string","value":"Gets the EMD language between the two languages"},"positive_passages":{"kind":"list like","value":[{"docid":"62965b2c20affbafeeb5c0cc7a3c475e","score":"0.0","text":"def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)","title":""}],"string":"[\n {\n \"docid\": \"62965b2c20affbafeeb5c0cc7a3c475e\",\n \"score\": \"0.0\",\n \"text\": \"def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7d19c984c03b3e59c0e29927e52e446b","score":"0.6914142","text":"def get_language(self):","title":""},{"docid":"38cf8daef8d0e9165e88916da0f08868","score":"0.67045707","text":"def getLanguage(self) -> ghidra.program.model.lang.Language:\n ...","title":""},{"docid":"88efbc8344a0bc887062d526ad6868f2","score":"0.6524406","text":"def Language(self) -> int:","title":""},{"docid":"25f46e6f2f129aea0b62e208273c88ba","score":"0.64493454","text":"def get_translation(language):","title":""},{"docid":"92e349e11ef96d37ee720c9e44ff81c6","score":"0.6397181","text":"def language(self):\n return self._mapped_lang","title":""},{"docid":"83cfc614d26d41e5c5b4cd9dcfed6d54","score":"0.6361355","text":"def get_translated_languages():","title":""},{"docid":"72af2abe9ac887512114fc6b329f6a58","score":"0.6298945","text":"def language(self):\n return getattr(\n self,\n constants.CONST_LANGUAGE,\n constants.DEFAULT_LANGUAGE,\n )","title":""},{"docid":"7374b79e2ca26f3f6ef3c6f14ae42a97","score":"0.62752056","text":"def getLanguage (self,c,setting):\n \n language = self.getString(c,setting)\n # g.trace(setting,language)\n \n return language","title":""},{"docid":"d018b5936457ce548d59d069e02048a5","score":"0.626775","text":"def get_language(event, context):\n logger.info('Calling get_language with event')\n logger.info(event)\n\n # Use UTF-8 encoding for comprehend\n if 'Text' in event:\n text = str(event['Text'])\n else:\n logger.error(\"There is no Text!\")\n raise Exception('Please provide Text!')\n\n # comprehend accepts up to 5000 UTF-8 encoded characters\n if len(text) >= 4900:\n text = text[:4899]\n elif len(text) < 20:\n logger.warning(\"Text is shorter than 20 characters!\")\n raise Exception('Please provide a text of at least 20 characters!')\n # logger.info(text)\n\n try:\n response = comprehend.detect_dominant_language(Text=text)\n except ClientError as e:\n logger.error(\"Received error: %s\", e, exc_info=True)\n raise\n except ParamValidationError as e:\n logger.error(\"The provided parameters are incorrect: %s\", e, exc_info=True)\n raise\n\n # logger.info(response)\n return response['Languages'][0]['LanguageCode']","title":""},{"docid":"91f4df655801a0f81560021370fb0325","score":"0.6215751","text":"def language(self):\n try:\n return self._get_response(\n url=self.LANGID_URL, sentence=self.sentence)\n except:\n return None","title":""},{"docid":"a6852d214fd73ca3c8b3c76ed0880cbe","score":"0.62144786","text":"def lang(self):\n # pick a sample to find language (don't pick the truth file)\n sample = [choose for choose in os.listdir(self.folder)\n if 'truth' not in choose][0]\n with open(os.path.join(self.folder, sample), 'r') as sample_file:\n first_line = sample_file.readlines()[0]\n lang = [match.group('lang')\n for match in re.finditer(Pan.LANG_REGEX, first_line)][0]\n return lang.lower()","title":""},{"docid":"64b70a35a3529c04521ca03c1783af78","score":"0.62131405","text":"def language(self):\n return self.language_version()[0]","title":""},{"docid":"762ed9baf7428d3c2c27c92906c3f34d","score":"0.61848724","text":"def language(self):\n lang=None\n if self.__dict__['TAG:language']:\n lang=self.__dict__['TAG:language']\n return lang","title":""},{"docid":"d9442ebc089fe4982af945a7c4eee87f","score":"0.6183641","text":"def lang(self):\n return self.lex_meta.lang","title":""},{"docid":"ff6c3d8673b836167af732a1190347c6","score":"0.617456","text":"def test_lang() -> str:\n lang = locale.getdefaultlocale()[0][0:2]\n if lang == 'fr':\n return 'dtc'\n return 'qdb'","title":""},{"docid":"86b093671c104aa8770479841f816a10","score":"0.615984","text":"def gncEmployeeGetLanguage(*args):\n return _gnucash_core_c.gncEmployeeGetLanguage(*args)","title":""},{"docid":"036561cb4fe47e070a4c9ddc370ac2e2","score":"0.60845643","text":"def get_language(self):\n\n return self.__msg_lang","title":""},{"docid":"99a60ae13aaed279959529adedcdb43d","score":"0.6080517","text":"def Language (self):\n return (self.headers.get ('index_language', 'any'),\n self.headers.get ('contents_language', 'any'))","title":""},{"docid":"1790cd720d52235c595dab8ffb7999cd","score":"0.6073051","text":"def get_lang():\n if os() == 'Windows':\n # Windows\n lang = locale.windows_locale[ctypes.windll.kernel32.GetUserDefaultUILanguage()]\n else:\n # Linux || MacOS\n lang = locale.getlocale(locale.LC_MESSAGES)[0]\n return lang","title":""},{"docid":"8f04f28b59d21d0f9e23d1b811974cc3","score":"0.6020242","text":"def moment_lang(self):\n return load_moment_locales().get(self.lang, 'en')","title":""},{"docid":"e019d75c7257253db6436ddc0730b7da","score":"0.5989116","text":"def lang(self):\n return self._lang","title":""},{"docid":"3065207a9592be2eab56e31bb0e0f886","score":"0.59747165","text":"def get_language(self):\n locale = self.get_locale()\n language = locale\n if '_' in locale:\n language = locale[0:locale.index('_')]\n return language","title":""},{"docid":"5123c9ed66643d7c0daa4ede7f2b7ffc","score":"0.5971464","text":"def check_language(text):\n if detect(text) == 'de':\n language = 'de'\n elif detect(text) == 'en':\n language = 'en'\n else:\n language = None\n return language","title":""},{"docid":"65d1e9eae9ab232b88af6f359a4eb6c0","score":"0.596331","text":"def language_tag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"6c4045103df8f4bf07a4b594e60c7fc2","score":"0.5960778","text":"def get_locale():\r\n # this is a demo case, we use url to get locale\r\n code = request.args.get('lang', 'en')\r\n return code","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"12f6c63ad26a4e47296bddee9b9653f2","score":"0.5934641","text":"def lang_(self):\n return self.doc.vocab.store[self.lex_meta.lang]","title":""},{"docid":"216919c5a4cf9279f362cc048048645b","score":"0.5924688","text":"def get_main_language(project_report):\n required1 = {\"MetaDataCollector\": {\"main_language\": \"\"}}\n required2 = {\"LanguageDetector\": {\"main_language\": \"\"}}\n\n if containedStructure(required1, project_report):\n lang = project_report[\"MetaDataCollector\"][\"main_language\"]\n return shorten_language(lang)\n elif containedStructure(required2, project_report):\n lang = project_report[\"LanguageDetector\"][\"main_language\"]\n return shorten_language(lang)\n return '-'","title":""},{"docid":"3125c2dce33044e62c80ca613fa15952","score":"0.59202147","text":"def convert_language(language):\n\n if language == 'StoreKing_NW':\n language = 'Kannada'\n if language not in LANGUAGES:\n language = 'Hindi'\n\n return language","title":""},{"docid":"8ce21ad58cb3eab50ede578a17775fc6","score":"0.59194607","text":"def get_locale():\n if \"lang\" in session:\n return session[\"lang\"]\n #Decides the most suitable language option\n #fi=Finnish has higher priority\n lang = request.accept_languages.best_match([\"fi\", \"en\"])\n return lang","title":""},{"docid":"fb14f5c2099e436683e91382ce62e904","score":"0.59086585","text":"def language_code(self):\n raise NotImplementedError()","title":""},{"docid":"71d7bb6cef50f1f302e8a531796e5ef0","score":"0.590221","text":"def __get_language__(self, lang_code: str) -> str:\n\n if lang_code not in self.ALLOWED_LANGUAGES.keys():\n raise ValueError('{} is not a supported language code'.format(lang_code))\n\n return self.ALLOWED_LANGUAGES[lang_code]","title":""},{"docid":"c660a06708cf14ec63c36cbcdc98b5f0","score":"0.5900211","text":"def get_lang(country_iso: str) -> str:\n res = country_lang.get(country_iso)\n if res:\n return res\n else:\n return 'en'","title":""},{"docid":"f71b058ed50499dc5964b614bdebe35c","score":"0.5899539","text":"def get_lang(self, language):\n if language not in self.compilers:\n self.console.print(\"Language is not supported!\", style=\"bold red\")\n Utils.close()\n return language","title":""},{"docid":"31f035ca7232dd83ae68d0fd8680c087","score":"0.5898755","text":"def getAvailableLanguages():\n\t#Make a list of all the locales found in NVDA's locale dir\n\tl=[x for x in os.listdir(\"locales\") if not x.startswith('.')]\n\tl=[x for x in l if os.path.isfile('locales/%s/LC_MESSAGES/twblue-documentation.mo' % x)]\n\t#Make sure that en (english) is in the list as it may not have any locale files, but is default\n\tif 'en' not in l:\n\t\tl.append('en')\n\t\tl.sort()\n\t#For each locale, ask Windows for its human readable display name\n\td=[]\n\tfor i in l:\n\t\tdesc=getLanguageDescription(i)\n\t\tlabel=\"%s, %s\"%(desc,i) if desc else i\n\t\td.append(label)\n\t#include a 'user default, windows' language, which just represents the default language for this user account\n\tl.append(\"system\")\n\t# Translators: the label for the Windows default NVDA interface language.\n\td.append(_(\"User default\"))\n\t#return a zipped up version of both the lists (a list with tuples of locale,label)\n\treturn zip(l,d)","title":""},{"docid":"f8a9d1153b9d1d09c331da8bf1f45873","score":"0.5883816","text":"def get_language(self):\n return self._language","title":""},{"docid":"29738bd87cc78dd4e2ff5aa73bc07f2f","score":"0.58760846","text":"def lang(self):\n return self.config_core.get('lang')","title":""},{"docid":"d7cf60544184706f9f81cdc9296ec99b","score":"0.5870653","text":"def language(self) -> Optional[Text]:\n\n return self.get(\"language\")","title":""},{"docid":"49d004470495efbf7e4526a851502aa6","score":"0.58666897","text":"def l_get(\n language_dict: Dict[str, Type[T_Lang]], update_msg_user_or_language_code: T_update_msg_user_or_language_code = None\n) -> Type[T_Lang]:\n lang = get_language_code(update_msg_user_or_language_code)\n\n # if it is None (default, or getting from message failed), use the default\n if lang is None:\n return language_dict['default']\n # end if\n\n if lang in language_dict:\n return language_dict[lang]\n # end if\n\n # try replacing \"de_DE\" => \"de-DE\"\n part = lang.replace(\"_\", \"-\")\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de-DE\" => \"de\"\n part = lang.split('-')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de_DE\" => \"de\"\n part = lang.split('_')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end if\n\n # nothing did match, use the default\n return language_dict['default']","title":""},{"docid":"8235dc2fa6170ed344df28e1700dbbc2","score":"0.5864678","text":"def getLanguage(self):\n spd = self.get(sessionPkgDataId, {})\n return spd.get('language', None)","title":""},{"docid":"16a9b23a1950dc54cc078c8a113ee638","score":"0.58440614","text":"def language(self) -> LangEnum:\n return self._lang_enum","title":""},{"docid":"e305eccb0d97870a645edc54619fa11d","score":"0.58301246","text":"def get_language(self):\n # Look through ancestors of this page for its language homepage\n # The language homepage is located at depth 3\n language_homepage = self.get_ancestors(inclusive=True).get(depth=3)\n\n # The slug of language homepages should always be set to the language code\n return language_homepage.slug","title":""},{"docid":"01a7c30c4fb5b54b7e1163687d05331e","score":"0.58280015","text":"def get_keyboard_language():\n # only language not variant layout\n user32 = ctypes.WinDLL('user32', use_last_error=True)\n curr_window = user32.GetForegroundWindow()\n thread_id = user32.GetWindowThreadProcessId(curr_window, 0)\n klid = user32.GetKeyboardLayout(thread_id)\n lid = klid & (2 ** 16 - 1)\n lid_hex = f\"{lid:#0{6}x}\"\n root = os.path.dirname(__file__)\n with open(root + '/misc/win-language-id.json', 'r') as f:\n win_layout = json.load(f)\n return win_layout[lid_hex]","title":""},{"docid":"35060b60835a1adab92ba7649db7b1db","score":"0.5827729","text":"def language_code(self):\n return self._language_code","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"60cd8d4c2b50df7167c55b050d8b4fed","score":"0.57999927","text":"def getLanguageDescription(language):\n\tdesc=None\n\tif platform.system() == \"Windows\":\n\t\tLCID=localeNameToWindowsLCID(language)\n\t\tif LCID!=0:\n\t\t\tbuf=ctypes.create_unicode_buffer(1024)\n\t\t\tif '_' not in language:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGDISPLAYNAME,buf,1024)\n\t\t\telse:\n\t\t\t\tres=0\n\t\t\tif res==0:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGUAGE,buf,1024)\n\t\t\tdesc=buf.value\n\telif platform.system() == \"Linux\" or not desc:\n\t\tdesc={\n\t\t\t\"am\":pgettext(\"languageName\",\"Amharic\"),\n\t\t\t\"an\":pgettext(\"languageName\",\"Aragonese\"),\n\t\t\t\"es\":pgettext(\"languageName\",\"Spanish\"),\n\t\t\t\"pt\":pgettext(\"languageName\",\"Portuguese\"),\n\t\t\t\"ru\":pgettext(\"languageName\",\"Russian\"),\n\t\t\t\"it\":pgettext(\"languageName\",\"italian\"),\n\t\t\t\"tr\":pgettext(\"languageName\",\"Turkey\"),\n\t\t\t\"gl\":pgettext(\"languageName\",\"Galician\"),\n\t\t\t\"ca\":pgettext(\"languageName\",\"Catala\"),\n\t\t\t\"eu\":pgettext(\"languageName\",\"Vasque\"),\n\t\t\t\"pl\":pgettext(\"languageName\",\"polish\"),\n\t\t\t\"ar\":pgettext(\"languageName\",\"Arabic\"),\n\t\t\t\"ne\":pgettext(\"languageName\",\"Nepali\"),\n\t\t\t\"sr\":pgettext(\"languageName\",\"Serbian (Latin)\"),\n\t\t}.get(language,None)\n\treturn desc","title":""},{"docid":"2e521127c2c1644e5b57354454f398c9","score":"0.5784645","text":"def SelectSupportedLanguage(language_codes):\n for lang in map(NormalizeLang, language_codes.split(',')):\n if lang in ALL_LANGUAGES:\n return lang\n first = lang.split('-')[0]\n if first in ALL_LANGUAGES:\n return first\n return None","title":""},{"docid":"45fcd201506a586053c88430aa858de5","score":"0.57824755","text":"def language(self):\n return self.json[\"response\"].get(\"language\", \"\")","title":""},{"docid":"8a89e360fd1b1356d970be4fb6b21d74","score":"0.57764006","text":"def test_23_language_detection_greek(self):\n res = detect_language(self.greek_text)\n self.assertEqual(res, 'el')\n return res","title":""},{"docid":"ef21f9e15c70ba159a29472690cf3950","score":"0.5774823","text":"def get_language(source, code, language=None):\n\n if language is not None:\n for l in extensions_mapping.values():\n if l.name == language:\n return l\n else:\n raise ValueError(\"Unknown forced language: \" + language)\n\n m = re.match(r'.*(\\..+)', os.path.basename(source))\n\n if m and m.group(1) in extensions_mapping:\n return extensions_mapping[m.group(1)]\n else:\n lang = lexers.guess_lexer(code).name.lower()\n for l in extensions_mapping.values():\n if l.name == lang:\n return l\n else:\n return None","title":""},{"docid":"8da823ae121d2b255c5a33172318a03e","score":"0.57725","text":"def Language(self):\n return \"\"","title":""},{"docid":"8bc68f83d53055dd7588e2b309bc48f3","score":"0.5762527","text":"def format_language(language):\n\n if language == ACCEPTED_LANGUAGES[0]:\n return 'eng'\n elif language == ACCEPTED_LANGUAGES[1]:\n return 'spa'\n elif language == ACCEPTED_LANGUAGES[2]:\n return 'fra'\n elif language == ACCEPTED_LANGUAGES[3]:\n return 'hin'","title":""},{"docid":"37f64e0382dccc912af07a8ecffe82e2","score":"0.5761884","text":"def get(ietf_tag: str) -> int:\n ietf_tag = ietf_tag.lower()\n languages = {\n \"en\": SupportedLanguage.EN,\n \"en-us\": SupportedLanguage.EN,\n \"en-gb\": SupportedLanguage.EN\n }\n\n return languages.get(ietf_tag, SupportedLanguage.EN)","title":""},{"docid":"49dcf7e294119248c293aa1a1ecfa77a","score":"0.5746692","text":"def get_language_from_message(message):\n if hasattr(message,'language'):\n return message.language\n return get_language_from_connection(message.persistant_connection)","title":""},{"docid":"fd487efa338808a4a785366461405f65","score":"0.5726152","text":"def language(self):\n\n return self._repo_data[\"language\"]","title":""},{"docid":"3dd19c7078cc54387873d7772600e4f7","score":"0.57207394","text":"def get_language(khoros_object, community_details=None):\n return get_community_field(khoros_object, 'language', community_details)","title":""},{"docid":"daadb6a97a97c036e36905d7fcc266de","score":"0.57153803","text":"def max_delang (self):\n return self._max_delang","title":""},{"docid":"a0c7697bba718c2fda31db17eabbad33","score":"0.5704921","text":"def get_default_language():\n return properties.LANGUAGE_CODE","title":""},{"docid":"4347a1ff6618c88e66f09364dd7fa2ae","score":"0.5693334","text":"def test_english(self):\n self.assertEqual(get_language('this is english'), 'en')","title":""},{"docid":"4459ad0d764cdc5ea1d93398d52beea2","score":"0.56914985","text":"def nb_language(self, engine_name, nb, language=None):\n return self.get_engine(engine_name).nb_language(nb, language)","title":""},{"docid":"8aa2db27f0e2c9c6eec736f9e249e08f","score":"0.5668158","text":"def get_language_code(self, handler):\n l = self.get_locale(handler)\n return str(l)","title":""},{"docid":"5ea780555d4bf7864d77a55a9e794d2e","score":"0.5646527","text":"def langsvlakheid(self):\n return self._langsvlakheid.get_waarde()","title":""},{"docid":"39076118f799675f003835a70c26b96e","score":"0.5592201","text":"def default_language(self):\n return self._lang","title":""},{"docid":"36cc39483704c665adf2d8a5f9ef33c2","score":"0.55814254","text":"def learning_language(self):\n if getattr(self, '_learning_language', None) is None:\n self._learning_language = self.app.get_setting(\n u'learning_language', u'en')\n return self._learning_language","title":""},{"docid":"597c596cd22ca4a0fb9414329da19b56","score":"0.5578478","text":"def get_language_choices():\n return properties.LANGUAGES","title":""},{"docid":"1314574931b7caa48350cdb3e69b9ef0","score":"0.5577607","text":"def language_GetCapLang(self):\n status = False\n ch_langs = ['de', 'deu', 'de-CH', 'ger', # Deutsch\n 'en', 'eng', 'en-US', 'en-GB', 'en-CA', # Englisch\n 'fr', 'fra', 'fr-CH', 'fre', # Französisch\n 'it', 'ita', 'it-CH', # Italienisch\n 'roh', 'rm'] # Romanisch\n \n url_status = False\n url_results = []\n langs_found = []\n results = []\n \n \"\"\"\n By GetCapabilities\n \"\"\"\n f = None\n if self.restful:\n url = urllib.basejoin(self.base_url, self.service_settings.REST.GetCapabilities)\n else:\n kvp = {\n 'request': 'GetCapabilities',\n 'service': self.service,\n 'version': self.version\n #'language':'de-CH'\n }\n url = self.build_kvp_url(self.base_url, kvp, swapcases=self.swapcases)\n try:\n f = URL2File(url, auth=self.auth)\n for line in f.readlines():\n # <Title xml:lang=\"fr\">\n l = re.compile('xml:lang=\"(\\w+)\"')\n found = l.findall(line)\n if found:\n langs_found.append(found[0])\n except Exception, e:\n url_results.append(\"%s for URL %s\" %(e, url))\n\n finally:\n if f:\n f.close()\n \n # Check Capabilities.Languages\n try:\n for lang in dict2list(self.gc_xmlroot.Languages.Language): \n langs_found.append(lang.value)\n results.append('Found following languages: ' + ', '.join(langs_found))\n except KeyError:\n results.append('XML Element \"Languages\" not found.')\n \n if DEBUG: print langs_found\n \n \"\"\"\n LANG-02\n Suche in Operation.Parameter(name=Language).Value\n \"\"\"\n # Get the Service Operations (same as in meta_ServiceOperations)\n \"\"\"\n if self.ows_common:\n #OWS_Common Workaround \n service_ops = dict2list(self.gc_xmlroot.OperationsMetadata.Operation)\n else:\n service_ops = dict2list(self.gc_xmlroot.Capability.Request)\n \n #if DEBUG: print service_ops\n langs_in_param_found = []\n for operation in service_ops:\n if not operation.has_key('Parameter'):\n continue\n else:\n params = dict2list(operation.Parameter)\n for param in params:\n if \"language\" == param.name.lower():\n for lang in dict2list(param.Value):\n langs_in_param_found.append(lang.value)\n langs_found.append(lang.value)\n \n if len(langs_in_param_found):\n self.setResultsOverview(\"LANG-02\", True, \"Found following languages: \" + ', '.join(langs_in_param_found))\n else:\n self.setResultsOverview(\"LANG-02\", False, \"Found no languages in Parameters\")\n\n \"\"\"\n\n \"\"\"\n By URL-naming\n LANG-03\n LANG-04\n \"\"\"\n #else:\n urls = self.base_url.split(\"/\")\n url_results = \"Service does not support languages via URL-Path\"\n for url_lang in urls:\n if url_lang in ch_langs:\n url_status = True\n langs_found.append(url_lang)\n results.append(\"Supported language %s found\" %url_lang)\n url_results = \"Supports languages via URL-Path '%s'\"%url_lang\n break\n \n \"\"\"\n Checking for HTTP status 300\n \"\"\"\n \n if url_status:\n http_status_code_redirect = 300\n http_status_code = None\n http_300 = False \n http_300_results = 'Server responded with HTTP status code 300 (multiple choices)'\n \n base_url = self.base_url.replace(\"/%s/\" %(url_lang), \"/\")\n url = self.build_kvp_url(base_url, kvp, self.swapcases)\n results.append('Checking for URL %s' %(url))\n try: \n f = URL2File(url, auth=self.auth)\n http_status_code = f.code \n except Exception, e:\n http_status_code = e.code\n finally:\n f.close()\n \n http_300 = http_status_code_redirect == http_status_code\n if not http_300:\n http_300_results = 'Server responded with HTTP status code %i (should be 300)' %(http_status_code)\n \n else:\n http_300 = False\n http_300_results = 'Service does not support redirection (HTTP Status Code 300)'\n self.setResultsOverview(\"LANG-04\", http_300, http_300_results)\n \n \"\"\"\n Checking equalness\n \"\"\"\n equal_langs = []\n \n for l in ch_langs:\n if l in langs_found:\n equal_langs.append(l)\n else:\n pass\n results.append(\"Found %s eCH-languages (%s) in GetCapabilities.\" %(len(equal_langs), ', '.join(equal_langs)))\n status = bool(len(equal_langs))\n \n if not status:\n results.append(\"Service does not support languages (via GetCapabilities)\")\n if not url_status:\n url_results = \"Service does not support languages (via URL)\"\n \n hints = \"LANG-xx\"\n # Anzahl > 0 der gefundenen übereinstimmenden Sprachangaben erfüllt LANG-01\n equal_langs = [\"'%s'\" %l for l in equal_langs]\n self.setResultsOverview(\"LANG-01\", status, results, data={'code':', '.join(equal_langs)})\n self.setResultsOverview(\"LANG-03\", url_status, url_results, data={'code':url_lang})\n\n #status = any([status, url_status, http_300, langs_in_param_found])\n status = any([status, url_status, http_300])\n return ResponseDict('language_GetCapLang', results, status, hints)","title":""},{"docid":"70af76ab2e7ccc1894522298d7193001","score":"0.5564876","text":"def language_tag(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"b825cbe6d7edd3f663e27a2df5e2a694","score":"0.55642813","text":"def get_language_code(language):\n\n # if SelfcheckLanguage[language] in SelfcheckLanguage:\n\n try:\n return SelfcheckLanguage[language].value\n except KeyError:\n return SelfcheckLanguage.UNKNOWN.value","title":""},{"docid":"b2b80e66f39eb3694cc93f0dfc6eb6c8","score":"0.55572534","text":"def _example_word(lang):\n return {\n \"de\": \"mann\",\n \"es\": \"hombre\"\n }.get(lang)","title":""},{"docid":"23e8ef306206adbf0712ef6783425a84","score":"0.5551857","text":"def test_22_language_detection_georgian(self):\n res = detect_language(self.georgian_text)\n self.assertEqual(res, 'ka')\n return res","title":""},{"docid":"bbe8eabf96228e3318a7dbef78bed21e","score":"0.55382514","text":"def get_restricted_translation(language):","title":""},{"docid":"6e1ae8d5c5b2b0da951bde342b697e7f","score":"0.55368966","text":"def detect_language(html) :\n h = html2text.HTML2Text()\n return langdetect.detect(h.handle(html))","title":""},{"docid":"f1b043acb4293797c4d7d8782085b0fb","score":"0.5533975","text":"def SelectLanguage(*langs):\n for lang in langs:\n if lang:\n supported_lang = SelectSupportedLanguage(lang)\n if supported_lang:\n return supported_lang\n # All arguments were None or invalid.\n return DEFAULT_LANGUAGE","title":""},{"docid":"b2519838fae7143708da9ef1a0071e33","score":"0.55263555","text":"def get_langinfo(isocode):\n page = \"http://www.ethnologue.com/language/\"+isocode","title":""},{"docid":"116ebdb2170d7d99284a0fc999bedc0c","score":"0.5515722","text":"def detect_language(text):\n\n languages = langdetect.detect_langs(text)\n # This is a list, I should return the topmost language 2 letters code in uppercase\n\n language = str(languages[0]).upper()\n language = language[0:2]\n return language","title":""},{"docid":"de840d1a4f23b9a8c04ad4ee0c78d02c","score":"0.5501173","text":"def get_language(self, request):\n if request.META.get('HTTP_ACCEPT_LANGUAGE'):\n best = self.get_best_language(\n request.META['HTTP_ACCEPT_LANGUAGE'])\n if best:\n return best\n return settings.LANGUAGE_CODE","title":""},{"docid":"812a0731fd886aff6bfc486fda7c291e","score":"0.5500768","text":"def detect_B(text):\n\n if text is None:\n return \"None\"\n try:\n lang = detect(text)\n\n if lang == 'es':\n return lang\n else:\n return 'en'\n except UnicodeDecodeError:\n return \"None\"","title":""},{"docid":"12550f06daa0c939cd1f11a17e4b4901","score":"0.54871047","text":"def find_language(regnskab_dict):\n for key, regnskab_tuples in regnskab_dict.items():\n for t in regnskab_tuples:\n if t.unitIdXbrl is not None and t.unitIdXbrl[0:5] == 'lang:':\n return t.unitIdXbrl[5:]\n return 'da'","title":""},{"docid":"c0c7a68ed0fd1e6022fd42027c80e24e","score":"0.54816854","text":"def nb_language(cls, nb, language=None):\n return nb_language(nb, language)","title":""},{"docid":"53339468ec2621b4d9b68bb8c646d467","score":"0.54690015","text":"def language_tag(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"5aceab5793405918a4af8ac38437e72d","score":"0.5465471","text":"def gengo_language_to_locale(lang):\n lang = REVERSE_LANGMAP.get(lang, lang)\n parts = lang.partition('-')\n return parts[0] + parts[1].replace('-', '_') + parts[2].upper()","title":""},{"docid":"fc684fc552261aa1a0a111055fcf3db9","score":"0.5461347","text":"def test_get_default_language():\n assert messages.get('UNKNOWN', 'foo') == messages.get('UNKNOWN', 'en')","title":""},{"docid":"b1490a48a304c69ef904edc4f23508b1","score":"0.5448692","text":"def get_language(self):\n return translation.get_language() # Assumes that middleware has set this properly.","title":""},{"docid":"4ba269d2ce9b75575efa709c03ac1679","score":"0.54374075","text":"def getLocations(self, language: str):\n if language not in self.locations.keys():\n return self.locations['de']\n else:\n return self.locations[language]","title":""},{"docid":"17e2768c96b0c43adf784c2cc7a648b3","score":"0.54363334","text":"def _language_name(self):\n languages = dict(settings.ALL_LANGUAGES)\n try:\n return languages[self.course_team.language]\n except KeyError:\n return self.course_team.language","title":""},{"docid":"c050815f7d7c5a0e33dbd062754bf878","score":"0.5428089","text":"def babel_language_in_locale(locale_id = 'en'):\n locale_obj = Locale.parse(flask_babel.get_locale())\n return locale_obj.get_language_name(locale_id)","title":""},{"docid":"6c2feee86d66db6f05c9ff0d67fc3f37","score":"0.5426774","text":"def get_default_programming_language(\n language: str\n) -> str:\n language = language.lower()\n for lang in config.DEFAULT_PROGRAMMING_LANGUAGES:\n if lang.replace('\\\\', '').lower() == language:\n return lang\n return \"\"","title":""},{"docid":"025879eba30135849ee96cfb440d0bcc","score":"0.54237074","text":"def language_version(self):\n return (None, None)","title":""},{"docid":"2e76f4cb6729101e7eac8a3c2918919f","score":"0.5418951","text":"def target_language(self):\n return self.target.config.name","title":""},{"docid":"ada6b3bf7115085ac89d052826564c84","score":"0.54109055","text":"def english(self) -> str:\n return self._english","title":""},{"docid":"277d65c1ed69f90c859004a3071a4fc7","score":"0.53834516","text":"def ui_language(self):\n return self._ui_language","title":""},{"docid":"90711d26c4fbf62f59ed7f992d8a1552","score":"0.5372895","text":"def canonizeLanguage(language):\n for code, name in LANGUAGES:\n if language == code or language == name:\n return code\n return ''","title":""},{"docid":"00223d98f7771684b2488a26b6afcac4","score":"0.53686786","text":"def LanguageIndex(self) -> int:","title":""},{"docid":"d041a335c9018f52897117de0d6b43ec","score":"0.5344354","text":"def current_language(request):\n languages = [x.split(';', 1)[0].lower() for x in request.META.get('HTTP_ACCEPT_LANGUAGE', '').split(',')]\n for language in languages:\n if language.startswith('zh'):\n return 'zh-tw'\n return 'en'","title":""},{"docid":"31c0766968b51ecda9a7dd834d2a9841","score":"0.53424793","text":"def get_lang(uid):\n return webdb.select('USERS', where='uid = \"' + str(uid) + '\"', limit=1)[0]['lang']","title":""},{"docid":"87e595a25126aeef4b825d780e5a1ebf","score":"0.5316689","text":"def get_lang_ext(self, lang):\n if lang not in self.language_extensions:\n return None\n return self.language_extensions[lang]","title":""},{"docid":"dc3a93bf0886f81449c29a183972b5bc","score":"0.53152317","text":"def get_language_with_ip(request):\n country = get_country_with_request(request)\n if country:\n return country.language_code\n else: # defaults to English\n return 'en'","title":""}],"string":"[\n {\n \"docid\": \"7d19c984c03b3e59c0e29927e52e446b\",\n \"score\": \"0.6914142\",\n \"text\": \"def get_language(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cf8daef8d0e9165e88916da0f08868\",\n \"score\": \"0.67045707\",\n \"text\": \"def getLanguage(self) -> ghidra.program.model.lang.Language:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88efbc8344a0bc887062d526ad6868f2\",\n \"score\": \"0.6524406\",\n \"text\": \"def Language(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f46e6f2f129aea0b62e208273c88ba\",\n \"score\": \"0.64493454\",\n \"text\": \"def get_translation(language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e349e11ef96d37ee720c9e44ff81c6\",\n \"score\": \"0.6397181\",\n \"text\": \"def language(self):\\n return self._mapped_lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83cfc614d26d41e5c5b4cd9dcfed6d54\",\n \"score\": \"0.6361355\",\n \"text\": \"def get_translated_languages():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72af2abe9ac887512114fc6b329f6a58\",\n \"score\": \"0.6298945\",\n \"text\": \"def language(self):\\n return getattr(\\n self,\\n constants.CONST_LANGUAGE,\\n constants.DEFAULT_LANGUAGE,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7374b79e2ca26f3f6ef3c6f14ae42a97\",\n \"score\": \"0.62752056\",\n \"text\": \"def getLanguage (self,c,setting):\\n \\n language = self.getString(c,setting)\\n # g.trace(setting,language)\\n \\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d018b5936457ce548d59d069e02048a5\",\n \"score\": \"0.626775\",\n \"text\": \"def get_language(event, context):\\n logger.info('Calling get_language with event')\\n logger.info(event)\\n\\n # Use UTF-8 encoding for comprehend\\n if 'Text' in event:\\n text = str(event['Text'])\\n else:\\n logger.error(\\\"There is no Text!\\\")\\n raise Exception('Please provide Text!')\\n\\n # comprehend accepts up to 5000 UTF-8 encoded characters\\n if len(text) >= 4900:\\n text = text[:4899]\\n elif len(text) < 20:\\n logger.warning(\\\"Text is shorter than 20 characters!\\\")\\n raise Exception('Please provide a text of at least 20 characters!')\\n # logger.info(text)\\n\\n try:\\n response = comprehend.detect_dominant_language(Text=text)\\n except ClientError as e:\\n logger.error(\\\"Received error: %s\\\", e, exc_info=True)\\n raise\\n except ParamValidationError as e:\\n logger.error(\\\"The provided parameters are incorrect: %s\\\", e, exc_info=True)\\n raise\\n\\n # logger.info(response)\\n return response['Languages'][0]['LanguageCode']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f4df655801a0f81560021370fb0325\",\n \"score\": \"0.6215751\",\n \"text\": \"def language(self):\\n try:\\n return self._get_response(\\n url=self.LANGID_URL, sentence=self.sentence)\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6852d214fd73ca3c8b3c76ed0880cbe\",\n \"score\": \"0.62144786\",\n \"text\": \"def lang(self):\\n # pick a sample to find language (don't pick the truth file)\\n sample = [choose for choose in os.listdir(self.folder)\\n if 'truth' not in choose][0]\\n with open(os.path.join(self.folder, sample), 'r') as sample_file:\\n first_line = sample_file.readlines()[0]\\n lang = [match.group('lang')\\n for match in re.finditer(Pan.LANG_REGEX, first_line)][0]\\n return lang.lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b70a35a3529c04521ca03c1783af78\",\n \"score\": \"0.62131405\",\n \"text\": \"def language(self):\\n return self.language_version()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762ed9baf7428d3c2c27c92906c3f34d\",\n \"score\": \"0.61848724\",\n \"text\": \"def language(self):\\n lang=None\\n if self.__dict__['TAG:language']:\\n lang=self.__dict__['TAG:language']\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9442ebc089fe4982af945a7c4eee87f\",\n \"score\": \"0.6183641\",\n \"text\": \"def lang(self):\\n return self.lex_meta.lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6c3d8673b836167af732a1190347c6\",\n \"score\": \"0.617456\",\n \"text\": \"def test_lang() -> str:\\n lang = locale.getdefaultlocale()[0][0:2]\\n if lang == 'fr':\\n return 'dtc'\\n return 'qdb'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86b093671c104aa8770479841f816a10\",\n \"score\": \"0.615984\",\n \"text\": \"def gncEmployeeGetLanguage(*args):\\n return _gnucash_core_c.gncEmployeeGetLanguage(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036561cb4fe47e070a4c9ddc370ac2e2\",\n \"score\": \"0.60845643\",\n \"text\": \"def get_language(self):\\n\\n return self.__msg_lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a60ae13aaed279959529adedcdb43d\",\n \"score\": \"0.6080517\",\n \"text\": \"def Language (self):\\n return (self.headers.get ('index_language', 'any'),\\n self.headers.get ('contents_language', 'any'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1790cd720d52235c595dab8ffb7999cd\",\n \"score\": \"0.6073051\",\n \"text\": \"def get_lang():\\n if os() == 'Windows':\\n # Windows\\n lang = locale.windows_locale[ctypes.windll.kernel32.GetUserDefaultUILanguage()]\\n else:\\n # Linux || MacOS\\n lang = locale.getlocale(locale.LC_MESSAGES)[0]\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f04f28b59d21d0f9e23d1b811974cc3\",\n \"score\": \"0.6020242\",\n \"text\": \"def moment_lang(self):\\n return load_moment_locales().get(self.lang, 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e019d75c7257253db6436ddc0730b7da\",\n \"score\": \"0.5989116\",\n \"text\": \"def lang(self):\\n return self._lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3065207a9592be2eab56e31bb0e0f886\",\n \"score\": \"0.59747165\",\n \"text\": \"def get_language(self):\\n locale = self.get_locale()\\n language = locale\\n if '_' in locale:\\n language = locale[0:locale.index('_')]\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5123c9ed66643d7c0daa4ede7f2b7ffc\",\n \"score\": \"0.5971464\",\n \"text\": \"def check_language(text):\\n if detect(text) == 'de':\\n language = 'de'\\n elif detect(text) == 'en':\\n language = 'en'\\n else:\\n language = None\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d1e9eae9ab232b88af6f359a4eb6c0\",\n \"score\": \"0.596331\",\n \"text\": \"def language_tag(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4045103df8f4bf07a4b594e60c7fc2\",\n \"score\": \"0.5960778\",\n \"text\": \"def get_locale():\\r\\n # this is a demo case, we use url to get locale\\r\\n code = request.args.get('lang', 'en')\\r\\n return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f6c63ad26a4e47296bddee9b9653f2\",\n \"score\": \"0.5934641\",\n \"text\": \"def lang_(self):\\n return self.doc.vocab.store[self.lex_meta.lang]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216919c5a4cf9279f362cc048048645b\",\n \"score\": \"0.5924688\",\n \"text\": \"def get_main_language(project_report):\\n required1 = {\\\"MetaDataCollector\\\": {\\\"main_language\\\": \\\"\\\"}}\\n required2 = {\\\"LanguageDetector\\\": {\\\"main_language\\\": \\\"\\\"}}\\n\\n if containedStructure(required1, project_report):\\n lang = project_report[\\\"MetaDataCollector\\\"][\\\"main_language\\\"]\\n return shorten_language(lang)\\n elif containedStructure(required2, project_report):\\n lang = project_report[\\\"LanguageDetector\\\"][\\\"main_language\\\"]\\n return shorten_language(lang)\\n return '-'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3125c2dce33044e62c80ca613fa15952\",\n \"score\": \"0.59202147\",\n \"text\": \"def convert_language(language):\\n\\n if language == 'StoreKing_NW':\\n language = 'Kannada'\\n if language not in LANGUAGES:\\n language = 'Hindi'\\n\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce21ad58cb3eab50ede578a17775fc6\",\n \"score\": \"0.59194607\",\n \"text\": \"def get_locale():\\n if \\\"lang\\\" in session:\\n return session[\\\"lang\\\"]\\n #Decides the most suitable language option\\n #fi=Finnish has higher priority\\n lang = request.accept_languages.best_match([\\\"fi\\\", \\\"en\\\"])\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb14f5c2099e436683e91382ce62e904\",\n \"score\": \"0.59086585\",\n \"text\": \"def language_code(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d7bb6cef50f1f302e8a531796e5ef0\",\n \"score\": \"0.590221\",\n \"text\": \"def __get_language__(self, lang_code: str) -> str:\\n\\n if lang_code not in self.ALLOWED_LANGUAGES.keys():\\n raise ValueError('{} is not a supported language code'.format(lang_code))\\n\\n return self.ALLOWED_LANGUAGES[lang_code]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c660a06708cf14ec63c36cbcdc98b5f0\",\n \"score\": \"0.5900211\",\n \"text\": \"def get_lang(country_iso: str) -> str:\\n res = country_lang.get(country_iso)\\n if res:\\n return res\\n else:\\n return 'en'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71b058ed50499dc5964b614bdebe35c\",\n \"score\": \"0.5899539\",\n \"text\": \"def get_lang(self, language):\\n if language not in self.compilers:\\n self.console.print(\\\"Language is not supported!\\\", style=\\\"bold red\\\")\\n Utils.close()\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f035ca7232dd83ae68d0fd8680c087\",\n \"score\": \"0.5898755\",\n \"text\": \"def getAvailableLanguages():\\n\\t#Make a list of all the locales found in NVDA's locale dir\\n\\tl=[x for x in os.listdir(\\\"locales\\\") if not x.startswith('.')]\\n\\tl=[x for x in l if os.path.isfile('locales/%s/LC_MESSAGES/twblue-documentation.mo' % x)]\\n\\t#Make sure that en (english) is in the list as it may not have any locale files, but is default\\n\\tif 'en' not in l:\\n\\t\\tl.append('en')\\n\\t\\tl.sort()\\n\\t#For each locale, ask Windows for its human readable display name\\n\\td=[]\\n\\tfor i in l:\\n\\t\\tdesc=getLanguageDescription(i)\\n\\t\\tlabel=\\\"%s, %s\\\"%(desc,i) if desc else i\\n\\t\\td.append(label)\\n\\t#include a 'user default, windows' language, which just represents the default language for this user account\\n\\tl.append(\\\"system\\\")\\n\\t# Translators: the label for the Windows default NVDA interface language.\\n\\td.append(_(\\\"User default\\\"))\\n\\t#return a zipped up version of both the lists (a list with tuples of locale,label)\\n\\treturn zip(l,d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8a9d1153b9d1d09c331da8bf1f45873\",\n \"score\": \"0.5883816\",\n \"text\": \"def get_language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29738bd87cc78dd4e2ff5aa73bc07f2f\",\n \"score\": \"0.58760846\",\n \"text\": \"def lang(self):\\n return self.config_core.get('lang')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cf60544184706f9f81cdc9296ec99b\",\n \"score\": \"0.5870653\",\n \"text\": \"def language(self) -> Optional[Text]:\\n\\n return self.get(\\\"language\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d004470495efbf7e4526a851502aa6\",\n \"score\": \"0.58666897\",\n \"text\": \"def l_get(\\n language_dict: Dict[str, Type[T_Lang]], update_msg_user_or_language_code: T_update_msg_user_or_language_code = None\\n) -> Type[T_Lang]:\\n lang = get_language_code(update_msg_user_or_language_code)\\n\\n # if it is None (default, or getting from message failed), use the default\\n if lang is None:\\n return language_dict['default']\\n # end if\\n\\n if lang in language_dict:\\n return language_dict[lang]\\n # end if\\n\\n # try replacing \\\"de_DE\\\" => \\\"de-DE\\\"\\n part = lang.replace(\\\"_\\\", \\\"-\\\")\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end of\\n\\n # try splitting it \\\"de-DE\\\" => \\\"de\\\"\\n part = lang.split('-')[0]\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end of\\n\\n # try splitting it \\\"de_DE\\\" => \\\"de\\\"\\n part = lang.split('_')[0]\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end if\\n\\n # nothing did match, use the default\\n return language_dict['default']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8235dc2fa6170ed344df28e1700dbbc2\",\n \"score\": \"0.5864678\",\n \"text\": \"def getLanguage(self):\\n spd = self.get(sessionPkgDataId, {})\\n return spd.get('language', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a9b23a1950dc54cc078c8a113ee638\",\n \"score\": \"0.58440614\",\n \"text\": \"def language(self) -> LangEnum:\\n return self._lang_enum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e305eccb0d97870a645edc54619fa11d\",\n \"score\": \"0.58301246\",\n \"text\": \"def get_language(self):\\n # Look through ancestors of this page for its language homepage\\n # The language homepage is located at depth 3\\n language_homepage = self.get_ancestors(inclusive=True).get(depth=3)\\n\\n # The slug of language homepages should always be set to the language code\\n return language_homepage.slug\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01a7c30c4fb5b54b7e1163687d05331e\",\n \"score\": \"0.58280015\",\n \"text\": \"def get_keyboard_language():\\n # only language not variant layout\\n user32 = ctypes.WinDLL('user32', use_last_error=True)\\n curr_window = user32.GetForegroundWindow()\\n thread_id = user32.GetWindowThreadProcessId(curr_window, 0)\\n klid = user32.GetKeyboardLayout(thread_id)\\n lid = klid & (2 ** 16 - 1)\\n lid_hex = f\\\"{lid:#0{6}x}\\\"\\n root = os.path.dirname(__file__)\\n with open(root + '/misc/win-language-id.json', 'r') as f:\\n win_layout = json.load(f)\\n return win_layout[lid_hex]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35060b60835a1adab92ba7649db7b1db\",\n \"score\": \"0.5827729\",\n \"text\": \"def language_code(self):\\n return self._language_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cd8d4c2b50df7167c55b050d8b4fed\",\n \"score\": \"0.57999927\",\n \"text\": \"def getLanguageDescription(language):\\n\\tdesc=None\\n\\tif platform.system() == \\\"Windows\\\":\\n\\t\\tLCID=localeNameToWindowsLCID(language)\\n\\t\\tif LCID!=0:\\n\\t\\t\\tbuf=ctypes.create_unicode_buffer(1024)\\n\\t\\t\\tif '_' not in language:\\n\\t\\t\\t\\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGDISPLAYNAME,buf,1024)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tres=0\\n\\t\\t\\tif res==0:\\n\\t\\t\\t\\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGUAGE,buf,1024)\\n\\t\\t\\tdesc=buf.value\\n\\telif platform.system() == \\\"Linux\\\" or not desc:\\n\\t\\tdesc={\\n\\t\\t\\t\\\"am\\\":pgettext(\\\"languageName\\\",\\\"Amharic\\\"),\\n\\t\\t\\t\\\"an\\\":pgettext(\\\"languageName\\\",\\\"Aragonese\\\"),\\n\\t\\t\\t\\\"es\\\":pgettext(\\\"languageName\\\",\\\"Spanish\\\"),\\n\\t\\t\\t\\\"pt\\\":pgettext(\\\"languageName\\\",\\\"Portuguese\\\"),\\n\\t\\t\\t\\\"ru\\\":pgettext(\\\"languageName\\\",\\\"Russian\\\"),\\n\\t\\t\\t\\\"it\\\":pgettext(\\\"languageName\\\",\\\"italian\\\"),\\n\\t\\t\\t\\\"tr\\\":pgettext(\\\"languageName\\\",\\\"Turkey\\\"),\\n\\t\\t\\t\\\"gl\\\":pgettext(\\\"languageName\\\",\\\"Galician\\\"),\\n\\t\\t\\t\\\"ca\\\":pgettext(\\\"languageName\\\",\\\"Catala\\\"),\\n\\t\\t\\t\\\"eu\\\":pgettext(\\\"languageName\\\",\\\"Vasque\\\"),\\n\\t\\t\\t\\\"pl\\\":pgettext(\\\"languageName\\\",\\\"polish\\\"),\\n\\t\\t\\t\\\"ar\\\":pgettext(\\\"languageName\\\",\\\"Arabic\\\"),\\n\\t\\t\\t\\\"ne\\\":pgettext(\\\"languageName\\\",\\\"Nepali\\\"),\\n\\t\\t\\t\\\"sr\\\":pgettext(\\\"languageName\\\",\\\"Serbian (Latin)\\\"),\\n\\t\\t}.get(language,None)\\n\\treturn desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e521127c2c1644e5b57354454f398c9\",\n \"score\": \"0.5784645\",\n \"text\": \"def SelectSupportedLanguage(language_codes):\\n for lang in map(NormalizeLang, language_codes.split(',')):\\n if lang in ALL_LANGUAGES:\\n return lang\\n first = lang.split('-')[0]\\n if first in ALL_LANGUAGES:\\n return first\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45fcd201506a586053c88430aa858de5\",\n \"score\": \"0.57824755\",\n \"text\": \"def language(self):\\n return self.json[\\\"response\\\"].get(\\\"language\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a89e360fd1b1356d970be4fb6b21d74\",\n \"score\": \"0.57764006\",\n \"text\": \"def test_23_language_detection_greek(self):\\n res = detect_language(self.greek_text)\\n self.assertEqual(res, 'el')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef21f9e15c70ba159a29472690cf3950\",\n \"score\": \"0.5774823\",\n \"text\": \"def get_language(source, code, language=None):\\n\\n if language is not None:\\n for l in extensions_mapping.values():\\n if l.name == language:\\n return l\\n else:\\n raise ValueError(\\\"Unknown forced language: \\\" + language)\\n\\n m = re.match(r'.*(\\\\..+)', os.path.basename(source))\\n\\n if m and m.group(1) in extensions_mapping:\\n return extensions_mapping[m.group(1)]\\n else:\\n lang = lexers.guess_lexer(code).name.lower()\\n for l in extensions_mapping.values():\\n if l.name == lang:\\n return l\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da823ae121d2b255c5a33172318a03e\",\n \"score\": \"0.57725\",\n \"text\": \"def Language(self):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc68f83d53055dd7588e2b309bc48f3\",\n \"score\": \"0.5762527\",\n \"text\": \"def format_language(language):\\n\\n if language == ACCEPTED_LANGUAGES[0]:\\n return 'eng'\\n elif language == ACCEPTED_LANGUAGES[1]:\\n return 'spa'\\n elif language == ACCEPTED_LANGUAGES[2]:\\n return 'fra'\\n elif language == ACCEPTED_LANGUAGES[3]:\\n return 'hin'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f64e0382dccc912af07a8ecffe82e2\",\n \"score\": \"0.5761884\",\n \"text\": \"def get(ietf_tag: str) -> int:\\n ietf_tag = ietf_tag.lower()\\n languages = {\\n \\\"en\\\": SupportedLanguage.EN,\\n \\\"en-us\\\": SupportedLanguage.EN,\\n \\\"en-gb\\\": SupportedLanguage.EN\\n }\\n\\n return languages.get(ietf_tag, SupportedLanguage.EN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49dcf7e294119248c293aa1a1ecfa77a\",\n \"score\": \"0.5746692\",\n \"text\": \"def get_language_from_message(message):\\n if hasattr(message,'language'):\\n return message.language\\n return get_language_from_connection(message.persistant_connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd487efa338808a4a785366461405f65\",\n \"score\": \"0.5726152\",\n \"text\": \"def language(self):\\n\\n return self._repo_data[\\\"language\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd19c7078cc54387873d7772600e4f7\",\n \"score\": \"0.57207394\",\n \"text\": \"def get_language(khoros_object, community_details=None):\\n return get_community_field(khoros_object, 'language', community_details)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daadb6a97a97c036e36905d7fcc266de\",\n \"score\": \"0.57153803\",\n \"text\": \"def max_delang (self):\\n return self._max_delang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c7697bba718c2fda31db17eabbad33\",\n \"score\": \"0.5704921\",\n \"text\": \"def get_default_language():\\n return properties.LANGUAGE_CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4347a1ff6618c88e66f09364dd7fa2ae\",\n \"score\": \"0.5693334\",\n \"text\": \"def test_english(self):\\n self.assertEqual(get_language('this is english'), 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4459ad0d764cdc5ea1d93398d52beea2\",\n \"score\": \"0.56914985\",\n \"text\": \"def nb_language(self, engine_name, nb, language=None):\\n return self.get_engine(engine_name).nb_language(nb, language)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa2db27f0e2c9c6eec736f9e249e08f\",\n \"score\": \"0.5668158\",\n \"text\": \"def get_language_code(self, handler):\\n l = self.get_locale(handler)\\n return str(l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea780555d4bf7864d77a55a9e794d2e\",\n \"score\": \"0.5646527\",\n \"text\": \"def langsvlakheid(self):\\n return self._langsvlakheid.get_waarde()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39076118f799675f003835a70c26b96e\",\n \"score\": \"0.5592201\",\n \"text\": \"def default_language(self):\\n return self._lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36cc39483704c665adf2d8a5f9ef33c2\",\n \"score\": \"0.55814254\",\n \"text\": \"def learning_language(self):\\n if getattr(self, '_learning_language', None) is None:\\n self._learning_language = self.app.get_setting(\\n u'learning_language', u'en')\\n return self._learning_language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597c596cd22ca4a0fb9414329da19b56\",\n \"score\": \"0.5578478\",\n \"text\": \"def get_language_choices():\\n return properties.LANGUAGES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1314574931b7caa48350cdb3e69b9ef0\",\n \"score\": \"0.5577607\",\n \"text\": \"def language_GetCapLang(self):\\n status = False\\n ch_langs = ['de', 'deu', 'de-CH', 'ger', # Deutsch\\n 'en', 'eng', 'en-US', 'en-GB', 'en-CA', # Englisch\\n 'fr', 'fra', 'fr-CH', 'fre', # Französisch\\n 'it', 'ita', 'it-CH', # Italienisch\\n 'roh', 'rm'] # Romanisch\\n \\n url_status = False\\n url_results = []\\n langs_found = []\\n results = []\\n \\n \\\"\\\"\\\"\\n By GetCapabilities\\n \\\"\\\"\\\"\\n f = None\\n if self.restful:\\n url = urllib.basejoin(self.base_url, self.service_settings.REST.GetCapabilities)\\n else:\\n kvp = {\\n 'request': 'GetCapabilities',\\n 'service': self.service,\\n 'version': self.version\\n #'language':'de-CH'\\n }\\n url = self.build_kvp_url(self.base_url, kvp, swapcases=self.swapcases)\\n try:\\n f = URL2File(url, auth=self.auth)\\n for line in f.readlines():\\n # <Title xml:lang=\\\"fr\\\">\\n l = re.compile('xml:lang=\\\"(\\\\w+)\\\"')\\n found = l.findall(line)\\n if found:\\n langs_found.append(found[0])\\n except Exception, e:\\n url_results.append(\\\"%s for URL %s\\\" %(e, url))\\n\\n finally:\\n if f:\\n f.close()\\n \\n # Check Capabilities.Languages\\n try:\\n for lang in dict2list(self.gc_xmlroot.Languages.Language): \\n langs_found.append(lang.value)\\n results.append('Found following languages: ' + ', '.join(langs_found))\\n except KeyError:\\n results.append('XML Element \\\"Languages\\\" not found.')\\n \\n if DEBUG: print langs_found\\n \\n \\\"\\\"\\\"\\n LANG-02\\n Suche in Operation.Parameter(name=Language).Value\\n \\\"\\\"\\\"\\n # Get the Service Operations (same as in meta_ServiceOperations)\\n \\\"\\\"\\\"\\n if self.ows_common:\\n #OWS_Common Workaround \\n service_ops = dict2list(self.gc_xmlroot.OperationsMetadata.Operation)\\n else:\\n service_ops = dict2list(self.gc_xmlroot.Capability.Request)\\n \\n #if DEBUG: print service_ops\\n langs_in_param_found = []\\n for operation in service_ops:\\n if not operation.has_key('Parameter'):\\n continue\\n else:\\n params = dict2list(operation.Parameter)\\n for param in params:\\n if \\\"language\\\" == param.name.lower():\\n for lang in dict2list(param.Value):\\n langs_in_param_found.append(lang.value)\\n langs_found.append(lang.value)\\n \\n if len(langs_in_param_found):\\n self.setResultsOverview(\\\"LANG-02\\\", True, \\\"Found following languages: \\\" + ', '.join(langs_in_param_found))\\n else:\\n self.setResultsOverview(\\\"LANG-02\\\", False, \\\"Found no languages in Parameters\\\")\\n\\n \\\"\\\"\\\"\\n\\n \\\"\\\"\\\"\\n By URL-naming\\n LANG-03\\n LANG-04\\n \\\"\\\"\\\"\\n #else:\\n urls = self.base_url.split(\\\"/\\\")\\n url_results = \\\"Service does not support languages via URL-Path\\\"\\n for url_lang in urls:\\n if url_lang in ch_langs:\\n url_status = True\\n langs_found.append(url_lang)\\n results.append(\\\"Supported language %s found\\\" %url_lang)\\n url_results = \\\"Supports languages via URL-Path '%s'\\\"%url_lang\\n break\\n \\n \\\"\\\"\\\"\\n Checking for HTTP status 300\\n \\\"\\\"\\\"\\n \\n if url_status:\\n http_status_code_redirect = 300\\n http_status_code = None\\n http_300 = False \\n http_300_results = 'Server responded with HTTP status code 300 (multiple choices)'\\n \\n base_url = self.base_url.replace(\\\"/%s/\\\" %(url_lang), \\\"/\\\")\\n url = self.build_kvp_url(base_url, kvp, self.swapcases)\\n results.append('Checking for URL %s' %(url))\\n try: \\n f = URL2File(url, auth=self.auth)\\n http_status_code = f.code \\n except Exception, e:\\n http_status_code = e.code\\n finally:\\n f.close()\\n \\n http_300 = http_status_code_redirect == http_status_code\\n if not http_300:\\n http_300_results = 'Server responded with HTTP status code %i (should be 300)' %(http_status_code)\\n \\n else:\\n http_300 = False\\n http_300_results = 'Service does not support redirection (HTTP Status Code 300)'\\n self.setResultsOverview(\\\"LANG-04\\\", http_300, http_300_results)\\n \\n \\\"\\\"\\\"\\n Checking equalness\\n \\\"\\\"\\\"\\n equal_langs = []\\n \\n for l in ch_langs:\\n if l in langs_found:\\n equal_langs.append(l)\\n else:\\n pass\\n results.append(\\\"Found %s eCH-languages (%s) in GetCapabilities.\\\" %(len(equal_langs), ', '.join(equal_langs)))\\n status = bool(len(equal_langs))\\n \\n if not status:\\n results.append(\\\"Service does not support languages (via GetCapabilities)\\\")\\n if not url_status:\\n url_results = \\\"Service does not support languages (via URL)\\\"\\n \\n hints = \\\"LANG-xx\\\"\\n # Anzahl > 0 der gefundenen übereinstimmenden Sprachangaben erfüllt LANG-01\\n equal_langs = [\\\"'%s'\\\" %l for l in equal_langs]\\n self.setResultsOverview(\\\"LANG-01\\\", status, results, data={'code':', '.join(equal_langs)})\\n self.setResultsOverview(\\\"LANG-03\\\", url_status, url_results, data={'code':url_lang})\\n\\n #status = any([status, url_status, http_300, langs_in_param_found])\\n status = any([status, url_status, http_300])\\n return ResponseDict('language_GetCapLang', results, status, hints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70af76ab2e7ccc1894522298d7193001\",\n \"score\": \"0.5564876\",\n \"text\": \"def language_tag(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b825cbe6d7edd3f663e27a2df5e2a694\",\n \"score\": \"0.55642813\",\n \"text\": \"def get_language_code(language):\\n\\n # if SelfcheckLanguage[language] in SelfcheckLanguage:\\n\\n try:\\n return SelfcheckLanguage[language].value\\n except KeyError:\\n return SelfcheckLanguage.UNKNOWN.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b80e66f39eb3694cc93f0dfc6eb6c8\",\n \"score\": \"0.55572534\",\n \"text\": \"def _example_word(lang):\\n return {\\n \\\"de\\\": \\\"mann\\\",\\n \\\"es\\\": \\\"hombre\\\"\\n }.get(lang)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e8ef306206adbf0712ef6783425a84\",\n \"score\": \"0.5551857\",\n \"text\": \"def test_22_language_detection_georgian(self):\\n res = detect_language(self.georgian_text)\\n self.assertEqual(res, 'ka')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe8eabf96228e3318a7dbef78bed21e\",\n \"score\": \"0.55382514\",\n \"text\": \"def get_restricted_translation(language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1ae8d5c5b2b0da951bde342b697e7f\",\n \"score\": \"0.55368966\",\n \"text\": \"def detect_language(html) :\\n h = html2text.HTML2Text()\\n return langdetect.detect(h.handle(html))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b043acb4293797c4d7d8782085b0fb\",\n \"score\": \"0.5533975\",\n \"text\": \"def SelectLanguage(*langs):\\n for lang in langs:\\n if lang:\\n supported_lang = SelectSupportedLanguage(lang)\\n if supported_lang:\\n return supported_lang\\n # All arguments were None or invalid.\\n return DEFAULT_LANGUAGE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2519838fae7143708da9ef1a0071e33\",\n \"score\": \"0.55263555\",\n \"text\": \"def get_langinfo(isocode):\\n page = \\\"http://www.ethnologue.com/language/\\\"+isocode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116ebdb2170d7d99284a0fc999bedc0c\",\n \"score\": \"0.5515722\",\n \"text\": \"def detect_language(text):\\n\\n languages = langdetect.detect_langs(text)\\n # This is a list, I should return the topmost language 2 letters code in uppercase\\n\\n language = str(languages[0]).upper()\\n language = language[0:2]\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de840d1a4f23b9a8c04ad4ee0c78d02c\",\n \"score\": \"0.5501173\",\n \"text\": \"def get_language(self, request):\\n if request.META.get('HTTP_ACCEPT_LANGUAGE'):\\n best = self.get_best_language(\\n request.META['HTTP_ACCEPT_LANGUAGE'])\\n if best:\\n return best\\n return settings.LANGUAGE_CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812a0731fd886aff6bfc486fda7c291e\",\n \"score\": \"0.5500768\",\n \"text\": \"def detect_B(text):\\n\\n if text is None:\\n return \\\"None\\\"\\n try:\\n lang = detect(text)\\n\\n if lang == 'es':\\n return lang\\n else:\\n return 'en'\\n except UnicodeDecodeError:\\n return \\\"None\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12550f06daa0c939cd1f11a17e4b4901\",\n \"score\": \"0.54871047\",\n \"text\": \"def find_language(regnskab_dict):\\n for key, regnskab_tuples in regnskab_dict.items():\\n for t in regnskab_tuples:\\n if t.unitIdXbrl is not None and t.unitIdXbrl[0:5] == 'lang:':\\n return t.unitIdXbrl[5:]\\n return 'da'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c7a68ed0fd1e6022fd42027c80e24e\",\n \"score\": \"0.54816854\",\n \"text\": \"def nb_language(cls, nb, language=None):\\n return nb_language(nb, language)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53339468ec2621b4d9b68bb8c646d467\",\n \"score\": \"0.54690015\",\n \"text\": \"def language_tag(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aceab5793405918a4af8ac38437e72d\",\n \"score\": \"0.5465471\",\n \"text\": \"def gengo_language_to_locale(lang):\\n lang = REVERSE_LANGMAP.get(lang, lang)\\n parts = lang.partition('-')\\n return parts[0] + parts[1].replace('-', '_') + parts[2].upper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc684fc552261aa1a0a111055fcf3db9\",\n \"score\": \"0.5461347\",\n \"text\": \"def test_get_default_language():\\n assert messages.get('UNKNOWN', 'foo') == messages.get('UNKNOWN', 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1490a48a304c69ef904edc4f23508b1\",\n \"score\": \"0.5448692\",\n \"text\": \"def get_language(self):\\n return translation.get_language() # Assumes that middleware has set this properly.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba269d2ce9b75575efa709c03ac1679\",\n \"score\": \"0.54374075\",\n \"text\": \"def getLocations(self, language: str):\\n if language not in self.locations.keys():\\n return self.locations['de']\\n else:\\n return self.locations[language]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e2768c96b0c43adf784c2cc7a648b3\",\n \"score\": \"0.54363334\",\n \"text\": \"def _language_name(self):\\n languages = dict(settings.ALL_LANGUAGES)\\n try:\\n return languages[self.course_team.language]\\n except KeyError:\\n return self.course_team.language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c050815f7d7c5a0e33dbd062754bf878\",\n \"score\": \"0.5428089\",\n \"text\": \"def babel_language_in_locale(locale_id = 'en'):\\n locale_obj = Locale.parse(flask_babel.get_locale())\\n return locale_obj.get_language_name(locale_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2feee86d66db6f05c9ff0d67fc3f37\",\n \"score\": \"0.5426774\",\n \"text\": \"def get_default_programming_language(\\n language: str\\n) -> str:\\n language = language.lower()\\n for lang in config.DEFAULT_PROGRAMMING_LANGUAGES:\\n if lang.replace('\\\\\\\\', '').lower() == language:\\n return lang\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025879eba30135849ee96cfb440d0bcc\",\n \"score\": \"0.54237074\",\n \"text\": \"def language_version(self):\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e76f4cb6729101e7eac8a3c2918919f\",\n \"score\": \"0.5418951\",\n \"text\": \"def target_language(self):\\n return self.target.config.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada6b3bf7115085ac89d052826564c84\",\n \"score\": \"0.54109055\",\n \"text\": \"def english(self) -> str:\\n return self._english\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277d65c1ed69f90c859004a3071a4fc7\",\n \"score\": \"0.53834516\",\n \"text\": \"def ui_language(self):\\n return self._ui_language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90711d26c4fbf62f59ed7f992d8a1552\",\n \"score\": \"0.5372895\",\n \"text\": \"def canonizeLanguage(language):\\n for code, name in LANGUAGES:\\n if language == code or language == name:\\n return code\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00223d98f7771684b2488a26b6afcac4\",\n \"score\": \"0.53686786\",\n \"text\": \"def LanguageIndex(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d041a335c9018f52897117de0d6b43ec\",\n \"score\": \"0.5344354\",\n \"text\": \"def current_language(request):\\n languages = [x.split(';', 1)[0].lower() for x in request.META.get('HTTP_ACCEPT_LANGUAGE', '').split(',')]\\n for language in languages:\\n if language.startswith('zh'):\\n return 'zh-tw'\\n return 'en'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c0766968b51ecda9a7dd834d2a9841\",\n \"score\": \"0.53424793\",\n \"text\": \"def get_lang(uid):\\n return webdb.select('USERS', where='uid = \\\"' + str(uid) + '\\\"', limit=1)[0]['lang']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e595a25126aeef4b825d780e5a1ebf\",\n \"score\": \"0.5316689\",\n \"text\": \"def get_lang_ext(self, lang):\\n if lang not in self.language_extensions:\\n return None\\n return self.language_extensions[lang]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3a93bf0886f81449c29a183972b5bc\",\n \"score\": \"0.53152317\",\n \"text\": \"def get_language_with_ip(request):\\n country = get_country_with_request(request)\\n if country:\\n return country.language_code\\n else: # defaults to English\\n return 'en'\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":798,"cells":{"query_id":{"kind":"string","value":"dcb6c0a8a52a7bb0ac92e1bdf7df1718"},"query":{"kind":"string","value":"get all the bookmars that a certai project has"},"positive_passages":{"kind":"list like","value":[{"docid":"4705871157183a2403386664a34c0255","score":"0.0","text":"def get_projectBookmarks(self, projectId):\n from Bookmark import Bookmark\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from bookmark where project=%s', (projectId,))\n bookmarks = list()\n for row in cursor:\n bookmark = Bookmark(row[0], row[1])\n bookmarks.append(bookmark)\n return bookmarks","title":""}],"string":"[\n {\n \"docid\": \"4705871157183a2403386664a34c0255\",\n \"score\": \"0.0\",\n \"text\": \"def get_projectBookmarks(self, projectId):\\n from Bookmark import Bookmark\\n cursor = self.dbconnect.get_cursor()\\n cursor.execute('select * from bookmark where project=%s', (projectId,))\\n bookmarks = list()\\n for row in cursor:\\n bookmark = Bookmark(row[0], row[1])\\n bookmarks.append(bookmark)\\n return bookmarks\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"edff7cd81254a3f14281af7177212839","score":"0.6072002","text":"def getBooks():\n ret = {}\n books = []\n iter = model.getBooks()\n while iter.next():\n books.append((iter.id, iter.name))\n\n ret['books'] = books\n return ret","title":""},{"docid":"c20ccd70c0b4dc0f21126c5b35a2add6","score":"0.60219944","text":"def get_all_books():\n\n # return Book.query.all()\n return Book.query.order_by(Book.title).all()","title":""},{"docid":"7bf9f7f1f1c9dad337225ac773f549ca","score":"0.5911421","text":"def books(self):\n return self._results_dict['books']","title":""},{"docid":"ffaac922289e589946ee40ee82d085cd","score":"0.59017754","text":"def makeBooksArray():\n books=[]\n metadata = metainfo.readmetadata()\n for key in metadata:\n if( (len(metadata[key]['subjects']) > 0) and( metadata[key]['subjects'] is not None) and (metadata[key]['downloads']>75) and ( metadata[key]['author'] is not None) and (\"en\" in metadata[key]['language']) ):\n #authorNames = extractNames(metadata[key]['author'])\n genreString = ', '.join(metadata[key]['subjects'])\n books.append(Book(key,metadata[key]['title'],metadata[key]['author'],genreString,0,0))\n return books","title":""},{"docid":"dd0291c5e68e86d1df97ac53b11c36a3","score":"0.5664254","text":"def bookkeeping_all():\n result = handlers.BookKeeping().retrieve_bookkeeping_all()\n return result","title":""},{"docid":"d1614d4de7151421f0c9791faa015e5c","score":"0.5599402","text":"def get_all_books() -> List[Dict]:\n with open(BOOKS, 'r') as in_file:\n return [_parse_from_line(line) for line in in_file]","title":""},{"docid":"237be531bc784d39ef4a4f3921d9d799","score":"0.5585883","text":"def get_books():\n books = Book.query.all()\n json_books = []\n for book in books:\n json_books.append(book.to_json())\n return jsonify(json_books), 200","title":""},{"docid":"ef68a6b4baab0918e4133a86782e5c38","score":"0.5565488","text":"def getClips(book_id):\n ret = {}\n clips = []\n it = model.getClipsByBookId(book_id)\n while it.next():\n clips.append((it.id, it.content))\n pass\n\n ret['clips'] = clips\n return ret","title":""},{"docid":"9822c9b8267f4bbe3be1a2a68d825825","score":"0.5564752","text":"def books():\n import raw\n return jsonify(raw.get_books())","title":""},{"docid":"dbc1d921dc9c507e31bd65a8ccb389bf","score":"0.55092233","text":"def get_book(self, top_n=None):\n return {\n 'asks': self.get_asks(top_n=top_n),\n 'bids': self.get_bids(top_n=top_n)\n }","title":""},{"docid":"97c3fb06b8fb3ae945e9ec38ceb52e37","score":"0.5496954","text":"def get_all_books_alphabetical(self):\n raise NotImplementedError","title":""},{"docid":"4e546d6dc1b8c1cf1b01fb12e3d27bfc","score":"0.54952437","text":"def get_all_books_release_year(self):\n raise NotImplementedError","title":""},{"docid":"3530625f47dfbaac23e3bb2548db518b","score":"0.5468389","text":"def getBorrowedBooks(self):\n return self.__borrowedBooks","title":""},{"docid":"9732865d0e328131cc0a5c7aed478c34","score":"0.546294","text":"def get_all_books(self, book_type):\n validate_type(book_type)\n return [book.serialize() for book in self.list_books if book.type == Type(book_type)]","title":""},{"docid":"1a73881df25c2175e71e8c0915b410a8","score":"0.5461085","text":"def get_bible(out):\n for book in BOOKS:\n book_chapters = IBook(book).chapter_to_text(version=\"basicenglish\")\n with open(Path(out) / f\"{book}.json\", \"w\") as out_json:\n json.dump(book_chapters, out_json)","title":""},{"docid":"df876f5293768f809cb674acdd1282ae","score":"0.5460983","text":"def get_books(self):\n files = [f for f in listdir(self.text_dir)]\n for author, title, period, url in self.json():\n filename = format_filename(author, title)\n if not filename in files:\n logger.debug(\"Getting %s\" % filename)\n book = self.extractor.download_book(url, False, author, title, period)\n else:\n logger.debug(\"%s already downloaded\" % filename)","title":""},{"docid":"1c25455989ae0ff94927d623ecc64382","score":"0.54381627","text":"def getNyTimesBooks():\n lists = [lst['list_name_encoded'] for lst in requests.get('https://api.nytimes.com/svc/books/v3/lists/names.json?api-key=1bfa24a95061415dbc8d4a4f136329a5').json()['results']]\n global printout\n for l in lists[:1]:\n results = requests.get('https://api.nytimes.com/svc/books/v3/lists.json?list='+l+'&api-key=1bfa24a95061415dbc8d4a4f136329a5').json()\n for r in results['results']:\n isbn = r['book_details'][0]['primary_isbn13']\n list = r['list_name']\n try:\n GRBook_req = requests.get('https://www.goodreads.com/book/isbn_to_id/'+isbn+'?key='+API_KEY['GOODREADS'])\n if(GRBook_req.status_code == 200):\n getGRBookByID(int(GRBook_req.text), list)\n except ExpatError as e:\n print(e)","title":""},{"docid":"48efe85434fc0b68dd59a1906bbfe648","score":"0.54263264","text":"def citations(self):\n return []","title":""},{"docid":"48ee710991bee60d2ba6ceafc328780f","score":"0.5416807","text":"def getLooks(self):\n pass","title":""},{"docid":"b3cb373aca17c84403726111cf0ef93f","score":"0.5414597","text":"def report_books(self):\n for book in self._books.itervalues():\n print(book.report())","title":""},{"docid":"6304b84bc10f74f192ff44ede1114e7a","score":"0.5382276","text":"def get_all_project_details() -> list:\n\n list_project = db_connector.collection(Collections.PROJECTS).find(\n {}, {\"_id\": 0}).sort(\"project_name\", pymongo.ASCENDING)\n list_projects_to_be_send = []\n for project in list_project:\n list_projects_to_be_send.append(project)\n return list_projects_to_be_send","title":""},{"docid":"672c738042ef32362a7eaa5ce36e731a","score":"0.5363474","text":"def get_chapters():\n q = 'project/'\n chapters = exec_jira_query(q)\n tcs = []\n for c in chapters:\n k = c['key']\n if k in relevant_chapter_keys:\n tcs.append(_build_chapter(c))\n return tcs","title":""},{"docid":"8ad5bfe68833e5763701cec6d8345a9c","score":"0.5345248","text":"def get_books(self, shelf):\n titles = self.parse_titles(shelf)\n isbns = self.parse_isbns(shelf)\n isbn13s = self.parse_isbn13s(shelf)\n authors = self.parse_author(shelf)\n\n books = []\n for i in range(len(titles)):\n b = Book(titles[i], isbns[i], isbn13s[i], authors[i])\n books.append(b)\n return books","title":""},{"docid":"74cf32b3febfbaa7e8c684f007192f04","score":"0.53447914","text":"def books(self):\n if not hasattr(self, '_books'):\n self._books = list(Bestseller.select().where(Bestseller.author == self))\n\n return self._books","title":""},{"docid":"e1a081d8885482958cb5ffbf91794e50","score":"0.53246725","text":"def bookshelves_subjects(self, book_id):\n select = \"SELECT DISTINCT ?subject ?bookshelf\"\n pattern = f\"\"\"\n WHERE {{\n <{book_id}> dcterms:subject [dcterms:title ?subject];\n pgterms:bookshelf [dcterms:title ?bookshelf] ;\n dcterms:type dcmitype:Text. \n }}\n ORDER BY ?subject\n \"\"\"\n query = self._namespace + select + pattern\n query = self._get_query_results(query)\n\n subjects = [result[\"subject\"] for result in query]\n bookshelves = [result[\"bookshelf\"] for result in query]\n\n return {\"bookshelves\": set(bookshelves), \"subjects\": set(subjects)}","title":""},{"docid":"035f5538e005f363b6d8bec20bd36a8e","score":"0.5317984","text":"def book_list(username):\n reviews=Review.objects.filter(creator__username__contains=username)\n return {'books_read':[r.book.title for r in reviews]}","title":""},{"docid":"bd584a6e594699b465eb8376480b73b8","score":"0.5293692","text":"def get_all_book_ids(self):\n raise NotImplementedError","title":""},{"docid":"be9122f82f2ad38b2c9797839d77dae1","score":"0.5280353","text":"def test_get_all_company_fundamentals(self):\n pass","title":""},{"docid":"a1fb51aa792d13daf074d9ca509842b5","score":"0.5274824","text":"def get_bibliography(self, id):\n select = \"SELECT DISTINCT ?title\"\n pattern = f\"\"\"\n WHERE {{\n ?book_id purl:creator <{id}> .\n {self._books}\n }}\n ORDER BY ?title\n \"\"\"\n\n query = self._namespace + select + pattern\n return [result[\"title\"] for result in self._get_query_results(query)]","title":""},{"docid":"35ed5a246c763eda36d66954f9ac9472","score":"0.5273621","text":"def getCaptials():\n query_strings = request.args.get('query')\n search_strings = request.args.get('search')\n book = capital.Capital()\n if not query_strings and not search_strings:\n return jsonify(book.fetch_20Capitals()), 200\n else:\n result = book.fetch_capitals(query_strings, search_strings)\n return jsonify(result), 200","title":""},{"docid":"f94f6c712c4fb595164c705a98aca96a","score":"0.5262072","text":"def report_all_books(self):\n print(\"= All Books at {library} =\".format(library=self))\n print(\"+ {:^30} | {:^30} | {:^10} +\"\n .format(\"Title\", \"Author\", \"Copies\"))\n for shelf in self._shelves.itervalues():\n print(\"Shelf - {shelf}\".format(shelf=shelf))\n shelf.report_books()","title":""},{"docid":"852fd0932e249ca1b9b0513846f2beb5","score":"0.52414143","text":"def get_all(self):\n\n self._init_dict_chal_keys()\n\n # REC FUTURE BIG : woups. Seems that underscore is a special char in the LIKE clauses. Will fix that later.\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n Teams.id.label('team_id'), Teams.name.label('team_name')\n ).join(Teams).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, award.team_id, award.team_name, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Hides descrip, icon and scores that the team should not know.\n key_ids_of_team = [\n award_infos['key_id']\n for award_id, _, award_team_id, __, award_infos in awards\n if award_team_id == self.team_id ]\n for award in awards:\n award_id, _, award_team_id, __, award_infos = award\n if award_infos['key_id'] not in key_ids_of_team and not award_infos['public']:\n award_infos['congrat_msg'] = None\n award_infos['congrat_img_url'] = None\n award_infos['score'] = None\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date.strftime('%Y-%m-%d %H:%M:%S'), award_team_name, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_team_id, award_team_name, award_infos in awards ]\n return awards","title":""},{"docid":"a10e9faa2f17a446fabe0eb42492f79a","score":"0.52363074","text":"def book(items):\n list1 = [ ]\n for i in items:\n if i[1] == 'book':\n list1.append(i)\n \n\n #print(list1)\n for i in list1:\n print(i[0],i[1],i[2],i[3])\n \n #print('\\n')\n #print(\"Book Category List: \")","title":""},{"docid":"9011e7ed69e422937eb5327e0fd5a057","score":"0.52341586","text":"def get_popularAuthors():","title":""},{"docid":"5bae812189214d0eca9a33a1b8283017","score":"0.5205308","text":"def all_books(self, date_format=DATE_FORMAT):\n return self.execute(queries.ALL_BOOKS, (date_format,)).fetchall()","title":""},{"docid":"8dab7fefb4df6626bf48f6bf501e8abe","score":"0.52029043","text":"def fetch_titles_from_page(page_number, get_books=True) -> ([Book], bool):\n assert type(page_number) is int\n assert type(get_books) is bool\n\n logger.debug(f\"Fetching catalog page #{page_number}... \")\n url = TITLES_URL + f\"&search_page={page_number}\"\n result = requests.get(url, headers=_get_scrape_headers())\n\n if result.status_code != 200:\n return [], False\n\n json_result = json.loads(result.text)\n if json_result[\"status\"] != \"SUCCESS\":\n return [], False\n\n if json_result[\"results\"] == \"No results\":\n return [], False\n\n books = []\n if get_books:\n html_results = json_result[\"results\"]\n scraper = BeautifulSoup(html_results, 'html.parser')\n catalog_results = scraper.find_all(\"li\", class_=\"catalog-result\")\n\n if catalog_results:\n for catalog_result in catalog_results:\n book = Book()\n result_data = catalog_result.find(\"div\", class_=\"result-data\")\n book.title = result_data.a.text.strip()\n # Remove enclosing \" or ' if any\n if book.title[0] is \"\\\"\" or book.title[0] is \"'\":\n book.title = book.title[1:]\n\n if book.title[-1] is \"\\\"\" or book.title[-1] is \"'\":\n book.title = book.title[:-1]\n\n book.url = result_data.a.attrs[\"href\"]\n book_author = result_data.find(class_=\"book-author\")\n if book_author and book_author.text:\n book.author = book_author.text.strip()\n else:\n logger.error(f\"Failed to scrape the author for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n if book_author and book_author.a and book_author.a.attrs[\"href\"]:\n book.author_url = book_author.a.attrs[\"href\"]\n else:\n # Books with various authors don't have an author url so this might be fine\n logger.warn(f\"Failed to scrape the author url for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n book.download_url = catalog_result.find(class_=\"download-btn\").a.attrs[\"href\"]\n book.size = catalog_result.find(class_=\"download-btn\").span.text.strip()\n books.append(book)\n logger.debug(f\"Fetched metadata for {len(books)} books from page #{page_number}\")\n\n else:\n logger.warn(f\"Failed to fetch books from the catalog page #{page_number}\")\n\n else:\n logger.debug(f\"Skipping scraping page #{page_number} for book metadata...\")\n\n return books, True","title":""},{"docid":"24177aea31d1974e83f92029e9fdd9e4","score":"0.51874995","text":"def get_project_cla_groups() -> List[dict]:\n return [project.to_dict() for project in get_project_cla_group_instance().all()]","title":""},{"docid":"20a060a2a5bce25663f6e65a1093082b","score":"0.51766706","text":"def get_book_all(self) -> (tuple, list):\n sql_expr = \\\n '''\n SELECT * FROM Books;\n '''\n sql_result = self._my_connect.execute_sql_read(sql_expr)\n return sql_result","title":""},{"docid":"4e5e47b31b5d480b438d8b922ac26c47","score":"0.5172181","text":"def allReviews(self,book):\n return self.query.filter_by(book_id=book.id).all()","title":""},{"docid":"c34a455b5b6e5dacc98dd48267a7c8db","score":"0.5162416","text":"def get_all_obgyn():\n \n ##first find where this condition is true for facility for obgyn\n obgyn_facility_list = Facility.query.filter_by(facility_category_name_english='Obstetrics and Gynecology').all()\n \n return obgyn_facility_list","title":""},{"docid":"36d5129ff7c35b11a63cd60cba04f239","score":"0.51620466","text":"def get_bible_books() -> {int: dict}:\n genres = get_bible_book_genres()\n\n with open(KEY_ENGLISH_PATH, 'r') as csvfile:\n reader = csv.reader(csvfile)\n headers = next(reader)\n\n book_index = headers.index(BOOK_KEY)\n name_index = headers.index(NAME_KEY)\n testament_index = headers.index(TESTAMENT_KEY)\n genre_index = headers.index(GENRE_KEY)\n dataset_index = headers.index(DATASET_KEY)\n\n return {\n int(book[book_index]): {\n 'name': book[name_index],\n 'testament': book[testament_index],\n 'genre_id': int(book[genre_index]),\n 'genre': genres[int(book[genre_index])],\n 'dataset': book[dataset_index]\n } for book in reader\n }","title":""},{"docid":"d1039cac6e05df4019fe627b9cd9b488","score":"0.5157313","text":"def harvest_all():\n db = db_connect()\n dois = None\n get_commits()\n get_citations(db, dois)\n get_mentions(since_version=None)\n list_records(dois)","title":""},{"docid":"7ea38c63d3d310e833fa6010f016c207","score":"0.5141538","text":"def cli(ctx, biomaterial_name=\"\", provider_id=\"\", biomaterial_id=\"\", organism_id=\"\", dbxref_id=\"\"):\n return ctx.gi.expression.get_biomaterials(biomaterial_name=biomaterial_name, provider_id=provider_id, biomaterial_id=biomaterial_id, organism_id=organism_id, dbxref_id=dbxref_id)","title":""},{"docid":"0552a8791165f00bd82c35a9076ab387","score":"0.5130115","text":"def get_test_bible_book_ids() -> {int}:\n return {book_id for (book_id, book) in get_bible_books().items() if book['dataset'] == 'test' }","title":""},{"docid":"b90c8edc53393d9501476ac214728a50","score":"0.5111732","text":"def get_all_books():\n\n book_info = db.books.find({}, {\"_id\": 0})\n book_info_cur = list(book_info)\n # print(book_info_cur[0][\"rating\"])\n sorted_cur = sorted(book_info_cur, key = lambda i: int(i.get('rating', 0)), reverse=True)\n if len(book_info_cur) != 0:\n return json.dumps(sorted_cur, indent=4), 200\n else:\n return \"Empty book collection in database\", 404","title":""},{"docid":"10b924ef88152a9fac406b6d482606bf","score":"0.51091206","text":"def get_all_cb_comps():\n u = urllib2.urlopen('http://api.crunchbase.com/v/1/companies.js')\n \n comps = u.read()\n\n return comps","title":""},{"docid":"37744c1787adacba8b8ce6ee9b38eb8a","score":"0.5083007","text":"def fetch_all_books(start_page=1, end_page=MAX_KNOWN_PAGE, need_update_page=False) -> [Book]:\n assert type(start_page) is int\n assert type(end_page) is int\n assert start_page < end_page\n assert type(need_update_page) is bool\n\n while need_update_page:\n logger.debug(f\"Checking if the # of LibriVox catalog pages is larger than {end_page}...\")\n _, need_update_page = fetch_titles_from_page(end_page, False)\n if need_update_page:\n end_page += 1\n logger.debug(f\"Found new catalog page #{end_page}\")\n\n logger.info(f\"Fetching LibriVox's book catalog from pages #{start_page} till #{end_page}...\")\n\n all_books = []\n \"\"\"\n # Commenting out multiprocess code for now as it crashes in Python 3.6.2 with the error:\n # \"__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.\"\n fetched_titles = []\n with Pool(NUM_PROCESSES) as pool:\n fetched_titles = pool.map(fetch_titles_from_page, [n for n in range(start_page, end_page)])\n for result in fetched_titles:\n if result and result[1]:\n all_books.extend(result[0])\n \"\"\"\n for page_num in range(start_page, end_page):\n catalog_page, could_fetch = fetch_titles_from_page(page_num)\n if could_fetch and catalog_page:\n all_books.extend(catalog_page)\n\n return all_books","title":""},{"docid":"8f76e593d6b23f2a302b628d8dc58ba0","score":"0.5065584","text":"def listConsents(self):\n Response = self.make_request()\n return Response","title":""},{"docid":"8e6648cba3aa87f3160051c3d7766ec1","score":"0.5063357","text":"def get_all(self):\n return [DocumentCorpus(self.collection, document) for document in self.collection.find()]","title":""},{"docid":"df84247f23d395f335bc7958cdf6d32f","score":"0.5062308","text":"def fetchCompanies():\n return desk.companies.list_companies()","title":""},{"docid":"afb6780e1b14530393c586bdff951aa2","score":"0.50400996","text":"def get_citations_needed_report(URL):\n # print('top report')\n response = requests.get(URL)\n content = response.content\n soup = BeautifulSoup(content, 'html.parser')\n result = soup.find_all('a', href=\"/wiki/Wikipedia:Citation_needed\")\n\n dictionary = []\n\n for link in result:\n P = link.parent.parent.parent\n P = P.text.strip()\n dictionary.append(P)\n\n # print('above dictionary')\n for P in dictionary:\n print(P, sep='\\n')\n print('\\n')\n\n return dictionary","title":""},{"docid":"2c0aea7c5e824b0c9aecad3c418b9157","score":"0.5029921","text":"def books_title(self):\n return [i.title for i in shelf.books_list]","title":""},{"docid":"a1ad1e8add187059eb6f82e5671699c0","score":"0.5021048","text":"def getBucketlists():","title":""},{"docid":"900b206cfe54379257569656e674a907","score":"0.5011742","text":"def test_books_get(self):\n pass","title":""},{"docid":"c0920b7316709655aa523d2dd5b7398d","score":"0.49976185","text":"def fetch_all_chapters(book) -> [Chapter]:\n logger.debug(f\"Downloading info for chapters in book: \\\"{book.title[:70]}\\\"...\")\n session = download_session.make_session()\n try:\n book_page = session.get(book.url, headers=_get_scrape_headers(), timeout=70)\n except download_session.get_download_exceptions() as e:\n logger.error(f\"Scraping timed out to download chapters from book \\\"{book.url}\\\"\")\n logger.error(e)\n return []\n\n if book_page.status_code != 200:\n logger.warn(f\"Failed to download chapters information for book \\\"{book.url}\\\"\")\n return []\n\n scraper = BeautifulSoup(book_page.text, \"html.parser\")\n _fetch_missing_book_metadata(book, scraper)\n\n # Get the chapters information\n chapters = []\n chapters_table = scraper.find(\"table\", class_=\"chapter-download\")\n if not chapters_table:\n logger.error(f\"Scraping failed to find the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n chapter_rows = chapters_table.find_all(\"tr\")[1:] # The first row is the table headers\n if not chapter_rows:\n logger.error(f\"Scraping failed to find the chapters rows in the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n num_row_elements = len(chapter_rows[0].find_all(\"td\"))\n if num_row_elements == 7:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.author = row_elements[2].text.strip()\n if row_elements[2].a:\n chapter.author_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.source_text = row_elements[3].text.strip()\n if row_elements[3].a:\n chapter.source_text_url = row_elements[3].a.attrs[\"href\"]\n\n if len(row_elements) > 4:\n chapter.reader_name = row_elements[4].text.strip()\n if row_elements[4].a:\n chapter.reader_url = row_elements[4].a.attrs[\"href\"]\n\n if len(row_elements) > 5:\n chapter.duration = row_elements[5].text.strip()\n\n if len(row_elements) > 6:\n chapter.language_code = row_elements[6].text.strip()\n # If no language_code was found, we fallback to use the book's language\n # This is not perfect as the book's language can be \"multilingual\" and chapters\n # are supposed to be recorded in only one language\n if len(row_elements) < 6:\n logger.warn(f\"Found an unexpected number of columns in the chapter row \\\"{chapter.title}\\\"\")\n\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n\n elif num_row_elements == 4:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.reader_name = row_elements[2].text.strip()\n # Chapters read by a group of people don't have a link to the reader's profile\n if row_elements[2].a:\n chapter.reader_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.duration = row_elements[3].text.strip()\n # The chapter's language will be set to the book's language via the chapter's init\n chapter.author = book.author\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for metadata of chapter \\\"{chapter.title}\\\"\")\n else:\n logger.error(f\"Found an unknown number ({num_row_elements}) of chapter_rows in \"\n f\"the book page of \\\"{book.url}\\\"\")\n\n logger.debug(f\"Finished downloading info for {len(chapters)} chapters in book \\\"{book.title[:50]}\\\"\")\n return chapters","title":""},{"docid":"82f50f6b850e4250535ba4a318356de3","score":"0.49940458","text":"def get_campos(self):\n campos = DBSession.query(Campos).all()\n return campos","title":""},{"docid":"04b04e5dc6586c5a26cd4999c0e5428a","score":"0.49917156","text":"def group_by_author(self, author: Author):\n return list(filter(lambda book: book.author == author, self.books))\n # return [book for book in self.books if book.author == author]","title":""},{"docid":"575ee14284891d05a646ecd5e8b93d04","score":"0.49862415","text":"def get_books(date, list_name):\n \n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?api-key=%s'%(str(date), list_name, key))\n response = response.json()\n\n num_results = response['num_results']\n fetched = 20\n names = [book['title'] for book in response['results']['books']]\n\n while num_results > fetched:\n time.sleep(0.125)\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?offset=%d&api-key=%s'%(str(date), list_name, fetched, key))\n response = response.json()\n names += [book['title'] for book in response['results']['books']]\n fetched += 20\n\n return set(names), datetime.datetime.strptime(response['results']['published_date'], \"%Y-%m-%d\")\n\n import datetime","title":""},{"docid":"6754e5ddfdd274ebad8c7dc16e92ecf5","score":"0.49814323","text":"def get_books_by_author_by(author_id):\n\n return Book.query.filter(Book.author_id == author_id).all()","title":""},{"docid":"059da709ae06cf5dc3abb55bfd751068","score":"0.49789444","text":"def address_book():\n return [(beings[k], locales[v]) for k,v in residencies.items()]","title":""},{"docid":"13ed1135b97bc4d8ad273ec5643edb03","score":"0.49710968","text":"def get_books(self, top_k=None, cache=True):\n # Reset the books saved in the cache if its length is under the threshold\n if top_k and len(self._books) < top_k:\n self._books = []\n # Get the top-k books, ordered from the author's book page\n books = []\n for i, book in enumerate(self.books(cache=cache)):\n book.register_author(self)\n books.append(book)\n if top_k and i + 1 >= top_k:\n break\n return books","title":""},{"docid":"fa7897ea3b6bb81356248b187a8dd014","score":"0.4967275","text":"def get_books_contained_by_version(bible_version: dict) -> [int]:\n table_path = TABLE_DIRECTORY / TABLE_NAME_FORMAT.format(table = bible_version['table'])\n\n with open(table_path, 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n\n headers = next(reader)\n book_index = headers.index(BOOK_KEY)\n\n return sorted({int(verse[book_index]) for verse in reader})","title":""},{"docid":"ad55ea24f2e82e232ae5f2893b4d496c","score":"0.49647325","text":"def get_bills(self):\n return Bill.objects.filter(club=self)","title":""},{"docid":"75f7e52d5e64c5f32df2d433bc20aaa9","score":"0.4963884","text":"def get_finished_books_by_criteria(search_criteria, patron_id):\n criteria = '%' + search_criteria + '%'\n search_result = (db_session.query(Finished_Book,\n Book,\n Book_Author,\n Author)\n .filter(Finished_Book.book_id == Book.id,\n Finished_Book.patron_id == patron_id,\n Book.id == Book_Author.book_id,\n Book_Author.author_id == Author.id,\n Book.title.like(criteria) |\n Author.name.like(criteria))\n .order_by(Book.title).all()) \n\n books_grouped_by_id = itertools.groupby(search_result, lambda x: x[1].id)\n\n list_of_books = []\n for book_id, result_set in books_grouped_by_id:\n\n\n for d in result_set:\n list_of_books += [(d[1].title, d[1].cover_png, d[0].date, d[3].name,\n \"\".join(r[3].name for r in result_set))]\n #end for\n #end for\n\n return list_of_books","title":""},{"docid":"8eefcef1d4d158585d11392c8d9cf137","score":"0.49480593","text":"def html_sample(self,limit=20):\n books = self.query.order_by(self.isbn).all()\n books = books[:limit]\n data = []\n for book in books:\n isbn = book.isbn\n desc = GoodReads.getDescription(isbn)\n data.append(desc)\n return data","title":""},{"docid":"f9fb78dccddb588333440f822406b273","score":"0.49452752","text":"def testBook():\n\n # book build\n books = []\n books.append(Book(\"The Hobbit\", \"J.R.R. Tolkien\", \"Doug\", [\"Wendy\"]))\n books.append(Book(\"Greek Myths\", \"Troy Miller\", \"Bob\", []))\n books.append(Book(\"Python Programming\", \"Ken Lambert\", \"Wendy\", [\"Bob\", \"Doug\"]))\n books.append(Book(\"Introduction to Computers\", \"Ted Murphy\", \"Wendy\", []))\n books.append(Book(\"P.H.P. Basics\", \"Michael Smith\", \"Wendy\", [\"Bob\"]))\n\n print(\"Current Books:\")\n print()\n for i in range(len(books)):\n print(\"\\tTitle: \", books[i].getTitle())\n print(\"\\tAuthor: \", books[i].getAuthor())\n print(\"\\tChecked Out To: \", books[i].getCurrentPatron())\n print(\"\\tCurrent Waiting List: \", books[i].getWaitingList())\n print()","title":""},{"docid":"4f274280e00eaca4f1c5bad1369dd8c2","score":"0.49442163","text":"def books(self, cache=True):\n if len(self._books) > 0 and cache:\n yield from self._books\n else:\n yield from self._search_books()","title":""},{"docid":"6af53be12cad589993d2fd84f01ca72c","score":"0.49382204","text":"def get(self):\n courses = list(Course.objects.all())\n return courses","title":""},{"docid":"6c9c566457fe64b0dab5057945228a3c","score":"0.4929902","text":"def get_book():\n # Check if an ID was provided as part of the URL.\n if 'id' in request.args:\n # If ID is provided, assign it to a variable.\n id = int(request.args['id'])\n else:\n # If no ID is provided, display an error in the browser.\n return \"Request error: no id field provided\"\n\n # prepare results variable (empty list)\n results = []\n\n import raw\n for book in raw.get_books():\n if book['id'] == id:\n results.append(book)\n\n # return data in json format\n return jsonify(results)","title":""},{"docid":"b48cade1199a9ddcd0bc3ec12b8a4ef6","score":"0.4925487","text":"def get_projects_by_accession(accession):\n project = Project()\n print(project.get_by_accession(accession))","title":""},{"docid":"09c8ca693e70b4c298d05c01aa7a575d","score":"0.492406","text":"def load_all_chapters(self):\n folder = join(\"/Volumes/SSD01/dialogues\",self.this_Book.txt_file_path)\n\n # self.files = listdir(join(\"data/Kindle-combined-txt\",str(isbn)))\n # no reason to get too complicated here\n self.txtfiles = [x for x in listdir(folder) if \".txt\" in x]\n\n # print(\"a sample of the text files:\")\n # print(self.txtfiles[:10])\n\n rawtext_by_chapter = []\n for fname in self.txtfiles:\n f = open(join(folder,fname),\"r\")\n rawtext_by_chapter.append(f.read())\n f.close()\n # word_lists_by_chapter = [listify(t) for t in rawtext_by_chapter]\n # apply spacy to each of them...\n word_lists_by_chapter = [[str(token) for token in nlp(t) if ((not token.is_punct) and (len(str(token)) != 0))] for t in rawtext_by_chapter]\n self.chapter_ends = cumsum(list(map(len,word_lists_by_chapter)))\n # add a 0 to the start, clip (to get the starts)\n # could just move the above array around too...\n self.chapter_beginnings = cumsum([0]+list(map(len,word_lists_by_chapter[:-1])))\n self.chapter_centers = (self.chapter_ends+self.chapter_beginnings)/2\n# print(list(map(len,self.word_lists_by_chapter)))\n# print(self.chapter_ends)\n# print(self.chapter_beginnings)\n# print(self.chapter_centers)\n# print(len(self.chapter_ends))\n# print(len(self.word_lists_by_chapter))\n self.all_word_list = list(itertools.chain(*word_lists_by_chapter))","title":""},{"docid":"f2cca76f77e96af07bcbb2595db675b9","score":"0.49230716","text":"def get_book():\n soup = Soup(CONTENT)\n title = str(soup.find_all(\"h2\")[0]).replace('<h2>', '').replace('</h2>', '').strip()\n desc = str([entry for entry in soup.find_all('div', class_=\"dotd-main-book-summary float-left\")][0]).split('<div')[4][2:-7].strip()\n img = soup.findAll('img')[5]['src']\n link = soup.find_all('div', class_=\"dotd-main-book-image float-left\")\n\n for div in link:\n link_p = div.find('a')['href']\n return Book(title, desc, img, link)","title":""},{"docid":"5b56f9049b0a5af56044011ccf3ea9a3","score":"0.49147058","text":"def get_all():\n return Doctor.query.all()","title":""},{"docid":"c50746a02d51d94ca427f68de0b57a4c","score":"0.49130303","text":"def get_mine(self):\n\n self._init_dict_chal_keys()\n\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n ).filter(Awards.teamid==self.team_id).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_infos in awards ]\n return awards","title":""},{"docid":"57ba6f0cc57d7662c698b5a07c807065","score":"0.4912252","text":"def get(self):\n all_blogss = get_all_blogss()\n print(\"All blogss=\",all_blogss)\n return all_blogss","title":""},{"docid":"147720b020aedaa97fc349619755f841","score":"0.49097124","text":"def get_authors(venue):\r\n papers=0\r\n results = dblp.search_pub(venue)\r\n\r\n hits = json.loads(results)[\"result\"][\"hits\"]\r\n for i in hits[\"hit\"]:\r\n info = i[\"info\"]\r\n if filter_papers(info, venue):\r\n papers+=1\r\n # print(info)\r\n if 'authors' in info:\r\n if isinstance(info[\"authors\"][\"author\"], list):\r\n for author in info[\"authors\"][\"author\"]:\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n else:\r\n author = info[\"authors\"][\"author\"]\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n\r\n return papers","title":""},{"docid":"87519999fe1b78f5a4f1835d9dfd27a6","score":"0.49049982","text":"def list_csrs():\n cmd = subprocess.Popen([PUPPETCA, '--list'], stdout = subprocess.PIPE, stderr = subprocess.STDOUT)\n out, err = cmd.communicate()\n csrs = out.split('\\n')\n return [r for r in csrs if r]","title":""},{"docid":"d9009a2f36c1438806386d3cfd6803ea","score":"0.4901347","text":"def resolve_labbook_list(self, info):\n return LabbookList(id=\"\")","title":""},{"docid":"16b462d0dbc43003f675551cd3ba7b18","score":"0.48985198","text":"def testimonials(self):\n testimonials = self.context.getRefs(relationship=\"course_testimonial\") \n return testimonials","title":""},{"docid":"ad48868230e24c2b9483e22cdb8e3c83","score":"0.48958287","text":"def springer_bib(year, conf, link):\n html_file = download_to_hash(link)\n soup = BeautifulSoup(open(html_file), 'lxml')\n res = ''\n for paper in soup.select('.chapter-item'):\n meta = paper.select('.content-type-list__meta')[0]\n title = meta.select('div')[0].get_text()\n authors_str = meta.select('div')[1].get_text()\n authors = authors_str2lst(authors_str)\n pdflink_a = paper.select('a.test-book-toc-download-link')\n pdflink = ''\n # some conference may not have a pdflink, e.g.\n # https://link.springer.com//book/10.1007/BFb0015518\n if pdflink_a:\n pdflink = pdflink_a[0]['href']\n if not pdflink.startswith('http'):\n pdflink = 'https://link.springer.com/' + pdflink\n id = gen_id(year, conf, authors, title)\n bib = gen_single_bib(id, title, ' and '.join(authors), pdflink, year, conf)\n res += bib\n return res","title":""},{"docid":"72543b0b93f999ed139508daec9bd423","score":"0.4895037","text":"def test_get_all_quotes_by_book(self):\n # hit the API endpoint\n response = self.client.post(\n reverse(\"quotes-book\", kwargs={\"version\": \"v1\"}), {\"book\": \"Don Quijote de la Mancha\"}\n )\n # fetch the data from db\n book = Book.objects.filter(title=\"Don Quijote de la Mancha\")[:1].get()\n expected = book.quotes\n serialized = QuoteSerializer(expected, many=True)\n self.assertEqual(response.data, serialized.data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"67cc7015b51ab776bba4148fff31bcc3","score":"0.4884307","text":"def list():\n return Company.query.all(), HTTPStatus.OK","title":""},{"docid":"b51ba71ff07fffae14bce8c9be190339","score":"0.48839167","text":"def get_CA(self):\n\n\t\treturn [r.Ca for r in self.res]","title":""},{"docid":"c8e49af7ea1ca8720574179626671b90","score":"0.4882444","text":"def getProjectMaintainers(self, project):\n tree = ElementTree.fromstring(''.join(core.show_project_meta(self.apiurl,\n project)))\n maintainers = []\n for person in tree.findall('person'):\n if person.get('role') == \"maintainer\":\n maintainers.append(person.get('userid'))\n return maintainers","title":""},{"docid":"46ce485964905983bcd771a5595c703a","score":"0.4879896","text":"def readBook2(self):\n # summary from https://www.amazon.ca/Essential-Calvin-Hobbes-Bill-\n # Watterson/dp/0836218051/ref=sr_1_1?crid=1YAN87VEA3JWD&keywords=the+\n # essential+calvin+and+hobbes&qid=1579321108&sprefix=the+essential+cal\n # %2Caps%2C181&sr=8-1\n print(form.border(\"\"\"\nTHE ESSENTIAL CALVIN AND HOBBES by Bill Watterson\n Beginning with the day Hobbes sprang into Calvin's tuna fish trap, the\n first two Calvin and Hobbes collections, Calvin and Hobbes and Something\n Under The Bed Is Drooling, are brought together in this treasury.\n Including black-and-white dailies and color Sundays, The Essential Calvin\n and Hobbes also features an original full-color 16-page story.\n \"\"\"))","title":""},{"docid":"0806836cfdeb94bad73bc76a354702ab","score":"0.4878542","text":"def most_client_report_order_books(self):\n\t\tclient_list = self.__borrow_controller.getReverseListClients()\n\t\tclient_list = self.__client_controller.getListFromIdsOrderBooks(client_list)\n\t\tprint(\"\\n\" + '-------=======-------')\n\t\tfor client in client_list:\n\t\t\tprint(client, end='')","title":""},{"docid":"bf2980bb0e099750f2f635458aa2c677","score":"0.4877945","text":"def get_book():\n id = get_input(\"id? \")\n r = requests.get(url = API_ENDPOINT + \"/\" + id)\n\n result_text = r.text\n print(\"\")\n print(\"result: \" + result_text)","title":""},{"docid":"230e1d7187be7c6342949083411ad154","score":"0.4870439","text":"def bid_list():\n # Grab all projects\n projects = request_api(project_path)\n\n # Parse and find \"Schnabel\" project id\n sfc_id = None\n for project in projects:\n if project[\"name\"] == \"Schnabel\":\n sfc_id = project[\"id\"]\n break\n\n # Grab all sections in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n sections = request_api(section_path, params=params)\n\n # Determine \"Bids\" section id\n bidding_id = None\n for section in sections:\n if section[\"name\"] == \"Bids\":\n bidding_id = section[\"id\"]\n break\n \n # Grab all tasks in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n tasks = request_api(task_path, params=params)\n\n # Filter tasks based on section id\n bid_tasks = []\n for task in tasks:\n if task[\"section_id\"] == bidding_id:\n bid_tasks.append(task)\n\n # Sort and return bid_tasks\n bid_tasks_sorted = quicksort(bid_tasks)\n return bid_tasks_sorted","title":""},{"docid":"01480a553da0b344569b406ff2d515bc","score":"0.4866259","text":"def getBook(self, _id):\n db = self.getBookDB()\n book=None\n try:\n value = (_id,)\n db.execute('select * from books where _id=?',value)\n book = self.getBookFromList(db.fetchone())\n except Exception as e:\n print(e)\n return book","title":""},{"docid":"7e2fbd99ee5e9dca5b830621f90e17f6","score":"0.48623312","text":"def get_available_cops():\n allIncidents = Incident.get_all()\n cops = []\n \n for i in allIncidents:\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\n# cops.append(i['operations_center']['id'])\n#conf\n cops.append(i['operations_center'])\n \n allReports = RelatoDeSituacao.get_all()\n \n for r in allReports:\n if (\n inicioAmostragem <= r.data_hora and \n r.data_hora <=terminoAmostragem and\n 'cop' in r.relator \n# and # todos tem que ter o COP\n#conf 'id' in r.relator['cop'] # todos tem que ter o id \n ):\n# cops.append(r.relator['cop']['id'])\n#conf \n cops.append(r.relator['cop'])\n\n allSincronizations = Sincronizacao.get_all()\n for sinc in allSincronizations:\n for action in sinc.acoes:\n if ( \n ((action.tipo == 'PONTUAL') and (action.inicio >= inicioAmostragem) and (action.inicio <= terminoAmostragem)) or\n ((action.tipo == 'INTERVALO') and (action.inicio >= inicioAmostragem and action.fim <= terminoAmostragem))\n ):\n cops.append(sinc.cop_responsavel['id'])\n \n return set(cops)","title":""},{"docid":"42bd5fff3bf83a8c1d6b707a4b206d95","score":"0.48550457","text":"def get_book(self, request, pk):\n query_set = Library.objects.get(id=pk)\n data = self.serializer_class(query_set, context={'request': request}).data\n return Response({'result': data}, status=status.HTTP_200_OK)","title":""},{"docid":"8243269654818ba0873eeae14ec49e4f","score":"0.48539725","text":"def print_catalog(self):\n print(\"THIS IS THE CATALOG OF ALL OUR BOOKS:\")\n for book, amount in self.books.items():\n print(book.title + ' - rating: ' + str(amount))","title":""},{"docid":"a58870f9f4fa8d5d3502ac32f7e3d318","score":"0.48537493","text":"def get_accounts():","title":""},{"docid":"a58870f9f4fa8d5d3502ac32f7e3d318","score":"0.48537493","text":"def get_accounts():","title":""},{"docid":"229b0e45cd060f50cefc112660732f2e","score":"0.4853242","text":"def extract_books(soup):\n books = {}\n for d in soup.find_all('div', 'bookMain'):\n asin = sub(r'_.*$', '', d['id'])\n title = d.find('span', 'title').text.strip()\n author = sub(r'by ', '', d.find('span', 'author').text.strip())\n author = sub('\\n', '', author)\n\n books[asin] = dict(asin=asin, title=title, author=author)\n\n return books","title":""},{"docid":"31d79332d6e68cd849b57c61ecb12b0d","score":"0.4850605","text":"def getBestBooks(catalog, number):\n books = catalog['books']\n bestbooks = lt.newList()\n for cont in range(1, number+1):\n book = lt.getElement(books, cont)\n lt.addLast(bestbooks, book)\n return bestbooks","title":""},{"docid":"c515019aac9c9b246e05855a0943f385","score":"0.4849605","text":"def get_all_biblio(page=1):\n query = BiblioEntry.query.paginate(page, ITEMS_PER_PAGE, False).items\n bibdat = convert_rows_to_dict(query)\n years = [str(value.year)\n for value in db.session.query(BiblioEntry.year).distinct()]\n tags = [str(value.tag)\n for value in db.session.query(BiblioEntry.tag).distinct()]\n templateVars = format_bibdatabase(bibdat)\n years.sort()\n templateVars[\"years\"] = years[::-1]\n templateVars[\"tags\"] = tags\n templateVars[\"nentries\"] = BiblioEntry.query.count()\n return render_template(\"references.html\", **templateVars)","title":""},{"docid":"0bbdb4c670cd4d67bb669d273d3d2c90","score":"0.48495772","text":"def papers(self):\n if self.all_papers == None:\n name = self.name\n orcid = self.orcid\n if not isinstance(name, str) and name != None:\n papers_name = list(ads.SearchQuery(\n author=name[0], fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n for kname in range(1, len(name)):\n papers_kname = list(ads.SearchQuery(\n author=name[kname], fl=self.fl, **self.kw))\n for pkn in papers_kname:\n if pkn.id not in papers_name_id:\n papers_name.append(pkn)\n papers_name_id.append(pkn.id)\n elif name != None:\n papers_name = list(ads.SearchQuery(\n author=name, fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n else:\n papers_name = []\n papers_name_id = []\n\n if orcid != None:\n papers_orcid = list(ads.SearchQuery(\n orcid=orcid, fl=self.fl, **self.kw))\n for porc in papers_orcid:\n if porc.id not in papers_name_id:\n papers_name.append(porc)\n papers_name_id.append(porc.id)\n\n self.ncite_all = [x.citation_count for x in papers_name]\n self.all_papers = PapersManager(papers_name)\n\n return self.all_papers","title":""},{"docid":"43387e866359b532e0f7ae9d8daf3237","score":"0.48454508","text":"def list_bitbucketserver_projects(self, almSettings):","title":""},{"docid":"321584db0441ace5dec0e13bdd715a5f","score":"0.48432878","text":"def test_get_all_audiobook_ok(client):\n\n with app.test_request_context():\n response = client.get(url_for(\"get_all_audio\", audioFileType=\"audiobook\"))\n\n assert response.status_code == 200\n res_data = response.get_json()\n record = list(filter(lambda x: x[\"id\"] == AUDIOBOOK_ID, res_data))\n assert len(record) == 1\n assert record[0][\"title\"] == \"Tale of two cities\"","title":""},{"docid":"e6c1dc6814ec66c490722a9594aee29a","score":"0.4835043","text":"def books_surname(self):\n return [i.author_sur for i in shelf.books_list]","title":""}],"string":"[\n {\n \"docid\": \"edff7cd81254a3f14281af7177212839\",\n \"score\": \"0.6072002\",\n \"text\": \"def getBooks():\\n ret = {}\\n books = []\\n iter = model.getBooks()\\n while iter.next():\\n books.append((iter.id, iter.name))\\n\\n ret['books'] = books\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20ccd70c0b4dc0f21126c5b35a2add6\",\n \"score\": \"0.60219944\",\n \"text\": \"def get_all_books():\\n\\n # return Book.query.all()\\n return Book.query.order_by(Book.title).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf9f7f1f1c9dad337225ac773f549ca\",\n \"score\": \"0.5911421\",\n \"text\": \"def books(self):\\n return self._results_dict['books']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaac922289e589946ee40ee82d085cd\",\n \"score\": \"0.59017754\",\n \"text\": \"def makeBooksArray():\\n books=[]\\n metadata = metainfo.readmetadata()\\n for key in metadata:\\n if( (len(metadata[key]['subjects']) > 0) and( metadata[key]['subjects'] is not None) and (metadata[key]['downloads']>75) and ( metadata[key]['author'] is not None) and (\\\"en\\\" in metadata[key]['language']) ):\\n #authorNames = extractNames(metadata[key]['author'])\\n genreString = ', '.join(metadata[key]['subjects'])\\n books.append(Book(key,metadata[key]['title'],metadata[key]['author'],genreString,0,0))\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0291c5e68e86d1df97ac53b11c36a3\",\n \"score\": \"0.5664254\",\n \"text\": \"def bookkeeping_all():\\n result = handlers.BookKeeping().retrieve_bookkeeping_all()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1614d4de7151421f0c9791faa015e5c\",\n \"score\": \"0.5599402\",\n \"text\": \"def get_all_books() -> List[Dict]:\\n with open(BOOKS, 'r') as in_file:\\n return [_parse_from_line(line) for line in in_file]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237be531bc784d39ef4a4f3921d9d799\",\n \"score\": \"0.5585883\",\n \"text\": \"def get_books():\\n books = Book.query.all()\\n json_books = []\\n for book in books:\\n json_books.append(book.to_json())\\n return jsonify(json_books), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef68a6b4baab0918e4133a86782e5c38\",\n \"score\": \"0.5565488\",\n \"text\": \"def getClips(book_id):\\n ret = {}\\n clips = []\\n it = model.getClipsByBookId(book_id)\\n while it.next():\\n clips.append((it.id, it.content))\\n pass\\n\\n ret['clips'] = clips\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9822c9b8267f4bbe3be1a2a68d825825\",\n \"score\": \"0.5564752\",\n \"text\": \"def books():\\n import raw\\n return jsonify(raw.get_books())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc1d921dc9c507e31bd65a8ccb389bf\",\n \"score\": \"0.55092233\",\n \"text\": \"def get_book(self, top_n=None):\\n return {\\n 'asks': self.get_asks(top_n=top_n),\\n 'bids': self.get_bids(top_n=top_n)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c3fb06b8fb3ae945e9ec38ceb52e37\",\n \"score\": \"0.5496954\",\n \"text\": \"def get_all_books_alphabetical(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e546d6dc1b8c1cf1b01fb12e3d27bfc\",\n \"score\": \"0.54952437\",\n \"text\": \"def get_all_books_release_year(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3530625f47dfbaac23e3bb2548db518b\",\n \"score\": \"0.5468389\",\n \"text\": \"def getBorrowedBooks(self):\\n return self.__borrowedBooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9732865d0e328131cc0a5c7aed478c34\",\n \"score\": \"0.546294\",\n \"text\": \"def get_all_books(self, book_type):\\n validate_type(book_type)\\n return [book.serialize() for book in self.list_books if book.type == Type(book_type)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a73881df25c2175e71e8c0915b410a8\",\n \"score\": \"0.5461085\",\n \"text\": \"def get_bible(out):\\n for book in BOOKS:\\n book_chapters = IBook(book).chapter_to_text(version=\\\"basicenglish\\\")\\n with open(Path(out) / f\\\"{book}.json\\\", \\\"w\\\") as out_json:\\n json.dump(book_chapters, out_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df876f5293768f809cb674acdd1282ae\",\n \"score\": \"0.5460983\",\n \"text\": \"def get_books(self):\\n files = [f for f in listdir(self.text_dir)]\\n for author, title, period, url in self.json():\\n filename = format_filename(author, title)\\n if not filename in files:\\n logger.debug(\\\"Getting %s\\\" % filename)\\n book = self.extractor.download_book(url, False, author, title, period)\\n else:\\n logger.debug(\\\"%s already downloaded\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c25455989ae0ff94927d623ecc64382\",\n \"score\": \"0.54381627\",\n \"text\": \"def getNyTimesBooks():\\n lists = [lst['list_name_encoded'] for lst in requests.get('https://api.nytimes.com/svc/books/v3/lists/names.json?api-key=1bfa24a95061415dbc8d4a4f136329a5').json()['results']]\\n global printout\\n for l in lists[:1]:\\n results = requests.get('https://api.nytimes.com/svc/books/v3/lists.json?list='+l+'&api-key=1bfa24a95061415dbc8d4a4f136329a5').json()\\n for r in results['results']:\\n isbn = r['book_details'][0]['primary_isbn13']\\n list = r['list_name']\\n try:\\n GRBook_req = requests.get('https://www.goodreads.com/book/isbn_to_id/'+isbn+'?key='+API_KEY['GOODREADS'])\\n if(GRBook_req.status_code == 200):\\n getGRBookByID(int(GRBook_req.text), list)\\n except ExpatError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48efe85434fc0b68dd59a1906bbfe648\",\n \"score\": \"0.54263264\",\n \"text\": \"def citations(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ee710991bee60d2ba6ceafc328780f\",\n \"score\": \"0.5416807\",\n \"text\": \"def getLooks(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3cb373aca17c84403726111cf0ef93f\",\n \"score\": \"0.5414597\",\n \"text\": \"def report_books(self):\\n for book in self._books.itervalues():\\n print(book.report())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6304b84bc10f74f192ff44ede1114e7a\",\n \"score\": \"0.5382276\",\n \"text\": \"def get_all_project_details() -> list:\\n\\n list_project = db_connector.collection(Collections.PROJECTS).find(\\n {}, {\\\"_id\\\": 0}).sort(\\\"project_name\\\", pymongo.ASCENDING)\\n list_projects_to_be_send = []\\n for project in list_project:\\n list_projects_to_be_send.append(project)\\n return list_projects_to_be_send\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672c738042ef32362a7eaa5ce36e731a\",\n \"score\": \"0.5363474\",\n \"text\": \"def get_chapters():\\n q = 'project/'\\n chapters = exec_jira_query(q)\\n tcs = []\\n for c in chapters:\\n k = c['key']\\n if k in relevant_chapter_keys:\\n tcs.append(_build_chapter(c))\\n return tcs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad5bfe68833e5763701cec6d8345a9c\",\n \"score\": \"0.5345248\",\n \"text\": \"def get_books(self, shelf):\\n titles = self.parse_titles(shelf)\\n isbns = self.parse_isbns(shelf)\\n isbn13s = self.parse_isbn13s(shelf)\\n authors = self.parse_author(shelf)\\n\\n books = []\\n for i in range(len(titles)):\\n b = Book(titles[i], isbns[i], isbn13s[i], authors[i])\\n books.append(b)\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cf32b3febfbaa7e8c684f007192f04\",\n \"score\": \"0.53447914\",\n \"text\": \"def books(self):\\n if not hasattr(self, '_books'):\\n self._books = list(Bestseller.select().where(Bestseller.author == self))\\n\\n return self._books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1a081d8885482958cb5ffbf91794e50\",\n \"score\": \"0.53246725\",\n \"text\": \"def bookshelves_subjects(self, book_id):\\n select = \\\"SELECT DISTINCT ?subject ?bookshelf\\\"\\n pattern = f\\\"\\\"\\\"\\n WHERE {{\\n <{book_id}> dcterms:subject [dcterms:title ?subject];\\n pgterms:bookshelf [dcterms:title ?bookshelf] ;\\n dcterms:type dcmitype:Text. \\n }}\\n ORDER BY ?subject\\n \\\"\\\"\\\"\\n query = self._namespace + select + pattern\\n query = self._get_query_results(query)\\n\\n subjects = [result[\\\"subject\\\"] for result in query]\\n bookshelves = [result[\\\"bookshelf\\\"] for result in query]\\n\\n return {\\\"bookshelves\\\": set(bookshelves), \\\"subjects\\\": set(subjects)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035f5538e005f363b6d8bec20bd36a8e\",\n \"score\": \"0.5317984\",\n \"text\": \"def book_list(username):\\n reviews=Review.objects.filter(creator__username__contains=username)\\n return {'books_read':[r.book.title for r in reviews]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd584a6e594699b465eb8376480b73b8\",\n \"score\": \"0.5293692\",\n \"text\": \"def get_all_book_ids(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9122f82f2ad38b2c9797839d77dae1\",\n \"score\": \"0.5280353\",\n \"text\": \"def test_get_all_company_fundamentals(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fb51aa792d13daf074d9ca509842b5\",\n \"score\": \"0.5274824\",\n \"text\": \"def get_bibliography(self, id):\\n select = \\\"SELECT DISTINCT ?title\\\"\\n pattern = f\\\"\\\"\\\"\\n WHERE {{\\n ?book_id purl:creator <{id}> .\\n {self._books}\\n }}\\n ORDER BY ?title\\n \\\"\\\"\\\"\\n\\n query = self._namespace + select + pattern\\n return [result[\\\"title\\\"] for result in self._get_query_results(query)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ed5a246c763eda36d66954f9ac9472\",\n \"score\": \"0.5273621\",\n \"text\": \"def getCaptials():\\n query_strings = request.args.get('query')\\n search_strings = request.args.get('search')\\n book = capital.Capital()\\n if not query_strings and not search_strings:\\n return jsonify(book.fetch_20Capitals()), 200\\n else:\\n result = book.fetch_capitals(query_strings, search_strings)\\n return jsonify(result), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f94f6c712c4fb595164c705a98aca96a\",\n \"score\": \"0.5262072\",\n \"text\": \"def report_all_books(self):\\n print(\\\"= All Books at {library} =\\\".format(library=self))\\n print(\\\"+ {:^30} | {:^30} | {:^10} +\\\"\\n .format(\\\"Title\\\", \\\"Author\\\", \\\"Copies\\\"))\\n for shelf in self._shelves.itervalues():\\n print(\\\"Shelf - {shelf}\\\".format(shelf=shelf))\\n shelf.report_books()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852fd0932e249ca1b9b0513846f2beb5\",\n \"score\": \"0.52414143\",\n \"text\": \"def get_all(self):\\n\\n self._init_dict_chal_keys()\\n\\n # REC FUTURE BIG : woups. Seems that underscore is a special char in the LIKE clauses. Will fix that later.\\n # Gets award informations\\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\\n awards_query_result = db.session.query(\\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\\n Teams.id.label('team_id'), Teams.name.label('team_name')\\n ).join(Teams).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\\n\\n # Joins award datas and key datas\\n awards = [\\n (award.award_id, award.date, award.team_id, award.team_name, self._award_infos(award.award_name))\\n for award in awards_query_result\\n ]\\n awards = [\\n award\\n for award in awards\\n if award[-1] is not None\\n ]\\n\\n # Hides descrip, icon and scores that the team should not know.\\n key_ids_of_team = [\\n award_infos['key_id']\\n for award_id, _, award_team_id, __, award_infos in awards\\n if award_team_id == self.team_id ]\\n for award in awards:\\n award_id, _, award_team_id, __, award_infos = award\\n if award_infos['key_id'] not in key_ids_of_team and not award_infos['public']:\\n award_infos['congrat_msg'] = None\\n award_infos['congrat_img_url'] = None\\n award_infos['score'] = None\\n\\n # Removes unneeded fields\\n awards = [\\n (award_id, award_date.strftime('%Y-%m-%d %H:%M:%S'), award_team_name, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\\n for award_id, award_date, award_team_id, award_team_name, award_infos in awards ]\\n return awards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10e9faa2f17a446fabe0eb42492f79a\",\n \"score\": \"0.52363074\",\n \"text\": \"def book(items):\\n list1 = [ ]\\n for i in items:\\n if i[1] == 'book':\\n list1.append(i)\\n \\n\\n #print(list1)\\n for i in list1:\\n print(i[0],i[1],i[2],i[3])\\n \\n #print('\\\\n')\\n #print(\\\"Book Category List: \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9011e7ed69e422937eb5327e0fd5a057\",\n \"score\": \"0.52341586\",\n \"text\": \"def get_popularAuthors():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bae812189214d0eca9a33a1b8283017\",\n \"score\": \"0.5205308\",\n \"text\": \"def all_books(self, date_format=DATE_FORMAT):\\n return self.execute(queries.ALL_BOOKS, (date_format,)).fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dab7fefb4df6626bf48f6bf501e8abe\",\n \"score\": \"0.52029043\",\n \"text\": \"def fetch_titles_from_page(page_number, get_books=True) -> ([Book], bool):\\n assert type(page_number) is int\\n assert type(get_books) is bool\\n\\n logger.debug(f\\\"Fetching catalog page #{page_number}... \\\")\\n url = TITLES_URL + f\\\"&search_page={page_number}\\\"\\n result = requests.get(url, headers=_get_scrape_headers())\\n\\n if result.status_code != 200:\\n return [], False\\n\\n json_result = json.loads(result.text)\\n if json_result[\\\"status\\\"] != \\\"SUCCESS\\\":\\n return [], False\\n\\n if json_result[\\\"results\\\"] == \\\"No results\\\":\\n return [], False\\n\\n books = []\\n if get_books:\\n html_results = json_result[\\\"results\\\"]\\n scraper = BeautifulSoup(html_results, 'html.parser')\\n catalog_results = scraper.find_all(\\\"li\\\", class_=\\\"catalog-result\\\")\\n\\n if catalog_results:\\n for catalog_result in catalog_results:\\n book = Book()\\n result_data = catalog_result.find(\\\"div\\\", class_=\\\"result-data\\\")\\n book.title = result_data.a.text.strip()\\n # Remove enclosing \\\" or ' if any\\n if book.title[0] is \\\"\\\\\\\"\\\" or book.title[0] is \\\"'\\\":\\n book.title = book.title[1:]\\n\\n if book.title[-1] is \\\"\\\\\\\"\\\" or book.title[-1] is \\\"'\\\":\\n book.title = book.title[:-1]\\n\\n book.url = result_data.a.attrs[\\\"href\\\"]\\n book_author = result_data.find(class_=\\\"book-author\\\")\\n if book_author and book_author.text:\\n book.author = book_author.text.strip()\\n else:\\n logger.error(f\\\"Failed to scrape the author for book \\\\\\\"{book.url}\\\\\\\" \\\"\\n f\\\"in catalog page #{page_number}\\\")\\n if book_author and book_author.a and book_author.a.attrs[\\\"href\\\"]:\\n book.author_url = book_author.a.attrs[\\\"href\\\"]\\n else:\\n # Books with various authors don't have an author url so this might be fine\\n logger.warn(f\\\"Failed to scrape the author url for book \\\\\\\"{book.url}\\\\\\\" \\\"\\n f\\\"in catalog page #{page_number}\\\")\\n book.download_url = catalog_result.find(class_=\\\"download-btn\\\").a.attrs[\\\"href\\\"]\\n book.size = catalog_result.find(class_=\\\"download-btn\\\").span.text.strip()\\n books.append(book)\\n logger.debug(f\\\"Fetched metadata for {len(books)} books from page #{page_number}\\\")\\n\\n else:\\n logger.warn(f\\\"Failed to fetch books from the catalog page #{page_number}\\\")\\n\\n else:\\n logger.debug(f\\\"Skipping scraping page #{page_number} for book metadata...\\\")\\n\\n return books, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24177aea31d1974e83f92029e9fdd9e4\",\n \"score\": \"0.51874995\",\n \"text\": \"def get_project_cla_groups() -> List[dict]:\\n return [project.to_dict() for project in get_project_cla_group_instance().all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a060a2a5bce25663f6e65a1093082b\",\n \"score\": \"0.51766706\",\n \"text\": \"def get_book_all(self) -> (tuple, list):\\n sql_expr = \\\\\\n '''\\n SELECT * FROM Books;\\n '''\\n sql_result = self._my_connect.execute_sql_read(sql_expr)\\n return sql_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e5e47b31b5d480b438d8b922ac26c47\",\n \"score\": \"0.5172181\",\n \"text\": \"def allReviews(self,book):\\n return self.query.filter_by(book_id=book.id).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34a455b5b6e5dacc98dd48267a7c8db\",\n \"score\": \"0.5162416\",\n \"text\": \"def get_all_obgyn():\\n \\n ##first find where this condition is true for facility for obgyn\\n obgyn_facility_list = Facility.query.filter_by(facility_category_name_english='Obstetrics and Gynecology').all()\\n \\n return obgyn_facility_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5129ff7c35b11a63cd60cba04f239\",\n \"score\": \"0.51620466\",\n \"text\": \"def get_bible_books() -> {int: dict}:\\n genres = get_bible_book_genres()\\n\\n with open(KEY_ENGLISH_PATH, 'r') as csvfile:\\n reader = csv.reader(csvfile)\\n headers = next(reader)\\n\\n book_index = headers.index(BOOK_KEY)\\n name_index = headers.index(NAME_KEY)\\n testament_index = headers.index(TESTAMENT_KEY)\\n genre_index = headers.index(GENRE_KEY)\\n dataset_index = headers.index(DATASET_KEY)\\n\\n return {\\n int(book[book_index]): {\\n 'name': book[name_index],\\n 'testament': book[testament_index],\\n 'genre_id': int(book[genre_index]),\\n 'genre': genres[int(book[genre_index])],\\n 'dataset': book[dataset_index]\\n } for book in reader\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1039cac6e05df4019fe627b9cd9b488\",\n \"score\": \"0.5157313\",\n \"text\": \"def harvest_all():\\n db = db_connect()\\n dois = None\\n get_commits()\\n get_citations(db, dois)\\n get_mentions(since_version=None)\\n list_records(dois)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea38c63d3d310e833fa6010f016c207\",\n \"score\": \"0.5141538\",\n \"text\": \"def cli(ctx, biomaterial_name=\\\"\\\", provider_id=\\\"\\\", biomaterial_id=\\\"\\\", organism_id=\\\"\\\", dbxref_id=\\\"\\\"):\\n return ctx.gi.expression.get_biomaterials(biomaterial_name=biomaterial_name, provider_id=provider_id, biomaterial_id=biomaterial_id, organism_id=organism_id, dbxref_id=dbxref_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0552a8791165f00bd82c35a9076ab387\",\n \"score\": \"0.5130115\",\n \"text\": \"def get_test_bible_book_ids() -> {int}:\\n return {book_id for (book_id, book) in get_bible_books().items() if book['dataset'] == 'test' }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90c8edc53393d9501476ac214728a50\",\n \"score\": \"0.5111732\",\n \"text\": \"def get_all_books():\\n\\n book_info = db.books.find({}, {\\\"_id\\\": 0})\\n book_info_cur = list(book_info)\\n # print(book_info_cur[0][\\\"rating\\\"])\\n sorted_cur = sorted(book_info_cur, key = lambda i: int(i.get('rating', 0)), reverse=True)\\n if len(book_info_cur) != 0:\\n return json.dumps(sorted_cur, indent=4), 200\\n else:\\n return \\\"Empty book collection in database\\\", 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b924ef88152a9fac406b6d482606bf\",\n \"score\": \"0.51091206\",\n \"text\": \"def get_all_cb_comps():\\n u = urllib2.urlopen('http://api.crunchbase.com/v/1/companies.js')\\n \\n comps = u.read()\\n\\n return comps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37744c1787adacba8b8ce6ee9b38eb8a\",\n \"score\": \"0.5083007\",\n \"text\": \"def fetch_all_books(start_page=1, end_page=MAX_KNOWN_PAGE, need_update_page=False) -> [Book]:\\n assert type(start_page) is int\\n assert type(end_page) is int\\n assert start_page < end_page\\n assert type(need_update_page) is bool\\n\\n while need_update_page:\\n logger.debug(f\\\"Checking if the # of LibriVox catalog pages is larger than {end_page}...\\\")\\n _, need_update_page = fetch_titles_from_page(end_page, False)\\n if need_update_page:\\n end_page += 1\\n logger.debug(f\\\"Found new catalog page #{end_page}\\\")\\n\\n logger.info(f\\\"Fetching LibriVox's book catalog from pages #{start_page} till #{end_page}...\\\")\\n\\n all_books = []\\n \\\"\\\"\\\"\\n # Commenting out multiprocess code for now as it crashes in Python 3.6.2 with the error:\\n # \\\"__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.\\\"\\n fetched_titles = []\\n with Pool(NUM_PROCESSES) as pool:\\n fetched_titles = pool.map(fetch_titles_from_page, [n for n in range(start_page, end_page)])\\n for result in fetched_titles:\\n if result and result[1]:\\n all_books.extend(result[0])\\n \\\"\\\"\\\"\\n for page_num in range(start_page, end_page):\\n catalog_page, could_fetch = fetch_titles_from_page(page_num)\\n if could_fetch and catalog_page:\\n all_books.extend(catalog_page)\\n\\n return all_books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f76e593d6b23f2a302b628d8dc58ba0\",\n \"score\": \"0.5065584\",\n \"text\": \"def listConsents(self):\\n Response = self.make_request()\\n return Response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6648cba3aa87f3160051c3d7766ec1\",\n \"score\": \"0.5063357\",\n \"text\": \"def get_all(self):\\n return [DocumentCorpus(self.collection, document) for document in self.collection.find()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df84247f23d395f335bc7958cdf6d32f\",\n \"score\": \"0.5062308\",\n \"text\": \"def fetchCompanies():\\n return desk.companies.list_companies()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb6780e1b14530393c586bdff951aa2\",\n \"score\": \"0.50400996\",\n \"text\": \"def get_citations_needed_report(URL):\\n # print('top report')\\n response = requests.get(URL)\\n content = response.content\\n soup = BeautifulSoup(content, 'html.parser')\\n result = soup.find_all('a', href=\\\"/wiki/Wikipedia:Citation_needed\\\")\\n\\n dictionary = []\\n\\n for link in result:\\n P = link.parent.parent.parent\\n P = P.text.strip()\\n dictionary.append(P)\\n\\n # print('above dictionary')\\n for P in dictionary:\\n print(P, sep='\\\\n')\\n print('\\\\n')\\n\\n return dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0aea7c5e824b0c9aecad3c418b9157\",\n \"score\": \"0.5029921\",\n \"text\": \"def books_title(self):\\n return [i.title for i in shelf.books_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ad1e8add187059eb6f82e5671699c0\",\n \"score\": \"0.5021048\",\n \"text\": \"def getBucketlists():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900b206cfe54379257569656e674a907\",\n \"score\": \"0.5011742\",\n \"text\": \"def test_books_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0920b7316709655aa523d2dd5b7398d\",\n \"score\": \"0.49976185\",\n \"text\": \"def fetch_all_chapters(book) -> [Chapter]:\\n logger.debug(f\\\"Downloading info for chapters in book: \\\\\\\"{book.title[:70]}\\\\\\\"...\\\")\\n session = download_session.make_session()\\n try:\\n book_page = session.get(book.url, headers=_get_scrape_headers(), timeout=70)\\n except download_session.get_download_exceptions() as e:\\n logger.error(f\\\"Scraping timed out to download chapters from book \\\\\\\"{book.url}\\\\\\\"\\\")\\n logger.error(e)\\n return []\\n\\n if book_page.status_code != 200:\\n logger.warn(f\\\"Failed to download chapters information for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return []\\n\\n scraper = BeautifulSoup(book_page.text, \\\"html.parser\\\")\\n _fetch_missing_book_metadata(book, scraper)\\n\\n # Get the chapters information\\n chapters = []\\n chapters_table = scraper.find(\\\"table\\\", class_=\\\"chapter-download\\\")\\n if not chapters_table:\\n logger.error(f\\\"Scraping failed to find the chapters table for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return chapters\\n\\n chapter_rows = chapters_table.find_all(\\\"tr\\\")[1:] # The first row is the table headers\\n if not chapter_rows:\\n logger.error(f\\\"Scraping failed to find the chapters rows in the chapters table for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return chapters\\n\\n num_row_elements = len(chapter_rows[0].find_all(\\\"td\\\"))\\n if num_row_elements == 7:\\n for row in chapter_rows:\\n chapter = Chapter(book)\\n chapter.title = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").text\\n chapter.download_url = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").attrs[\\\"href\\\"]\\n row_elements = row.find_all(\\\"td\\\")\\n if not row_elements or not row_elements[0] or not row_elements[0].a:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n break\\n\\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \\\"\\\").strip())\\n if len(row_elements) > 2:\\n chapter.author = row_elements[2].text.strip()\\n if row_elements[2].a:\\n chapter.author_url = row_elements[2].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 3:\\n chapter.source_text = row_elements[3].text.strip()\\n if row_elements[3].a:\\n chapter.source_text_url = row_elements[3].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 4:\\n chapter.reader_name = row_elements[4].text.strip()\\n if row_elements[4].a:\\n chapter.reader_url = row_elements[4].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 5:\\n chapter.duration = row_elements[5].text.strip()\\n\\n if len(row_elements) > 6:\\n chapter.language_code = row_elements[6].text.strip()\\n # If no language_code was found, we fallback to use the book's language\\n # This is not perfect as the book's language can be \\\"multilingual\\\" and chapters\\n # are supposed to be recorded in only one language\\n if len(row_elements) < 6:\\n logger.warn(f\\\"Found an unexpected number of columns in the chapter row \\\\\\\"{chapter.title}\\\\\\\"\\\")\\n\\n chapters.append(chapter)\\n\\n else:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n\\n elif num_row_elements == 4:\\n for row in chapter_rows:\\n chapter = Chapter(book)\\n chapter.title = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").text\\n chapter.download_url = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").attrs[\\\"href\\\"]\\n row_elements = row.find_all(\\\"td\\\")\\n if not row_elements or not row_elements[0] or not row_elements[0].a:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n break\\n\\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \\\"\\\").strip())\\n if len(row_elements) > 2:\\n chapter.reader_name = row_elements[2].text.strip()\\n # Chapters read by a group of people don't have a link to the reader's profile\\n if row_elements[2].a:\\n chapter.reader_url = row_elements[2].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 3:\\n chapter.duration = row_elements[3].text.strip()\\n # The chapter's language will be set to the book's language via the chapter's init\\n chapter.author = book.author\\n chapters.append(chapter)\\n\\n else:\\n logger.error(f\\\"Scraping failed for metadata of chapter \\\\\\\"{chapter.title}\\\\\\\"\\\")\\n else:\\n logger.error(f\\\"Found an unknown number ({num_row_elements}) of chapter_rows in \\\"\\n f\\\"the book page of \\\\\\\"{book.url}\\\\\\\"\\\")\\n\\n logger.debug(f\\\"Finished downloading info for {len(chapters)} chapters in book \\\\\\\"{book.title[:50]}\\\\\\\"\\\")\\n return chapters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f50f6b850e4250535ba4a318356de3\",\n \"score\": \"0.49940458\",\n \"text\": \"def get_campos(self):\\n campos = DBSession.query(Campos).all()\\n return campos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b04e5dc6586c5a26cd4999c0e5428a\",\n \"score\": \"0.49917156\",\n \"text\": \"def group_by_author(self, author: Author):\\n return list(filter(lambda book: book.author == author, self.books))\\n # return [book for book in self.books if book.author == author]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575ee14284891d05a646ecd5e8b93d04\",\n \"score\": \"0.49862415\",\n \"text\": \"def get_books(date, list_name):\\n \\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?api-key=%s'%(str(date), list_name, key))\\n response = response.json()\\n\\n num_results = response['num_results']\\n fetched = 20\\n names = [book['title'] for book in response['results']['books']]\\n\\n while num_results > fetched:\\n time.sleep(0.125)\\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?offset=%d&api-key=%s'%(str(date), list_name, fetched, key))\\n response = response.json()\\n names += [book['title'] for book in response['results']['books']]\\n fetched += 20\\n\\n return set(names), datetime.datetime.strptime(response['results']['published_date'], \\\"%Y-%m-%d\\\")\\n\\n import datetime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6754e5ddfdd274ebad8c7dc16e92ecf5\",\n \"score\": \"0.49814323\",\n \"text\": \"def get_books_by_author_by(author_id):\\n\\n return Book.query.filter(Book.author_id == author_id).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059da709ae06cf5dc3abb55bfd751068\",\n \"score\": \"0.49789444\",\n \"text\": \"def address_book():\\n return [(beings[k], locales[v]) for k,v in residencies.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ed1135b97bc4d8ad273ec5643edb03\",\n \"score\": \"0.49710968\",\n \"text\": \"def get_books(self, top_k=None, cache=True):\\n # Reset the books saved in the cache if its length is under the threshold\\n if top_k and len(self._books) < top_k:\\n self._books = []\\n # Get the top-k books, ordered from the author's book page\\n books = []\\n for i, book in enumerate(self.books(cache=cache)):\\n book.register_author(self)\\n books.append(book)\\n if top_k and i + 1 >= top_k:\\n break\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7897ea3b6bb81356248b187a8dd014\",\n \"score\": \"0.4967275\",\n \"text\": \"def get_books_contained_by_version(bible_version: dict) -> [int]:\\n table_path = TABLE_DIRECTORY / TABLE_NAME_FORMAT.format(table = bible_version['table'])\\n\\n with open(table_path, 'r', encoding='utf-8') as csvfile:\\n reader = csv.reader(csvfile)\\n\\n headers = next(reader)\\n book_index = headers.index(BOOK_KEY)\\n\\n return sorted({int(verse[book_index]) for verse in reader})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad55ea24f2e82e232ae5f2893b4d496c\",\n \"score\": \"0.49647325\",\n \"text\": \"def get_bills(self):\\n return Bill.objects.filter(club=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f7e52d5e64c5f32df2d433bc20aaa9\",\n \"score\": \"0.4963884\",\n \"text\": \"def get_finished_books_by_criteria(search_criteria, patron_id):\\n criteria = '%' + search_criteria + '%'\\n search_result = (db_session.query(Finished_Book,\\n Book,\\n Book_Author,\\n Author)\\n .filter(Finished_Book.book_id == Book.id,\\n Finished_Book.patron_id == patron_id,\\n Book.id == Book_Author.book_id,\\n Book_Author.author_id == Author.id,\\n Book.title.like(criteria) |\\n Author.name.like(criteria))\\n .order_by(Book.title).all()) \\n\\n books_grouped_by_id = itertools.groupby(search_result, lambda x: x[1].id)\\n\\n list_of_books = []\\n for book_id, result_set in books_grouped_by_id:\\n\\n\\n for d in result_set:\\n list_of_books += [(d[1].title, d[1].cover_png, d[0].date, d[3].name,\\n \\\"\\\".join(r[3].name for r in result_set))]\\n #end for\\n #end for\\n\\n return list_of_books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eefcef1d4d158585d11392c8d9cf137\",\n \"score\": \"0.49480593\",\n \"text\": \"def html_sample(self,limit=20):\\n books = self.query.order_by(self.isbn).all()\\n books = books[:limit]\\n data = []\\n for book in books:\\n isbn = book.isbn\\n desc = GoodReads.getDescription(isbn)\\n data.append(desc)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9fb78dccddb588333440f822406b273\",\n \"score\": \"0.49452752\",\n \"text\": \"def testBook():\\n\\n # book build\\n books = []\\n books.append(Book(\\\"The Hobbit\\\", \\\"J.R.R. Tolkien\\\", \\\"Doug\\\", [\\\"Wendy\\\"]))\\n books.append(Book(\\\"Greek Myths\\\", \\\"Troy Miller\\\", \\\"Bob\\\", []))\\n books.append(Book(\\\"Python Programming\\\", \\\"Ken Lambert\\\", \\\"Wendy\\\", [\\\"Bob\\\", \\\"Doug\\\"]))\\n books.append(Book(\\\"Introduction to Computers\\\", \\\"Ted Murphy\\\", \\\"Wendy\\\", []))\\n books.append(Book(\\\"P.H.P. Basics\\\", \\\"Michael Smith\\\", \\\"Wendy\\\", [\\\"Bob\\\"]))\\n\\n print(\\\"Current Books:\\\")\\n print()\\n for i in range(len(books)):\\n print(\\\"\\\\tTitle: \\\", books[i].getTitle())\\n print(\\\"\\\\tAuthor: \\\", books[i].getAuthor())\\n print(\\\"\\\\tChecked Out To: \\\", books[i].getCurrentPatron())\\n print(\\\"\\\\tCurrent Waiting List: \\\", books[i].getWaitingList())\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f274280e00eaca4f1c5bad1369dd8c2\",\n \"score\": \"0.49442163\",\n \"text\": \"def books(self, cache=True):\\n if len(self._books) > 0 and cache:\\n yield from self._books\\n else:\\n yield from self._search_books()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af53be12cad589993d2fd84f01ca72c\",\n \"score\": \"0.49382204\",\n \"text\": \"def get(self):\\n courses = list(Course.objects.all())\\n return courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9c566457fe64b0dab5057945228a3c\",\n \"score\": \"0.4929902\",\n \"text\": \"def get_book():\\n # Check if an ID was provided as part of the URL.\\n if 'id' in request.args:\\n # If ID is provided, assign it to a variable.\\n id = int(request.args['id'])\\n else:\\n # If no ID is provided, display an error in the browser.\\n return \\\"Request error: no id field provided\\\"\\n\\n # prepare results variable (empty list)\\n results = []\\n\\n import raw\\n for book in raw.get_books():\\n if book['id'] == id:\\n results.append(book)\\n\\n # return data in json format\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48cade1199a9ddcd0bc3ec12b8a4ef6\",\n \"score\": \"0.4925487\",\n \"text\": \"def get_projects_by_accession(accession):\\n project = Project()\\n print(project.get_by_accession(accession))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c8ca693e70b4c298d05c01aa7a575d\",\n \"score\": \"0.492406\",\n \"text\": \"def load_all_chapters(self):\\n folder = join(\\\"/Volumes/SSD01/dialogues\\\",self.this_Book.txt_file_path)\\n\\n # self.files = listdir(join(\\\"data/Kindle-combined-txt\\\",str(isbn)))\\n # no reason to get too complicated here\\n self.txtfiles = [x for x in listdir(folder) if \\\".txt\\\" in x]\\n\\n # print(\\\"a sample of the text files:\\\")\\n # print(self.txtfiles[:10])\\n\\n rawtext_by_chapter = []\\n for fname in self.txtfiles:\\n f = open(join(folder,fname),\\\"r\\\")\\n rawtext_by_chapter.append(f.read())\\n f.close()\\n # word_lists_by_chapter = [listify(t) for t in rawtext_by_chapter]\\n # apply spacy to each of them...\\n word_lists_by_chapter = [[str(token) for token in nlp(t) if ((not token.is_punct) and (len(str(token)) != 0))] for t in rawtext_by_chapter]\\n self.chapter_ends = cumsum(list(map(len,word_lists_by_chapter)))\\n # add a 0 to the start, clip (to get the starts)\\n # could just move the above array around too...\\n self.chapter_beginnings = cumsum([0]+list(map(len,word_lists_by_chapter[:-1])))\\n self.chapter_centers = (self.chapter_ends+self.chapter_beginnings)/2\\n# print(list(map(len,self.word_lists_by_chapter)))\\n# print(self.chapter_ends)\\n# print(self.chapter_beginnings)\\n# print(self.chapter_centers)\\n# print(len(self.chapter_ends))\\n# print(len(self.word_lists_by_chapter))\\n self.all_word_list = list(itertools.chain(*word_lists_by_chapter))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cca76f77e96af07bcbb2595db675b9\",\n \"score\": \"0.49230716\",\n \"text\": \"def get_book():\\n soup = Soup(CONTENT)\\n title = str(soup.find_all(\\\"h2\\\")[0]).replace('<h2>', '').replace('</h2>', '').strip()\\n desc = str([entry for entry in soup.find_all('div', class_=\\\"dotd-main-book-summary float-left\\\")][0]).split('<div')[4][2:-7].strip()\\n img = soup.findAll('img')[5]['src']\\n link = soup.find_all('div', class_=\\\"dotd-main-book-image float-left\\\")\\n\\n for div in link:\\n link_p = div.find('a')['href']\\n return Book(title, desc, img, link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b56f9049b0a5af56044011ccf3ea9a3\",\n \"score\": \"0.49147058\",\n \"text\": \"def get_all():\\n return Doctor.query.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50746a02d51d94ca427f68de0b57a4c\",\n \"score\": \"0.49130303\",\n \"text\": \"def get_mine(self):\\n\\n self._init_dict_chal_keys()\\n\\n # Gets award informations\\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\\n awards_query_result = db.session.query(\\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\\n ).filter(Awards.teamid==self.team_id).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\\n\\n # Joins award datas and key datas\\n awards = [\\n (award.award_id, award.date, self._award_infos(award.award_name))\\n for award in awards_query_result\\n ]\\n awards = [\\n award\\n for award in awards\\n if award[-1] is not None\\n ]\\n\\n # Removes unneeded fields\\n awards = [\\n (award_id, award_date, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\\n for award_id, award_date, award_infos in awards ]\\n return awards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ba6f0cc57d7662c698b5a07c807065\",\n \"score\": \"0.4912252\",\n \"text\": \"def get(self):\\n all_blogss = get_all_blogss()\\n print(\\\"All blogss=\\\",all_blogss)\\n return all_blogss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147720b020aedaa97fc349619755f841\",\n \"score\": \"0.49097124\",\n \"text\": \"def get_authors(venue):\\r\\n papers=0\\r\\n results = dblp.search_pub(venue)\\r\\n\\r\\n hits = json.loads(results)[\\\"result\\\"][\\\"hits\\\"]\\r\\n for i in hits[\\\"hit\\\"]:\\r\\n info = i[\\\"info\\\"]\\r\\n if filter_papers(info, venue):\\r\\n papers+=1\\r\\n # print(info)\\r\\n if 'authors' in info:\\r\\n if isinstance(info[\\\"authors\\\"][\\\"author\\\"], list):\\r\\n for author in info[\\\"authors\\\"][\\\"author\\\"]:\\r\\n update_authors(author[\\\"@pid\\\"], author[\\\"text\\\"], info[\\\"key\\\"])\\r\\n else:\\r\\n author = info[\\\"authors\\\"][\\\"author\\\"]\\r\\n update_authors(author[\\\"@pid\\\"], author[\\\"text\\\"], info[\\\"key\\\"])\\r\\n\\r\\n return papers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87519999fe1b78f5a4f1835d9dfd27a6\",\n \"score\": \"0.49049982\",\n \"text\": \"def list_csrs():\\n cmd = subprocess.Popen([PUPPETCA, '--list'], stdout = subprocess.PIPE, stderr = subprocess.STDOUT)\\n out, err = cmd.communicate()\\n csrs = out.split('\\\\n')\\n return [r for r in csrs if r]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9009a2f36c1438806386d3cfd6803ea\",\n \"score\": \"0.4901347\",\n \"text\": \"def resolve_labbook_list(self, info):\\n return LabbookList(id=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b462d0dbc43003f675551cd3ba7b18\",\n \"score\": \"0.48985198\",\n \"text\": \"def testimonials(self):\\n testimonials = self.context.getRefs(relationship=\\\"course_testimonial\\\") \\n return testimonials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad48868230e24c2b9483e22cdb8e3c83\",\n \"score\": \"0.48958287\",\n \"text\": \"def springer_bib(year, conf, link):\\n html_file = download_to_hash(link)\\n soup = BeautifulSoup(open(html_file), 'lxml')\\n res = ''\\n for paper in soup.select('.chapter-item'):\\n meta = paper.select('.content-type-list__meta')[0]\\n title = meta.select('div')[0].get_text()\\n authors_str = meta.select('div')[1].get_text()\\n authors = authors_str2lst(authors_str)\\n pdflink_a = paper.select('a.test-book-toc-download-link')\\n pdflink = ''\\n # some conference may not have a pdflink, e.g.\\n # https://link.springer.com//book/10.1007/BFb0015518\\n if pdflink_a:\\n pdflink = pdflink_a[0]['href']\\n if not pdflink.startswith('http'):\\n pdflink = 'https://link.springer.com/' + pdflink\\n id = gen_id(year, conf, authors, title)\\n bib = gen_single_bib(id, title, ' and '.join(authors), pdflink, year, conf)\\n res += bib\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72543b0b93f999ed139508daec9bd423\",\n \"score\": \"0.4895037\",\n \"text\": \"def test_get_all_quotes_by_book(self):\\n # hit the API endpoint\\n response = self.client.post(\\n reverse(\\\"quotes-book\\\", kwargs={\\\"version\\\": \\\"v1\\\"}), {\\\"book\\\": \\\"Don Quijote de la Mancha\\\"}\\n )\\n # fetch the data from db\\n book = Book.objects.filter(title=\\\"Don Quijote de la Mancha\\\")[:1].get()\\n expected = book.quotes\\n serialized = QuoteSerializer(expected, many=True)\\n self.assertEqual(response.data, serialized.data)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67cc7015b51ab776bba4148fff31bcc3\",\n \"score\": \"0.4884307\",\n \"text\": \"def list():\\n return Company.query.all(), HTTPStatus.OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51ba71ff07fffae14bce8c9be190339\",\n \"score\": \"0.48839167\",\n \"text\": \"def get_CA(self):\\n\\n\\t\\treturn [r.Ca for r in self.res]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e49af7ea1ca8720574179626671b90\",\n \"score\": \"0.4882444\",\n \"text\": \"def getProjectMaintainers(self, project):\\n tree = ElementTree.fromstring(''.join(core.show_project_meta(self.apiurl,\\n project)))\\n maintainers = []\\n for person in tree.findall('person'):\\n if person.get('role') == \\\"maintainer\\\":\\n maintainers.append(person.get('userid'))\\n return maintainers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46ce485964905983bcd771a5595c703a\",\n \"score\": \"0.4879896\",\n \"text\": \"def readBook2(self):\\n # summary from https://www.amazon.ca/Essential-Calvin-Hobbes-Bill-\\n # Watterson/dp/0836218051/ref=sr_1_1?crid=1YAN87VEA3JWD&keywords=the+\\n # essential+calvin+and+hobbes&qid=1579321108&sprefix=the+essential+cal\\n # %2Caps%2C181&sr=8-1\\n print(form.border(\\\"\\\"\\\"\\nTHE ESSENTIAL CALVIN AND HOBBES by Bill Watterson\\n Beginning with the day Hobbes sprang into Calvin's tuna fish trap, the\\n first two Calvin and Hobbes collections, Calvin and Hobbes and Something\\n Under The Bed Is Drooling, are brought together in this treasury.\\n Including black-and-white dailies and color Sundays, The Essential Calvin\\n and Hobbes also features an original full-color 16-page story.\\n \\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0806836cfdeb94bad73bc76a354702ab\",\n \"score\": \"0.4878542\",\n \"text\": \"def most_client_report_order_books(self):\\n\\t\\tclient_list = self.__borrow_controller.getReverseListClients()\\n\\t\\tclient_list = self.__client_controller.getListFromIdsOrderBooks(client_list)\\n\\t\\tprint(\\\"\\\\n\\\" + '-------=======-------')\\n\\t\\tfor client in client_list:\\n\\t\\t\\tprint(client, end='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2980bb0e099750f2f635458aa2c677\",\n \"score\": \"0.4877945\",\n \"text\": \"def get_book():\\n id = get_input(\\\"id? \\\")\\n r = requests.get(url = API_ENDPOINT + \\\"/\\\" + id)\\n\\n result_text = r.text\\n print(\\\"\\\")\\n print(\\\"result: \\\" + result_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230e1d7187be7c6342949083411ad154\",\n \"score\": \"0.4870439\",\n \"text\": \"def bid_list():\\n # Grab all projects\\n projects = request_api(project_path)\\n\\n # Parse and find \\\"Schnabel\\\" project id\\n sfc_id = None\\n for project in projects:\\n if project[\\\"name\\\"] == \\\"Schnabel\\\":\\n sfc_id = project[\\\"id\\\"]\\n break\\n\\n # Grab all sections in \\\"Schnabel\\\" project\\n params = {\\\"project_id\\\": sfc_id}\\n sections = request_api(section_path, params=params)\\n\\n # Determine \\\"Bids\\\" section id\\n bidding_id = None\\n for section in sections:\\n if section[\\\"name\\\"] == \\\"Bids\\\":\\n bidding_id = section[\\\"id\\\"]\\n break\\n \\n # Grab all tasks in \\\"Schnabel\\\" project\\n params = {\\\"project_id\\\": sfc_id}\\n tasks = request_api(task_path, params=params)\\n\\n # Filter tasks based on section id\\n bid_tasks = []\\n for task in tasks:\\n if task[\\\"section_id\\\"] == bidding_id:\\n bid_tasks.append(task)\\n\\n # Sort and return bid_tasks\\n bid_tasks_sorted = quicksort(bid_tasks)\\n return bid_tasks_sorted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01480a553da0b344569b406ff2d515bc\",\n \"score\": \"0.4866259\",\n \"text\": \"def getBook(self, _id):\\n db = self.getBookDB()\\n book=None\\n try:\\n value = (_id,)\\n db.execute('select * from books where _id=?',value)\\n book = self.getBookFromList(db.fetchone())\\n except Exception as e:\\n print(e)\\n return book\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2fbd99ee5e9dca5b830621f90e17f6\",\n \"score\": \"0.48623312\",\n \"text\": \"def get_available_cops():\\n allIncidents = Incident.get_all()\\n cops = []\\n \\n for i in allIncidents:\\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\\n# cops.append(i['operations_center']['id'])\\n#conf\\n cops.append(i['operations_center'])\\n \\n allReports = RelatoDeSituacao.get_all()\\n \\n for r in allReports:\\n if (\\n inicioAmostragem <= r.data_hora and \\n r.data_hora <=terminoAmostragem and\\n 'cop' in r.relator \\n# and # todos tem que ter o COP\\n#conf 'id' in r.relator['cop'] # todos tem que ter o id \\n ):\\n# cops.append(r.relator['cop']['id'])\\n#conf \\n cops.append(r.relator['cop'])\\n\\n allSincronizations = Sincronizacao.get_all()\\n for sinc in allSincronizations:\\n for action in sinc.acoes:\\n if ( \\n ((action.tipo == 'PONTUAL') and (action.inicio >= inicioAmostragem) and (action.inicio <= terminoAmostragem)) or\\n ((action.tipo == 'INTERVALO') and (action.inicio >= inicioAmostragem and action.fim <= terminoAmostragem))\\n ):\\n cops.append(sinc.cop_responsavel['id'])\\n \\n return set(cops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42bd5fff3bf83a8c1d6b707a4b206d95\",\n \"score\": \"0.48550457\",\n \"text\": \"def get_book(self, request, pk):\\n query_set = Library.objects.get(id=pk)\\n data = self.serializer_class(query_set, context={'request': request}).data\\n return Response({'result': data}, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8243269654818ba0873eeae14ec49e4f\",\n \"score\": \"0.48539725\",\n \"text\": \"def print_catalog(self):\\n print(\\\"THIS IS THE CATALOG OF ALL OUR BOOKS:\\\")\\n for book, amount in self.books.items():\\n print(book.title + ' - rating: ' + str(amount))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58870f9f4fa8d5d3502ac32f7e3d318\",\n \"score\": \"0.48537493\",\n \"text\": \"def get_accounts():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58870f9f4fa8d5d3502ac32f7e3d318\",\n \"score\": \"0.48537493\",\n \"text\": \"def get_accounts():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"229b0e45cd060f50cefc112660732f2e\",\n \"score\": \"0.4853242\",\n \"text\": \"def extract_books(soup):\\n books = {}\\n for d in soup.find_all('div', 'bookMain'):\\n asin = sub(r'_.*$', '', d['id'])\\n title = d.find('span', 'title').text.strip()\\n author = sub(r'by ', '', d.find('span', 'author').text.strip())\\n author = sub('\\\\n', '', author)\\n\\n books[asin] = dict(asin=asin, title=title, author=author)\\n\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d79332d6e68cd849b57c61ecb12b0d\",\n \"score\": \"0.4850605\",\n \"text\": \"def getBestBooks(catalog, number):\\n books = catalog['books']\\n bestbooks = lt.newList()\\n for cont in range(1, number+1):\\n book = lt.getElement(books, cont)\\n lt.addLast(bestbooks, book)\\n return bestbooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c515019aac9c9b246e05855a0943f385\",\n \"score\": \"0.4849605\",\n \"text\": \"def get_all_biblio(page=1):\\n query = BiblioEntry.query.paginate(page, ITEMS_PER_PAGE, False).items\\n bibdat = convert_rows_to_dict(query)\\n years = [str(value.year)\\n for value in db.session.query(BiblioEntry.year).distinct()]\\n tags = [str(value.tag)\\n for value in db.session.query(BiblioEntry.tag).distinct()]\\n templateVars = format_bibdatabase(bibdat)\\n years.sort()\\n templateVars[\\\"years\\\"] = years[::-1]\\n templateVars[\\\"tags\\\"] = tags\\n templateVars[\\\"nentries\\\"] = BiblioEntry.query.count()\\n return render_template(\\\"references.html\\\", **templateVars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbdb4c670cd4d67bb669d273d3d2c90\",\n \"score\": \"0.48495772\",\n \"text\": \"def papers(self):\\n if self.all_papers == None:\\n name = self.name\\n orcid = self.orcid\\n if not isinstance(name, str) and name != None:\\n papers_name = list(ads.SearchQuery(\\n author=name[0], fl=self.fl, **self.kw))\\n papers_name_id = [x.id for x in papers_name]\\n for kname in range(1, len(name)):\\n papers_kname = list(ads.SearchQuery(\\n author=name[kname], fl=self.fl, **self.kw))\\n for pkn in papers_kname:\\n if pkn.id not in papers_name_id:\\n papers_name.append(pkn)\\n papers_name_id.append(pkn.id)\\n elif name != None:\\n papers_name = list(ads.SearchQuery(\\n author=name, fl=self.fl, **self.kw))\\n papers_name_id = [x.id for x in papers_name]\\n else:\\n papers_name = []\\n papers_name_id = []\\n\\n if orcid != None:\\n papers_orcid = list(ads.SearchQuery(\\n orcid=orcid, fl=self.fl, **self.kw))\\n for porc in papers_orcid:\\n if porc.id not in papers_name_id:\\n papers_name.append(porc)\\n papers_name_id.append(porc.id)\\n\\n self.ncite_all = [x.citation_count for x in papers_name]\\n self.all_papers = PapersManager(papers_name)\\n\\n return self.all_papers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43387e866359b532e0f7ae9d8daf3237\",\n \"score\": \"0.48454508\",\n \"text\": \"def list_bitbucketserver_projects(self, almSettings):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321584db0441ace5dec0e13bdd715a5f\",\n \"score\": \"0.48432878\",\n \"text\": \"def test_get_all_audiobook_ok(client):\\n\\n with app.test_request_context():\\n response = client.get(url_for(\\\"get_all_audio\\\", audioFileType=\\\"audiobook\\\"))\\n\\n assert response.status_code == 200\\n res_data = response.get_json()\\n record = list(filter(lambda x: x[\\\"id\\\"] == AUDIOBOOK_ID, res_data))\\n assert len(record) == 1\\n assert record[0][\\\"title\\\"] == \\\"Tale of two cities\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c1dc6814ec66c490722a9594aee29a\",\n \"score\": \"0.4835043\",\n \"text\": \"def books_surname(self):\\n return [i.author_sur for i in shelf.books_list]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":799,"cells":{"query_id":{"kind":"string","value":"e048fbeb7c6ddb0b67d7a05a70285c70"},"query":{"kind":"string","value":"Gets the side of the line p is on."},"positive_passages":{"kind":"list like","value":[{"docid":"61392af388f6f7d1f80e07e4e0dd26b4","score":"0.711668","text":"def line_side(pl, p, pr):\n\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\n\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\n\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\n\tif xp > 0:\n\t return 1\n\telif xp < 0:\n\t return -1\n\telse:\n\t return 0","title":""}],"string":"[\n {\n \"docid\": \"61392af388f6f7d1f80e07e4e0dd26b4\",\n \"score\": \"0.711668\",\n \"text\": \"def line_side(pl, p, pr):\\n\\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\\n\\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\\n\\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\\n\\tif xp > 0:\\n\\t return 1\\n\\telif xp < 0:\\n\\t return -1\\n\\telse:\\n\\t return 0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cb2c6c21f616e1864d15179f7c84d096","score":"0.7492487","text":"def get_side(self):\n return self.get_perimeter() / 4","title":""},{"docid":"dc079eec7d6fd53e500597fcb4a05fcb","score":"0.7384879","text":"def point_side_line(p1, p2, p):\n\n # Get vector from p1 to p2\n a = p2 - p1\n # Get vector from p1 to q\n b = p - p1\n # The sign of the cross product determines point side\n s = np.sign(np.cross(a,b))\n\n # s>0 -> LEFT side of the line\n # s=0 -> ON side of the line\n # s<0 -> RIGHT side of the line\n return s","title":""},{"docid":"d9f66d4996016c63cce2787af84ca213","score":"0.731372","text":"def getSide(self):\n return self.side","title":""},{"docid":"c6b22f78f373225ad3d6b028b74f755a","score":"0.7087271","text":"def side(self) -> int:\n return self._side","title":""},{"docid":"546c66e2ae21ff0ed1122999b1f1a98a","score":"0.6932154","text":"def side(self):\n return self._side","title":""},{"docid":"546c66e2ae21ff0ed1122999b1f1a98a","score":"0.6932154","text":"def side(self):\n return self._side","title":""},{"docid":"319b8e8eaec902c8d4a61daea65f714e","score":"0.67851067","text":"def side(self):\n\t\treturn self.get(54)","title":""},{"docid":"99ce10f146becbd285f82f30503daa8d","score":"0.64582276","text":"def get_side_left(self):\n return self.filter_var(self.side_left)","title":""},{"docid":"9bac596a77cc5e3988f13cf4d3cf5c97","score":"0.6424401","text":"def point_line_side(pointx, pointy, x1, y1, x2, y2):\n return (x2-x1) * (pointy-y1) - (y2-y1) * (pointx-x1)","title":""},{"docid":"ca1c895aa5e8ba15ce9142beafd4aa8b","score":"0.6313351","text":"def get_side(self, player):\n if self.__team == player.get_team():\n return \"ALLIES\"\n return \"ENEMIES\"","title":""},{"docid":"388ef22d1cbe686519f8df605e5cceda","score":"0.61742723","text":"def get_side_right(self):\n return self.filter_var(self.side_right)","title":""},{"docid":"5bf47b198470cfb534d7252195d40d25","score":"0.61605984","text":"def side1(self) -> float:\n return self._side1","title":""},{"docid":"c96cab5dc570f59ec86ef0529726dbd8","score":"0.61603254","text":"def get_side(a, b):\n x = cosine_sign(a, b) #computes z component of out of plane component\n if x < 0:\n return LEFT\n elif x > 0: \n return RIGHT\n elif x == 0.:\n return \"zero\"\n else:\n return None","title":""},{"docid":"d44143d085f4d3f1bf40a5ecb61ddcff","score":"0.61546004","text":"def side2(self) -> float:\n return self._side2","title":""},{"docid":"5cb2c8f9f85a895ec83d33b86194efab","score":"0.6146379","text":"def distance_from_line(self, p):\n rp = pt.diff(p, self.start) # make relative to start of line\n rp_along_d = self.direction.copy().scale(rp.dot_product(self.direction)) # Get component in direction of line\n rpr = pt.diff(rp, rp_along_d) # perpendicular\n return rpr.length()","title":""},{"docid":"30bc1e85f3f9f64b5b8e912b0e9b661e","score":"0.6144422","text":"def get_flop(self):\n return self.Flop","title":""},{"docid":"4812ef8c1c078aebffa2a372682cc043","score":"0.60142285","text":"def complementary_side(self):\n if self == PuzzlePieceSide.top:\n return PuzzlePieceSide.bottom\n\n if self == PuzzlePieceSide.right:\n return PuzzlePieceSide.left\n\n if self == PuzzlePieceSide.bottom:\n return PuzzlePieceSide.top\n\n if self == PuzzlePieceSide.left:\n return PuzzlePieceSide.right","title":""},{"docid":"fe908da91ed5367c91c1ecdf043f099e","score":"0.5988404","text":"def _determineSide(self, testPlayer):\n playerList = self._playerOrder(self._playerTurn)\n lastPlayer = playerList[-1]\n if testPlayer < lastPlayer:\n testPlayer += 4\n return testPlayer - lastPlayer - 1","title":""},{"docid":"ac3dc604b14c12375571c029bbfb43ba","score":"0.5897389","text":"def get_side(given_name):\n return get_group(given_name, group='side') or ''","title":""},{"docid":"452b59b17bdf485a91f4f306fc899d97","score":"0.58860266","text":"def point_is_on_leftside_of_line(self, point):\n\n if (point.y < self.point2.y and point.y >= self.point1.y) or (point.y >= self.point2.y and point.y < self.point1.y):\n\n # The equation to determine this comes from here: https://math.stackexchange.com/questions/274712/calculate-on-which-side-of-a-straight-line-is-a-given-point-located\n\n d = ((point.x - self.point1.x)*(self.point2.y - self.point1.y)) - ((point.y - self.point1.y)*(self.point2.x - self.point1.x))\n\n left = (self.point1.y - self.point2.y)\n # If d and left are both positive or both negative, then the point is on the left side of the line\n\n if (d < 0 and left < 0) or (d > 0 and left > 0):\n return True\n\n else:\n return False","title":""},{"docid":"e4811fb638efc5f5d24f044525e886ee","score":"0.5875098","text":"def linha_pos(p):\n\treturn p[0]","title":""},{"docid":"9f2e4c3181ec788edc55e9f45175d7f0","score":"0.5852361","text":"def side(pitch):\n return pitch / SQRT3","title":""},{"docid":"c7ecd08a42dbabcd7f85d608af4e0d73","score":"0.58335465","text":"def getPerimeter(self):\n return self.side * 4","title":""},{"docid":"505dc43b635a26a2b30b27a6ba5dc9e6","score":"0.5772587","text":"def side(self, value):\n if value in (self.LEFT, self.RIGHT):\n self._side = value\n else:\n raise ValueError('Side value must be LEFT (-1) or RIGHT (1)')","title":""},{"docid":"65b7f0c5539dfef34becf6dbd667105f","score":"0.57487196","text":"def left(self):\n return self.corner.x()","title":""},{"docid":"c813cffbd7d40171bb2fb28d280983df","score":"0.5639955","text":"def side_name(self):\n return str(self).split(\".\")[1]","title":""},{"docid":"3adb71702fb9cd2cc0ece1f8045760ba","score":"0.56043947","text":"def get_semi_perimeter(self):\n return (self.side1 + self.side2 + self.side3) / 2","title":""},{"docid":"9a066b8bd9a856bd12319defc923aef4","score":"0.559953","text":"def getSide( name ):\n \n # make sure is not a full dag path\n edit = name.split('|')[-1]\n \n # make sure is lowerCase and return the side\n edit = edit.lower()\n \n if edit.startswith('l_'): return 'l'\n if edit.startswith('r_'): return 'r'\n \n return ''","title":""},{"docid":"5e2991d7d4d56b1bce69d751f18d4576","score":"0.5562073","text":"def side_lengths(self):\n d12 = self.p1.euclidean_distance(self.p2)\n d23 = self.p2.euclidean_distance(self.p3)\n d31 = self.p3.euclidean_distance(self.p1)\n return d12, d23, d31","title":""},{"docid":"e3bd52e2d680737939d8ba267bcddba0","score":"0.5545313","text":"def grating_direction(self, p):\n normal = self.normal(p)\n xaxis = Vec(1, 0, 0)\n a = normal.cross(xaxis)\n rotation = Mat().rotateAxis(90, a)\n return normal.transformDir(rotation).normalize()","title":""},{"docid":"60b97ef5e685d6009ee908fbd9a55829","score":"0.55079645","text":"def getQuarter(self, point):\n # Checks on which side of the bottom-left to top-right diagonal the point\n # is.\n posDiag = point[0] + point[1] > 0\n\n # Checks on which side of the top-left to bottom-right diagonal the point\n # is.\n negDiag = point[1] > point[0]\n\n if posDiag:\n if negDiag:\n return 0\n else:\n return 1\n if negDiag:\n return 3\n return 2","title":""},{"docid":"7cace02a999fdba75f2c696856f3d90b","score":"0.5465378","text":"def get_location_to_move_to(self, side):\n temp = PoseStamped()\n if self.get_domino_direction() == \"L\":\n if side == \"top\":\n temp.pose.position.y += VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if self.get_domino_direction() == \"R\":\n if side == \"top\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y += VERT_VERT_OFFSET\n return temp\n #if \"side\" == \"left\":\n # temp.pose.position.y += HORIZ_VERT_OFFSET\n #if \"side\" == \"right\":\n # temp.pose.position.y -= HORIZ_VERT_OFFSET","title":""},{"docid":"2ba517229a96a0ae281a986f32b12561","score":"0.5462002","text":"def get_perimeter(self):\n return self.side1 + self.side2 + self.side3","title":""},{"docid":"f0c094c6beb4f14393aec743aeed28b9","score":"0.54574496","text":"def get_distance_from_starting_side(img, mode, x_edge_left, x_edge_right):\n if mode == \"left\":\n return img.shape[1] - x_edge_right\n else:\n return x_edge_left","title":""},{"docid":"4f67f669222f5bb0958ed5913d501004","score":"0.5418983","text":"def get_pos(self, x, y):\n return x * self.SIDE_SQUARES + y","title":""},{"docid":"f46217b5d4a3bf9930b48694b85137e4","score":"0.54149246","text":"def get_relative(self, p):\n return (p - self.absolute.bottom_left) / self.absolute.size.to_float()","title":""},{"docid":"feafb19adaf2e87952196d4b826f8166","score":"0.5411935","text":"def direction_ratio(self, point):\n return [(point.x - self.x),(point.y - self.y),(point.z - self.z)]","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"eaad10f40fe76b681998e34ee83a1a8c","score":"0.5405452","text":"def get_right(self):\n\t\tif self.computed:\n\t\t\treturn self.dimensions[1]\n\t\treturn 1","title":""},{"docid":"d6e47540c05fecce63ccfae23df225ce","score":"0.53937054","text":"def getPerpendicularLineHomogenous(p1, p2):\n b1 = (p2[1] - p1[1]) / (p2[0] - p1[0]) if p1[0] != p2[0] else float('inf')\n chord_cent = [(p2[0] + p1[0]) / 2, (p2[1] + p1[1]) / 2, 1]\n print(\"Chord_cent1 \", chord_cent)\n if b1 == 0:\n return float('inf'), chord_cent\n if b1 == float('inf'):\n return 0, chord_cent\n return -1 / b1, chord_cent","title":""},{"docid":"ff766003e683a29f7d2c6416c280a635","score":"0.53896916","text":"def getArea(self):\n #return self.side * self.side\n return 2*self.side","title":""},{"docid":"7df14a41fa16a48c86b48839d61fdc82","score":"0.53799415","text":"def midpoint(self, p):\n\n x = (p[0][0] + p[1][0] + p[2][0] + p[3][0]) / 4\n y = (p[0][1] + p[1][1] + p[2][1] + p[3][1]) / 4\n return x, y","title":""},{"docid":"f73dab94634347f82d0dc28547542c25","score":"0.53726375","text":"def getLineWidth(self):\n return self._lineWidth","title":""},{"docid":"06f05f2ccca3c0281d049deaa4f03d8c","score":"0.53612983","text":"def linestyle(self):\n return self._linestyle","title":""},{"docid":"4076cda62c00ab929cf05a1aae6cf65c","score":"0.53612137","text":"def get_offset_right(self):\n\t\treturn self.offset[1]","title":""},{"docid":"f90514e3e77fcadcb9a2c86e09e4e1b1","score":"0.5358349","text":"def getdLine(self):\n return self._dline","title":""},{"docid":"b6fa7a36eb53e525f484638168cc8b5b","score":"0.53573775","text":"def height(self):\r\n return self._side","title":""},{"docid":"1b5f5bd755b344a8a75bc6c57f86475c","score":"0.5345058","text":"def calculate_perimeter(self):\n perimeter = self.__side * 4\n return perimeter","title":""},{"docid":"f87b30c945a5567bfc2240bcd77dd08c","score":"0.5343993","text":"def quadrant(self, pt):\r\n if pt[X] >= self.origin[X]:\r\n if pt[Y] >= self.origin[Y]:\r\n return NE\r\n else:\r\n return SE\r\n else:\r\n if pt[Y] >= self.origin[Y]:\r\n return NW\r\n else:\r\n return SW","title":""},{"docid":"bd23c5c0554d9d9997c69b4457f3c274","score":"0.5329019","text":"def line_from_pts(p1, p2):\n\n ##### STUDENT CODE START #####\n l = np.cross(p1, p2)\n l = l / np.linalg.norm(l[:2], 2)\n\n ##### STUDENT CODE END #####\n \n return l","title":""},{"docid":"21a35e959d4908f30b97bf4848e75ec6","score":"0.5325119","text":"def side_classification(self):\n sides = self.side_lengths()\n\n if isequal(sides[0], sides[1]) and isequal(sides[1], sides[2]) and isequal(sides[2], sides[0]):\n return \"equilateral\" # três lados iguais: equilátero\n\n if isequal(sides[0], sides[1]) or isequal(sides[1], sides[2]) or isequal(sides[2], sides[0]):\n return \"isosceles\" # dois lados iguais, mas não os três: isósceles\n\n return \"scalene\" # lados diferentes: escaleno","title":""},{"docid":"2641821b1ed811817a778bb890aaa36c","score":"0.532211","text":"def get_direction(self):\n if self.is_left_pressed():\n self._current = Point(-1, 0)\n elif self.is_right_pressed():\n self._current = Point(1, 0)\n elif self.is_up_pressed():\n self._current = Point(0, -1)\n elif self.is_down_pressed():\n self._current = Point(0, 1)\n\n return self._current","title":""},{"docid":"be4b4aedcdfc2898a74a38f5ad3bef25","score":"0.5311293","text":"def get_prow_direction(self):\r\n return self.__prow_direction","title":""},{"docid":"4824af83cbfcd0c9b4827977a55c8929","score":"0.5307189","text":"def right(self):\n return self.corner.x() + self.size.width()","title":""},{"docid":"1e76bbc1bed440819b0e30feab13317b","score":"0.52988553","text":"def left_position(self):\n try:\n return self.__left_position\n except AttributeError:\n self.__left_position = self.start_position if self.feature.clockwise \\\n else 2 * self.start_position - self.stop_position\n return self.__left_position","title":""},{"docid":"31b0b084c0645ffe85794cb9c11aaf33","score":"0.52933043","text":"def Direction(self, *args):\n return _Geom2d.Geom2d_Line_Direction(self, *args)","title":""},{"docid":"3c17ae41ecdb1315c0c732d7dacbe2d1","score":"0.52901244","text":"def getLeftQuadraturePosition(self):\r\n return -self.leftTalon.getSensorCollection().getQuadraturePosition()","title":""},{"docid":"508bb380003269349fc110de0c40a413","score":"0.528571","text":"def getEdgeThickness(self):\n front = self[0].point.z + self[0].edgePlane()\n back = self[1].point.z + self[1].edgePlane()\n return back - front","title":""},{"docid":"ab32697a754caf7edf5eef8b455737f6","score":"0.5277734","text":"def signed_dist_to_line(self, p, q) -> float:\n if p.y == q.y:\n return self.y - p.y\n elif p.x == q.x:\n return self.x - p.x\n else:\n a = 1 / (q.x - p.x)\n b = -1 / (q.y - p.y)\n c = p.y / (q.y - p.y) - p.x / (q.x - p.x)\n return (a * self.x + b * self.y + c) / math.sqrt(a ** 2 + b ** 2)","title":""},{"docid":"8e3305c5054dc703ff0d2b23d82d1e5e","score":"0.52753776","text":"def line_orientation(p: Position, q: Position, r: Position) -> OrientationResult:\r\n val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))\r\n\r\n if np.isclose(val, 0.0):\r\n return OrientationResult.COLLINEAR\r\n\r\n if val > 0:\r\n return OrientationResult.CLOCKWISE\r\n return OrientationResult.ANTICLOCKWISE","title":""},{"docid":"dfb13ef9256803cb070b6e95a2d28ecf","score":"0.52742714","text":"def side_func(x):\n y=(window_height-HEIGHT)/2.0 - sqrt(3) * (x-window_width/2.0)\n if (x>=250):\n y=(window_height-HEIGHT)/2.0 + sqrt(3) * (x-window_width/2.0)\n return y","title":""},{"docid":"1086cb68103222f6a4abfcea670b4adb","score":"0.52640045","text":"def perimeter(self) -> float:\n return 2*(self.side1+self.side2)","title":""},{"docid":"87f215dbff347887aad936ea533ed38a","score":"0.5243852","text":"def on_surface(self, point):\n return point[0] - self.rect.x, point[1] - self.rect.y","title":""},{"docid":"064023801890f585ef985bafa019b550","score":"0.5243692","text":"def perpendicular(self, through: Point | Line) -> Line | Plane:\n if self.dim != 3 and isinstance(through, Line):\n raise NotImplementedError(f\"Expected dimension 3 but found dimension {self.dim}.\")\n\n p = self.array[..., :-1]\n p = Point(np.append(p, np.zeros(p.shape[:-1] + (1,), dtype=p.dtype), axis=-1), copy=False)\n return through.join(p)","title":""},{"docid":"b20216c62a07a2c561a93c157149e1ef","score":"0.5235033","text":"def frame_left(self) -> float:\r\n return self.position.x - self.half_width","title":""},{"docid":"028d0164af79b05c3ac256b9c71d08c3","score":"0.522857","text":"def direction(self) -> Point:\n first_vec = self.first.subtract(self.second)\n second_vec = self.first.subtract(self.third)\n return first_vec.vector_product(second_vec)","title":""},{"docid":"8268a4baf77c92f8ebf10101ad82ba1c","score":"0.52246726","text":"def SPL(self):\n return self.S().P().L()","title":""},{"docid":"d8d39586dbd0d67cf187c96d7c2580c5","score":"0.52229756","text":"def lineLength (line):\r\n return math.sqrt((line[1].x - line[0].x) ** 2 + (line[1].y - line[0].y) ** 2)","title":""},{"docid":"69259cea9e83b1ffc23ae212e788078f","score":"0.5217991","text":"def side_length(X1,y1,x2,y2):\n return math.sqrt((X1-X2)**2 + (y1 - y2)**2))","title":""},{"docid":"7b047d858bc17cad65646b9791437d70","score":"0.52150047","text":"def getThickness(self):\n return self[1].point.z - self[0].point.z","title":""},{"docid":"7bf01ad6f6db18d2c958c702f21d424a","score":"0.5213559","text":"def linewidth():","title":""},{"docid":"732448dd60c29e209140f9bca58d8ae5","score":"0.52098674","text":"def frame_right(self) -> float:\r\n return self.position.x + self.half_width","title":""},{"docid":"acc3c505cc6af72b33e15aa92f07b3c7","score":"0.51981115","text":"def get_connected(self, side=None):\n end_side=[]\n if self._orientation==1:\n end_side.append('W')\n return end_side\n elif self._orientation==2:\n end_side.append('N')\n return end_side\n elif self._orientation==3:\n end_side.append('E')\n return end_side\n else:\n end_side.append('S')\n return end_side","title":""},{"docid":"c439f1be62b3408051c86203f5f0318b","score":"0.51950645","text":"def _width_of_current_line(self) -> int:\n return self._tw.width_of_current_line","title":""},{"docid":"c92873d5d9b9e8feafb4a5fe82708e6a","score":"0.5189287","text":"def dof_point(self) -> PointType:\n return self.ref.sub_entity(*self.entity).midpoint()","title":""},{"docid":"e6575bde7e407ed2671b78943f03ad74","score":"0.51873684","text":"def pentagon_perimeter(side: Number) -> Number:\n return 5*side","title":""},{"docid":"de905baaaa716709c4113fc4938a03bb","score":"0.5185853","text":"def get_side_of_head(s:bs4.element.Tag) -> SideOfHead:\n title = s.title.contents[0].lower()\n description = s.description.contents[0].lower()\n if 'rt' in title or 'right' in description:\n return SideOfHead.RIGHT\n elif 'lt' in title or 'left' in description:\n return SideOfHead.LEFT\n else:\n error(f'side of head cannot be determined from:\\n\\ttitle:{title}\\n\\t description:{description}')\n return None","title":""},{"docid":"3d092e967e0ec30a79077167779e0415","score":"0.51793754","text":"def get_offset_left(self):\n\t\treturn self.offset[0]","title":""},{"docid":"a4be2789fba83d16e1bf5707f434fb23","score":"0.517089","text":"def column(self) -> int:\n\n return int(self[-2:])","title":""},{"docid":"5743ed02767f576faa0f5a98e9b8430c","score":"0.51668185","text":"def side_lobe_level():\n return -13.26","title":""},{"docid":"3a8cacc4e6a6e14b4df8722e1f377b38","score":"0.5161773","text":"def get_shape(sides):\n pass","title":""},{"docid":"31be2fda9a74fdd41f7ebe74fe131f23","score":"0.5157701","text":"def get_pin_direction(self, line):\n is_input = self._get_bit_in_register('DIRECTION', line)\n return Directions.IN if is_input else Directions.OUT","title":""},{"docid":"c9ac0edf3447d8db00b4d251ccdc5281","score":"0.51407963","text":"def _get_level(self):\n if not hasattr(self.__p, 'pPr'):\n return 0\n return int(self.__p.pPr.get('lvl', 0))","title":""},{"docid":"18fc0904c9be3ee6edecbd6ba0bd0794","score":"0.51407915","text":"def pointDistance( self, p ):\n disp = self.p2 - self.p1\n segLen = disp.magnitude()\n norm = disp / segLen\n dispP = p - self.p1\n dp = norm.dot( dispP )\n if ( dp < 0 ): \n return (p - self.p1).magnitude()\n elif ( dp > segLen ):\n return ( p - self.p2).magnitude()\n else:\n A = -norm.y\n B = norm.x\n C = -( A * self.p1.x + B * self.p1.y )\n return abs( A * p.x + B * p.y + C )","title":""},{"docid":"79fa7b3b4b53af8196ccbd5dc769c301","score":"0.5140242","text":"def sideinfo(self):\n return self._get_element(*self._side_info_locator)","title":""},{"docid":"a688a18fc0e8075e19decfe1f28a08e0","score":"0.5128","text":"def line(self):\n return self.__line","title":""},{"docid":"5d85367c9d9286952cef6560643372c3","score":"0.5127137","text":"def right(self):\n return self._x + self._width","title":""},{"docid":"6b5e166a12cb1538013848c4b2f848a2","score":"0.51181984","text":"def line(p1, p2):\n A = (p1[1] - p2[1])\n B = (p2[0] - p1[0])\n C = (p1[0]*p2[1] - p2[0]*p1[1])\n return A, B, -C","title":""},{"docid":"611a49d6ca4dc570aaf11508efcccfa2","score":"0.5113819","text":"def perpendicular(self):\n Vperp = Vector2(-self.mData[1], self.mData[0])\n return Vperp","title":""},{"docid":"ae882b581d115c24f67cdfe1172c7eac","score":"0.5109769","text":"def distance_to(self, p):\n\t\treturn (self - p).length()","title":""},{"docid":"ac64bdf0462135e4d1d630ee1a66a143","score":"0.5109033","text":"def float2SideLeft(self):\n self.mLeft = self.getFloatSideLeft()\n return True","title":""},{"docid":"02812c3794e0b2e346c612557edd9d1d","score":"0.5107515","text":"def midPoint( self ):\n try:\n return ( self.p1 + self.p2 ) * 0.5\n except TypeError:\n print type( self.p1 ), type( self.p2 )","title":""},{"docid":"984e3ad3e8d3118d918353047d3d1428","score":"0.510563","text":"def coluna_pos(p):\n\treturn p[1]","title":""},{"docid":"bfae792a2165e1b3e43234a27b076f87","score":"0.51040936","text":"def right(self) -> float:\n return self.x + self.w","title":""},{"docid":"55dd12c453218080573b3268eebd75a4","score":"0.5101551","text":"def right(self):\n return self.left + self.width","title":""},{"docid":"59f689ee7b14e64d8b2fd2cde977e06b","score":"0.50990087","text":"def point_to_line(tower):\n return True, tower","title":""},{"docid":"5388fc94cbc0fdd59754edf92694cbee","score":"0.5096413","text":"def line_width(self):\n return self._line_width","title":""},{"docid":"8175b4c1543f79275f8b383fec11f42c","score":"0.5087364","text":"def perpendicular(self, through):\n if self.contains(through):\n n = self.dim + 1\n\n l = self\n\n if n > 3:\n # additional point is required to determine the exact line\n arr = np.zeros(n)\n for i in range(n):\n arr[-i - 1] = 1\n o = Point(arr)\n if not self.contains(o):\n break\n e = join(self, o)\n basis = e.basis_matrix\n line_pts = basis.dot(self.basis_matrix.T)\n l = Line(np.cross(*line_pts.T))\n\n from .operators import harmonic_set\n p = l.meet(infty)\n q = harmonic_set(I, J, p)\n\n if n > 3:\n q = Point(basis.T.dot(q.array))\n\n return Line(through, q)\n\n return self.mirror(through).join(through)","title":""}],"string":"[\n {\n \"docid\": \"cb2c6c21f616e1864d15179f7c84d096\",\n \"score\": \"0.7492487\",\n \"text\": \"def get_side(self):\\n return self.get_perimeter() / 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc079eec7d6fd53e500597fcb4a05fcb\",\n \"score\": \"0.7384879\",\n \"text\": \"def point_side_line(p1, p2, p):\\n\\n # Get vector from p1 to p2\\n a = p2 - p1\\n # Get vector from p1 to q\\n b = p - p1\\n # The sign of the cross product determines point side\\n s = np.sign(np.cross(a,b))\\n\\n # s>0 -> LEFT side of the line\\n # s=0 -> ON side of the line\\n # s<0 -> RIGHT side of the line\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f66d4996016c63cce2787af84ca213\",\n \"score\": \"0.731372\",\n \"text\": \"def getSide(self):\\n return self.side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b22f78f373225ad3d6b028b74f755a\",\n \"score\": \"0.7087271\",\n \"text\": \"def side(self) -> int:\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546c66e2ae21ff0ed1122999b1f1a98a\",\n \"score\": \"0.6932154\",\n \"text\": \"def side(self):\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546c66e2ae21ff0ed1122999b1f1a98a\",\n \"score\": \"0.6932154\",\n \"text\": \"def side(self):\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319b8e8eaec902c8d4a61daea65f714e\",\n \"score\": \"0.67851067\",\n \"text\": \"def side(self):\\n\\t\\treturn self.get(54)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ce10f146becbd285f82f30503daa8d\",\n \"score\": \"0.64582276\",\n \"text\": \"def get_side_left(self):\\n return self.filter_var(self.side_left)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bac596a77cc5e3988f13cf4d3cf5c97\",\n \"score\": \"0.6424401\",\n \"text\": \"def point_line_side(pointx, pointy, x1, y1, x2, y2):\\n return (x2-x1) * (pointy-y1) - (y2-y1) * (pointx-x1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1c895aa5e8ba15ce9142beafd4aa8b\",\n \"score\": \"0.6313351\",\n \"text\": \"def get_side(self, player):\\n if self.__team == player.get_team():\\n return \\\"ALLIES\\\"\\n return \\\"ENEMIES\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388ef22d1cbe686519f8df605e5cceda\",\n \"score\": \"0.61742723\",\n \"text\": \"def get_side_right(self):\\n return self.filter_var(self.side_right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf47b198470cfb534d7252195d40d25\",\n \"score\": \"0.61605984\",\n \"text\": \"def side1(self) -> float:\\n return self._side1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96cab5dc570f59ec86ef0529726dbd8\",\n \"score\": \"0.61603254\",\n \"text\": \"def get_side(a, b):\\n x = cosine_sign(a, b) #computes z component of out of plane component\\n if x < 0:\\n return LEFT\\n elif x > 0: \\n return RIGHT\\n elif x == 0.:\\n return \\\"zero\\\"\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44143d085f4d3f1bf40a5ecb61ddcff\",\n \"score\": \"0.61546004\",\n \"text\": \"def side2(self) -> float:\\n return self._side2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb2c8f9f85a895ec83d33b86194efab\",\n \"score\": \"0.6146379\",\n \"text\": \"def distance_from_line(self, p):\\n rp = pt.diff(p, self.start) # make relative to start of line\\n rp_along_d = self.direction.copy().scale(rp.dot_product(self.direction)) # Get component in direction of line\\n rpr = pt.diff(rp, rp_along_d) # perpendicular\\n return rpr.length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30bc1e85f3f9f64b5b8e912b0e9b661e\",\n \"score\": \"0.6144422\",\n \"text\": \"def get_flop(self):\\n return self.Flop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4812ef8c1c078aebffa2a372682cc043\",\n \"score\": \"0.60142285\",\n \"text\": \"def complementary_side(self):\\n if self == PuzzlePieceSide.top:\\n return PuzzlePieceSide.bottom\\n\\n if self == PuzzlePieceSide.right:\\n return PuzzlePieceSide.left\\n\\n if self == PuzzlePieceSide.bottom:\\n return PuzzlePieceSide.top\\n\\n if self == PuzzlePieceSide.left:\\n return PuzzlePieceSide.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe908da91ed5367c91c1ecdf043f099e\",\n \"score\": \"0.5988404\",\n \"text\": \"def _determineSide(self, testPlayer):\\n playerList = self._playerOrder(self._playerTurn)\\n lastPlayer = playerList[-1]\\n if testPlayer < lastPlayer:\\n testPlayer += 4\\n return testPlayer - lastPlayer - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3dc604b14c12375571c029bbfb43ba\",\n \"score\": \"0.5897389\",\n \"text\": \"def get_side(given_name):\\n return get_group(given_name, group='side') or ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"452b59b17bdf485a91f4f306fc899d97\",\n \"score\": \"0.58860266\",\n \"text\": \"def point_is_on_leftside_of_line(self, point):\\n\\n if (point.y < self.point2.y and point.y >= self.point1.y) or (point.y >= self.point2.y and point.y < self.point1.y):\\n\\n # The equation to determine this comes from here: https://math.stackexchange.com/questions/274712/calculate-on-which-side-of-a-straight-line-is-a-given-point-located\\n\\n d = ((point.x - self.point1.x)*(self.point2.y - self.point1.y)) - ((point.y - self.point1.y)*(self.point2.x - self.point1.x))\\n\\n left = (self.point1.y - self.point2.y)\\n # If d and left are both positive or both negative, then the point is on the left side of the line\\n\\n if (d < 0 and left < 0) or (d > 0 and left > 0):\\n return True\\n\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4811fb638efc5f5d24f044525e886ee\",\n \"score\": \"0.5875098\",\n \"text\": \"def linha_pos(p):\\n\\treturn p[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2e4c3181ec788edc55e9f45175d7f0\",\n \"score\": \"0.5852361\",\n \"text\": \"def side(pitch):\\n return pitch / SQRT3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ecd08a42dbabcd7f85d608af4e0d73\",\n \"score\": \"0.58335465\",\n \"text\": \"def getPerimeter(self):\\n return self.side * 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505dc43b635a26a2b30b27a6ba5dc9e6\",\n \"score\": \"0.5772587\",\n \"text\": \"def side(self, value):\\n if value in (self.LEFT, self.RIGHT):\\n self._side = value\\n else:\\n raise ValueError('Side value must be LEFT (-1) or RIGHT (1)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b7f0c5539dfef34becf6dbd667105f\",\n \"score\": \"0.57487196\",\n \"text\": \"def left(self):\\n return self.corner.x()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c813cffbd7d40171bb2fb28d280983df\",\n \"score\": \"0.5639955\",\n \"text\": \"def side_name(self):\\n return str(self).split(\\\".\\\")[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adb71702fb9cd2cc0ece1f8045760ba\",\n \"score\": \"0.56043947\",\n \"text\": \"def get_semi_perimeter(self):\\n return (self.side1 + self.side2 + self.side3) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a066b8bd9a856bd12319defc923aef4\",\n \"score\": \"0.559953\",\n \"text\": \"def getSide( name ):\\n \\n # make sure is not a full dag path\\n edit = name.split('|')[-1]\\n \\n # make sure is lowerCase and return the side\\n edit = edit.lower()\\n \\n if edit.startswith('l_'): return 'l'\\n if edit.startswith('r_'): return 'r'\\n \\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2991d7d4d56b1bce69d751f18d4576\",\n \"score\": \"0.5562073\",\n \"text\": \"def side_lengths(self):\\n d12 = self.p1.euclidean_distance(self.p2)\\n d23 = self.p2.euclidean_distance(self.p3)\\n d31 = self.p3.euclidean_distance(self.p1)\\n return d12, d23, d31\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3bd52e2d680737939d8ba267bcddba0\",\n \"score\": \"0.5545313\",\n \"text\": \"def grating_direction(self, p):\\n normal = self.normal(p)\\n xaxis = Vec(1, 0, 0)\\n a = normal.cross(xaxis)\\n rotation = Mat().rotateAxis(90, a)\\n return normal.transformDir(rotation).normalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60b97ef5e685d6009ee908fbd9a55829\",\n \"score\": \"0.55079645\",\n \"text\": \"def getQuarter(self, point):\\n # Checks on which side of the bottom-left to top-right diagonal the point\\n # is.\\n posDiag = point[0] + point[1] > 0\\n\\n # Checks on which side of the top-left to bottom-right diagonal the point\\n # is.\\n negDiag = point[1] > point[0]\\n\\n if posDiag:\\n if negDiag:\\n return 0\\n else:\\n return 1\\n if negDiag:\\n return 3\\n return 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cace02a999fdba75f2c696856f3d90b\",\n \"score\": \"0.5465378\",\n \"text\": \"def get_location_to_move_to(self, side):\\n temp = PoseStamped()\\n if self.get_domino_direction() == \\\"L\\\":\\n if side == \\\"top\\\":\\n temp.pose.position.y += VERT_VERT_OFFSET\\n if side == \\\"bottom\\\":\\n temp.pose.position.y -= VERT_VERT_OFFSET\\n if self.get_domino_direction() == \\\"R\\\":\\n if side == \\\"top\\\":\\n temp.pose.position.y -= VERT_VERT_OFFSET\\n if side == \\\"bottom\\\":\\n temp.pose.position.y += VERT_VERT_OFFSET\\n return temp\\n #if \\\"side\\\" == \\\"left\\\":\\n # temp.pose.position.y += HORIZ_VERT_OFFSET\\n #if \\\"side\\\" == \\\"right\\\":\\n # temp.pose.position.y -= HORIZ_VERT_OFFSET\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba517229a96a0ae281a986f32b12561\",\n \"score\": \"0.5462002\",\n \"text\": \"def get_perimeter(self):\\n return self.side1 + self.side2 + self.side3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c094c6beb4f14393aec743aeed28b9\",\n \"score\": \"0.54574496\",\n \"text\": \"def get_distance_from_starting_side(img, mode, x_edge_left, x_edge_right):\\n if mode == \\\"left\\\":\\n return img.shape[1] - x_edge_right\\n else:\\n return x_edge_left\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f67f669222f5bb0958ed5913d501004\",\n \"score\": \"0.5418983\",\n \"text\": \"def get_pos(self, x, y):\\n return x * self.SIDE_SQUARES + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46217b5d4a3bf9930b48694b85137e4\",\n \"score\": \"0.54149246\",\n \"text\": \"def get_relative(self, p):\\n return (p - self.absolute.bottom_left) / self.absolute.size.to_float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feafb19adaf2e87952196d4b826f8166\",\n \"score\": \"0.5411935\",\n \"text\": \"def direction_ratio(self, point):\\n return [(point.x - self.x),(point.y - self.y),(point.z - self.z)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaad10f40fe76b681998e34ee83a1a8c\",\n \"score\": \"0.5405452\",\n \"text\": \"def get_right(self):\\n\\t\\tif self.computed:\\n\\t\\t\\treturn self.dimensions[1]\\n\\t\\treturn 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e47540c05fecce63ccfae23df225ce\",\n \"score\": \"0.53937054\",\n \"text\": \"def getPerpendicularLineHomogenous(p1, p2):\\n b1 = (p2[1] - p1[1]) / (p2[0] - p1[0]) if p1[0] != p2[0] else float('inf')\\n chord_cent = [(p2[0] + p1[0]) / 2, (p2[1] + p1[1]) / 2, 1]\\n print(\\\"Chord_cent1 \\\", chord_cent)\\n if b1 == 0:\\n return float('inf'), chord_cent\\n if b1 == float('inf'):\\n return 0, chord_cent\\n return -1 / b1, chord_cent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff766003e683a29f7d2c6416c280a635\",\n \"score\": \"0.53896916\",\n \"text\": \"def getArea(self):\\n #return self.side * self.side\\n return 2*self.side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df14a41fa16a48c86b48839d61fdc82\",\n \"score\": \"0.53799415\",\n \"text\": \"def midpoint(self, p):\\n\\n x = (p[0][0] + p[1][0] + p[2][0] + p[3][0]) / 4\\n y = (p[0][1] + p[1][1] + p[2][1] + p[3][1]) / 4\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73dab94634347f82d0dc28547542c25\",\n \"score\": \"0.53726375\",\n \"text\": \"def getLineWidth(self):\\n return self._lineWidth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06f05f2ccca3c0281d049deaa4f03d8c\",\n \"score\": \"0.53612983\",\n \"text\": \"def linestyle(self):\\n return self._linestyle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4076cda62c00ab929cf05a1aae6cf65c\",\n \"score\": \"0.53612137\",\n \"text\": \"def get_offset_right(self):\\n\\t\\treturn self.offset[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90514e3e77fcadcb9a2c86e09e4e1b1\",\n \"score\": \"0.5358349\",\n \"text\": \"def getdLine(self):\\n return self._dline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fa7a36eb53e525f484638168cc8b5b\",\n \"score\": \"0.53573775\",\n \"text\": \"def height(self):\\r\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5f5bd755b344a8a75bc6c57f86475c\",\n \"score\": \"0.5345058\",\n \"text\": \"def calculate_perimeter(self):\\n perimeter = self.__side * 4\\n return perimeter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87b30c945a5567bfc2240bcd77dd08c\",\n \"score\": \"0.5343993\",\n \"text\": \"def quadrant(self, pt):\\r\\n if pt[X] >= self.origin[X]:\\r\\n if pt[Y] >= self.origin[Y]:\\r\\n return NE\\r\\n else:\\r\\n return SE\\r\\n else:\\r\\n if pt[Y] >= self.origin[Y]:\\r\\n return NW\\r\\n else:\\r\\n return SW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd23c5c0554d9d9997c69b4457f3c274\",\n \"score\": \"0.5329019\",\n \"text\": \"def line_from_pts(p1, p2):\\n\\n ##### STUDENT CODE START #####\\n l = np.cross(p1, p2)\\n l = l / np.linalg.norm(l[:2], 2)\\n\\n ##### STUDENT CODE END #####\\n \\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a35e959d4908f30b97bf4848e75ec6\",\n \"score\": \"0.5325119\",\n \"text\": \"def side_classification(self):\\n sides = self.side_lengths()\\n\\n if isequal(sides[0], sides[1]) and isequal(sides[1], sides[2]) and isequal(sides[2], sides[0]):\\n return \\\"equilateral\\\" # três lados iguais: equilátero\\n\\n if isequal(sides[0], sides[1]) or isequal(sides[1], sides[2]) or isequal(sides[2], sides[0]):\\n return \\\"isosceles\\\" # dois lados iguais, mas não os três: isósceles\\n\\n return \\\"scalene\\\" # lados diferentes: escaleno\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2641821b1ed811817a778bb890aaa36c\",\n \"score\": \"0.532211\",\n \"text\": \"def get_direction(self):\\n if self.is_left_pressed():\\n self._current = Point(-1, 0)\\n elif self.is_right_pressed():\\n self._current = Point(1, 0)\\n elif self.is_up_pressed():\\n self._current = Point(0, -1)\\n elif self.is_down_pressed():\\n self._current = Point(0, 1)\\n\\n return self._current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4b4aedcdfc2898a74a38f5ad3bef25\",\n \"score\": \"0.5311293\",\n \"text\": \"def get_prow_direction(self):\\r\\n return self.__prow_direction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4824af83cbfcd0c9b4827977a55c8929\",\n \"score\": \"0.5307189\",\n \"text\": \"def right(self):\\n return self.corner.x() + self.size.width()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e76bbc1bed440819b0e30feab13317b\",\n \"score\": \"0.52988553\",\n \"text\": \"def left_position(self):\\n try:\\n return self.__left_position\\n except AttributeError:\\n self.__left_position = self.start_position if self.feature.clockwise \\\\\\n else 2 * self.start_position - self.stop_position\\n return self.__left_position\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b0b084c0645ffe85794cb9c11aaf33\",\n \"score\": \"0.52933043\",\n \"text\": \"def Direction(self, *args):\\n return _Geom2d.Geom2d_Line_Direction(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c17ae41ecdb1315c0c732d7dacbe2d1\",\n \"score\": \"0.52901244\",\n \"text\": \"def getLeftQuadraturePosition(self):\\r\\n return -self.leftTalon.getSensorCollection().getQuadraturePosition()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508bb380003269349fc110de0c40a413\",\n \"score\": \"0.528571\",\n \"text\": \"def getEdgeThickness(self):\\n front = self[0].point.z + self[0].edgePlane()\\n back = self[1].point.z + self[1].edgePlane()\\n return back - front\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab32697a754caf7edf5eef8b455737f6\",\n \"score\": \"0.5277734\",\n \"text\": \"def signed_dist_to_line(self, p, q) -> float:\\n if p.y == q.y:\\n return self.y - p.y\\n elif p.x == q.x:\\n return self.x - p.x\\n else:\\n a = 1 / (q.x - p.x)\\n b = -1 / (q.y - p.y)\\n c = p.y / (q.y - p.y) - p.x / (q.x - p.x)\\n return (a * self.x + b * self.y + c) / math.sqrt(a ** 2 + b ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3305c5054dc703ff0d2b23d82d1e5e\",\n \"score\": \"0.52753776\",\n \"text\": \"def line_orientation(p: Position, q: Position, r: Position) -> OrientationResult:\\r\\n val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))\\r\\n\\r\\n if np.isclose(val, 0.0):\\r\\n return OrientationResult.COLLINEAR\\r\\n\\r\\n if val > 0:\\r\\n return OrientationResult.CLOCKWISE\\r\\n return OrientationResult.ANTICLOCKWISE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb13ef9256803cb070b6e95a2d28ecf\",\n \"score\": \"0.52742714\",\n \"text\": \"def side_func(x):\\n y=(window_height-HEIGHT)/2.0 - sqrt(3) * (x-window_width/2.0)\\n if (x>=250):\\n y=(window_height-HEIGHT)/2.0 + sqrt(3) * (x-window_width/2.0)\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1086cb68103222f6a4abfcea670b4adb\",\n \"score\": \"0.52640045\",\n \"text\": \"def perimeter(self) -> float:\\n return 2*(self.side1+self.side2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f215dbff347887aad936ea533ed38a\",\n \"score\": \"0.5243852\",\n \"text\": \"def on_surface(self, point):\\n return point[0] - self.rect.x, point[1] - self.rect.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064023801890f585ef985bafa019b550\",\n \"score\": \"0.5243692\",\n \"text\": \"def perpendicular(self, through: Point | Line) -> Line | Plane:\\n if self.dim != 3 and isinstance(through, Line):\\n raise NotImplementedError(f\\\"Expected dimension 3 but found dimension {self.dim}.\\\")\\n\\n p = self.array[..., :-1]\\n p = Point(np.append(p, np.zeros(p.shape[:-1] + (1,), dtype=p.dtype), axis=-1), copy=False)\\n return through.join(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20216c62a07a2c561a93c157149e1ef\",\n \"score\": \"0.5235033\",\n \"text\": \"def frame_left(self) -> float:\\r\\n return self.position.x - self.half_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028d0164af79b05c3ac256b9c71d08c3\",\n \"score\": \"0.522857\",\n \"text\": \"def direction(self) -> Point:\\n first_vec = self.first.subtract(self.second)\\n second_vec = self.first.subtract(self.third)\\n return first_vec.vector_product(second_vec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8268a4baf77c92f8ebf10101ad82ba1c\",\n \"score\": \"0.52246726\",\n \"text\": \"def SPL(self):\\n return self.S().P().L()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d39586dbd0d67cf187c96d7c2580c5\",\n \"score\": \"0.52229756\",\n \"text\": \"def lineLength (line):\\r\\n return math.sqrt((line[1].x - line[0].x) ** 2 + (line[1].y - line[0].y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69259cea9e83b1ffc23ae212e788078f\",\n \"score\": \"0.5217991\",\n \"text\": \"def side_length(X1,y1,x2,y2):\\n return math.sqrt((X1-X2)**2 + (y1 - y2)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b047d858bc17cad65646b9791437d70\",\n \"score\": \"0.52150047\",\n \"text\": \"def getThickness(self):\\n return self[1].point.z - self[0].point.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf01ad6f6db18d2c958c702f21d424a\",\n \"score\": \"0.5213559\",\n \"text\": \"def linewidth():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732448dd60c29e209140f9bca58d8ae5\",\n \"score\": \"0.52098674\",\n \"text\": \"def frame_right(self) -> float:\\r\\n return self.position.x + self.half_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc3c505cc6af72b33e15aa92f07b3c7\",\n \"score\": \"0.51981115\",\n \"text\": \"def get_connected(self, side=None):\\n end_side=[]\\n if self._orientation==1:\\n end_side.append('W')\\n return end_side\\n elif self._orientation==2:\\n end_side.append('N')\\n return end_side\\n elif self._orientation==3:\\n end_side.append('E')\\n return end_side\\n else:\\n end_side.append('S')\\n return end_side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c439f1be62b3408051c86203f5f0318b\",\n \"score\": \"0.51950645\",\n \"text\": \"def _width_of_current_line(self) -> int:\\n return self._tw.width_of_current_line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92873d5d9b9e8feafb4a5fe82708e6a\",\n \"score\": \"0.5189287\",\n \"text\": \"def dof_point(self) -> PointType:\\n return self.ref.sub_entity(*self.entity).midpoint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6575bde7e407ed2671b78943f03ad74\",\n \"score\": \"0.51873684\",\n \"text\": \"def pentagon_perimeter(side: Number) -> Number:\\n return 5*side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de905baaaa716709c4113fc4938a03bb\",\n \"score\": \"0.5185853\",\n \"text\": \"def get_side_of_head(s:bs4.element.Tag) -> SideOfHead:\\n title = s.title.contents[0].lower()\\n description = s.description.contents[0].lower()\\n if 'rt' in title or 'right' in description:\\n return SideOfHead.RIGHT\\n elif 'lt' in title or 'left' in description:\\n return SideOfHead.LEFT\\n else:\\n error(f'side of head cannot be determined from:\\\\n\\\\ttitle:{title}\\\\n\\\\t description:{description}')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d092e967e0ec30a79077167779e0415\",\n \"score\": \"0.51793754\",\n \"text\": \"def get_offset_left(self):\\n\\t\\treturn self.offset[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4be2789fba83d16e1bf5707f434fb23\",\n \"score\": \"0.517089\",\n \"text\": \"def column(self) -> int:\\n\\n return int(self[-2:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5743ed02767f576faa0f5a98e9b8430c\",\n \"score\": \"0.51668185\",\n \"text\": \"def side_lobe_level():\\n return -13.26\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8cacc4e6a6e14b4df8722e1f377b38\",\n \"score\": \"0.5161773\",\n \"text\": \"def get_shape(sides):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31be2fda9a74fdd41f7ebe74fe131f23\",\n \"score\": \"0.5157701\",\n \"text\": \"def get_pin_direction(self, line):\\n is_input = self._get_bit_in_register('DIRECTION', line)\\n return Directions.IN if is_input else Directions.OUT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ac0edf3447d8db00b4d251ccdc5281\",\n \"score\": \"0.51407963\",\n \"text\": \"def _get_level(self):\\n if not hasattr(self.__p, 'pPr'):\\n return 0\\n return int(self.__p.pPr.get('lvl', 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18fc0904c9be3ee6edecbd6ba0bd0794\",\n \"score\": \"0.51407915\",\n \"text\": \"def pointDistance( self, p ):\\n disp = self.p2 - self.p1\\n segLen = disp.magnitude()\\n norm = disp / segLen\\n dispP = p - self.p1\\n dp = norm.dot( dispP )\\n if ( dp < 0 ): \\n return (p - self.p1).magnitude()\\n elif ( dp > segLen ):\\n return ( p - self.p2).magnitude()\\n else:\\n A = -norm.y\\n B = norm.x\\n C = -( A * self.p1.x + B * self.p1.y )\\n return abs( A * p.x + B * p.y + C )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fa7b3b4b53af8196ccbd5dc769c301\",\n \"score\": \"0.5140242\",\n \"text\": \"def sideinfo(self):\\n return self._get_element(*self._side_info_locator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a688a18fc0e8075e19decfe1f28a08e0\",\n \"score\": \"0.5128\",\n \"text\": \"def line(self):\\n return self.__line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d85367c9d9286952cef6560643372c3\",\n \"score\": \"0.5127137\",\n \"text\": \"def right(self):\\n return self._x + self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5e166a12cb1538013848c4b2f848a2\",\n \"score\": \"0.51181984\",\n \"text\": \"def line(p1, p2):\\n A = (p1[1] - p2[1])\\n B = (p2[0] - p1[0])\\n C = (p1[0]*p2[1] - p2[0]*p1[1])\\n return A, B, -C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"611a49d6ca4dc570aaf11508efcccfa2\",\n \"score\": \"0.5113819\",\n \"text\": \"def perpendicular(self):\\n Vperp = Vector2(-self.mData[1], self.mData[0])\\n return Vperp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae882b581d115c24f67cdfe1172c7eac\",\n \"score\": \"0.5109769\",\n \"text\": \"def distance_to(self, p):\\n\\t\\treturn (self - p).length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac64bdf0462135e4d1d630ee1a66a143\",\n \"score\": \"0.5109033\",\n \"text\": \"def float2SideLeft(self):\\n self.mLeft = self.getFloatSideLeft()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02812c3794e0b2e346c612557edd9d1d\",\n \"score\": \"0.5107515\",\n \"text\": \"def midPoint( self ):\\n try:\\n return ( self.p1 + self.p2 ) * 0.5\\n except TypeError:\\n print type( self.p1 ), type( self.p2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984e3ad3e8d3118d918353047d3d1428\",\n \"score\": \"0.510563\",\n \"text\": \"def coluna_pos(p):\\n\\treturn p[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfae792a2165e1b3e43234a27b076f87\",\n \"score\": \"0.51040936\",\n \"text\": \"def right(self) -> float:\\n return self.x + self.w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dd12c453218080573b3268eebd75a4\",\n \"score\": \"0.5101551\",\n \"text\": \"def right(self):\\n return self.left + self.width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59f689ee7b14e64d8b2fd2cde977e06b\",\n \"score\": \"0.50990087\",\n \"text\": \"def point_to_line(tower):\\n return True, tower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5388fc94cbc0fdd59754edf92694cbee\",\n \"score\": \"0.5096413\",\n \"text\": \"def line_width(self):\\n return self._line_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8175b4c1543f79275f8b383fec11f42c\",\n \"score\": \"0.5087364\",\n \"text\": \"def perpendicular(self, through):\\n if self.contains(through):\\n n = self.dim + 1\\n\\n l = self\\n\\n if n > 3:\\n # additional point is required to determine the exact line\\n arr = np.zeros(n)\\n for i in range(n):\\n arr[-i - 1] = 1\\n o = Point(arr)\\n if not self.contains(o):\\n break\\n e = join(self, o)\\n basis = e.basis_matrix\\n line_pts = basis.dot(self.basis_matrix.T)\\n l = Line(np.cross(*line_pts.T))\\n\\n from .operators import harmonic_set\\n p = l.meet(infty)\\n q = harmonic_set(I, J, p)\\n\\n if n > 3:\\n q = Point(basis.T.dot(q.array))\\n\\n return Line(through, q)\\n\\n return self.mirror(through).join(through)\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
eb85624807aa0c6481b333d5603773be | Tests the insert method. | [
{
"docid": "d80cf976fc8e4c59b66f82ca08877bc9",
"score": "0.762313",
"text": "def test_insert(self):\n test_text = 'this is an insert test post'\n doc_id = self.engine.insert({'text': test_text})\n\n result = self.engine.find_by_id(doc_id)\n self.assertEqual(self._get_id([resu... | [
{
"docid": "42e6d55ed9b8167441d0c0db9c55427c",
"score": "0.8176738",
"text": "def test_insert(self):\n\n ids = self._helper.insert('test_table', (\n {'col_b': randint(0, 99)},\n {'col_b': randint(0, 99)},\n {'col_b': randint(0, 99)}\n ))\n self.asser... |
94d0024e99bf646bff8fd64e8a0db8e4 | generates a new date from person_id preferences | [
{
"docid": "b5233295c049c8cc5179ac1568ad534c",
"score": "0.67847145",
"text": "def generate_from_person_id(date_person_id):\r\n\r\n person_prefs = Preference.query.filter(Preference.date_person_id==date_person_id).first()\r\n \r\n # person_prefs.attribute == \r\n print(f'\\n\\n{person_prefs}'... | [
{
"docid": "a38fa82d82be41e76567c98bb83f0bf3",
"score": "0.60629743",
"text": "def create_date_person(user_id, name, relationship_type):\r\n\r\n date_person = DatePerson(user_id=user_id, name=name, relationship_type=relationship_type)\r\n\r\n db.session.add(date_person)\r\n db.session.commit()\... |
1589c5865da94ec1a570d5900ce7fe81 | Create a property package | [
{
"docid": "a837c1b42e44072798c7ada49de8282d",
"score": "0.529537",
"text": "def pp_toluene():\n\n return PropertyPackage(phases=1, phase_names=['toluene'])",
"title": ""
}
] | [
{
"docid": "d4becaaefdda3b48b49cdee6a733c8b9",
"score": "0.66099685",
"text": "def create_property(key, value):\n\n prop = manifest.CTD_ANON_4()\n prop.PropertyKey = unicode(key)\n prop.PropertyValue = unicode(value)\n\n return prop",
"title": ""
},
{
"docid": "8f881dd8a34bdb0fe2... |
5c1b4edc37cf0c9d7eaae82e09ff2489 | This method sets self.country_code from given locale | [
{
"docid": "09124be737d4905ae4c97bb5cff1c361",
"score": "0.6249225",
"text": "def get_country_code_from_locale(self):\n regex_pattern = re.compile('[-_](.*$)', re.U)\n match = regex_pattern.findall(self.locale)\n if match:\n return match[0].upper()\n else:\n ... | [
{
"docid": "2cd480f0e5b77b98260f1207398ba917",
"score": "0.7170048",
"text": "def country_code(self, country_code):\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "2cd480f0e5b77b98260f1207398ba917",
"score": "0.7170048",
"text": "def country_code(self, count... |
a5a14d993f096de597bd893e0ee12422 | Return the Body Mass Index (BMI) for the given weight, height, and measurement system. | [
{
"docid": "31680e17a6bc9efb79d34c03894346b1",
"score": "0.784507",
"text": "def calculate_bmi(height, weight, system='m'):\n if system == 'm':\n bmi = (weight / (height ** 2))\n else:\n bmi = 703 * (weight / (height ** 2))\n return bmi",
"title": ""
}
] | [
{
"docid": "f5996c4338c038a932b2454500eb8fee",
"score": "0.8331378",
"text": "def body_mass_index(weight, height):\n bmi = weight / (height ** 2) * 10000\n return bmi",
"title": ""
},
{
"docid": "9078e04477ee72b02fc0ca32e01c4981",
"score": "0.7788883",
"text": "def calculate_bm... |
81869966304cac1edc9a4a0a291c2a06 | >>> cover_phone_number('01234 567 890') '01234 ' | [
{
"docid": "6a782a16b67a0e7bc8c81c9310fd264e",
"score": "0.74416536",
"text": "def cover_phone_number(no):\n result = ''\n for order, digit in enumerate(no):\n if order < 5:\n result = result + digit\n else:\n if order in (5, 8, 11):\n result = re... | [
{
"docid": "10ef34eb9301b5071bdb4651a7f4541b",
"score": "0.7311842",
"text": "def phone(number):\n\treturn",
"title": ""
},
{
"docid": "6973b0b1aa9a6a9c68ae05a6f689a096",
"score": "0.72521627",
"text": "def get_raw_phone_number(phone):\n if not phone:\n return\n if phone... |
fcec292034e2345fb9fb6face4ca9787 | Initializes an empty graph that will store | [
{
"docid": "78ac43c9ec2bf6ad7fc97f1d39a653c3",
"score": "0.80484927",
"text": "def __init__(self):\n self.graph = {}",
"title": ""
}
] | [
{
"docid": "f9edf2b2716187983888b6916bdc16e5",
"score": "0.8202905",
"text": "def __init__(self):\n super(Graph, self).__init__()\n self.vertex = []\n self.edges = {}",
"title": ""
},
{
"docid": "cd62b0588bbf781e12dcf50f6b2377a3",
"score": "0.8162918",
"text": "d... |
d0a2a8fec99873f3f23741f91d1b5447 | Get appliance OSPF interfaces configuration | [
{
"docid": "fae438d4fd00c57de550c3c278de6b0b",
"score": "0.77904",
"text": "def get_appliance_ospf_interfaces_config(\n self,\n ne_id: str,\n) -> dict:\n return self._get(\"/ospf/config/interfaces/{}\".format(ne_id))",
"title": ""
}
] | [
{
"docid": "3234037c36bb52ad62356d0e92c5e305",
"score": "0.6962521",
"text": "def config_if(self):\n\n resource = \"/ip/address\"\n\n for intf in self.interfaces:\n\n response = self.request_put(resource, intf)\n #print(response)",
"title": ""
},
{
"docid"... |
7495509b729bd83921a95fa42410dfce | check input, extract data from csv file find copynumber files created by varscan | [
{
"docid": "dd3b2cc955a7893deea1dfc1c0bbed64",
"score": "0.5857006",
"text": "def main(): # run analyses of copynumber files using functions defined above\n # initialize lists\n individual_samples = set()\n ancestor_clone_pairs = set()\n missing_pairs = set()\n analyzed_pairs = set()\n\n ... | [
{
"docid": "712e9b347ffc2d5821cf6bec6902a509",
"score": "0.65452796",
"text": "def read_csv_file():\n\n csv_open = open(csv_file, 'rU')\n csv_reader = csv.reader(csv_open)\n row1 = next(csv_reader)\n\n clone_regex = re.compile('(Clone).*')\n clones = ([c.group(0) for cell in row1 for c in... |
6d7c635ef6190c251b027ad60d043f1b | Encode image file into string using base64. | [
{
"docid": "083775d02d746f13cdbf4045f2c8b572",
"score": "0.84103495",
"text": "def encodeImage(imageFile):\n encoded_string = ''\n if not os.path.exist(imageFile):\n print(\"File does not exist\",imageFile)\n with open(imgFile, \"rb\") as image_file:\n encoded_... | [
{
"docid": "e3eb3d717edbcc83c69c8e8a7f2f63ec",
"score": "0.8400944",
"text": "def encode_image(filename):\n with open(filename, \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n return encoded_string.decode(\"utf-8\")",
"title": ""
},
{
"docid": "20... |
7b760be8345a0f6b5f9b92938d3443f2 | Insert a new tray by adding tray to database. | [
{
"docid": "34cdcc6da48271164ffd28891aa06cdc",
"score": "0.8211067",
"text": "def insert_tray(tray: NewTrayInsert):\n with get_session() as sess:\n tray = Tray(**tray.dict())\n sess.add(tray)",
"title": ""
}
] | [
{
"docid": "986f26d9d9e21b32706b815174baa564",
"score": "0.60956424",
"text": "def insert(self):\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "1333b9854ffb8e3522d004fb384a98e6",
"score": "0.6006402",
"text": "def insert_transaction(self, item... |
60b1d7c2af15251da1f18ba1fdaec554 | show the stdout and stderr logs from a container | [
{
"docid": "d2edbb196b20ba3bf704ef2110636600",
"score": "0.68574196",
"text": "def logs(docker, container):\n info = docker.inspect_container(container)\n real_user = os.environ['SUDO_USER']\n if check_contenair_user(info, real_user):\n print docker.logs(container, stdout=True, stderr=Tr... | [
{
"docid": "18b4643ddd8f6a3602c022aca55b9799",
"score": "0.6842843",
"text": "def app_logs():\n run(f'docker logs {service_container_name}')",
"title": ""
},
{
"docid": "318a9b46d67867f3bdb8d0e98f9815cf",
"score": "0.6645457",
"text": "def logs(c):\n docker_compose(c, 'logs -f ... |
84c47483d857f890fe9beeda7750176d | returns the color of the drawn circle on the screen it can be different from the mass radius depending on mass.z | [
{
"docid": "352421a22a01d4800f90a2cba9f68d4d",
"score": "0.6111481",
"text": "def show_color(self):\n if not self.bound:\n return self.color\n r, g, b = self.color\n x = (WIND + self.z) / (2 * WIND)\n r = int(abs(r * x))\n g = int(abs(g * x))\n b = in... | [
{
"docid": "7a7d93903e0a47e10ecb16bf8d297757",
"score": "0.707346",
"text": "def _drawcircle(self,x,y,rad):\n color=\"red\"\n return self.c.create_oval(x-rad,y-rad,x+rad,y+rad,width=rad/5,fill=color,outline='black')",
"title": ""
},
{
"docid": "5251198223462dcdfb636d832cd01f64",
... |
7fba9ede4b0716e61c11ec7b2329ca94 | Generates a random character in [AZaz09]. | [
{
"docid": "c2cee4d2d87f3a14e49abaa85bf7e0d1",
"score": "0.75228137",
"text": "def random_alpha_num_char():\n num = random.randint(0, 26 + 26 + 10)\n if num < 26:\n return chr(num + 65)\n num -= 26\n if num < 26:\n return chr(num + 97)\n return chr(num + 48)",
"title": "... | [
{
"docid": "b331f1497ca3a7668249f633afa29201",
"score": "0.7927985",
"text": "def random_charachter() -> chr:\r\n return chr(int(random.randrange(32, 126, 1)))",
"title": ""
},
{
"docid": "1e58f69b2bcd64cf7c038a4f1183e9af",
"score": "0.79179096",
"text": "def random_char(alph):\n ... |
480ab2652d0d43eb768f1e1958a2b2af | Sets the volume of the player. | [
{
"docid": "5188a5b1a0c43f191664c6fbf30dc6f7",
"score": "0.71377057",
"text": "async def _volume(self, ctx: commands.Context, *, volume: int):\n\n await ctx.message.delete(delay=5)\n if not ctx.voice_state.is_playing:\n return await ctx.send('Nothing being played at the moment.'... | [
{
"docid": "0605d5ecd7672b195973cb02635dd7d5",
"score": "0.8093429",
"text": "async def volume(self, ctx, volume: int=None):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n\n if not volume:\n return await ctx.send(f'🔈 **{player.volume}%**')\n\n await player.set_... |
48396caa299375cb51992b31e43c3e59 | Takes key delimiter to avoid false collisions | [
{
"docid": "7dcf97ff70b889f82261618afbdd3bbb",
"score": "0.0",
"text": "def __init__(self, delim):\n \n self.__headers__= []\n self.__keys__ = []\n self.__delim__= delim",
"title": ""
}
] | [
{
"docid": "034355288292464d0dae3d826be50cda",
"score": "0.6467016",
"text": "def key_to_path(self, key):\n assert type(key) == str\n if self.config['separator'] in key:\n return key.split(self.config['separator'])\n else:\n return [key]",
"title": ""
},
{
"docid": "e75ff1... |
d2c6b52742fafc7dd50224580b53e7da | Register an endpoint with this client. | [
{
"docid": "b4e161884b7e6e24370777782bd9d197",
"score": "0.0",
"text": "def register(self, name, cls, routes):\n self.__dict__[name] = cls(self, routes[0], routes[1])",
"title": ""
}
] | [
{
"docid": "8e9c617398c09504bf9c8a7a7e5c2be9",
"score": "0.74871415",
"text": "def register(self, endpoint):\n endpoint = endpoint if not self.prefix else self.prefix + endpoint\n self.routes.append((endpoint, self.handler))",
"title": ""
},
{
"docid": "05b66432fa3d0bd4403cee00... |
df15ea22cd573caba7e021139fb6add6 | Updates configuration to continue parsing from the last state. | [
{
"docid": "fe75c85fbfa26e293115d1958ec091d3",
"score": "0.0",
"text": "def configureLastEnv(self, lastBlock):\n\t\tself.lastOffset = self.db.getLastOffset(lastBlock.fileName)\n\t\tself.setDatFileList(lastBlock.fileName)\n\t\tself.blockCount = lastBlock.blockNumber + 1",
"title": ""
}
] | [
{
"docid": "8c847d2d8c5d8e067e9b9c4cec258a93",
"score": "0.67347",
"text": "def config_reloaded(self):\n self.normalize_config()\n self.rebuild_alias_maps()",
"title": ""
},
{
"docid": "ac27db10d59c79d20bbbffb7541fc7c3",
"score": "0.657165",
"text": "def reload(self):\n... |
cdbfad92f73dfb254a59e293ab82bb5c | The ID of the basic GA instance. | [
{
"docid": "10168b545ae147fb3b30235bea337f67",
"score": "0.0",
"text": "def accelerator_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"accelerator_id\")",
"title": ""
}
] | [
{
"docid": "623bf3ecf10b26f32a0e22e015278b41",
"score": "0.7669954",
"text": "def analytics_id(self):\r\n return self.__analytics_id",
"title": ""
},
{
"docid": "748ad9b2fba9e34c2af90a9740a955f9",
"score": "0.71005523",
"text": "def unique_id(self):\n return f\"{self.hass.d... |
7751137b5ef6a8be9faa1f2ea7c6c57b | Encodes a Prensor into a tuple of lists of Tensors. | [
{
"docid": "45a99029ad517ac5a66a7d00aa3e38fc",
"score": "0.62981755",
"text": "def _to_components(self, value: \"Prensor\") -> List[tf.Tensor]:\n result = []\n self._append_to_components(value, result)\n return result",
"title": ""
}
] | [
{
"docid": "ffa4a990d841e423269f90aac54c0873",
"score": "0.59007865",
"text": "def encode(self, X: Tensor) -> List[Tensor]:\n return self.encoder(X)",
"title": ""
},
{
"docid": "615d83ebd482ecaa56c904bb4eba76ba",
"score": "0.5785119",
"text": "def encode_tensor(obj: Tensor) ->... |
d9c067aea9f5fa263434023f4e0cdca0 | Stub method, no functionality so that calling ``.get_input_entity()`` from ``.resolve()`` doesn't fail. | [
{
"docid": "caf590479c1c5e4deb68e1ad9f005bca",
"score": "0.54916525",
"text": "def get_input_entity(self, peer):\n return peer",
"title": ""
}
] | [
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53908837",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53908837",
"text": "def _get_input(self):\n return self.__input",
"title": ... |
d25728fa22bded19bc0c2391aeb198a2 | If data is identical but at a different level in the tree, strip_template() will not find it. | [
{
"docid": "20e0b7836f784a80bbc2864de19aabf5",
"score": "0.47186264",
"text": "def test_different_level(self):\r\n self.assertStrips(\r\n '<div><p>Foo</p><p>Bar</p></div>',\r\n '<p>Foo</p><p>Bar</p>',\r\n '<div><p>Foo</p><p>Bar</p></div>',\r\n 0,\r\n ... | [
{
"docid": "0db6bc4ac2ff1dfc3d0d454d25159543",
"score": "0.6451072",
"text": "def strip_template(tree1, tree2, check_ids=True, debug=False):\r\n # TODO:\r\n # Solve the sidebar problem -- delete them\r\n\r\n # Assemble a list of trees to compare. Obviously, first we just compare the\r\n #... |
f1b046b956850e508fcf55a6df4b6b7e | Collate function for PGL dataloader. | [
{
"docid": "8178101d28df8f18d5dfdd9f1325e9e4",
"score": "0.0",
"text": "def __init__(self, graph_wrapper, label_name='Log10_Kd', is_inference=False):\n assert label_name in ['Log10_Kd', 'Log10_Ki', 'KIBA']\n super(DTACollateFunc, self).__init__()\n self.graph_wrapper = graph_wrapper... | [
{
"docid": "d92a08224e9af357bb87b0169edadf67",
"score": "0.6742523",
"text": "def collater(self, samples):\n\t\treturn collate(\n\t\t\tsamples, self.src_embedding, self.tgt_embedding, self.src_dict, self.sql_dict, \n\t\t\tpad_idx=self.src_dict.pad(), eos_idx=self.src_dict.eos(), \n\t\t\tunk_idx=self.sr... |
1b765e94847150c9962d5efd934b7ad4 | Check that the GRIB2 URL exist and is of useful length. | [
{
"docid": "576189b698c80c0c60c6cfe0bfefe259",
"score": "0.76307017",
"text": "def _check_grib(self, url):\n head = requests.head(url)\n check_exists = head.ok\n if check_exists:\n check_content = int(head.raw.info()[\"Content-Length\"]) > 1_000_000\n return ch... | [
{
"docid": "204ea2128c5700896e1ad6969fecb6de",
"score": "0.6853001",
"text": "def checkUrlExists(window,url):\n\tcon = None\n\ttry:\n\t\tcon=db.connect('dlList.db')\n\t\tcursor = con.cursor()\n\t\tsql = \"SELECT * FROM downloads WHERE fileurl='\"+url+\"'\"\n\t\tcursor.execute(sql)\n\t\trows = cursor.fet... |
7e8618d600b26c25ea152421e904908e | Initiate training in the framework. | [
{
"docid": "3107dd14c7a1b9bc7e0d61ff6ac437e3",
"score": "0.0",
"text": "def train(self, nncfg=None):\n for nnpatch in self.nnpatches:\n for nnmodel in nnpatch.nnmodels:\n\n # If `nncfg` is already provided at NNModel creation, prioritize !\n if ((nnmodel.n... | [
{
"docid": "c04dac0951e3d7f0955f14e704fab09e",
"score": "0.8627881",
"text": "def start_training(self):\n self.__init__()",
"title": ""
},
{
"docid": "3dfadb6fb2382607383e18917e51ef57",
"score": "0.8184445",
"text": "def train(self):\n self.module.start_training()\n ... |
f176e2207ac746f2af84b220abbd5d6d | ez_as_py.Window.get_rect() Returns the dimensions of the window. | [
{
"docid": "b3f19e2eba6ae3e7aa3944f36429696d",
"score": "0.8090245",
"text": "def get_rect(self):\n\n return self._window.get_rect()",
"title": ""
}
] | [
{
"docid": "a8725c2b8728a3ac539d861550ecbb31",
"score": "0.80813",
"text": "def get_window_rect(self):\n return self._selenium_web_driver().get_window_rect()",
"title": ""
},
{
"docid": "dc06c84eba3e7d9bdbfdbf195fcfd780",
"score": "0.7673315",
"text": "def get_window_rect(wind... |
126ed6e8cc263f3169177ded98785fb3 | Stops the mojo shell. | [
{
"docid": "fb731e4dd192d39fac66ae02a8ddbee4",
"score": "0.6821146",
"text": "def StopShell(self):\n subprocess.check_call(self._CreateADBCommand(['shell',\n 'am',\n 'force-stop',\n ... | [
{
"docid": "deb13e56e09dd66c4b83baf4edb8bbf6",
"score": "0.70763576",
"text": "def stop(self):\n self.execute('pl_stop')",
"title": ""
},
{
"docid": "54870459170defb80c47588d993b09ec",
"score": "0.69280404",
"text": "def stop(self):\n\t\tPopen([\"screen\", \"-S\", self.name, \... |
05a5d5586961a6e30bf575dacccdff7a | The name of the Log Store. | [
{
"docid": "851950395880187338a3968561079308",
"score": "0.739993",
"text": "def sls_logstore(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"sls_logstore\")",
"title": ""
}
] | [
{
"docid": "e9740687d067a0cf19e6252460c0df26",
"score": "0.8303079",
"text": "def store_name(self) -> str:\n return pulumi.get(self, \"store_name\")",
"title": ""
},
{
"docid": "5010d70b0c4cdfe1aba9bb172116dba0",
"score": "0.8030705",
"text": "def store_name(self):\n\n ... |
095c89e054db19a35f4b9d6db87667c9 | Try to get data about the board by parameter 'board_url'. Function is called upon GET request, checks request parameters and get data from DB if record exists | [
{
"docid": "04915bb04d226f57f3da84c20d9eb072",
"score": "0.7731758",
"text": "def get_board(data):\n # validate all required fields exists\n for required_field in [\"board_url\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n re... | [
{
"docid": "73ccee6729593ec4a71baee0ddf10528",
"score": "0.58711916",
"text": "def get_boards(self) -> Optional[List[Board]]:\n http_method = 'GET'\n detail = 'boards'\n values = {}\n url_postfix = 'boards'\n response = None\n try:\n response = make_r... |
9ba5ec79a9302af8d0123c4c9aad8a67 | Sorts all the fabrics in the system in to physical shelves, taking into account maximum capacity, proximity grouping by pattern, and checking if there is actually quantity before assigning a shelf | [
{
"docid": "aca164ffe7dc6941b1a4f576fa3a245d",
"score": "0.74741846",
"text": "def sort_fabrics():\n max_shelf_qty = Decimal('240')\n shelves = Shelf.objects.all().order_by('tower', 'name')\n current_shelf_index = 0\n shelf = shelves[current_shelf_index]\n cell_style = \"\"\"\n ... | [
{
"docid": "b4cb2524fec347b6ea4a5fcff1ed1911",
"score": "0.5964946",
"text": "def PantrySorterEmptyShelf(Shelf: Shelf, stackableFood, unstackableFood):\n print(stackableFood, unstackableFood)\n unstackableFood = sorted(unstackableFood, reverse=True)\n stackableFood = sorted(stackableFood, rever... |
6ac45f39cb1314b9c014133ecf90e9db | Convert the product attribute form a string to an enum and return it. | [
{
"docid": "f8be53873402eb15ea70d1f29c7c3e7a",
"score": "0.6536794",
"text": "def product_as_enum(self):\n return Fit.field_enums.product_enum(self.manufacturer, self.product)",
"title": ""
}
] | [
{
"docid": "32267688a1610b39258f5bd86ac3ab87",
"score": "0.7099658",
"text": "def convert_attr_type_to_enum(attr_value):\r\n if str(attr_value.type) == 'AttrType.INTS':\r\n return 7\r\n elif str(attr_value.type) == 'AttrType.UNDEFINED':\r\n return 0\r\n elif str(attr_value.type) =... |
da0b6b90adf08064d06211309e30161c | Init. Remove if found. Convert to uppercase. Convert Os to 0s if found. | [
{
"docid": "20f2ed6679f7ab64cf9345c9245666cb",
"score": "0.0",
"text": "def __init__(self, tag: str):\n if tag.startswith('#'):\n tag = tag[1:]\n tag = tag.replace('O', '0')\n tag = tag.upper()\n self._tag = tag",
"title": ""
}
] | [
{
"docid": "d296894557ee5f0907bdb0319940102c",
"score": "0.530492",
"text": "def swapcase(self):\n return \"\"",
"title": ""
},
{
"docid": "086fdf579ea6e309c3117571e27ef338",
"score": "0.5259351",
"text": "def __init__(self, inString):\r\n # replacing every keyboard symbol ... |
9dc543c1b6023a5fcc5e4833ba4c797f | Returns the amount of damage this weapon will do | [
{
"docid": "589d861fab5d94fe94d7d1d2b235c62f",
"score": "0.0",
"text": "def get_amount(self):\n return 5",
"title": ""
}
] | [
{
"docid": "d071e053f13c61fa0c111ee136d462df",
"score": "0.8673491",
"text": "def damage(self, weapon):\n damage = self.ability_scores.strength.modifier\n for die in range(weapon.multiplier):\n damage += Dice.roll(weapon.damage_die)\n return damage",
"title": ""
},
... |
d31396fa7e71a367f792e2fd3dee2c06 | A convenience method to create a Worker that uses a shared thread pool for performing operations for GUI clients in a background thread | [
{
"docid": "f0bc37e7f8d034b9e1461a4fbe8fc835",
"score": "0.75226104",
"text": "def createGuiWorker() -> ghidra.util.worker.Worker:\n ...",
"title": ""
}
] | [
{
"docid": "d033a6b043ea48866266aacd0e9d067c",
"score": "0.71658456",
"text": "def create_worker(self):",
"title": ""
},
{
"docid": "b26e67465cf3a9098c42cf2a2533bda0",
"score": "0.6285358",
"text": "def worker_pool(self, *, max_workers=None):\n return concurrent.futures.Thread... |
458659e045cff9aa4dc4d0a41aa4ed9d | Stop the client. Must be invoked when done with the client for graceful exit. If already stopped, will not do anything. Cannot be cancelled if you try, the client will still fully shut down as much as possible, although CancelledError will still be raised. | [
{
"docid": "57620fd574d7a038b1ee2c20ccbaddad",
"score": "0.6313185",
"text": "async def stop(self) -> None:\n cancelled_tasks = []\n logger.debug(\"Stopping ProvisioningMQTTClient...\")\n\n if self._process_dps_responses_task:\n logger.debug(\"Cancelling '_process_dps_res... | [
{
"docid": "4a6bf9e069d2867b325f8891a1068339",
"score": "0.7935486",
"text": "def stop(self):\n self.info(\"Stopping client\")\n with self.__lock:\n self.__isocket.disconnect(self.__raddr)\n self.__isocket.close()\n\n self.__done = True\n\n self.__ba... |
b6d7b86e4b7bd65a0ada5fbd5a190a1b | Initialize internal set which will hold all tokens. | [
{
"docid": "180d6e6813da247d9c756bbaaac23ba4",
"score": "0.7904349",
"text": "def __init__(self):\n self._tokens = set()",
"title": ""
}
] | [
{
"docid": "0c47979dfc53180adde213d01b1ccd89",
"score": "0.6796376",
"text": "def __init__(self):\n\t\tself.s = set()",
"title": ""
},
{
"docid": "02efeb7887570eb52bfc0a23403e9739",
"score": "0.6761306",
"text": "def __init__(self):\n self.all_words = set()\n self.uniqu... |
01c89c0f326c0ef8a2645d1ed77f5ee1 | Take a list of blocks and place them into the world in one go. | [
{
"docid": "c78b2f5eb4f94285754458a48d9118a6",
"score": "0.52899325",
"text": "def sendBlocks(blockList, x=0, y=0, z=0, retries=5, flags='0100011'):\n body = str.join(\"\\n\", ['~{} ~{} ~{} {}'.format(*bp) for bp in blockList])\n try:\n response = setBlock(x, y, z, body, flags)\n ret... | [
{
"docid": "f3b2707d26453c19feb0ecb5eb8cd48a",
"score": "0.6527537",
"text": "def update_body_blocks(self) -> None:\n for i in range(0, len(self.body_blocks) - 1):\n self.body_blocks[i].setpos(self.body_blocks[i + 1].pos())",
"title": ""
},
{
"docid": "9909accc7b21b06034cd9... |
7b201d064d8a0dad0a690c43f7b08b79 | Return a randomly shuffled copy of a list | [
{
"docid": "e9b8bf65db11c18fca07b2f27e6b7946",
"score": "0.80562687",
"text": "def shuffle(input_list):\n output_list = list(input_list)\n random.shuffle(output_list)\n return output_list",
"title": ""
}
] | [
{
"docid": "6ec0efb610d74977bfdf18b035d06be7",
"score": "0.8064399",
"text": "def sample_list(l):\n outl = l[:]\n random.shuffle(outl)\n return outl[0]",
"title": ""
},
{
"docid": "3b6a6728b7215bc758b15f145b70a21b",
"score": "0.795556",
"text": "def partial_shuffle_orig(ls, ... |
e0ddcae60b69a4318a11a246c1454df9 | Applies 2D Gaussian Blur. | [
{
"docid": "224cc2963a2ab7b5c1f2b34bf9aa12ba",
"score": "0.0",
"text": "def __init__(self, in_channels, out_channels, ksize=5):\n\n super(GaussianConv2d, self).__init__()\n weight = (np.arange(ksize, dtype=np.float32) - ksize // 2) ** 2\n weight = np.sqrt(weight[None, :] + weight[:, None])\n ... | [
{
"docid": "687ae079bfddc7119b5409b0a5bdd133",
"score": "0.7160371",
"text": "def _gaussian_blur(self, img, kernel_size):\r\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "50efc7ccae0b729fdb6b07bd4e9a7ca3",
"score": "0.70999116",
"tex... |
5986bd6a59c7c3d96fa913666a852520 | Get the tools name by locating the line starting with 'STATS SUMMARY' which contains the tool name in the format e.g 'STATS SUMMARY blr.cli.tool_name. Return tool name | [
{
"docid": "9b1cfc2336ee1482c2814c3c7d9e4afd",
"score": "0.79888105",
"text": "def get_tool_name(file):\n for line in file:\n if line.startswith(\"STATS SUMMARY\"):\n return line.strip().split(\".\")[-1]\n\n return None",
"title": ""
}
] | [
{
"docid": "9d45da86d3750ac2f3c9160162bb916c",
"score": "0.6053547",
"text": "def GetInfo(self, tool):\n return (INFO % tool.replace('\\\\', '/'))",
"title": ""
},
{
"docid": "924224d8f4de85f9f0c86155e8c450d4",
"score": "0.5898121",
"text": "def extract_tool_details(self, node):\n... |
696d3c1b102fbc3a1cbd5a1e81a7febb | Get a list of tracks and save them to the database Return the number of track saved | [
{
"docid": "ed30a712872bcfd2eb98e0d4efd63b99",
"score": "0.53171325",
"text": "def write_track_to_datastore(track, user, location): \n logging.info(\"Saving track \\\"%s\\\" by \\\"%s\\\" (id: %s, created at: %s) to datastore ...\" % \\\n (track['title'], user.username, track['id'], track[... | [
{
"docid": "7113dbafa0725fb0b398d2bfaf371758",
"score": "0.65143496",
"text": "def getTrackCount(self):",
"title": ""
},
{
"docid": "c22c60d02e82c1160fd75ea7d88939ed",
"score": "0.6346815",
"text": "def task_7_song_counter():\n list = Song.objects.all()\n return len(list)",
... |
33b66aeb908a685ae028f257f6cdab5c | returns y_predicted.shape[2] binary clf curves calculated axis[1]wise this is a numpy implementation | [
{
"docid": "1a2d8e903a57cfb31df4f796ac19f1ca",
"score": "0.7147785",
"text": "def _last_axis_binary_clf_curve(y_true, y_predicted):\n assert y_true.shape == y_predicted.shape\n axis = -1\n sort_idx = list(np.ogrid[[slice(x) for x in y_predicted.shape]])\n sort_idx[axis] = y_predicted.argsort... | [
{
"docid": "e6770e82198d0d0915e0a55298116c41",
"score": "0.7294076",
"text": "def predicty(self, x):\n \"\"\" checked: yis0 + yis1 = 1, even with posteriorLabelGivenHypo \"\"\"\n yis0 = np.zeros(1)\n yis1 = np.zeros(1)\n for iconfig in range(self.ntotPerm):\n y = s... |
101d81614ab0a5a889c0c5c8f30c8a93 | Method to get all connection types | [
{
"docid": "1c2bedd0979e2655c426ccbacf5c59b7",
"score": "0.75797945",
"text": "def connectionTypes(request: HttpRequest) -> Response:\n if request.method == \"GET\":\n res = Connections.getConnectionTypes()\n return Response(res.json())",
"title": ""
}
] | [
{
"docid": "98b5f976a955658a2f18d8853d873c9f",
"score": "0.70869887",
"text": "def network_connections(self, type: str = 'inet') -> List[dict]:\n schema = ConnectionSchema()\n return schema.dump(\n schema.load(psutil.net_connections(kind=type), many=True), # type: ignore\n ... |
3ed796d7fb0d790e934c120b07f97043 | Will produce the isolation source linear combination predictions. | [
{
"docid": "36765d0d6933adb56f64042231c3107b",
"score": "0.0",
"text": "def seqenv(self):\n return Seqenv(self, self.p.seqenv_dir)",
"title": ""
}
] | [
{
"docid": "98a17fef7ec9649ac5bc58e7b786d499",
"score": "0.5872101",
"text": "def predict(self, cfg):",
"title": ""
},
{
"docid": "3d2569a6dfe82ae752eb4dc1b5e5c312",
"score": "0.57712275",
"text": "def linear_nonlinear_combining_model():\r\n# combining_type = ['arima_ffnn', 'arima... |
b15d38c3c411fae60885cd3a5ea2f768 | Here we know the architecture num_nodes = num_inter + 2, so we add another 1 | [
{
"docid": "6f301c58d3a55bc85294f05fb5ae76fc",
"score": "0.0",
"text": "def generate_arch(self, n, num_nodes, num_ops=8):\n # def _get_arch():\n # arch = []\n # for i in range(2, num_nodes):\n # p1 = np.random.randint(0, i)\n # op1 = np.random.r... | [
{
"docid": "b090a3bd6217391aa1e78b910d578ed3",
"score": "0.5962054",
"text": "def number_of_edges(self):",
"title": ""
},
{
"docid": "7bbfecdfee7c69775885f676dc237ff0",
"score": "0.5906666",
"text": "def add_node(self, node_for_adding, **attr):\n\n self.num_states += 1\n\n ... |
ec8f1251875f46906e98641a9876f069 | Test all known migration cases. | [
{
"docid": "46833f24f414b7e1599600f9531df70b",
"score": "0.0",
"text": "def test_compiled_string_is_expected():\n builder = DjangoTagMigrationBuilder()\n file_migration = builder.build_migration(DJANGO_TEMPLATE)\n compiled = file_migration.compile()\n assert compiled == TRANSIFEX_TEMPLATE\n\... | [
{
"docid": "c12c7465e867794386171e686d0362be",
"score": "0.7462017",
"text": "def run_migration_checks():\n check_model_state()\n check_migration_state()",
"title": ""
},
{
"docid": "0336de207b599a5978811d3f4062925c",
"score": "0.7103489",
"text": "def migration():",
"title... |
ce1c96a4d80733f9dded1747970509c6 | Uses the frequency as the fit parameters | [
{
"docid": "4e7b8a5e8df2789f646c09fbe8cd8f6e",
"score": "0.0",
"text": "def get_fit_old(self):\n def fit_PSD_target_function(f, f_QP, f_RO):\n tail = True\n # tail = False\n if tail:\n return (4 * f_RO ** 2 * f_QP) / ((2 * f_QP) ** 2 + (2 * np.pi * ... | [
{
"docid": "4a53186e3fdf2d627b95ae8f897e6ed3",
"score": "0.70693344",
"text": "def set_frequency(self, frequency):",
"title": ""
},
{
"docid": "a7ad01f2056a664fe2cdc21a5693024b",
"score": "0.66535306",
"text": "def fit(self):",
"title": ""
},
{
"docid": "a7ad01f2056a664fe... |
28e8d58e031b6e9984ce908b755a5e1d | Update parameter dictionary, while also performing some basic checks. | [
{
"docid": "ca91c3445747af00598d0000a0615a04",
"score": "0.0",
"text": "def update(self, values, check_conflict=False, check_already_exists=True, path=\"\"):\n # check if values is not a dictionary\n if not isinstance(values, dict):\n values = values._dict_items\n # check... | [
{
"docid": "b6f27b5b1708baf25527a92069a0bb4d",
"score": "0.7634509",
"text": "def update_parameters(self):",
"title": ""
},
{
"docid": "52a41537d757e88af18f861bdb7b2c79",
"score": "0.75093085",
"text": "def update_params(self) -> None:\n raise NotImplementedError",
"title"... |
7dcb2c2e1cd2aabf754f610640b6b72d | Return the node corresponding to line n of external file given by path. | [
{
"docid": "e096dfb90f0f6450e621e8b673c3c206",
"score": "0.7063621",
"text": "def find_line(path: str, n: int) -> tuple[Position, int]:\n if path == root_path:\n p, offset = c.gotoCommands.find_file_line(n, root)\n else:\n # Find an @<file> node with t... | [
{
"docid": "9782e0ee913fb0e1297522a755a2c9c4",
"score": "0.61165345",
"text": "def getLineNumber(lnr, path):\n global realmap\n lnr = int(lnr)\n\n if not path in realmap:\n realmap[path] = getDecoded(path)\n\n if realmap.get(path) == \"not found\":\n return str(lnr)\n\n lastLn... |
ecb3abf80a6c753d2a9bb905fc601082 | It is an array of string values. If the operator is In or NotIn, the values array must be nonempty. If the operator is Exists or DoesNotExist, the values array must be empty. | [
{
"docid": "83bedbf18fa8b31ed46d6fdafb101161",
"score": "0.46206558",
"text": "def values(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"values\")",
"title": ""
}
] | [
{
"docid": "2144af1d3298794a7da56c26a4c8fea0",
"score": "0.6495714",
"text": "def contains_operator(metadata, values, logical_operator=\" or \"):\n if isinstance(values, (list, tuple)):\n filters = [f\"contains({metadata},'{item}')\" for item in values]\n filters_joined = logical_operat... |
4a1939a5eb8a098543846af3f2c5decb | method which returns the status (1 for win of 1, 2 for win of 2, 0 for full board and a tie) we decided to keep the numbers as numbers and not as constant because we because we have noticed that the constants create more confusion in this case after a conversation with lab support | [
{
"docid": "454fd2f109148f7d7a8a5fd2f612be93",
"score": "0.0",
"text": "def get_winner(self):\r\n\r\n\r\n player = self.get_current_player()\r\n\r\n\r\n for i in range(self.ROWS):\r\n\r\n for j in range(self.COLOUMNS - 3):\r\n\r\n if self.board[i][j] == player and... | [
{
"docid": "518cc98d4e81758d6d7a4d60382824b4",
"score": "0.7457999",
"text": "def check_status(mat, stat):\n if stat == GAME_OVER:\n return GAME_OVER\n for r in range(len(mat)):\n if COAL_CELL in mat[r]:\n return PLAY\n return WIN",
"title": ""
},
{
"docid":... |
44d2eda6e096b216d06513fa51569107 | Saidas do bairro de sabados, domingos e feriados | [
{
"docid": "379dc0eb871f2c7d74aefb80aa0e57e1",
"score": "0.0",
"text": "def SaidasBairroSabadosDomingoseFeriados6823(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z52'] + \"\"\"\nEsse onibus nao possui horario nesses dias, deseja consultar as empresas?\n-> /empresas\"\"\"\n... | [
{
"docid": "618777769afbe97dc7f9f8b42bdbeaab",
"score": "0.68243456",
"text": "def SaidasBairroSabadosDomingoseFeriados178(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z5'] + \"\"\"\nSabados:\n|06:00| A D\n|06:45| \n|07:30| D\n|08:30| A D\n|09:30|\n|10:30| A D\n|11:00| SC ... |
b8931249e149b6f7e5829d521b73dc28 | Ensure we can create a new account object and it returns status code as 201. | [
{
"docid": "d448f0b7f5691e066ec1b8497ab7f3ed",
"score": "0.0",
"text": "def test_given_valid_details_for_registration(self):\n\n response = self.client.post(self.register_url, self.valid_registration_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)",
... | [
{
"docid": "54b1493f11cffca22ef66264bd13bcfe",
"score": "0.7521507",
"text": "def test_create_account(self):\n\n response = self.client.post(\n '/api/v1/users', data=json.dumps(create_account), content_type='application/json')\n result = json.loads(response.data.decode())\n ... |
3916e3eb546ea428885852405cc3f11e | Ensambla el sistema de ecuaciones | [
{
"docid": "5ae8f9ab0e1f89c7367e33b976ebc38c",
"score": "0.53271186",
"text": "def ensamblar_sistema(self):\n \n Ngdl = self.Nnodos * self.Ndimensiones\n\n self.K = np.zeros((Ngdl,Ngdl), dtype=np.double)\n self.f = np.zeros((Ngdl), dtype=np.double)\n self.u = np.zeros(... | [
{
"docid": "230aeb41b55eff2e4983667e22fba701",
"score": "0.6246159",
"text": "def iniciaconfiguracion():\n\tos.system(\"CLS\")\n\truta = os.getcwd()\n\trutatask = ruta + \"\\\\syncloud.exe\"\n\ttimetask = \"\"\n\tkey = _winreg.CreateKey(_winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\\\Olesistemas\\\\syncloud')\... |
2541eb9e1e33ce147bdb8184afa7d8db | Returns a secure filename that is mostly guaranteed to be unique. | [
{
"docid": "210a598d950e07f6021996eb18ce2257",
"score": "0.7789519",
"text": "def secure_uuid_filename(_obj, data):\n\n _, ext = path.splitext(data.filename)\n uid = uuid1()\n return secure_filename('{}{}'.format(uid, ext))",
"title": ""
}
] | [
{
"docid": "8fcf0c370b38efe3b8877bdd8508d2ad",
"score": "0.79068273",
"text": "def generate_secret_filename_for(filename):\n secret = os.urandom(constants.FILENAME_SECRET_LENGTH).hex()\n return secret + filename",
"title": ""
},
{
"docid": "dc03048562e6273eb0e7720bb1857366",
"score... |
6eb19d0511ab549c0fa61b646922241f | Called after an object has been moved into this object. Anything inside a character is an item, in their inventory or equipped. It's assumed that coordinate 0 is the character's inventory, and coordinates 1+ are their equipment slots. | [
{
"docid": "3d7df2a78ba3426f8d90607e1587d03b",
"score": "0.51111734",
"text": "def at_object_receive(self, obj: DefaultObject, source_location: typing.Optional[DefaultObject], move_type=\"move\", **kwargs):\n obj.db.coordinates = 0",
"title": ""
}
] | [
{
"docid": "fee4d1485ac8e479d3529f2925b77351",
"score": "0.61136115",
"text": "def move_being_on_map(self, obj, dx, dy):\n newx = obj.x + dx\n newy = obj.y + dy\n # checks\n self.rules.assert_remove_ok(obj)\n self.rules.assert_unoccupied(obj.place, newx, newy)\n ... |
1e3e6aca4e87ac8d47d5b6e268cb6a89 | Delete singleton for given arguments in a class decorated with SingletonWithRegardsTo | [
{
"docid": "85015b99acc71fc329f8ac21ee521fd7",
"score": "0.7398244",
"text": "def delete_singleton_for(x, *args) -> None:\n del x.__it__[args]",
"title": ""
}
] | [
{
"docid": "3287075af136606fc1de66d043c06c40",
"score": "0.6534979",
"text": "def singleton(cls):\n \n # Ex: from class 'Fucker' to singleton name \"_FuckerSingleton\"\n # 'closure' for the class name, needed to recover the global object name:\n singletonName = '_{}Singleton'.format(cls.__n... |
793273eae081136d8f3febb8da253e13 | Get the boolean value for a given config parameter. | [
{
"docid": "93fc056817c3321dd9b9a5b6340c474d",
"score": "0.7363348",
"text": "def getConfigBoolean(category, identifier):\n global config\n return config.getboolean(category, identifier)",
"title": ""
}
] | [
{
"docid": "b2ae98a2a0e1024fbd755982bb0f400a",
"score": "0.79825675",
"text": "def get_bool(self, name, default=False):\n param = self.module.params.get(name, default)\n\n if param in (True, False):\n return param\n elif isinstance(param, str):\n return BOOLEAN... |
c7bc01c8c595a41fbdf5c05e887f3f7a | Return list of column names. | [
{
"docid": "a8a1f72d635808bf5123e00e24d47356",
"score": "0.75188255",
"text": "def columns(self):\n if self._default_index:\n return list(self._df.columns)\n return list(self._df.index.names) + list(self._df.columns)",
"title": ""
}
] | [
{
"docid": "b50d29d4eb3280d1ab00bfa8ce62d58b",
"score": "0.87906796",
"text": "def getColumnsNames(self):\n self.do('show columns from ' + self.__nameTable + ';')\n return list([name[0] for name in self.done()])",
"title": ""
},
{
"docid": "715fc82ab8aac2989f607f23723d0699",
... |
e83d260d7944ed2df03968de04210f5e | Selectionne toute les equipes | [
{
"docid": "ed0a094b03dc336c2dbcf9cba7d0d249",
"score": "0.0",
"text": "def nom_equipes(id_equipe):\n c=conn.cursor()\n p = (id_equipe, )\n c.execute(\"\"\"SELECT nom FROM TEAM WHERE id = ?\"\"\", p)\n r=c.fetchall()\n return r[0][0]",
"title": ""
}
] | [
{
"docid": "fc04a92527757372ee40f2afea1f1ae0",
"score": "0.5983597",
"text": "def select( self ):",
"title": ""
},
{
"docid": "50cf8f958d662ede8e29dba741222c64",
"score": "0.58359784",
"text": "def GetSelectedScalarComponents(self):\n ...",
"title": ""
},
{
"docid"... |
99c0d72e525d482329b2e642eb8c4c8b | Reset state_identifiers to None | [
{
"docid": "e6e6ca6d6b2e6722e34171e9ebc6f862",
"score": "0.88936627",
"text": "def reset_state(self):\n self.state_identifiers = {k:None for k in self._get_state()}",
"title": ""
}
] | [
{
"docid": "3711c9c93b006e8b4cf3574e222340bb",
"score": "0.7362603",
"text": "def reset_state(self):",
"title": ""
},
{
"docid": "38bcc6fe5d3b74fe76d2c3754b12f469",
"score": "0.7149735",
"text": "def reset(self, state):\n pass",
"title": ""
},
{
"docid": "48a521a59... |
588065c092536f7288acf9dbfbdc684a | Start the FTP client | [
{
"docid": "c87cc3a7d39919e3dc281d1fb5e9d120",
"score": "0.7409453",
"text": "def start(self):\n while True:\n try:\n userInput = raw_input('ftp>')\n except KeyboardInterrupt:\n self.commandSock.close()\n break\n\n if u... | [
{
"docid": "40a5e6d4c8c6e9800aed333d8fbbeba8",
"score": "0.7501968",
"text": "def startFtp(self, host, port):\n self.ftpfactory = factory = FileClientFactory(self) # setting up the factory\n factory.protocol = FileClientProtocol\n factory.deferred = defer.Deferred()\n factory... |
9ac724d1ba5c72f1a5fe4c4654ab92af | 1. If value is bigger than Node, go right. If empty, add Node. 2. if value is smaller than Node, go left. If empty, add Node. 3. if value is same as Node, overwrite and return. | [
{
"docid": "d303aa8d096e0c55418dde460440f6a9",
"score": "0.6986899",
"text": "def insert(self, value): \n if value > self.value:\n if self.right is None:\n self.right = Node(value)\n else:\n #Insert on right node. \n self.right.insert(value)\n\n if value < self.value... | [
{
"docid": "cc0d4b56d0a03baa884dd027f8872a66",
"score": "0.7236795",
"text": "def add(self, value: object) -> None:\n # make new node\n new_node = TreeNode(value)\n\n\n\n if self.root is None:\n self.root = new_node\n\n else:\n current = self.root\n ... |
441d22117b67a3254b48a5d80db5e675 | Searches through the class's attrs for methods with the same name as HTTP ones and adds the bottle.request decorator to them. The route is set via self.route . | [
{
"docid": "46576f42443c93c02aa41745f2b78883",
"score": "0.69266075",
"text": "def __new__(cls, name, bases, attrs):\n if 'route' in attrs: # to allow inheritance\n # Get the given route\n # or routes if a list is provided\n routes = attrs['route']\\\n ... | [
{
"docid": "23abe07965fcb8e4521a1b2d07e3cff9",
"score": "0.6665122",
"text": "def __getattr__(self, name, *args, **kwargs):\n if name not in ('get', 'post', 'put', 'head', 'delete'):\n raise AttributeError\n return partial(self._request, name, *args, **kwargs)",
"title": ""
... |
454fdc73af98476e48abd7b0554cd692 | Arms vehicle and fly to aTargetAltitude. | [
{
"docid": "29bf935b4f5da125599c399f93de029e",
"score": "0.74334264",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print(\"Basic pre-arm checks\")\n # Don't try to arm until autopilot is ready\n while not vehicle.is_armable:\n print(\" Waiting for vehicle to initialise...\")\n ... | [
{
"docid": "af213d26adaf5f294494c15dc4f2d00c",
"score": "0.7867513",
"text": "def arm_and_takeoff(self, aTargetAltitude):\r\n print(\"Basic pre-arm checks\")\r\n # Don't let the user try to arm until autopilot is ready\r\n while not vehicle.is_armable:\r\n print(\" Waitin... |
f603e37c07d87db035c521a1654b497b | currently only suppor one example at a time | [
{
"docid": "c0621f075dbd648f6f37506eded4f33b",
"score": "0.0",
"text": "def saliency_expl(expl_method, Xs, ys, model):\n print('Computing explanation by {}...'.format(expl_method))\n\n assert Xs.shape[0] == 1\n assert ys.shape[0] == 1\n\n if expl_method == 'SG':\n sg_r = 0.2\n ... | [
{
"docid": "f84a606a4456d9f6f52b70f06ae01465",
"score": "0.70378304",
"text": "def add_examples(self,src):\n return",
"title": ""
},
{
"docid": "1274e76c0963b153550760a5a6822c52",
"score": "0.6776039",
"text": "def sample(self):\n pass",
"title": ""
},
{
"do... |
79741a40c9400c9e7569054ca8891dc6 | example function to load model The prefix allows the loading of different models | [
{
"docid": "fcd7e1764c0ffc983e0b39ea4aff7f8d",
"score": "0.76179326",
"text": "def model_load(prefix='sl',country='all', training=True):\n \n models = [f for f in os.listdir(MODEL_DIR) if re.search(\"sl\",f)]\n\n if len(models) == 0:\n raise Exception(\"Models with prefix '{}' cannot be ... | [
{
"docid": "c0fd5de7decb08a60cafccf195b1f345",
"score": "0.76663965",
"text": "def loadmodel(self, nameprefix):\n self.topicmodeler.loadmodel(nameprefix)",
"title": ""
},
{
"docid": "97c0da6e305b6175b926cc9ce18bb622",
"score": "0.76616585",
"text": "def load_model():\n pass... |
22309e7e09d72f5df4eb925c102f7779 | CreateFilmSchema accepts a valid title | [
{
"docid": "1ad064f3127428bf4539e89d332f18a6",
"score": "0.6432382",
"text": "def test_title_passes_validation(self):\n result = self.schema.deserialize(self.params)\n self.assertEqual(self.params['title'], result['title'])",
"title": ""
}
] | [
{
"docid": "85eb6fd2eec38707529103ac3078cfba",
"score": "0.63343024",
"text": "def create_movie():\n body = request.get_json()\n validated_body = {}\n required_fields = ['title', 'release_date']\n for field in required_fields:\n resp = body.get(field, None)\n if resp is None:\n... |
daa1ef20b040cb30b2c08026e8c2b7d8 | Draw a pixel at (x,y) on the given canvas | [
{
"docid": "0995007b44a80ad80146cc37fd0a1fff",
"score": "0.7908712",
"text": "def draw_pixel(canvas, x, y, color='#FF0000'):\n x1, y1 = x - 1, y - 1\n x2, y2 = x + 1, y + 1\n canvas.create_oval(x1, y1, x2, y2, fill=color, outline=color)",
"title": ""
}
] | [
{
"docid": "eb9d7b8bdc51c63d8b12792d5b48a0d3",
"score": "0.7462939",
"text": "def draw_pixel(self, x, y):\n # TODO Support for colors\n if (x, y) not in self.coord2rectangle:\n r = self.canvas.create_rectangle(x, y, x+1, y+1, fill=\"black\")\n self.coord2rectangle[(x,... |
4ff1bc27f33dfb14c2e012b3e212a2a1 | Ensure virtual machine endpoint returns a valid response | [
{
"docid": "defb997169eda472584e5d8d9cd0b157",
"score": "0.6642315",
"text": "def test_endpoint_virtual_machine(self):\n\n for i in range(60):\n utils.build_vm_full(f\"api-test-vm-{i}.example.com\")\n\n resp = self.client.get(\"/api/plugins/prometheus-sd/virtual-machines/\")\n ... | [
{
"docid": "67a12dd4e5a05e76ce0ee3a18313dd21",
"score": "0.6154137",
"text": "def verify(self, response):",
"title": ""
},
{
"docid": "41f9543e83d54ae65b2a593f033a6b4d",
"score": "0.60561776",
"text": "def check(self, response):\n pass",
"title": ""
},
{
"docid": "... |
e99ea12864f0f0d98f66a723a580ef33 | Find all water touching point (i,j) and set to ii. The recursive version works for teeny, tiny grids only, so we need another way. | [
{
"docid": "1a6ad161e90035cd4a4d77e21a0f1e1a",
"score": "0.67142487",
"text": "def flood_fill_water(imask, i, j, ii):\n Mp, Lp = imask.shape\n Lm = Lp-2\n Mm = Mp-2\n jj = imask[j,i]\n\n llist = []\n llist.append((j,i))\n while len(llist) > 0:\n (j,i) = llist.pop()\n i... | [
{
"docid": "419f98b652d4ab344feb2043d0c6ed25",
"score": "0.5981496",
"text": "def swimInWater(self, grid: List[List[int]]) -> int:\n\n n = len(grid)\n\n to_visit = []\n\n def add_to_visit(i, j):\n heapq.heappush(to_visit, (grid[i][j], i, j))\n\n def neighbors(i, j)... |
00f6d034adfcd3beaf86e7505d0ae22f | Start the agent after initialisation. | [
{
"docid": "1491c91070d75bdf257c2ff5acaeafb3",
"score": "0.7184218",
"text": "def on_initialized(self) -> None:\n self.start()",
"title": ""
}
] | [
{
"docid": "3b4be7c1b90499b9dfd73790ef730100",
"score": "0.78729284",
"text": "def agent_start(self, state):\n pass",
"title": ""
},
{
"docid": "232a87f307a67be276259c654c633733",
"score": "0.7734188",
"text": "def init_agent(self):",
"title": ""
},
{
"docid": "4eb... |
398a8a80ee96b0ca0e45d459aeaefb5d | BGR format, packed into 2 bytes by dropping LSBs. | [
{
"docid": "7ffc6cbaac3fab19fdefcecb63cfc96c",
"score": "0.53539056",
"text": "def save_bgr565(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n data[2*offset], data[2 * offset + 1] = compress565(\n pixels[4 * offset + 2],\n ... | [
{
"docid": "06a6a687383b0f339c9d45e713944124",
"score": "0.648248",
"text": "def rgb2bgr(tpl):\n return (tpl[2], tpl[1], tpl[0])",
"title": ""
},
{
"docid": "187a8dd5a5e69a8eec16f24f10e9f27a",
"score": "0.637679",
"text": "def from_rgb2bgr(im):\n return cv2.cvtColor(im, cv2.COLOR... |
e12dde588b95e7dc2285219334173982 | Creates the logistical function for percent increase in exp. | [
{
"docid": "70f7cf4e4f10fc320823fa801ad7d3b2",
"score": "0.6326081",
"text": "def exp_algorithm(self):\n exp_logistic = 1 / (\n 3 + math.exp(-(3 / 4) * (self.playerLevel / 16) - 4)) + 1\n return exp_logistic",
"title": ""
}
] | [
{
"docid": "f1bc095663e2201c1472cfae0a3b0016",
"score": "0.7222441",
"text": "def log(x):\n return 1 / ( 1 + math.exp(-x))",
"title": ""
},
{
"docid": "427a834b19c5cab5d04c7074822f5036",
"score": "0.70191973",
"text": "def exp(x):\n return F.exp(x)",
"title": ""
},
{
... |
76d8a4a962caf2411134a7ab3da14128 | Sort the segments in blue and red | [
{
"docid": "2546b0ed2ad9cad8b4b2ae20845f8f6f",
"score": "0.8025422",
"text": "def sort_segments(case):\n blue = []\n red = []\n for segment in case:\n\n if segment[-1] == 'B':\n blue.append(int(segment[:-1]))\n else:\n red.append(int(segment[:-1]))\n\n # s... | [
{
"docid": "1355250a07537a8dcc0629f5dc7e0575",
"score": "0.6966242",
"text": "def sortColors(self, nums) -> None:\n end, end_0, start_1, start_2, count_1 = len(nums) - 1, -1, -1, -1, 0\n for i in range(end, -1, -1):\n if nums[i] == 2:\n start_2 = i\n el... |
1ab6d1d0f03396b3d9278e24a23c3d29 | Use the given status structure and upate all of the redis variables with the current values. | [
{
"docid": "373481df6af4d56f205a3446b94fda97",
"score": "0.7114745",
"text": "def updateRedis(self,status):\n\n self.redis.set('pumpcall',status['PC'])\n self.redis.set('pumpon',status['P'])\n self.redis.set('northcall',status['NC'])\n self.redis.set('northon',status['N'])\n ... | [
{
"docid": "13f3868cf43499654a670d77e85c5e99",
"score": "0.6291984",
"text": "def updateStatus(self):\n\n try :\n st = self.api.getSystemStatus()\n\n if st:\n self.updateRedis(st)\n\n bFast = False\n for key in ['P','PC','N','NC','S','SC'... |
3f40489d1f123e20b33fb7002aa842da | get the last execution result of the EoX API auto sync | [
{
"docid": "f09a18a493f4a14a124658503b9a2d05",
"score": "0.795451",
"text": "def get_cisco_eox_api_auto_sync_last_execution_result(self):\n return self._config_options[ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_RESULT]",
"title": ""
}
] | [
{
"docid": "b6025beb0654ff0149e33ae1f554e28d",
"score": "0.68530107",
"text": "def get_cisco_eox_api_auto_sync_last_execution_time(self):\n return self._config_options[ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_TIME]",
"title": ""
},
{
"docid": "32229a5cc40f87b900278b6fac6d9fa3",
... |
6358dfa57d11a64dec215b90d16ad948 | generated source for method parseAndGetClassForMessage | [
{
"docid": "35bae810b20ef8b26a8550ad4e6b7e87",
"score": "0.7376051",
"text": "def parseAndGetClassForMessage(cls, message):\n try:\n json_obj = json.loads(message)\n if json_obj is None:\n # Nothing found\n raise Exception(\"Could not find [\" ... | [
{
"docid": "9693b8b81c159ca8ea2de795504711e8",
"score": "0.6514559",
"text": "def _parse_msg(self, msg):\n raise NotImplementedError(\"Implement!\")",
"title": ""
},
{
"docid": "5594dba28b3e43f98d8eba50df9e984e",
"score": "0.650433",
"text": "def generate_message_classes(messa... |
a098c3b6f3646c90f7b6e6f563f3ac81 | Write values from row of septwq table using cursor and sql, using num for the first element. | [
{
"docid": "4a18dea0a97aaebfd50360a841c4eb2d",
"score": "0.75467116",
"text": "def writeSeptRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n data = (num, row[1].lower()) + tuple(row[4:7]) + tuple(row[8:12]) + tuple(row[13:16]) + (row[2],)\n cursor.execute(sql, data)",
... | [
{
"docid": "aaafeb3175ba43a527c8dfb69b771262",
"score": "0.71464074",
"text": "def writePestRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n data = (num, row[2].lower()) + tuple(row[3:7]) + (row[8], 0, 0, 0, 0, 0, 0, 0, 0, row[10])\n cursor.execute(sql, data)",
"ti... |
b2c5a670fd92894d410e96d40abdcf0e | Return a dictionary mapping package to recipe name. | [
{
"docid": "dd851b24009a5f0216ea3f9c824a09c4",
"score": "0.534241",
"text": "def _pkgmap(d):\n\n target_os = d.getVar(\"TARGET_OS\", True)\n target_vendor = d.getVar(\"TARGET_VENDOR\", True)\n basedir = os.path.dirname(d.getVar(\"PKGDATA_DIR\", True))\n\n dirs = (\"%s%s-%s\" % (arch, target_... | [
{
"docid": "15852747fb8674970bb432c0bdfb20d2",
"score": "0.61707795",
"text": "def recipes():\n return {'recipes': [recipe(), recipe()]}",
"title": ""
},
{
"docid": "40cf200d1b8aefffb7a98b88a7f76b4d",
"score": "0.61030656",
"text": "def packages_from_entry(self, entry):\r\n ... |
923aa58fcc2054b3c100e2f5d852d0ad | This tests to see if the function that removed quotes worked and that there are no remaining quotations in the data | [
{
"docid": "b4f723e2ff61fcd43c3cec1e918d1837",
"score": "0.8019426",
"text": "def test_replace_quotes():\n df_result_quotes = replace_quotes(output_not_cleaned)\n assert '\"' not in df_result_quotes.values\n print('TEST PASSED, for replace_quotes')",
"title": ""
}
] | [
{
"docid": "5d27fa81384f85a59c3d6ccc79418bb4",
"score": "0.6825093",
"text": "def test_remove_quotes(self):\n # Set up\n sample_sheet = CasavaSampleSheet(fp=cStringIO.StringIO(\"\"\"FCID,Lane,SampleID,SampleRef,Index,Description,Control,Recipe,Operator,SampleProject\n\"D190HACXX\",1,\"PB\"... |
4ce8dae4ac4053b4ab3ca20a68802c00 | Calculate average distance of two clusters | [
{
"docid": "f67bd78a9d76772e123e3a2250a45b7b",
"score": "0.69087136",
"text": "def mean(average_dis1, num_of_distance1, average_dis2, num_of_distance2, num_of_distances):\r\n return (average_dis1*num_of_distance1 + average_dis2*num_of_distance2)/num_of_distances",
"title": ""
}
] | [
{
"docid": "397ef63681c0bead1b04d694faf2c1b2",
"score": "0.7762529",
"text": "def _calculate_average_distance(matrix, clusters, c_x, c_y):\n\n pass",
"title": ""
},
{
"docid": "3ab2226ee7caf41831be5816611eb289",
"score": "0.7740244",
"text": "def avg_cluster_distance(self, mat... |
f634af849b1a0f84d7a22d842504efad | Set up a blob selection survey | [
{
"docid": "77bcf48d7ed0a69707ea80974df3ea53",
"score": "0.58597565",
"text": "def testBlobs(self):\n nside = 32\n survey_length = 2.0 # days\n\n surveys = []\n # Set up the DD\n dd_surveys = generate_dd_surveys(nside=nside)\n surveys.append(dd_surveys)\n\n ... | [
{
"docid": "4a9668099875beb4a9c885505b4d2517",
"score": "0.53254974",
"text": "def test_bokeh_selector(self):\n from straxen.analyses.bokeh_waveform_plot import DataSelectionHist\n p = self.st.get_array(nt_test_run_id, 'peak_basics')\n ds = DataSelectionHist('ds')\n fig = ds.... |
caba96953d0db8ab81c413e65e5f9dde | Returns the loom input tensor, used for building feed dictionaries. May be fed a single result or a sequence of results from `Compiler.build_loom_inputs()` or `Compiler.build_loom_input_batched()`. | [
{
"docid": "a4a7142f25ab9382865e61641721e6ce",
"score": "0.82956433",
"text": "def loom_input_tensor(self):\n self._check_loom_initialized()\n return self._wet.input_tensor",
"title": ""
}
] | [
{
"docid": "b1e438b5032c09b23af1bd83e8f00646",
"score": "0.63694286",
"text": "def input_tensor(interpreter):\r\n tensor_index = interpreter.get_input_details()[0]['index']\r\n return interpreter.tensor(tensor_index)()[0]",
"title": ""
},
{
"docid": "8978a692cfa1077b16d93793236519fb",
... |
a65c1835300f919c202a781fd914cf72 | Builds a layer which connects to the input_layer according to the given parameters. | [
{
"docid": "77f3d7c659762805d395fbc0fee40fbb",
"score": "0.61406976",
"text": "def create_output_layer(input_layer, weights_tuple, delta, layer_name, refrac):\n# print('Number of output neurons {} for size {}x{}'.format(\\\n# total_output_neurons, t_n, t_m))... | [
{
"docid": "3a81c94c57960a2260aafdee1e162199",
"score": "0.6962431",
"text": "def create_layer(input_layer, layer):\n\n # Common to all layers\n layer_type = layer[0]\n name = layer[1]\n\n\n if layer_type == CONV:\n weights = weight_variable(layer[2], name+'_conv_weights')\n stride = l... |
4c9e79ec6f0453fbd09830ae83effa3f | Return a dict description of the Operation | [
{
"docid": "28710bff765d22500247aae734742e28",
"score": "0.5967061",
"text": "def describe(self) -> dict:\n\n description = super().describe()\n\n description.update(\n {\n \"args\": {\"keys\": self.keys}\n }\n )\n\n return description",
... | [
{
"docid": "edb73633f6a56bc0d0a4cd54c806823b",
"score": "0.84622484",
"text": "def describe(self) -> dict:\n\n return {\n \"operation\": type(self).__name__\n }",
"title": ""
},
{
"docid": "084ed9cb0338e34f3fc6cb6189dc59d2",
"score": "0.73279786",
"text": "de... |
e02c3b6520f5cfe70a6eeb8665f0faab | "To ensure the robustness of the model, five random datasets were created to repeat the training and testing of the CNN classifiers (5fold crossvalidation)" | [
{
"docid": "35b086822916871823d5cf5d63c5a3bd",
"score": "0.5881104",
"text": "def run(self, *inputs, **kwargs) -> None:\n validation_folds = self.config.training_crossval_folds\n split_ratios = [1.0 / validation_folds] * validation_folds\n\n \"\"\"\n \"All baseline MR data we... | [
{
"docid": "8b71a66a734b075d1133dcee50313951",
"score": "0.66847914",
"text": "def train_model():\r\n x_train = []\r\n x_test = []\r\n x_valid = []\r\n\r\n y_train = []\r\n y_test = []\r\n y_valid = []\r\n\r\n # reading train\r\n read_images(fpath_train, x_train, y_train, FEMALE)... |
012e9d167c4fb65431c816f0d2bcbe37 | The name of the category. | [
{
"docid": "e9c3dee8d88435fba7afa382d2833fa5",
"score": "0.0",
"text": "def name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"name\")",
"title": ""
}
] | [
{
"docid": "89d5292e894ed33a2743b5ac6e40c96c",
"score": "0.9356435",
"text": "def category_name(self) -> str:\n return pulumi.get(self, \"category_name\")",
"title": ""
},
{
"docid": "0708909944d1765b981bfd898b8dc4cb",
"score": "0.93421",
"text": "def category_name(self):\n ... |
25b75ea7bf5cb76a12789e7989129fb6 | Returns a ApplicationCache Object to interact with the browser app cache. | [
{
"docid": "30b841e126a587fbf9af547ebf5dc15d",
"score": "0.83621204",
"text": "def get_application_cache(self) -> ApplicationCache:\n return self._selenium_web_driver().application_cache",
"title": ""
}
] | [
{
"docid": "ee705e4bfe2f7971b08deb8b63d3f503",
"score": "0.6836842",
"text": "def create_cache():\n class _App(object):\n config = {}\n\n cache_config = {\n 'CACHE_TYPE': 'filesystem',\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\n 'CACHE_THRESHOLD': 100,\n 'CACH... |
e6be7d743ec3c3a768eef75172fd2f8f | find users with their username that start with letter that user has entered | [
{
"docid": "2c1b08376ea3c1ba98ed3949f10267b2",
"score": "0.83855855",
"text": "def users(letter):\n return User.objects.filter(username__istartswith=letter)",
"title": ""
}
] | [
{
"docid": "d8e566029b9c771acb958a39552c0017",
"score": "0.85593385",
"text": "def getUsersStartingWith(letters):",
"title": ""
},
{
"docid": "8664fa1d80739b81e26240163caa7420",
"score": "0.6641168",
"text": "def check_username(self, username):\n match = re.findall(r\"^[a-zA-Z... |
60a13f80bbbd52ab881d64ae67e7d878 | Test LimitTracker used in auvimport. | [
{
"docid": "376124658927b62692caa4f6e6bed547",
"score": "0.5882347",
"text": "def test_limit_tracker(self):\n\n # check direct values\n direct_track = LimitTracker()\n\n direct_track.check(1.0)\n\n self.assertEqual(direct_track.minimum, 1.0)\n self.assertEqual(direct_t... | [
{
"docid": "5e84c8ee36f03c4d423562b65adc6087",
"score": "0.6538664",
"text": "def test_rate_limit_interface():\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\n min_rate_def = RateDefinition(per_second=10)\n per_user_rate_def = PerUserRateDefinition(per_user... |
ec66effa18f2468179bea5514cc33f27 | Changes each face when a turn is made | [
{
"docid": "fbd8f96f5e48ea6fb17378d7c6c7e8a1",
"score": "0.0",
"text": "def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\n falseSide1, falseSide2,\n falseCopy1, falseCopy2,\n trueSide1, trueSide2, \n trueCopy1, trueCopy2,\n twoSide1, ... | [
{
"docid": "ffc8669634c0df6b38b564f4c3edc344",
"score": "0.6458799",
"text": "def __init__(self):\n self.face = 0",
"title": ""
},
{
"docid": "0695201c5269bde707b3ec1907317f36",
"score": "0.6264643",
"text": "def changeTurn(self):\n self._players[self._current].deactiva... |
9003693612e2496d1b8ec20c13e967fc | Takes a project UUID and either creates a new document in the desprojects index or updates the document if one already exists for that project. | [
{
"docid": "809657dc207ab465236a7ea3711a9617",
"score": "0.74024814",
"text": "def index_or_update_project(self, uuid):\n from designsafe.apps.api.projects.models import Project\n\n client = get_service_account_client()\n project_model = Project(client)\n project = project_model.search({'uui... | [
{
"docid": "3eb70541f86495a1a812503637a60949",
"score": "0.58575755",
"text": "def reindex_projects(self):\n client = get_service_account_client()\n query = {'name': 'designsafe.project'}\n\n in_loop = True\n offset = 0\n while in_loop:\n listing = client.meta.listMetadata(q=json.d... |
68e37ec088ae4267c593779e2ace4a03 | r"""The number of graphs in the dataset. | [
{
"docid": "179a0ee94a6a7e7d1a87da777afe7be5",
"score": "0.0",
"text": "def __len__(self): # -> Literal[1]:\n ...",
"title": ""
}
] | [
{
"docid": "0355f1833468a8bd7a3a10e653f39a43",
"score": "0.8215049",
"text": "def number_of_graphs(self):\n return self.get_table_row_count(self.GRAPH_TABLE._name_)",
"title": ""
},
{
"docid": "d5cf06f4008d0a5795c841c5b8657580",
"score": "0.75362337",
"text": "def n_datasets(s... |
e436a651273cc71240d278e3bae026c5 | Show the given variable on the plot. Undoes `ignore_variable`. If a dataframe name is specified, all of its columns will be shown. | [
{
"docid": "13c4e6f3f61e09eaa750fd207f32f767",
"score": "0.4953621",
"text": "def show_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.show_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} doe... | [
{
"docid": "f3416a3c42880a57556602bd26db1797",
"score": "0.57033026",
"text": "def ignore_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.ignore_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active... |
2be6d369e19ba2cf155c71d55a982262 | Start a player off with the default starting deck, an empty hand, and an empty discard pile. Set the game stage to 'early_game'. Initialize a turn counter. Draw a random starting hand from the deck. | [
{
"docid": "9ab052ff693422df14ac76b389410470",
"score": "0.7242505",
"text": "def __init__(self):\n\n self.hand = [] # empty hand\n self.discard = [] # empty discard pile\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\n self.game_stage = GameStage.early_game # ... | [
{
"docid": "5100836fd6ace5c376bcbe31fb141463",
"score": "0.7105495",
"text": "def start_new_game(self, player_hand):\n self.deck_of_cards += self.hand\n self.deck_of_cards += player_hand\n self.deck_of_cards.shuffle()",
"title": ""
},
{
"docid": "73b8d8128052bfd7bd5a587d... |
878430aedb1cf2faa0d7a646f80b93dd | Returns True if `n` is a prime number. Uses a sieve based approach. | [
{
"docid": "f6562e93978739d735e33a2f03e547ef",
"score": "0.82913023",
"text": "def is_prime(n):\n if n < 2:\n return False\n ps = primes()\n p = next(ps)\n while p * p <= n:\n if n % p == 0:\n return False\n p = next(ps)\n return True",
"title": ""
}
... | [
{
"docid": "78a25bc4f1590805de217f19646d793b",
"score": "0.8519759",
"text": "def isPrime(n):\n\tif n > 1:\n\t\tsqrtN = int(math.sqrt(n))\n\t\tfor i in xrange(2, sqrtN + 1):\n\t\t\tif n % i == 0:\n\t\t\t\treturn False\n\t\treturn True\n\telse:\n\t\treturn False",
"title": ""
},
{
"docid": "d... |
c4d430e66bb3d21f6d5a3c0b14362f75 | Gets the EMD language between the two languages | [
{
"docid": "62965b2c20affbafeeb5c0cc7a3c475e",
"score": "0.0",
"text": "def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)",
"title": ""
}
] | [
{
"docid": "7d19c984c03b3e59c0e29927e52e446b",
"score": "0.6914142",
"text": "def get_language(self):",
"title": ""
},
{
"docid": "38cf8daef8d0e9165e88916da0f08868",
"score": "0.67045707",
"text": "def getLanguage(self) -> ghidra.program.model.lang.Language:\n ...",
"title... |
dcb6c0a8a52a7bb0ac92e1bdf7df1718 | get all the bookmars that a certai project has | [
{
"docid": "4705871157183a2403386664a34c0255",
"score": "0.0",
"text": "def get_projectBookmarks(self, projectId):\n from Bookmark import Bookmark\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from bookmark where project=%s', (projectId,))\n bookmarks = li... | [
{
"docid": "edff7cd81254a3f14281af7177212839",
"score": "0.6072002",
"text": "def getBooks():\n ret = {}\n books = []\n iter = model.getBooks()\n while iter.next():\n books.append((iter.id, iter.name))\n\n ret['books'] = books\n return ret",
"title": ""
},
{
"docid":... |
e048fbeb7c6ddb0b67d7a05a70285c70 | Gets the side of the line p is on. | [
{
"docid": "61392af388f6f7d1f80e07e4e0dd26b4",
"score": "0.711668",
"text": "def line_side(pl, p, pr):\n\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\n\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\n\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\n\tif xp > 0:\n\t return 1\n\telif xp < 0:\n\t ret... | [
{
"docid": "cb2c6c21f616e1864d15179f7c84d096",
"score": "0.7492487",
"text": "def get_side(self):\n return self.get_perimeter() / 4",
"title": ""
},
{
"docid": "dc079eec7d6fd53e500597fcb4a05fcb",
"score": "0.7384879",
"text": "def point_side_line(p1, p2, p):\n\n # Get vecto... |